psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels

Abstract

AbstractSingle cell RNA-seq has been successfully combined with pseudotime inference methods to investigate biological processes which have sequential labels, such as time series studies of development and differentiation. Pseudotime methods developed to date ignore the labels, and where there is substantial variation in the data not associated with the labels (such as cell cycle variation or batch effects), they can fail to find relevant genes. We introduce psupertime, a supervised pseudotime approach which outperforms benchmark pseudotime methods by explicitly using the sequential labels as input. psupertime uses a simple, regression-based model, which by acknowledging the labels assures that genes relevant to the process, rather than to major drivers of variation, are found. psupertime is applicable to the wide range of single cell RNA-seq datasets with sequential labels, derived from either experimental design or user-selected cell cluster sequences, and provides a tool for targeted identification of genes regulated along biological processes.