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Linking co-expression modules with phenotypes

The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost d...

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Detalles Bibliográficos
Autores principales: Kumar, Rakesh, Ojha, Krishna Kumar, Yadav, Harlokesh Narayan, Singh, Vijay Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997497/
https://www.ncbi.nlm.nih.gov/pubmed/36909689
http://dx.doi.org/10.6026/97320630018438
Descripción
Sumario:The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status.