Robust discovery of causal gene networks via measurement error estimation and correction

Discovering causal relations among genes from observational data is a fundamental problem in systems biology, especially in humans where direct gene perturbations are unethical/infeasible. “Mediation or Mendelian-Randomization” based methods can infer causality from gene expression and matched genotype data, but expression measurement errors are prevalent (e.g.,RNA-seq counts of low/moderate expressed genes) and can mislead most such methods into making wrong causal inferences.
We propose a two-step framework to discover causal gene networks under measurement noise. The first step predicts the variance of measurement errors in RNA-seq read counts of all genes when no technical replicates are available, with a machine learning model trained on gene/sample-specific features (like average expression, gene length, GC content). Our framework’s second step incorporates the estimated measurement errors to correct/extend mediation-based causality methods like CIT. For instance, our newly proposed RobustCIT method conducts four regression-based statistical tests verifying a chain of conditions of causality as the original CIT, but with regression coefficients, residuals and associated P-values appropriately corrected using measurement error variance.