epicompare is now available via DockerHub as a containerised environment with Rstudio and all necessary dependencies pre-installed.
First, install Docker if you have not already.
Create an image of the Docker container in command line:
docker pull neurogenomicslab/epicompareOnce the image has been created, you can launch it with:
docker run \
-d \
-e ROOT=true \
-e PASSWORD="<your_password>" \
-v ~/Desktop:/Desktop \
-v /Volumes:/Volumes \
-p 8787:8787 \
neurogenomicslab/epicompare<your_password> above with whatever you want your password to be.-v flags for your particular use case.-d ensures the container will run in “detached” mode,
which means it will persist even after you’ve closed your command line session.If you are using a system that does not allow Docker (as is the case for many institutional computing clusters), you can instead install Docker images via Singularity.
singularity pull docker://neurogenomicslab/epicompareFinally, launch the containerised Rstudio by entering the following URL in any web browser: http://localhost:8787/
Login using the credentials set during the Installation steps.
utils::sessionInfo()## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] EpiCompare_1.13.2 BiocStyle_2.37.1
##
## loaded via a namespace (and not attached):
## [1] splines_4.5.1
## [2] BiocIO_1.19.0
## [3] bitops_1.0-9
## [4] ggplotify_0.1.3
## [5] filelock_1.0.3
## [6] tibble_3.3.0
## [7] R.oo_1.27.1
## [8] XML_3.99-0.19
## [9] lifecycle_1.0.4
## [10] httr2_1.2.1
## [11] lattice_0.22-7
## [12] magrittr_2.0.4
## [13] plotly_4.11.0
## [14] sass_0.4.10
## [15] rmarkdown_2.29
## [16] jquerylib_0.1.4
## [17] yaml_2.3.10
## [18] plotrix_3.8-4
## [19] ggtangle_0.0.7
## [20] cowplot_1.2.0
## [21] DBI_1.2.3
## [22] RColorBrewer_1.1-3
## [23] lubridate_1.9.4
## [24] abind_1.4-8
## [25] GenomicRanges_1.61.5
## [26] purrr_1.1.0
## [27] R.utils_2.13.0
## [28] BiocGenerics_0.55.1
## [29] RCurl_1.98-1.17
## [30] yulab.utils_0.2.1
## [31] rappdirs_0.3.3
## [32] IRanges_2.43.2
## [33] S4Vectors_0.47.2
## [34] enrichplot_1.29.2
## [35] ggrepel_0.9.6
## [36] tidytree_0.4.6
## [37] ChIPseeker_1.45.0
## [38] codetools_0.2-20
## [39] DelayedArray_0.35.3
## [40] DOSE_4.3.0
## [41] tidyselect_1.2.1
## [42] aplot_0.2.9
## [43] UCSC.utils_1.5.0
## [44] farver_2.1.2
## [45] matrixStats_1.5.0
## [46] stats4_4.5.1
## [47] BiocFileCache_2.99.6
## [48] base64enc_0.1-3
## [49] Seqinfo_0.99.2
## [50] GenomicAlignments_1.45.4
## [51] jsonlite_2.0.0
## [52] systemfonts_1.2.3
## [53] tools_4.5.1
## [54] treeio_1.33.0
## [55] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [56] Rcpp_1.1.0
## [57] glue_1.8.0
## [58] SparseArray_1.9.1
## [59] xfun_0.53
## [60] qvalue_2.41.0
## [61] MatrixGenerics_1.21.0
## [62] GenomeInfoDb_1.45.11
## [63] dplyr_1.1.4
## [64] withr_3.0.2
## [65] BiocManager_1.30.26
## [66] fastmap_1.2.0
## [67] boot_1.3-32
## [68] caTools_1.18.3
## [69] digest_0.6.37
## [70] timechange_0.3.0
## [71] R6_2.6.1
## [72] mime_0.13
## [73] gridGraphics_0.5-1
## [74] seqPattern_1.41.0
## [75] GO.db_3.21.0
## [76] gtools_3.9.5
## [77] dichromat_2.0-0.1
## [78] RSQLite_2.4.3
## [79] R.methodsS3_1.8.2
## [80] tidyr_1.3.1
## [81] generics_0.1.4
## [82] data.table_1.17.8
## [83] rtracklayer_1.69.1
## [84] bsplus_0.1.5
## [85] httr_1.4.7
## [86] htmlwidgets_1.6.4
## [87] S4Arrays_1.9.1
## [88] downloadthis_0.5.0
## [89] pkgconfig_2.0.3
## [90] gtable_0.3.6
## [91] blob_1.2.4
## [92] S7_0.2.0
## [93] impute_1.83.0
## [94] XVector_0.49.1
## [95] htmltools_0.5.8.1
## [96] bookdown_0.44
## [97] fgsea_1.35.6
## [98] scales_1.4.0
## [99] Biobase_2.69.1
## [100] png_0.1-8
## [101] ggfun_0.2.0
## [102] knitr_1.50
## [103] tzdb_0.5.0
## [104] reshape2_1.4.4
## [105] rjson_0.2.23
## [106] uuid_1.2-1
## [107] nlme_3.1-168
## [108] curl_7.0.0
## [109] cachem_1.1.0
## [110] stringr_1.5.2
## [111] BiocVersion_3.22.0
## [112] KernSmooth_2.23-26
## [113] parallel_4.5.1
## [114] AnnotationDbi_1.71.1
## [115] restfulr_0.0.16
## [116] pillar_1.11.1
## [117] grid_4.5.1
## [118] vctrs_0.6.5
## [119] gplots_3.2.0
## [120] dbplyr_2.5.1
## [121] evaluate_1.0.5
## [122] magick_2.9.0
## [123] readr_2.1.5
## [124] tinytex_0.57
## [125] GenomicFeatures_1.61.6
## [126] cli_3.6.5
## [127] compiler_4.5.1
## [128] Rsamtools_2.25.3
## [129] rlang_1.1.6
## [130] crayon_1.5.3
## [131] labeling_0.4.3
## [132] plyr_1.8.9
## [133] fs_1.6.6
## [134] ggiraph_0.9.1
## [135] stringi_1.8.7
## [136] genomation_1.41.1
## [137] viridisLite_0.4.2
## [138] gridBase_0.4-7
## [139] BiocParallel_1.43.4
## [140] Biostrings_2.77.2
## [141] lazyeval_0.2.2
## [142] GOSemSim_2.35.1
## [143] Matrix_1.7-4
## [144] BSgenome_1.77.2
## [145] hms_1.1.3
## [146] patchwork_1.3.2
## [147] bit64_4.6.0-1
## [148] ggplot2_4.0.0
## [149] KEGGREST_1.49.1
## [150] SummarizedExperiment_1.39.2
## [151] AnnotationHub_3.99.6
## [152] igraph_2.1.4
## [153] memoise_2.0.1
## [154] bslib_0.9.0
## [155] ggtree_3.99.0
## [156] fastmatch_1.1-6
## [157] bit_4.6.0
## [158] ape_5.8-1