DESCRIPTION

Package: tradeSeq
Type: Package
Title: trajectory-based differential expression analysis for sequencing data
Date: 2019-03-17
Version: 1.7.07
Authors@R: c(person("Koen", "Van den Berge", role = c("aut"),
                     email = "koen.vandenberge@ugent.be"),
              person("Hector", "Roux de Bezieux", role = c("aut", "cre"),
                     email = "hector.rouxdebezieux@berkeley.edu",
		     comment = c(ORCID = "0000-0002-1489-8339")),
		person("Kelly","Street", role = c("aut","ctb")),
		person("Lieven","Clement", role=c("aut","ctb")),
		person("Sandrine","Dudoit", role="ctb"))
Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: false
URL: https://statomics.github.io/tradeSeq/index.html
Depends: R (>= 3.6)
Collate:
    'AllGenerics.R'
    'utils.R'
    'associationTest.R'
    'clusterExpressionPatterns.R'
    'conditionTest.R'
    'data.R'
    'diffEndTest.R'
    'earlyDETest.R'
    'evaluateK.R'
    'fitGAM.R'
    'getSmootherPvalues.R'
    'getSmootherTestStats.R'
    'nknots.R'
    'patternTest.R'
    'plotGeneCount.R'
    'plotSmoothers.R'
    'predictCells.R'
    'predictSmooth.R'
    'startVsEndTest.R'
RoxygenNote: 7.1.1
Imports: 
    mgcv,
    edgeR,
    SingleCellExperiment,
    SummarizedExperiment,
    slingshot,
    magrittr,
    RColorBrewer,
    BiocParallel,
    Biobase,
    pbapply,
    igraph,
    ggplot2,
    princurve,
    methods,
    S4Vectors,
    tibble,
    Matrix,
    TrajectoryUtils,
    viridis,
    matrixStats,
    MASS
Suggests:
    knitr,
    rmarkdown,
    testthat,
    covr,
    clusterExperiment
VignetteBuilder: knitr
biocViews:
    Clustering,
    Regression,
    TimeCourse,
    DifferentialExpression,
    GeneExpression,
    RNASeq,
    Sequencing,
    Software,
    SingleCell,
    Transcriptomics,
    MultipleComparison,
    Visualization
BugReports: https://github.com/statOmics/tradeSeq/issues