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. 2022 Jul 5;23(4):bbac246. doi: 10.1093/bib/bbac246

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© The Author(s) 2022. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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The PathMe approach modified from figure in Ref. 83. Multiomics features mapped to a particular pathway are summarized into pathway-level scores via a sparse denoising multimodal autoencoder architecture. Hidden layer 1 consists of up to hidden units for each omics modality, where p_j represents the number of features of omics type j. The hidden units of omics modality j are tightly connected to the input features of the same omics type; however, there are no connections from the input features of the other data modalities. Hidden layer 2 comprises one hidden unit that represents the overall score of the multiomics pathway. Consensus sparse NMF clustering can be applied in subsequent steps by concatenating the P multiomics pathway scores of each patient.

Inline graphic — The PathMe approach modified from figure in Ref. 83. Multiomics features mapped to a particular pathway are summarized into pathway-level scores via a sparse denoising multimodal autoencoder architecture. Hidden layer 1 consists of up to hidden units for each omics modality, where p_j represents the number of features of omics type j. The hidden units of omics modality j are tightly connected to the input features of the same omics type; however, there are no connections from the input features of the other data modalities. Hidden layer 2 comprises one hidden unit that represents the overall score of the multiomics pathway. Consensus sparse NMF clustering can be applied in subsequent steps by concatenating the P multiomics pathway scores of each patient.