Many current RNA-sequencing data analysis methods compare expressions one gene at a time, taking little consideration of the correlations among genes. In this study, we propose a method to convert such one-dimensional comparison approaches into a two-dimensional evaluation of the ratio of standard deviations of two constructed random variables. This method allows the identification of differentially expressed genes while controlling a preset significance level conditional on the read count mean-variance relationship. Meanwhile, correlations among genes are naturally accommodated due to the clustering of genes with similar distribution in the proposed σ- σ plot. The proposed distribution-free method is designated as DFseq, because it does not depend on a parametric distribution to fit read count. As a result, compared with parametric methods, DFseq can effectively handle genes with a bimodal distribution and/or genes with excessive 0 read counts, as well as genes with outlying observations. Besides, DFseq is an ideal platform for comparing performance of different differential gene expression detection methods.

中文翻译：

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DFseq：用于检测RNA测序数据差异基因表达的无分布方法。

当前许多RNA测序数据分析方法一次只比较一个基因的表达，却很少考虑基因之间的相关性。在这项研究中，我们提出了一种方法，可以将这种一维比较方法转换为两个构造的随机变量的标准差比率的二维评估。该方法允许识别差异表达的基因，同时控制预设的显着性水平，该水平取决于读取计数均值-方差关系。同时，由于在建议的σ-σ图中具有相似分布的基因聚类，因此自然可以适应基因之间的相关性。提议的无分布方法称为DFseq，因为它不依赖于参数分布来适合读取计数。结果，与参数方法相比，DFseq可以有效处理具有双峰分布的基因和/或读取计数超过0的基因，以及具有偏远观察结果的基因。此外，DFseq是比较不同差异基因表达检测方法性能的理想平台。