Probabilistic index models for testing differential expression in single cell RNA sequencing data

Abstract

Single-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.