Explainable multi-task learning for multi-modality biological data analysis

Xin Tang, Jiawei Zhang, Yichun He, Xinhe Zhang, Zuwan Lin, Sebastian Partarrieu, Emma Bou Hanna, Zhaolin Ren, Hao Shen, Yuhong Yang, Xiao Wang, Na Li, Jie Ding () and Jia Liu ()
Additional contact information
Xin Tang: Harvard University
Jiawei Zhang: University of Minnesota Twin Cities
Yichun He: Harvard University
Xinhe Zhang: Harvard University
Zuwan Lin: Harvard University
Sebastian Partarrieu: Harvard University
Emma Bou Hanna: Harvard University
Zhaolin Ren: Harvard University
Hao Shen: Harvard University
Yuhong Yang: University of Minnesota Twin Cities
Xiao Wang: Broad Institute of MIT and Harvard
Na Li: Harvard University
Jie Ding: University of Minnesota Twin Cities
Jia Liu: Harvard University

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: Abstract Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.

Date: 2023
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DOI: 10.1038/s41467-023-37477-x

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