Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)
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Published:2023-12-19
Issue:2
Volume:20
Page:57-74
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ISSN:1744-4292
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Container-title:Molecular Systems Biology
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language:en
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Short-container-title:Mol Syst Biol
Author:
Joodaki Mehdi, Shaigan MinaORCID, Parra Victor, Bülow Roman DORCID, Kuppe ChristophORCID, Hölscher David LORCID, Cheng MingboORCID, Nagai James SORCID, Goedertier Michaël, Bouteldja Nassim, Tesar VladimirORCID, Barratt Jonathan, Roberts Ian SDORCID, Coppo Rosanna, Kramann RafaelORCID, Boor PeterORCID, Costa Ivan GORCID
Abstract
AbstractAlthough clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.
Funder
Deutsche Forschungsgemeinschaft Bundesministerium für Bildung und Forschung EC | ERC | HORIZON EUROPE European Research Council
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems
Reference58 articles.
1. Albergante L, Mirkes E, Bac J, Chen H, Martin A, Faure L, Barillot E, Pinello L, Gorban A, Zinovyev A (2020) Robust and scalable learning of complex intrinsic dataset geometry via ElPiGraph. Entropy 3:296 2. Baghy K, Dezso K, László V, Fullár A, Péterfia B, Paku S, Nagy P, Schaff Z, Iozzo RV, Kovalszky I (2011) Ablation of the decorin gene enhances experimental hepatic fibrosis and impairs hepatic healing in mice. Lab Invest 3:439–451 3. Bonneel N, Van De Panne M, Paris S, Heidrich W (2011) Displacement interpolation using Lagrangian mass transport. In: Proceedings of the 2011 SIGGRAPH Asia conference, pp 1–12 4. Bülow RD, Hölscher DL, Costa IG, Boor P (2023) Extending the landscape of omics technologies by pathomics. npj Syst Biol Appl 1:38 5. Berry T, Harlim J (2016) Variable bandwidth diffusion kernels. Appl Comput Harmon Anal 1:68–96
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