Package: maPredictDSC
Version: 1.10.0
Date: 2013-6-27
Title: Phenotype prediction using microarray data: approach of the best
overall team in the IMPROVER Diagnostic Signature Challenge
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Depends: R (>= 2.15.0), MASS, affy, limma, gcrma, ROC, class, e1071, caret, hgu133plus2.db, ROCR, AnnotationDbi, LungCancerACvsSCCGEO
Suggests: parallel
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types.
License: GPL-2
URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC
biocViews: Microarray, Classification
Collate: aggregateDSC.R perfDSC.R predictDSC.R maPredictDSC.R
Imports:
LazyLoad: yes
Packaged: 2016-05-04 05:01:21 UTC; biocbuild
NeedsCompilation: no
Package: edgeR
Version: 3.14.0
Date: 2016-04-19
Title: Empirical Analysis of Digital Gene Expression Data in R
Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.
Author: Yunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Xiaobei Zhou <xiaobei.zhou@uzh.ch>, Mark Robinson <mark.robinson@imls.uzh.ch>, Gordon Smyth <smyth@wehi.edu.au>
Maintainer: Yunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Mark Robinson <mark.robinson@imls.uzh.ch>, Davis McCarthy <dmccarthy@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>
License: GPL (>=2)
Depends: R (>= 2.15.0), limma
Imports: graphics, stats, utils, methods
Suggests: MASS, statmod, splines, locfit, KernSmooth
URL: http://bioinf.wehi.edu.au/edgeR
biocViews: GeneExpression, Transcription, AlternativeSplicing,
Coverage, DifferentialExpression, DifferentialSplicing,
GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression,
TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect,
MultipleComparison, Normalization, QualityControl
NeedsCompilation: yes
Packaged: 2016-05-04 03:09:43 UTC; biocbuild
Package: snapCGH
Title: Segmentation, normalisation and processing of aCGH data.
Version: 1.42.0
Date: 2009-10-08
Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas
Hardcastle, Natalie P. Thorne
Description: Methods for segmenting, normalising and processing aCGH
data; including plotting functions for visualising raw and
segmented data for individual and multiple arrays.
Maintainer: John Marioni <marioni@uchicago.edu>
Depends: limma, DNAcopy, methods
Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils
License: GPL
biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing
NeedsCompilation: yes
Packaged: 2016-05-04 02:49:21 UTC; biocbuild
Package: tRanslatome
Type: Package
Title: Comparison between multiple levels of gene expression
Version: 1.10.0
Date: 2015-08-20
Author: Toma Tebaldi, Erik Dassi, Galena Kostoska
Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>, Erik Dassi <erik.dassi@unitn.it>
Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase
Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots.
License: GPL-3
LazyLoad: yes
biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,
DifferentialExpression, Microarray, HighThroughputSequencing,
QualityControl, GO, MultipleComparisons, Bioinformatics
Packaged: 2016-05-04 04:58:42 UTC; biocbuild
NeedsCompilation: no
providing 
.