Mungesumstats现在可以通过DockerHub作为一个容器化的环境，预装了Rstudio和所有必要的依赖项。
首先,安装码头工人如果你还没有。
的映像码头工人命令行容器:
Docker拉神经基因组板/mungesumstats一旦创建了映像，您可以使用以下命令启动它:
docker run \ -d \ -e ROOT=true \ -e PASSWORD="" \ -v ~/Desktop:/Desktop \ -v /Volumes:/Volumes \ -p 8787:8787 \ neurogenome slab/mungesumstats . < your_password >上面有你想要的密码。- v为您的特定用例标记。- d确保容器将以“分离”模式运行，这意味着即使在您关闭命令行会话后，它也将持续存在。如果你正在使用一个不允许Docker的系统(就像许多机构计算集群的情况一样)，你可以替换通过Singularity安装Docker镜像．
奇点拉docker://neurogenomicslab/mungesumstats最后，通过在任何浏览器中输入以下URL来启动容器化的Rstudio:http://localhost:8787/
使用在安装步骤中设置的凭据登录。
跑龙套:sessionInfo ()## R正在开发中(不稳定)(2022-12-10 r83428) ##平台:x86_64-pc-linux-gnu(64位)##运行在Ubuntu 22.04.1 LTS ## ##矩阵产品:默认## BLAS: /home/biocbuild/bbs-3.17-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_phone = c# # [11] LC_MEASUREMENT=en_US。UTF-8 LC_IDENTIFICATION=C ## ##时区:美国/New_York ## tzcode源:系统(glibc) ## ##附加的基本包:## [1]stats graphics grDevices utils datasets methods base ## ##其他附加包:## [1]MungeSumstats_1.7.12 biocstyle_id .27.0 ## ##通过命名空间加载(且未附加):# # # # [1] tidyselect_1.2.0 [2] dplyr_1.0.10 # # [3] blob_1.2.3 # # [4] filelock_1.0.2 # # [5] R.utils_2.12.2 # # [6] Biostrings_2.67.0 # # [7] bitops_1.0-7 # # [8] fastmap_1.1.0 # # [9] rcurl_1.98 - 1.9 # # [10] BiocFileCache_2.7.1 # # [11] VariantAnnotation_1.45.0 # # [12] GenomicAlignments_1.35.0 # # [13] xml_3.99 - 0.13 # # [14] digest_0.6.31 # # [15] lifecycle_1.0.3 # # [16] ellipsis_0.3.2 # # [17] KEGGREST_1.39.0 # # [18] RSQLite_2.2.19 # # [19] googleAuthR_2.0.0 # # [20] magrittr_2.0.3 # # [21] compiler_4.3.0 # #[22] rlang_1.0.6 ## [23] sass_0.4.4 ## [24] progress_1.2.2 ## [25] tools_4.3.0 ## [26] utf8_1.2.2 ## [27] yaml_2.3.6 ## [28] data.table_1.14.6 ## [29] rtracklayer_1.59.0 ## [30] knitr_1.41 ## [31] prettyunits_1.1.1 ## [32] curl_4.3.3 ## [33] bit_4.0.5 ## [34] DelayedArray_0.25.0 ## [35] xml2_1.3.3 ## [36] BiocParallel_1.33.7 ## [37] BiocGenerics_0.45.0 ## [38] R.oo_1.25.0 ## [39] grid_4.3.0 ## [40] stats4_4.3.0 ## [41] fansi_1.0.3 ## [42] biomaRt_2.55.0 ## [43] SummarizedExperiment_1.29.1 ## [44] cli_3.5.0 ## [45] rmarkdown_2.19 ## [46] crayon_1.5.2 ## [47] generics_0.1.3 ## [48] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1 ## [49] httr_1.4.4 ## [50] rjson_0.2.21 ## [51] DBI_1.1.3 ## [52] cachem_1.0.6 ## [53] stringr_1.5.0 ## [54] zlibbioc_1.45.0 ## [55] assertthat_0.2.1 ## [56] parallel_4.3.0 ## [57] AnnotationDbi_1.61.0 ## [58] BiocManager_1.30.19 ## [59] XVector_0.39.0 ## [60] restfulr_0.0.15 ## [61] matrixStats_0.63.0 ## [62] vctrs_0.5.1 ## [63] Matrix_1.5-3 ## [64] jsonlite_1.8.4 ## [65] bookdown_0.31 ## [66] IRanges_2.33.0 ## [67] hms_1.1.2 ## [68] S4Vectors_0.37.3 ## [69] bit64_4.0.5 ## [70] GenomicFiles_1.35.0 ## [71] GenomicFeatures_1.51.4 ## [72] jquerylib_0.1.4 ## [73] glue_1.6.2 ## [74] codetools_0.2-18 ## [75] stringi_1.7.8 ## [76] GenomeInfoDb_1.35.8 ## [77] BiocIO_1.9.1 ## [78] GenomicRanges_1.51.4 ## [79] tibble_3.1.8 ## [80] pillar_1.8.1 ## [81] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.22 ## [82] rappdirs_0.3.3 ## [83] htmltools_0.5.4 ## [84] GenomeInfoDbData_1.2.9 ## [85] BSgenome_1.67.1 ## [86] R6_2.5.1 ## [87] dbplyr_2.2.1 ## [88] evaluate_0.19 ## [89] lattice_0.20-45 ## [90] Biobase_2.59.0 ## [91] highr_0.9 ## [92] R.methodsS3_1.8.2 ## [93] png_0.1-8 ## [94] Rsamtools_2.15.0 ## [95] gargle_1.2.1 ## [96] memoise_2.0.1 ## [97] bslib_0.4.2 ## [98] Rcpp_1.0.9 ## [99] xfun_0.36 ## [100] fs_1.5.2 ## [101] MatrixGenerics_1.11.0 ## [102] pkgconfig_2.0.3