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• W4385978018 abstract "The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives." @default.
• W4385978018 created "2023-08-19" @default.
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• W4385978018 date "2023-08-01" @default.
• W4385978018 modified "2023-10-17" @default.
• W4385978018 title "Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures" @default.
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• W4385978018 doi "https://doi.org/10.1016/j.crmeth.2023.100563" @default.
• W4385978018 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37671028" @default.
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