SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Autor: Rahimikollu J; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA., Xiao H; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA., Rosengart A; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA., Rosen ABI; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA., Tabib T; Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA., Zdinak PM; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA., He K; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA., Bing X; Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada., Bunea F; Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA., Wegkamp M; Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.; Department of Mathematics, Cornell University, Ithaca, NY, USA., Poholek AC; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA. poholeka@pitt.edu., Joglekar AV; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. joglekar@pitt.edu., Lafyatis RA; Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA. lafyatis@pitt.edu., Das J; Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. jishnu@pitt.edu.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 May; Vol. 21 (5), pp. 835-845. Date of Electronic Publication: 2024 Feb 19.
DOI: 10.1038/s41592-024-02175-z
Abstrakt: Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
(© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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