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 Under development (unstable) (2025-01-22 r87618)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## 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_1.15.12 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1
## [2] dplyr_1.1.4
## [3] blob_1.2.4
## [4] R.utils_2.12.3
## [5] Biostrings_2.75.3
## [6] bitops_1.0-9
## [7] fastmap_1.2.0
## [8] RCurl_1.98-1.16
## [9] VariantAnnotation_1.53.1
## [10] GenomicAlignments_1.43.0
## [11] XML_3.99-0.18
## [12] digest_0.6.37
## [13] lifecycle_1.0.4
## [14] KEGGREST_1.47.0
## [15] RSQLite_2.3.9
## [16] magrittr_2.0.3
## [17] compiler_4.5.0
## [18] rlang_1.1.5
## [19] sass_0.4.9
## [20] tools_4.5.0
## [21] yaml_2.3.10
## [22] data.table_1.16.4
## [23] rtracklayer_1.67.0
## [24] knitr_1.49
## [25] S4Arrays_1.7.2
## [26] bit_4.5.0.1
## [27] curl_6.2.0
## [28] DelayedArray_0.33.5
## [29] ieugwasr_1.0.1
## [30] abind_1.4-8
## [31] BiocParallel_1.41.0
## [32] BiocGenerics_0.53.6
## [33] R.oo_1.27.0
## [34] grid_4.5.0
## [35] stats4_4.5.0
## [36] SummarizedExperiment_1.37.0
## [37] cli_3.6.3
## [38] rmarkdown_2.29
## [39] crayon_1.5.3
## [40] generics_0.1.3
## [41] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [42] httr_1.4.7
## [43] rjson_0.2.23
## [44] DBI_1.2.3
## [45] cachem_1.1.0
## [46] stringr_1.5.1
## [47] parallel_4.5.0
## [48] AnnotationDbi_1.69.0
## [49] BiocManager_1.30.25
## [50] XVector_0.47.2
## [51] restfulr_0.0.15
## [52] matrixStats_1.5.0
## [53] vctrs_0.6.5
## [54] Matrix_1.7-2
## [55] jsonlite_1.8.9
## [56] bookdown_0.42
## [57] IRanges_2.41.2
## [58] S4Vectors_0.45.2
## [59] bit64_4.6.0-1
## [60] GenomicFiles_1.43.0
## [61] GenomicFeatures_1.59.1
## [62] jquerylib_0.1.4
## [63] glue_1.8.0
## [64] codetools_0.2-20
## [65] stringi_1.8.4
## [66] GenomeInfoDb_1.43.4
## [67] BiocIO_1.17.1
## [68] GenomicRanges_1.59.1
## [69] UCSC.utils_1.3.1
## [70] tibble_3.2.1
## [71] pillar_1.10.1
## [72] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24
## [73] htmltools_0.5.8.1
## [74] GenomeInfoDbData_1.2.13
## [75] BSgenome_1.75.1
## [76] R6_2.5.1
## [77] evaluate_1.0.3
## [78] lattice_0.22-6
## [79] Biobase_2.67.0
## [80] R.methodsS3_1.8.2
## [81] png_0.1-8
## [82] Rsamtools_2.23.1
## [83] memoise_2.0.1
## [84] bslib_0.9.0
## [85] SparseArray_1.7.5
## [86] xfun_0.50
## [87] MatrixGenerics_1.19.1
## [88] pkgconfig_2.0.3