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 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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] MungeSumstats_1.12.0 BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4
## [3] blob_1.2.4 R.utils_2.12.3
## [5] Biostrings_2.72.0 bitops_1.0-7
## [7] fastmap_1.1.1 RCurl_1.98-1.14
## [9] VariantAnnotation_1.50.0 GenomicAlignments_1.40.0
## [11] XML_3.99-0.16.1 digest_0.6.35
## [13] lifecycle_1.0.4 KEGGREST_1.44.0
## [15] RSQLite_2.3.6 magrittr_2.0.3
## [17] googleAuthR_2.0.1 compiler_4.4.0
## [19] rlang_1.1.3 sass_0.4.9
## [21] tools_4.4.0 utf8_1.2.4
## [23] yaml_2.3.8 data.table_1.15.4
## [25] rtracklayer_1.64.0 knitr_1.46
## [27] S4Arrays_1.4.0 bit_4.0.5
## [29] curl_5.2.1 DelayedArray_0.30.0
## [31] abind_1.4-5 BiocParallel_1.38.0
## [33] BiocGenerics_0.50.0 R.oo_1.26.0
## [35] grid_4.4.0 stats4_4.4.0
## [37] fansi_1.0.6 SummarizedExperiment_1.34.0
## [39] cli_3.6.2 rmarkdown_2.26
## [41] crayon_1.5.2 generics_0.1.3
## [43] httr_1.4.7 rjson_0.2.21
## [45] DBI_1.2.2 cachem_1.0.8
## [47] stringr_1.5.1 zlibbioc_1.50.0
## [49] assertthat_0.2.1 parallel_4.4.0
## [51] AnnotationDbi_1.66.0 BiocManager_1.30.22
## [53] XVector_0.44.0 restfulr_0.0.15
## [55] matrixStats_1.3.0 vctrs_0.6.5
## [57] Matrix_1.7-0 jsonlite_1.8.8
## [59] bookdown_0.39 IRanges_2.38.0
## [61] S4Vectors_0.42.0 bit64_4.0.5
## [63] GenomicFeatures_1.56.0 jquerylib_0.1.4
## [65] glue_1.7.0 codetools_0.2-20
## [67] stringi_1.8.3 GenomeInfoDb_1.40.0
## [69] BiocIO_1.14.0 GenomicRanges_1.56.0
## [71] UCSC.utils_1.0.0 tibble_3.2.1
## [73] pillar_1.9.0 htmltools_0.5.8.1
## [75] GenomeInfoDbData_1.2.12 BSgenome_1.72.0
## [77] R6_2.5.1 evaluate_0.23
## [79] lattice_0.22-6 Biobase_2.64.0
## [81] R.methodsS3_1.8.2 png_0.1-8
## [83] Rsamtools_2.20.0 gargle_1.5.2
## [85] memoise_2.0.1 bslib_0.7.0
## [87] SparseArray_1.4.0 xfun_0.43
## [89] fs_1.6.4 MatrixGenerics_1.16.0
## [91] pkgconfig_2.0.3