Unico: a unified model for cell-type resolution genomics from heterogeneous omics data

Zeyuan Johnson Chen; Elior Rahmani; Eran Halperin

doi:10.1186/s13059-025-03776-3

Unico: a unified model for cell-type resolution genomics from heterogeneous omics data

Genome Biol. 2025 Oct 3;26(1):333. doi: 10.1186/s13059-025-03776-3.

Authors

Zeyuan Johnson Chen^#^{1

2}, Elior Rahmani^#³, Eran Halperin⁴

Affiliations

¹ Department of Computer Science, University of California, Los Angeles, CA, USA.
² Department of Computational Medicine, University of California, Los Angeles, CA, USA.
³ Department of Computational Medicine, University of California, Los Angeles, CA, USA. eliorrahmani@mednet.ucla.edu.
⁴ Department of Computational Medicine, University of California, Los Angeles, CA, USA. ehalperin@cs.ucla.edu.

^# Contributed equally.

Abstract

Most population-scale genomic datasets collected to date consist of "bulk" samples obtained from heterogeneous tissues, reflecting mixtures of different cell types. We introduce Unico, a Unified cross-omics computational method designed to deconvolve standard two-dimensional bulk matrices (samples by features) into three-dimensional tensors (samples by features by cell types). Unico is the first principled model-based deconvolution method that is theoretically justified for any tissue-level genomic data. By deconvolving bulk gene expression and DNA methylation datasets, we demonstrate Unico's superior performance compared to existing methods, enhancing the ability to conduct powerful, large-scale genomic studies at cell-type resolution.

Keywords: Cell-type specificity; Computational models; DNA methylation; Decomposition; Deconvolution; Epigenomics; Nonparametric models; RNA expression; Statistical methods.

MeSH terms

Algorithms
DNA Methylation
Genomics* / methods
Humans
Software*

Grants and funding

5R01HG010505-04/HG/NHGRI NIH HHS/United States