PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

In the investigation of molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along a continuous cell trajectory, which can be estimated by pseudotime inference from single-cell RNA-sequencing (scRNA-seq) data. However, existing methods that identify DE genes based on inferred pseudotime do not account for the uncertainty in pseudotime inference. Also, they either have ill-posed p-values that hinder the control of false discovery rate (FDR) or have restrictive models that reduce the power of DE gene identification. To overcome these drawbacks, we propose PseudotimeDE, a robust method that accounts for the uncertainty in pseudotime inference and thus identifies DE genes along cell pseudotime with well-calibrated p-values. PseudotimeDE is flexible in allowing users to specify the pseudotime inference method and to choose the appropriate model for scRNA-seq data. Comprehensive simulations and real-data applications verify that PseudotimeDE provides well-calibrated p-values essential for controlling FDR and downstream analysis and that PseudotimeDE is more powerful than existing methods to identify DE genes.