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Associate Professor
Department of Biostatistics
Johns Hopkins University
Baltimore, Maryland

Estimates of correlation between pairs of genes, also called co-expression analysis, are commonly used to construct networks between genes using gene expression data. Here, we show that the distribution of these correlations depends on the expression level of the involved genes, which we refer to this as a mean-correlation relationship in bulk RNA-seq data. This dependence introduces a bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. 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, 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.