| Autor: |
Mehdi Joodaki, Mina Shaigan, Victor Parra, Roman D. Buelow, Christoph Kuppe, David L. Holscher, Mingbo Cheng, James S. Nagai, Nassim Bouteldja, Vladimir Tesar, Jonathan Barratt, Ian S. D. Roberts, Rosanna Coppo, Rafael Kramann, Peter Boor, Ivan G. Costa |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Zdroj: |
Cold Spring Harbor : Cold Spring Harbor Laboratory, bioRxiv : the preprint server for biology [1]-24 (2022). doi:10.1101/2022.12.16.520739 |
| Popis: |
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and 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 and 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 and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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