SEaCorAl: Identifying and contrasting the regulation-correlation bias in RNA-Seq paired expression data of patient groups
SEaCorAl: Identifying and contrasting the regulation-correlation bias in RNA-Seq paired expression data of patient groups
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DOI：
10.1016/j.compbiomed.2021.104567
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发表时间：
2021-06-24
影响因子：
7.7
通讯作者：
Farina, Lorenzo
中科院分区：
文献类型：
--
作者：
Petti, Manuela;Verrienti, Antonella;Farina, Lorenzo
癌症基因组图谱数据库提供了使用配对计数分析全基因组表达 RNA-Seq 癌症数据的可能性，即通过从同一个体采集样本，以成对的正常细胞和癌细胞收集表达数据的研究。基因表达谱的相关性是研究共表达组的最常见分析，用于寻找组学大数据的生物学解释。本文的目的有三个：首先，我们首次证明 RNA-Seq 配对表达数据中存在“调节相关偏差”，即基因对的表达状态（上调或下调）与相应相关系数的符号之间的人为联系。其次，我们提供了一个统计模型，能够从理论上解释存在这种偏差的原因。第三，我们提出了一种偏差消除算法SEaCorAl，能够有效减少偏差影响并提高相关分析的生物学意义。 SEaCorAl 算法的验证是通过显示检测具有生物学意义的正相关关联的能力显着增加以及所得无偏相关网络的模块性显着增加来进行的。
The Cancer Genome Atlas database offers the possibility of analyzing genome-wide expression RNA-Seq cancer data using paired counts, that is, studies where expression data are collected in pairs of normal and cancer cells, by taking samples from the same individual. Correlation of gene expression profiles is the most common analysis to study co-expression groups, which is used to find biological interpretation of -omics big data. The aim of the paper is threefold: firstly we show for the first time, the presence of a "regulation-correlation bias" in RNA-Seq paired expression data, that is an artifactual link between the expression status (up- or down-regulation) of a gene pair and the sign of the corresponding correlation coefficient. Secondly, we provide a statistical model able to theoretically explain the reasons for the presence of such a bias. Thirdly, we present a bias-removal algorithm, called SEaCorAl, able to effectively reduce bias effects and improve the biological significance of correlation analysis. Validation of the SEaCorAl algorithm is performed by showing a significant increase in the ability to detect biologically meaningful associations of positive correlations and a significant increase of the modularity of the resulting unbiased correlation network.