ArticleMolecular systems biology2024
Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).
Article in Molecular systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.Bioinformatics (Oxford, England) · 2026Article
- scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes.bioRxiv : the preprint server for biology · 2026Article
- MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- Optimal transport analysis of high-dimensional flow cytometry data in immuno-oncology.Frontiers in immunology · 2026Article
- PILOT-GM-VAE: patient-level analysis of single-cell disease atlas with optimal transport of Gaussian mixture variational autoencoders.Briefings in bioinformatics · 2025Article
- Article
- scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.Bioinformatics (Oxford, England) · 2025Article
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- Biases in machine-learning models of human single-cell data.Nature cell biology · 2025Review
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics.Briefings in bioinformatics · 2024Article
- Visualizing scRNA-Seq data at population scale with GloScope.Genome biology · 2024Article
- Visualizing scRNA-Seq Data at Population Scale with GloScope.bioRxiv : the preprint server for biology · 2024Article
- QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport.bioRxiv : the preprint server for biology · 2024Article
- Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease.eLife · 2023Article
- scLKME: A Landmark-based Approach for Generating Multi-cellular Sample Embeddings from Single-cell Data.bioRxiv : the preprint server for biology · 2023Article
- Extending the landscape of omics technologies by pathomics.NPJ systems biology and applications · 2023Article
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17 authors.
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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.
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