PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.
Genome Biol
; 20(1): 59, 2019 03 19.
Article
em En
| MEDLINE
| ID: mdl-30890159
ABSTRACT
Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https//github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Gráficos por Computador
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Análise de Sequência de RNA
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Regulação da Expressão Gênica no Desenvolvimento
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Biologia Computacional
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Análise de Célula Única
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Sequenciamento de Nucleotídeos em Larga Escala
Limite:
Animals
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Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article