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Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE
Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological sy...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196363/ https://www.ncbi.nlm.nih.gov/pubmed/21964415 http://dx.doi.org/10.1038/nbt.1991 |
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author | Qiu, Peng Simonds, Erin F. Bendall, Sean C. Gibbs, Kenneth D. Bruggner, Robert V. Linderman, Michael D. Sachs, Karen Nolan, Garry P. Plevritis, Sylvia K. |
author_facet | Qiu, Peng Simonds, Erin F. Bendall, Sean C. Gibbs, Kenneth D. Bruggner, Robert V. Linderman, Michael D. Sachs, Karen Nolan, Garry P. Plevritis, Sylvia K. |
author_sort | Qiu, Peng |
collection | PubMed |
description | Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system under investigation. To objectively uncover cellular heterogeneity from single-cell measurements, we present a novel computational approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE). We applied SPADE to cytometry data of mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. In addition, SPADE produced a map of intracellular signal activation across the landscape of human hematopoietic development. SPADE revealed a functionally distinct cell population, natural killer (NK) cells, without using any NK-specific parameters. SPADE is a versatile method that facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations. |
format | Online Article Text |
id | pubmed-3196363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
record_format | MEDLINE/PubMed |
spelling | pubmed-31963632012-04-02 Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE Qiu, Peng Simonds, Erin F. Bendall, Sean C. Gibbs, Kenneth D. Bruggner, Robert V. Linderman, Michael D. Sachs, Karen Nolan, Garry P. Plevritis, Sylvia K. Nat Biotechnol Article Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system under investigation. To objectively uncover cellular heterogeneity from single-cell measurements, we present a novel computational approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE). We applied SPADE to cytometry data of mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. In addition, SPADE produced a map of intracellular signal activation across the landscape of human hematopoietic development. SPADE revealed a functionally distinct cell population, natural killer (NK) cells, without using any NK-specific parameters. SPADE is a versatile method that facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations. 2011-10-02 /pmc/articles/PMC3196363/ /pubmed/21964415 http://dx.doi.org/10.1038/nbt.1991 Text en Users may view, print, copy, download and 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 Qiu, Peng Simonds, Erin F. Bendall, Sean C. Gibbs, Kenneth D. Bruggner, Robert V. Linderman, Michael D. Sachs, Karen Nolan, Garry P. Plevritis, Sylvia K. Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title | Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title_full | Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title_fullStr | Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title_full_unstemmed | Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title_short | Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE |
title_sort | extracting a cellular hierarchy from high-dimensional cytometry data with spade |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196363/ https://www.ncbi.nlm.nih.gov/pubmed/21964415 http://dx.doi.org/10.1038/nbt.1991 |
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