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Characterization of cell fate probabilities in single-cell data with Palantir

Single-cell RNA sequencing 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 by treating cell fate as a p... Full description

Journal Title: Nature biotechnology 2019-04, Vol.37 (4), p.451-460
Main Author: Setty, Manu
Other Authors: Kiseliovas, Vaidotas , Levine, Jacob , Gayoso, Adam , Mazutis, Linas , Pe'er, Dana
Format: Electronic Article Electronic Article
Language: English
Subjects:
RNA
Publisher: United States: Nature Publishing Group
ID: ISSN: 1087-0156
Link: https://www.ncbi.nlm.nih.gov/pubmed/30899105
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7549125
title: Characterization of cell fate probabilities in single-cell data with Palantir
format: Article
creator:
  • Setty, Manu
  • Kiseliovas, Vaidotas
  • Levine, Jacob
  • Gayoso, Adam
  • Mazutis, Linas
  • Pe'er, Dana
subjects:
  • Algorithms
  • Animals
  • Biotechnology
  • Bone marrow
  • Bone Marrow Cells - cytology
  • Bone Marrow Cells - metabolism
  • Cell Differentiation - genetics
  • Cell fate
  • Cell Lineage - genetics
  • Differentiation
  • Entropy
  • Erythropoiesis - genetics
  • Gene expression
  • Gene Expression Regulation, Developmental
  • Gene sequencing
  • Hematopoiesis - genetics
  • Hemopoiesis
  • Humans
  • Markov Chains
  • Mice
  • Models, Biological
  • Models, Statistical
  • Plastic properties
  • Plasticity
  • Ribonucleic acid
  • RNA
  • Sequence Analysis, RNA - statistics & numerical data
  • Single-Cell Analysis - statistics & numerical data
  • Statistical analysis
  • Trajectories
  • Transcription factors
ispartof: Nature biotechnology, 2019-04, Vol.37 (4), p.451-460
description: Single-cell RNA sequencing 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 by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time 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 single-cell RNA sequencing 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, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
language: eng
source:
identifier: ISSN: 1087-0156
fulltext: no_fulltext
issn:
  • 1087-0156
  • 1546-1696
url: Link


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titleCharacterization of cell fate probabilities in single-cell data with Palantir
creatorSetty, Manu ; Kiseliovas, Vaidotas ; Levine, Jacob ; Gayoso, Adam ; Mazutis, Linas ; Pe'er, Dana
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descriptionSingle-cell RNA sequencing 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 by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time 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 single-cell RNA sequencing 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, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
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subjectAlgorithms ; Animals ; Biotechnology ; Bone marrow ; Bone Marrow Cells - cytology ; Bone Marrow Cells - metabolism ; Cell Differentiation - genetics ; Cell fate ; Cell Lineage - genetics ; Differentiation ; Entropy ; Erythropoiesis - genetics ; Gene expression ; Gene Expression Regulation, Developmental ; Gene sequencing ; Hematopoiesis - genetics ; Hemopoiesis ; Humans ; Markov Chains ; Mice ; Models, Biological ; Models, Statistical ; Plastic properties ; Plasticity ; Ribonucleic acid ; RNA ; Sequence Analysis, RNA - statistics & numerical data ; Single-Cell Analysis - statistics & numerical data ; Statistical analysis ; Trajectories ; Transcription factors
ispartofNature biotechnology, 2019-04, Vol.37 (4), p.451-460
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descriptionSingle-cell RNA sequencing 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 by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time 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 single-cell RNA sequencing 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, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
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0M.S and D.P. conceived the study, designed and developed Palantir, developed additional analysis methods, analyzed the data and wrote the manuscript. M.S implemented Palantir and all other analysis methods. V.K. and L.M. designed, optimized and executed all single cell RNA-seq experiments. J.L and D.P developed an early theory on application of Markov chains to single cell data. M.S and A.G developed trend-based clustering analysis.
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abstractSingle-cell RNA sequencing 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 by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time 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 single-cell RNA sequencing 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, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
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