Package: CellNOptR
Type: Package
Title: Training of boolean logic models of signalling networks using
prior knowledge networks and perturbation data.
Version: 1.18.0
Date: 2014-03-13
Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve
Maintainer: T.Cokelaer <cokelaer@ebi.ac.uk>
Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML
Suggests: RUnit, BiocGenerics, igraph
biocViews: CellBasedAssays, CellBiology, Proteomics, TimeCourse
Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.
License: GPL-3
LazyLoad: yes
SystemRequirements: Graphviz version >= 2.2
NeedsCompilation: yes
Packaged: 2016-05-04 04:18:04 UTC; biocbuild
Package: SplicingGraphs
Title: Create, manipulate, visualize splicing graphs, and assign
RNA-seq reads to them
Version: 1.12.0
Author: D. Bindreither, M. Carlson, M. Morgan, H. Pages
License: Artistic-2.0
Description: This package allows the user to create, manipulate, and visualize
splicing graphs and their bubbles based on a gene model for a given
organism. Additionally it allows the user to assign RNA-seq reads to
the edges of a set of splicing graphs, and to summarize them in
different ways.
Maintainer: H. Pages <hpages@fredhutch.org>
Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7)
Imports: methods, utils, igraph, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>= 2.3.21), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz
Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit
Collate: utils.R igraph-utils.R SplicingGraphs-class.R
plotTranscripts-methods.R sgedgesByGene-methods.R
txpath-methods.R sgedges-methods.R sgraph-methods.R
rsgedgesByGene-methods.R bubbles-methods.R assignReads.R
countReads-methods.R toy_data.R zzz.R
biocViews: Genetics, Annotation, DataRepresentation, Visualization,
Sequencing, RNASeq, GeneExpression, AlternativeSplicing,
Transcription
NeedsCompilation: no
Packaged: 2016-05-04 04:58:04 UTC; biocbuild
Package: TDARACNE
Type: Package
Title: Network reverse engineering from time course data.
Version: 1.22.0
Date: 2009-11-11
Author: Zoppoli P.,Morganella S., Ceccarelli M.
Maintainer: Zoppoli Pietro <zoppoli.pietro@gmail.com>
Depends: GenKern, Rgraphviz, Biobase
biocViews: Microarray, TimeCourse
Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.
License: GPL-2
collate: IcEfx.R PercentileC.R RangeRank2.R CalcMI_time2.R bootstrap.R
saveTime.R MItimeIcE2.R MItimeThreshperm2.R DPI_TDAracne.R
DPI2_TDAracne.R ToTheGraph_timeShiftmax2.R TDARACNE.R
TDARACNEdataPublished.R plotRgraphviz.R
LazyLoad: yes
NeedsCompilation: no
Packaged: 2016-05-04 03:53:55 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
Package: biocGraph
Title: Graph examples and use cases in Bioinformatics
Description: This package provides examples and code that make use of
the different graph related packages produced by Bioconductor.
Version: 1.34.0
Date: 2012-04-27
Author: Li Long <li.long@isb-sib.ch>, Robert Gentleman
<rgentlem@fhcrc.org>, Seth Falcon <sethf@fhcrc.org> Florian
Hahne <fhahne@fhcrc.org>
Maintainer: Florian Hahne <florian.hahne@novartis.com>
Depends: Rgraphviz, graph
Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods
Suggests: fibroEset, geneplotter, hgu95av2.db
License: Artistic-2.0
LazyLoad: Yes
biocViews: Visualization, GraphAndNetwork
NeedsCompilation: no
Packaged: 2016-05-04 03:01:06 UTC; biocbuild
providing 
.