RNA-seq has been widely used to 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 (GEO) 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.
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
- Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y. (2012) GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics [Epub ahead of print]. [abstract]