Securing diagonal integration of multimodal single-cell data against ambiguous mapping

Area of Science:

• Single-cell multimodal omics
• Computational biology
• Bioinformatics

Background:

• Single-cell multimodal omics technologies generate large datasets requiring sophisticated integration methods.
• Diagonal integration is a flexible approach for heterogeneous single-cell data but risks artificial integrations due to ambiguous cell mapping.
• Existing diagonal integration methods lack mechanisms to detect spurious cell alignments.

Purpose of the Study:

• To introduce SONATA, a novel diagnostic method for detecting artificial integrations in diagonal data integration.
• To address the challenge of ambiguous cell mapping in multimodal single-cell data integration.
• To enhance the reliability and interpretability of multimodal single-cell data analysis.

Main Methods:

• SONATA quantifies cell-cell ambiguity within the data manifold to identify ambiguous alignments.
• The method acts as an add-on to existing diagonal integration pipelines.
• Evaluated using simulated and real multimodal single-cell datasets.

Main Results:

• Artificial integrations are widespread and overlooked in mainstream diagonal integration methods.
• SONATA effectively distinguishes biologically meaningful integrations from spurious ones.
• The diagnostic tool provides actionable insights into potential integration failures.

Conclusions:

• SONATA offers a robust framework for ensuring the reliability of multimodal single-cell data integration.
• The method safeguards against misleading integrations, improving data interpretability.
• SONATA is a valuable tool for researchers working with complex single-cell omics data.