Authors
Xuanwei Lin, Pengzhen Hu, Hebing Chen, Ximeng Liu, Xiaochen Bo, Hao Li
Published in
IEEE transactions on computational biology and bioinformatics. Volume PP. Sep 01, 2026. Epub Sep 01, 2026.
Abstract
Recent advances in single-cell multimodal omics sequencing enable the joint profiling of multiple molecular layers within individual cells. Despite this progress, computational integration remains challenging because cross-modal alignment must be achieved without discarding modality-specific information. This paper introduces scCMIA, a mutual-information-guided framework for robust single-cell cross-modal integration. scCMIA decomposes the representation of each modality into a semantic latent variable for shared cellular states and a modality-specific latent variable for non-shared information required for reconstruction. The framework combines contrastive cross-modal alignment, mutual-information-guided decoupling, and a unified CrossVQ codebook to support both accurate reconstruction and interpretable discrete representation learning. Benchmarking across paired single-cell multi-omics datasets demonstrates that scCMIA achieves strong alignment and reconstruction performance, improves downstream label transfer and cell-type classification, and enables code-level analysis of cross-modal coupling patterns across cell types. These results show that scCMIA provides an effective and interpretable framework for single-cell cross-modal integration.
PMID:
42678848
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.
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