Network inference with Granger causality ensembles on single-cell transcriptomics.
Cell Rep
; 38(6): 110333, 2022 02 08.
Article
in En
| MEDLINE
| ID: mdl-35139376
ABSTRACT
Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cell Differentiation
/
Gene Expression Profiling
/
Gene Regulatory Networks
/
Transcriptome
Type of study:
Etiology_studies
Limits:
Animals
Language:
En
Journal:
Cell Rep
Year:
2022
Document type:
Article
Affiliation country:
United States