V-SVA

V-SVA identifies and annotates hidden sources of variation in single-cell RNA sequencing (scRNA-seq) data to distinguish technical artifacts from biological signals and inform downstream analyses.

Key Features:

• Detection of Hidden Variation: Identifies surrogate variables representing unwanted variation such as batch effects and biologically meaningful variation such as cell-type-associated signals in scRNA-seq data.
• Integration with SVA Algorithms: Implements IA-SVA, SVA, and ZINB-WaVE for surrogate variable detection and correction while preserving biological variation.
• Gene Discovery and Annotation: Performs gene-level discovery associated with identified sources of variation and annotates genes using publicly available databases and gene sets.
• Data Visualization: Applies dimension reduction methods to visualize complex scRNA-seq data and relationships between surrogate variables and cells.

Scientific Applications:

• Distinguishing Technical and Biological Variation: Enables separation of batch effects and other technical artifacts from genuine biological signals in single-cell transcriptomics studies.
• Identification of Cell-Type-Associated Programs: Supports discovery and characterization of gene expression programs associated with specific cell types or biological processes at single-cell resolution.

Methodology:

Uses IA-SVA, SVA, and ZINB-WaVE to detect surrogate variables and correct unwanted variation while preserving biological signals, applies dimension reduction for visualization, and performs gene discovery with annotation against publicly available databases and gene sets.