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] MungeSumstats_1.9.17 BiocStyle_2.29.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0
## [2] dplyr_1.1.2
## [3] blob_1.2.4
## [4] filelock_1.0.2
## [5] R.utils_2.12.2
## [6] Biostrings_2.69.2
## [7] bitops_1.0-7
## [8] fastmap_1.1.1
## [9] RCurl_1.98-1.12
## [10] BiocFileCache_2.9.1
## [11] VariantAnnotation_1.47.1
## [12] GenomicAlignments_1.37.0
## [13] XML_3.99-0.14
## [14] digest_0.6.33
## [15] lifecycle_1.0.3
## [16] KEGGREST_1.41.0
## [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_1.2.3
## [26] yaml_2.3.7
## [27] data.table_1.14.8
## [28] rtracklayer_1.61.1
## [29] knitr_1.43
## [30] prettyunits_1.1.1
## [31] S4Arrays_1.1.5
## [32] curl_5.0.2
## [33] bit_4.0.5
## [34] DelayedArray_0.27.10
## [35] xml2_1.3.5
## [36] abind_1.4-5
## [37] BiocParallel_1.35.4
## [38] BiocGenerics_0.47.0
## [39] R.oo_1.25.0
## [40] grid_4.3.1
## [41] stats4_4.3.1
## [42] fansi_1.0.4
## [43] biomaRt_2.57.1
## [44] SummarizedExperiment_1.31.1
## [45] cli_3.6.1
## [46] rmarkdown_2.24
## [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_1.47.0
## [56] assertthat_0.2.1
## [57] parallel_4.3.1
## [58] AnnotationDbi_1.63.2
## [59] BiocManager_1.30.22
## [60] XVector_0.41.1
## [61] restfulr_0.0.15
## [62] matrixStats_1.0.0
## [63] vctrs_0.6.3
## [64] Matrix_1.6-1
## [65] jsonlite_1.8.7
## [66] bookdown_0.35
## [67] IRanges_2.35.2
## [68] hms_1.1.3
## [69] S4Vectors_0.39.1
## [70] bit64_4.0.5
## [71] GenomicFiles_1.37.0
## [72] GenomicFeatures_1.53.1
## [73] jquerylib_0.1.4
## [74] glue_1.6.2
## [75] codetools_0.2-19
## [76] stringi_1.7.12
## [77] GenomeInfoDb_1.37.2
## [78] BiocIO_1.11.0
## [79] GenomicRanges_1.53.1
## [80] tibble_3.2.1
## [81] pillar_1.9.0
## [82] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24
## [83] rappdirs_0.3.3
## [84] htmltools_0.5.6
## [85] GenomeInfoDbData_1.2.10
## [86] BSgenome_1.69.0
## [87] R6_2.5.1
## [88] dbplyr_2.3.3
## [89] evaluate_0.21
## [90] lattice_0.21-8
## [91] Biobase_2.61.0
## [92] R.methodsS3_1.8.2
## [93] png_0.1-8
## [94] Rsamtools_2.17.0
## [95] gargle_1.5.2
## [96] memoise_2.0.1
## [97] bslib_0.5.1
## [98] SparseArray_1.1.11
## [99] xfun_0.40
## [100] fs_1.6.3
## [101] MatrixGenerics_1.13.1
## [102] pkgconfig_2.0.3