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Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-...

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Detalles Bibliográficos
Autores principales: Pandey, Kushagra, Zafar, Hamim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410915/
https://www.ncbi.nlm.nih.gov/pubmed/35639499
http://dx.doi.org/10.1093/nar/gkac412
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author Pandey, Kushagra
Zafar, Hamim
author_facet Pandey, Kushagra
Zafar, Hamim
author_sort Pandey, Kushagra
collection PubMed
description Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of ∼ millions of cells.
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spelling pubmed-94109152022-08-26 Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET Pandey, Kushagra Zafar, Hamim Nucleic Acids Res Methods Online Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of ∼ millions of cells. Oxford University Press 2022-05-25 /pmc/articles/PMC9410915/ /pubmed/35639499 http://dx.doi.org/10.1093/nar/gkac412 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Pandey, Kushagra
Zafar, Hamim
Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title_full Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title_fullStr Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title_full_unstemmed Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title_short Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
title_sort inference of cell state transitions and cell fate plasticity from single-cell with margaret
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410915/
https://www.ncbi.nlm.nih.gov/pubmed/35639499
http://dx.doi.org/10.1093/nar/gkac412
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