MungeSumstats is now available via ghcr.io 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 ghcr.io/neurogenomics/MungeSumstats
Once 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 8900:8787 \
ghcr.io/neurogenomics/MungeSumstats
<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://ghcr.io/neurogenomics/MungeSumstats
For troubleshooting, see the Singularity documentation.
Finally, launch the containerised Rstudio by entering the following URL in any web browser: http://localhost:8900/
Login using the credentials set during the Installation steps.
utils::sessionInfo()
## R version 4.5.0 Patched (2025-04-21 r88169)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MungeSumstats_1.17.0 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.49.0
## [2] SummarizedExperiment_1.39.0
## [3] rjson_0.2.23
## [4] xfun_0.52
## [5] bslib_0.9.0
## [6] Biobase_2.69.0
## [7] lattice_0.22-7
## [8] vctrs_0.6.5
## [9] tools_4.5.0
## [10] bitops_1.0-9
## [11] generics_0.1.3
## [12] stats4_4.5.0
## [13] curl_6.2.2
## [14] parallel_4.5.0
## [15] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [16] tibble_3.2.1
## [17] AnnotationDbi_1.71.0
## [18] RSQLite_2.3.9
## [19] blob_1.2.4
## [20] pkgconfig_2.0.3
## [21] R.oo_1.27.0
## [22] Matrix_1.7-3
## [23] data.table_1.17.0
## [24] BSgenome_1.77.0
## [25] S4Vectors_0.47.0
## [26] lifecycle_1.0.4
## [27] stringr_1.5.1
## [28] compiler_4.5.0
## [29] Rsamtools_2.25.0
## [30] Biostrings_2.77.0
## [31] GenomicFiles_1.45.0
## [32] codetools_0.2-20
## [33] GenomeInfoDb_1.45.3
## [34] htmltools_0.5.8.1
## [35] sass_0.4.10
## [36] RCurl_1.98-1.17
## [37] yaml_2.3.10
## [38] pillar_1.10.2
## [39] crayon_1.5.3
## [40] jquerylib_0.1.4
## [41] R.utils_2.13.0
## [42] BiocParallel_1.43.0
## [43] cachem_1.1.0
## [44] DelayedArray_0.35.1
## [45] abind_1.4-8
## [46] tidyselect_1.2.1
## [47] digest_0.6.37
## [48] stringi_1.8.7
## [49] dplyr_1.1.4
## [50] restfulr_0.0.15
## [51] bookdown_0.43
## [52] VariantAnnotation_1.55.0
## [53] fastmap_1.2.0
## [54] grid_4.5.0
## [55] cli_3.6.5
## [56] SparseArray_1.9.0
## [57] magrittr_2.0.3
## [58] S4Arrays_1.9.0
## [59] GenomicFeatures_1.61.0
## [60] XML_3.99-0.18
## [61] UCSC.utils_1.5.0
## [62] bit64_4.6.0-1
## [63] rmarkdown_2.29
## [64] XVector_0.49.0
## [65] httr_1.4.7
## [66] matrixStats_1.5.0
## [67] bit_4.6.0
## [68] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24
## [69] png_0.1-8
## [70] R.methodsS3_1.8.2
## [71] memoise_2.0.1
## [72] evaluate_1.0.3
## [73] knitr_1.50
## [74] GenomicRanges_1.61.0
## [75] IRanges_2.43.0
## [76] BiocIO_1.19.0
## [77] rtracklayer_1.69.0
## [78] rlang_1.1.6
## [79] glue_1.8.0
## [80] DBI_1.2.3
## [81] BiocManager_1.30.25
## [82] BiocGenerics_0.55.0
## [83] jsonlite_2.0.0
## [84] ieugwasr_1.0.3
## [85] R6_2.6.1
## [86] MatrixGenerics_1.21.0
## [87] GenomicAlignments_1.45.0