SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data.
Brief Bioinform
; 22(4)2021 07 20.
Article
em En
| MEDLINE
| ID: mdl-33230549
ABSTRACT
Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https//github.com/xiaoyulu95/SSMD.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Antígenos de Diferenciação
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Biologia Computacional
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Perfilação da Expressão Gênica
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Bases de Dados Genéticas
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Transcriptoma
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Microambiente Celular
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
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INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article