RCI-NB is an algorithm for discovering patient-specific root causes of disease from single cell RNA-sequencing (scRNA-seq) data. scRNA-seq datasets contain non-negative counts measured by an error prone sequencing process. RCI-NB 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 regression and goodness of fit testing procedures that bypass Poisson measurement error.

The Experiments folder contains code to replicate the experimental results in the paper. Please cite the article if you use any of the code in this repository.

Press the green button up top and download the zip file. Then:

library(devtools)

install_local("Directory of RCINB-main.zip")

library(RCINB)

Generate the grouth truth DAG with 10 variables and an expected neighborhood size of two:

DAG = generate_NB_DAG_r(10,2)

Generate ten thousand samples from the DAG:

data = sample_NB_DAG_r(10000, DAG)

Run RCI-NB:

out = RCI_NB(data$data,DAG$Y,data$C,data$Xp)

Print expected Shapley values:

print(out$shaps)