scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data.

Abstract

With the continuous application of single-cell data, effectively correcting batch effects and accurately identifying cell types has emerged as a critical challenge in biomedical research. However, existing methods often struggle to disentangle technical effects from genuine biological variation, limiting their performance on heterogeneous datasets. Here, we introduce single-cell Batch Correction Network (scBCN), an integration framework that combines robust inter-batch similar cluster identification with a deep residual neural network to correct batch effects while preserving biological variability. To evaluate the performance of scBCN, we conduct benchmarking experiments on various simulated and real datasets, demonstrating its superiority in both batch correction and biological variation conservation. Furthermore, scBCN shows its applicability in cross-species and cross-omics data integration, underscoring its potential for uncovering and characterizing cell type-specific gene expression patterns.