Correlated Gene Modules Uncovered by High-Precision Single-Cell Transcriptomics

Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to cluster into correlated modules. We hypothesized that correlated modules could be better captured by measuring correlations in steady-state gene expression fluctuations. Here, we developed a high-precision single-cell RNA-seq method called MALBAC-DT to measure these correlations in homogenous cell populations. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules (CGMs). We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.