Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 May 19:10:1118824.
doi: 10.3389/fmed.2023.1118824. eCollection 2023.

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

Katelyn Queen  1 , My-Nhi Nguyen  1 , Frank D Gilliland  1 , Sung Chun  2 , Benjamin A Raby  2   3   4 , Joshua Millstein  1
Affiliations

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

Katelyn Queen et al. Front Med (Lausanne). .

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.

Keywords: asthma; asthma control; differential co-expression; gene expression; inflammation.

PubMed Disclaimer

Conflict of interest statement

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.

Figures

Figure 1
Figure 1
(A) The distribution of 6-month ACT scores in ABRIDGE Whole Blood gene expression, with scores being calculated with information about wheezing with and without exercise, patient waking due to wheezing, and the need for rescue medications in the last 6 months. (B) The distribution of 7-day ACT scores in CAMP Whole Blood gene expression, with scores being calculated with information about the need for rescue and preventative medications, activity limits, and patient waking due to wheezing in the past 7 days.
Figure 2
Figure 2
Violin plots for the most statistically significant gene-gene covariance measures (Equation 5) and 6-month ACT score components relationships for the ABRIDGE cohort, where each dot represents values for one patient. Kruskal–Wallis was used to test for global differences, and Wilcoxon signed-rank was used to test for pairwise differences. (A) IDO1 and ADORA3 covariance in 6-month frequency of waking from wheezing; (B) ALOX15 and ADORA3 covariance in 6-month Albuterol use; (C) ALOX15 and ADORA3 covariance in 6-month frequency of wheezing with exercising; (D) ALOX15 and ADORA3 covariance in 6-month frequency of waking from wheezing.
Figure 3
Figure 3
Violin plots for the most statistically significant gene-gene covariance measures (Equation 5) and 7-day ACT score components relationships for the CAMP cohort, where each dot represents values for one patient. Kruskal–Wallis was used to test for global differences, and Wilcoxon signed-rank was used to test for pairwise differences. (A) IDO1 and ADORA3 covariance in 7-day frequency of rescue bronchodilator use; (B) ALOX15 and ADORA3 covariance in 7-day frequency of rescue bronchodilator use; (C) ALOX15 and IDO1 covariance in 7-day activity limit.
Figure 4
Figure 4
Violin plots for comparing unadjusted (A) ADORA3, (B) ALOX15, and (C) IDO1 expression across 6-month ACT score levels in the ABRIDGE cohort.
Figure 5
Figure 5
Violin plots for comparing unadjusted (A) ADORA3, (B) ALOX15, and (C) IDO1 expression across 7-day ACT score levels in the CAMP cohort.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Chowdhury HA, Bhattacharyya DK, Kalita JK. (Differential) co-expression analysis of gene expression: a survey of best practices. IEEE/ACM Trans Comput Biol Bioinform. (2020) 17:1154–73. 10.1109/tcbb.2019.2893170 - DOI - PubMed
    1. Bhuva DD, Cursons J, Smyth GK, Davis MJ. Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer. Genome Biol. (2019) 20:236. 10.1186/s13059-019-1851-8 - DOI - PMC - PubMed
    1. Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinformatics. (2006) 7:509. 10.1186/1471-2105-7-509 - DOI - PMC - PubMed
    1. Widmann M. One-dimensional CCA and SVD, and their relationship to regression maps. J Clim. (2005) 18:2785–92. 10.1175/JCLI3424.1 - DOI
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. (1995) 57:289–300. 10.1111/j.2517-6161.1995.tb02031.x - DOI