MOADE: a multimodal autoencoder for dissociating bulk multi-omics data

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and...

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Veröffentlicht in:Genome Biology Jg. 26; H. 1; S. 325 - 28
Hauptverfasser: Sun, Jiao, Malik, Ayesha A., Lin, Tong, Bratton, Ayla, Pan, Yue, Smith, Kyle, Onar-Thomas, Arzu, Robinson, Giles W., Zhang, Wei, Northcott, Paul A., Li, Qian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 30.09.2025
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Animal Genetics and Genomics
Autoencoder
Bioinformatics
Biomedical and Life Sciences
Brain
Cancer
Computational Biology - methods
Deep learning
DNA methylation
Evolutionary Biology
Gene Expression Profiling
Genomics - methods
High-resolution purification
Human Genetics
Humans
Life Sciences
Methodology
Microbial Genetics and Genomics
Multimodal
Multiomics
Origin cell mapping
Pediatrics
Plant Genetics and Genomics
Proteomics
Single-Cell Analysis - methods
Software
Tissues
Transcriptome
Transcriptomics
Tumor microenvironment
Tumors
Multimodal
Tumor microenvironment
Autoencoder
High-resolution purification
Origin cell mapping
ISSN:1474-760X, 1474-7596, 1474-760X
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Zusammenfassung:In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-025-03805-1