MOADE: a multimodal autoencoder for dissociating bulk multi-omics data - 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 (Genome Biology, 2025)
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.