Package: scry
Title: Small-Count Analysis Methods for High-Dimensional Data
Version: 1.0.1
Description: Many modern biological datasets consist of small counts that are 
            not well fit by standard linear-Gaussian methods such as principal
            component analysis. This package provides implementations of 
            count-based feature selection and dimension reduction algorithms.
            These methods can be used to facilitate unsupervised analysis
            of any high-dimensional data such as single-cell RNA-seq.
Authors@R: c(person("F. William", "Townes", email = "will.townes@gmail.com",
            role = c("aut", "cre", "cph")),
            person("Kelly", "Street", email = "street.kelly@gmail.com", 
            role="aut"))
License: Artistic-2.0
Depends: R (>= 4.0), stats, methods
Imports: glmpca (>= 0.2.0), Matrix, SingleCellExperiment,
        SummarizedExperiment
Suggests: BiocGenerics, knitr, testthat, covr, rmarkdown, ggplot2,
        DuoClustering2018
VignetteBuilder: knitr
LazyData: false
URL: https://bioconductor.org/packages/scry.html
BugReports: https://github.com/kstreet13/scry/issues
RoxygenNote: 7.1.1
Encoding: UTF-8
biocViews: DimensionReduction, GeneExpression, Normalization,
        PrincipalComponent, RNASeq, Software, Sequencing, SingleCell,
        Transcriptomics
git_url: https://git.bioconductor.org/packages/scry
git_branch: RELEASE_3_11
git_last_commit: f2cc014
git_last_commit_date: 2020-07-28
Date/Publication: 2020-07-30
NeedsCompilation: no
Packaged: 2020-07-31 05:17:09 UTC; biocbuild
Author: F. William Townes [aut, cre, cph],
  Kelly Street [aut]
Maintainer: F. William Townes <will.townes@gmail.com>
Built: R 4.0.2; ; 2020-07-31 16:00:02 UTC; windows
