Package: simpleaffy
Title: Very simple high level analysis of Affymetrix data
Version: 2.48.0
Author: Crispin J Miller
Description: Provides high level functions for reading Affy .CEL files,
phenotypic data, and then computing simple things with it, such
as t-tests, fold changes and the like. Makes heavy use of the
affy library. Also has some basic scatter plot functions and
mechanisms for generating high resolution journal figures...
Maintainer: Crispin Miller <cmiller@picr.man.ac.uk>
Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma
Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma
License: GPL (>= 2)
URL: http://www.bioconductor.org
http://bioinformatics.picr.man.ac.uk/simpleaffy/
LazyLoad: yes
biocViews: Microarray, OneChannel, QualityControl, Preprocessing,
Transcription, DataImport, DifferentialExpression, Annotation,
ReportWriting, Visualization
NeedsCompilation: yes
Packaged: 2016-05-04 02:39:53 UTC; biocbuild
Package: sva
Title: Surrogate Variable Analysis
Version: 3.20.0
Author: Jeffrey T. Leek <jtleek@gmail.com>, W. Evan Johnson <wej@bu.edu>,
Hilary S. Parker <hiparker@jhsph.edu>, Elana J. Fertig <ejfertig@jhmi.edu>,
Andrew E. Jaffe <ajaffe@jhsph.edu>, John D. Storey <jstorey@princeton.edu>
Description: The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch
effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing
batch effects with known control probes (Leek 2014 biorXiv).
Removing batch effects and using surrogate variables in
differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).
Maintainer: Jeffrey T. Leek <jtleek@gmail.com>, John D. Storey
<jstorey@princeton.edu>, W. Evan Johnson <wej@bu.edu>
Depends: R (>= 2.8), mgcv, genefilter
Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
License: Artistic-2.0
biocViews: Microarray, StatisticalMethod, Preprocessing,
MultipleComparison, Sequencing, RNASeq, BatchEffect,
Normalization
NeedsCompilation: yes
Packaged: 2016-05-04 04:16:38 UTC; biocbuild
Package: Hiiragi2013
Type: Package
Title: Cell-to-cell expression variability followed by signal
reinforcement progressively segregates early mouse lineages
Version: 1.8.0
Author: Andrzej Oles, Wolfgang Huber
Maintainer: Andrzej Oles <andrzej.oles@embl.de>
Description: This package contains the experimental data and a complete executable transcript (vignette) of the statistical analysis presented in the paper "Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages" by Y. Ohnishi, W. Huber, A. Tsumura, M. Kang, P. Xenopoulos, K. Kurimoto, A. K. Oles, M. J. Arauzo-Bravo, M. Saitou, A.-K. Hadjantonakis and T. Hiiragi; Nature Cell Biology (2014) 16(1): 27-37. doi: 10.1038/ncb2881."
License: Artistic-2.0
LazyLoad: true
Depends: R (>= 3.0.0), affy, Biobase, boot, clue, cluster, genefilter, geneplotter, gplots, gtools, KEGGREST, MASS, mouse4302.db, RColorBrewer, xtable
Imports: grid, lattice, latticeExtra
Suggests: ArrayExpress, BiocStyle
biocViews: ExperimentData, MicroarrayData, qPCRData,
ReproducibleResearch
NeedsCompilation: no
Packaged: 2016-05-07 20:43:09 UTC; biocbuild
Package: cellHTS2
Version: 2.36.0
Title: Analysis of cell-based screens - revised version of cellHTS
Author: Ligia Bras, Wolfgang Huber <whuber@embl.de>, Michael Boutros <m.boutros@dkfz.de>, Gregoire Pau <gpau@embl.de>, Florian Hahne <florian.hahne@novartis.com>
Maintainer: Joseph Barry <joseph.barry@embl.de>
Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid
Suggests: ggplot2
Imports: prada, GSEABase, Category, stats4
Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction.
Reference: Michael Boutros and Ligia P. Bras and Wolfgang Huber.
Analysis of cell-based RNAi screens. Genome Biology 7:7 R66
(2006).
License: Artistic-2.0
URL: http://www.dkfz.de/signaling
biocViews: CellBasedAssays, Preprocessing, Visualization
Collate: AllClasses.R AllGenerics.R adjustVariance.R checkColumns.R
checkControls.R convertOldCellHTS.R envisionPlateReader.R
getDynamicRange.R getMeasureRepAgreement.R getTopTable.R
getZfactor.R gseaModule.R glossary.R htmlFunctions.R
imageScreen.R makePlot.R methods-cellHTS.R methods-ROC.R
normalizePlates.R oneRowPerId.R perPlateScaling.R
plateConfModule.R plateListModule.R plateSummariesModule.R
plotSpatialEffects.R progressReport.R readHTAnalystData.R
readPlateList.R rsa.R screenResultsModule.R
screenDescriptionModule.R screenScriptModule.R
screenSummaryModule.R settings.R summarizeReplicates.R
tableOutput.R templateDescriptionFile.R writeHTML-methods.R
writeReport.R write.tabdel.R zzz.R
NeedsCompilation: no
Packaged: 2016-05-04 03:03:58 UTC; biocbuild
Package: CNTools
Version: 1.28.0
Title: Convert segment data into a region by sample matrix to allow for
other high level computational analyses.
Author: Jianhua Zhang
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
Depends: R (>= 2.10), methods, tools, stats, genefilter
Description: This package provides tools to convert the output of
segmentation analysis using DNAcopy to a matrix structure with
overlapping segments as rows and samples as columns so that
other computational analyses can be applied to segmented data
Keyword: copy number
License: LGPL
ZipData: no
biocViews: Microarray, CopyNumberVariation
NeedsCompilation: yes
Packaged: 2016-05-04 03:13:58 UTC; biocbuild
providing 
.