Abstract
Purpose
Omics data are crucial for medical diagnosis as it contains intrinsic biomedical information. Multi-omics integrated analysis has become a new direction for scientists to explore life mechanisms. Nevertheless, the quality of complex omics data often varies greatly due to different samples or even different omics types, it is challenging to dynamically capture the uncertainty for different kinds of omics data.
Methods
This paper proposes a uncertainty-aware dynamic integration framework for multi-omics classification. The framework consists of three modules: deep embedding, confidence prediction, and downstream tasks. The deep embedding module extract key information from multi-omics data to obtain a low-dimensional feature representation which is used to train downstream tasks. Combined with the deep embedding module, the confidence prediction module is used to dynamically capture the uncertainty of the data. We introduce “confidNet” to assign a confidence value for each type of omics data, which is used for dynamic integration between multi-omics.
Results
Compared with other integration methods, the proposed method can contain more crucial biomedical information in the obtained low-dimensional representation. Our framework realizes reliable integration among multiple omics, and it can still achieve high accuracy on small sample data sets. We have verified the effectiveness of the model in a large number of experiments.
Conclusion
Our framework can be widely applied to high-dimensional omics data and has great potential to facilitate medical decision-making and biological analysis.
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Funding
This work was supported by the Project of State Key Laboratory of ASIC and System (No. 2021KF015); the National Natural Science Foundation of China (Grant nos. 61972456); Natural Science Foundation of Tianjin (No. 20JCYBJC00140; Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, P.R.China (KFKT-2020101).
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LD and CL conceived and designed research. CL collects data and conducted experiments. RW and JC coordinated the research. CL analyzed data and wrote the manuscript. All authors read, revised, and approved the manuscript.
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The TCGA data was download from https://xenabrowser.net/datapages/ The single-cell data was from the paper “Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer” (Cantini, Laura et al. 2021)
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This article does not contain any studies performed with human participants or animals by any of the authors.
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The authors declare no competing interests.
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Du, L., Liu, C., Wei, R. et al. Uncertainty-aware dynamic integration for multi-omics classification of tumors. J Cancer Res Clin Oncol 149, 3301–3312 (2023). https://doi.org/10.1007/s00432-022-04219-3
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DOI: https://doi.org/10.1007/s00432-022-04219-3