ACDC：一种检测与表型或暴露相关的共表达的通用方法。
ACDC: a general approach for detecting phenotype or exposure associated co-expression.
作者信息
Queen Katelyn, Nguyen My-Nhi, Gilliland Frank D, Chun Sung, Raby Benjamin A, Millstein Joshua
机构信息
Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Division of Pulmonary Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
出版信息
Front Med (Lausanne). 2023 May 19;10:1118824. doi: 10.3389/fmed.2023.1118824. eCollection 2023.
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.
背景
现有的基于模块的差异共表达方法通过测试转录丰度的一致变化来识别跨表型或暴露结构的基因-基因关系差异。当前方法仅允许评估单一、二元或分类暴露或表型的共表达变化，限制了从这些分析中可获得的信息。
方法
在此，我们提出一种检测差异共表达的新方法，该方法同时适用于具有二元、有序或连续数据类型的多种表型或暴露。
结果
我们报告了该方法在两组哮喘控制水平不同的哮喘患者队列中的应用，以识别基因共表达与哮喘控制测试分数之间的关联。结果表明，ADORA3、ALOX15和IDO1的表达水平及协方差均与哮喘控制相关。
结论
ACDC是对现有方法的灵活扩展，能够检测不同外部变量间的差异共表达。
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