Correlated Gene Modules Uncovered by Single-Cell Transcriptomics with High Detectability and Accuracy

Abstract

Abstract Single cell transcriptome sequencing has become extremely useful for cell typing. However, such differential expression data has shed little light on regulatory relationships among genes. Here, by examining pairwise correlations between mRNA levels of any two genes under steady-state conditions, we uncovered correlated gene modules (CGMs), clusters of intercorrelated genes that carry out certain biological functions together. We report a novel single-cell RNA-seq method called MALBAC-DT with higher detectability and accuracy, allowing determination of the covariance matrix of the expressed mRNAs for a homogenous cell population. We observed a prevalence of positive correlations between pairs of genes, with higher correlations corresponding to higher likelihoods of protein-protein interactions. Some CGMs, such as the p53 module in a cancer cell line, are cell type specific, while others, such as the protein synthesis CGM, are shared by different cell types. CGMs distinguished direct targets of p53 and exposed different modes of regulation of these genes in different cell types. Our covariance analyses of steady-state fluctuations provides a powerful way to advance our functional understanding of gene-to-gene interactions.