Package: cpvSNP
Type: Package
Title: Gene set analysis methods for SNP association p-values that lie
in genes in given gene sets
Version: 1.4.0
Date: 2015-04-09
Author: Caitlin McHugh, Jessica Larson, and Jason Hackney
Maintainer: Caitlin McHugh <mchughc@uw.edu>
Imports: methods, corpcor, BiocParallel, ggplot2, plyr
Depends: R (>= 2.10), GenomicFeatures, GSEABase (>= 1.24.0)
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle
Description: Gene set analysis methods exist to combine SNP-level
association p-values into gene sets, calculating a single
association p-value for each gene set. This package implements
two such methods that require only the calculated SNP p-values,
the gene set(s) of interest, and a correlation matrix (if
desired). One method (GLOSSI) requires independent SNPs and the
other (VEGAS) can take into account correlation (LD) among the
SNPs. Built-in plotting functions are available to help users
visualize results.
License: Artistic-2.0
biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment,
GenomicVariation
NeedsCompilation: no
Packaged: 2016-05-04 05:57:21 UTC; biocbuild
Package: PROMISE
Type: Package
Title: PRojection Onto the Most Interesting Statistical Evidence
Description: A general tool to identify genomic features with a
specific biologically interesting pattern of associations with
multiple endpoint variables as described in Pounds et. al.
(2009) Bioinformatics 25: 2013-2019
Version: 1.24.0
Date: 2014-6-24
Author: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
Maintainer: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
Depends: R (>= 3.1.0), Biobase, GSEABase
Imports: Biobase, GSEABase, stats
License: GPL (>= 2)
LazyLoad: yes
biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression
NeedsCompilation: no
Packaged: 2016-05-04 03:35:30 UTC; biocbuild
Package: EnrichmentBrowser
Version: 2.2.2
Date: 2016-05-19
Title: Seamless navigation through combined results of set-based and
network-based enrichment analysis
Author: Ludwig Geistlinger, Gergely Csaba, Ralf Zimmer
Maintainer: Ludwig Geistlinger <Ludwig.Geistlinger@bio.ifi.lmu.de>
Depends: R (>= 3.0.0), Biobase, GSEABase, pathview
Imports: AnnotationDbi, ComplexHeatmap, DESeq2, EDASeq, GO.db, KEGGREST, KEGGgraph, MASS, ReportingTools, Rgraphviz, S4Vectors, SPIA, SummarizedExperiment, biocGraph, edgeR, geneplotter, graph, hwriter, limma, safe, topGO
Suggests: ALL, BiocStyle, airway, hgu95av2.db
Description: The EnrichmentBrowser package implements essential functionality
for the enrichment analysis of gene expression data. The analysis combines
the advantages of set-based and network-based enrichment analysis in order
to derive high-confidence gene sets and biological pathways that are
differentially regulated in the expression data under investigation.
Besides, the package facilitates the visualization and exploration of such
sets and pathways.
License: Artistic-2.0
biocViews: Microarray, RNASeq, GeneExpression, DifferentialExpression,
Pathways, GraphAndNetwork, Network, GeneSetEnrichment,
NetworkEnrichment, Visualization, ReportWriting
NeedsCompilation: no
Packaged: 2016-05-20 04:51:09 UTC; biocbuild
Package: npGSEA
Type: Package
Title: Permutation approximation methods for gene set enrichment
analysis (non-permutation GSEA)
Version: 1.8.0
Date: 2015-5-6
Author: Jessica Larson and Art Owen
Maintainer: Jessica Larson <larson.jess@gmail.com>
Imports: Biobase, methods, BiocGenerics, graphics, stats
Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle
Depends: GSEABase (>= 1.24.0)
Description: Current gene set enrichment methods rely upon permutations
for inference. These approaches are computationally expensive
and have minimum achievable p-values based on the number of
permutations, not on the actual observed statistics. We have
derived three parametric approximations to the permutation
distributions of two gene set enrichment test statistics. We
are able to reduce the computational burden and granularity
issues of permutation testing with our method, which is
implemented in this package. npGSEA calculates gene set
enrichment statistics and p-values without the computational
cost of permutations. It is applicable in settings where one
or many gene sets are of interest. There are also built-in
plotting functions to help users visualize results.
License: Artistic-2.0
biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways
Collate: 'getIncidence.R' 'miscFunctions.R' 'miscDataPrepFunctions.R'
'npGSEA.R' 'AllClasses.R' 'AllGenerics.R'
'npGSEAResultBeta-accessors.R' 'npGSEAResultChiSq-accessors.R'
'npGSEAResultNorm-accessors.R' 'npGSEAPlot-methods.R'
'show-methods.R' 'pValues-methods.R'
Packaged: 2016-05-04 05:26:04 UTC; biocbuild
NeedsCompilation: no
Package: gCMAP
Type: Package
Title: Tools for Connectivity Map-like analyses
Version: 1.16.0
Date: 2015-02-25
Depends: GSEABase, limma (>= 3.20.0)
Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq
Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa
Enhances: bigmemory, bigmemoryExtras (>= 1.1.2)
Author: Thomas Sandmann <sandmann.thomas@gene.com>, Richard Bourgon
<bourgon.richard@gene.com> and Sarah Kummerfeld
<kummerfeld.sarah@gene.com>
Maintainer: Thomas Sandmann <sandmann.thomas@gene.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
NeedsCompilation: no
Packaged: 2016-05-04 04:44:19 UTC; biocbuild
Package: GSVAdata
Title: Data employed in the vignette of the GSVA package
Version: 1.8.0
Author: Robert Castelo <robert.castelo@upf.edu>
Description: This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Armstrong et al. Nat Genet 30:41-47, 2002; Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010
Maintainer: Robert Castelo <robert.castelo@upf.edu>
Depends: R (>= 2.10), Biobase, GSEABase, hgu95a.db
License: GPL (>= 2)
biocViews: ExperimentData, RNASeqData, Homo_sapiens_Data, CancerData,
LeukemiaCancerData
NeedsCompilation: no
Packaged: 2016-05-07 20:19:15 UTC; biocbuild
providing 
.