mungesumstats 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/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 8787:8787 \
neurogenomicslab/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://neurogenomicslab/mungesumstatsFinally, 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.
utils::sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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_GB 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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] XVector_0.42.0 GenomicRanges_1.54.0 S4Vectors_0.40.1
## [4] IRanges_2.36.0 MungeSumstats_1.10.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0
## [2] dplyr_1.1.3
## [3] blob_1.2.4
## [4] filelock_1.0.2
## [5] R.utils_2.12.2
## [6] Biostrings_2.70.1
## [7] bitops_1.0-7
## [8] fastmap_1.1.1
## [9] RCurl_1.98-1.12
## [10] BiocFileCache_2.10.1
## [11] VariantAnnotation_1.48.0
## [12] GenomicAlignments_1.38.0
## [13] XML_3.99-0.14
## [14] digest_0.6.33
## [15] lifecycle_1.0.3
## [16] KEGGREST_1.42.0
## [17] RSQLite_2.3.1
## [18] googleAuthR_2.0.1
## [19] magrittr_2.0.3
## [20] compiler_4.3.1
## [21] rlang_1.1.1
## [22] sass_0.4.7
## [23] progress_1.2.2
## [24] tools_4.3.1
## [25] utf8_1.2.4
## [26] yaml_2.3.7
## [27] data.table_1.14.8
## [28] rtracklayer_1.62.0
## [29] knitr_1.44
## [30] prettyunits_1.2.0
## [31] S4Arrays_1.2.0
## [32] curl_5.1.0
## [33] bit_4.0.5
## [34] DelayedArray_0.28.0
## [35] xml2_1.3.5
## [36] abind_1.4-5
## [37] BiocParallel_1.36.0
## [38] BiocGenerics_0.48.0
## [39] R.oo_1.25.0
## [40] grid_4.3.1
## [41] stats4_4.3.1
## [42] fansi_1.0.5
## [43] biomaRt_2.58.0
## [44] SummarizedExperiment_1.32.0
## [45] cli_3.6.1
## [46] rmarkdown_2.25
## [47] crayon_1.5.2
## [48] generics_0.1.3
## [49] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [50] httr_1.4.7
## [51] rjson_0.2.21
## [52] DBI_1.1.3
## [53] cachem_1.0.8
## [54] stringr_1.5.0
## [55] zlibbioc_1.48.0
## [56] assertthat_0.2.1
## [57] parallel_4.3.1
## [58] AnnotationDbi_1.64.0
## [59] BiocManager_1.30.22
## [60] restfulr_0.0.15
## [61] matrixStats_1.0.0
## [62] vctrs_0.6.4
## [63] Matrix_1.6-1.1
## [64] jsonlite_1.8.7
## [65] bookdown_0.36
## [66] hms_1.1.3
## [67] bit64_4.0.5
## [68] GenomicFiles_1.38.0
## [69] GenomicFeatures_1.54.0
## [70] jquerylib_0.1.4
## [71] glue_1.6.2
## [72] codetools_0.2-19
## [73] stringi_1.7.12
## [74] GenomeInfoDb_1.38.0
## [75] BiocIO_1.12.0
## [76] tibble_3.2.1
## [77] pillar_1.9.0
## [78] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24
## [79] rappdirs_0.3.3
## [80] htmltools_0.5.6.1
## [81] GenomeInfoDbData_1.2.11
## [82] BSgenome_1.70.0
## [83] R6_2.5.1
## [84] dbplyr_2.4.0
## [85] evaluate_0.22
## [86] lattice_0.22-5
## [87] Biobase_2.62.0
## [88] R.methodsS3_1.8.2
## [89] png_0.1-8
## [90] Rsamtools_2.18.0
## [91] gargle_1.5.2
## [92] memoise_2.0.1
## [93] bslib_0.5.1
## [94] SparseArray_1.2.0
## [95] xfun_0.40
## [96] fs_1.6.3
## [97] MatrixGenerics_1.14.0
## [98] pkgconfig_2.0.3