CellDrift: inferring perturbation responses in temporally sampled single-cell data

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Cellular responses to perturbations are reflected in gene expression changes.
• Existing methods often overlook the temporal consistency of gene expression patterns.
• Analyzing dynamic gene expression is crucial for understanding biological processes.

Purpose of the Study:

• To develop a novel method, CellDrift, for analyzing temporal gene expression patterns in response to perturbations.
• To improve the identification and quantification of cell-level responses over time.
• To leverage temporal gene expression consistency for more accurate perturbation analysis.

Main Methods:

• Developed CellDrift, a generalized linear model-based functional data analysis method.
• CellDrift identifies covarying temporal patterns across various cell types.
• The method is designed to handle longitudinal datasets and impute missing time points.

Main Results:

• CellDrift outperforms existing approaches in identifying temporally varied perturbation patterns.
• Demonstrated superior performance in imputing missing time points in gene expression data.
• Successfully applied to COVID-19 progression and gastrointestinal development datasets.

Conclusions:

• CellDrift is a powerful tool for analyzing dynamic gene expression in response to perturbations.
• The method can identify specific gene programs linked to biological processes, trajectories, and outcomes.
• CellDrift offers a robust approach for longitudinal data analysis in systems biology and disease research.