JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.
PLoS Comput Biol
; 17(3): e1008804, 2021 03.
Article
em En
| MEDLINE
| ID: mdl-33724985
ABSTRACT
With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have become critical. Typically, single-cell datasets from different technologies cannot be directly combined or concatenated, due to the innate difference in the data, such as the number of measured parameters and the distributions. Even datasets generated by the same technology are often affected by the batch effect. A computational approach for aligning different datasets and hence identifying related clusters will be useful for data integration and interpretation in large scale single-cell experiments. Our proposed algorithm called JSOM, a variation of the Self-organizing map, aligns two related datasets that contain similar clusters, by constructing two maps-low-dimensional discretized representation of datasets-that jointly evolve according to both datasets. Here we applied the JSOM algorithm to flow cytometry, mass cytometry, and single-cell RNA sequencing datasets. The resulting JSOM maps not only align the related clusters in the two datasets but also preserve the topology of the datasets so that the maps could be used for further analysis, such as clustering.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Biologia Computacional
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Aprendizado de Máquina não Supervisionado
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos