scTITANS

scTITANS analyzes time-series single-cell RNA sequencing (scRNA-seq) data to identify temporally differentially expressed genes and dynamic cell subclusters while correcting for cell asynchrony.

Key Features:

• Correction of Cell Asynchrony: Uses pseudotime derived from trajectory inference analysis to correct for asynchrony among cells sampled at different time points.
• Time-Dependent Covariate: Incorporates a time-dependent covariate based on time-series analysis methods to model temporal effects in gene expression and clustering.
• Handling Heterogeneity: Manages cellular heterogeneity by leveraging timing information embedded in biological processes to improve interpretation.
• Quantitative and Accurate Analysis: Provides a quantitative framework aimed at increased accuracy for identifying temporal variations in gene expression and cell subclusters compared to existing approaches.

Scientific Applications:

• Dynamic cellular process analysis: Characterizes temporal dynamics of cellular processes using time-series scRNA-seq data.
• Temporal differential expression: Identifies differentially expressed genes across time points while accounting for cell asynchrony.
• Cell subcluster dynamics: Detects formation and temporal variation of cell subclusters across multiple time points.

Methodology:

Leverages pseudotime from trajectory inference analysis to correct cell asynchrony and integrates a time-dependent covariate from time-series analysis methods to identify temporally differentially expressed genes and dynamic subclusters.