Cancer Biostatistics Monthly Seminar
A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
Dr. Hongkai Ji, PhD
Professor
Associate Chair
Director of the Graduate Program
Department of Biostatistics
Bloomberg School of Public Health
Johns Hopkins University
Abstract
Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
Meeting ID: 895 1762 3227
Passcode: 5f74neBi
This group meets monthly from Noon – 1 p.m. The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.