SSBER

SSBER removes batch effects from single-cell RNA sequencing (scRNA-seq) data by incorporating biological prior knowledge to mitigate technical variation while preserving genuine biological signals.

Key Features:

• Batch-effect correction: Performs batch-effect correction on scRNA-seq datasets to reduce technical discrepancies between batches.
• Biological priors integration: Integrates biological prior knowledge to guide the correction process and avoid altering true biological variation.
• Composition-aware handling: Effective when there is substantial variation in cell type composition across batches, reducing the risk of overcorrection.
• Preservation of biological signals: Maintains genuine biological variation during the correction process.
• Cross-batch cell type resolution: Handles datasets where cell type structures differ markedly between batches or where similar cell types appear across different batches.
• High-dimensional data support: Designed to operate on high-dimensional scRNA-seq data.
• Robust performance: Demonstrates improved performance compared to existing algorithms under challenging conditions described in the input.

Scientific Applications:

• Multi-batch integration: Integration of scRNA-seq datasets generated across diverse laboratories and sequencing platforms.
• Heterogeneous tissue analysis: Analysis of heterogeneous scRNA-seq datasets collected from various tissues.
• Downstream analyses facilitation: Enables more reliable downstream analyses of scRNA-seq data by reducing technical artifacts.
• Reproducibility and accuracy improvement: Improves the accuracy and reproducibility of single-cell transcriptome studies.

Methodology:

SSBER incorporates biological prior knowledge to guide batch-effect correction in high-dimensional scRNA-seq datasets.