switchde: inference of switch-like differential expression along single-cell trajectories

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Pseudotime analysis of single-cell RNA sequencing (scRNA-seq) data is widely used to infer biological processes like cell differentiation.
• Existing methods primarily focus on discovering trajectories but pay less attention to modeling differential gene expression along these paths.

Purpose of the Study:

• To introduce switchde, a statistical framework and R package for identifying switch-like differential gene expression along pseudotemporal trajectories.
• To provide interpretable parameter estimates for the speed and location of gene regulation.
• To offer a method that accounts for zero-inflation common in scRNA-seq data.

Main Methods:

• switchde employs a statistical framework for fitting models of gene expression changes along pseudotime.
• The package allows for fast model fitting and provides parameter estimates for gene regulation dynamics.
• It calculates a P-value to assess switch-like differential expression and optionally models zero-inflation.

Main Results:

• switchde enables the identification of genes exhibiting switch-like differential expression along pseudotemporal trajectories.
• The method provides interpretable parameters for the rate and position of gene regulation.
• It offers a robust approach for analyzing differential expression in scRNA-seq data, including handling zero-inflation.

Conclusions:

• switchde is a valuable tool for researchers studying dynamic biological processes using scRNA-seq data.
• The package facilitates a deeper understanding of gene regulation patterns during cellular transitions.
• switchde is available as an R package via the Bioconductor project.