scSensitiveGeneDefine

scSensitiveGeneDefine identifies and manages sensitive genes in single-cell RNA sequencing (scRNA-seq) data that exhibit high variability in response to environmental stimuli, using Shannon entropy and coefficients of variation to mitigate noise in cell type annotation and unsupervised clustering.

Key Features:

• Sensitive Gene Detection: Utilizes CV-rank and coefficients of variation (CV) together with Shannon entropy to detect genes with high cell-to-cell variability termed sensitive genes.
• Noise Reduction: Identifies and removes sensitive genes to reduce noise and improve the accuracy of unsupervised clustering and cell type annotation in scRNA-seq analyses.
• Pathway Enrichment Analysis: Demonstrates that sensitive genes are often enriched in pathways related to cellular stress responses, providing functional context for their variability.

Scientific Applications:

• Cell Type Annotation: Improves identification of cell subsets and states by reducing the influence of highly variable sensitive genes on annotation.
• Unsupervised Clustering Enhancement: Brings unsupervised clustering results closer to ground-truth cell labels by removing sensitive-gene-induced noise.
• Comparative Analysis: Facilitates comparison among cell marker genes, housekeeping genes (HK genes), and sensitive genes to elucidate their distinct roles in cellular function.

Methodology:

Computational steps explicitly include CV-rank calculation of coefficients of variation, application of Shannon entropy to quantify expression uncertainty, and validation across 11 single-cell RNA-seq datasets from various human tissues.