ScFPC-DE: Robust Differential Expression Analysis Along Single Cell Trajectories via Functional Principal Component Analysis.

ABSTRACT:

Motivation

Identifying temporally differentially expressed genes (TDEGs) along pseudotime trajectories from single cell RNA sequencing (scRNA-seq) data helps characterize the cellular states that underlie the dynamic process of cellular development. However, existing tests based on generalized additive models (GAMs) suffer from increased false positive rates under zero inflation caused by high dropouts, a ubiquitous technical artifact of scRNA-seq data. Furthermore, by testing each gene independently, existing tests ignore the variance-covariance structure shared across genes along the trajectory, leading to suboptimal power and reduced interpretability.

Results

We present scFPC-DE, a trajectory-based differential expression analysis (TDEA) method based on functional data an

SUBMITTER: Candelaria RJL 

PROVIDER: S-EPMC12637488 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature