Package: gCMAP
Type: Package
Title: Tools for Connectivity Map-like analyses
Version: 1.28.0
Date: 2017-01-26
Depends: GSEABase, limma (>= 3.20.0)
Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9),
        parallel, annotate, genefilter, AnnotationDbi, DESeq,
        grDevices, graphics, stats, utils, bigmemory, bigmemoryExtras
        (>= 1.1.2)
Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa
Author: Thomas Sandmann <sandmann.t@gmail.com>, Richard Bourgon
        <bourgon.richard@gene.com> and Sarah Kummerfeld
        <kummerfeld.sarah@gene.com>
Maintainer: Thomas Sandmann <sandmann.t@gmail.com>
Description: The gCMAP package provides a toolkit for comparing
        differential gene expression profiles through gene set
        enrichment analysis. Starting from normalized microarray or
        RNA-seq gene expression values (stored in lists of
        ExpressionSet and CountDataSet objects) the package performs
        differential expression analysis using the limma or DESeq
        packages. Supplying a simple list of gene identifiers, global
        differential expression profiles or data from complete
        experiments as input, users can use a unified set of several
        well-known gene set enrichment analysis methods to retrieve
        experiments with similar changes in gene expression. To take
        into account the directionality of gene expression changes,
        gCMAPQuery introduces the SignedGeneSet class, directly
        extending GeneSet from the GSEABase package.  To increase
        performance of large queries, multiple gene sets are stored as
        sparse incidence matrices within CMAPCollection eSets. gCMAP
        offers implementations of 1. Fisher's exact test (Fisher, J R
        Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al,
        Science, 2006) 3. Parametric and non-parametric t-statistic
        summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4.
        Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon,
        Biometrics Bulletin, 1945) as well as wrappers for the 5.
        camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer
        (Wu et al, Bioinformatics, 2010) functions from the limma
        package and 7. wraps the gsea method from the mgsa package
        (Bauer et al, NAR, 2010). All methods return CMAPResult
        objects, an S4 class inheriting from AnnotatedDataFrame,
        containing enrichment statistics as well as annotation data and
        providing simple high-level summary plots.
License: Artistic-2.0
LazyLoad: yes
ByteCompile: TRUE
Collate: 'AllClasses.R' 'AllGenerics.R' 'SignedGeneSet-accessors.R'
        'utility-functions.R' 'camera_score-methods.R'
        'connectivity_score-methods.R' 'featureScore-methods.R'
        'fisher_score-methods.R' 'geneIndex-methods.R'
        'gsealm_jg_score-methods.R' 'gsealm_score-methods.R'
        'incidence-methods.R' 'mgsa_score-methods.R'
        'mapIdentifiers-methods.R' 'minSetSize-methods.R'
        'mroast_score-methods.R' 'romer_score-methods.R'
        'wilcox_score-methods.R' 'CMAPCollection-accessors.R'
        'CMAPResults-accessors.R'
biocViews: Microarray, Software, Pathways, Annotation
OS_type: unix
git_url: https://git.bioconductor.org/packages/gCMAP
git_branch: RELEASE_3_9
git_last_commit: 638e459
git_last_commit_date: 2019-05-02
Date/Publication: 2019-05-02
NeedsCompilation: no
Packaged: 2019-05-03 00:35:02 UTC; biocbuild
Built: R 3.6.1; ; 2019-08-07 23:23:17 UTC; unix
