Package: rgsepd
Type: Package
Title: Gene Set Enrichment / Projection Displays
Version: 1.4.2
Date: 2016-04-02
Author: Karl Stamm
Maintainer: Karl Stamm <karl.stamm@gmail.com>
Description: R/GSEPD is a bioinformatics package for R to help
disambiguate transcriptome samples (a matrix of RNA-Seq counts
at RefSeq IDs) by automating differential expression (with
DESeq2), then gene set enrichment (with GOSeq), and finally a
N-dimensional projection to quantify in which ways each sample
is like either treatment group.
Depends: R (>= 3.3.0), DESeq2, goseq (>= 1.17)
Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi
Suggests: boot, tools, RUnit, BiocGenerics, knitr, xtable
License: GPL-3
biocViews: Software, DifferentialExpression, GeneSetEnrichment, RNASeq
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2016-05-16 05:08:53 UTC; biocbuild
Package: DEXSeq
Version: 1.18.4
Title: Inference of differential exon usage in RNA-Seq
Author: Simon Anders <sanders@fs.tum.de> and Alejandro Reyes
<alejandro.reyes@embl.de>
Maintainer: Alejandro Reyes <alejandro.reyes@embl.de>
Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter
Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>=
2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors
Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr
Enhances:
Description: The package is focused on finding differential exon usage
using RNA-seq exon counts between samples with different
experimental designs. It provides functions that allows the
user to make the necessary statistical tests based on a model
that uses the negative binomial distribution to estimate the
variance between biological replicates and generalized linear
models for testing. The package also provides functions for the
visualization and exploration of the results.
License: GPL (>= 3)
URL:
biocViews: Sequencing, RNASeq, DifferentialExpression
Packaged: 2016-05-19 02:54:44 UTC; biocbuild
VignetteBuilder: knitr
NeedsCompilation: no
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: FourCSeq
Type: Package
Title: Package analyse 4C sequencing data
Version: 1.6.2
Date: 2015-06-16
Author: Felix A. Klein, EMBL Heidelberg
Maintainer: Felix A. Klein <felix.klein@embl.de>
Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2 (>= 1.9.11), splines, methods, LSD
Imports: DESeq2, Biobase, Biostrings, GenomicRanges, SummarizedExperiment, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix
Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene
VignetteBuilder: knitr
Description: FourCSeq is an R package dedicated to the analysis of
(multiplexed) 4C sequencing data. The package provides a
pipeline to detect specific interactions between DNA elements
and identify differential interactions between conditions. The
statistical analysis in R starts with individual bam files for
each sample as inputs. To obtain these files, the package
contains a python script (extdata/python/demultiplex.py) to
demultiplex libraries and trim off primer sequences. With a
standard alignment software the required bam files can be then
be generated.
License: GPL (>= 3)
biocViews: Software, Preprocessing, Sequencing
NeedsCompilation: no
Packaged: 2016-05-16 04:50:38 UTC; biocbuild
Package: XBSeq
Type: Package
Title: Test for differential expression for RNA-seq data
Version: 1.2.2
Date: 2016-02-05
Author: Yuanhang Liu
Maintainer: Yuanhang Liu <liuy12@uthscsa.edu>
Description: We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.
License: GPL (>=3)
Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, Delaporte, magrittr
Depends: DESeq2, R (>= 3.2.0)
Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat
VignetteBuilder: knitr
biocViews: RNASeq, DifferentialExpression, Sequencing, Software,
ExperimentalDesign
URL: https://github.com/Liuy12/XBSeq
NeedsCompilation: no
Packaged: 2016-05-16 05:37:30 UTC; biocbuild
providing 
.