Interaction-based transcriptome analysis via differential network inference.
Brief Bioinform
; 23(6)2022 Nov 19.
Article
in English
| MEDLINE | ID: covidwho-2087743
ABSTRACT
Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Gene Regulatory Networks
/
COVID-19
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
Bib
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