DECODE: Deep learning-based common deconvolution framework for various omics data

Deconvolution algorithm enables estimation of cell type abundances from tissue-level data, providing a crucial way for exploring plentiful cohort data at the cellular level. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for multiomics data from different cohorts. A deconvolution algorithm applicable to various omics data can use cell abundance as a bridge to improve the comparability of different cohorts. Here, we developed DECODE, a universal deconvolution framework of both cell type and cell state designed for transcriptomics, proteomics and metabolomics data, which seamlessly integrates diverse multiomics tissue datasets in cellular level. DECODE fills the gap in metabolomics deconvolution and significantly outperformed state-of-the-art methods on different omics data across donors, disease conditions, healthy states, and measurement platforms. In addition, DECODE exhibits high robustness in scenarios that are closer to real applications, that it can accurately deconvolve known cell types even when the reference single-cell data incomplete all cell types of target tissue. DECODE will serve as a powerful tool for the fully extending multiomics cohort data into cellular level.