背景:单细胞RNA测序(scRNA-seq)技术能够以单细胞分辨率评估转录组范围的变化。由于不同受试者的环境暴露和遗传背景的异质性,受试者效应是多个受试者的scRNA-seq数据变异的主要来源,这严重混淆了细胞类型特异性差异表达(DE)分析。此外,丢失事件在scRNA-seq数据中很普遍,导致数据中过多的零,这进一步加剧了DE分析中的挑战。
结果:我们开发了iDESC来检测scRNA-seq数据中两组受试者之间的细胞类型特异性DE基因。iDESC使用零膨胀负二项式混合模型来考虑受试者效应和辍学。脱失事件的发生率(脱失率)被证明取决于基因表达水平,它是通过跨基因汇集信息来建模的。主题效应被建模为负二项式分量的对数均值中的随机效应。我们评估并比较了iDESC与11种现有DE分析方法的性能。使用模拟数据,我们证明了与现有方法相比,iDESC具有良好的I型误差控制和更高的功率。将具有良好控制的I型错误的那些方法应用于来自相同组织和疾病的三个真实scRNA-seq数据集表明,iDESC的结果实现了数据集之间的最佳一致性和最佳疾病相关性。
结论:iDESC能够通过将受试者效应与疾病效应分开,并考虑辍学来鉴定DE基因,从而获得更准确和可靠的DE分析结果。提示在对多个受试者的scRNA-seq数据的DE分析中考虑受试者效应和退出的重要性。
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.
RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.
CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.