Capybara

Capybara quantifies cell identity and fate transitions at single-cell resolution from single-cell RNA sequencing (scRNA-seq) data to characterize continuous and mixed cellular identities.

Key Features:

• R implementation: Implemented in R for computational analysis of single-cell data.
• Sparse scRNA-seq handling: Operates on highly-sparse single-cell RNA sequencing (scRNA-seq) data to address challenges of sparse measurements.
• Continuous identity scoring: Assesses cell identity as a continuum rather than forcing discrete categorical labels.
• Multi-identity detection: Identifies cells exhibiting multiple identities simultaneously to quantify mixed states and fate transitions.
• Prior-knowledge independence: Functions without requiring prior biological knowledge for classification.
• Benchmarking: Benchmarked against existing classifiers and reported superior performance in annotating cells and identifying critical transitions.

Scientific Applications:

• Hematopoiesis: Delineates cell fate dynamics within the hematopoietic differentiation hierarchy.
• Developmental biology: Quantifies dynamics of cell identity during developmental processes.
• Cell reprogramming: Applied to reprogramming strategies to reveal regional patterning and identify potential in vivo correlates of engineered cell types.
• Disease progression: Provides insights into disease-associated fate transitions by measuring changes in cell identity.
• Cell engineering evaluation: Supports assessment of efficiency and fidelity in cell engineering approaches.

Methodology:

Implemented in R to analyze highly-sparse single-cell RNA sequencing (scRNA-seq) data by scoring continuous cell identities, detecting cells with multiple identities, and benchmarking results against existing classifiers.