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
Package: Polyfit
Type: Package
Title: Add-on to DESeq to improve p-values and q-values
Version: 1.6.0
Date: 2014-08-06
Author: Conrad Burden
biocViews: DifferentialExpression, Sequencing, RNASeq, GeneExpression
Maintainer: Conrad Burden <conrad.burden@anu.edu.au>
Depends: DESeq
Suggests: BiocStyle
Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values.
License: GPL (>= 3)
Packaged: 2016-05-04 05:35:12 UTC; biocbuild
NeedsCompilation: no
Package: SeqGSEA
Type: Package
Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating
differential expression and splicing
Version: 1.12.0
Date: 2015-08-21
Author: Xi Wang <Xi.Wang@newcastle.edu.au>
Maintainer: Xi Wang <Xi.Wang@mdc-berlin.de>
Description: The package generally provides methods for gene set
enrichment analysis of high-throughput RNA-Seq data by
integrating differential expression and splicing. It uses
negative binomial distribution to model read count data, which
accounts for sequencing biases and biological variation. Based
on permutation tests, statistical significance can also be
achieved regarding each gene's differential expression and
splicing, respectively.
License: GPL (>= 3)
Depends: Biobase, doParallel, DESeq
Imports: methods, biomaRt
Suggests: easyRNASeq, GenomicRanges
biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression,
DifferentialExpression
NeedsCompilation: no
Packaged: 2016-05-05 03:51:13 UTC; biocbuild
Package: TCC
Type: Package
Title: TCC: Differential expression analysis for tag count data with
robust normalization strategies
Version: 1.12.1
Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji
Kadota
Maintainer: Jianqiang Sun <wukong@bi.a.u-tokyo.ac.jp>, Tomoaki
Nishiyama <tomoakin@staff.kanazawa-u.ac.jp>
Description: This package provides a series of functions for performing
differential expression analysis from RNA-seq count data using
robust normalization strategy (called DEGES). The basic idea of
DEGES is that potential differentially expressed genes or
transcripts (DEGs) among compared samples should be removed
before data normalization to obtain a well-ranked gene list
where true DEGs are top-ranked and non-DEGs are bottom ranked.
This can be done by performing a multi-step normalization
strategy (called DEGES for DEG elimination strategy). A major
characteristic of TCC is to provide the robust normalization
methods for several kinds of count data (two-group with or
without replicates, multi-group/multi-factor, and so on) by
virtue of the use of combinations of functions in depended
packages.
Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC
Imports: samr
Suggests: RUnit, BiocGenerics
Enhances: snow
License: GPL-2
Copyright: Authors listed above
biocViews: Sequencing, DifferentialExpression, RNASeq
NeedsCompilation: no
Packaged: 2016-05-27 04:20:14 UTC; biocbuild
Package: EDDA
Type: Package
Title: Experimental Design in Differential Abundance analysis
Version: 1.10.0
Date: 2015-01-06
Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan
Maintainer: Chia Kuan Hui Burton <chiakhb@gis.a-star.edu.sg>, Niranjan Nagarajan <nagarajann@gis.a-star.edu.sg>
Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527).
License: GPL (>= 2)
Depends: Rcpp (>= 0.10.4), parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
LinkingTo: Rcpp
biocViews: Sequencing, ExperimentalDesign, Normalization, RNASeq,
ChIPSeq
URL: http://edda.gis.a-star.edu.sg/
http://genomebiology.com/2014/15/12/527
NeedsCompilation: yes
Packaged: 2016-05-04 05:20:25 UTC; biocbuild
providing 
.