{ "_id": "6acbb952fe5cc25786e92e2a", "Package": "EMTscore", "Type": "Package", "Title": "Calculate EMT Scores Based on Omics Data", "Version": "0.99.10", "Date": "2026-08-04", "Authors@R": "c(\nperson(given = \"Haimei\", family = \"Wen\",\nemail = \"hudie.luoluo@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0006-1600-1210\")),\nperson(given = \"Daniel\", family = \"Lopez\", role = \"aut\"),\nperson(given = \"Tian\", family = \"Hong\", role = \"aut\"),\nperson(\"National Institutes of Health\", role = \"fnd\",\ncomment = c(grant = \"R35GM149531\")),\nperson(\"National Science Foundation\", role = \"fnd\",\ncomment = c(grant = \"2243562\")))", "Description": "Epithelial-mesenchymal transition (EMT) is an important\nform of cellular plasticity that is fully or partially\nactivated in several biological scenarios including development\nand disease progression. EMT involves altered expression of\nhundreds of protein-coding and non-protein-coding genes. Recent\nstudies showed the prevalence of partial EMT in multiple\nprocesses such as various cancers and organ fibrosis, which\nnecessitates rigorous quantification of the degree of EMT.\nWhile traditional gene set scoring methods such as gene set\nvariation analysis have been used to generate EMT scores from\nomics data, multiple EMT scoring algorithms and EMT gene sets\nhave been used by different groups without standardization.\nFurthermore, comparisons of EMT scores computed from different\nmethods and/or different EMT gene sets are generally difficult\ndue to both the context dependent nature of EMT and the lack of\ntools that comprehensively integrate varying components for EMT\nscoring. To address this problem, EMTscore enables users to\nselect scoring methods from a list of previously used\nalgorithms and EMT gene sets from a list of gene sets produced\nfrom different experiments. Several visualization methods are\nprovided for making publication-quality plots of EMT scores\nfrom omics data. The package also implements a\nprincipal-component-analysis based method for scoring divergent\nEMT processes from a single dataset. Overall, EMTscore provides\nan integrated solution for assessing the degree and complexity\nof EMT from omics data, and paves the way for standardizing the\ncomparison of EMT programs across multiple contexts.", "License": "GPL-3", "URL": "https://github.com/wenmm/EMTscore", "BugReports": "https://github.com/wenmm/EMTscore/issues", "Encoding": "UTF-8", "biocViews": "Software, GeneExpression, GeneSetEnrichment, SingleCell,\nTranscriptomics, Visualization, RNASeq, DimensionReduction,\nPrincipalComponent, MultipleComparison", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "LazyData": "false", "NeedsCompilation": "no", "Config/pak/sysreqs": "cmake libglpk-dev make libmagick++-dev gsfonts\nlibicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev perl\npython3 zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-08-04 21:30:00 UTC", "RemoteUrl": "https://github.com/bioc/EMTscore", "RemoteRef": "HEAD", "RemoteSha": "66ca8aebe6cfdea9e287c9751594618ade19c4a8", "Packaged": { "Date": "2026-10-11 15:53:47 UTC", "User": "root" }, "Author": "Haimei Wen [aut, cre] (ORCID: ),\nDaniel Lopez [aut],\nTian Hong [aut],\nNational Institutes of Health [fnd] (grant: R35GM149531),\nNational Science Foundation [fnd] (grant: 2243562)", "Maintainer": "Haimei Wen ", "_user": "bioc", "_type": "src", "_file": "EMTscore_0.99.10.tar.gz", "_fileid": "https://r2.ropensci.org/959a556739ee1f55ac195b1a15511c7036c1d3adf5c7b261ecf0c0026ec67d2a", "_filesize": 9261758, "_sha256": "959a556739ee1f55ac195b1a15511c7036c1d3adf5c7b261ecf0c0026ec67d2a", "_expires": "2027-01-19T16:29:04.000Z", "_created": "2026-10-11T15:53:47.000Z", "_published": "2026-10-11T16:29:06.224Z", "_buildurl": "https://github.com/r-universe/bioc/actions/runs/38151429777", "_status": "success", "_host": "GitHub-Actions", "_bioccheck": { "error": 0, "warning": 1, "note": 5 }, "_upstream": "https://github.com/bioc/EMTscore", "_commit": { "id": "66ca8aebe6cfdea9e287c9751594618ade19c4a8", "author": "wenmm ", "committer": "wenmm ", "message": "Bump to 0.99.10: final review fixes (message wording, vignette assignments)\n\nAddresses the two remaining items from the Bioconductor review:\n\no R/Pathway_score_correlation.R: the cor.test() error handler now reports\n \"Invalid correlation for ...\" instead of \"Error in correlation for ...\",\n clearing the BiocCheck note about redundant 'stop'/'warn*' wording in signal\n conditions (as suggested in the review diff).\no vignettes/EMTscore.Rmd: use <- for the two remaining top-level assignments\n (colors, line 267; emt_names, line 342), clearing the lintr\n assignment_linter output. Named function arguments keep =.\n\nNo functional changes.\n\nCo-Authored-By: Claude Opus 5 \n", "time": 1785879000 }, "_maintainer": { "name": "Haimei Wen", "email": "hudie.luoluo@gmail.com", "login": "wenmm", "description": "Bioinformatics, Omics data.", "uuid": 31941752, "orcid": "0009-0006-1600-1210" }, "_distro": "resolute", "_jobs": [ { "job": 114510908623, "time": 244, "config": "bioc-checks", "r": "4.6.1", "check": "WARNING", "artifact": "11707263764" }, { "job": 114510908654, "time": 541, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11707129732" }, { "job": 114510908653, "time": 563, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11707289313" }, { "job": 114510908601, "time": 363, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11708073038" }, { "job": 114510908581, "time": 402, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11708247480" }, { "job": 114505602126, 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"title": "Calculate correlations between pathway or signature scores across samples", "topics": [ "correlate_sample_scores" ] }, { "page": "data_for_plot", "title": "Example data prepared for plotting", "topics": [ "data_for_plot" ] }, { "page": "data_prepare", "title": "Prepare data for plotting", "topics": [ "data_prepare" ] }, { "page": "Execute_AUCell", "title": "Execute AUCell Scoring from GMT File", "topics": [ "Execute_AUCell" ] }, { "page": "Execute_AUCell_parallel", "title": "Compute AUCell Scores for Multiple Gene Sets", "topics": [ "Execute_AUCell_parallel" ] }, { "page": "Execute_E_M_plot", "title": "Plot E vs M scores", "topics": [ "Execute_E_M_plot" ] }, { "page": "Execute_GSVA", "title": "Execute GSVA Scoring from GMT File", "topics": [ "Execute_GSVA" ] }, { "page": "Execute_GSVA_parallel", "title": "Compute GSVA Scores for Multiple Gene Sets in Parallel", "topics": [ "Execute_GSVA_parallel" ] }, { "page": "Execute_JAS", "title": "Execute JASMINE Scoring from GMT File", 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