Trajectory-based differential expression analysis for single-cell sequencing data

Koen Van Den Berge,Koen Van Den Berge,Hector Roux De Bézieux,Wouter Saelens,Lieven Clement,Lieven Clement,Robrecht Cannoodt,Yvan Saeys,Kelly Street,Sandrine Dudoit

doi:10.1038/s41467-020-14766-3

Abstract

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

Highlights

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression
The method tests whether a model with a bifurcation significantly increases the likelihood of the data as compared with a model without a bifurcation, essentially testing whether gene expression is differentially associated with the two lineages
In the generalized additive model (GAM), each lineage is represented by a separate cubic smoothing spline, and the flexibility of GAMs allows us to adjust for other covariates or confounders as fixed effects in the model

Summary

Introduction

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Recent developments have allowed the inference of complex trajectories[3,4,5] These advances enable researchers to study dynamic biological processes, such as complex differentiation patterns from a progenitor population to multiple differentiated cellular states[6,7], and have the promise to provide transcriptome-wide insights into these processes. Differential expression (DE) analysis of individual genes along lineages is often performed on discrete groups of cells in the developmental pathway, e.g., by comparing clusters of cells along the trajectory or clusters of differentiated cell types Such discrete DE approaches do not exploit the continuous expression resolution that can be obtained from the pseudotemporal ordering of cells along lineages provided by TI methods. The pseudotimes are continuous, and cells are never at the exact same pseudotime value

Methods

Results

Conclusion