condiments

condiments conducts differential topology analysis to estimate and interpret cell trajectories across multiple conditions in single-cell RNA-sequencing and other 'omic datasets.

Key Features:

• Trajectory Integration: Integrates datasets from multiple conditions into a single trajectory for unified downstream analysis.
• Differential Progression Detection: Compares conditions along the trajectory's path to detect large-scale changes indicative of differential progression.
• Differential Gene Behavior Analysis: Identifies genes that exhibit different behaviors between conditions along a differentiation path to detect subtler changes.
• Multi-level Interpretation: Enables interpretation of differences between conditions at the trajectory, cell population, and gene expression levels.
• Format Flexibility: Accepts any input format without restriction, though primarily geared toward scRNASeq data.

Scientific Applications:

• Developmental Process Investigation: Analyzes embryogenesis, cellular differentiation, and regeneration at single-cell resolution.
• Condition Comparison: Infers and compares differentiation trajectories between biological groups or conditions, such as wild-type and knock-out stem cell populations.
• Dynamic Biological System Analysis: Represents cellular states as a continuum rather than discrete groups for studying stem cell differentiation into mature cell types.

Methodology:

The framework integrates datasets from multiple conditions into a single trajectory, then compares conditions along the trajectory's path to detect differential progression and identifies genes with different behaviors between conditions along differentiation paths.