scRANK

scRANK prioritizes cell types in single-cell RNA sequencing (scRNA-seq) analyses by integrating disease-relevant prior knowledge, including molecular mechanisms and drug information, to rank cell types according to their relevance to disease and treatment contexts.

Key Features:

• Integration of Prior Knowledge: scRANK incorporates molecular mechanisms and drug information as structured prior knowledge to prioritize cell types based on relevance to predefined biological contexts.
• Data-Driven Alignment: The method aligns scRNA-seq-derived results with established molecular mechanisms and drug associations to rank cell types by concordance with prior knowledge.
• Expert-User Information Utilization: scRANK combines expert-provided knowledge with automated processes to emphasize cell types most biologically meaningful for a given condition.
• Cell-Cell Communication Analysis: The approach assesses perturbations in cell-cell communication networks between disease and control states to refine cell-type prioritization.
• Complementary Methodology: scRANK provides an automated ranking approach that complements conventional techniques such as proportion estimation and differential gene counting.

Scientific Applications:

• Disease Research: Identifying cell types whose expression profiles align with known disease mechanisms and treatments to support studies of cellular heterogeneity in complex diseases.
• Drug Discovery and Development: Using integrated drug information to identify cell types relevant to therapeutic targets and treatment response.

Methodology:

The methodology structures molecular mechanisms and drug data as prior knowledge, matches scRNA-seq-derived results to that prior knowledge, and analyzes cell-cell communication perturbations between disease and control states to produce automated cell-type rankings.