aggregateBioVar

aggregateBioVar performs differential gene expression analysis for multi-subject single-cell RNA sequencing (scRNA-seq) studies by modeling subject-level biological variation and using pseudobulk aggregation to improve false discovery rate control.

Key Features:

• Statistical Modeling: Implements an advanced statistical model for analyzing gene counts that captures variability between subjects in scRNA-seq data.
• Parameter Estimation: Provides methods for estimating model parameters from gene count data.
• False Discovery Rate Control: Incorporates approaches that reduce inflated FDRs observed with naïve differential expression analyses.
• Pseudobulk Aggregation: Aggregates single-cell counts into subject-level pseudobulk counts prior to differential expression testing.
• Simulation-Based Evaluation: Uses simulation studies to compare testing methods and demonstrate the effects of subject-specific variability on inference.

Scientific Applications:

• Enhanced FDR Management: Applied in studies with subject-to-subject variation in gene expression distributions to mitigate false-positive differential expression findings.
• Cross-Species Analysis: Validated on datasets from human samples and animal models to assess performance across biological contexts.

Methodology:

Performs simulation studies comparing testing methods and implements pseudobulk aggregation that combines single-cell counts into bulk-like per-subject matrices before conducting differential expression tests.