Bayesian information sharing enhances detection of regulatory associations in rare cell types.
Bioinformatics
; 37(Suppl_1): i349-i357, 2021 07 12.
Article
en En
| MEDLINE
| ID: mdl-34252956
ABSTRACT
MOTIVATION Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. RESULTS:
We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION The code for ShareNet is available at http//sharenet.csail.mit.edu and https//github.com/alexw16/sharenet.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Análisis de la Célula Individual
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos