# BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration

## TL;DR

BiCLUM is a new method for combining unpaired single-cell multi-omics data, improving integration by aligning both cell and feature levels, leading to better biological insights.

## Contribution

BiCLUM introduces a novel bilateral contrastive learning framework that simultaneously aligns cell and feature levels for unpaired single-cell multi-omics integration.

## Key findings

- BiCLUM outperforms existing methods in integrating RNA+ATAC and RNA+protein datasets.
- BiCLUM embeddings preserve biologically meaningful regulatory relationships between chromatin accessibility and gene expression.
- BiCLUM enables robust downstream analyses like transcription factor activity inference and cell–cell interaction mapping.

## Abstract

The integration of single-cell multi-omics data provides a powerful approach for understanding the complex interplay between different molecular modalities, such as RNA expression, chromatin accessibility and protein abundance, measured through assays like scRNA-seq, scATAC-seq and CITE-seq, at single-cell resolution. However, most existing single-cell technologies focus on individual modalities, limiting a comprehensive understanding of their interconnections. Integrating such diverse and often unpaired datasets remains a challenging task due to unknown cell correspondences across distinct feature spaces and limited insights into cell-type-specific activities in non-scRNA-seq modalities. In this work, we propose BiCLUM, a Bilateral Contrastive Learning approach for Unpaired single-cell Multi-omics integration, which simultaneously enforces cell-level and feature-level alignment across modalities. BiCLUM first transforms one modality, such as scATAC-seq, into the data space of another modality, such as scRNA-seq, using prior genomic knowledge. It then learns cell and gene embeddings simultaneously through a bilateral contrastive learning framework, incorporating both cell-level and feature-level contrastive losses. Across multiple RNA+ATAC and RNA+protein datasets, BiCLUM consistently outperforms or matches existing integration methods in both visualization and quantitative benchmarks. Importantly, BiCLUM embeddings preserve biologically meaningful regulatory relationships between chromatin accessibility and gene expression, as evidenced by significantly higher gene–peak correlations than random controls. Downstream analyses further demonstrate that BiCLUM-derived embeddings facilitate transcription factor activity inference, identification of cell-type-specific marker genes, functional enrichment, and cell–cell interaction mapping. Comprehensive hyperparameter sensitivity and ablation analyses further establish BiCLUM as a robust and interpretable framework that not only achieves effective cross-modal alignment but also retains the underlying regulatory and functional landscape across single-cell modalities.

With the rise of single-cell multi-omics technologies, researchers can now probe multiple molecular layers (e.g., transcriptome and epigenome) within individual cells, enabling a more comprehensive understanding of cellular states and functions. However, integrating unpaired multi-omics data from different modalities poses significant challenges due to batch effects, non-overlapping feature spaces, and the lack of cell-to-cell correspondence. To address these issues, we introduce BiCLUM, a bilateral contrastive learning–based integration framework that innovatively models both cell-level and feature-level dependencies in a unified manner. Instead of relying solely on cell-level matching, BiCLUM constructs mutual nearest neighbor (MNN) pairs at both the cell and feature levels, and employs a bilateral contrastive objective to enforce consistent alignment across modalities. This design enables BiCLUM to capture multi-omic correspondences from two complementary perspectives, enhancing the robustness and biological interpretability of the integration. We evaluate BiCLUM across a diverse set of benchmark datasets and show that it consistently outperforms existing integration methods in terms of cell-type alignment, omics mixing, and downstream trajectory inference. Our work highlights the effectiveness of contrastive learning in resolving modality discrepancies and provides a robust framework for integrative single-cell analysis.