GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell transcriptome data integration is crucial for understanding complex biological systems.
• Batch effects are a major challenge in integrating datasets from different sources.
• Existing contrastive learning methods show promise but require further optimization for batch effect removal.

Purpose of the Study:

• To develop an advanced contrastive learning framework, GLOBE, for effective batch effect correction.
• To improve the stability and consistency of batch effect approximation in transcriptome data integration.
• To provide a transparent method for generating batch-corrected gene matrices for downstream analyses.

Main Methods:

• GLOBE employs adaptive translation transformations for stable batch effect approximation.
• A hardness-aware and consistency-aware loss function aligns representations, ensuring batch effect invariance.
• The framework generates a transparent batch-corrected gene matrix.

Main Results:

• GLOBE demonstrates superior performance in robust batch mixing and biological signal conservation across diverse datasets.
• Benchmarking shows GLOBE outperforms existing state-of-the-art batch correction methods.
• Application to mouse neocortex datasets successfully removed batch effects while preserving cellular structures.

Conclusions:

• GLOBE offers an effective and robust solution for batch effect removal in single-cell transcriptome data integration.
• The framework's transparent approach supports diverse downstream analyses.
• GLOBE advances the field by providing a stable and biologically accurate data integration method.