Package: BASiCS
Type: Package
Title: Bayesian Analysis of Single-Cell Sequencing data
Version: 2.16.0
Date: 2024-03-25
Authors@R: c(person("Catalina", "Vallejos", role=c("aut", "cre"),
        email="catalina.vallejos@igmm.ed.ac.uk",
        comment=c(ORCID = "0000-0003-3638-1960")), 
        person("Nils", "Eling", role=c("aut")), 
        person("Alan", "O'Callaghan", role = c("aut")),
        person("Sylvia", "Richardson", role = c("ctb")), 
        person("John", "Marioni", role=c("ctb"))) 
Description: Single-cell mRNA sequencing can uncover novel cell-to-cell
    heterogeneity in gene expression levels in seemingly homogeneous populations 
    of cells. However, these experiments are prone to high levels of technical 
    noise, creating new challenges for identifying genes that show genuine 
    heterogeneous expression within the population of cells under study. BASiCS 
    (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian 
    hierarchical model to perform statistical analyses of single-cell RNA 
    sequencing datasets in the context of supervised experiments (where the groups 
    of cells of interest are known a priori, e.g. experimental conditions or cell 
    types). BASiCS performs built-in data normalisation (global scaling) and 
    technical noise quantification (based on spike-in genes). BASiCS provides an 
    intuitive detection criterion for highly (or lowly) variable genes within a 
    single group of cells. Additionally, BASiCS can compare gene expression 
    patterns between two or more pre-specified groups of cells. Unlike traditional 
    differential expression tools, BASiCS quantifies changes in expression that lie 
    beyond comparisons of means, also allowing the study of changes in cell-to-cell 
    heterogeneity. The latter can be quantified via a biological over-dispersion 
    parameter that measures the excess of variability that is observed with respect 
    to Poisson sampling noise, after normalisation and technical noise removal. 
    Due to the strong mean/over-dispersion confounding that is typically observed 
    for scRNA-seq datasets, BASiCS also tests for changes in residual 
    over-dispersion, defined by residual values with respect to a global 
    mean/over-dispersion trend. 
License: GPL-3
Depends: R (>= 4.2), SingleCellExperiment
Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2,
        graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3),
        S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment,
        viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat,
        reshape2, BiocParallel, posterior, hexbin
Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, magick
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
        GeneExpression, Transcriptomics, SingleCell,
        DifferentialExpression, Bayesian, CellBiology, ImmunoOncology
SystemRequirements: C++11
NeedsCompilation: yes
URL: https://github.com/catavallejos/BASiCS
BugReports: https://github.com/catavallejos/BASiCS/issues
RoxygenNote: 7.3.1
Encoding: UTF-8
LazyData: false
Config/testthat/edition: 3
Collate: 'AllClasses.R' 'AllGenerics.R' 'BASiCS_CalculateERCC.R'
        'BASiCS_CorrectOffset.R' 'BASiCS_DenoisedCounts.R'
        'BASiCS_DenoisedRates.R' 'BASiCS_DetectHVG_LVG.R'
        'BASiCS_DiagHist.R' 'BASiCS_DiagPlot.R'
        'BASiCS_DivideAndConquer.R' 'BASiCS_Draw.R'
        'BASiCS_EffectiveSize.R' 'BASiCS_Filter.R' 'BASiCS_LoadChain.R'
        'BASiCS_MCMC.R' 'BASiCS_MockSCE.R' 'BASiCS_Package.R'
        'BASiCS_PlotDE.R' 'BASiCS_PlotOffset.R' 'BASiCS_PlotVG.R'
        'BASiCS_PlotVarianceDecomp.R' 'BASiCS_PriorParam.R'
        'BASiCS_ShowFit.R' 'BASiCS_Sim.R' 'BASiCS_TestDE.R'
        'BASiCS_VarThresholdSearchHVG_LVG.R' 'BASiCS_VarianceDecomp.R'
        'HiddenBASiCS_Sim.R' 'HiddenHeaderBASiCS_Sim.R'
        'HiddenHeaderTest_DE.R' 'HiddenVarDecomp.R' 'utils_Misc.R'
        'Methods.R' 'RcppExports.R' 'data.R' 'makeExampleBASiCS_Data.R'
        'newBASiCS_Chain.R' 'newBASiCS_Data.R' 'utils_Data.R'
        'utils_DivideAndConquer.R' 'utils_MCMC.R' 'utils_Store.R'
        'utils_Tests.R' 'utils_VG.R' 'welcome.R'
git_url: https://git.bioconductor.org/packages/BASiCS
git_branch: RELEASE_3_19
git_last_commit: e753c97
git_last_commit_date: 2024-04-30
Repository: Bioconductor 3.19
Date/Publication: 2024-05-01
Packaged: 2024-05-01 21:12:35 UTC; biocbuild
Author: Catalina Vallejos [aut, cre] (<https://orcid.org/0000-0003-3638-1960>),
  Nils Eling [aut],
  Alan O'Callaghan [aut],
  Sylvia Richardson [ctb],
  John Marioni [ctb]
Maintainer: Catalina Vallejos <catalina.vallejos@igmm.ed.ac.uk>
Built: R 4.4.0; x86_64-w64-mingw32; 2024-05-02 12:27:25 UTC; windows
Archs: x64
