GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data
GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data
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DOI：
10.1093/bioinformatics/bts515
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发表时间：
2012-11-01
期刊：
影响因子：
5.8
通讯作者：
Zhang, Yong
中科院分区：
文献类型：
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作者：
Feng, Jianxing;Meyer, Clifford A.;Zhang, Yong
动机：RNA-seq已广泛应用于转录组分析，可有效测量基因表达水平。尽管测序成本正在迅速下降，但基因表达综合数据库中几乎70%的人类RNA-seq样本没有生物重复，2011年发表的未复制RNA-seq数据多于已复制RNA-seq数据。尽管有大量的单重复研究，但目前还没有令人满意的方法来检测只有单个生物重复时的差异表达基因。结果：我们提出了GFOLD（广义折叠变化）算法，从RNA-seq数据中产生具有生物学意义的差异表达基因排名。GFOLD基于对数折叠变化的后验分布为表达式变化分配可靠的统计量。这样，GFOLD就克服了现有RNA-seq分析方法计算p值和折叠变化的缺点，在只有单个生物重复的情况下，给出了更稳定、更有生物学意义的基因排序。
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.