Case-control analysis of single-cell RNA-seq studies
Single-cell RNA-seq (scRNA-seq) assays are being increasingly utilized to investigate specific hypotheses in both basic biology and clinically-applied studies. The design of most such studies can be often reduced to a comparison between two or more groups of samples, such as disease cases and healthy controls, or treatment and placebo. Comparative analysis between groups of scRNA-seq samples brings additional statistical considerations, and currently there is a lack of tools to address this common scenario. Based on our experience with comparative designs, here we present a computational suite (Cacoa - case-control analysis) to carry out statistical tests, exploration, and visualization of scRNA-seq sample cohorts. Using multiple example datasets, we demonstrate how application of these techniques can provide additional insights, and avoid issues stemming from inter-individual variability, limited sample size, and high dimensionality of the data.