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.
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] EpiCompare_1.11.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3
## [2] sys_3.4.3
## [3] jsonlite_1.8.9
## [4] magrittr_2.0.3
## [5] ggtangle_0.0.4
## [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] zlibbioc_1.52.0
## [12] vctrs_0.6.5
## [13] memoise_2.0.1
## [14] Rsamtools_2.23.1
## [15] b64_0.1.3
## [16] RCurl_1.98-1.16
## [17] ggtree_3.15.0
## [18] htmltools_0.5.8.1
## [19] S4Arrays_1.7.1
## [20] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [21] plotrix_3.8-4
## [22] AnnotationHub_3.15.0
## [23] curl_6.0.1
## [24] SparseArray_1.7.2
## [25] gridGraphics_0.5-1
## [26] sass_0.4.9
## [27] KernSmooth_2.23-24
## [28] bslib_0.8.0
## [29] htmlwidgets_1.6.4
## [30] plyr_1.8.9
## [31] lubridate_1.9.3
## [32] plotly_4.10.4
## [33] impute_1.81.0
## [34] cachem_1.1.0
## [35] buildtools_1.0.0
## [36] GenomicAlignments_1.43.0
## [37] igraph_2.1.1
## [38] mime_0.12
## [39] downloadthis_0.4.1
## [40] lifecycle_1.0.4
## [41] pkgconfig_2.0.3
## [42] Matrix_1.7-1
## [43] R6_2.5.1
## [44] fastmap_1.2.0
## [45] GenomeInfoDbData_1.2.13
## [46] MatrixGenerics_1.19.0
## [47] digest_0.6.37
## [48] aplot_0.2.3
## [49] enrichplot_1.27.1
## [50] colorspace_2.1-1
## [51] patchwork_1.3.0
## [52] AnnotationDbi_1.69.0
## [53] S4Vectors_0.45.2
## [54] GenomicRanges_1.59.1
## [55] RSQLite_2.3.8
## [56] labeling_0.4.3
## [57] bsplus_0.1.4
## [58] filelock_1.0.3
## [59] timechange_0.3.0
## [60] fansi_1.0.6
## [61] httr_1.4.7
## [62] abind_1.4-8
## [63] compiler_4.4.2
## [64] withr_3.0.2
## [65] bit64_4.5.2
## [66] BiocParallel_1.41.0
## [67] DBI_1.2.3
## [68] gplots_3.2.0
## [69] R.utils_2.12.3
## [70] ChIPseeker_1.43.0
## [71] rappdirs_0.3.3
## [72] DelayedArray_0.33.2
## [73] rjson_0.2.23
## [74] caTools_1.18.3
## [75] gtools_3.9.5
## [76] tools_4.4.2
## [77] ape_5.8
## [78] R.oo_1.27.0
## [79] glue_1.8.0
## [80] restfulr_0.0.15
## [81] nlme_3.1-166
## [82] GOSemSim_2.33.0
## [83] grid_4.4.2
## [84] gridBase_0.4-7
## [85] reshape2_1.4.4
## [86] fgsea_1.33.0
## [87] generics_0.1.3
## [88] BSgenome_1.75.0
## [89] gtable_0.3.6
## [90] tzdb_0.4.0
## [91] R.methodsS3_1.8.2
## [92] seqPattern_1.39.0
## [93] tidyr_1.3.1
## [94] hms_1.1.3
## [95] data.table_1.16.2
## [96] utf8_1.2.4
## [97] XVector_0.47.0
## [98] BiocGenerics_0.53.3
## [99] ggrepel_0.9.6
## [100] BiocVersion_3.21.1
## [101] pillar_1.9.0
## [102] stringr_1.5.1
## [103] yulab.utils_0.1.8
## [104] splines_4.4.2
## [105] dplyr_1.1.4
## [106] treeio_1.31.0
## [107] BiocFileCache_2.15.0
## [108] lattice_0.22-6
## [109] rtracklayer_1.67.0
## [110] bit_4.5.0
## [111] tidyselect_1.2.1
## [112] GO.db_3.20.0
## [113] maketools_1.3.1
## [114] Biostrings_2.75.1
## [115] knitr_1.49
## [116] IRanges_2.41.1
## [117] SummarizedExperiment_1.37.0
## [118] stats4_4.4.2
## [119] xfun_0.49
## [120] Biobase_2.67.0
## [121] matrixStats_1.4.1
## [122] stringi_1.8.4
## [123] UCSC.utils_1.3.0
## [124] lazyeval_0.2.2
## [125] ggfun_0.1.7
## [126] yaml_2.3.10
## [127] boot_1.3-31
## [128] evaluate_1.0.1
## [129] codetools_0.2-20
## [130] tibble_3.2.1
## [131] qvalue_2.39.0
## [132] BiocManager_1.30.25
## [133] ggplotify_0.1.2
## [134] cli_3.6.3
## [135] munsell_0.5.1
## [136] jquerylib_0.1.4
## [137] Rcpp_1.0.13-1
## [138] GenomeInfoDb_1.43.2
## [139] dbplyr_2.5.0
## [140] png_0.1-8
## [141] XML_3.99-0.17
## [142] parallel_4.4.2
## [143] readr_2.1.5
## [144] ggplot2_3.5.1
## [145] blob_1.2.4
## [146] DOSE_4.1.0
## [147] bitops_1.0-9
## [148] viridisLite_0.4.2
## [149] tidytree_0.4.6
## [150] scales_1.3.0
## [151] genomation_1.39.0
## [152] purrr_1.0.2
## [153] crayon_1.5.3
## [154] rlang_1.1.4
## [155] cowplot_1.1.3
## [156] fastmatch_1.1-4
## [157] KEGGREST_1.47.0