{ "_id": "6a1ac47b1d7bb097a09db301", "Package": "mixOmics", "Type": "Package", "Title": "Omics Data Integration Project", "Version": "6.37.0", "Authors@R": "c(person(\"Kim-Anh\", \"Le Cao\", role = \"aut\", email = \"kimanh.lecao@unimelb.edu.au\"),\nperson(\"Florian\", \"Rohart\", role = \"aut\"),\nperson(\"Ignacio\", \"Gonzalez\", role = \"aut\"),\nperson(\"Sebastien\", \"Dejean\", role = \"aut\"),\n## key contributors\nperson(\"Al J\", \"Abadi\", role = \"ctb\", email = \"al.jal.abadi@gmail.com\"),\nperson(\"Max\", \"Bladen\", role = \"ctb\", email = \"mbladen19@gmail.com\"),\nperson(\"Benoit\", \"Gautier\", role = \"ctb\"),\nperson(\"Francois\", \"Bartolo\", role = \"ctb\"),\n## also contributions from\nperson(\"Pierre\", \"Monget\", role = \"ctb\"),\nperson(\"Jeff\", \"Coquery\", role = \"ctb\"),\nperson(\"FangZou\", \"Yao\", role = \"ctb\"),\nperson(\"Benoit\", \"Liquet\", role = \"ctb\"),\nperson(\"Eva\", \"Hamrud\", role = \"ctb\"),\nperson(\"Derek\", \"Lei\", role = c(\"ctb\", \"cre\"), email = \"mixomicsdeveloper@gmail.com\"))", "Description": "Multivariate methods are well suited to large omics data\nsets where the number of variables (e.g. genes, proteins,\nmetabolites) is much larger than the number of samples\n(patients, cells, mice). They have the appealing properties of\nreducing the dimension of the data by using instrumental\nvariables (components), which are defined as combinations of\nall variables. Those components are then used to produce useful\ngraphical outputs that enable better understanding of the\nrelationships and correlation structures between the different\ndata sets that are integrated. mixOmics offers a wide range of\nmultivariate methods for the exploration and integration of\nbiological datasets with a particular focus on variable\nselection. The package proposes several sparse multivariate\nmodels we have developed to identify the key variables that are\nhighly correlated, and/or explain the biological outcome of\ninterest. The data that can be analysed with mixOmics may come\nfrom high throughput sequencing technologies, such as omics\ndata (transcriptomics, metabolomics, proteomics, metagenomics\netc) but also beyond the realm of omics (e.g. spectral\nimaging). The methods implemented in mixOmics can also handle\nmissing values without having to delete entire rows with\nmissing data. A non exhaustive list of methods include variants\nof generalised Canonical Correlation Analysis, sparse Partial\nLeast Squares and sparse Discriminant Analysis. Recently we\nimplemented integrative methods to combine multiple data sets:\nN-integration with variants of Generalised Canonical\nCorrelation Analysis and P-integration with variants of\nmulti-group Partial Least Squares.", "License": "GPL (>= 2)", "URL": "http://www.mixOmics.org", "BugReports": "https://github.com/mixOmicsTeam/mixOmics/issues/", "VignetteBuilder": "knitr", "Date": "2021-07-15", "Packaged": { "Date": "2026-05-30 07:39:21 UTC", "User": "root" }, "NeedsCompilation": "no", "biocViews": "ImmunoOncology, Microarray, Sequencing, Metabolomics,\nMetagenomics, Proteomics, GenePrediction, MultipleComparison,\nClassification, Regression", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "Config/pak/sysreqs": "cmake libfreetype6-dev libglpk-dev libglu1-mesa-dev\nmake texlive libicu-dev libpng-dev libuv1-dev libxml2-dev\nlibgl1-mesa-dev zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:49:08 UTC", "RemoteUrl": "https://github.com/bioc/mixOmics", "RemoteRef": "HEAD", "RemoteSha": "e7b0056bbc898bf4c79be45a88f4ecad8b734b51", "Author": "Kim-Anh Le Cao [aut],\nFlorian Rohart [aut],\nIgnacio Gonzalez [aut],\nSebastien Dejean [aut],\nAl J Abadi [ctb],\nMax Bladen [ctb],\nBenoit Gautier [ctb],\nFrancois Bartolo [ctb],\nPierre Monget [ctb],\nJeff Coquery [ctb],\nFangZou Yao [ctb],\nBenoit Liquet [ctb],\nEva Hamrud [ctb],\nDerek Lei [ctb, cre]", "Maintainer": "Derek Lei ", "MD5sum": "b8ebc187db21f3bcb2e4a6485adae25b", "_user": "bioc", "_type": "src", "_file": "mixOmics_6.37.0.tar.gz", "_fileid": "975d42faa3039f4deac806c64b15da8b9feecafa6c1e4bdb2a40cb2cc9dca0c6", "_filesize": 19566639, "_sha256": "975d42faa3039f4deac806c64b15da8b9feecafa6c1e4bdb2a40cb2cc9dca0c6", "_created": "2026-05-30T07:39:21.000Z", "_published": "2026-05-30T11:05:31.063Z", "_jobs": [ { "job": 78634156608, "time": 247, "config": "bioc-checks", "r": "4.6.0", "check": "WARNING", "artifact": "7306570484" }, { "job": 78634156611, 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models (PLS) with Discriminant Analysis", "topics": [ "mint.plsda" ] }, { "page": "mint.spls", "title": "P-integration with variable selection", "topics": [ "mint.spls" ] }, { "page": "mint.splsda", "title": "P-integration with Discriminant Analysis and variable selection", "topics": [ "mint.splsda" ] }, { "page": "mixOmics", "title": "PLS-derived methods: one function to rule them all!", "topics": [ "mixOmics" ] }, { "page": "multidrug", "title": "Multidrug Resistence Data", "topics": [ "multidrug" ] }, { "page": "nearZeroVar", "title": "Identification of zero- or near-zero variance predictors", "topics": [ "nearZeroVar" ] }, { "page": "network", "title": "Relevance Network for (r)CCA and (s)PLS regression", "topics": [ "network", "network.default", "network.pls", "network.rcc", "network.spls" ] }, { "page": "nipals", "title": "Non-linear Iterative Partial Least Squares (NIPALS) algorithm", "topics": [ "nipals" ] }, { "page": "nutrimouse", "title": "Nutrimouse Dataset", "topics": [ 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"plot.perf.plsda.mthd", "plot.perf.sgccda.mthd", "plot.perf.splsda.mthd" ] }, { "page": "plot.perf.pls", "title": "Plot for model performance for PLS analyses", "topics": [ "plot.perf.pls", "plot.perf.pls.mthd", "plot.perf.spls.mthd" ] }, { "page": "plot.rcc", "title": "Canonical Correlations Plot", "topics": [ "plot.rcc" ] }, { "page": "plot.tune", "title": "Plot model performance", "topics": [ "plot.tune", "plot.tune.block.splsda", "plot.tune.spca", "plot.tune.spls", "plot.tune.spls1", "plot.tune.splsda" ] }, { "page": "plotArrow", "title": "Arrow sample plot", "topics": [ "plotArrow" ] }, { "page": "plotDiablo", "title": "Graphical output for the DIABLO framework", "topics": [ "plot.sgccda", "plotDiablo" ] }, { "page": "plotIndiv", "title": "Plot of Individuals (Experimental Units)", "topics": [ "plotIndiv", "plotIndiv.mint.pls", "plotIndiv.mint.plsda", "plotIndiv.mint.spls", "plotIndiv.mint.splsda", "plotIndiv.mixo_pls", "plotIndiv.pca", "plotIndiv.rgcca", "plotIndiv.sgcca" ] }, { "page": "plotLoadings", "title": "Plot of Loading vectors", "topics": [ "plotLoadings", "plotLoadings.mint.pls", "plotLoadings.mint.plsda", "plotLoadings.mint.spls", "plotLoadings.mint.splsda", "plotLoadings.mixo_pls", "plotLoadings.mixo_plsda", "plotLoadings.mixo_spls", "plotLoadings.mixo_splsda", "plotLoadings.pca", "plotLoadings.pls", "plotLoadings.rcc", "plotLoadings.rgcca", "plotLoadings.sgcca", "plotLoadings.sgccda", "plotLoadings.spls" ] }, { "page": "plotMarkers", "title": "Plot the values for multivariate markers in block analyses", "topics": [ "plotMarkers" ] }, { "page": "plotVar", "title": "Plot of Variables", "topics": [ "plotVar", "plotVar.pca", "plotVar.pls", "plotVar.plsda", "plotVar.rcc", "plotVar.rgcca", "plotVar.sgcca", "plotVar.spca", "plotVar.spls", "plotVar.splsda" ] }, { "page": "pls", "title": "Partial Least Squares (PLS) Regression", "topics": [ "pls" ] }, { "page": "plsda", "title": "Partial Least Squares Discriminant Analysis (PLS-DA).", "topics": [ "plsda" ] }, { "page": "predict", "title": "Predict Method for (mint).(block).(s)pls(da) methods", "topics": [ "predict", "predict.block.pls", "predict.block.spls", "predict.mint.block.pls", "predict.mint.block.plsda", "predict.mint.block.spls", "predict.mint.block.splsda", "predict.mint.pls", "predict.mint.plsda", "predict.mint.spls", "predict.mint.splsda", "predict.mixo_pls", "predict.mixo_spls", "predict.pls", "predict.plsda", "predict.spls", "predict.splsda" ] }, { "page": "S3methods-print", "title": "Print Methods for CCA, (s)PLS, PCA and Summary objects", "topics": [ "print", "print.ipca", "print.mint.pls", "print.mint.plsda", "print.mint.spls", "print.mint.splsda", "print.mixo_pls", "print.mixo_plsda", "print.mixo_spls", "print.mixo_splsda", "print.pca", "print.perf.mint.splsda.mthd", "print.perf.pls.mthd", "print.perf.plsda.mthd", "print.perf.sgccda.mthd", "print.perf.splsda.mthd", "print.predict", "print.rcc", "print.rgcca", "print.sgcca", "print.sgccda", "print.sipca", "print.spca", "print.summary", "print.tune.block.splsda", "print.tune.mint.splsda", "print.tune.pca", "print.tune.pls", "print.tune.rcc", "print.tune.spca", "print.tune.spls1", "print.tune.splsda" ] }, { "page": "rcc", "title": "Regularized Canonical Correlation Analysis", "topics": [ "rcc", "rcc.default" ] }, { "page": "selectVar", "title": "Output of selected variables", "topics": [ "select.var", "selectVar", "selectVar.mixo_pls", "selectVar.mixo_spls", "selectVar.pca", "selectVar.rgcca", "selectVar.sgcca" ] }, { "page": "sipca", "title": "Independent Principal Component Analysis", "topics": [ "sipca" ] }, { "page": "spca", "title": "Sparse Principal Components Analysis", "topics": [ "spca" ] }, { "page": "spls", "title": "Sparse Partial Least Squares (sPLS)", "topics": [ "spls" ] }, { "page": "splsda", "title": "Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)", "topics": [ "splsda" ] }, { "page": "srbct", "title": "Small version of the small round blue cell tumors of childhood data", "topics": [ "srbct" ] }, { "page": "stemcells", "title": "Human Stem Cells Data", "topics": [ "stemcells" ] }, { "page": "study_split", "title": "divides a data matrix in a list of matrices defined by a factor", "topics": [ "study_split" ] }, { "page": "summary", "title": "Summary Methods for CCA and PLS objects", "topics": [ "summary", "summary.mixo_pls", "summary.mixo_spls", "summary.pca", "summary.rcc" ] }, { "page": "tune", "title": "Wrapper function to tune pls-derived methods.", "topics": [ "tune" ] }, { "page": "tune.block.plsda", "title": "Tuning function for block.plsda method (N-integration with Discriminant Analysis)", "topics": [ "tune.block.plsda" ] }, { "page": "tune.block.splsda", "title": "Tuning function for block.splsda method (N-integration with sparse Discriminant Analysis)", "topics": [ "tune.block.splsda" ] }, { "page": "tune.mint.plsda", "title": "Estimate the parameters of mint.plsda method", "topics": [ "tune.mint.plsda" ] }, { "page": "tune.mint.splsda", "title": "Estimate the parameters of mint.splsda method", "topics": [ "tune.mint.splsda" ] }, { "page": "tune.pca", "title": "Tune the number of principal components in PCA", "topics": [ "tune.pca" ] }, { "page": "tune.pls", "title": "Tuning functions for PLS method", "topics": [ "tune.pls" ] }, { "page": "tune.plsda", "title": "Tuning functions for PLS-DA method", "topics": [ "tune.plsda" ] }, { "page": "tune.rcc", "title": "Estimate the parameters of regularization for Regularized CCA", "topics": [ "tune.rcc" ] }, { "page": "tune.spca", "title": "Tune number of selected variables for spca", "topics": [ "tune.spca" ] }, { "page": "tune.spls", "title": "Tuning functions for sPLS method", "topics": [ "tune.spls" ] }, { "page": "tune.splsda", "title": "Tuning functions for sPLS-DA method", "topics": [ "tune.splsda" ] }, { "page": "tune.splslevel", "title": "Parallelized Tuning function for multilevel sPLS method using BiocParallel", "topics": [ "tune.splslevel" ] }, { "page": "unmap", "title": "Dummy matrix for an outcome factor", "topics": [ "unmap" ] }, { "page": "vac18", "title": "Vaccine study Data", "topics": [ "vac18" ] }, { "page": "vac18.simulated", "title": "Simulated data based on the vac18 study for multilevel analysis", "topics": [ "vac18.simulated" ] }, { "page": "vip", "title": "Variable Importance in the Projection (VIP)", "topics": [ "vip" ] }, { "page": "withinVariation", "title": "Within matrix decomposition for repeated measurements (cross-over design)", "topics": [ "withinVariation" ] }, { "page": "wrapper.rgcca", "title": "mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)", "topics": [ "wrapper.rgcca" ] }, { "page": "wrapper.sgcca", "title": "mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)", "topics": [ "wrapper.sgcca" ] }, { "page": "yeast", "title": "Yeast metabolomic study", "topics": [ "yeast" ] } ], "_readme": "https://github.com/bioc/mixOmics/raw/HEAD/README.md", "_rundeps": [ "base64enc", "BH", "BiocParallel", "bslib", "cachem", "cli", "codetools", "corpcor", "cpp11", "digest", "dplyr", "ellipse", "evaluate", "farver", "fastmap", "fontawesome", "formatR", "fs", "futile.logger", "futile.options", "generics", "ggplot2", "ggrepel", "glue", "gridExtra", "gtable", "highr", "htmltools", "htmlwidgets", "igraph", "isoband", "jquerylib", "jsonlite", "knitr", "labeling", "lambda.r", "lattice", "lifecycle", "magrittr", "MASS", "Matrix", "matrixStats", "memoise", "mime", "pillar", "pkgconfig", "plyr", "purrr", "R6", "rappdirs", "rARPACK", "RColorBrewer", "Rcpp", "RcppEigen", "reshape2", "rgl", "rlang", "rmarkdown", "RSpectra", "S7", "sass", "scales", "snow", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "tinytex", "utf8", "vctrs", "viridisLite", "withr", "xfun", "yaml" ], "_vignettes": [ { "source": "vignette.Rmd", "filename": "vignette.html", "title": "mixOmics vignette", "author": "Kim-Anh Le Cao", "engine": "knitr::rmarkdown", "headings": [ "Preamble", "Introduction", "Input data", "Methods", "Some background knowledge", "Overview", "Key publications", "Outline of this Vignette", "Other methods not covered in this vignette", "Let's get started", "Installation", "Load the package", "Upload data", "Quick start in mixOmics", "PCA on the multidrug study", "Load the data", "Example: PCA", "Choose the number of components", "PCA with fewer components", "Identify the informative variables", "Sample plots", "Variable plot: correlation circle plot", "Biplot: samples and variables", "Example: sparse PCA", "Choose the number of variables to select", "Final sparse PCA", "Sample and variable plots", "PLS on the liver toxicity study", "Load the data", "Example: sPLS1 regression", "Number of dimensions using the $Q^2$ criterion", "Number of variables to select in $\\boldsymbol X$", "Final sPLS1 model", "Sample plots", "Performance assessment of sPLS1", "Example: PLS2 regression", "Number of dimensions using the $Q^2$ criterion", "Number of variables to select in both $\\boldsymbol X$ and $\\boldsymbol Y$", "Final sPLS2 model", "Numerical outputs", "Importance variables", "Graphical outputs", "Performance", "PLS-DA on the SRBCT case study", "Load the data", "Example: PLS-DA", "Initial exploration with PCA", "Number of components in PLS-DA", "Final PLS-DA model", "Classification performance", "Background prediction", "Example: sPLS-DA", "Number of variables to select", "Final model and performance", "Variable selection and stability", "Sample visualisation", "Variable visualisation", "Take a detour: prediction", "AUROC outputs complement performance evaluation", "N-Integration", "Block sPLS-DA on the TCGA case study", "Load the data", "Parameter choice", "Design matrix", "Number of components", "Number of variables to select", "Final model", "Sample plots", "plotDiablo", "plotIndiv", "plotArrow", "Variable plots", "plotVar", "circosPlot", "network", "plotLoadings", "cimDiablo", "Model performance and prediction", 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