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• W2155839154 abstract "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 Contact: xsliu@jimmy.harvard.edu or yzhang@tongji.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online." @default.
• W2155839154 created "2016-06-24" @default.
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• W2155839154 date "2012-08-24" @default.
• W2155839154 modified "2023-10-14" @default.
• W2155839154 title "GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data" @default.
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• W2155839154 doi "https://doi.org/10.1093/bioinformatics/bts515" @default.
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