Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions.
Bioinformatics
; 31(11): 1816-23, 2015 Jun 01.
Article
em En
| MEDLINE
| ID: mdl-25638814
ABSTRACT
MOTIVATION Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time. RESULTS:
We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes. AVAILABILITY AND IMPLEMENTATION The Matlab source code is available at http//treschgroup.de/Genealogies.html.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Diferenciação Celular
/
Modelos Estatísticos
/
Imagem com Lapso de Tempo
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article