Lamian: Differential Pseudotime Analysis
This note is for Hou, W., Ji, Z., Chen, Z., Wherry, E. J., Hicks, S. C., & Ji, H. (2021). A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples (p. 2021.07.10.451910). bioRxiv.
the majority of the methods were designed to infer gene expression changes along the reconstructed trajectory within one biological sample.
However, scRNA-seq experiments today standardly generate data with multiple biological samples across multiple conditions
Changes in pseudotemporal trajectories across conditions can occur in multiple ways, including
- topological differences
- changes in the proportion (or density or abundance) of cells along a cell lineage across conditions
- changes in the gene expression itself along pseudotime across conditions
Given scRNA-seq data from multiple biological samples with known covariates, such as age, sex, sample type, disease status, Lamian can be used to
- construct pseudotemporal trajectories and evaluate the uncertainty of the topologies
- evaluate differential changes in the topological structure associated with sample covariates
- describe how gene expression and cell density change along the pseudotime
- characterize how sample covariates modifies the pseudotemporal dynamics of gene expression and cell density
Lamian accounts for variability across biological samples. And hence Lamian is able to more appropriately control the false discovery rate (FDR) when analyzing multi-sample data.