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It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.", "biocViews": [ "Microarray", "Normalization", "Software", "Technology" ], "Author": "Sean Davis ", "Maintainer": "Sean Davis ", "URL": "http://watson.nci.nih.gov/~sdavis", "git_url": "https://git.bioconductor.org/packages/ACME", "git_branch": "RELEASE_3_18", "git_last_commit": "1b192d9", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ACME_2.58.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ACME_2.58.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ACME_2.58.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ACME_2.58.0.tgz", "vignettes": [ "vignettes/ACME/inst/doc/ACME.pdf" ], "vignetteTitles": [ "ACME" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ACME/inst/doc/ACME.R" ], "suggestsMe": [ "oligo" ], "dependencyCount": "6", "Rank": 978 }, "ADaCGH2": { "Package": "ADaCGH2", "Version": "2.42.0", "Depends": [ "R (>= 3.2.0)", "parallel", "ff", "GLAD" ], "Imports": [ "bit", "DNAcopy", "tilingArray", "waveslim", "cluster", "aCGH", "snapCGH" ], "Suggests": [ "CGHregions", "Cairo", "limma" ], "Enhances": [ "Rmpi" ], "License": "GPL (>= 3)", "Archs": "x64", "MD5sum": "0ce28a95b156205f60eeda851b36c958", "NeedsCompilation": "yes", "Title": "Analysis of big data from aCGH experiments using parallel computing and ff objects", "Description": "Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.", "biocViews": [ "CopyNumberVariants", "Microarray", "Software" ], "Author": "Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan.", "Maintainer": "Ramon Diaz-Uriarte ", "URL": "https://github.com/rdiaz02/adacgh2", "git_url": "https://git.bioconductor.org/packages/ADaCGH2", "git_branch": "RELEASE_3_18", "git_last_commit": "b61cec8", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ADaCGH2_2.42.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ADaCGH2_2.42.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ADaCGH2_2.42.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ADaCGH2_2.42.0.tgz", "vignettes": [ "vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf", "vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf", "vignettes/ADaCGH2/inst/doc/benchmarks.pdf" ], "vignetteTitles": [ "ADaCGH2-long-examples.pdf", "ADaCGH2 Overview", "benchmarks.pdf" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ADaCGH2/inst/doc/ADaCGH2.R" ], "dependencyCount": "100", "Rank": 1203 }, "ADAM": { "Package": "ADAM", "Version": "1.18.0", "Depends": [ "R(>= 3.5)", "stats", "utils", "methods" ], "Imports": [ "Rcpp (>= 0.12.18)", "GO.db (>= 3.6.0)", "KEGGREST (>= 1.20.2)", "knitr", "pbapply (>= 1.3-4)", "dplyr (>= 0.7.6)", "DT (>= 0.4)", "stringr (>= 1.3.1)", "SummarizedExperiment (>= 1.10.1)" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "testthat", "rmarkdown", "BiocStyle" ], "License": "GPL (>= 2)", "Archs": "x64", "MD5sum": "7f051085afca603520dbb5cb010e58fe", "NeedsCompilation": "yes", "Title": "ADAM: Activity and Diversity Analysis Module", "Description": "ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes).", "biocViews": [ "GeneExpression", "GeneSetEnrichment", "KEGG", "Microarray", "Pathways", "Software" ], "Author": "André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths]", "Maintainer": "Jose Luiz Rybarczyk Filho ", "SystemRequirements": "C++11", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/ADAM", "git_branch": "RELEASE_3_18", "git_last_commit": "1652823", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ADAM_1.18.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ADAM_1.18.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ADAM_1.18.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ADAM_1.18.0.tgz", "vignettes": [ "vignettes/ADAM/inst/doc/ADAM.html" ], "vignetteTitles": [ "\"Using ADAM\"" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ADAM/inst/doc/ADAM.R" ], "dependsOnMe": [ "ADAMgui" ], "dependencyCount": "95", "Rank": 715 }, "ADAMgui": { "Package": "ADAMgui", "Version": "1.18.0", "Depends": [ "R(>= 3.6)", "stats", "utils", "methods", "ADAM" ], "Imports": [ "GO.db (>= 3.5.0)", "dplyr (>= 0.7.6)", "shiny (>= 1.1.0)", "stringr (>= 1.3.1)", "stringi (>= 1.2.4)", "varhandle (>= 2.0.3)", "ggplot2 (>= 3.0.0)", "ggrepel (>= 0.8.0)", "ggpubr (>= 0.1.8)", "ggsignif (>= 0.4.0)", "reshape2 (>= 1.4.3)", "RColorBrewer (>= 1.1-2)", "colorRamps (>= 2.3)", "DT (>= 0.4)", "data.table (>= 1.11.4)", "gridExtra (>= 2.3)", "shinyjs (>= 1.0)", "knitr", "testthat" ], "Suggests": [ "markdown", "BiocStyle" ], "License": "GPL (>= 2)", "MD5sum": "d6b76d93be7f193cd913f44f6ff41a64", "NeedsCompilation": "no", "Title": "Activity and Diversity Analysis Module Graphical User Interface", "Description": "ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function.", "biocViews": [ "GeneSetEnrichment", "KEGG", "Pathways", "Software" ], "Author": "Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths]", "Maintainer": "Jose Luiz Rybarczyk Filho ", "URL": "TBA", "VignetteBuilder": "knitr", "BugReports": "https://github.com/jrybarczyk/ADAMgui/issues", "git_url": "https://git.bioconductor.org/packages/ADAMgui", "git_branch": "RELEASE_3_18", "git_last_commit": "604f8c4", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ADAMgui_1.18.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ADAMgui_1.18.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ADAMgui_1.18.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ADAMgui_1.18.0.tgz", "vignettes": [ "vignettes/ADAMgui/inst/doc/ADAMgui.html" ], "vignetteTitles": [ "\"Using ADAMgui\"" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ADAMgui/inst/doc/ADAMgui.R" ], "dependencyCount": "161", "Rank": 854 }, "adductomicsR": { "Package": "adductomicsR", "Version": "1.18.0", "Depends": [ "R (>= 3.6)", "adductData", "ExperimentHub", "AnnotationHub" ], "Imports": [ "parallel (>= 3.3.2)", "data.table (>= 1.10.4)", "OrgMassSpecR (>= 0.4.6)", "foreach (>= 1.4.3)", "mzR (>= 2.14.0)", "ade4 (>= 1.7.6)", "rvest (>= 0.3.2)", "pastecs (>= 1.3.18)", "reshape2 (>= 1.4.2)", "pracma (>= 2.0.4)", "DT (>= 0.2)", "fpc (>= 2.1.10)", "doSNOW (>= 1.0.14)", "fastcluster (>= 1.1.22)", "RcppEigen (>= 0.3.3.3.0)", "bootstrap (>= 2017.2)", "smoother (>= 1.1)", "dplyr (>= 0.7.5)", "zoo (>= 1.8)", "stats (>= 3.5.0)", "utils (>= 3.5.0)", "graphics (>= 3.5.0)", "grDevices (>= 3.5.0)", "methods (>= 3.5.0)", "datasets (>= 3.5.0)" ], "Suggests": [ "knitr (>= 1.15.1)", "rmarkdown (>= 1.5)", "Rdisop (>= 1.34.0)", "testthat" ], "License": "Artistic-2.0", "MD5sum": "82e24a09ba891b76e9497a6725b96fe0", "NeedsCompilation": "no", "Title": "Processing of adductomic mass spectral datasets", "Description": "Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks.", "biocViews": [ "DataImport", "GUI", "MassSpectrometry", "Metabolomics", "Software", "ThirdPartyClient" ], "Author": "Josie Hayes ", "Maintainer": "Josie Hayes ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/JosieLHayes/adductomicsR/issues", "git_url": "https://git.bioconductor.org/packages/adductomicsR", "git_branch": "RELEASE_3_18", "git_last_commit": "35a34c9", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/adductomicsR_1.18.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/adductomicsR_1.18.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/adductomicsR_1.18.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/adductomicsR_1.18.0.tgz", "vignettes": [ "vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html" ], "vignetteTitles": [ "Adductomics workflow" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R" ], "dependencyCount": "143", "Rank": 1339 }, "ADImpute": { "Package": "ADImpute", "Version": "1.12.0", "Depends": [ "R (>= 4.0)" ], "Imports": [ "checkmate", "BiocParallel", "data.table", "DrImpute", "kernlab", "MASS", "Matrix", "methods", "rsvd", "S4Vectors", "SAVER", "SingleCellExperiment", "stats", "SummarizedExperiment", "utils" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat" ], "License": "GPL-3 + file LICENSE", "MD5sum": "488025707dc1ae48d36b1e936cd86e18", "NeedsCompilation": "no", "Title": "Adaptive Dropout Imputer (ADImpute)", "Description": "Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.", "biocViews": [ "GeneExpression", "Network", "Preprocessing", "Sequencing", "SingleCell", "Software", "Transcriptomics" ], "Author": "Ana Carolina Leote [cre, aut] ()", "Maintainer": "Ana Carolina Leote ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/anacarolinaleote/ADImpute/issues", "git_url": "https://git.bioconductor.org/packages/ADImpute", "git_branch": "RELEASE_3_18", "git_last_commit": "327d96d", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ADImpute_1.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ADImpute_1.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ADImpute_1.12.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ADImpute_1.12.0.tgz", "vignettes": [ "vignettes/ADImpute/inst/doc/ADImpute_tutorial.html" ], "vignetteTitles": [ "ADImpute tutorial" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/ADImpute/inst/doc/ADImpute_tutorial.R" ], "dependencyCount": "58", "Rank": 1036 }, "adSplit": { "Package": "adSplit", "Version": "1.72.0", "Depends": [ "R (>= 2.1.0)", "methods (>= 2.1.0)" ], "Imports": [ "AnnotationDbi", "Biobase (>= 1.5.12)", "cluster (>= 1.9.1)", "GO.db (>= 1.8.1)", "graphics", "grDevices", "KEGGREST (>= 1.30.1)", "multtest (>= 1.6.0)", "stats (>= 2.1.0)" ], "Suggests": [ "golubEsets (>= 1.0)", "vsn (>= 1.5.0)", "hu6800.db (>= 1.8.1)" ], "License": "GPL (>= 2)", "Archs": "x64", "MD5sum": "4d50affb50e6f6a11d8c4e379b6116d8", "NeedsCompilation": "yes", "Title": "Annotation-Driven Clustering", "Description": "This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined.", "biocViews": [ "Clustering", "Microarray", "Software" ], "Author": "Claudio Lottaz, Joern Toedling", "Maintainer": "Claudio Lottaz ", "URL": "http://compdiag.molgen.mpg.de/software/adSplit.shtml", "git_url": "https://git.bioconductor.org/packages/adSplit", "git_branch": "RELEASE_3_18", "git_last_commit": "194ddb2", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/adSplit_1.72.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/adSplit_1.72.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/adSplit_1.72.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/adSplit_1.72.0.tgz", "vignettes": [ "vignettes/adSplit/inst/doc/tr_2005_02.pdf" ], "vignetteTitles": [ "Annotation-Driven Clustering" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/adSplit/inst/doc/tr_2005_02.R" ], "dependencyCount": "55", "Rank": 1203 }, "adverSCarial": { "Package": "adverSCarial", "Version": "1.0.0", "Imports": [ "gtools", "S4Vectors", "methods", "DelayedArray" ], "Suggests": [ "knitr", "RUnit", "BiocGenerics", "TENxPBMCData", "CHETAH", "stringr", "LoomExperiment" ], "License": "MIT + file LICENSE", "MD5sum": "196840790cf7fd0a0cbfe6d1aeb60e07", "NeedsCompilation": "no", "Title": "adverSCarial, generate and analyze the vulnerability of scRNA-seq classifiers to adversarial attacks", "Description": "adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single gene attack involves making a small modification to the input to alter the classification. The max change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks.", "biocViews": [ "Classification", "SingleCell", "Software", "Transcriptomics" ], "Author": "Ghislain FIEVET [aut, cre] (), Sébastien HERGALANT [aut] ()", "Maintainer": "Ghislain FIEVET ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/adverSCarial", "git_branch": "RELEASE_3_18", "git_last_commit": "141ed01", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/adverSCarial_1.0.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/adverSCarial_1.0.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/adverSCarial_1.0.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/adverSCarial_1.0.0.tgz", "vignettes": [ "vignettes/adverSCarial/inst/doc/vign01_adverSCarial.html", "vignettes/adverSCarial/inst/doc/vign02_overView_analysis.html", "vignettes/adverSCarial/inst/doc/vign03_adapt_classifier.html", "vignettes/adverSCarial/inst/doc/vign04_advRandWalkMinChange.html" ], "vignetteTitles": [ "Vign01_adverSCarial", "Vign02_overView_analysis", "Vign03_adaptClassifier", "Vign04_advRandWalkMinChange" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/adverSCarial/inst/doc/vign01_adverSCarial.R", "vignettes/adverSCarial/inst/doc/vign02_overView_analysis.R" ], "dependencyCount": "23", "Rank": 2114 }, "AffiXcan": { "Package": "AffiXcan", "Version": "1.20.0", "Depends": [ "R (>= 3.6)", "SummarizedExperiment" ], "Imports": [ "MultiAssayExperiment", "BiocParallel", "crayon" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown" ], "License": "GPL-3", "MD5sum": "0fdb85714a4ee94e3c04117e56d35724", "NeedsCompilation": "no", "Title": "A Functional Approach To Impute Genetically Regulated Expression", "Description": "Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). 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Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. 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BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. 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CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. 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CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data.", "biocViews": [ "CopyNumberVariation", "ExomeSeq", "ImmunoOncology", "Normalization", "QualityControl", "Software" ], "Author": "Yuchao Jiang, Nancy R. 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It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. 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Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.", "biocViews": [ "DifferentialExpression", "GeneExpression", "GeneSetEnrichment", "ImmunoOncology", "Pathways", "RNASeq", "Software", "Transcriptomics" ], "Author": "Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. 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CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. 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This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.", "biocViews": [ "AssayDomain", "Clustering", "DifferentialExpression", "GeneExpression", "Infrastructure", "Normalization", "QualityControl", "RNASeq", "Sequencing", "Software", "Transcription", "Transcriptomics" ], "Author": "Stefano Mangiola [aut, cre, rev] (), Michael Milton [aut, rev] (), Martin Morgan [ctb, rev], Vincent Carey [ctb, rev], Julie Iskander [rev], Tony Papenfuss [rev], Silicon Valley Foundation CZF2019-002443 [fnd], NIH NHGRI 5U24HG004059-18 [fnd], Victoria Cancer Agency ECRF21036 [fnd], NHMRC 1116955 [fnd]", "Maintainer": "Stefano Mangiola ", "URL": "https://github.com/stemangiola/CuratedAtlasQueryR", "VignetteBuilder": "knitr", "BugReports": "https://github.com/stemangiola/CuratedAtlasQueryR/issues", "git_url": "https://git.bioconductor.org/packages/CuratedAtlasQueryR", "git_branch": "RELEASE_3_18", "git_last_commit": "bb3acd7", "git_last_commit_date": "2023-12-06", "Date/Publication": "2023-12-07", "source.ver": "src/contrib/CuratedAtlasQueryR_1.0.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/CuratedAtlasQueryR_1.0.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/CuratedAtlasQueryR_1.0.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/CuratedAtlasQueryR_1.0.1.tgz", "vignettes": [ "vignettes/CuratedAtlasQueryR/inst/doc/Introduction.html" ], "vignetteTitles": [ "CuratedAtlasQueryR" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/CuratedAtlasQueryR/inst/doc/Introduction.R" ], "dependencyCount": "180", "Rank": 2096 }, "customCMPdb": { "Package": "customCMPdb", "Version": "1.12.0", "Depends": [ "R (>= 4.0)" ], "Imports": [ "AnnotationHub", "RSQLite", "XML", "utils", "ChemmineR", "methods", "stats", "rappdirs", "BiocFileCache" ], "Suggests": [ "knitr", "rmarkdown", "testthat", "BiocStyle" ], "License": "Artistic-2.0", "MD5sum": "1aa1425d87c45ac42f2c7ed808a7a583", "NeedsCompilation": "no", "Title": "Customize and Query Compound Annotation Database", "Description": "This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections.", "biocViews": [ "AnnotationHubSoftware", "Cheminformatics", "Software" ], "Author": "Yuzhu Duan [aut, cre], Thomas Girke [aut]", "Maintainer": "Yuzhu Duan ", "URL": "https://github.com/yduan004/customCMPdb/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/yduan004/customCMPdb/issues", "git_url": "https://git.bioconductor.org/packages/customCMPdb", "git_branch": "RELEASE_3_18", "git_last_commit": "b93db2f", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/customCMPdb_1.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/customCMPdb_1.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/customCMPdb_1.12.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/customCMPdb_1.12.0.tgz", "vignettes": [ "vignettes/customCMPdb/inst/doc/customCMPdb.html" ], "vignetteTitles": [ "customCMPdb" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/customCMPdb/inst/doc/customCMPdb.R" ], "dependencyCount": "117", "Rank": 1663 }, "customProDB": { "Package": "customProDB", "Version": "1.42.1", "Depends": [ "R (>= 3.5.0)", "IRanges", "AnnotationDbi", "biomaRt (>= 2.17.1)" ], "Imports": [ "S4Vectors (>= 0.9.25)", "DBI", "GenomeInfoDb", "GenomicRanges", "Rsamtools (>= 1.10.2)", "GenomicAlignments", "Biostrings (>= 2.26.3)", "GenomicFeatures (>= 1.32.0)", "stringr", "RCurl", "plyr", "VariantAnnotation (>= 1.13.44)", "rtracklayer", "RSQLite", "AhoCorasickTrie", "methods" ], "Suggests": [ "RMariaDB", "BSgenome.Hsapiens.UCSC.hg19" ], "License": "Artistic-2.0", "MD5sum": "e15acd30625fe6d1cd3abfc8e0f3b902", "NeedsCompilation": "no", "Title": "Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search", "Description": "Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. 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Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. 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This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. 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DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. 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DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks.", "biocViews": [ "Coverage", "DifferentialMethylation", "HiddenMarkovModel", "Sequencing", "Software" ], "Author": "Farhad Shokoohi", "Maintainer": "Farhad Shokoohi ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/shokoohi/DMCHMM/issues", "git_url": "https://git.bioconductor.org/packages/DMCHMM", "git_branch": "RELEASE_3_18", "git_last_commit": "bae8dc8", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DMCHMM_1.24.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DMCHMM_1.24.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/DMCHMM_1.24.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/DMCHMM_1.24.0.tgz", "vignettes": [ "vignettes/DMCHMM/inst/doc/DMCHMM.html" ], "vignetteTitles": [ "DMCHMM: Differentially Methylated CpG using Hidden Markov Model" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/DMCHMM/inst/doc/DMCHMM.R" ], "dependencyCount": "59", "Rank": 1729 }, "DMRcaller": { "Package": "DMRcaller", "Version": "1.34.0", "Depends": [ "R (>= 3.5)", "GenomicRanges", "IRanges", "S4Vectors (>= 0.23.10)" ], "Imports": [ "parallel", "Rcpp", "RcppRoll", "betareg", "grDevices", "graphics", "methods", "stats", "utils" ], "Suggests": [ "knitr", "RUnit", "BiocGenerics" ], "License": "GPL-3", "MD5sum": "1c5b37620b4950eb63e32c3063235e8e", "NeedsCompilation": "no", "Title": "Differentially Methylated Regions caller", "Description": "Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects.", "biocViews": [ "Coverage", "DNAMethylation", "DifferentialMethylation", "Sequencing", "Software" ], "Author": "Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt ", "Maintainer": "Nicolae Radu Zabet ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/DMRcaller", "git_branch": "RELEASE_3_18", "git_last_commit": "7b214e9", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DMRcaller_1.34.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DMRcaller_1.34.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/DMRcaller_1.34.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/DMRcaller_1.34.0.tgz", "vignettes": [ "vignettes/DMRcaller/inst/doc/DMRcaller.pdf" ], "vignetteTitles": [ "DMRcaller" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/DMRcaller/inst/doc/DMRcaller.R" ], "dependencyCount": "30", "Rank": 978 }, "DMRcate": { "Package": "DMRcate", "Version": "2.16.1", "Depends": [ "R (>= 4.0.0)" ], "Imports": [ "ExperimentHub", "bsseq", "GenomeInfoDb", "limma", "edgeR", "minfi", "missMethyl", "GenomicRanges", "plyr", "Gviz", "IRanges", "stats", "utils", "S4Vectors", "methods", "graphics", "SummarizedExperiment", "biomaRt" ], "Suggests": [ "knitr", "RUnit", "BiocGenerics", "IlluminaHumanMethylation450kanno.ilmn12.hg19", "IlluminaHumanMethylationEPICanno.ilm10b4.hg19", "FlowSorted.Blood.EPIC", "tissueTreg", "DMRcatedata" ], "License": "file LICENSE", "MD5sum": "6c37b23c3dc4f68915ab665f3572ad95", "NeedsCompilation": "no", "Title": "Methylation array and sequencing spatial analysis methods", "Description": "De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions.", "biocViews": [ "Coverage", "DNAMethylation", "DataImport", "DifferentialExpression", "DifferentialMethylation", "Epigenetics", "GeneExpression", "Genetics", "GenomeAnnotation", "MethylationArray", "Microarray", "MultipleComparison", "OneChannel", "Preprocessing", "QualityControl", "Sequencing", "Software", "TimeCourse", "TwoChannel", "WholeGenome" ], "Author": "Tim Peters", "Maintainer": "Tim Peters ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/DMRcate", "git_branch": "RELEASE_3_18", "git_last_commit": "4cd2397", "git_last_commit_date": "2023-12-04", "Date/Publication": "2023-12-05", "source.ver": "src/contrib/DMRcate_2.16.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DMRcate_2.16.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/DMRcate_2.16.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/DMRcate_2.16.1.tgz", "vignettes": [ "vignettes/DMRcate/inst/doc/DMRcate.pdf" ], "vignetteTitles": [ "The DMRcate package user's guide" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/DMRcate/inst/doc/DMRcate.R" ], "dependsOnMe": [ "ChAMP", "methylationArrayAnalysis" ], "suggestsMe": [ "missMethyl" ], "dependencyCount": "225", "Rank": 231 }, "DMRScan": { "Package": "DMRScan", "Version": "1.24.0", "Depends": [ "R (>= 3.6.0)" ], "Imports": [ "Matrix", "MASS", "RcppRoll", "GenomicRanges", "IRanges", "GenomeInfoDb", "methods", "mvtnorm", "stats", "parallel" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "BiocManager" ], "License": "GPL-3", "MD5sum": "698b81baf921ef394adedb8d8ff7ba7f", "NeedsCompilation": "no", "Title": "Detection of Differentially Methylated Regions", "Description": "This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.", "biocViews": [ "Sequencing", "Software", "Technology", "WholeGenome" ], "Author": "Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut]", "Maintainer": "Christian M Page ", "URL": "https://github.com/christpa/DMRScan", "VignetteBuilder": "knitr", "BugReports": "https://github.com/christpa/DMRScan/issues", "PackageStatus": "Active", "git_url": "https://git.bioconductor.org/packages/DMRScan", "git_branch": "RELEASE_3_18", "git_last_commit": "fded058", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DMRScan_1.24.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DMRScan_1.24.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/DMRScan_1.24.0.tgz", "vignettes": [ "vignettes/DMRScan/inst/doc/DMRScan_vignette.html" ], "vignetteTitles": [ "DMR Scan Vignette" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/DMRScan/inst/doc/DMRScan_vignette.R" ], "dependencyCount": "25", "Rank": 1938 }, "dmrseq": { "Package": "dmrseq", "Version": "1.22.1", "Depends": [ "R (>= 3.5)", "bsseq" ], "Imports": [ "GenomicRanges", "nlme", "ggplot2", "S4Vectors", "RColorBrewer", "bumphunter", "DelayedMatrixStats (>= 1.1.13)", "matrixStats", "BiocParallel", "outliers", "methods", "locfit", "IRanges", "grDevices", "graphics", "stats", "utils", "annotatr", "AnnotationHub", "rtracklayer", "GenomeInfoDb", "splines" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "TxDb.Hsapiens.UCSC.hg19.knownGene", "org.Hs.eg.db" ], "License": "MIT + file LICENSE", "MD5sum": "12917aa3916bb932e0f7be6ca29c8cc8", "NeedsCompilation": "no", "Title": "Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing", "Description": "This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.", "biocViews": [ "DNAMethylation", "DifferentialMethylation", "Epigenetics", "FunctionalGenomics", "ImmunoOncology", "MultipleComparison", "Regression", "Sequencing", "Software", "WholeGenome" ], "Author": "Keegan Korthauer [cre, aut] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut]", "Maintainer": "Keegan Korthauer ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/dmrseq", "git_branch": "RELEASE_3_18", "git_last_commit": "daaf07a", "git_last_commit_date": "2024-02-20", "Date/Publication": "2024-02-20", "source.ver": "src/contrib/dmrseq_1.22.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/dmrseq_1.22.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/dmrseq_1.22.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/dmrseq_1.22.1.tgz", "vignettes": [ "vignettes/dmrseq/inst/doc/dmrseq.html" ], "vignetteTitles": [ "Analyzing Bisulfite-seq data with dmrseq" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/dmrseq/inst/doc/dmrseq.R" ], "importsMe": [ "biscuiteer" ], "dependencyCount": "174", "Rank": 480 }, "DNABarcodeCompatibility": { "Package": "DNABarcodeCompatibility", "Version": "1.18.0", "Depends": [ "R (>= 3.6.0)" ], "Imports": [ "dplyr", "tidyr", "numbers", "purrr", "stringr", "DNABarcodes", "stats", "utils", "methods" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "testthat" ], "License": "file LICENSE", "MD5sum": "b1e2988c254cf089a2f887abf139b7bf", "NeedsCompilation": "no", "Title": "A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms", "Description": "The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.", "biocViews": [ "Preprocessing", "Sequencing", "Software" ], "Author": "Céline Trébeau [cre] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] ()", "Maintainer": "Céline Trébeau ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues", "git_url": "https://git.bioconductor.org/packages/DNABarcodeCompatibility", "git_branch": "RELEASE_3_18", "git_last_commit": "44ca03d", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DNABarcodeCompatibility_1.18.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DNABarcodeCompatibility_1.18.0.zip", "vignettes": [ "vignettes/DNABarcodeCompatibility/inst/doc/introduction.html" ], "vignetteTitles": [ "Introduction to DNABarcodeCompatibility" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/DNABarcodeCompatibility/inst/doc/introduction.R" ], "dependencyCount": "35", "Rank": 1938 }, "DNABarcodes": { "Package": "DNABarcodes", "Version": "1.32.0", "Depends": [ "Matrix", "parallel" ], "Imports": [ "Rcpp (>= 0.11.2)", "BH" ], "LinkingTo": [ "Rcpp", "BH" ], "Suggests": [ "knitr", "BiocStyle", "rmarkdown" ], "License": "GPL-2", "MD5sum": "6047e393e89e644fbf9aa4aa5a9faa64", "NeedsCompilation": "yes", "Title": "A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments", "Description": "The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode.", "biocViews": [ "Preprocessing", "Sequencing", "Software" ], "Author": "Tilo Buschmann ", "Maintainer": "Tilo Buschmann ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/DNABarcodes", "git_branch": "RELEASE_3_18", "git_last_commit": "5d76e74", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DNABarcodes_1.32.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DNABarcodes_1.32.0.zip", "vignettes": [ "vignettes/DNABarcodes/inst/doc/DNABarcodes.html" ], "vignetteTitles": [ "DNABarcodes" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/DNABarcodes/inst/doc/DNABarcodes.R" ], "importsMe": [ "DNABarcodeCompatibility" ], "dependencyCount": "11", "Rank": 2178 }, "DNAcopy": { "Package": "DNAcopy", "Version": "1.76.0", "License": "GPL (>= 2)", "MD5sum": "1b80859f79a39def302664f11b91a98f", "NeedsCompilation": "yes", "Title": "DNA Copy Number Data Analysis", "Description": "Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.", "biocViews": [ "CopyNumberVariation", "Microarray", "Software" ], "Author": "Venkatraman E. 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This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes.", "biocViews": [ "GeneFusionDetection", "Genetics", "Sequencing", "Software", "TargetedResequencing" ], "Author": "Christoffer Trier Maansson [aut, cre] (), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd]", "Maintainer": "Christoffer Trier Maansson ", "URL": "https://github.com/CTrierMaansson/DNAfusion", "VignetteBuilder": "knitr", "BugReports": "https://github.com/CTrierMaansson/DNAfusion/issues", "git_url": "https://git.bioconductor.org/packages/DNAfusion", "git_branch": "RELEASE_3_18", "git_last_commit": "6684cb7", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/DNAfusion_1.4.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/DNAfusion_1.4.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/DNAfusion_1.4.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/DNAfusion_1.4.0.tgz", "vignettes": [ "vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html" ], "vignetteTitles": [ "Introduction to DNAfusion" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R" ], "dependencyCount": "103", "Rank": 1899 }, "DNAshapeR": { "Package": "DNAshapeR", "Version": "1.30.0", "Depends": [ "R (>= 3.4)", "GenomicRanges" ], "Imports": [ "Rcpp (>= 0.12.1)", "Biostrings", "fields" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "AnnotationHub", "knitr", "rmarkdown", "testthat", "BSgenome.Scerevisiae.UCSC.sacCer3", "BSgenome.Hsapiens.UCSC.hg19", "caret" ], "License": "GPL-2", "MD5sum": "f2b90849d52bce75d88a4970f08c2544", "NeedsCompilation": "yes", "Title": "High-throughput prediction of DNA shape features", "Description": "DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. 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The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. 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While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model.", "biocViews": [ "GeneTarget", "GraphAndNetwork", "MotifAnnotation", "Software", "Transcription" ], "Author": "Zheng Wei, Zhana Duren, Shining Ma", "Maintainer": "Zheng Wei ", "URL": "https://github.com/wzthu/enrichTF", "VignetteBuilder": "knitr", "BugReports": "https://github.com/wzthu/enrichTF/issues", "git_url": "https://git.bioconductor.org/packages/enrichTF", "git_branch": "RELEASE_3_18", "git_last_commit": "fab8b47", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/enrichTF_1.18.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/enrichTF_1.18.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/enrichTF_1.18.0.tgz", "vignettes": [ "vignettes/enrichTF/inst/doc/enrichTF.html" ], "vignetteTitles": [ "An Introduction to enrichTF" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/enrichTF/inst/doc/enrichTF.R" ], "dependencyCount": "224", "Rank": 2178 }, "enrichViewNet": { "Package": "enrichViewNet", "Version": "1.0.0", "Depends": [ "R (>= 4.2.0)" ], "Imports": [ "gprofiler2", "strex", "RCy3", "jsonlite", "stringr", "enrichplot", "methods" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat" ], "License": "Artistic-2.0", "MD5sum": "5b2eb51ee4f57abd3b77da182e7e5d1a", "NeedsCompilation": "no", "Title": "From functional enrichment results to biological networks", "Description": "This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes.", "biocViews": [ "BiologicalQuestion", "GO", "Network", "NetworkEnrichment", "Software" ], "Author": "Astrid Deschênes [aut, cre] (), Pascal Belleau [aut] (), Robert L. Faure [aut] (), Maria J. Fernandes [aut] (), Alexander Krasnitz [aut], David A. 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The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. 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This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. 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The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. 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This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. 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GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. 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"hasLICENSE": false, "Rfiles": [ "vignettes/gaggle/inst/doc/gaggle.R" ], "dependencyCount": "9", "Rank": 2178 }, "GAprediction": { "Package": "GAprediction", "Version": "1.28.0", "Depends": [ "R (>= 3.3)" ], "Imports": [ "glmnet", "stats", "utils", "Matrix" ], "Suggests": [ "knitr", "rmarkdown" ], "License": "GPL (>=2)", "MD5sum": "5aceb1005e134eeeb2fc6a77941d8348", "NeedsCompilation": "no", "Title": "Prediction of gestational age with Illumina HumanMethylation450 data", "Description": "[GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data.", "biocViews": [ "BiomedicalInformatics", "DNAMethylation", "Epigenetics", "ImmunoOncology", "Regression", "Software" ], "Author": "Jon Bohlin", "Maintainer": "Jon Bohlin ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/GAprediction", "git_branch": "RELEASE_3_18", "git_last_commit": "d93600c", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/GAprediction_1.28.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/GAprediction_1.28.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/GAprediction_1.28.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/GAprediction_1.28.0.tgz", "vignettes": [ "vignettes/GAprediction/inst/doc/GAprediction.html" ], "vignetteTitles": [ "GAprediction" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/GAprediction/inst/doc/GAprediction.R" ], "dependencyCount": "17", "Rank": 1339 }, "garfield": { "Package": "garfield", "Version": "1.30.0", "Suggests": [ "knitr" ], "License": "GPL-3", "Archs": "x64", "MD5sum": "bf1974925a6c10b95ada01ff6d46bf15", "NeedsCompilation": "yes", "Title": "GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction", "Description": "GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. 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Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.", "biocViews": [ "Classification", "Clustering", "FeatureExtraction", "Software" ], "Author": "Mattia Chiesa , Luca Piacentini ", "Maintainer": "Mattia Chiesa ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/GARS", "git_branch": "RELEASE_3_18", "git_last_commit": "acbc127", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/GARS_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/GARS_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/GARS_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/GARS_1.22.0.tgz", "vignettes": [ "vignettes/GARS/inst/doc/GARS.pdf" ], "vignetteTitles": [ "GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/GARS/inst/doc/GARS.R" ], "dependencyCount": "265", "Rank": 1794 }, "GateFinder": { "Package": "GateFinder", "Version": "1.22.0", "Imports": [ "splancs", "mvoutlier", "methods", "stats", "diptest", "flowCore", "flowFP" ], "Suggests": [ "RUnit", "BiocGenerics" ], "License": "Artistic-2.0", "Archs": "x64", "MD5sum": "4fcb13ba6ee293d3ffb30122b652df12", "NeedsCompilation": "no", "Title": "Projection-based Gating Strategy Optimization for Flow and Mass Cytometry", "Description": "Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type.", "biocViews": [ "CellBiology", "Clustering", "FlowCytometry", "ImmunoOncology", "Software" ], "Author": "Nima Aghaeepour , Erin F. 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The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. 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GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population.", "biocViews": [ "GeneticVariability", "Genetics", "HiddenMarkovModel", "QualityControl", "SNP", "Sequencing", "Software" ], "Author": "Tomoyuki Furuta [aut, cre] ()", "Maintainer": "Tomoyuki Furuta ", "URL": "https://github.com/tomoyukif/GBScleanR", "SystemRequirements": "GNU make, C++11", "VignetteBuilder": "knitr", "BugReports": "https://github.com/tomoyukif/GBScleanR/issues", "git_url": "https://git.bioconductor.org/packages/GBScleanR", "git_branch": "RELEASE_3_18", "git_last_commit": "f849068", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/GBScleanR_1.6.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/GBScleanR_1.6.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/GBScleanR_1.6.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/GBScleanR_1.6.0.tgz", "vignettes": [ "vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html" ], "vignetteTitles": [ "BasicUsageOfGBScleanR.html" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R" ], "dependencyCount": "63", "Rank": 2012 }, "gcapc": { "Package": "gcapc", "Version": "1.26.0", "Depends": [ "R (>= 3.4)" ], "Imports": [ "BiocGenerics", "GenomeInfoDb", "S4Vectors", "IRanges", "Biostrings", "BSgenome", "GenomicRanges", "Rsamtools", "GenomicAlignments", "matrixStats", "MASS", "splines", "grDevices", "graphics", "stats", "methods" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "BSgenome.Hsapiens.UCSC.hg19", "BSgenome.Mmusculus.UCSC.mm10" ], "License": "GPL-3", "Archs": "x64", "MD5sum": "16c0a9ac78843641871680c0e0d48dfc", "NeedsCompilation": "no", "Title": "GC Aware Peak Caller", "Description": "Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.", "biocViews": [ "BatchEffect", "ChIPSeq", "PeakDetection", "Sequencing", "Software" ], "Author": "Mingxiang Teng and Rafael A. 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This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.", "biocViews": [ "DimensionReduction", "GenomeWideAssociation", "PrincipalComponent", "SNP", "Software" ], "Author": "Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (), John D. Storey [aut] ()", "Maintainer": "Alejandro Ochoa ", "URL": "https://github.com/StoreyLab/gcatest", "VignetteBuilder": "knitr", "BugReports": "https://github.com/StoreyLab/gcatest/issues", "git_url": "https://git.bioconductor.org/packages/gcatest", "git_branch": "RELEASE_3_18", "git_last_commit": "f3875f7", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/gcatest_2.2.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/gcatest_2.2.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/gcatest_2.2.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/gcatest_2.2.0.tgz", "vignettes": [ "vignettes/gcatest/inst/doc/gcatest.pdf" ], "vignetteTitles": [ "gcat Package" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/gcatest/inst/doc/gcatest.R" ], "dependencyCount": "13", "Rank": 1729 }, "gCrisprTools": { "Package": "gCrisprTools", "Version": "2.8.0", "Depends": [ "R (>= 4.1)" ], "Imports": [ "Biobase", "limma", "RobustRankAggreg", "ggplot2", "SummarizedExperiment", "grid", "rmarkdown", "grDevices", "graphics", "methods", "ComplexHeatmap", "stats", "utils", "parallel" ], "Suggests": [ "edgeR", "knitr", "AnnotationDbi", "org.Mm.eg.db", "org.Hs.eg.db", "BiocGenerics", "markdown", "RUnit", "sparrow", "msigdbr", "fgsea" ], "License": "Artistic-2.0", "MD5sum": "113ef693d51de0459865934e9a16d90a", "NeedsCompilation": "no", "Title": "Suite of Functions for Pooled Crispr Screen QC and Analysis", "Description": "Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the \"sparrow\" package, and streamlines many formal requirements to aid in interpretablity.", "biocViews": [ "BiomedicalInformatics", "CRISPR", "CellBiology", "DifferentialExpression", "ExperimentalDesign", "FunctionalGenomics", "GeneSetEnrichment", "Genetics", "ImmunoOncology", "MultipleComparison", "Normalization", "Pharmacogenetics", "Pharmacogenomics", "PooledScreens", "Preprocessing", "QualityControl", "RNASeq", "Regression", "Software", "SystemsBiology", "Visualization" ], "Author": "Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty", "Maintainer": "Russell Bainer ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/gCrisprTools", "git_branch": "RELEASE_3_18", "git_last_commit": "b31fbe6", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/gCrisprTools_2.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/gCrisprTools_2.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/gCrisprTools_2.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/gCrisprTools_2.8.0.tgz", "vignettes": [ "vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html", "vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html", "vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html" ], "vignetteTitles": [ "Contrast_Comparisons_gCrisprTools", "Example_Workflow_gCrisprTools", "gCrisprTools_Vignette" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R", "vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R", "vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R" ], "dependencyCount": "94", "Rank": 1429 }, "gcrma": { "Package": "gcrma", "Version": "2.74.0", "Depends": [ "R (>= 2.6.0)", "affy (>= 1.23.2)", "graphics", "methods", "stats", "utils" ], "Imports": [ "Biobase", "affy (>= 1.23.2)", "affyio (>= 1.13.3)", "XVector", "Biostrings (>= 2.11.32)", "splines", "BiocManager" ], "Suggests": [ "affydata", "tools", "splines", "hgu95av2cdf", "hgu95av2probe" ], "License": "LGPL", "MD5sum": "d28b6c225b2677e476da8d0348af4695", "NeedsCompilation": "yes", "Title": "Background Adjustment Using Sequence Information", "Description": "Background adjustment using sequence information", "biocViews": [ "Microarray", "OneChannel", "Preprocessing", "Software" ], "Author": "Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry", "Maintainer": "Z. 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Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. 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To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have celltype specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally TF activity data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.", "biocViews": [ "ATACSeq", "BiomedicalInformatics", "ChIPSeq", "GeneExpression", "GeneRegulation", "GeneSetEnrichment", "Genetics", "GraphAndNetwork", "NetworkInference", "RNASeq", "Regression", "Software", "Transcription", "Transcriptomics" ], "Author": "Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb]", "Maintainer": "Christian Arnold ", "URL": "https://grp-zaugg.embl-community.io/GRaNIE", "VignetteBuilder": "knitr", "BugReports": "https://git.embl.de/grp-zaugg/GRaNIE/issues", "git_url": "https://git.bioconductor.org/packages/GRaNIE", "git_branch": "RELEASE_3_18", "git_last_commit": "35f2aa9", "git_last_commit_date": "2023-10-26", "Date/Publication": "2023-10-26", "source.ver": "src/contrib/GRaNIE_1.6.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/GRaNIE_1.6.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/GRaNIE_1.6.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/GRaNIE_1.6.1.tgz", "vignettes": [ "vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html", "vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.html", "vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html" ], "vignetteTitles": [ "Package Details", "Single-cell eGRN inference", "Workflow example" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R", "vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.R", "vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R" ], "dependencyCount": "178", "Rank": 710 }, "granulator": { "Package": "granulator", "Version": "1.10.0", "Depends": [ "R (>= 4.1)" ], "Imports": [ "cowplot", "e1071", "epiR", "dplyr", "dtangle", "ggplot2", "ggplotify", "grDevices", "limSolve", "magrittr", "MASS", "nnls", "parallel", "pheatmap", "purrr", "rlang", "stats", "tibble", "tidyr", "utils" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat" ], "License": "GPL-3", "MD5sum": "05c4025460a33eb195280f676bd5695f", "NeedsCompilation": "no", "Title": "Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data", "Description": "granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.", "biocViews": [ "DifferentialExpression", "GeneExpression", "RNASeq", "Regression", "SingleCell", "Software", "StatisticalMethod", "Transcriptomics" ], "Author": "Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico Ferrero [aut]", "Maintainer": "Sabina Pfister ", "URL": "https://github.com/xanibas/granulator", "VignetteBuilder": "knitr", "BugReports": "https://github.com/xanibas/granulator/issues", "git_url": "https://git.bioconductor.org/packages/granulator", "git_branch": "RELEASE_3_18", "git_last_commit": "deb1923", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/granulator_1.10.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/granulator_1.10.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/granulator_1.10.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/granulator_1.10.0.tgz", "vignettes": [ "vignettes/granulator/inst/doc/granulator.html" ], "vignetteTitles": [ "Deconvoluting bulk RNA-seq data with granulator" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/granulator/inst/doc/granulator.R" ], "suggestsMe": [ "deconvR" ], "dependencyCount": "125", "Rank": 627 }, "graper": { "Package": "graper", "Version": "1.18.0", "Depends": [ "R (>= 3.6)" ], "Imports": [ "Matrix", "Rcpp", "stats", "ggplot2", "methods", "cowplot", "matrixStats" ], "LinkingTo": [ "Rcpp", "RcppArmadillo", "BH" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "testthat" ], "License": "GPL (>= 2)", "MD5sum": "e969608b591224df6c92d73194adea89", "NeedsCompilation": "yes", "Title": "Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes", "Description": "This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. 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The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. 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It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. 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Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets.", "biocViews": [ "Bayesian", "GeneExpression", "GeneRegulation", "GraphAndNetwork", "Network", "NetworkInference", "PrincipalComponent", "Regression", "Software", "SystemsBiology", "Transcription", "Transcriptomics" ], "Author": "Luis F. 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With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. 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Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows.", "biocViews": [ "CRISPR", "FunctionalGenomics", "GeneSetEnrichment", "GeneTarget", "KEGG", "Normalization", "Pathways", "PooledScreens", "QualityControl", "Software", "Visualization" ], "Author": "Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. 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GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index.", "biocViews": [ "GraphAndNetwork", "MotifDiscovery", "NetworkEnrichment", "Software", "SystemsBiology" ], "Author": "Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] ()", "Maintainer": "Fabrício Almeida-Silva ", "URL": "https://github.com/almeidasilvaf/magrene", "VignetteBuilder": "knitr", "BugReports": "https://support.bioconductor.org/t/magrene", "git_url": "https://git.bioconductor.org/packages/magrene", "git_branch": "RELEASE_3_18", "git_last_commit": "268c106", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/magrene_1.4.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/magrene_1.4.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/magrene_1.4.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/magrene_1.4.0.tgz", "vignettes": [ "vignettes/magrene/inst/doc/magrene.html" ], "vignetteTitles": [ "Introduction to magrene" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/magrene/inst/doc/magrene.R" ], "dependencyCount": "13", "Rank": 1970 }, "MAI": { "Package": "MAI", "Version": "1.8.0", "Depends": [ "R (>= 3.5.0)" ], "Imports": [ "caret", "parallel", "doParallel", "foreach", "e1071", "future.apply", "future", "missForest", "pcaMethods", "tidyverse", "stats", "utils", "methods", "SummarizedExperiment", "S4Vectors" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "testthat (>= 3.0.0)" ], "License": "GPL-3", "MD5sum": "ac8dbd997d58657f8ed6b64f5cdee7a9", "NeedsCompilation": "no", "Title": "Mechanism-Aware Imputation", "Description": "A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. 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For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro).", "biocViews": [ "Classification", "DNAMethylation", "Epigenetics", "ImmunoOncology", "Software" ], "Author": "Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph]", "Maintainer": "Helena Carén ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/MethPed", "git_branch": "RELEASE_3_18", "git_last_commit": "1b88a6f", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/MethPed_1.30.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MethPed_1.30.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MethPed_1.30.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MethPed_1.30.0.tgz", "vignettes": [ "vignettes/MethPed/inst/doc/MethPed-vignette.html" ], "vignetteTitles": [ "MethPed User Guide" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MethPed/inst/doc/MethPed-vignette.R" ], "dependencyCount": "8", "Rank": 1729 }, "MethReg": { "Package": "MethReg", "Version": "1.12.0", "Depends": [ "R (>= 4.0)" ], "Imports": [ "dplyr", "plyr", "GenomicRanges", "SummarizedExperiment", "DelayedArray", "ggplot2", "ggpubr", "tibble", "tidyr", "S4Vectors", "sesameData", "sesame", "AnnotationHub", "ExperimentHub", "stringr", "readr", "methods", "stats", "Matrix", "MASS", "rlang", "pscl", "IRanges", "sfsmisc", "progress", "utils" ], "Suggests": [ "rmarkdown", "BiocStyle", "testthat (>= 2.1.0)", "parallel", "downloader", "R.utils", "doParallel", "reshape2", "JASPAR2022", "TFBSTools", "motifmatchr", "matrixStats", "biomaRt", "dorothea", "viper", "stageR", "BiocFileCache", "png", "htmltools", "knitr", "jpeg", "BSgenome.Hsapiens.UCSC.hg38", "BSgenome.Hsapiens.UCSC.hg19", "openxlsx", "data.table", "downloader" ], "License": "GPL-3", "MD5sum": "0c092ea7b8883b6c125e64e16030bca6", "NeedsCompilation": "no", "Title": "Assessing the regulatory potential of DNA methylation regions or sites on gene transcription", "Description": "Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.", "biocViews": [ "Epigenetics", "GeneExpression", "GeneTarget", "MethylationArray", "Regression", "Software", "Transcription" ], "Author": "Tiago Silva [aut, cre] (), Lily Wang [aut]", "Maintainer": "Tiago Silva ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/TransBioInfoLab/MethReg/issues/", "git_url": "https://git.bioconductor.org/packages/MethReg", "git_branch": "RELEASE_3_18", "git_last_commit": "30684d3", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/MethReg_1.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MethReg_1.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MethReg_1.12.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MethReg_1.12.0.tgz", "vignettes": [ "vignettes/MethReg/inst/doc/MethReg.html" ], "vignetteTitles": [ "MethReg: estimating regulatory potential of DNA methylation in gene transcription" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MethReg/inst/doc/MethReg.R" ], "dependencyCount": "186", "Rank": 1048 }, "methrix": { "Package": "methrix", "Version": "1.16.0", "Depends": [ "R (>= 3.6)", "data.table (>= 1.12.4)", "SummarizedExperiment" ], "Imports": [ "rtracklayer", "DelayedArray", "HDF5Array", "BSgenome", "DelayedMatrixStats", "parallel", "methods", "ggplot2", "S4Vectors", "matrixStats", "graphics", "stats", "utils", "GenomicRanges", "IRanges" ], "Suggests": [ "knitr", "rmarkdown", "DSS", "bsseq", "plotly", "BSgenome.Mmusculus.UCSC.mm9", "MafDb.1Kgenomes.phase3.GRCh38", "MafDb.1Kgenomes.phase3.hs37d5", "BSgenome.Hsapiens.UCSC.hg19", "GenomicScores", "Biostrings", "RColorBrewer", "GenomeInfoDb", "testthat (>= 2.1.0)" ], "License": "MIT + file LICENSE", "Archs": "x64", "MD5sum": "7bdda59fc802fc09d10ca9a4916cfe94", "NeedsCompilation": "no", "Title": "Fast and efficient summarization of generic bedGraph files from Bisufite sequencing", "Description": "Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.", "biocViews": [ "Coverage", "DNAMethylation", "Sequencing", "Software" ], "Author": "Anand Mayakonda [aut, cre] (), Reka Toth [aut] (), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb]", "Maintainer": "Anand Mayakonda ", "URL": "https://github.com/CompEpigen/methrix", "VignetteBuilder": "knitr", "BugReports": "https://github.com/CompEpigen/methrix/issues", "git_url": "https://git.bioconductor.org/packages/methrix", "git_branch": "RELEASE_3_18", "git_last_commit": "6ed5233", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/methrix_1.16.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/methrix_1.16.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/methrix_1.16.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/methrix_1.16.0.tgz", "vignettes": [ "vignettes/methrix/inst/doc/methrix.html" ], "vignetteTitles": [ "Methrix tutorial" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/methrix/inst/doc/methrix.R" ], "dependencyCount": "85", "Rank": 994 }, "MethTargetedNGS": { "Package": "MethTargetedNGS", "Version": "1.34.0", "Depends": [ "R (>= 3.1.2)", "stringr", "seqinr", "gplots", "Biostrings" ], "License": "Artistic-2.0", "MD5sum": "0bce9edc9682a9ca23653409301d7225", "NeedsCompilation": "no", "Title": "Perform Methylation Analysis on Next Generation Sequencing Data", "Description": "Perform step by step methylation analysis of Next Generation Sequencing data.", "biocViews": [ "Alignment", "DataImport", "Genetics", "ResearchField", "SequenceMatching", "Sequencing", "Software" ], "Author": "Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. 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Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.", "biocViews": [ "BatchEffect", "DNAMethylation", "GUI", "MethylationArray", "Microarray", "QualityControl", "Software", "TwoChannel", "Visualization" ], "Author": "Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb]", "Maintainer": "L.J.Sinke ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/MethylAid", "git_branch": "RELEASE_3_18", "git_last_commit": "2ab1e43", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/MethylAid_1.36.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MethylAid_1.36.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MethylAid_1.36.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MethylAid_1.36.0.tgz", "vignettes": [ "vignettes/MethylAid/inst/doc/MethylAid.pdf" ], "vignetteTitles": [ "MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MethylAid/inst/doc/MethylAid.R" ], "dependsOnMe": [ "MethylAidData" ], "dependencyCount": "169", "Rank": 947 }, "methylCC": { "Package": "methylCC", "Version": "1.16.0", "Depends": [ "R (>= 3.6)", "FlowSorted.Blood.450k" ], "Imports": [ "Biobase", "GenomicRanges", "IRanges", "S4Vectors", "dplyr", "magrittr", "minfi", "bsseq", "quadprog", "plyranges", "stats", "utils", "bumphunter", "genefilter", "methods", "IlluminaHumanMethylation450kmanifest", "IlluminaHumanMethylation450kanno.ilmn12.hg19" ], "Suggests": [ "rmarkdown", "knitr", "testthat (>= 2.1.0)", "BiocGenerics", "BiocStyle", "tidyr", "ggplot2" ], "License": "GPL-3", "MD5sum": "77566e7338f615ca5305d6e00465908b", "NeedsCompilation": "no", "Title": "Estimate the cell composition of whole blood in DNA methylation samples", "Description": "A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing).", "biocViews": [ "DNAMethylation", "MethylSeq", "MethylationArray", "Microarray", "Sequencing", "Software", "WholeGenome" ], "Author": "Stephanie C. Hicks [aut, cre] (), Rafael Irizarry [aut] ()", "Maintainer": "Stephanie C. Hicks ", "URL": "https://github.com/stephaniehicks/methylCC/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/stephaniehicks/methylCC/", "git_url": "https://git.bioconductor.org/packages/methylCC", "git_branch": "RELEASE_3_18", "git_last_commit": "5122b11", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/methylCC_1.16.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/methylCC_1.16.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/methylCC_1.16.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/methylCC_1.16.0.tgz", "vignettes": [ "vignettes/methylCC/inst/doc/methylCC.html" ], "vignetteTitles": [ "The methylCC user's guide" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/methylCC/inst/doc/methylCC.R" ], "dependencyCount": "159", "Rank": 1095 }, "methylclock": { "Package": "methylclock", "Version": "1.8.0", "Depends": [ "R (>= 4.1.0)", "methylclockData", "devtools", "quadprog" ], "Imports": [ "Rcpp (>= 1.0.6)", "ExperimentHub", "dplyr", "impute", "PerformanceAnalytics", "Biobase", "ggpmisc", "tidyverse", "ggplot2", "ggpubr", "minfi", "tibble", "RPMM", "stats", "graphics", "tidyr", "gridExtra", "preprocessCore", "dynamicTreeCut", "planet" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "BiocStyle", "knitr", "GEOquery", "rmarkdown" ], "License": "MIT + file LICENSE", "Archs": "x64", "MD5sum": "b6151e23d6086f38d6489dcc277c81c9", "NeedsCompilation": "yes", "Title": "Methylclock - DNA methylation-based clocks", "Description": "This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock.", "biocViews": [ "BiologicalQuestion", "DNAMethylation", "Normalization", "Preprocessing", "Software", "StatisticalMethod" ], "Author": "Dolors Pelegri-Siso [aut, cre] (), Juan R. Gonzalez [aut] ()", "Maintainer": "Dolors Pelegri-Siso ", "URL": "https://github.com/isglobal-brge/methylclock", "VignetteBuilder": "knitr", "BugReports": "https://github.com/isglobal-brge/methylclock/issues", "git_url": "https://git.bioconductor.org/packages/methylclock", "git_branch": "RELEASE_3_18", "git_last_commit": "bcf2548", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/methylclock_1.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/methylclock_1.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/methylclock_1.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/methylclock_1.8.0.tgz", "vignettes": [ "vignettes/methylclock/inst/doc/methylclock.html" ], "vignetteTitles": [ "DNAm age using diffrent methylation clocks" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/methylclock/inst/doc/methylclock.R" ], "dependencyCount": "295", "Rank": 574 }, "methylGSA": { "Package": "methylGSA", "Version": "1.20.0", "Depends": [ "R (>= 3.5)" ], "Imports": [ "RobustRankAggreg", "ggplot2", "stringr", "stats", "clusterProfiler", "missMethyl", "org.Hs.eg.db", "reactome.db", "BiocParallel", "GO.db", "AnnotationDbi", "shiny", "IlluminaHumanMethylation450kanno.ilmn12.hg19", "IlluminaHumanMethylationEPICanno.ilm10b4.hg19" ], "Suggests": [ "knitr", "rmarkdown", "testthat", "enrichplot" ], "License": "GPL-2", "MD5sum": "e802fab6abc1a9bac64bc36dd588c19d", "NeedsCompilation": "no", "Title": "Gene Set Analysis Using the Outcome of Differential Methylation", "Description": "The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. 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Ernst [aut] (), Leo Lahti [aut] (), Basil Courbayre [ctb], Giulio Benedetti [ctb] ()", "Maintainer": "Tuomas Borman ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/miaViz", "git_branch": "RELEASE_3_18", "git_last_commit": "09da1eb", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/miaViz_1.10.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/miaViz_1.10.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/miaViz_1.10.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/miaViz_1.10.0.tgz", "vignettes": [ "vignettes/miaViz/inst/doc/miaViz.html" ], "vignetteTitles": [ "miaViz" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/miaViz/inst/doc/miaViz.R" ], "suggestsMe": [ "miaSim" ], "dependencyCount": "154", "Rank": 435 }, "MiChip": { "Package": "MiChip", "Version": "1.56.0", "Depends": [ "R (>= 2.3.0)", "Biobase" ], "Imports": [ "Biobase" ], "License": "GPL (>= 2)", "MD5sum": "28daafdf6588314a9fad7a75b1671c10", "NeedsCompilation": "no", "Title": "MiChip Parsing and Summarizing Functions", "Description": "This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages.", "biocViews": [ "Microarray", "Preprocessing", "Software" ], "Author": "Jonathon Blake ", "Maintainer": "Jonathon Blake ", "git_url": "https://git.bioconductor.org/packages/MiChip", "git_branch": "RELEASE_3_18", "git_last_commit": "b3be377", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/MiChip_1.56.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MiChip_1.56.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MiChip_1.56.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MiChip_1.56.0.tgz", "vignettes": [ "vignettes/MiChip/inst/doc/MiChip.pdf" ], "vignetteTitles": [ "MiChip miRNA Microarray Processing" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MiChip/inst/doc/MiChip.R" ], "dependencyCount": "6", "Rank": 1843 }, "microbiome": { "Package": "microbiome", "Version": "1.24.0", "Depends": [ "R (>= 3.6.0)", "phyloseq", "ggplot2" ], "Imports": [ "Biostrings", "compositions", "dplyr", "reshape2", "Rtsne", "scales", "stats", "tibble", "tidyr", "utils", "vegan" ], "Suggests": [ "BiocGenerics", "BiocStyle", "Cairo", "knitr", "rmarkdown", "testthat" ], "License": "BSD_2_clause + file LICENSE", "MD5sum": "2d185cf8b8bf7d23f295dee1af621a0f", "NeedsCompilation": "no", "Title": "Microbiome Analytics", "Description": "Utilities for microbiome analysis.", "biocViews": [ "Metagenomics", "Microbiome", "Sequencing", "Software", "SystemsBiology" ], "Author": "Leo Lahti [aut, cre] (), Sudarshan Shetty [aut] ()", "Maintainer": "Leo Lahti ", "URL": "http://microbiome.github.io/microbiome", "VignetteBuilder": "knitr", "BugReports": "https://github.com/microbiome/microbiome/issues", "git_url": "https://git.bioconductor.org/packages/microbiome", "git_branch": "RELEASE_3_18", "git_last_commit": "4e08c43", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/microbiome_1.24.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/microbiome_1.24.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/microbiome_1.24.0.tgz", "vignettes": [ "vignettes/microbiome/inst/doc/vignette.html" ], "vignetteTitles": [ "microbiome R package" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/microbiome/inst/doc/vignette.R" ], "importsMe": "MicrobiomeSurv", "suggestsMe": [ "ANCOMBC" ], "dependencyCount": "89", "Rank": 153 }, "microbiomeDASim": { "Package": "microbiomeDASim", "Version": "1.16.0", "Depends": [ "R (>= 3.6.0)" ], "Imports": [ "graphics", "ggplot2", "MASS", "tmvtnorm", "Matrix", "mvtnorm", "pbapply", "stats", "phyloseq", "metagenomeSeq", "Biobase" ], "Suggests": [ "testthat (>= 2.1.0)", "knitr", "devtools" ], "License": "MIT + file LICENSE", "MD5sum": "9ddf99ab393766f7bcb9dee13b3707f9", "NeedsCompilation": "no", "Title": "Microbiome Differential Abundance Simulation", "Description": "A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance.", "biocViews": [ "Microbiome", "Software", "Visualization" ], "Author": "Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson", "Maintainer": "Justin Williams ", "URL": "https://github.com/williazo/microbiomeDASim", "VignetteBuilder": "knitr", "BugReports": "https://github.com/williazo/microbiomeDASim/issues", "git_url": "https://git.bioconductor.org/packages/microbiomeDASim", "git_branch": "RELEASE_3_18", "git_last_commit": "f2a8522", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/microbiomeDASim_1.16.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/microbiomeDASim_1.16.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/microbiomeDASim_1.16.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/microbiomeDASim_1.16.0.tgz", "vignettes": [ "vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf" ], "vignetteTitles": [ "microbiomeDASim" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R" ], "dependencyCount": "97", "Rank": 1550 }, "microbiomeExplorer": { "Package": "microbiomeExplorer", "Version": "1.12.0", "Depends": [ "shiny", "magrittr", "metagenomeSeq", "Biobase" ], "Imports": [ "shinyjs (>= 2.0.0)", "shinydashboard", "shinycssloaders", "shinyWidgets", "rmarkdown (>= 1.9.0)", "DESeq2", "RColorBrewer", "dplyr", "tidyr", "purrr", "rlang", "knitr", "readr", "DT (>= 0.12.0)", "biomformat", "tools", "stringr", "vegan", "matrixStats", "heatmaply", "car", "broom", "limma", "reshape2", "tibble", "forcats", "lubridate", "methods", "plotly (>= 4.9.1)" ], "Suggests": [ "V8", "testthat (>= 2.1.0)" ], "License": "MIT + file LICENSE", "MD5sum": "c1e966728a968697ad53fd6405c156c5", "NeedsCompilation": "no", "Title": "Microbiome Exploration App", "Description": "The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.", "biocViews": [ "Classification", "Clustering", "DifferentialExpression", "GeneticVariability", "ImmunoOncology", "Metagenomics", "Microbiome", "MultipleComparison", "Normalization", "Sequencing", "Software", "Visualization" ], "Author": "Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd]", "Maintainer": "Janina Reeder ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/microbiomeExplorer", "git_branch": "RELEASE_3_18", "git_last_commit": "83cb131", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/microbiomeExplorer_1.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/microbiomeExplorer_1.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/microbiomeExplorer_1.12.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/microbiomeExplorer_1.12.0.tgz", "vignettes": [ "vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html" ], "vignetteTitles": [ "microbiomeExplorer" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R" ], "dependencyCount": "198", "Rank": 1025 }, "microbiomeMarker": { "Package": "microbiomeMarker", "Version": "1.8.0", "Depends": [ "R (>= 4.1.0)" ], "Imports": [ "dplyr", "phyloseq", "magrittr", "purrr", "MASS", "utils", "ggplot2", "tibble", "rlang", "stats", "coin", "ggtree", "tidytree", "methods", "IRanges", "tidyr", "patchwork", "ggsignif", "metagenomeSeq", "DESeq2", "edgeR", "BiocGenerics", "Biostrings", "yaml", "biomformat", "S4Vectors", "Biobase", "ComplexHeatmap", "ANCOMBC", "caret", "limma", "ALDEx2", "multtest", "plotROC", "vegan", "pROC", "BiocParallel" ], "Suggests": [ "testthat", "covr", "glmnet", "Matrix", "kernlab", "e1071", "ranger", "knitr", "rmarkdown", "BiocStyle", "withr" ], "License": "GPL-3", "MD5sum": "facf2f8071da6049b688fe3d16efb0e2", "NeedsCompilation": "no", "Title": "microbiome biomarker analysis toolkit", "Description": "To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers.", "biocViews": [ "DifferentialExpression", "Metagenomics", "Microbiome", "Software" ], "Author": "Yang Cao [aut, cre]", "Maintainer": "Yang Cao ", "URL": "https://github.com/yiluheihei/microbiomeMarker", "VignetteBuilder": "knitr", "BugReports": "https://github.com/yiluheihei/microbiomeMarker/issues", "git_url": "https://git.bioconductor.org/packages/microbiomeMarker", "git_branch": "RELEASE_3_18", "git_last_commit": "577f9da", "git_last_commit_date": "2023-11-04", "Date/Publication": "2023-11-05", "source.ver": "src/contrib/microbiomeMarker_1.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/microbiomeMarker_1.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/microbiomeMarker_1.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/microbiomeMarker_1.8.0.tgz", "vignettes": [ "vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.html" ], "vignetteTitles": [ "Tools for microbiome marker identification" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.R" ], "dependencyCount": "314", "Rank": 341 }, "MicrobiomeProfiler": { "Package": "MicrobiomeProfiler", "Version": "1.8.0", "Depends": [ "R (>= 4.2.0)" ], "Imports": [ "clusterProfiler (>= 4.5.2)", "config", "DT", "enrichplot", "golem", "gson", "methods", "magrittr", "shiny (>= 1.6.0)", "shinyWidgets", "shinycustomloader", "htmltools", "ggplot2", "graphics", "stats", "utils" ], "Suggests": [ "rmarkdown", "knitr", "testthat (>= 3.0.0)", "prettydoc" ], "License": "GPL-2", "MD5sum": "47851ff17ed03e5b448a09513c7bf122", "NeedsCompilation": "no", "Title": "An R/shiny package for microbiome functional enrichment analysis", "Description": "This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis.", "biocViews": [ "KEGG", "Microbiome", "Software", "Visualization" ], "Author": "Guangchuang Yu [aut, ths] (), Meijun Chen [aut, cre] ()", "Maintainer": "Meijun Chen ", "URL": "https://github.com/YuLab-SMU/MicrobiomeProfiler/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/YuLab-SMU/MicrobiomeProfiler/issues", "git_url": "https://git.bioconductor.org/packages/MicrobiomeProfiler", "git_branch": "RELEASE_3_18", "git_last_commit": "4aff8e7", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/MicrobiomeProfiler_1.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MicrobiomeProfiler_1.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MicrobiomeProfiler_1.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MicrobiomeProfiler_1.8.0.tgz", "vignettes": [ "vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html" ], "vignetteTitles": [ "Introduction to MicrobiotaProcess" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R" ], "dependencyCount": "162", "Rank": 812 }, "MicrobiotaProcess": { "Package": "MicrobiotaProcess", "Version": "1.14.1", "Depends": [ "R (>= 4.0.0)" ], "Imports": [ "ape", "tidyr", "ggplot2", "magrittr", "dplyr", "Biostrings", "ggrepel", "vegan", "zoo", "ggtree", "tidytree (>= 0.4.2)", "MASS", "methods", "rlang", "tibble", "grDevices", "stats", "utils", "coin", "ggsignif", "patchwork", "ggstar", "tidyselect", "SummarizedExperiment", "foreach", "treeio (>= 1.17.2)", "pillar", "cli", "plyr", "dtplyr", "ggtreeExtra", "data.table", "ggfun (>= 0.1.1)" ], "Suggests": [ "rmarkdown", "prettydoc", "testthat", "knitr", "nlme", "phangorn", "DECIPHER", "randomForest", "jsonlite", "biomformat", "scales", "yaml", "withr", "S4Vectors", "purrr", "seqmagick", "glue", "ggupset", "ggVennDiagram", "gghalves", "ggalluvial (>= 0.11.1)", "forcats", "phyloseq", "aplot", "ggnewscale", "ggside", "ggh4x", "hopach", "parallel", "shadowtext", "DirichletMultinomial", "ggpp", "BiocManager" ], "License": "GPL (>= 3.0)", "Archs": "x64", "MD5sum": "3cbe1b72a8f91301c18ffca401297c4b", "NeedsCompilation": "no", "Title": "A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework", "Description": "MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework).", "biocViews": [ "FeatureExtraction", "Microbiome", "MultipleComparison", "Software", "Visualization" ], "Author": "Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] ()", "Maintainer": "Shuangbin Xu ", "URL": "https://github.com/YuLab-SMU/MicrobiotaProcess/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/YuLab-SMU/MicrobiotaProcess/issues", "git_url": "https://git.bioconductor.org/packages/MicrobiotaProcess", "git_branch": "RELEASE_3_18", "git_last_commit": "9fc267f", "git_last_commit_date": "2024-03-26", "Date/Publication": "2024-03-27", "source.ver": "src/contrib/MicrobiotaProcess_1.14.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/MicrobiotaProcess_1.14.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/MicrobiotaProcess_1.14.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/MicrobiotaProcess_1.14.1.tgz", "vignettes": [ "vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html" ], "vignetteTitles": [ "Introduction to MicrobiotaProcess" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R" ], "dependencyCount": "108", "Rank": 293 }, "microRNA": { "Package": "microRNA", "Version": "1.60.0", "Depends": [ "R (>= 2.10)" ], "Imports": [ "Biostrings (>= 2.11.32)" ], "License": "Artistic-2.0", "MD5sum": "04b7af542581242a8e6ba5d4f19c14ca", "NeedsCompilation": "yes", "Title": "Data and functions for dealing with microRNAs", "Description": "Different data resources for microRNAs and some functions for manipulating them.", "biocViews": [ "GenomeAnnotation", "Infrastructure", "SequenceMatching", "Software" ], "Author": "R. Gentleman, S. Falcon", "Maintainer": "\"James F. 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MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. 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At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. 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The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. 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It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.", "biocViews": [ "Clustering", "DataImport", "DataRepresentation", "DifferentialExpression", "GeneExpression", "ImmunoOncology", "Infrastructure", "MultipleComparison", "QualityControl", "RNASeq", "Sequencing", "Software", "Visualization" ], "Author": "Cole Trapnell", "Maintainer": "Cole Trapnell ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/monocle", "git_branch": "RELEASE_3_18", "git_last_commit": "dc2a481", "git_last_commit_date": "2024-04-01", "Date/Publication": "2024-04-01", "source.ver": "src/contrib/monocle_2.30.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/monocle_2.30.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/monocle_2.30.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/monocle_2.30.1.tgz", "vignettes": [ "vignettes/monocle/inst/doc/monocle-vignette.pdf" ], "vignetteTitles": [ "Monocle: Cell counting, differential expression, and trajectory analysis for single-cell RNA-Seq experiments" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/monocle/inst/doc/monocle-vignette.R" ], "dependsOnMe": [ "cicero", "phemd" ], "importsMe": [ "uSORT" ], "suggestsMe": [ "scran", "sincell" ], "dependencyCount": "78", "Rank": 116 }, "Moonlight2R": { "Package": "Moonlight2R", "Version": "1.0.0", "Depends": [ "R (>= 4.3)", "doParallel", "foreach" ], "Imports": [ "parmigene", "randomForest", "gplots", "circlize", "RColorBrewer", "HiveR", "clusterProfiler", "DOSE", "Biobase", "grDevices", "graphics", "GEOquery", "stats", "purrr", "RISmed", "grid", "utils", "ComplexHeatmap", "GenomicRanges", "dplyr", "fuzzyjoin", "rtracklayer", "magrittr", "qpdf", "readr", "seqminer", "stringr", "tibble", "tidyHeatmap", "tidyr", "AnnotationHub", "easyPubMed", "org.Hs.eg.db" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat (>= 3.0.0)", "devtools", "roxygen2", "png" ], "License": "GPL-3", "MD5sum": "45af1bc580f9231cb2a83eae7517d3b8", "NeedsCompilation": "no", "Title": "Identify oncogenes and tumor suppressor genes from omics data", "Description": "The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.", "biocViews": [ "DNAMethylation", "DifferentialExpression", "DifferentialMethylation", "GeneExpression", "GeneRegulation", "GeneSetEnrichment", "MethylationArray", "Network", "NetworkEnrichment", "Pathways", "Software", "Survival" ], "Author": "Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut], Xi Steven Chen [aut], Antonio Colaprico [aut], Catharina Olsen [aut], Matteo Tiberti [cre, aut], Elena Papaleo [aut]", "Maintainer": "Matteo Tiberti ", "URL": "https://github.com/ELELAB/Moonlight2R", "SystemRequirements": "CScapeSomatic", "VignetteBuilder": "knitr", "BugReports": "https://github.com/ELELAB/Moonlight2R/issues", "git_url": "https://git.bioconductor.org/packages/Moonlight2R", "git_branch": "RELEASE_3_18", "git_last_commit": "f5bfcb9", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/Moonlight2R_1.0.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/Moonlight2R_1.0.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/Moonlight2R_1.0.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/Moonlight2R_1.0.0.tgz", "vignettes": [ "vignettes/Moonlight2R/inst/doc/Moonlight2R.html" ], "vignetteTitles": [ "A workflow to study mechanistic indicators for driver gene prediction with Moonlight" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": true, "hasLICENSE": false, "Rfiles": [ "vignettes/Moonlight2R/inst/doc/Moonlight2R.R" ], "dependencyCount": "223", "Rank": 2109 }, "MoonlightR": { "Package": "MoonlightR", "Version": "1.28.0", "Depends": [ "R (>= 3.5)", "doParallel", "foreach" ], "Imports": [ "parmigene", "randomForest", "SummarizedExperiment", "gplots", "circlize", "RColorBrewer", "HiveR", "clusterProfiler", "DOSE", "Biobase", "limma", "grDevices", "graphics", "TCGAbiolinks", "GEOquery", "stats", "RISmed", "grid", "utils" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat", "devtools", "roxygen2", "png", "edgeR" ], "License": "GPL (>= 3)", "Archs": "x64", "MD5sum": "2a389ad9b7fe5ffc5e2c47717eb0b4c0", "NeedsCompilation": "no", "Title": "Identify oncogenes and tumor suppressor genes from omics data", "Description": "Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.", "biocViews": [ "DNAMethylation", "DifferentialExpression", "DifferentialMethylation", "GeneExpression", "GeneRegulation", "GeneSetEnrichment", "MethylationArray", "Network", "NetworkEnrichment", "Pathways", "Software", "Survival" ], "Author": "Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. 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What is precursor ion purity? -What we call \"Precursor ion purity\" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation.", "biocViews": [ "MassSpectrometry", "Metabolomics", "Software" ], "Author": "Thomas N. 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Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.", "biocViews": [ "DifferentialExpression", "ExperimentalDesign", "ImmunoOncology", "MassSpectrometry", "MultipleComparison", "Normalization", "Preprocessing", "Proteomics", "Regression", "Software", "TimeCourse" ], "Author": "Lieven Clement [aut, cre] (), Laurent Gatto [aut] (), Oliver M. 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The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. 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It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS.", "biocViews": [ "DataImport", "ImmunoOncology", "Infrastructure", "MassSpectrometry", "Metabolomics", "Proteomics", "Software" ], "Author": "Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer", "Maintainer": "Steffen Neumann ", "URL": "https://github.com/sneumann/mzR/", "SystemRequirements": "C++11, GNU make", "VignetteBuilder": "knitr", "BugReports": "https://github.com/sneumann/mzR/issues/", "git_url": "https://git.bioconductor.org/packages/mzR", "git_branch": "RELEASE_3_18", "git_last_commit": "1764563", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/mzR_2.36.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/mzR_2.36.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/mzR_2.36.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/mzR_2.36.0.tgz", "vignettes": [ "vignettes/mzR/inst/doc/mzR.html" ], "vignetteTitles": [ "Accessin raw mass spectrometry and identification data" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/mzR/inst/doc/mzR.R" ], "dependsOnMe": [ "MSnbase" ], "importsMe": [ "adductomicsR", "CluMSID", "DIAlignR", "MSnID", "msPurity", "peakPantheR", "RMassBank", "SIMAT", "TargetDecoy", "topdownr", "xcms", "yamss" ], "suggestsMe": [ "AnnotationHub", "MetaboAnnotation", "MsBackendRawFileReader", "MsBackendSql", "msdata", "MsDataHub", "MsExperiment", "MsQuality", "PSMatch", "qcmetrics", "RforProteomics", "Spectra" ], "dependencyCount": "10", "Rank": 106 }, "NADfinder": { "Package": "NADfinder", "Version": "1.26.0", "Depends": [ "R (>= 3.5.0)", "BiocGenerics", "IRanges", "GenomicRanges", "S4Vectors", "SummarizedExperiment" ], "Imports": [ "graphics", "methods", "baseline", "signal", "GenomicAlignments", "GenomeInfoDb", "rtracklayer", "limma", "trackViewer", "stats", "utils", "Rsamtools", "metap", "EmpiricalBrownsMethod", "ATACseqQC", "corrplot", "csaw" ], "Suggests": [ "RUnit", "BiocStyle", "knitr", "BSgenome.Mmusculus.UCSC.mm10", "testthat", "BiocManager", "rmarkdown" ], "License": "GPL (>= 2)", "MD5sum": "d60f5020a7e46305fafc626c59a11851", "NeedsCompilation": "no", "Title": "Call wide peaks for sequencing data", "Description": "Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.", "biocViews": [ "DNASeq", "GeneRegulation", "PeakDetection", "Sequencing", "Software" ], "Author": "Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu", "Maintainer": "Jianhong Ou , Lihua Julie Zhu ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/NADfinder", "git_branch": "RELEASE_3_18", "git_last_commit": "d34108b", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/NADfinder_1.26.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/NADfinder_1.26.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/NADfinder_1.26.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/NADfinder_1.26.0.tgz", "vignettes": [ "vignettes/NADfinder/inst/doc/NADfinder.html" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/NADfinder/inst/doc/NADfinder.R" ], "dependencyCount": "249", "Rank": 1843 }, "NanoMethViz": { "Package": "NanoMethViz", "Version": "2.8.1", "Depends": [ "R (>= 4.0.0)", "methods", "ggplot2 (>= 3.4.0)" ], "Imports": [ "cpp11 (>= 0.2.5)", "readr", "cli", "S4Vectors", "SummarizedExperiment", "BiocSingular", "bsseq", "forcats", "assertthat", "AnnotationDbi", "Rcpp", "dplyr", "data.table", "dbscan", "e1071", "fs", "GenomicRanges", "Biostrings", "ggrastr", "glue", "graphics", "IRanges", "limma (>= 3.44.0)", "patchwork", "purrr", "rlang", "R.utils", "Rsamtools", "scales (>= 1.2.0)", "scico", "stats", "stringr", "tibble", "tidyr", "utils", "withr", "zlibbioc" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "BiocStyle", "DSS", "Mus.musculus (>= 1.3.1)", "Homo.sapiens (>= 1.3.1)", "org.Hs.eg.db", "TxDb.Hsapiens.UCSC.hg19.knownGene", "TxDb.Hsapiens.UCSC.hg38.knownGene", "org.Mm.eg.db", "TxDb.Mmusculus.UCSC.mm10.knownGene", "TxDb.Mmusculus.UCSC.mm39.refGene", "knitr", "rmarkdown", "rtracklayer", "testthat (>= 3.0.0)", "covr" ], "License": "Apache License (>= 2.0)", "Archs": "x64", "MD5sum": "5ddef080c0bf460814f94f28ee5f98d5", "NeedsCompilation": "yes", "Title": "Visualise methlation data from Oxford Nanopore sequencing", "Description": "NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.", "biocViews": [ "DNAMethylation", "DataImport", "DifferentialMethylation", "Epigenetics", "LongRead", "Software", "Visualization" ], "Author": "Shian Su [cre, aut]", "Maintainer": "Shian Su ", "URL": "https://github.com/shians/NanoMethViz", "SystemRequirements": "C++17", "VignetteBuilder": "knitr", "BugReports": "https://github.com/Shians/NanoMethViz/issues", "git_url": "https://git.bioconductor.org/packages/NanoMethViz", "git_branch": "RELEASE_3_18", "git_last_commit": "1d8a149", "git_last_commit_date": "2023-11-07", "Date/Publication": "2023-11-08", "source.ver": "src/contrib/NanoMethViz_2.8.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/NanoMethViz_2.8.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/NanoMethViz_2.8.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/NanoMethViz_2.8.1.tgz", "vignettes": [ "vignettes/NanoMethViz/inst/doc/UsersGuide.html" ], "vignetteTitles": [ "User's Guide" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/NanoMethViz/inst/doc/UsersGuide.R" ], "dependencyCount": "149", "Rank": 1009 }, "NanoStringDiff": { "Package": "NanoStringDiff", "Version": "1.32.0", "Depends": [ "Biobase" ], "Imports": [ "matrixStats", "methods", "Rcpp" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "testthat", "BiocStyle" ], "License": "GPL", "MD5sum": "3614c5bcf8aeea002875aa005d746092", "NeedsCompilation": "yes", "Title": "Differential Expression Analysis of NanoString nCounter Data", "Description": "This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized.", "biocViews": [ "DifferentialExpression", "Normalization", "Software" ], "Author": "hong wang , tingting zhai , chi wang ", "Maintainer": "tingting zhai ,hong wang ", "git_url": "https://git.bioconductor.org/packages/NanoStringDiff", "git_branch": "RELEASE_3_18", "git_last_commit": "4afdd7f", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/NanoStringDiff_1.32.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/NanoStringDiff_1.32.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/NanoStringDiff_1.32.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/NanoStringDiff_1.32.0.tgz", "vignettes": [ "vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf" ], "vignetteTitles": [ "NanoStringDiff Vignette" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R" ], "suggestsMe": [ "NanoTube" ], "dependencyCount": "8", "Rank": 861 }, "NanoStringNCTools": { "Package": "NanoStringNCTools", "Version": "1.10.1", "Depends": [ "R (>= 3.6)", "Biobase", "S4Vectors", "ggplot2" ], "Imports": [ "BiocGenerics", "Biostrings", "ggbeeswarm", "ggiraph", "ggthemes", "grDevices", "IRanges", "methods", "pheatmap", "RColorBrewer", "stats", "utils" ], "Suggests": [ "biovizBase", "ggbio", "RUnit", "rmarkdown", "knitr", "qpdf" ], "License": "MIT", "Archs": "x64", "MD5sum": "cfe38ccd4db66d51e0223e4584f674ba", "NeedsCompilation": "no", "Title": "NanoString nCounter Tools", "Description": "Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data.", "biocViews": [ "CellBasedAssays", "DataImport", "GeneExpression", "ProprietaryPlatforms", "Proteomics", "RNASeq", "Software", "Transcription", "Transcriptomics", "mRNAMicroarray" ], "Author": "Patrick Aboyoun [aut], Nicole Ortogero [cre], Zhi Yang [ctb]", "Maintainer": "Nicole Ortogero ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/NanoStringNCTools", "git_branch": "RELEASE_3_18", "git_last_commit": "e7a3f23", "git_last_commit_date": "2024-04-11", "Date/Publication": "2024-04-15", "source.ver": "src/contrib/NanoStringNCTools_1.10.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/NanoStringNCTools_1.10.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/NanoStringNCTools_1.10.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/NanoStringNCTools_1.10.1.tgz", "vignettes": [ "vignettes/NanoStringNCTools/inst/doc/Introduction.html" ], "vignetteTitles": [ "Introduction to the NanoStringRCCSet Class" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/NanoStringNCTools/inst/doc/Introduction.R" ], "dependsOnMe": [ "GeomxTools", "GeoMxWorkflows" ], "importsMe": [ "GeoDiff" ], "dependencyCount": "84", "Rank": 338 }, "nanotatoR": { "Package": "nanotatoR", "Version": "1.18.0", "Depends": [ "R (>= 4.1)" ], "Imports": [ "hash(>= 2.2.6)", "openxlsx(>= 4.0.17)", "rentrez(>= 1.1.0)", "stats", "rlang", "stringr", "knitr", "testthat", "utils", "AnnotationDbi", "httr", "GenomicRanges", "tidyverse", "VarfromPDB", "org.Hs.eg.db", "curl", "dplyr", "XML", "XML2R" ], "Suggests": [ "rmarkdown", "yaml" ], "License": "file LICENSE", "MD5sum": "e808c68591d98effbfdba4dbac07ac91", "NeedsCompilation": "no", "Title": "Next generation structural variant annotation and classification", "Description": "Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions (INDELs) and, more recently, small copy number variants (CNVs). However, due to utilization of short reads, it is not well suited for identification of structural variants (SV). Optical mapping (OM) from Bionano Genomics, utilizes long fluorescently labeled megabase size DNA molecules for de novo genome assembly and identification of SVs with a much higher sensitivity than WGS. Nevertheless, currently available SV annotation tools have limited number of functions. NanotatoR is an R package written to provide a set of annotations for SVs identified by OM. It uses Database of Genomic Variants (DGV), Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER) as well as a subset (154 samples) of 1000 Genome Project to calculate the population frequencies of the SVs (an optional internal cohort SV frequency calculation is also available). NanotatoR creates a primary gene list (PG) from NCBI databases based on proband’s phenotype specific keywords and compares the list to the set of genes overlapping/near SVs. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type (e.g., INDELs, Inversions, Translocations). 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However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. 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Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata.", "biocViews": [ "Bayesian", "DNASeq", "DifferentialExpression", "GUI", "GeneExpression", "ImmunoOncology", "Metagenomics", "Microbiome", "RNASeq", "Sequencing", "Software", "Transcriptomics", "Visualization" ], "Author": "Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut]", "Maintainer": "Daniel Giguere ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/omicplotR", "git_branch": "RELEASE_3_18", "git_last_commit": "5fc732a", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/omicplotR_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/omicplotR_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/omicplotR_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/omicplotR_1.22.0.tgz", "vignettes": [ "vignettes/omicplotR/inst/doc/omicplotR.html" ], "vignetteTitles": [ "omicplotR: A tool for visualization of omic datasets as compositions" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/omicplotR/inst/doc/omicplotR.R" ], "dependencyCount": "111", "Rank": 1309 }, "omicRexposome": { "Package": "omicRexposome", "Version": "1.24.3", "Depends": [ "R (>= 3.5.0)", "Biobase" ], "Imports": [ "stats", "utils", "grDevices", "graphics", "methods", "rexposome", "limma", "sva", "ggplot2", "ggrepel", "PMA", "omicade4", "gridExtra", "MultiDataSet", "SmartSVA", "isva", "parallel", "SummarizedExperiment", "stringr" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "snpStats", "brgedata" ], "License": "MIT + file LICENSE", "MD5sum": "5504120962d682e799611cb01e10c603", "NeedsCompilation": "no", "Title": "Exposome and omic data associatin and integration analysis", "Description": "omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).", "biocViews": [ "DifferentialExpression", "DifferentialMethylation", "Epigenetics", "GeneExpression", "GeneRegulation", "ImmunoOncology", "MultipleComparison", "Proteomics", "Regression", "Software", "StatisticalMethod", "Transcriptomics", "Visualization", "WorkflowStep" ], "Author": "Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut]", "Maintainer": "Xavier Escribà Montagut ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/omicRexposome", "git_branch": "RELEASE_3_18", "git_last_commit": "7937bee", "git_last_commit_date": "2024-02-12", "Date/Publication": "2024-02-15", "source.ver": "src/contrib/omicRexposome_1.24.3.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/omicRexposome_1.24.3.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/omicRexposome_1.24.3.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/omicRexposome_1.24.3.tgz", "vignettes": [ "vignettes/omicRexposome/inst/doc/exposome_omic_integration.html" ], "vignetteTitles": [ "Exposome Data Integration with Omic Data" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/omicRexposome/inst/doc/exposome_omic_integration.R" ], "dependencyCount": "235", "Rank": 1604 }, "OMICsPCA": { "Package": "OMICsPCA", "Version": "1.20.0", "Depends": [ "R (>= 3.5.0)", "OMICsPCAdata" ], "Imports": [ "HelloRanges", "fpc", "stats", "MultiAssayExperiment", "pdftools", "methods", "grDevices", "utils", "clValid", "NbClust", "cowplot", "rmarkdown", "kableExtra", "rtracklayer", "IRanges", "GenomeInfoDb", "reshape2", "ggplot2", "factoextra", "rgl", "corrplot", "MASS", "graphics", "FactoMineR", "PerformanceAnalytics", "tidyr", "data.table", "cluster", "magick" ], "Suggests": [ "knitr", "RUnit", "BiocGenerics" ], "License": "GPL-3", "MD5sum": "d115069df9bde574c2439532f584f19a", "NeedsCompilation": "no", "Title": "An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples", "Description": "OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.", "biocViews": [ "BiologicalQuestion", "BiomedicalInformatics", "Clustering", "DataRepresentation", "DimensionReduction", "Epigenetics", "EpigeneticsWorkflow", "FunctionalGenomics", "GUI", "GeneticVariability", "ImmunoOncology", "MultipleComparison", "PrincipalComponent", "SingleCell", "Software", "Transcription", "Visualization", "Workflow" ], "Author": "Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]", "Maintainer": "Subhadeep Das ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/OMICsPCA", "git_branch": "RELEASE_3_18", "git_last_commit": "fbf1917", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/OMICsPCA_1.20.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/OMICsPCA_1.20.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/OMICsPCA_1.20.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/OMICsPCA_1.20.0.tgz", "vignettes": [ "vignettes/OMICsPCA/inst/doc/vignettes.html" ], "vignetteTitles": [ "OMICsPCA" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/OMICsPCA/inst/doc/vignettes.R" ], "dependencyCount": "221", "Rank": 1389 }, "omicsPrint": { "Package": "omicsPrint", "Version": "1.22.0", "Depends": [ "R (>= 3.5)", "MASS" ], "Imports": [ "methods", "matrixStats", "graphics", "stats", "SummarizedExperiment", "MultiAssayExperiment", "RaggedExperiment" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "testthat", "GEOquery", "VariantAnnotation", "Rsamtools", "BiocParallel", "GenomicRanges", "FDb.InfiniumMethylation.hg19", "snpStats" ], "License": "GPL (>= 2)", "Archs": "x64", "MD5sum": "4e45c908441cedfc0dac097c558e61f1", "NeedsCompilation": "no", "Title": "Cross omic genetic fingerprinting", "Description": "omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation).", "biocViews": [ "DNAMethylation", "Epigenetics", "GeneticVariability", "Genetics", "ImmunoOncology", "QualityControl", "Software", "Transcription", "Transcriptomics" ], "Author": "Maarten van Iterson [aut], Davy Cats [cre]", "Maintainer": "Davy Cats ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/omicsPrint", "git_branch": "RELEASE_3_18", "git_last_commit": "25859e6", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/omicsPrint_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/omicsPrint_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/omicsPrint_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/omicsPrint_1.22.0.tgz", "vignettes": [ "vignettes/omicsPrint/inst/doc/omicsPrint.html" ], "vignetteTitles": [ "omicsPrint: detection of data linkage errors in multiple omics studies" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/omicsPrint/inst/doc/omicsPrint.R" ], "dependencyCount": "54", "Rank": 1663 }, "omicsViewer": { "Package": "omicsViewer", "Version": "1.6.0", "Depends": [ "R (>= 4.2)" ], "Imports": [ "survminer", "survival", "fastmatch", "reshape2", "stringr", "beeswarm", "grDevices", "DT", "shiny", "shinythemes", "shinyWidgets", "plotly", "networkD3", "httr", "matrixStats", "RColorBrewer", "Biobase", "fgsea", "openxlsx", "psych", "shinybusy", "ggseqlogo", "htmlwidgets", "graphics", "grid", "stats", "utils", "methods", "shinyjs", "curl", "flatxml", "ggplot2", "S4Vectors", "SummarizedExperiment", "RSQLite", "Matrix", "shinycssloaders", "ROCR" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "unittest" ], "License": "GPL-2", "MD5sum": "7d650ff242a133610199a574a29abfde", "NeedsCompilation": "no", "Title": "Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer", "Description": "omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. 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Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall.", "biocViews": [ "Classification", "CpGIsland", "DNAMethylation", "DifferentialMethylation", "Epigenetics", "Software", "ddPCR" ], "Author": "Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut]", "Maintainer": "Hans Petter Brodal ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/PoDCall", "git_branch": "RELEASE_3_18", "git_last_commit": "e26a93b", "git_last_commit_date": "2024-01-16", "Date/Publication": "2024-01-16", "source.ver": "src/contrib/PoDCall_1.10.1.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/PoDCall_1.10.1.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/PoDCall_1.10.1.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/PoDCall_1.10.1.tgz", "vignettes": [ "vignettes/PoDCall/inst/doc/PoDCall.html" ], "vignetteTitles": [ "PoDCall: Positive Droplet Caller for DNA Methylation ddPCR" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/PoDCall/inst/doc/PoDCall.R" ], "dependencyCount": "91", "Rank": 1429 }, "podkat": { "Package": "podkat", "Version": "1.34.0", "Depends": [ "R (>= 3.5.0)", "methods", "Rsamtools (>= 1.99.1)", "GenomicRanges" ], "Imports": [ "Rcpp (>= 0.11.1)", "parallel", "stats (>= 4.3.0)", "graphics", "grDevices", "utils", "Biobase", "BiocGenerics", "Matrix", "GenomeInfoDb", "IRanges", "Biostrings", "BSgenome (>= 1.32.0)" ], "LinkingTo": [ "Rcpp", "Rhtslib (>= 1.15.3)" ], "Suggests": [ "BSgenome.Hsapiens.UCSC.hg38.masked", "TxDb.Hsapiens.UCSC.hg38.knownGene", "BSgenome.Mmusculus.UCSC.mm10.masked", "GWASTools (>= 1.13.24)", "VariantAnnotation", "SummarizedExperiment", "knitr" ], "License": "GPL (>= 2)", "MD5sum": "b06fbdc3f282bde5ec8df0d062d3ecb6", "NeedsCompilation": "yes", "Title": "Position-Dependent Kernel Association Test", "Description": "This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results.", "biocViews": [ "Annotation", "DataImport", "Genetics", "Sequencing", "Software", "VariantAnnotation", "WholeGenome" ], "Author": "Ulrich Bodenhofer", "Maintainer": "Ulrich Bodenhofer ", "URL": "http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/podkat", "git_branch": "RELEASE_3_18", "git_last_commit": "0a4ab12", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/podkat_1.34.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/podkat_1.34.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/podkat_1.34.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/podkat_1.34.0.tgz", "vignettes": [ "vignettes/podkat/inst/doc/podkat.pdf" ], "vignetteTitles": [ "PODKAT - An R Package for Association Testing Involving Rare and Private Variants" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/podkat/inst/doc/podkat.R" ], "dependencyCount": "51", "Rank": 1550 }, "pogos": { "Package": "pogos", "Version": "1.22.0", "Depends": [ "R (>= 3.5.0)", "rjson (>= 0.2.15)", "httr (>= 1.3.1)" ], "Imports": [ "methods", "S4Vectors", "utils", "shiny", "ontoProc", "ggplot2", "graphics" ], "Suggests": [ "knitr", "DT", "ontologyPlot", "testthat", "rmarkdown", "BiocStyle" ], "License": "Artistic-2.0", "Archs": "x64", "MD5sum": "14955bb127999aaa92633bc1ce3a1227", "NeedsCompilation": "no", "Title": "PharmacOGenomics Ontology Support", "Description": "Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies.", "biocViews": [ "ImmunoOncology", "Pharmacogenomics", "PooledScreens", "Software" ], "Author": "Vince Carey ", "Maintainer": "VJ Carey ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/pogos", "git_branch": "RELEASE_3_18", "git_last_commit": "7f46f03", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/pogos_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/pogos_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/pogos_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/pogos_1.22.0.tgz", "vignettes": [ "vignettes/pogos/inst/doc/pogos.html" ], "vignetteTitles": [ "pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/pogos/inst/doc/pogos.R" ], "suggestsMe": [ "BiocOncoTK" ], "dependencyCount": "127", "Rank": 1249 }, "polyester": { "Package": "polyester", "Version": "1.38.0", "Depends": [ "R (>= 3.0.0)" ], "Imports": [ "Biostrings (>= 2.32.0)", "IRanges", "S4Vectors", "logspline", "limma", "zlibbioc" ], "Suggests": [ "knitr", "ballgown", "markdown" ], "License": "Artistic-2.0", "MD5sum": "acc47ee29fb9c22fb1ad4bbfafe8fd4d", "NeedsCompilation": "no", "Title": "Simulate RNA-seq reads", "Description": "This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression.", "biocViews": [ "DifferentialExpression", "Sequencing", "Software" ], "Author": "Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek", "Maintainer": "Jack Fu , Jeff Leek ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/polyester", "git_branch": "RELEASE_3_18", "git_last_commit": "d768346", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/polyester_1.38.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/polyester_1.38.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/polyester_1.38.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/polyester_1.38.0.tgz", "vignettes": [ "vignettes/polyester/inst/doc/polyester.html" ], "vignetteTitles": [ "The Polyester package for simulating RNA-seq reads" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/polyester/inst/doc/polyester.R" ], "dependencyCount": "21", "Rank": 674 }, "POMA": { "Package": "POMA", "Version": "1.12.0", "Depends": [ "R (>= 4.0)" ], "Imports": [ "broom", "caret", "ComplexHeatmap", "dbscan", "dplyr", "DESeq2", "ggplot2", "ggrepel", "glasso (>= 1.11)", "glmnet", "impute", "limma", "magrittr", "mixOmics", "randomForest", "RankProd (>= 3.14)", "rmarkdown", "SummarizedExperiment", "tibble", "tidyr", "uwot", "vegan" ], "Suggests": [ "BiocStyle", "covr", "ggraph", "knitr", "patchwork", "plotly", "tidyverse", "testthat (>= 2.3.2)" ], "License": "GPL-3", "MD5sum": "89690c830c8b6120d4d436f05e34f4eb", "NeedsCompilation": "no", "Title": "Tools for Omics Data Analysis", "Description": "A reproducible and easy-to-use toolkit for visualization, pre-processing, exploration, and statistical analysis of omics datasets. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package has a Shiny app version called POMAShiny that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. See Castellano-Escuder P, González-Domínguez R, Carmona-Pontaque F, et al. (2021) for more details.", "biocViews": [ "BatchEffect", "Classification", "Clustering", "DecisionTree", "DimensionReduction", "MultidimensionalScaling", "Normalization", "Preprocessing", "PrincipalComponent", "RNASeq", "Regression", "Software", "StatisticalMethod", "Visualization" ], "Author": "Pol Castellano-Escuder [aut, cre] ()", "Maintainer": "Pol Castellano-Escuder ", "URL": "https://github.com/pcastellanoescuder/POMA", "VignetteBuilder": "knitr", "BugReports": "https://github.com/pcastellanoescuder/POMA/issues", "git_url": "https://git.bioconductor.org/packages/POMA", "git_branch": "RELEASE_3_18", "git_last_commit": "6414754", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/POMA_1.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/POMA_1.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/POMA_1.12.0.tgz", "vignettes": [ "vignettes/POMA/inst/doc/POMA-demo.html", "vignettes/POMA/inst/doc/POMA-eda.html", "vignettes/POMA/inst/doc/POMA-normalization.html" ], "vignetteTitles": [ "POMA Workflow", "POMA EDA Example", "POMA Normalization Methods" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/POMA/inst/doc/POMA-demo.R", "vignettes/POMA/inst/doc/POMA-eda.R", "vignettes/POMA/inst/doc/POMA-normalization.R" ], "suggestsMe": [ "fobitools" ], "dependencyCount": "174", "Rank": 994 }, "powerTCR": { "Package": "powerTCR", "Version": "1.22.0", "Imports": [ "cubature", "doParallel", "evmix", "foreach", "magrittr", "methods", "parallel", "purrr", "stats", "truncdist", "vegan", "VGAM" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "RUnit", "BiocGenerics" ], "License": "Artistic-2.0", "MD5sum": "6033da999bfcba4c2e80a45e358c3855", "NeedsCompilation": "no", "Title": "Model-Based Comparative Analysis of the TCR Repertoire", "Description": "This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits.", "biocViews": [ "BiomedicalInformatics", "Clustering", "Software" ], "Author": "Hillary Koch", "Maintainer": "Hillary Koch ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/powerTCR", "git_branch": "RELEASE_3_18", "git_last_commit": "78a217e", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/powerTCR_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/powerTCR_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/powerTCR_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/powerTCR_1.22.0.tgz", "vignettes": [ "vignettes/powerTCR/inst/doc/powerTCR.html" ], "vignetteTitles": [ "Vignette Title" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/powerTCR/inst/doc/powerTCR.R" ], "importsMe": [ "scRepertoire" ], "dependencyCount": "36", "Rank": 325 }, "POWSC": { "Package": "POWSC", "Version": "1.10.0", "Depends": [ "R (>= 4.1)", "Biobase", "SingleCellExperiment", "MAST" ], "Imports": [ "pheatmap", "ggplot2", "RColorBrewer", "grDevices", "SummarizedExperiment", "limma" ], "Suggests": [ "rmarkdown", "knitr", "testthat (>= 3.0.0)", "BiocStyle" ], "License": "GPL-2", "MD5sum": "e2ed51f94e9a1b37aa91fe74a007f4d7", "NeedsCompilation": "no", "Title": "Simulation, power evaluation, and sample size recommendation for single cell RNA-seq", "Description": "Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.", "biocViews": [ "DifferentialExpression", "ImmunoOncology", "SingleCell", "Software" ], "Author": "Kenong Su [aut, cre], Hao Wu [aut]", "Maintainer": "Kenong Su ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/POWSC", "git_branch": "RELEASE_3_18", "git_last_commit": "128061c", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/POWSC_1.10.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/POWSC_1.10.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/POWSC_1.10.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/POWSC_1.10.0.tgz", "vignettes": [ "vignettes/POWSC/inst/doc/POWSC.html" ], "vignetteTitles": [ "The POWSC User's Guide" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/POWSC/inst/doc/POWSC.R" ], "dependencyCount": "71", "Rank": 1389 }, "ppcseq": { "Package": "ppcseq", "Version": "1.10.0", "Depends": [ "R (>= 4.1.0)", "rstan (>= 2.18.1)" ], "Imports": [ "benchmarkme", "dplyr", "edgeR", "foreach", "ggplot2", "graphics", "lifecycle", "magrittr", "methods", "parallel", "purrr", "Rcpp (>= 0.12.0)", "RcppParallel (>= 5.0.1)", "rlang", "rstantools (>= 2.1.1)", "stats", "tibble", "tidybayes", "tidyr (>= 0.8.3.9000)", "utils" ], "LinkingTo": [ "BH (>= 1.66.0)", "Rcpp (>= 0.12.0)", "RcppEigen (>= 0.3.3.3.0)", "RcppParallel (>= 5.0.1)", "rstan (>= 2.18.1)", "StanHeaders (>= 2.18.0)" ], "Suggests": [ "knitr", "testthat", "BiocStyle", "rmarkdown" ], "License": "GPL-3", "MD5sum": "5a95706ac4269643dd1eb6efd4bccb67", "NeedsCompilation": "yes", "Title": "Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models", "Description": "Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.", "biocViews": [ "Clustering", "DifferentialExpression", "GeneExpression", "Normalization", "QualityControl", "RNASeq", "Sequencing", "Software", "Transcription", "Transcriptomics" ], "Author": "Stefano Mangiola [aut, cre] ()", "Maintainer": "Stefano Mangiola ", "URL": "https://github.com/stemangiola/ppcseq", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "BugReports": "https://github.com/stemangiola/ppcseq/issues", "git_url": "https://git.bioconductor.org/packages/ppcseq", "git_branch": "RELEASE_3_18", "git_last_commit": "8195177", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ppcseq_1.10.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ppcseq_1.10.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ppcseq_1.10.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ppcseq_1.10.0.tgz", "vignettes": [ "vignettes/ppcseq/inst/doc/introduction.html" ], "vignetteTitles": [ "Overview of the ppcseq package" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ppcseq/inst/doc/introduction.R" ], "dependencyCount": "94", "Rank": 1794 }, "PPInfer": { "Package": "PPInfer", "Version": "1.28.0", "Depends": [ "biomaRt", "fgsea", "kernlab", "ggplot2", "igraph", "STRINGdb", "yeastExpData" ], "Imports": [ "httr", "grDevices", "graphics", "stats", "utils" ], "License": "Artistic-2.0", "MD5sum": "f6b346ab6e0c90b546825aa50fb52b59", "NeedsCompilation": "no", "Title": "Inferring functionally related proteins using protein interaction networks", "Description": "Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. 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Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. 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QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. 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This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape).", "biocViews": [ "Clustering", "DifferentialExpression", "GeneExpression", "Microarray", "MultipleComparison", "Network", "Software", "StatisticalMethod", "Visualization" ], "Author": "Yu Zhang [aut, cre], Qin Ma [aut]", "Maintainer": "Yu Zhang ", "URL": "http://github.com/zy26/QUBIC", "SystemRequirements": "C++11, Rtools (>= 3.1)", "VignetteBuilder": "knitr", "BugReports": "http://github.com/zy26/QUBIC/issues", "git_url": "https://git.bioconductor.org/packages/QUBIC", "git_branch": "RELEASE_3_18", "git_last_commit": "49a174a", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/QUBIC_1.30.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/QUBIC_1.30.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/QUBIC_1.30.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/QUBIC_1.30.0.tgz", "vignettes": [ "vignettes/QUBIC/inst/doc/qubic_vignette.pdf" ], "vignetteTitles": [ "QUBIC Tutorial" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/QUBIC/inst/doc/qubic_vignette.R" ], "importsMe": [ "mosbi" ], "suggestsMe": [ "runibic" ], "dependencyCount": "53", "Rank": 574 }, "qusage": { "Package": "qusage", "Version": "2.36.0", "Depends": [ "R (>= 2.10)", "limma (>= 3.14)", "methods" ], "Imports": [ "utils", "Biobase", "nlme", "emmeans", "fftw" ], "License": "GPL (>= 2)", "MD5sum": "f4675daa1494bbf070a084d05a2e672b", "NeedsCompilation": "no", "Title": "qusage: Quantitative Set Analysis for Gene Expression", "Description": "This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). 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Finally an HTML report is generated with `report()`.", "biocViews": [ "Classification", "Coverage", "Epigenetics", "Sequencing", "Software", "Transcriptomics" ], "Author": "Henry Miller [aut, cre, cph] (), Daniel Montemayor [ctb] (), Simon Levy [ctb] (), Anna Vines [ctb] (), Alexander Bishop [ths, cph] ()", "Maintainer": "Henry Miller ", "URL": "https://github.com/Bishop-Laboratory/RLSeq, https://bishop-laboratory.github.io/RLSeq/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/Bishop-Laboratory/RLSeq/issues", "git_url": "https://git.bioconductor.org/packages/RLSeq", "git_branch": "devel", "git_last_commit": "81d0655", "git_last_commit_date": "2022-11-07", "Date/Publication": "2023-04-23", "source.ver": "src/contrib/RLSeq_1.5.2.tar.gz", "vignettes": [ "vignettes/RLSeq/inst/doc/RLSeq.html" ], "vignetteTitles": [ "Analyzing R-loop Data with RLSeq" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/RLSeq/inst/doc/RLSeq.R" ], "dependencyCount": "209", "Rank": 2178 }, "Rmagpie": { "Package": "Rmagpie", "Version": "1.58.0", "Depends": [ "R (>= 2.6.1)", "Biobase (>= 2.5.5)" ], "Imports": [ "Biobase (>= 2.5.5)", "e1071", "graphics", "grDevices", "kernlab", "methods", "pamr", "stats", "utils" ], "Suggests": [ "xtable" ], "License": "GPL (>= 3)", "Archs": "x64", "MD5sum": "939fbdcb38d890daf9880baea89c8685", "NeedsCompilation": "no", "Title": "MicroArray Gene-expression-based Program In Error rate estimation", "Description": "Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.", "biocViews": [ "Classification", "Microarray", "Software" ], "Author": "Camille Maumet , with contributions from C. Ambroise J. 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Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records.", "biocViews": [ "Bioinformatics", "ImmunoOncology", "MassSpectrometry", "Metabolomics", "Software" ], "Author": "Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze", "Maintainer": "RMassBank at Eawag ", "SystemRequirements": "OpenBabel", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/RMassBank", "git_branch": "RELEASE_3_18", "git_last_commit": "0a05d66", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/RMassBank_3.12.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/RMassBank_3.12.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/RMassBank_3.12.0.tgz", "vignettes": [ "vignettes/RMassBank/inst/doc/RMassBank.html", "vignettes/RMassBank/inst/doc/RMassBankNonstandard.html", "vignettes/RMassBank/inst/doc/RMassBankXCMS.html" ], "vignetteTitles": [ "RMassBank: The workflow by example", "RMassBank: Non-standard usage", "RMassBank for XCMS" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/RMassBank/inst/doc/RMassBank.R", "vignettes/RMassBank/inst/doc/RMassBankNonstandard.R", "vignettes/RMassBank/inst/doc/RMassBankXCMS.R" ], "suggestsMe": [ "RMassBankData" ], "dependencyCount": "143", "Rank": 854 }, "rmelting": { "Package": "rmelting", "Version": "1.18.0", "Depends": [ "R (>= 3.6)" ], "Imports": [ "Rdpack", "rJava (>= 0.9-8)" ], "Suggests": [ "readxl", "knitr", "rmarkdown", "reshape2", "pander", "testthat" ], "License": "GPL-2 | GPL-3", "Archs": "x64", "MD5sum": "a73d7dd41b3d77dce067644b04412e9d", "NeedsCompilation": "no", "Title": "R Interface to MELTING 5", "Description": "R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters.", "biocViews": [ "BiomedicalInformatics", "Cheminformatics", "Software" ], "Author": "J. 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However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.", "biocViews": [ "GeneExpression", "Software", "Transcription" ], "Author": "Zytnicki Matthias [aut, cre]", "Maintainer": "Zytnicki Matthias ", "SystemRequirements": "C++11", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/Rmmquant", "git_branch": "RELEASE_3_18", "git_last_commit": "89552c3", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/Rmmquant_1.20.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/Rmmquant_1.20.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/Rmmquant_1.20.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/Rmmquant_1.20.0.tgz", "vignettes": [ "vignettes/Rmmquant/inst/doc/Rmmquant.html" ], "vignetteTitles": [ "The Rmmquant package" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/Rmmquant/inst/doc/Rmmquant.R" ], "dependencyCount": "195", "Rank": 1843 }, "rmspc": { "Package": "rmspc", "Version": "1.8.0", "Imports": [ "processx", "BiocManager", "rtracklayer", "stats", "tools", "methods", "GenomicRanges", "stringr" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "testthat (>= 3.0.0)" ], "License": "GPL-3", "Archs": "x64", "MD5sum": "fd5ce900bcff0c0a4b4423a7cc261aec", "NeedsCompilation": "no", "Title": "Multiple Sample Peak Calling", "Description": "The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as \"peaks\"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples.", "biocViews": [ "ChIPSeq", "ChipOnChip", "DataImport", "RNASeq", "Sequencing", "Software" ], "Author": "Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre]", "Maintainer": "Meriem Bahda ", "URL": "https://genometric.github.io/MSPC/", "SystemRequirements": ".NET 6.0", "VignetteBuilder": "knitr", "BugReports": "https://github.com/Genometric/MSPC/issues", "git_url": "https://git.bioconductor.org/packages/rmspc", "git_branch": "RELEASE_3_18", "git_last_commit": "4214dd6", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/rmspc_1.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/rmspc_1.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/rmspc_1.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/rmspc_1.8.0.tgz", "vignettes": [ "vignettes/rmspc/inst/doc/rmpsc.html" ], "vignetteTitles": [ "User guide to the rmspc package" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/rmspc/inst/doc/rmpsc.R" ], "dependencyCount": "61", "Rank": 1429 }, "RNAAgeCalc": { "Package": "RNAAgeCalc", "Version": "1.14.0", "Depends": [ "R (>= 3.6)" ], "Imports": [ "ggplot2", "recount", "impute", "AnnotationDbi", "org.Hs.eg.db", "stats", "SummarizedExperiment", "methods" ], "Suggests": [ "knitr", "rmarkdown", "testthat" ], "License": "GPL-2", "Archs": "x64", "MD5sum": "8601edc9bd38ec2387cd9cd9a78b0da8", "NeedsCompilation": "no", "Title": "A multi-tissue transcriptional age calculator", "Description": "It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.", "biocViews": [ "GeneExpression", "RNASeq", "Software" ], "Author": "Xu Ren [aut, cre], Pei Fen Kuan [aut]", "Maintainer": "Xu Ren ", "URL": "https://github.com/reese3928/RNAAgeCalc", "VignetteBuilder": "knitr", "BugReports": "https://github.com/reese3928/RNAAgeCalc/issues", "git_url": "https://git.bioconductor.org/packages/RNAAgeCalc", "git_branch": "RELEASE_3_18", "git_last_commit": "da4cd15", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/RNAAgeCalc_1.14.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/RNAAgeCalc_1.14.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/RNAAgeCalc_1.14.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/RNAAgeCalc_1.14.0.tgz", "vignettes": [ "vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html" ], "vignetteTitles": [ "RNAAgeCalc" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R" ], "dependencyCount": "170", "Rank": 1048 }, "RNAdecay": { "Package": "RNAdecay", "Version": "1.22.2", "Depends": [ "R (>= 3.5)" ], "Imports": [ "stats", "grDevices", "grid", "ggplot2", "gplots", "utils", "TMB", "nloptr", "scales" ], "Suggests": [ "parallel", "knitr", "reshape2", "rmarkdown" ], "License": "GPL-2", "MD5sum": "5c17b35bac4e238886bf0631aaaa460b", "NeedsCompilation": "yes", "Title": "Maximum Likelihood Decay Modeling of RNA Degradation Data", "Description": "RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. 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The editing frequencies can be tested against binary, continuous or survival outcomes. 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The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. 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However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.", "biocViews": [ "Bayesian", "Clustering", "Coverage", "DNAMethylation", "DifferentialExpression", "DifferentialMethylation", "Epigenetics", "FeatureExtraction", "GeneExpression", "GeneRegulation", "Genetics", "ImmunoOncology", "Regression", "Sequencing", "SingleCell", "Software" ], "Author": "Andreas C. Kapourani [aut, cre] (), John Riddell [ctb]", "Maintainer": "Andreas C. Kapourani ", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "BugReports": "https://github.com/andreaskapou/scMET/issues", "git_url": "https://git.bioconductor.org/packages/scMET", "git_branch": "RELEASE_3_18", "git_last_commit": "d18b5a2", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/scMET_1.4.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/scMET_1.4.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/scMET_1.4.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/scMET_1.4.0.tgz", "vignettes": [ "vignettes/scMET/inst/doc/scMET_vignette.html" ], "vignetteTitles": [ "scMET analysis using synthetic data" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/scMET/inst/doc/scMET_vignette.R" ], "dependencyCount": "114", "Rank": 1729 }, "scmeth": { "Package": "scmeth", "Version": "1.22.0", "Depends": [ "R (>= 3.5.0)" ], "Imports": [ "knitr", "rmarkdown", "bsseq", "AnnotationHub", "GenomicRanges", "reshape2", "stats", "utils", "BSgenome", "DelayedArray (>= 0.5.15)", "annotatr", "SummarizedExperiment (>= 1.5.6)", "GenomeInfoDb", "Biostrings", "DT", "HDF5Array (>= 1.7.5)" ], "Suggests": [ "BSgenome.Mmusculus.UCSC.mm10", "BSgenome.Hsapiens.NCBI.GRCh38", "TxDb.Hsapiens.UCSC.hg38.knownGene", "org.Hs.eg.db", "Biobase", "BiocGenerics", "ggplot2", "ggthemes" ], "License": "GPL-2", "MD5sum": "52beac81d58da27e01cc5e1a0b6584b2", "NeedsCompilation": "no", "Title": "Functions to conduct quality control analysis in methylation data", "Description": "Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report.", "biocViews": [ "DNAMethylation", "ImmunoOncology", "Preprocessing", "QualityControl", "SingleCell", "Software" ], "Author": "Divy Kangeyan ", "Maintainer": "Divy Kangeyan ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/aryeelab/scmeth/issues", "git_url": "https://git.bioconductor.org/packages/scmeth", "git_branch": "RELEASE_3_18", "git_last_commit": "39a201a", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/scmeth_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/scmeth_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/scmeth_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/scmeth_1.22.0.tgz", "vignettes": [ "vignettes/scmeth/inst/doc/my-vignette.html" ], "vignetteTitles": [ "Vignette Title" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/scmeth/inst/doc/my-vignette.R" ], "suggestsMe": [ "biscuiteer" ], "dependencyCount": "168", "Rank": 1159 }, "SCnorm": { "Package": "SCnorm", "Version": "1.24.0", "Depends": [ "R (>= 3.4.0)" ], "Imports": [ "SingleCellExperiment", "SummarizedExperiment", "stats", "methods", "graphics", "grDevices", "parallel", "quantreg", "cluster", "moments", "data.table", "BiocParallel", "S4Vectors", "ggplot2", "forcats", "BiocGenerics" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "devtools" ], "License": "GPL (>= 2)", "MD5sum": "cfac086eb57336e865c999c4c0b42a0c", "NeedsCompilation": "no", "Title": "Normalization of single cell RNA-seq data", "Description": "This package implements SCnorm — a method to normalize single-cell RNA-seq data.", "biocViews": [ "ImmunoOncology", "Normalization", "RNASeq", "SingleCell", "Software" ], "Author": "Rhonda Bacher", "Maintainer": "Rhonda Bacher ", "URL": "https://github.com/rhondabacher/SCnorm", "VignetteBuilder": "knitr", "BugReports": "https://github.com/rhondabacher/SCnorm/issues", "git_url": "https://git.bioconductor.org/packages/SCnorm", "git_branch": "RELEASE_3_18", "git_last_commit": "a500413", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/SCnorm_1.24.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/SCnorm_1.24.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/SCnorm_1.24.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/SCnorm_1.24.0.tgz", "vignettes": [ "vignettes/SCnorm/inst/doc/SCnorm.pdf" ], "vignetteTitles": [ "SCnorm Vignette" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/SCnorm/inst/doc/SCnorm.R" ], "dependencyCount": "75", "Rank": 861 }, "scone": { "Package": "scone", "Version": "1.26.0", "Depends": [ "R (>= 3.4)", "methods", "SummarizedExperiment" ], "Imports": [ "graphics", "stats", "utils", "aroma.light", "BiocParallel", "class", "cluster", "compositions", "diptest", "edgeR", "fpc", "gplots", "grDevices", "hexbin", "limma", "matrixStats", "mixtools", "RColorBrewer", "boot", "rhdf5", "RUVSeq", "rARPACK", "MatrixGenerics", "SingleCellExperiment" ], "Suggests": [ "BiocStyle", "DT", "ggplot2", "knitr", "miniUI", "NMF", "plotly", "reshape2", "rmarkdown", "scran", "scRNAseq", "shiny", "testthat", "visNetwork", "doParallel", "batchtools", "splatter", "scater", "kableExtra", "mclust", "TENxPBMCData" ], "License": "Artistic-2.0", "Archs": "x64", "MD5sum": "0e7dd77999fc8cbf842fee6452c82a1b", "NeedsCompilation": "no", "Title": "Single Cell Overview of Normalized Expression data", "Description": "SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.", "biocViews": [ "Coverage", "GeneExpression", "ImmunoOncology", "Normalization", "Preprocessing", "QualityControl", "RNASeq", "Sequencing", "SingleCell", "Software", "Transcriptomics" ], "Author": "Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb]", "Maintainer": "Davide Risso ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/YosefLab/scone/issues", "git_url": "https://git.bioconductor.org/packages/scone", "git_branch": "RELEASE_3_18", "git_last_commit": "a7873ee", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/scone_1.26.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/scone_1.26.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/scone_1.26.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/scone_1.26.0.tgz", "vignettes": [ "vignettes/scone/inst/doc/PsiNorm.html", "vignettes/scone/inst/doc/sconeTutorial.html" ], "vignetteTitles": [ "PsiNorm normalization", "Introduction to SCONE" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/scone/inst/doc/PsiNorm.R", "vignettes/scone/inst/doc/sconeTutorial.R" ], "dependencyCount": "183", "Rank": 805 }, "Sconify": { "Package": "Sconify", "Version": "1.22.0", "Depends": [ "R (>= 3.5)" ], "Imports": [ "tibble", "dplyr", "FNN", "flowCore", "Rtsne", "ggplot2", "magrittr", "utils", "stats", "readr" ], "Suggests": [ "knitr", "rmarkdown", "testthat" ], "License": "Artistic-2.0", "MD5sum": "61762bc5d7e8560ae818942a21da33a5", "NeedsCompilation": "no", "Title": "A toolkit for performing KNN-based statistics for flow and mass cytometry data", "Description": "This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps\"fold change\" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.", "biocViews": [ "FlowCytometry", "ImmunoOncology", "MultipleComparison", "SingleCell", "Software", "Visualization" ], "Author": "Tyler J Burns", "Maintainer": "Tyler J Burns ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/Sconify", "git_branch": "RELEASE_3_18", "git_last_commit": "ffdb315", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/Sconify_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/Sconify_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/Sconify_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/Sconify_1.22.0.tgz", "vignettes": [ "vignettes/Sconify/inst/doc/DataQuality.html", "vignettes/Sconify/inst/doc/FindingIdealK.html", "vignettes/Sconify/inst/doc/Step1.PreProcessing.html", "vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html", "vignettes/Sconify/inst/doc/Step3.PostProcessing.html" ], "vignetteTitles": [ "Data Quality", "Finding Ideal K", "How to process FCS files for downstream use in R", "General Scone Analysis", "Final Post-Processing Steps for Scone" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/Sconify/inst/doc/DataQuality.R", "vignettes/Sconify/inst/doc/FindingIdealK.R", "vignettes/Sconify/inst/doc/Step1.PreProcessing.R", "vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R", "vignettes/Sconify/inst/doc/Step3.PostProcessing.R" ], "dependencyCount": "62", "Rank": 1729 }, "SCOPE": { "Package": "SCOPE", "Version": "1.14.0", "Depends": [ "R (>= 3.6.0)", "GenomicRanges", "IRanges", "Rsamtools", "GenomeInfoDb", "BSgenome.Hsapiens.UCSC.hg19" ], "Imports": [ "stats", "grDevices", "graphics", "utils", "DescTools", "RColorBrewer", "gplots", "foreach", "parallel", "doParallel", "DNAcopy", "BSgenome", "Biostrings", "BiocGenerics", "S4Vectors" ], "Suggests": [ "knitr", "rmarkdown", "WGSmapp", "BSgenome.Hsapiens.UCSC.hg38", "BSgenome.Mmusculus.UCSC.mm10", "testthat (>= 2.1.0)" ], "License": "GPL-2", "MD5sum": "b8e4ce76717f217322282d378de1b3bd", "NeedsCompilation": "no", "Title": "A normalization and copy number estimation method for single-cell DNA sequencing", "Description": "Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.", "biocViews": [ "Alignment", "CopyNumberVariation", "Coverage", "DNASeq", "DataImport", "Normalization", "QualityControl", "Sequencing", "SingleCell", "Software", "WholeGenome" ], "Author": "Rujin Wang, Danyu Lin, Yuchao Jiang", "Maintainer": "Rujin Wang ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/SCOPE", "git_branch": "RELEASE_3_18", "git_last_commit": "11e3191", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/SCOPE_1.14.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/SCOPE_1.14.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/SCOPE_1.14.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/SCOPE_1.14.0.tgz", "vignettes": [ "vignettes/SCOPE/inst/doc/SCOPE_vignette.html" ], "vignetteTitles": [ "SCOPE: Single-cell Copy Number Estimation" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/SCOPE/inst/doc/SCOPE_vignette.R" ], "dependencyCount": "102", "Rank": 1339 }, "scoreInvHap": { "Package": "scoreInvHap", "Version": "1.24.0", "Depends": [ "R (>= 3.6.0)" ], "Imports": [ "Biostrings", "methods", "snpStats", "VariantAnnotation", "GenomicRanges", "BiocParallel", "graphics", "SummarizedExperiment" ], "Suggests": [ "testthat", "knitr", "BiocStyle", "rmarkdown" ], "License": "file LICENSE", "MD5sum": "7505f8161571dcc7b9b752fc62dedce5", "NeedsCompilation": "no", "Title": "Get inversion status in predefined regions", "Description": "scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions.", "biocViews": [ "Genetics", "GenomicVariation", "SNP", "Software" ], "Author": "Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre]", "Maintainer": "Dolors Pelegri-Siso ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/scoreInvHap", "git_branch": "RELEASE_3_18", "git_last_commit": "644e352", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/scoreInvHap_1.24.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/scoreInvHap_1.24.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/scoreInvHap_1.24.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/scoreInvHap_1.24.0.tgz", "vignettes": [ "vignettes/scoreInvHap/inst/doc/scoreInvHap.html" ], "vignetteTitles": [ "Inversion genotyping with scoreInvHap" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": true, "Rfiles": [ "vignettes/scoreInvHap/inst/doc/scoreInvHap.R" ], "dependencyCount": "104", "Rank": 1794 }, "scp": { "Package": "scp", "Version": "1.12.0", "Depends": [ "R (>= 4.2.0)", "QFeatures (>= 1.3.5)" ], "Imports": [ "methods", "stats", "utils", "SingleCellExperiment", "SummarizedExperiment", "MultiAssayExperiment", "MsCoreUtils", "matrixStats", "S4Vectors", "dplyr", "magrittr" ], "Suggests": [ "scpdata", "testthat", "knitr", "BiocStyle", "rmarkdown", "ggplot2", "patchwork", "impute", "scater", "sva", "preprocessCore", "vsn", "uwot" ], "License": "Artistic-2.0", "Archs": "x64", "MD5sum": "c4f759cfb996e6150ecec65b7de3e343", "NeedsCompilation": "no", "Title": "Mass Spectrometry-Based Single-Cell Proteomics Data Analysis", "Description": "Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. 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The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes.", "biocViews": [ "Annotation", "ChIPSeq", "ChIPchip", "Classification", "Coverage", "DNA3DStructure", "DataImport", "Epigenetics", "FunctionalGenomics", "HiC", "Regression", "Software" ], "Author": "Jonas Ibn-Salem [aut, cre]", "Maintainer": "Jonas Ibn-Salem ", "URL": "https://github.com/ibn-salem/sevenC", "VignetteBuilder": "knitr", "BugReports": "https://github.com/ibn-salem/sevenC/issues", "git_url": "https://git.bioconductor.org/packages/sevenC", "git_branch": "RELEASE_3_18", "git_last_commit": "3b7cf4e", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/sevenC_1.22.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/sevenC_1.22.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/sevenC_1.22.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/sevenC_1.22.0.tgz", "vignettes": [ "vignettes/sevenC/inst/doc/sevenC.html" ], "vignetteTitles": [ "Introduction to sevenC" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/sevenC/inst/doc/sevenC.R" ], "dependencyCount": "77", "Rank": 1794 }, "SGCP": { "Package": "SGCP", "Version": "1.2.0", "Depends": [ "R (>= 4.3.0)" ], "Imports": [ "ggplot2", "expm", "caret", "plyr", "dplyr", "GO.db", "annotate", "SummarizedExperiment", "genefilter", "GOstats", "RColorBrewer", "xtable", "Rgraphviz", "reshape2", "openxlsx", "ggridges", "DescTools", "org.Hs.eg.db", "methods", "grDevices", "stats", "RSpectra", "graph" ], "Suggests": [ "knitr", "BiocManager" ], "License": "GPL-3", "MD5sum": "46986c7dd18ef80a29c389500ed5023b", "NeedsCompilation": "no", "Title": "SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks", "Description": "SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. 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Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. 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The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.", "biocViews": [ "DifferentialExpression", "ExonArray", "GeneExpression", "Microarray", "MultiChannel", "MultipleComparison", "OneChannel", "Preprocessing", "QualityControl", "Software", "Transcription", "TwoChannel" ], "Author": "Brig Mecham and John D. Storey ", "Maintainer": "John D. 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The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. 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SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. 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We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. 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It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. 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Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. 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Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). 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DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data.", "biocViews": [ "DifferentialExpression", "GenomicVariation", "RNASeq", "SNP", "Sequencing", "Software", "VariantAnnotation" ], "Author": "Lachlan Baer [aut, cre] (), Stevie Pederson [aut] ()", "Maintainer": "Lachlan Baer ", "URL": "https://github.com/baerlachlan/tadar", "VignetteBuilder": "knitr", "BugReports": "https://github.com/baerlachlan/tadar/issues", "git_url": "https://git.bioconductor.org/packages/tadar", "git_branch": "RELEASE_3_18", "git_last_commit": "fdabf0f", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/tadar_1.0.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/tadar_1.0.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/tadar_1.0.0.tgz", "vignettes": [ "vignettes/tadar/inst/doc/dar.html" ], "vignetteTitles": [ "DAR analysis" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/tadar/inst/doc/dar.R" ], "dependencyCount": "155", "Rank": 2114 }, "TADCompare": { "Package": "TADCompare", "Version": "1.12.1", "Depends": [ "R (>= 4.0)" ], "Imports": [ "dplyr", "PRIMME", "cluster", "Matrix", "magrittr", "HiCcompare", "ggplot2", "tidyr", "ggpubr", "RColorBrewer", "reshape2", "cowplot" ], "Suggests": [ "BiocStyle", "knitr", "rmarkdown", "microbenchmark", "testthat", "covr", "pheatmap", "rGREAT", "SpectralTAD", "magick", "qpdf" ], "License": "MIT + file LICENSE", "MD5sum": "bf2880cfb4ff92e40345c7764c9a5077", "NeedsCompilation": "no", "Title": "TADCompare: Identification and characterization of differential TADs", "Description": "TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. 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Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn.", "biocViews": [ "CRISPR", "PooledScreens", "Sequencing", "SingleCell", "Software", "Technology" ], "Author": "Andreas R. Gschwind [aut, cre] (), Lars Velten [aut] (), Lars M. Steinmetz [aut]", "Maintainer": "Andreas R. 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(2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.", "biocViews": [ "Software", "StatisticalMethod", "Transcription" ], "Author": "Mahmoud Ahmed [aut, cre]", "Maintainer": "Mahmoud Ahmed ", "URL": "https://github.com/MahShaaban/target", "VignetteBuilder": "knitr", "BugReports": "https://github.com/MahShaaban/target/issues", "git_url": "https://git.bioconductor.org/packages/target", "git_branch": "RELEASE_3_18", "git_last_commit": "8bae527", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/target_1.16.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/target_1.16.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/target_1.16.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/target_1.16.0.tgz", "vignettes": [ "vignettes/target/inst/doc/extend-target.html", "vignettes/target/inst/doc/target.html" ], "vignetteTitles": [ "Using target to predict combined binding", "Using the target package" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/target/inst/doc/extend-target.R", "vignettes/target/inst/doc/target.R" ], "dependencyCount": "48", "Rank": 1899 }, "TargetDecoy": { "Package": "TargetDecoy", "Version": "1.8.0", "Depends": [ "R (>= 4.1)" ], "Imports": [ "ggplot2", "ggpubr", "methods", "miniUI", "mzID", "mzR", "shiny", "stats" ], "Suggests": [ "BiocStyle", "knitr", "msdata", "sessioninfo", "rmarkdown", "gridExtra", "testthat (>= 3.0.0)", "covr" ], "License": "Artistic-2.0", "MD5sum": "ff49065b40e049f06bbe1ad9c74200fc", "NeedsCompilation": "no", "Title": "Diagnostic Plots to Evaluate the Target Decoy Approach", "Description": "A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method.", "biocViews": [ "MassSpectrometry", "Proteomics", "QualityControl", "Software", "Visualization" ], "Author": "Elke Debrie [aut, cre], Lieven Clement [aut] (), Milan Malfait [aut] ()", "Maintainer": "Elke Debrie ", "URL": "https://www.bioconductor.org/packages/TargetDecoy, https://statomics.github.io/TargetDecoy/, https://github.com/statOmics/TargetDecoy/", "VignetteBuilder": "knitr", "BugReports": "https://github.com/statOmics/TargetDecoy/issues", "git_url": "https://git.bioconductor.org/packages/TargetDecoy", "git_branch": "RELEASE_3_18", "git_last_commit": "bfb3135", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/TargetDecoy_1.8.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/TargetDecoy_1.8.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/TargetDecoy_1.8.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/TargetDecoy_1.8.0.tgz", "vignettes": [ "vignettes/TargetDecoy/inst/doc/TargetDecoy.html" ], "vignetteTitles": [ "Introduction to TargetDecoy" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/TargetDecoy/inst/doc/TargetDecoy.R" ], "dependencyCount": "123", "Rank": 2012 }, "TargetScore": { "Package": "TargetScore", "Version": "1.40.0", "Depends": [ "pracma", "Matrix" ], "Suggests": [ "TargetScoreData", "gplots", "Biobase", "GEOquery" ], "License": "GPL-2", "Archs": "x64", "MD5sum": "92d3b6ca138c6c3081262a758ec63766", "NeedsCompilation": "no", "Title": "TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information", "Description": "Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. 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In addition to the basic calculations of the affected copy number of a variant, the Zygosity-Predictor can integrate the influence of several variants on a gene and ultimately make a statement if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, the Zygosity-Predictor can address whether unmutated copies of tumor-suppressor genes are present. Beyond this, it is possible to make this statement for all genes of an organism. The Zygosity-Predictor was primarily developed to handle SNVs and INDELs (later addressed as small-variants) of somatic and germline origin. In order not to overlook severe effects outside of the small-variant context, it has been extended with the assessment of large scale deletions, which cause losses of whole genes or parts of them.", "biocViews": [ "BiomedicalInformatics", "FunctionalPrediction", "GenePrediction", "Software", "SomaticMutation" ], "Author": "Marco Rheinnecker [aut, cre] (), Marc Ruebsam [aut], Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara Hutter [aut]", "Maintainer": "Marco Rheinnecker ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/ZygosityPredictor", "git_branch": "RELEASE_3_18", "git_last_commit": "e9e07df", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "source.ver": "src/contrib/ZygosityPredictor_1.2.0.tar.gz", "win.binary.ver": "bin/windows/contrib/4.3/ZygosityPredictor_1.2.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/ZygosityPredictor_1.2.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/ZygosityPredictor_1.2.0.tgz", "vignettes": [ "vignettes/ZygosityPredictor/inst/doc/Usage.html" ], "vignetteTitles": [ "ZygosityPredictor_Usage" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/ZygosityPredictor/inst/doc/Usage.R" ], "dependencyCount": "102", "Rank": 2067 }, "BDMMAcorrect": { "Package": "BDMMAcorrect", "Version": "1.20.0", "Depends": [ "R (>= 3.5)", "vegan", "ellipse", "ggplot2", "ape", "SummarizedExperiment" ], "Imports": [ "Rcpp (>= 0.12.12)", "RcppArmadillo", "RcppEigen", "stats" ], "LinkingTo": [ "Rcpp", "RcppArmadillo", "RcppEigen" ], "Suggests": [ "knitr", "rmarkdown", "BiocGenerics" ], "License": "GPL (>= 2)", "NeedsCompilation": "yes", "Title": "Meta-analysis for the metagenomic read counts data from different cohorts", "Description": "Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. 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This package has the underlying models implemented in C++ and the output and analysis features implemented in R.", "biocViews": [ "BiomedicalInformatics", "CellBiology", "GeneExpression", "ImmunoOncology", "MathematicalBiology", "RNASeq", "SingleCell", "Software", "SystemsBiology" ], "Author": "Thomas D. Sherman, Raymond Cheng, Elana J. Fertig", "Maintainer": "Thomas D. Sherman , Elana J. 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Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information.", "biocViews": [ "GeneExpression", "GeneRegulation", "GraphAndNetwork", "Network", "NetworkEnrichment", "NetworkInference", "Software", "SystemsBiology", "Transcription", "Visualization" ], "Author": "Remy Nicolle, Thibault Venzac and Mohamed Elati", "Maintainer": "Remy Nicolle ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/CoRegNet", "git_branch": "RELEASE_3_18", "git_last_commit": "93ca417", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "win.binary.ver": "bin/windows/contrib/4.3/CoRegNet_1.40.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/CoRegNet_1.40.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/CoRegNet_1.40.0.tgz", "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rank": 2178 }, "crisprseekplus": { "Package": "crisprseekplus", "Version": "1.28.0", "Depends": [ "R (>= 3.3.0)", "shiny", "shinyjs", "CRISPRseek" ], "Imports": [ "DT", "utils", "GUIDEseq", "GenomicRanges", "GenomicFeatures", "BiocManager", "BSgenome", "AnnotationDbi", "hash" ], "Suggests": [ "testthat", "rmarkdown", "knitr", "R.rsp" ], "License": "GPL-3 + file LICENSE", "NeedsCompilation": "no", "Title": "crisprseekplus", "Description": "Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis.", "biocViews": [ "GeneRegulation", "SequenceMatching", "Software" ], "Author": "Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber ", "Maintainer": "Alper Kucukural ", "URL": "https://github.com/UMMS-Biocore/crisprseekplus", "VignetteBuilder": "knitr, R.rsp", "BugReports": "https://github.com/UMMS-Biocore/crisprseekplus/issues/new", "git_url": "https://git.bioconductor.org/packages/crisprseekplus", "git_branch": "RELEASE_3_18", "git_last_commit": "8338d72", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "win.binary.ver": "bin/windows/contrib/4.3/crisprseekplus_1.28.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/crisprseekplus_1.28.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/crisprseekplus_1.28.0.tgz", "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rank": 2178 }, "dpeak": { "Package": "dpeak", "Version": "1.14.0", "Depends": [ "R (>= 4.0.0)", "methods", "stats", "utils", "graphics", "Rcpp" ], "Imports": [ "MASS", "IRanges", "BSgenome", "grDevices", "parallel" ], "LinkingTo": [ "Rcpp" ], "Suggests": [ "BSgenome.Ecoli.NCBI.20080805" ], "License": "GPL (>= 2)", "NeedsCompilation": "yes", "Title": "dPeak (Deconvolution of Peaks in ChIP-seq Analysis)", "Description": "dPeak is a statistical framework for the high resolution identification of protein-DNA interaction sites using PET and SET ChIP-Seq and ChIP-exo data. It provides computationally efficient and user friendly interface to process ChIP-seq and ChIP-exo data, implement exploratory analysis, fit dPeak model, and export list of predicted binding sites for downstream analysis.", "biocViews": [ "ChIPSeq", "Genetics", "Sequencing", "Software", "Transcription" ], "Author": "Dongjun Chung, Carter Allen", "Maintainer": "Dongjun Chung ", "SystemRequirements": "GNU make, meme, fimo", "BugReports": "https://github.com/dongjunchung/dpeak/issues", "git_url": "https://git.bioconductor.org/packages/dpeak", "git_branch": "RELEASE_3_18", "git_last_commit": "174adc9", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "win.binary.ver": "bin/windows/contrib/4.3/dpeak_1.14.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/dpeak_1.14.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/dpeak_1.14.0.tgz", "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rank": 2178 }, "farms": { "Package": "farms", "Version": "1.54.0", "Depends": [ "R (>= 2.8)", "affy (>= 1.20.0)", "MASS", "methods" ], "Imports": [ "affy", "MASS", "Biobase (>= 1.13.41)", "methods", "graphics" ], "Suggests": [ "affydata", "Biobase", "utils" ], "License": "LGPL (>= 2.1)", "NeedsCompilation": "no", "Title": "FARMS - Factor Analysis for Robust Microarray Summarization", "Description": "The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called \"I/NI-calls\"", "biocViews": [ "GeneExpression", "Microarray", "Preprocessing", "QualityControl", "Software" ], "Author": "Djork-Arne Clevert ", "Maintainer": "Djork-Arne Clevert ", "URL": "http://www.bioinf.jku.at/software/farms/farms.html", "git_url": "https://git.bioconductor.org/packages/farms", "git_branch": "RELEASE_3_18", "git_last_commit": "90b6bd5", "git_last_commit_date": "2023-10-24", "Date/Publication": "2023-10-24", "win.binary.ver": "bin/windows/contrib/4.3/farms_1.54.0.zip", "mac.binary.big-sur-x86_64.ver": "bin/macosx/big-sur-x86_64/contrib/4.3/farms_1.54.0.tgz", "mac.binary.big-sur-arm64.ver": "bin/macosx/big-sur-arm64/contrib/4.3/farms_1.54.0.tgz", "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rank": 2178, "suggestsMe": [ "DEqMS" ] }, "FCBF": { "Package": "FCBF", "Version": "2.10.0", "Depends": [ "R (>= 4.1)" ], "Imports": [ "ggplot2", "gridExtra", "pbapply", "parallel", "SummarizedExperiment", "stats", "mclust" ], "Suggests": [ "caret", "mlbench", "SingleCellExperiment", "knitr", "rmarkdown", "testthat", "BiocManager" ], "License": "MIT + file LICENSE", "NeedsCompilation": "no", "Title": "Fast Correlation Based Filter for Feature Selection", "Description": "This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. 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Novianti ", "Maintainer": "Mark A. van de Wiel ", "PackageStatus": "Deprecated", "Rank": 2178 }, "deco": { "Package": "deco", "Version": "1.18.0", "Depends": [ "R (>= 3.5.0)", "AnnotationDbi", "BiocParallel", "SummarizedExperiment", "limma" ], "Imports": [ "stats", "methods", "ggplot2", "foreign", "graphics", "BiocStyle", "Biobase", "cluster", "gplots", "RColorBrewer", "locfit", "made4", "ade4", "sfsmisc", "scatterplot3d", "gdata", "grDevices", "utils", "reshape2", "gridExtra" ], "Suggests": [ "knitr", "curatedTCGAData", "MultiAssayExperiment", "Homo.sapiens", "rmarkdown" ], "License": "GPL (>=3)", "NeedsCompilation": "no", "Title": "Decomposing Heterogeneous Cohorts using Omic Data Profiling", "Description": "This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.", "biocViews": [ "Bayesian", "BiomedicalInformatics", "Clustering", "DifferentialExpression", "ExonArray", "FeatureExtraction", "GeneExpression", "MicroRNAArray", "Microarray", "MultipleComparison", "Proteomics", "RNASeq", "Sequencing", "Software", "Transcription", "Transcriptomics", "mRNAMicroarray" ], "Author": "Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. 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Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information.", "biocViews": [ "Classification", "GO", "Genetics", "Pathways", "Regression", "Software" ], "Author": "Junfang Chen and Emanuel Schwarz", "Maintainer": "Junfang Chen ", "VignetteBuilder": "knitr", "PackageStatus": "Deprecated", "Rank": 2178 }, "OmicsLonDA": { "Package": "OmicsLonDA", "Version": "1.18.0", "Depends": [ "R(>= 3.6)" ], "Imports": [ "SummarizedExperiment", "gss", "plyr", "zoo", "pracma", "ggplot2", "BiocParallel", "parallel", "grDevices", "graphics", "stats", "utils", "methods", "BiocGenerics" ], "Suggests": [ "knitr", "rmarkdown", "testthat", "devtools", "BiocManager" ], "License": "MIT + file LICENSE", "NeedsCompilation": "no", "Title": "Omics Longitudinal Differential Analysis", "Description": "Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups.", "biocViews": [ "Lipidomics", "Metabolomics", "Microbiome", "Proteomics", "Regression", "Software", "Survival", "TimeCourse", "Transcriptomics" ], "Author": "Ahmed A. 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The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. 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The purpose of the HPAStainR function is to query the visual staining data in the Human Protein Atlas to return a table of staining ranked cell types. The function also has multiple arguments to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. The other functions exist exclusively to easily acquire the data required to run HPAStainR.", "biocViews": [ "GeneExpression", "GeneSetEnrichment", "Software" ], "Author": "Tim O. Nieuwenhuis [aut, cre] ()", "Maintainer": "Tim O. 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This method provides a systematic comparisons between a gene set (such as a list of differentially expressed genes) and well-studied \"basic pathway networks\" (KEGG pathways), measuring the importance of pathways and genes for the gene set. 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All the requests to access the pointer will be sent to the underlying file system and eventually handled by a customized data-reading function. The main purpose of the package is to reduce the memory consumption when using R's vector to represent a large data. 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For each omic dataset you furnish, multiSight provides classification models with feature selection you can use as biosignature: (i) To forecast phenotypes (e.g. to diagnostic tasks, histological subtyping), (ii) To design Pathways and gene ontology enrichments (Over Representation Analysis), (iii) To build Network inference linked to PubMed querying to make assumptions easier and data-driven. Main analysis are embedded in an user-friendly graphical interface.", "biocViews": [ "Classification", "DifferentialExpression", "GeneSetEnrichment", "Network", "NetworkInference", "Pathways", "RNASeq", "Software", "miRNA" ], "Author": "Florian Jeanneret [cre, aut] (), Stephane Gazut [aut]", "Maintainer": "Florian Jeanneret ", "VignetteBuilder": "knitr", "BugReports": "https://github.com/Fjeanneret/multiSight/issues", "PackageStatus": "Deprecated", "Rank": 2178 }, "mbOmic": { "Package": "mbOmic", "Version": "1.6.0", "Depends": [ "R (>= 4.1.0)" ], "Imports": [ "parallel", "doParallel", "psych", "WGCNA", "data.table", "igraph", "visNetwork", "cluster", "clusterSim", "methods", "graphics", "stats" ], "Suggests": [ "testthat (>= 3.0.0)", "knitr", "rmarkdown", "devtools", "impute" ], "License": "Artistic-2.0", "Title": "Integrative analysis of the microbiome and metabolome", "Description": "The mbOmic package contains a set of analysis functions for microbiomics and metabolomics data, designed to analyze the inter-omic correlation between microbiology and metabolites. 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