  Accurately inferring the root causes of disease from sequencing data can
improve the discovery of novel therapeutic targets. However, existing root
causal inference algorithms require perfectly measured continuous random
variables. Single cell RNA sequencing (scRNA-seq) datasets contain large
numbers of cells but non-negative counts measured by an error prone process. We
therefore introduce an algorithm called Root Causal Inference with Negative
Binomials (RCI-NB) that accounts for count-based measurement error by
separating negative binomial distributions into their gamma and Poisson
components; the gamma distributions form a fully identifiable but latent post
non-linear causal model representing the true RNA expression levels, which we
only observe with Poisson corruption. RCI-NB identifies patient-specific root
causal contributions from scRNA-seq datasets by integrating novel sparse
regression and goodness of fit testing procedures that bypass Poisson
measurement error. Experiments demonstrate significant improvements over
existing alternatives.
