{ "_id": "6a1ac1a41d7bb097a09d8836", "Package": "multiClust", "Type": "Package", "Title": "multiClust: An R-package for Identifying Biologically Relevant\nClusters in Cancer Transcriptome Profiles", "Version": "1.43.0", "Date": "2021-07-27", "Authors@R": "c(\nperson(\"Nathan\",\"Lawlor\", email = \"nathan.lawlor03@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Peiyong\", \"Guan\", role = \"aut\"),\nperson(\"Alec\",\"Fabbri\", email = \"fabbrialhs@gmail.com\", role = \"aut\"),\nperson(\"Krish\", \"Karuturi\", email = \"Krishna.Karuturi@jax.org\",\nrole = \"aut\"),\nperson(\"Joshy\", \"George\", email = \"Joshy.George@jax.org\", role = \"aut\")\n)", "Description": "Clustering is carried out to identify patterns in\ntranscriptomics profiles to determine clinically relevant\nsubgroups of patients. Feature (gene) selection is a critical\nand an integral part of the process. Currently, there are many\nfeature selection and clustering methods to identify the\nrelevant genes and perform clustering of samples. However,\nchoosing an appropriate methodology is difficult. In addition,\nextensive feature selection methods have not been supported by\nthe available packages. Hence, we developed an integrative\nR-package called multiClust that allows researchers to\nexperiment with the choice of combination of methods for gene\nselection and clustering with ease. Using multiClust, we\nidentified the best performing clustering methodology in the\ncontext of clinical outcome. Our observations demonstrate that\nsimple methods such as variance-based ranking perform well on\nthe majority of data sets, provided that the appropriate number\nof genes is selected. However, different gene ranking and\nselection methods remain relevant as no methodology works for\nall studies.", "License": "GPL (>= 2)", "biocViews": "FeatureExtraction, Clustering, GeneExpression, Survival", "VignetteBuilder": "knitr", "RoxygenNote": "5.0.0", "NeedsCompilation": "no", "Author": "Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri\n[aut], Krish Karuturi [aut], Joshy George [aut]", "Maintainer": "Nathan Lawlor ", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:42:13 UTC", "RemoteUrl": "https://github.com/bioc/multiClust", "RemoteRef": "HEAD", "RemoteSha": "479fd5a68cfdae6b04942675d4901c49b29e8ce1", "Packaged": { "Date": "2026-05-30 07:30:31 UTC", "User": "root" }, "MD5sum": "69454a25882986a4fc2e48d4f538cf5b", "_user": "bioc", "_type": "src", "_file": "multiClust_1.43.0.tar.gz", "_fileid": "936a40d64dc63b59f624e79d67fda37e84da53e7b092bc014cde9234f1216f94", "_filesize": 3079815, "_sha256": 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Transcriptomics", "uuid": 16104148 }, "_distro": "noble", "_registered": true, "_dependencies": [ { "package": "mclust", "role": "Imports" }, { "package": "ctc", "role": "Imports" }, { "package": "survival", "role": "Imports" }, { "package": "cluster", "role": "Imports" }, { "package": "dendextend", "role": "Imports" }, { "package": "amap", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "gplots", "role": "Suggests" }, { "package": "RUnit", "role": "Suggests" }, { "package": "BiocGenerics", "role": "Suggests" }, { "package": "preprocessCore", "role": "Suggests" }, { "package": "Biobase", "role": "Suggests" }, { "package": "GEOquery", "role": "Suggests" } ], "_owner": "bioc", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-44", "n": 2 }, { "week": "2026-18", "n": 2 } ], "_tags": [], "_bioc": [ { "branch": "devel", "version": "1.43.0", "bioc": "3.24" }, { "branch": "release", "version": "1.42.0", "bioc": "3.23" } ], "_topics": [ "featureextraction", "clustering", "geneexpression", "survival" ], "_userbio": { "uuid": 2286807, "type": "organization", "name": "Bioconductor", "description": "Software for the analysis and comprehension of high-throughput genomic data" }, "_downloads": { "count": 458, "source": "https://www.bioconductor.org/packages/stats/bioc/multiClust" }, "_mentions": 2, "_searchresults": 9, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/multiClust.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_realowner": "bioc", "_cranurl": false, "_exports": [ "avg_probe_exp", "cluster_analysis", "input_file", "nor.min.max", "number_clusters", "number_probes", "probe_ranking", "surv_analysis", "WriteMatrixToFile" ], "_help": [ { "page": "avg_probe_exp", "title": "Function to produce a matrix containing the average expression of each gene probe within each sample cluster.", "topics": [ "avg_probe_exp" ] }, { "page": "cluster_analysis", "title": "Function to perform Kmeans or Hierarchical clustering analysis of the selected gene probe expression data.", "topics": [ "cluster_analysis" ] }, { "page": "input_file", "title": "Function to read-in the gene expression file and assign gene probe names as the rownames.", "topics": [ "input_file" ] }, { "page": "nor.min.max", "title": "Function to normalize data to bring values into alignment. This function uses feature scaling to normalize values in a dataset between 0 and 1.", "topics": [ "nor.min.max" ] }, { "page": "number_clusters", "title": "Function to determine the number of clusters to be used to cluster gene probes and samples.", "topics": [ "number_clusters" ] }, { "page": "number_probes", "title": "Function to determine the number of gene probes to select for in the gene feature selection process.", "topics": [ "number_probes" ] }, { "page": "probe_ranking", "title": "Function to select for genes using one of the available gene probe ranking options.", "topics": [ "probe_ranking" ] }, { "page": "surv_analysis", "title": "Function to produce Kaplan-Meier Survival Plots of selected gene expression data.", "topics": [ "surv_analysis" ] }, { "page": "WriteMatrixToFile", "title": "Function to write a data matrix to a text file.", "topics": [ "WriteMatrixToFile" ] } ], "_readme": "https://github.com/bioc/multiClust/raw/HEAD/README.md", "_rundeps": [ "amap", "cli", "cluster", "cpp11", "ctc", "dendextend", "farver", "ggplot2", "glue", "gridExtra", "gtable", "isoband", "labeling", "lattice", "lifecycle", "magrittr", "Matrix", "mclust", "R6", "RColorBrewer", "rlang", "S7", "scales", "survival", "vctrs", "viridis", "viridisLite", "withr" ], "_vignettes": [ { "source": "multiClust.Rmd", "filename": "multiClust.html", "title": "A Guide to multiClust", "author": "Nathan Lawlor", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "1. Getting Started", "1.1 Obtaining a Gene Expression Dataset and Clinical Information", "1.2 Normalization of Gene Expression Datasets", "1.3 Formatting the Patient Clinical Information", "2. Loading Your Gene Probe Expression Dataset into R", "2.1 Loading Text Files Containing Gene Expression Matrix", "3. Gene Selection Algorithms", "3.1 Determining the Number of Desired Probes or Genes", "3.2 Choosing a Gene Selection Algorithm", "4. Cluster Analysis of Selected Genes and Samples", "4.1 Determining the Number of Clusters to Divide Samples Into", "4.2 Kmeans or Hierarchical Clustering of Genes/Probes and Samples", "5. Obtaining the Average Expression for Each Gene/Probe in Each Cluster", "6. Clinical Analysis of Selected Gene Probes and Samples", "7. 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