Dataset Information

ACDC: a general approach for detecting phenotype or exposure associated co-expression.

ABSTRACT:

Background

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

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

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

ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.

SUBMITTER: Queen K

PROVIDER: S-EPMC10235619 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

ACDC: a general approach for detecting phenotype or exposure associated co-expression.

Queen Katelyn K Nguyen My-Nhi MN Gilliland Frank D FD Chun Sung S Raby Benjamin A BA Millstein Joshua J

Frontiers in medicine 20230519

<h4>Background</h4>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.<h4>Methods</h4>Here, we propose a novel approach for detection of differen ...[more]

PMID: 37275375

Similar Datasets

Project description:BackgroundCo-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer).MethodsCo-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks. Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as "bottlenecks" rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth.ConclusionIdentifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.

Dataset Information

ACDC: a general approach for detecting phenotype or exposure associated co-expression.

Background

Methods

Results

Conclusion

Publications

ACDC: a general approach for detecting phenotype or exposure associated co-expression.

Similar Datasets

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