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- Title
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).
- Authors
Joodaki, Mehdi; Shaigan, Mina; Parra, Victor; Bülow, Roman D; Kuppe, Christoph; Hölscher, David L; Cheng, Mingbo; Nagai, James S; Goedertier, Michaël; Bouteldja, Nassim; Tesar, Vladimir; Barratt, Jonathan; Roberts, Ian SD; Coppo, Rosanna; Kramann, Rafael; Boor, Peter; Costa, Ivan G
- Abstract
Although 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. Synopsis: PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients. It allows the estimation of sample-level clustering and trajectories Statistical methods allow the interpretation of results, i.e., association of clusters/trajectories with cell clusters, genes and tissue structures. PILOT is showcased in scRNA-seq of myocardial infarction and pathomics data of kidney IgA nephropathy. PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients.
- Subjects
GENOMICS; IGA glomerulonephritis; MYOCARDIAL infarction; GENE expression; CELL populations; NUTRITIONAL genomics
- Publication
Molecular Systems Biology, 2024, Vol 20, Issue 2, p57
- ISSN
1744-4292
- Publication type
Article
- DOI
10.1038/s44320-023-00003-8