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Accurately inferring the root causes of disease from sequencing data canimprove the discovery of novel therapeutic targets. However, existing rootcausal inference algorithms require perfectly measured continuous randomvariables. Single cell RNA sequencing (scRNA-seq) datasets contain largenumbers of cells but non-negative counts measured by an error prone process. Wetherefore introduce an algorithm called Root Causal Inference with NegativeBinomials (RCI-NB) that accounts for count-based measurement error byseparating negative binomial distributions into their gamma and Poissoncomponents; the gamma distributions form a fully identifiable but latent postnon-linear causal model representing the true RNA expression levels, which weonly observe with Poisson corruption. RCI-NB identifies patient-specific rootcausal contributions from scRNA-seq datasets by integrating novel sparseregression and goodness of fit testing procedures that bypass Poissonmeasurement error. Experiments demonstrate significant improvements overexisting alternatives.

Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial