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Comparative Study
. 2012 Nov 1;28(21):2782-8.
doi: 10.1093/bioinformatics/bts515. Epub 2012 Aug 24.

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data

Jianxing Feng  1 , Clifford A Meyer, Qian Wang, Jun S Liu, X Shirley Liu, Yong Zhang
Affiliations
Comparative Study

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data

Jianxing Feng et al. Bioinformatics. .

Abstract

Motivation: RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available.

Results: We present the GFOLD (generalized fold change) algorithm to produce biologically meaningful rankings of differentially expressed genes from RNA-seq data. GFOLD assigns reliable statistics for expression changes based on the posterior distribution of log fold change. In this way, GFOLD overcomes the shortcomings of P-value and fold change calculated by existing RNA-seq analysis methods and gives more stable and biological meaningful gene rankings when only a single biological replicate is available.

Availability: The open source C/C++ program is available at http://www.tongji.edu.cn/∼zhanglab/GFOLD/index.html

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