Quantitative Issues in Cancer Research Working Seminar

Phillip Nicol
Doctoral Student, Department of Biostatistics, Harvard University

“Robust Identification of Perturbed Cell Types in Single-cell RNA-seq Data”

ABSTRACT: Many single-cell RNA-seq experiments aim to identify cell types that are transcriptionally different between two or more biological conditions. Existing computational approaches to this problem are sensitive to bias induced by pseudoreplication, non-independence of cells belonging to the same sample or patient. We introduce pcDiffPop, a statistical method that uses linear mixed-effects models to find significantly perturbed cell types while controlling for the sample-level variability present in single-cell RNA-seq data. pcDiffPop operates by estimating the distance between the group means in a low-dimensional embedding space. Using both real and simulated single-cell datasets, we show that pcDiffPop is accurate and, unlike competing methods, robust in the presence of pseudoreplication bias. pcDiffPop is also computationally efficient (scalable to datasets with millions of cells) and capable of controlling for other possible confounders such as age or batch. We demonstrate pcDiffPop by using it to compare cell types between responders and non-responders to immunotherapy. On melanoma samples, we identify a macrophage signature associated with poor response to checkpoint inhibitors. We also demonstrate how pcDiffPop can be used to formally test whether two cell clusters are distinct. Our examples highlight the utility of pcDiffPop as a tool for the exploratory analysis of single-cell RNA-seq data.