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

## TL;DR

PseudotimeDE improves the detection of genes changing during cell development by accounting for uncertainty in pseudotime estimation from single-cell RNA data.

## Contribution

PseudotimeDE introduces a novel method for differential gene expression analysis along pseudotime with well-calibrated p-values.

## Key findings

- PseudotimeDE outperforms existing methods in false discovery rate control.
- The method adapts to various pseudotime inference techniques and handles uncertainty effectively.

## Abstract

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

The online version contains supplementary material available at (10.1186/s13059-021-02341-y).