Package: davidTiling
Version: 1.12.0
Title: Data and analysis scripts for David, Huber et al. yeast tiling
array paper
Author: Wolfgang Huber <huber@ebi.ac.uk>, Joern Toedling <toedling@ebi.ac.uk>
Maintainer: Wolfgang Huber <huber@ebi.ac.uk>
Depends: R (>= 2.10), Biobase (>= 2.5.5), tilingArray, GO.db
Description: This package contains the data for the paper by L. David et al. in PNAS 2006 (PMID 16569694): 8 CEL files of Affymetrix genechips, an ExpressionSet object with the raw feature data, a probe annotation data structure for the chip and the yeast genome annotation (GFF file) that was used. In addition, some custom-written analysis functions are provided, as well as R scripts in the scripts directory.
biocViews: ExperimentData, Genome, Saccharomyces_cerevisiae_Data,
MicroarrayData, ReproducibleResearch
Reference: A high-resolution map of transcription in the yeast genome.
David L, Huber W, Granovskaia M, Toedling J, Palm CJ, Bofkin L,
Jones T, Davis RW, Steinmetz LM. Proc Natl Acad Sci U S A. 2006
Apr 4;103(14):5320-5.
License: LGPL
URL: http://www.ebi.ac.uk/huber
NeedsCompilation: no
Packaged: 2016-05-07 20:10:48 UTC; biocbuild
Package: topGO
Type: Package
Title: Enrichment Analysis for Gene Ontology
Version: 2.24.0
Date: 2016-02-03
Author: Adrian Alexa, Jorg Rahnenfuhrer
Maintainer: Adrian Alexa <adrian.alexa@gmail.com>
Description: topGO package provides tools for testing GO terms while
accounting for the topology of the GO graph. Different test
statistics and different methods for eliminating local
similarities and dependencies between GO terms can be
implemented and applied.
License: LGPL
Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>=
1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi
(>= 1.7.19), SparseM (>= 0.73)
Imports: lattice, matrixStats, DBI
Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest
Collate: AllClasses.R topGOmethods.R topGOgraph.R topGOalgo.R
topGOfunctions.R topGOannotations.R topGOtests.R topGOviz.R
zzz.R
biocViews: Microarray, Visualization
NeedsCompilation: no
Packaged: 2016-05-04 03:00:37 UTC; biocbuild
Package: SemDist
Version: 1.6.0
Date: 2014-09-04
Title: Information Accretion-based Function Predictor Evaluation
Author: Ian Gonzalez and Wyatt Clark
Maintainer: Ian Gonzalez <gonzalez.isv@gmail.com>
Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate
Suggests: GOSemSim
Description: This package implements methods to calculate information
accretion for a given version of the gene ontology and uses this
data to calculate remaining uncertainty, misinformation, and
semantic similarity for given sets of predicted annotations and
true annotations from a protein function predictor.
biocViews: Classification, Annotation, GO, Software
License: GPL (>= 2)
URL: http://github.com/iangonzalez/SemDist
NeedsCompilation: no
Packaged: 2016-05-04 05:40:52 UTC; biocbuild
Package: ExpressionView
Version: 1.24.0
Date: Sep 23, 2012
Title: Visualize biclusters identified in gene expression data
Author: Andreas Luscher <andreas.luescher@a3.epfl.ch>
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>
Description: ExpressionView visualizes possibly overlapping biclusters
in a gene expression matrix. It can use the result of the ISA
method (eisa package) or the algorithms in the biclust
package or others. The viewer itself was developed using Adobe
Flex and runs in a flash-enabled web browser.
Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi
Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi
Suggests: ALL, hgu95av2.db, biclust, affy
biocViews: Classification, Visualization, Microarray, GeneExpression,
GO, KEGG
Collate: AllGenerics.R export.R launch.R order.R zzz.R
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2016-05-04 03:33:33 UTC; biocbuild
Package: mvGST
Type: Package
Title: Multivariate and directional gene set testing
Version: 1.6.0
Date: 2014-10-02
Author: John R. Stevens and Dennis S. Mecham
Maintainer: John R. Stevens <john.r.stevens@usu.edu>
Description: mvGST provides platform-independent tools to identify GO
terms (gene sets) that are differentially active (up or down)
in multiple contrasts of interest. Given a matrix of one-sided
p-values (rows for genes, columns for contrasts), mvGST uses
meta-analytic methods to combine p-values for all genes
annotated to each gene set, and then classify each gene set as
being significantly more active (1), less active (-1), or not
significantly differentially active (0) in each contrast of
interest. With multiple contrasts of interest, each gene set
is assigned to a profile (across contrasts) of differential
activity. Tools are also provided for visualizing (in a GO
graph) the gene sets classified to a given profile.
Depends: R (>= 2.10.0), GO.db, Rgraphviz
Imports: gProfileR, stringr, topGO, GOstats, annotate, AnnotationDbi, graph
Suggests: hgu133plus2.db, org.Hs.eg.db
License: GPL-3
biocViews: Microarray, OneChannel, RNASeq, DifferentialExpression, GO,
Pathways, GeneSetEnrichment, GraphAndNetwork
NeedsCompilation: no
Packaged: 2016-05-05 04:50:02 UTC; biocbuild
providing 
.