scViewer

scViewer provides visualization and statistical analysis of single-cell RNA sequencing (scRNA-seq) data to explore gene expression, co-expression, and differential expression across cell types and experimental conditions.

Key Features:

• Integration with Seurat RDS Objects: Accepts processed Seurat RDS objects as input, ensuring compatibility with Seurat-based single-cell workflows.
• Comprehensive Visualization Capabilities: Generates detailed, publication-ready plots for gene expression visualization.
• Cell-Type-Specific Gene Expression Exploration: Supports exploration of gene expression patterns specific to annotated cell types.
• Co-Expression Analysis: Performs co-expression analysis of two genes to assess joint expression patterns.
• Differential Expression Analysis: Performs differential expression across biological conditions while accounting for cell-level and subject-level variation using negative binomial mixed modeling.
• Multi-Condition Comparison: Supports gene-level differential expression and co-expression comparisons across multiple experimental conditions or disease states.

Scientific Applications:

• Cellular Process Insights: Enables identification of cell-type-specific expression and co-expression patterns to investigate cellular processes.
• Disease Research: Applicable to disease-focused studies and demonstrated on a single-cell dataset of brain cells from an Alzheimer’s disease study.

Methodology:

Computations accept processed Seurat RDS objects and apply negative binomial mixed modeling and other statistical methods for analysis and visualization.