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The workflow loads the `flowCore`, `flowStats` and `flowViz` packages and its dependencies. It loads the ITN data with 15 samples, each of which includes, in addition to FSC and SSC, 5 fluorescence channels: CD3, CD4, CD8, CD69 and HLADR.", "biocViews": [ "ImmunoOncologyWorkflow", "ProteomicsWorkflow", "Workflow" ], "Author": "Bioconductor Package Maintainer [aut, cre]", "Maintainer": "Bioconductor Package Maintainer ", "URL": "https://www.bioconductor.org/help/workflows/highthroughputassays/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/highthroughputassays", "git_branch": "RELEASE_3_9", "git_last_commit": "809d5bd", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/highthroughputassays_1.8.0.tar.gz", "vignettes": [ "vignettes/highthroughputassays/inst/doc/high-throughput-assays.html" ], "vignetteTitles": [ "Using Bioconductor with High Throughput Assays" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/highthroughputassays/inst/doc/high-throughput-assays.R" ], "dependencyCount": "83", "Rank": 20 }, "liftOver": { "Package": "liftOver", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "gwascat", "GenomicRanges", "rtracklayer", "Homo.sapiens", "BiocGenerics" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle" ], "License": "Artistic-2.0", "MD5sum": "55c652dded0305dae4fc2ac4deea85f6", "NeedsCompilation": "no", "Title": "Changing genomic coordinate systems with rtracklayer::liftOver", "Description": "The liftOver facilities developed in conjunction with the UCSC browser track infrastructure are available for transforming data in GRanges formats. This is illustrated here with an image of the EBI/NHGRI GWAS catalog that is, as of May 10 2017, distributed with coordinates defined by NCBI build hg38.", "biocViews": [ "BasicWorkflow", "Workflow" ], "Author": "Bioconductor Package Maintainer [aut, cre]", "Maintainer": "Bioconductor Package Maintainer ", "URL": "https://www.bioconductor.org/help/workflows/liftOver/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/liftOver", "git_branch": "RELEASE_3_9", "git_last_commit": "ee71f68", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/liftOver_1.8.0.tar.gz", "vignettes": [ "vignettes/liftOver/inst/doc/liftov.html" ], "vignetteTitles": [ "Changing genomic coordinate systems with rtracklayer::liftOver" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/liftOver/inst/doc/liftov.R" ], "dependencyCount": "82", "Rank": 4 }, "maEndToEnd": { "Package": "maEndToEnd", "Version": "2.4.0", "Depends": [ "R (>= 3.5.0)", "Biobase", "oligoClasses", "ArrayExpress", "pd.hugene.1.0.st.v1", "hugene10sttranscriptcluster.db", "oligo", "arrayQualityMetrics", "limma", "topGO", "ReactomePA", "clusterProfiler", "gplots", "ggplot2", "geneplotter", "pheatmap", "RColorBrewer", "dplyr", "tidyr", "stringr", "matrixStats", "genefilter", "openxlsx", "Rgraphviz" ], "Suggests": [ "BiocStyle", "knitr", "devtools", "rmarkdown" ], "License": "MIT", "MD5sum": "b0a805e7d8e69b28e8c4bbc63f4d1079", "NeedsCompilation": "no", "Title": "An end to end workflow for differential gene expression using Affymetrix microarrays", "Description": "In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current \"Gene\" type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis.", "biocViews": [ "GeneExpressionWorkflow", "Workflow" ], "Author": "Bernd Klaus [aut, cre], Stefanie Reisenauer [aut]", "Maintainer": "Bernd Klaus ", "URL": "https://www.bioconductor.org/help/workflows/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/maEndToEnd", "git_branch": "RELEASE_3_9", "git_last_commit": "ff896ca", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/maEndToEnd_2.4.0.tar.gz", "vignettes": [ "vignettes/maEndToEnd/inst/doc/MA-Workflow.html" ], "vignetteTitles": [ "An end to end workflow for differential gene expression using Affymetrix microarrays" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/maEndToEnd/inst/doc/MA-Workflow.R" ], "dependencyCount": "198", "Rank": 7 }, "methylationArrayAnalysis": { "Package": "methylationArrayAnalysis", "Version": "1.8.1", "Depends": [ "R (>= 3.3.0)", "knitr", "rmarkdown", "BiocStyle", "limma", "minfi", "IlluminaHumanMethylation450kanno.ilmn12.hg19", "IlluminaHumanMethylation450kmanifest", "RColorBrewer", "missMethyl", "matrixStats", "minfiData", "Gviz", "DMRcate", "stringr", "FlowSorted.Blood.450k" ], "License": "Artistic-2.0", "MD5sum": "b2af4a1f66beec85eb59a2be2dade6dd", "NeedsCompilation": "no", "Title": "A cross-package Bioconductor workflow for analysing methylation array data.", "Description": "Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This Bioconductor workflow uses multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.", "biocViews": [ "EpigeneticsWorkflow", "Workflow" ], "Author": "Jovana Maksimovic [aut, cre]", "Maintainer": "Jovana Maksimovic ", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/methylationArrayAnalysis", "git_branch": "RELEASE_3_9", "git_last_commit": "4f472a0", "git_last_commit_date": "2019-06-06", "Date/Publication": "2019-06-07", "source.ver": "src/contrib/methylationArrayAnalysis_1.8.1.tar.gz", "vignettes": [ "vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.html" ], "vignetteTitles": [ "A cross-package Bioconductor workflow for analysing methylation array data" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.R" ], "dependencyCount": "195", "Rank": 8 }, "proteomics": { "Package": "proteomics", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "mzR", "mzID", "MSnID", "MSnbase", "rpx", "MLInterfaces", "pRoloc", "pRolocdata", "MSGFplus", "rols", "hpar" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle" ], "License": "Artistic-2.0", "MD5sum": "74ee30ebc486c01107f367e6bc0ecbcc", "NeedsCompilation": "no", "Title": "Mass spectrometry and proteomics data analysis", "Description": "This workflow illustrates R / Bioconductor infrastructure for proteomics. Topics covered focus on support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, data processing and analysis.", "biocViews": [ "ImmunoOncologyWorkflow", "ProteomicsWorkflow", "Workflow" ], "Author": "Laurent Gatto [aut, cre]", "Maintainer": "Laurent Gatto ", "URL": "https://www.bioconductor.org/help/workflows/proteomics/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/proteomics", "git_branch": "RELEASE_3_9", "git_last_commit": "3ea1968", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-06", "source.ver": "src/contrib/proteomics_1.8.0.tar.gz", "vignettes": [ "vignettes/proteomics/inst/doc/proteomics.html" ], "vignetteTitles": [ "An R/Bioc proteomics workflow" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/proteomics/inst/doc/proteomics.R" ], "dependencyCount": "193", "Rank": 6 }, "recountWorkflow": { "Package": "recountWorkflow", "Version": "1.8.1", "Depends": [ "R (>= 3.4.0)" ], "Imports": [ "recount (>= 1.2.3)", "GenomicRanges", "limma", "edgeR", "DESeq2", "regionReport (>= 1.11.2)", "clusterProfiler (>= 3.5.5)", "org.Hs.eg.db (>= 3.4.1)", "gplots", "derfinder", "rtracklayer (>= 1.36.4)", "GenomicFeatures", "bumphunter (>= 1.17.2)", "derfinderPlot" ], "Suggests": [ "BiocStyle (>= 2.5.19)", "BiocWorkflowTools", "knitr", "sessioninfo", "rmarkdown", "knitcitations" ], "License": "Artistic-2.0", "MD5sum": "7eda31ab2e06e5bc58a42b86c80396e3", "NeedsCompilation": "no", "Title": "recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor", "Description": "The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data.", "biocViews": [ "ResourceQueryingWorkflow", "Workflow" ], "Author": "Leonardo Collado-Torres [aut, cre], Abhinav Nellore [ctb], Andrew E. Jaffe [ctb]", "Maintainer": "Leonardo Collado-Torres ", "URL": "https://github.com/LieberInstitute/recountWorkflow", "VignetteBuilder": "knitr", "BugReports": "https://support.bioconductor.org/t/recountWorkflow/", "git_url": "https://git.bioconductor.org/packages/recountWorkflow", "git_branch": "RELEASE_3_9", "git_last_commit": "216b263", "git_last_commit_date": "2019-05-22", "Date/Publication": "2019-05-29", "source.ver": "src/contrib/recountWorkflow_1.8.1.tar.gz", "vignettes": [ "vignettes/recountWorkflow/inst/doc/recount-workflow.html" ], "vignetteTitles": [ "recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor" ], "hasREADME": false, "hasNEWS": true, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/recountWorkflow/inst/doc/recount-workflow.R" ], "dependencyCount": "210", "Rank": 22 }, "RNAseq123": { "Package": "RNAseq123", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "Glimma (>= 1.1.9)", "limma", "edgeR", "gplots", "RColorBrewer", "Mus.musculus" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle" ], "License": "Artistic-2.0", "MD5sum": "46a31e4035b479ed6655defe4876bf1d", "NeedsCompilation": "no", "Title": "RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR", "Description": "R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016).", "biocViews": [ "GeneExpressionWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Charity Law, Monther Alhamdoosh, Shian Su, Xueyi Dong, Luyi Tian, Gordon Smyth and Matthew Ritchie", "Maintainer": "Matthew Ritchie ", "URL": "https://f1000research.com/articles/5-1408/v3", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/RNAseq123", "git_branch": "RELEASE_3_9", "git_last_commit": "c02c562", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/RNAseq123_1.8.0.tar.gz", "vignettes": [ "vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.html", "vignettes/RNAseq123/inst/doc/limmaWorkflow.html" ], "vignetteTitles": [ "RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (Chinese version)", "RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (English version)" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.R", "vignettes/RNAseq123/inst/doc/limmaWorkflow.R" ], "dependencyCount": "91", "Rank": 3 }, "rnaseqDTU": { "Package": "rnaseqDTU", "Version": "1.4.0", "Depends": [ "R (>= 3.5.0)", "DRIMSeq", "DEXSeq", "stageR", "DESeq2", "edgeR", "rafalib", "devtools" ], "Suggests": [ "knitr", "rmarkdown" ], "License": "Artistic-2.0", "MD5sum": "5b342faf084de3d51ce0d7fb1ab2bbec", "NeedsCompilation": "no", "Title": "RNA-seq workflow for differential transcript usage following Salmon quantification", "Description": "RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU.", "biocViews": [ "GeneExpressionWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Michael Love [aut, cre], Charlotte Soneson [aut], Rob Patro [aut]", "Maintainer": "Michael Love ", "URL": "https://github.com/mikelove/rnaseqDTU/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/rnaseqDTU", "git_branch": "RELEASE_3_9", "git_last_commit": "061e64e", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/rnaseqDTU_1.4.0.tar.gz", "vignettes": [ "vignettes/rnaseqDTU/inst/doc/rnaseqDTU.html" ], "vignetteTitles": [ "RNA-seq workflow for differential transcript usage following Salmon quantification" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/rnaseqDTU/inst/doc/rnaseqDTU.R" ], "dependencyCount": "168", "Rank": 14 }, "rnaseqGene": { "Package": "rnaseqGene", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "BiocStyle", "airway", "Rsamtools", "GenomicFeatures", "GenomicAlignments", "BiocParallel", "magrittr", "DESeq2", "apeglm", "vsn", "dplyr", "ggplot2", "pheatmap", "RColorBrewer", "PoiClaClu", "ggbeeswarm", "genefilter", "AnnotationDbi", "org.Hs.eg.db", "ReportingTools", "Gviz", "sva", "RUVSeq", "fission" ], "Suggests": [ "knitr", "rmarkdown" ], "License": "Artistic-2.0", "MD5sum": "5089953a4acc3fc8c7192edb90727559", "NeedsCompilation": "no", "Title": "RNA-seq workflow: gene-level exploratory analysis and differential expression", "Description": "Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.", "biocViews": [ "GeneExpressionWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Michael Love [aut, cre]", "Maintainer": "Michael Love ", "URL": "https://github.com/mikelove/rnaseqGene/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/rnaseqGene", "git_branch": "RELEASE_3_9", "git_last_commit": "1ca74bc", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/rnaseqGene_1.8.0.tar.gz", "vignettes": [ "vignettes/rnaseqGene/inst/doc/rnaseqGene.html" ], "vignetteTitles": [ "RNA-seq workflow at the gene level" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/rnaseqGene/inst/doc/rnaseqGene.R" ], "dependencyCount": "186", "Rank": 1 }, "RnaSeqGeneEdgeRQL": { "Package": "RnaSeqGeneEdgeRQL", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "edgeR", "gplots", "org.Mm.eg.db", "GO.db" ], "Suggests": [ "knitr", "knitcitations" ], "License": "Artistic-2.0", "MD5sum": "4ee23409f7836e901faf7cbe253bd259", "NeedsCompilation": "no", "Title": "Gene-level RNA-seq differential expression and pathway analysis using Rsubread and the edgeR quasi-likelihood pipeline", "Description": "This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis. The analysis includes publication quality plots, GO and KEGG analyses, and the analysis of a expression signature as generated by a prior experiment.", "biocViews": [ "GeneExpressionWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Yunshun Chen, Aaron Lun, Gordon Smyth", "Maintainer": "Yunshun Chen ", "URL": "http://f1000research.com/articles/5-1438", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/RnaSeqGeneEdgeRQL", "git_branch": "RELEASE_3_9", "git_last_commit": "c1a3f5b", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/RnaSeqGeneEdgeRQL_1.8.0.tar.gz", "vignettes": [ "vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html" ], "vignetteTitles": [ "From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.R" ], "dependencyCount": "46", "Rank": 11 }, "sequencing": { "Package": "sequencing", "Version": "1.8.0", "Depends": [ "R (>= 3.3.0)", "GenomicRanges", "GenomicAlignments", "Biostrings", "Rsamtools", "ShortRead", "BiocParallel", "rtracklayer", "VariantAnnotation", "AnnotationHub", "BSgenome.Hsapiens.UCSC.hg19", "RNAseqData.HNRNPC.bam.chr14" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle" ], "License": "Artistic-2.0", "MD5sum": "bcbcb0670933d4f0493229b8d1f703d2", "NeedsCompilation": "no", "Title": "Introduction to Bioconductor for Sequence Data", "Description": "Bioconductor enables the analysis and comprehension of high- throughput genomic data. We have a vast number of packages that allow rigorous statistical analysis of large data while keeping technological artifacts in mind. Bioconductor helps users place their analytic results into biological context, with rich opportunities for visualization. Reproducibility is an important goal in Bioconductor analyses. Different types of analysis can be carried out using Bioconductor, for example; Sequencing : RNASeq, ChIPSeq, variants, copy number etc.; Microarrays: expression, SNP, etc.; Domain specific analysis : Flow cytometry, Proteomics etc. For these analyses, one typically imports and works with diverse sequence-related file types, including fasta, fastq, BAM, gtf, bed, and wig files, among others. Bioconductor packages support import, common and advanced sequence manipulation operations such as trimming, transformation, and alignment including quality assessment.", "biocViews": [ "BasicWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Sonali Arora [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre]", "Maintainer": "Bioconductor Package Maintainer ", "URL": "https://www.bioconductor.org/help/workflows/sequencing/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/sequencing", "git_branch": "RELEASE_3_9", "git_last_commit": "6cd53a3", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/sequencing_1.8.0.tar.gz", "vignettes": [ "vignettes/sequencing/inst/doc/sequencing.html" ], "vignetteTitles": [ "Introduction to Bioconductor for Sequence Data" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/sequencing/inst/doc/sequencing.R" ], "dependencyCount": "104", "Rank": 13 }, "simpleSingleCell": { "Package": "simpleSingleCell", "Version": "1.8.0", "Imports": [ "BiocStyle", "callr", "rmarkdown" ], "Suggests": [ "knitr", "readxl", "R.utils", "Matrix", "SingleCellExperiment", "scater", "scran", "DropletUtils", "org.Hs.eg.db", "org.Mm.eg.db", "EnsDb.Hsapiens.v86", "TxDb.Mmusculus.UCSC.mm10.ensGene", "dynamicTreeCut", "cluster", "igraph", "Rtsne", "pheatmap", "limma", "edgeR", "BiocParallel", "BiocFileCache", "BiocNeighbors", "BiocSingular", "batchelor", "scRNAseq", "TENxBrainData" ], "License": "Artistic-2.0", "MD5sum": "ba46f140144a3221074ef544b22df79b", "NeedsCompilation": "no", "Title": "A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor", "Description": "This workflow implements a low-level analysis pipeline for scRNA-seq data using scran, scater and other Bioconductor packages. It describes how to perform quality control on the libraries, normalization of cell-specific biases, basic data exploration, cell cycle phase identification, doublet detection and batch correction. Procedures to detect highly variable genes, significantly correlated genes and subpopulation-specific marker genes are also shown. These analyses are demonstrated on publicly available scRNA-seq data sets from a variety of protocols including SMART-seq2 and 10X Genomics.", "biocViews": [ "ImmunoOncologyWorkflow", "SingleCellWorkflow", "Workflow" ], "Author": "Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut]", "Maintainer": "Aaron Lun ", "URL": "https://www.bioconductor.org/help/workflows/simpleSingleCell/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/simpleSingleCell", "git_branch": "RELEASE_3_9", "git_last_commit": "0a95c65", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/simpleSingleCell_1.8.0.tar.gz", "vignettes": [ "vignettes/simpleSingleCell/inst/doc/batch.html", "vignettes/simpleSingleCell/inst/doc/bigdata.html", "vignettes/simpleSingleCell/inst/doc/de.html", "vignettes/simpleSingleCell/inst/doc/doublets.html", "vignettes/simpleSingleCell/inst/doc/intro.html", "vignettes/simpleSingleCell/inst/doc/misc.html", "vignettes/simpleSingleCell/inst/doc/qc.html", "vignettes/simpleSingleCell/inst/doc/reads.html", "vignettes/simpleSingleCell/inst/doc/spike.html", "vignettes/simpleSingleCell/inst/doc/tenx.html", "vignettes/simpleSingleCell/inst/doc/umis.html", "vignettes/simpleSingleCell/inst/doc/var.html" ], "vignetteTitles": [ "05. 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Advanced variance modelling" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/simpleSingleCell/inst/doc/batch.R", "vignettes/simpleSingleCell/inst/doc/bigdata.R", "vignettes/simpleSingleCell/inst/doc/de.R", "vignettes/simpleSingleCell/inst/doc/doublets.R", "vignettes/simpleSingleCell/inst/doc/intro.R", "vignettes/simpleSingleCell/inst/doc/misc.R", "vignettes/simpleSingleCell/inst/doc/qc.R", "vignettes/simpleSingleCell/inst/doc/reads.R", "vignettes/simpleSingleCell/inst/doc/spike.R", "vignettes/simpleSingleCell/inst/doc/tenx.R", "vignettes/simpleSingleCell/inst/doc/umis.R", "vignettes/simpleSingleCell/inst/doc/var.R" ], "dependencyCount": "30", "Rank": 2 }, "SingscoreAMLMutations": { "Package": "SingscoreAMLMutations", "Version": "1.0.1", "Depends": [ "R (>= 3.6.0)" ], "Imports": [ "dcanr", "edgeR", "ggplot2", "gridExtra", "GSEABase", "org.Hs.eg.db", "plyr", "reshape2", "rtracklayer", "singscore", "SummarizedExperiment", "TCGAbiolinks" ], "Suggests": [ "knitr", "rmarkdown", "BiocStyle", "BiocWorkflowTools", "spelling" ], "License": "Artistic-2.0", "MD5sum": "c3f0740d5d57586c086ca5e3b6be7fc3", "NeedsCompilation": "no", "Title": "Using singscore to predict mutations in AML from transcriptomic signatures", "Description": "This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis.", "biocViews": [ "GeneExpressionWorkflow", "GenomicVariantsWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Dharmesh D. Bhuva [aut, cre] (), Momeneh Foroutan [aut] (), Yi Xie [aut] (), Ruqian Lyu [aut], Joseph Cursons [aut] (), Melissa J. Davis [aut] ()", "Maintainer": "Dharmesh D. 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These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. 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Identify structural location of variants and compute amino acid coding changes for non-synonymous variants. Use SIFT and PolyPhen database packages to predict consequence of amino acid coding changes", "biocViews": [ "AnnotationWorkflow", "ImmunoOncologyWorkflow", "Workflow" ], "Author": "Bioconductor Package Maintainer [cre]", "Maintainer": "Bioconductor Package Maintainer ", "URL": "https://www.bioconductor.org/help/workflows/variants/", "VignetteBuilder": "knitr", "git_url": "https://git.bioconductor.org/packages/variants", "git_branch": "RELEASE_3_9", "git_last_commit": "659b328", "git_last_commit_date": "2019-05-02", "Date/Publication": "2019-05-03", "source.ver": "src/contrib/variants_1.8.0.tar.gz", "vignettes": [ "vignettes/variants/inst/doc/Annotating_Genomic_Variants.html" ], "vignetteTitles": [ "Annotating Genomic Variants" ], "hasREADME": false, "hasNEWS": false, "hasINSTALL": false, "hasLICENSE": false, "Rfiles": [ "vignettes/variants/inst/doc/Annotating_Genomic_Variants.R" ], "dependencyCount": "80", "Rank": 15 } }