ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

Mann-Krzisnik, D.; Li, Y.

doi:10.1101/2025.01.24.634799

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bioRxiv · 10.1101/2025.01.24.634799

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

Mann-Krzisnik, D.·Li, Y.

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Abstract

Integrating multimodal single-cell data such as scRNA-seq and scATAC-seq is key for decoding gene regulatory networks. Still, integration remains challenging due to issues related to feature harmonization and limited quantity of paired data. To address these challenges, we introduce ECLARE, a novel framework combining multi-teacher ensemble knowledge distillation with contrastive learning for integrating unpaired single-cell multi-omic data. Briefly, ECLARE trains teacher models on paired datasets to guide a student model for aligning unpaired data, leveraging a refined contrastive objective and optimal-transport-based loss for precise cross-modality alignment. In computational benchmarking, experiments demonstrate ECLAREs competitive performance in cell pairing accuracy, multimodal integration and biological structure preservation, indicating that multi-teacher knowledge distillation provides an effective means to improve a diagonal integration model beyond its zero-shot capabilities. In biological case studies, we demonstrate ECLAREs applicability using unpaired snRNA-seq and snATAC-seq datasets in major depressive disorder (MDD). Firstly, our results revealed transcription factors and target gene combinations differentially regulated in depression with sex- and cell-type specificity. These findings further reveal gene regulatory interactions in excitatory neurons that are highly relevant to MDD neuropathology, such as those involving EGR1, SOX2, and NR3C1. Secondly, we show that ECLARE can learn continuous data manifolds useful for deciphering longitudinal biological processes in neurodevelopment and disease, revealing altered neurodevelopmental programs as potential regulators of depression in females, strongly associated with EGR1 target genes. Altogether, we propose ECLARE as a robust solution for diagonal integration of unpaired multimodal single-cell data that enables the study of altered gene regulation in disease.

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Mann-Krzisnik, D., Li, Y.. 2025-01-27. ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data. https://doi.org/10.1101/2025.01.24.634799

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