{ "_id": "6a104101acfb0bcc41c9d3d0", "Package": "scapGNN", "Type": "Package", "Title": "Graph Neural Network-Based Framework for Single Cell Active\nPathways and Gene Modules Analysis", "Version": "0.1.4", "Date": "2023-8-7", "Authors@R": "c(person(\"Xudong Han\", role = c(\"aut\", \"cre\",\"cph\"), email = \"hanxd1217@163.com\"),\nperson(\"Xujiang Guo\", role = \"fnd\"))", "Author": "Xudong Han [aut, cre, cph], Xujiang Guo [fnd]", "Maintainer": "Xudong Han ", "Description": "It is a single cell active pathway analysis tool based on\nthe graph neural network (F. Scarselli (2009)\n; Thomas N. Kipf (2017)\n) to construct the gene-cell association\nnetwork, infer pathway activity scores from different single\ncell modalities data, integrate multiple modality data on the\nsame cells into one pathway activity score matrix, identify\ncell phenotype activated gene modules and parse association\nnetworks of gene modules under multiple cell phenotype. In\naddition, abundant visualization programs are provided to\ndisplay the results.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 08:01:37 UTC", "User": "root" }, "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibuv1-dev libxml2-dev libssl-dev python3 zlib1g-dev", "Repository": "https://xudonghan-bio.r-universe.dev", "Date/Publication": "2023-08-08 10:31:45 UTC", "RemoteUrl": "https://github.com/cran/scapGNN", "RemoteRef": "HEAD", "RemoteSha": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "MD5sum": "8fafb5bc39d4f1671f8074d5fba16973", "_user": "xudonghan-bio", "_type": "src", "_file": "scapGNN_0.1.4.tar.gz", "_fileid": "f6556811767362d0727e890d7e7a6210a79a7293193a734cb9627964c375ae92", "_filesize": 3480209, "_sha256": "f6556811767362d0727e890d7e7a6210a79a7293193a734cb9627964c375ae92", "_created": "2026-05-11T08:01:37.000Z", "_published": "2026-05-22T11:41:53.304Z", "_distro": "noble", "_jobs": [ { "job": 77372419237, "time": 229, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6913275339" }, { "job": 77372419488, "time": 193, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6913262185" }, { "job": 77372419731, "time": 197, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "6913253983" }, { "job": 77372419195, "time": 198, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6913254568" }, { "job": 77372419132, "time": 225, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6913204496" }, { "job": 77372418886, "time": 129, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7159633301" }, { "job": 77372419247, "time": 152, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "6913250345" }, { "job": 77372419403, "time": 179, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "6913258129" }, { "job": 77372419623, "time": 145, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "6913248144" } ], "_buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/scapGNN", "_commit": { "id": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "author": "Xudong Han ", "committer": "cran-robot ", "message": "version 0.1.4\n", "time": 1691490705 }, "_maintainer": { "name": "Xudong Han", "email": "hanxd1217@163.com", "login": "xudonghan-bio", "uuid": 49810640 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "ActivePathways", "role": "Imports" }, { "package": "AdaptGauss", "role": "Imports" }, { "package": "coop", "role": "Imports" }, { "package": "igraph", "role": "Imports" }, { "package": "mixtools", "role": "Imports" }, { "package": "reticulate", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "xudonghan-bio", "count": 5, "uuid": 49810640 } ], "_userbio": { "uuid": 49810640, "type": "user", "name": "XuDong Han", "description": "Bioinformatics and Basic Medicine" }, "_downloads": { "count": 233, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/scapGNN" }, "_searchresults": 7, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/scapGNN.html", "manual.pdf" ], "_realowner": "xudonghan-bio", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2022-06-10" }, { "version": "0.1.1", "date": "2022-06-27" }, { "version": "0.1.2", "date": "2022-11-23" }, { "version": "0.1.3", "date": "2022-12-02" }, { "version": "0.1.4", "date": "2023-08-08" } ], "_exports": [ "ConNetGNN", "cpGModule", "InteNet", "load_path_data", "plotCCNetwork", "plotGANetwork", "plotMulPhenGM", "Preprocessing", "RunLTMG", "RWR", "scPathway" ], "_datasets": [ { "name": "ATAC_net", "title": "Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset", "object": "ATAC_net", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ConNetGNN_data", "title": "The results of ConNetGNN() function", "object": "ConNetGNN_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "H9_0h_cpGM_data", "title": "Cell-activated gene modules under the 0-hour phenotype", "object": "H9_0h_cpGM_data", "class": [ "data.frame" ], "fields": [ "Genes", "AS", "Pvalue", "FDR" ], "rows": 10, "table": true, "tojson": true }, { "name": "H9_24h_cpGM_data", "title": "Cell-activated gene modules under the 24-hour phenotype", "object": "H9_24h_cpGM_data", "class": [ "data.frame" ], "fields": [ "Genes", "AS", "Pvalue", "FDR" ], "rows": 7, "table": true, "tojson": true }, { "name": "H9_36h_cpGM_data", "title": "Cell-activated gene modules under the 36-hour phenotype", "object": "H9_36h_cpGM_data", "class": [ "data.frame" ], "fields": [ "Genes", "AS", "Pvalue", "FDR" ], "rows": 8, "table": true, "tojson": true }, { "name": "Hv_exp", "title": "Single-cell gene expression profiles", "object": "Hv_exp", "class": [ "matrix", "array" ], "fields": [ "H9_0h_1", "H9_0h_2", "H9_0h_3", "H9_0h_4", "H9_0h_5", "H9_0h_6", "H9_0h_7", "H9_0h_8", "H9_0h_9", "H9_0h_10", "H9_0h_11", "H9_0h_12", "H9_0h_13", "H9_0h_14", "H9_0h_15", "H9_0h_16", "H9_0h_17", "H9_0h_18", "H9_0h_19", "H9_0h_20", "H9_0h_21", "H9_0h_22", "H9_0h_23", "H9_0h_24", "H9_0h_25", "H9_0h_26", "H9_0h_27", "H9_0h_28", "H9_0h_29", "H9_0h_30", "H9_24h_31", "H9_24h_32", "H9_24h_33", "H9_24h_34", "H9_24h_35", "H9_24h_36", "H9_24h_37", "H9_24h_38", "H9_24h_39", "H9_24h_40", "H9_24h_41", "H9_24h_42", "H9_24h_43", "H9_24h_44", "H9_24h_45", "H9_24h_46", "H9_24h_47", "H9_24h_48", "H9_24h_49", "H9_24h_50", "H9_24h_51", "H9_24h_52", "H9_24h_53", "H9_24h_54", "H9_24h_55", "H9_24h_56", "H9_24h_57", "H9_24h_58", "H9_24h_59", "H9_24h_60", "H9_36h_61", "H9_36h_62", "H9_36h_63", "H9_36h_64", "H9_36h_65", "H9_36h_66", "H9_36h_67", "H9_36h_68", "H9_36h_69", "H9_36h_70", "H9_36h_71", "H9_36h_72", "H9_36h_73", "H9_36h_74", "H9_36h_75", "H9_36h_76", "H9_36h_77", "H9_36h_78", "H9_36h_79", "H9_36h_80", "H9_36h_81", "H9_36h_82", "H9_36h_83", "H9_36h_84", "H9_36h_85", "H9_36h_86", "H9_36h_87", "H9_36h_88", "H9_36h_89", "H9_36h_90" ], "rows": 500, "table": true, "tojson": true }, { "name": "RNA_ATAC_IntNet", "title": "Results of InteNet() for integrating scRNA-seq and scATAC-seq data.", "object": "RNA_ATAC_IntNet", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "RNA_net", "title": "Results of ConNetGNN() for scRNA-seq data from SNARE-seq dataset", "object": "RNA_net", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "scPathway_data", "title": "Single cell pathway activity matrix", "object": "scPathway_data", "class": [ "matrix", "array" ], "fields": [ "H9_0h_1", "H9_0h_2", "H9_0h_3", "H9_0h_4", "H9_0h_5", "H9_0h_6", "H9_0h_7", "H9_0h_8", "H9_0h_9", "H9_0h_10", "H9_0h_11", "H9_0h_12", "H9_0h_13", "H9_0h_14", "H9_0h_15", "H9_0h_16", "H9_0h_17", "H9_0h_18", "H9_0h_19", "H9_0h_20", "H9_0h_21", "H9_0h_22", "H9_0h_23", "H9_0h_24", "H9_0h_25", "H9_0h_26", "H9_0h_27", "H9_0h_28", "H9_0h_29", "H9_0h_30", "H9_24h_31", "H9_24h_32", "H9_24h_33", "H9_24h_34", "H9_24h_35", "H9_24h_36", "H9_24h_37", "H9_24h_38", "H9_24h_39", "H9_24h_40", "H9_24h_41", "H9_24h_42", "H9_24h_43", "H9_24h_44", "H9_24h_45", "H9_24h_46", "H9_24h_47", "H9_24h_48", "H9_24h_49", "H9_24h_50", "H9_24h_51", "H9_24h_52", "H9_24h_53", "H9_24h_54", "H9_24h_55", "H9_24h_56", "H9_24h_57", "H9_24h_58", "H9_24h_59", "H9_24h_60", "H9_36h_61", "H9_36h_62", "H9_36h_63", "H9_36h_64", "H9_36h_65", "H9_36h_66", "H9_36h_67", "H9_36h_68", "H9_36h_69", "H9_36h_70", "H9_36h_71", "H9_36h_72", "H9_36h_73", "H9_36h_74", "H9_36h_75", "H9_36h_76", "H9_36h_77", "H9_36h_78", "H9_36h_79", "H9_36h_80", "H9_36h_81", "H9_36h_82", "H9_36h_83", "H9_36h_84", "H9_36h_85", "H9_36h_86", "H9_36h_87", "H9_36h_88", "H9_36h_89", "H9_36h_90" ], "rows": 20, "table": true, "tojson": true } ], "_help": [ { "page": "ATAC_net", "title": "Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset", "topics": [ "ATAC_net" ] }, { "page": "BIC_LTMG", "title": "BIC_LTMG", "topics": [ "BIC_LTMG" ] }, { "page": "BIC_ZIMG", "title": "BIC_ZIMG", "topics": [ "BIC_ZIMG" ] }, { "page": "ConNetGNN", "title": "Construct association networks for gene-gene, cell-cell, and gene-cell based on graph neural network (GNN)", "topics": [ "ConNetGNN" ] }, { "page": "ConNetGNN_data", "title": "The results of ConNetGNN() function", "topics": [ "ConNetGNN_data" ] }, { "page": "cpGModule", "title": "Identify cell phenotype activated gene module", "topics": [ "cpGModule" ] }, { "page": "create_scapGNN_env", "title": "Create the create_scapGNN_env environment on miniconda", "topics": [ "create_scapGNN_env" ] }, { "page": "Fit_LTMG", "title": "Fitting function for Left-truncated mixed Gaussian", "topics": [ "Fit_LTMG" ] }, { "page": "Global_Zcut", "title": "Global_Zcut", "topics": [ "Global_Zcut" ] }, { "page": "H9_0h_cpGM_data", "title": "Cell-activated gene modules under the 0-hour phenotype", "topics": [ "H9_0h_cpGM_data" ] }, { "page": "H9_24h_cpGM_data", "title": "Cell-activated gene modules under the 24-hour phenotype", "topics": [ "H9_24h_cpGM_data" ] }, { "page": "H9_36h_cpGM_data", "title": "Cell-activated gene modules under the 36-hour phenotype", "topics": [ "H9_36h_cpGM_data" ] }, { "page": "Hv_exp", "title": "Single-cell gene expression profiles", "topics": [ "Hv_exp" ] }, { "page": "instPyModule", "title": "Install the pyhton module through the reticulate R package", "topics": [ "instPyModule" ] }, { "page": "InteNet", "title": "Integrate network data from single-cell RNA-seq and ATAC-seq", "topics": [ "InteNet" ] }, { "page": "isLoaded", "title": "The internal functions of the 'scapGNN' package", "topics": [ "isLoaded" ] }, { "page": "load_path_data", "title": "load pathway or gene set's gmt file", "topics": [ "load_path_data" ] }, { "page": "LTMG", "title": "Left-truncated mixed Gaussian", "topics": [ "LTMG" ] }, { "page": "LTMG-class", "title": "An S4 class to represent the input data for LTMG.", "topics": [ "LTMG-class" ] }, { "page": "plotCCNetwork", "title": "Visualize cell cluster association network graph", "topics": [ "plotCCNetwork" ] }, { "page": "plotGANetwork", "title": "Visualize gene association network graph of a gene module or pathway at the specified cell phenotype", "topics": [ "plotGANetwork" ] }, { "page": "plotMulPhenGM", "title": "Visualize gene association network graph for activated gene modules under multiple cell phenotypes", "topics": [ "plotMulPhenGM" ] }, { "page": "Preprocessing", "title": "Data preprocessing", "topics": [ "Preprocessing" ] }, { "page": "Pure_CDF", "title": "Pure_CDF", "topics": [ "Pure_CDF" ] }, { "page": "RNA_ATAC_IntNet", "title": "Results of InteNet() for integrating scRNA-seq and scATAC-seq data.", "topics": [ "RNA_ATAC_IntNet" ] }, { "page": "RNA_net", "title": "Results of ConNetGNN() for scRNA-seq data from SNARE-seq dataset", "topics": [ "RNA_net" ] }, { "page": "RunLTMG", "title": "Run Left-truncated mixed Gaussian", "topics": [ ".RunLTMG", "RunLTMG", "RunLTMG,LTMG-method" ] }, { "page": "RWR", "title": "Function that performs a random Walk with restart (RWR) on a given graph", "topics": [ "RWR" ] }, { "page": "scPathway", "title": "Infer pathway activation score matrix at single-cell resolution", "topics": [ "scPathway" ] }, { "page": "scPathway_data", "title": "Single cell pathway activity matrix", "topics": [ "scPathway_data" ] } ], "_rundeps": [ "ActivePathways", "AdaptGauss", "askpass", "base64enc", "bslib", "cachem", "cli", "commonmark", "coop", "cpp11", "crosstalk", "curl", "data.table", "DataVisualizations", "digest", "dplyr", "evaluate", "farver", "fastmap", "fontawesome", "fs", "generics", "ggplot2", "glue", "gtable", "here", "highr", "htmltools", "htmlwidgets", "httpuv", "httr", "igraph", "isoband", "jquerylib", "jsonlite", "kernlab", "knitr", "labeling", "later", "lattice", "lazyeval", "lifecycle", "magrittr", "MASS", "Matrix", "memoise", "mime", "mixtools", "nlme", "openssl", "otel", "pillar", "pkgconfig", "plotly", "plyr", "png", "pracma", "promises", "purrr", "R6", "rappdirs", "RColorBrewer", "Rcpp", "RcppParallel", "RcppTOML", "reshape2", "reticulate", "rlang", "rmarkdown", "rprojroot", "S7", "sass", "scales", "segmented", "shiny", "sourcetools", "sp", "stringi", "stringr", "survival", "sys", "tibble", "tidyr", "tidyselect", "tinytex", "utf8", "vctrs", "viridisLite", "withr", "xfun", "xtable", "yaml" ], "_vignettes": [ { "source": "vignette.Rmd", "filename": "vignette.html", "title": "scapGNN: Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis", "author": "Xudong Han and Xuejiang Guo", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Data preprocessing", "single-cell RNA-seq data", "single-cell ATAC-seq data", "Construct gene-cell association network", "Infer pathway activity score matrix at the single-cell level", "Identification of activated gene modules", "Data analysis and visualization combined with Seurat package" ], "created": "2022-06-10 07:50:02", "modified": "2023-08-08 10:31:45", "commits": 4 } ], "_score": 2, "_indexed": true, "_nocasepkg": "scapgnn", "_universes": [ "xudonghan-bio" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.4", "date": "2026-05-11T08:04:48.000Z", "distro": "noble", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "ab8fd7f3c45bf64cecf191e1a71cdb05664c215548faf1835ec0c1a2e5b4bc6b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.6.0", "os": "linux", "version": "0.1.4", "date": "2026-05-11T08:04:09.000Z", "distro": "noble", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "d79746d47ada056d2c14ab31f79975790795ffad9b7d16ef90b0958bb18e0004", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.5.3", "os": "mac", "version": "0.1.4", "date": "2026-05-11T08:04:00.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "3d1b85a11ba84a05fe82fb4259a2020fde457429eced456f06d21cc12c9ad187", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.6.0", "os": "mac", "version": "0.1.4", "date": "2026-05-11T08:04:00.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "58b1edf309030fb9aa8e468de5530e14229f9a9e1300a2c200be44b935396412", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.7.0", "os": "win", "version": "0.1.4", "date": "2026-05-11T08:03:07.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "79ad57c4ebf3a0eeaec17360534ee27af7fc19494343c06b62a8a248e314be76", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.5.3", "os": "win", "version": "0.1.4", "date": "2026-05-11T08:03:46.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "26c4e644f21e65613e787606ac79e491083b1a9415ea2dcb1c220db6b9883bfd", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.6.0", "os": "win", "version": "0.1.4", "date": "2026-05-11T08:03:08.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "914aea59831e1104355b12febcdecab5cd2cc4abfb223d39e7f267cb20f1bd40", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" }, { "r": "4.6.0", "os": "wasm", "version": "0.1.4", "date": "2026-05-22T11:41:36.000Z", "commit": "afde8b0524c8323d1fba680f867a21a49dcb53fd", "fileid": "b2cdbaef3d736a39b7f1d3bab4d7de2d74af9bed3ee997791033298fe7affeee", "status": "success", "buildurl": "https://github.com/r-universe/xudonghan-bio/actions/runs/25657704110" } ] }