Evaluation of read count based RNAseq analysis methods.
Evaluation of read count based RNAseq analysis methods.
复制标题
DOI：
10.1186/1471-2164-14-s8-s2
复制
发表时间：
2013
期刊：
影响因子：
4.4
通讯作者：
Shyr Y
中科院分区：
文献类型：
--
作者：
Guo Y;Li CI;Ye F;Shyr Y
RNAseq技术正在取代微阵列技术，成为基因表达谱分析的首选工具。虽然提供了比微阵列更丰富的数据，但RNAseq数据的分析更具挑战性。迄今为止，还没有就进行稳健RNAseq分析的最佳方法达成共识。在这项研究中，我们设计了一个全面的实验来评估六种基于读取计数的RNAseq分析方法（DESeq, DEGseq, edgeR, NBPSeq， TSPM和baySeq），使用真实和模拟数据。我们发现，基于p值，六种方法产生了相似的折叠变化和差异表达基因的合理重叠。然而，这六种方法都存在过度敏感的问题。通过对实际数据的运行时间和模拟数据下的受试者工作特征曲线（AUC-ROC）面积的评估，我们发现edgeR比其他方法在速度和精度之间取得了更好的平衡。
RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis. In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity. Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.