ACDC: a general approach for detecting phenotype or exposure associated co-expression.
asthma
asthma control
differential co-expression
gene expression
inflammation
Journal
Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047
Informations de publication
Date de publication:
2023
2023
Historique:
received:
08
12
2022
accepted:
02
05
2023
medline:
5
6
2023
pubmed:
5
6
2023
entrez:
5
6
2023
Statut:
epublish
Résumé
Existing module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses. Here, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. We report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control. ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.
Sections du résumé
Background
UNASSIGNED
Existing module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses.
Methods
UNASSIGNED
Here, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types.
Results
UNASSIGNED
We report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control.
Conclusion
UNASSIGNED
ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.
Identifiants
pubmed: 37275375
doi: 10.3389/fmed.2023.1118824
pmc: PMC10235619
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1118824Subventions
Organisme : NCI NIH HHS
ID : P01 CA196569
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL118455
Pays : United States
Informations de copyright
Copyright © 2023 Queen, Nguyen, Gilliland, Chun, Raby and Millstein.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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