RT Journal Article
SR Electronic
T1 Co-expression analysis is biased by a mean-correlation relationship
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.13.944777
DO 10.1101/2020.02.13.944777
A1 Yi Wang
A1 Stephanie C. Hicks
A1 Kasper D. Hansen
YR 2020
UL http://biorxiv.org/content/early/2020/02/13/2020.02.13.944777.abstract
AB Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. Here, we show that the distribution of such correlations depend on the expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces a bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.