Go Back   SEQanswers > Literature Watch

Similar Threads
Thread Thread Starter Forum Replies Last Post
RNA-Seq: Differential expression analysis for sequence count data. Newsbot! Literature Watch 3 09-25-2012 09:19 AM
RNA-Seq: RNASEQR--a streamlined and accurate RNA-seq sequence analysis program. Newsbot! Literature Watch 1 02-07-2012 06:40 PM
RNA-Seq: ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count da Newsbot! Literature Watch 0 11-18-2011 02:20 AM
RNA-Seq: IsoLasso: A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembl Newsbot! Literature Watch 0 09-29-2011 06:00 AM
RNA-Seq: X-MATE: A flexible system for mapping short read data. Newsbot! Literature Watch 0 01-11-2011 07:20 AM

Thread Tools
Old 08-04-2011, 02:00 AM   #1
RSS Posting Maniac

Join Date: Feb 2008
Posts: 1,443
Default RNA-Seq: A Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data.

Syndicated from PubMed RSS Feeds

A Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data.

Bioinformatics. 2011 Aug 2;

Authors: Zhou YH, Xia K, Wright FA

MOTIVATION: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA-sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that "borrows" information across genes, while easily incorporating design effects and additional covariates. RESULTS: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data, and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. AVAILABILITY: An R package containing examples and sample datasets is available at CONTACT:;

PMID: 21810900 [PubMed - as supplied by publisher]

Newsbot! is offline   Reply With Quote

Thread Tools

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

All times are GMT -8. The time now is 12:34 PM.

Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2020, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO