Cargando…
Characterization of cell fate probabilities in single-cell data with Palantir
Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fa...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549125/ https://www.ncbi.nlm.nih.gov/pubmed/30899105 http://dx.doi.org/10.1038/s41587-019-0068-4 |
_version_ | 1783592741610979328 |
---|---|
author | Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe’er, Dana |
author_facet | Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe’er, Dana |
author_sort | Setty, Manu |
collection | PubMed |
description | Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow scRNA-seq data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems. |
format | Online Article Text |
id | pubmed-7549125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-75491252020-10-12 Characterization of cell fate probabilities in single-cell data with Palantir Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe’er, Dana Nat Biotechnol Article Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow scRNA-seq data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems. 2019-03-21 2019-04 /pmc/articles/PMC7549125/ /pubmed/30899105 http://dx.doi.org/10.1038/s41587-019-0068-4 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe’er, Dana Characterization of cell fate probabilities in single-cell data with Palantir |
title | Characterization of cell fate probabilities in single-cell data with Palantir |
title_full | Characterization of cell fate probabilities in single-cell data with Palantir |
title_fullStr | Characterization of cell fate probabilities in single-cell data with Palantir |
title_full_unstemmed | Characterization of cell fate probabilities in single-cell data with Palantir |
title_short | Characterization of cell fate probabilities in single-cell data with Palantir |
title_sort | characterization of cell fate probabilities in single-cell data with palantir |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549125/ https://www.ncbi.nlm.nih.gov/pubmed/30899105 http://dx.doi.org/10.1038/s41587-019-0068-4 |
work_keys_str_mv | AT settymanu characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir AT kiseliovasvaidotas characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir AT levinejacob characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir AT gayosoadam characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir AT mazutislinas characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir AT peerdana characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir |