Sha Ying, Phan John H, Wang May D
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6461-4. doi: 10.1109/EMBC.2015.7319872.
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.
我们比较了过滤RNA测序低表达基因的方法，并研究了过滤对差异表达基因（DEG）检测的影响。尽管RNA测序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抽样噪声难以区分。存在噪声的低表达基因会降低检测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，我们还研究了RNA测序流程对最佳过滤阈值的影响。结果表明，使DEG总数最大化的过滤阈值与使DEG检测灵敏度最大化的阈值密切相关。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学意义的RNA测序流程因素。
