Package: corral
Title: Correspondence Analysis for Single Cell Data
Version: 1.20.0
Date: 2023-02-09
Authors@R: 
    c(person(given = "Lauren", family = "Hsu",
             role = c("aut", "cre"),
             email = "lrnshoe@gmail.com",
             comment = c(ORCID = "0000-0002-6035-7381")),
     person(given = "Aedin", family = "Culhane",
             role = c("aut"),
             email = "Aedin.Culhane@ul.ie",
             comment = c(ORCID = "0000-0002-1395-9734")))
Description: Correspondence analysis (CA) is a matrix factorization
        method, and is similar to principal components analysis (PCA).
        Whereas PCA is designed for application to continuous,
        approximately normally distributed data, CA is appropriate for
        non-negative, count-based data that are in the same additive
        scale. The corral package implements CA for dimensionality
        reduction of a single matrix of single-cell data, as well as a
        multi-table adaptation of CA that leverages data-optimized
        scaling to align data generated from different sequencing
        platforms by projecting into a shared latent space. corral
        utilizes sparse matrices and a fast implementation of SVD, and
        can be called directly on Bioconductor objects (e.g.,
        SingleCellExperiment) for easy pipeline integration. The
        package also includes additional options, including variations
        of CA to address overdispersion in count data (e.g.,
        Freeman-Tukey chi-squared residual), as well as the option to
        apply CA-style processing to continuous data (e.g., proteomic
        TOF intensities) with the Hellinger distance adaptation of CA.
Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix,
        methods, MultiAssayExperiment, pals, reshape2,
        SingleCellExperiment, SummarizedExperiment, transport
Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr,
        rmarkdown, scater, testthat
License: GPL-2
RoxygenNote: 7.1.2
VignetteBuilder: knitr
biocViews: BatchEffect, DimensionReduction, GeneExpression,
        Preprocessing, PrincipalComponent, Sequencing, SingleCell,
        Software, Visualization
Encoding: UTF-8
Config/pak/sysreqs: libicu-dev zlib1g-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2025-10-29 15:00:12 UTC
RemoteUrl: https://github.com/bioc/corral
RemoteRef: RELEASE_3_22
RemoteSha: 0d657a1640ad7b5e8fe2c07b063293d49fe93edc
NeedsCompilation: no
Packaged: 2025-11-11 13:55:06 UTC; root
Author: Lauren Hsu [aut, cre] (ORCID: <https://orcid.org/0000-0002-6035-7381>),
  Aedin Culhane [aut] (ORCID: <https://orcid.org/0000-0002-1395-9734>)
Maintainer: Lauren Hsu <lrnshoe@gmail.com>
Built: R 4.5.2; ; 2025-11-11 14:04:34 UTC; windows
