Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10482562" target="_blank" >RIV/00064165:_____/24:10482562 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11110/24:10482562

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZpmYThhQIP" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZpmYThhQIP</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s44320-023-00003-8" target="_blank" >10.1038/s44320-023-00003-8</a>

Result language

angličtina

Original language name

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Original language description

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. 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30217 - Urology and nephrology

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Molecular Systems Biology

ISSN

1744-4292

e-ISSN

1744-4292

Volume of the periodical

20

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

57-74

UT code for WoS article

001219709500003

EID of the result in the Scopus database

2-s2.0-85184143907