CellDrift

CellDrift identifies covarying temporal gene-expression programs and quantifies perturbation responses from time-series single-cell RNA-sequencing (scRNA-seq) data.

Key Features:

• Generalized Linear Model (GLM)-based Functional Data Analysis: Employs a GLM-based functional data analysis framework to detect and quantify covarying temporal expression patterns.
• Temporal Pattern Identification: Captures time-dependent variations in gene expression to characterize how perturbations influence cellular responses over time.
• Imputation of Missing Time Points: Imputes missing temporal samples to preserve continuity in longitudinal single-cell studies.
• Cross-cell-type and Perturbation Quantification: Quantifies covarying temporal patterns across different cell types and perturbation conditions.
• Benchmark Performance: Demonstrated superior performance compared to other approaches in identifying temporally varied perturbation-response patterns.

Scientific Applications:

• COVID-19 Disease Progression: Identifies gene programs associated with COVID-19 progression by analyzing temporal changes in scRNA-seq profiles.
• Gastrointestinal Tract Development: Maps sequential biological pathways and outcomes during gastrointestinal tract development via temporal gene-expression dynamics.

Methodology:

Applies a generalized linear model–based functional data analysis framework to time-series single-cell RNA-seq data and performs imputation of missing time points.