PDF(Pubmed)
Abstract：
BACKGROUND: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.
RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh\'s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .
CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.
RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh\'s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .
CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.
摘要：
背景：差异表达基因（DEG）的有效鉴定对于单细胞RNA测序（scRNA-seq）谱具有挑战性。许多现有的算法具有高的假阳性率(FPR),并且常常不能识别弱的生物信号。
结果：我们提出了一种在scRNA-seq数据中鉴定DEGs的新方法，称为RankCompV3。它基于基因对的相对表达顺序（REO）的比较，这些基因对是通过比较一组单细胞谱中一对基因的表达水平而确定的。在比较的两组中，统计表达水平始终高于或低于目的基因的基因的数量。分别,结果在3×3列联表中制成表格，通过McCullagh方法进行测试，以确定基因是否失调。在模拟和真实scRNA-seq数据中，RankCompV3严格控制FPR并表现出高精度，优于其他11种常见的单细胞DEG检测算法。使用常规单细胞或合成假块剖面进行的分析产生了与真实事实高度一致的DEG。此外,RankCompV3显示出比其他方法对弱生物信号更高的灵敏度。该算法是使用Julia实现的，可以在R中调用。源代码可在https://github.com/pathint/RankCompV3获得。JL.
结论：基于REOs的算法是分析单细胞RNA谱并以高精度和灵敏度鉴定DEGs的有价值的工具。
结果：我们提出了一种在scRNA-seq数据中鉴定DEGs的新方法，称为RankCompV3。它基于基因对的相对表达顺序（REO）的比较，这些基因对是通过比较一组单细胞谱中一对基因的表达水平而确定的。在比较的两组中，统计表达水平始终高于或低于目的基因的基因的数量。分别,结果在3×3列联表中制成表格，通过McCullagh方法进行测试，以确定基因是否失调。在模拟和真实scRNA-seq数据中，RankCompV3严格控制FPR并表现出高精度，优于其他11种常见的单细胞DEG检测算法。使用常规单细胞或合成假块剖面进行的分析产生了与真实事实高度一致的DEG。此外,RankCompV3显示出比其他方法对弱生物信号更高的灵敏度。该算法是使用Julia实现的，可以在R中调用。源代码可在https://github.com/pathint/RankCompV3获得。JL.
结论：基于REOs的算法是分析单细胞RNA谱并以高精度和灵敏度鉴定DEGs的有价值的工具。
