{ "_id": "6a1ad6e41d7bb097a09e5f9b", "Package": "corral", "Title": "Correspondence Analysis for Single Cell Data", "Version": "1.23.0", "Date": "2023-02-09", "Authors@R": "c(person(given = \"Lauren\", family = \"Hsu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"lrnshoe@gmail.com\",\ncomment = c(ORCID = \"0000-0002-6035-7381\")),\nperson(given = \"Aedin\", family = \"Culhane\",\nrole = c(\"aut\"),\nemail = \"Aedin.Culhane@ul.ie\",\ncomment = c(ORCID = \"0000-0002-1395-9734\")))", "Description": "Correspondence analysis (CA) is a matrix factorization\nmethod, and is similar to principal components analysis (PCA).\nWhereas PCA is designed for application to continuous,\napproximately normally distributed data, CA is appropriate for\nnon-negative, count-based data that are in the same additive\nscale. The corral package implements CA for dimensionality\nreduction of a single matrix of single-cell data, as well as a\nmulti-table adaptation of CA that leverages data-optimized\nscaling to align data generated from different sequencing\nplatforms by projecting into a shared latent space. corral\nutilizes sparse matrices and a fast implementation of SVD, and\ncan be called directly on Bioconductor objects (e.g.,\nSingleCellExperiment) for easy pipeline integration. The\npackage also includes additional options, including variations\nof CA to address overdispersion in count data (e.g.,\nFreeman-Tukey chi-squared residual), as well as the option to\napply CA-style processing to continuous data (e.g., proteomic\nTOF intensities) with the Hellinger distance adaptation of CA.", "License": "GPL-2", "RoxygenNote": "7.1.2", "VignetteBuilder": "knitr", "biocViews": "BatchEffect, DimensionReduction, GeneExpression,\nPreprocessing, PrincipalComponent, Sequencing, SingleCell,\nSoftware, Visualization", "Encoding": "UTF-8", "Config/pak/sysreqs": "libicu-dev zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:53:00 UTC", "RemoteUrl": "https://github.com/bioc/corral", "RemoteRef": "HEAD", "RemoteSha": "757a1c2e8175797e2c4d878eb97be106bd1c6602", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-30 08:48:38 UTC", "User": "root" }, "Author": "Lauren Hsu [aut, cre] (ORCID: ),\nAedin Culhane [aut] (ORCID: )", 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