Computational methods for the integrative analysis of single-cell data.
Brief Bioinform
; 22(1): 20-29, 2021 01 18.
Article
em En
| MEDLINE
| ID: mdl-32363378
ABSTRACT
Recent advances in single-cell technologies are providing exciting opportunities for dissecting tissue heterogeneity and investigating cell identity, fate and function. This is a pristine, exploding field that is flooding biologists with a new wave of data, each with its own specificities in terms of complexity and information content. The integrative analysis of genomic data, collected at different molecular layers from diverse cell populations, holds promise to address the full-scale complexity of biological systems. However, the combination of different single-cell genomic signals is computationally challenging, as these data are intrinsically heterogeneous for experimental, technical and biological reasons. Here, we describe the computational methods for the integrative analysis of single-cell genomic data, with a focus on the integration of single-cell RNA sequencing datasets and on the joint analysis of multimodal signals from individual cells.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
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Análise de Célula Única
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RNA-Seq
Limite:
Animals
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Humans
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article