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 4.5.0 (2025-04-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] EpiCompare_1.13.0 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3
## [2] jsonlite_2.0.0
## [3] magrittr_2.0.3
## [4] magick_2.8.6
## [5] ggtangle_0.0.6
## [6] GenomicFeatures_1.61.3
## [7] farver_2.1.2
## [8] rmarkdown_2.29
## [9] fs_1.6.6
## [10] BiocIO_1.19.0
## [11] vctrs_0.6.5
## [12] memoise_2.0.1
## [13] Rsamtools_2.25.0
## [14] b64_0.1.6
## [15] RCurl_1.98-1.17
## [16] ggtree_3.17.0
## [17] tinytex_0.57
## [18] htmltools_0.5.8.1
## [19] S4Arrays_1.9.1
## [20] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [21] plotrix_3.8-4
## [22] AnnotationHub_3.99.5
## [23] curl_6.2.3
## [24] SparseArray_1.9.0
## [25] gridGraphics_0.5-1
## [26] sass_0.4.10
## [27] KernSmooth_2.23-26
## [28] bslib_0.9.0
## [29] htmlwidgets_1.6.4
## [30] plyr_1.8.9
## [31] httr2_1.1.2
## [32] lubridate_1.9.4
## [33] plotly_4.10.4
## [34] impute_1.83.0
## [35] cachem_1.1.0
## [36] GenomicAlignments_1.45.0
## [37] igraph_2.1.4
## [38] mime_0.13
## [39] downloadthis_0.4.1
## [40] lifecycle_1.0.4
## [41] pkgconfig_2.0.3
## [42] Matrix_1.7-3
## [43] R6_2.6.1
## [44] fastmap_1.2.0
## [45] MatrixGenerics_1.21.0
## [46] digest_0.6.37
## [47] aplot_0.2.5
## [48] enrichplot_1.29.1
## [49] patchwork_1.3.0
## [50] AnnotationDbi_1.71.0
## [51] S4Vectors_0.47.0
## [52] GenomicRanges_1.61.0
## [53] RSQLite_2.4.0
## [54] labeling_0.4.3
## [55] bsplus_0.1.5
## [56] filelock_1.0.3
## [57] timechange_0.3.0
## [58] httr_1.4.7
## [59] abind_1.4-8
## [60] compiler_4.5.0
## [61] withr_3.0.2
## [62] bit64_4.6.0-1
## [63] BiocParallel_1.43.3
## [64] DBI_1.2.3
## [65] gplots_3.2.0
## [66] R.utils_2.13.0
## [67] ChIPseeker_1.45.0
## [68] rappdirs_0.3.3
## [69] DelayedArray_0.35.1
## [70] rjson_0.2.23
## [71] caTools_1.18.3
## [72] gtools_3.9.5
## [73] tools_4.5.0
## [74] ape_5.8-1
## [75] R.oo_1.27.1
## [76] glue_1.8.0
## [77] restfulr_0.0.15
## [78] nlme_3.1-168
## [79] GOSemSim_2.35.0
## [80] grid_4.5.0
## [81] gridBase_0.4-7
## [82] reshape2_1.4.4
## [83] fgsea_1.35.2
## [84] generics_0.1.4
## [85] BSgenome_1.77.0
## [86] gtable_0.3.6
## [87] tzdb_0.5.0
## [88] R.methodsS3_1.8.2
## [89] seqPattern_1.41.0
## [90] tidyr_1.3.1
## [91] hms_1.1.3
## [92] data.table_1.17.4
## [93] XVector_0.49.0
## [94] BiocGenerics_0.55.0
## [95] ggrepel_0.9.6
## [96] BiocVersion_3.22.0
## [97] pillar_1.10.2
## [98] stringr_1.5.1
## [99] yulab.utils_0.2.0
## [100] splines_4.5.0
## [101] dplyr_1.1.4
## [102] BiocFileCache_2.99.5
## [103] treeio_1.33.0
## [104] lattice_0.22-7
## [105] rtracklayer_1.69.0
## [106] bit_4.6.0
## [107] tidyselect_1.2.1
## [108] GO.db_3.21.0
## [109] Biostrings_2.77.1
## [110] knitr_1.50
## [111] bookdown_0.43
## [112] IRanges_2.43.0
## [113] SummarizedExperiment_1.39.0
## [114] stats4_4.5.0
## [115] xfun_0.52
## [116] Biobase_2.69.0
## [117] matrixStats_1.5.0
## [118] stringi_1.8.7
## [119] UCSC.utils_1.5.0
## [120] lazyeval_0.2.2
## [121] ggfun_0.1.8
## [122] yaml_2.3.10
## [123] boot_1.3-31
## [124] evaluate_1.0.3
## [125] codetools_0.2-20
## [126] tibble_3.2.1
## [127] qvalue_2.41.0
## [128] BiocManager_1.30.25
## [129] ggplotify_0.1.2
## [130] cli_3.6.5
## [131] jquerylib_0.1.4
## [132] dichromat_2.0-0.1
## [133] Rcpp_1.0.14
## [134] GenomeInfoDb_1.45.4
## [135] dbplyr_2.5.0
## [136] png_0.1-8
## [137] XML_3.99-0.18
## [138] parallel_4.5.0
## [139] readr_2.1.5
## [140] ggplot2_3.5.2
## [141] blob_1.2.4
## [142] DOSE_4.3.0
## [143] bitops_1.0-9
## [144] viridisLite_0.4.2
## [145] tidytree_0.4.6
## [146] scales_1.4.0
## [147] genomation_1.41.0
## [148] purrr_1.0.4
## [149] crayon_1.5.3
## [150] rlang_1.1.6
## [151] cowplot_1.1.3
## [152] fastmatch_1.1-6
## [153] KEGGREST_1.49.0