A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables studying dynamic gene regulation in biological processes.
• Existing pseudotime analysis methods lack robust multi-sample comparison capabilities across conditions.
• Comparing pseudotemporal patterns across multiple samples is crucial for understanding biological variability.

Approach:

• Introduced Lamian, a statistically rigorous computational framework for differential multi-sample pseudotime analysis.
• Lamian accounts for cross-sample variability to reduce false discoveries.
• The framework detects changes in gene expression, cell density, and trajectory topology.

Key Points:

• Lamian enables identification of biological process changes associated with sample covariates.
• It reduces sample-specific false discoveries not generalizable to new samples.
• Demonstrated advantages using simulations and real scRNA-seq data, including COVID-19 patient immune responses.

Conclusions:

• Lamian provides a robust method for differential pseudotime analysis across multiple samples.
• The framework enhances the understanding of cellular gene expression programs in continuous biological processes.
• Lamian is valuable for comparative studies in various biological contexts.