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/MungeSumstatsOnce 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/MungeSumstatsFor 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 4.6.0 Patched (2026-05-01 r89994)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/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_1.21.0 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1
## [2] dplyr_1.2.1
## [3] blob_1.3.0
## [4] R.utils_2.13.0
## [5] Biostrings_2.81.1
## [6] bitops_1.0-9
## [7] fastmap_1.2.0
## [8] RCurl_1.98-1.18
## [9] VariantAnnotation_1.59.0
## [10] GenomicAlignments_1.49.0
## [11] XML_3.99-0.23
## [12] digest_0.6.39
## [13] lifecycle_1.0.5
## [14] KEGGREST_1.53.0
## [15] RSQLite_3.52.0
## [16] magrittr_2.0.5
## [17] compiler_4.6.0
## [18] rlang_1.2.0
## [19] sass_0.4.10
## [20] tools_4.6.0
## [21] yaml_2.3.12
## [22] data.table_1.18.4
## [23] rtracklayer_1.73.0
## [24] knitr_1.51
## [25] S4Arrays_1.13.0
## [26] bit_4.6.0
## [27] curl_7.1.0
## [28] DelayedArray_0.39.2
## [29] ieugwasr_1.1.0
## [30] abind_1.4-8
## [31] BiocParallel_1.47.0
## [32] BiocGenerics_0.59.2
## [33] R.oo_1.27.1
## [34] grid_4.6.0
## [35] stats4_4.6.0
## [36] SummarizedExperiment_1.43.0
## [37] cli_3.6.6
## [38] rmarkdown_2.31
## [39] crayon_1.5.3
## [40] generics_0.1.4
## [41] otel_0.2.0
## [42] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [43] httr_1.4.8
## [44] rjson_0.2.23
## [45] BiocBaseUtils_1.15.1
## [46] DBI_1.3.0
## [47] cachem_1.1.0
## [48] stringr_1.6.0
## [49] parallel_4.6.0
## [50] AnnotationDbi_1.75.0
## [51] BiocManager_1.30.27
## [52] XVector_0.53.0
## [53] restfulr_0.0.16
## [54] matrixStats_1.5.0
## [55] vctrs_0.7.3
## [56] Matrix_1.7-5
## [57] jsonlite_2.0.0
## [58] bookdown_0.46
## [59] IRanges_2.47.1
## [60] S4Vectors_0.51.2
## [61] bit64_4.8.0
## [62] GenomicFiles_1.49.0
## [63] GenomicFeatures_1.65.0
## [64] jquerylib_0.1.4
## [65] glue_1.8.1
## [66] codetools_0.2-20
## [67] stringi_1.8.7
## [68] GenomeInfoDb_1.49.0
## [69] BiocIO_1.23.3
## [70] GenomicRanges_1.65.0
## [71] UCSC.utils_1.9.0
## [72] tibble_3.3.1
## [73] pillar_1.11.1
## [74] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24
## [75] htmltools_0.5.9
## [76] Seqinfo_1.3.0
## [77] BSgenome_1.81.0
## [78] R6_2.6.1
## [79] evaluate_1.0.5
## [80] lattice_0.22-9
## [81] Biobase_2.73.1
## [82] R.methodsS3_1.8.2
## [83] png_0.1-9
## [84] Rsamtools_2.29.0
## [85] cigarillo_1.3.0
## [86] memoise_2.0.1
## [87] bslib_0.11.0
## [88] SparseArray_1.13.2
## [89] xfun_0.57
## [90] MatrixGenerics_1.25.0
## [91] pkgconfig_2.0.3