Differential gene expression analysis using coexpression and RNA-Seq data
Author(s) -
Ei-Wen Yang,
Thomas Girke,
Tao Jiang
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt363
Subject(s) - inference , computer science , computational biology , rna seq , markov random field , biology , maximum a posteriori estimation , coalescent theory , expression (computer science) , gene , gene expression , data mining , mathematics , genetics , artificial intelligence , statistics , maximum likelihood , transcriptome , phylogenetics , image segmentation , programming language , segmentation
RNA-Seq is increasingly being used for differential gene expression analysis, which was dominated by the microarray technology in the past decade. However, inferring differential gene expression based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. The differential expression estimation may be biased against low read count values such that the differential expression of genes with high read counts is more easily detected. The estimation bias may further propagate in downstream analyses at the systems biology level if it is not corrected.
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