CINS: Cell Interaction Network inference from Single cell expression data.
PLoS Comput Biol
; 18(9): e1010468, 2022 09.
Article
em En
| MEDLINE
| ID: mdl-36095011
ABSTRACT
Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Perfilação da Expressão Gênica
/
Análise de Célula Única
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article