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 Under development (unstable) (2025-01-20 r87609)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-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_1.11.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3
## [2] jsonlite_1.8.9
## [3] magrittr_2.0.3
## [4] magick_2.8.5
## [5] ggtangle_0.0.6
## [6] GenomicFeatures_1.59.1
## [7] farver_2.1.2
## [8] rmarkdown_2.29
## [9] fs_1.6.5
## [10] BiocIO_1.17.1
## [11] vctrs_0.6.5
## [12] memoise_2.0.1
## [13] Rsamtools_2.23.1
## [14] b64_0.1.3
## [15] RCurl_1.98-1.16
## [16] ggtree_3.15.0
## [17] tinytex_0.54
## [18] htmltools_0.5.8.1
## [19] S4Arrays_1.7.3
## [20] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [21] plotrix_3.8-4
## [22] AnnotationHub_3.15.0
## [23] curl_6.2.0
## [24] SparseArray_1.7.5
## [25] gridGraphics_0.5-1
## [26] sass_0.4.9
## [27] KernSmooth_2.23-26
## [28] bslib_0.9.0
## [29] htmlwidgets_1.6.4
## [30] plyr_1.8.9
## [31] lubridate_1.9.4
## [32] plotly_4.10.4
## [33] impute_1.81.0
## [34] cachem_1.1.0
## [35] GenomicAlignments_1.43.0
## [36] igraph_2.1.4
## [37] mime_0.12
## [38] downloadthis_0.4.1
## [39] lifecycle_1.0.4
## [40] pkgconfig_2.0.3
## [41] Matrix_1.7-2
## [42] R6_2.6.0
## [43] fastmap_1.2.0
## [44] GenomeInfoDbData_1.2.13
## [45] MatrixGenerics_1.19.1
## [46] digest_0.6.37
## [47] aplot_0.2.4
## [48] enrichplot_1.27.4
## [49] colorspace_2.1-1
## [50] patchwork_1.3.0
## [51] AnnotationDbi_1.69.0
## [52] S4Vectors_0.45.4
## [53] GenomicRanges_1.59.1
## [54] RSQLite_2.3.9
## [55] labeling_0.4.3
## [56] bsplus_0.1.4
## [57] filelock_1.0.3
## [58] timechange_0.3.0
## [59] httr_1.4.7
## [60] abind_1.4-8
## [61] compiler_4.5.0
## [62] withr_3.0.2
## [63] bit64_4.6.0-1
## [64] BiocParallel_1.41.0
## [65] DBI_1.2.3
## [66] gplots_3.2.0
## [67] R.utils_2.12.3
## [68] ChIPseeker_1.43.0
## [69] rappdirs_0.3.3
## [70] DelayedArray_0.33.6
## [71] rjson_0.2.23
## [72] caTools_1.18.3
## [73] gtools_3.9.5
## [74] tools_4.5.0
## [75] ape_5.8-1
## [76] R.oo_1.27.0
## [77] glue_1.8.0
## [78] restfulr_0.0.15
## [79] nlme_3.1-167
## [80] GOSemSim_2.33.0
## [81] grid_4.5.0
## [82] gridBase_0.4-7
## [83] reshape2_1.4.4
## [84] fgsea_1.33.2
## [85] generics_0.1.3
## [86] BSgenome_1.75.1
## [87] gtable_0.3.6
## [88] tzdb_0.4.0
## [89] R.methodsS3_1.8.2
## [90] seqPattern_1.39.0
## [91] tidyr_1.3.1
## [92] hms_1.1.3
## [93] data.table_1.16.4
## [94] XVector_0.47.2
## [95] BiocGenerics_0.53.6
## [96] ggrepel_0.9.6
## [97] BiocVersion_3.21.1
## [98] pillar_1.10.1
## [99] stringr_1.5.1
## [100] yulab.utils_0.2.0
## [101] splines_4.5.0
## [102] dplyr_1.1.4
## [103] treeio_1.31.0
## [104] BiocFileCache_2.15.1
## [105] lattice_0.22-6
## [106] rtracklayer_1.67.0
## [107] bit_4.5.0.1
## [108] tidyselect_1.2.1
## [109] GO.db_3.20.0
## [110] Biostrings_2.75.3
## [111] knitr_1.49
## [112] bookdown_0.42
## [113] IRanges_2.41.3
## [114] SummarizedExperiment_1.37.0
## [115] stats4_4.5.0
## [116] xfun_0.50
## [117] Biobase_2.67.0
## [118] matrixStats_1.5.0
## [119] stringi_1.8.4
## [120] UCSC.utils_1.3.1
## [121] lazyeval_0.2.2
## [122] ggfun_0.1.8
## [123] yaml_2.3.10
## [124] boot_1.3-31
## [125] evaluate_1.0.3
## [126] codetools_0.2-20
## [127] tibble_3.2.1
## [128] qvalue_2.39.0
## [129] BiocManager_1.30.25
## [130] ggplotify_0.1.2
## [131] cli_3.6.4
## [132] munsell_0.5.1
## [133] jquerylib_0.1.4
## [134] Rcpp_1.0.14
## [135] GenomeInfoDb_1.43.4
## [136] dbplyr_2.5.0
## [137] png_0.1-8
## [138] XML_3.99-0.18
## [139] parallel_4.5.0
## [140] readr_2.1.5
## [141] ggplot2_3.5.1
## [142] blob_1.2.4
## [143] DOSE_4.1.0
## [144] bitops_1.0-9
## [145] viridisLite_0.4.2
## [146] tidytree_0.4.6
## [147] scales_1.3.0
## [148] genomation_1.39.0
## [149] purrr_1.0.4
## [150] crayon_1.5.3
## [151] rlang_1.1.5
## [152] cowplot_1.1.3
## [153] fastmatch_1.1-6
## [154] KEGGREST_1.47.0