Fujian Medical University, China.
Fujian Medical University and Harbin Medical University.
Brief Bioinform. 2019 Mar 22;20(2):482-491. doi: 10.1093/bib/bbx135.
To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.
为了检测小规模细胞系实验中的差异表达基因（DEGs），通常每个状态只有两个或三个技术重复，常用的统计方法，如微阵列显著性分析（SAM）、limma 和 RankProd（RP），缺乏统计学功效，而倍数变化方法则缺乏任何统计学控制。在这项研究中，我们证明了基因对之间的样本内相对表达顺序（REO）在细胞系的技术重复中非常稳定，但在某些处理后，如基因敲低、基因转染和药物处理后，往往会广泛破坏。基于这一发现，我们定制了 RankComp 算法，该算法最初是为通过 REO 比较进行个体化差异表达分析而设计的，用于在小规模细胞系数据中确定具有一定统计控制的 DEGs。在模拟和真实数据中，新算法名为 CellComp，与原始的 RankComp、SAM、limma 和 RP 方法相比，它具有更高的精度和更高的灵敏度。因此，CellComp 为分析小规模细胞系数据提供了一种有效的工具。
