Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data.
Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data.
复制标题
DOI：
10.1109/embc.2015.7319872
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wang MD
中科院分区：
文献类型：
--
作者：
Sha Y;Phan JH;Wang MD
我们比较了过滤 RNA-seq 低表达基因的方法，并研究了过滤对检测差异表达基因 (DEG) 的影响。尽管RNA-seq技术提高了基因表达定量的动态范围，但低表达基因可能与采样噪声无法区分。噪音低表达基因的存在会降低检测 DEG 的灵敏度。因此，识别和过滤这些低表达基因可能会提高 DEG 检测的灵敏度。使用SEQC基准数据集，我们研究了不同过滤方法对DEG检测灵敏度的影响。此外，我们研究了 RNA-seq 管道对最佳过滤阈值的影响。结果表明，最大化 DEG 总数的过滤阈值与最大化 DEG 检测灵敏度的阈值密切相关。转录组参考注释、表达定量方法和DEG检测方法是影响最佳过滤阈值的具有统计显着性的RNA-seq流程因素。
We compare methods for filtering RNA-seq lowexpression genes and investigate the effect of filtering on detection of differentially expressed genes (DEGs). Although RNA-seq technology has improved the dynamic range of gene expression quantification, low-expression genes may be indistinguishable from sampling noise. The presence of noisy, low-expression genes can decrease the sensitivity of detecting DEGs. Thus, identification and filtering of these low-expression genes may improve DEG detection sensitivity. Using the SEQC benchmark dataset, we investigate the effect of different filtering methods on DEG detection sensitivity. Moreover, we investigate the effect of RNA-seq pipelines on optimal filtering thresholds. Results indicate that the filtering threshold that maximizes the total number of DEGs closely corresponds to the threshold that maximizes DEG detection sensitivity. Transcriptome reference annotation, expression quantification method, and DEG detection method are statistically significant RNA-seq pipeline factors that affect the optimal filtering threshold.