scQA

scQA integrates qualitative dropout patterns and quantitative expression measures to identify cell types and associated key genes from single-cell RNA sequencing (scRNA-seq) data.

Key Features:

• Dual-Perspective Analysis: Incorporates dropout events alongside quantitative expression metrics to detect cell types and their associated key genes.
• Bidirectional Clustering: Implements a bidirectional clustering strategy that does not require pre-specifying the number of cell types and iteratively minimizes landmarks to approximately a dozen while maximizing inclusion of quasi-trend-preserved genes.
• Label Propagation Strategy: Uses label propagation to cluster cells based on transcriptomic profiles without prior assumptions about cell type numbers.
• Key Gene Identification: Extracts key genes associated with identified cell types and supports external and internal validation of these gene associations.

Scientific Applications:

• Tissue architecture analysis: Identifies cell types and key genes to dissect complex tissue structures from scRNA-seq data.
• Disease mechanism investigation: Maps cell-type-specific gene signatures relevant to disease mechanisms using scRNA-seq datasets.
• Developmental biology: Resolves cellular heterogeneity and key gene expression during developmental processes from scRNA-seq profiles.
• Method benchmarking and validation: Demonstrated robustness across 20 publicly available scRNA-seq datasets and reported consistent performance improvements over other leading tools.

Methodology:

The computational workflow uses an iterative process balancing qualitative (dropout events) and quantitative expression data, applies bidirectional clustering to minimize landmarks to approximately a dozen while maximizing quasi-trend-preserved genes, and employs label propagation for cell clustering and key-gene extraction.