BATMAN

BATMAN performs batch-effect correction and integration of single-cell RNA sequencing (scRNA-Seq) datasets to enable combined downstream analyses such as cell-type-specific expression quantitative trait loci (eQTL) discovery.

Key Features:

• Batch Effect Correction: Performs batch-effect correction to remove systematic differences between scRNA-Seq datasets for cohesive combined analysis.
• Preservation of Local Structure: Preserves intra-dataset local cell-to-cell relationships, avoiding distortions associated with mutual nearest neighbors-based methods.
• Performance Superiority: Demonstrates improved batch correction metrics in simulations and analyses of real datasets, with reported improvements up to 80% over state-of-the-art methods.
• Facilitation of Downstream Analyses: Enhances the power of integrated datasets for downstream tasks, including identification of cell-type-specific expression quantitative trait loci (eQTLs).

Scientific Applications:

• Large-scale scRNA-Seq Integration: Integrating multiple batches or experiments in large-scale scRNA-Seq studies to characterize cellular heterogeneity and function.
• eQTL Mapping: Enabling mapping of cell-type-specific expression quantitative trait loci (eQTLs) using integrated single-cell datasets.

Methodology:

Implements an integration approach based on minimum-weight matching that aligns datasets while minimizing distortions and preserving local structure, contrasted with mutual nearest neighbors methods.