Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics.

Area of Science:

• Oncology
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cancer heterogeneity.
• Accurate differential gene expression analysis is needed for clinical case/control studies (e.g., treatment responders vs. non-responders).
• Existing methods often yield false positives and fail to capture patient-specific data structures or confounders.

Purpose of the Study:

• To introduce BEANIE, a novel nonparametric statistical method for differential gene expression analysis of gene signatures in clinical scRNA-seq data.
• To address limitations of current methods, including high false positive rates and inadequate handling of patient-specific hierarchies and confounders.
• To provide a robust tool for hypothesis generation in cancer research.

Main Methods:

• Developed BEANIE, a nonparametric statistical approach for differential expression analysis.
• Applied BEANIE to simulated and real-world clinical datasets from breast cancer, lung cancer, and melanoma.
• Evaluated BEANIE's performance against existing methods in terms of specificity and sensitivity.

Main Results:

• BEANIE demonstrates superior specificity while maintaining high sensitivity compared to existing methods in simulations.
• The method effectively analyzes gene signatures in clinically relevant groups within scRNA-seq data.
• BEANIE successfully handles patient-specific hierarchical structures and sample-driven confounders.

Conclusions:

• BEANIE offers a robust methodological strategy for identifying differentially expressed gene signatures in cancer.
• The method enhances biological insights into unique and shared gene signatures across different tumor states.
• BEANIE is applicable to single-study analyses, meta-analyses, and cross-validation across cell types.