Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs.
Brief Bioinform
; 22(4)2021 07 20.
Article
in English
| MEDLINE | ID: covidwho-1003509
ABSTRACT
Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Cellular Senescence
/
Gene Regulatory Networks
/
COVID-19
/
Lung
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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