Linking co-expression modules with phenotypes

Rakesh Kumar; Krishna Kumar Ojha; Harlokesh Narayan Yadav; Vijay Kumar Singh

doi:10.6026/97320630018438

Linking co-expression modules with phenotypes

Bioinformation. 2022 Apr 30;18(4):438-441. doi: 10.6026/97320630018438. eCollection 2022.

Authors

Rakesh Kumar¹, Krishna Kumar Ojha¹, Harlokesh Narayan Yadav², Vijay Kumar Singh¹

Affiliations

¹ Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar 824236, India.
² Department of Pharmacology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi - 110029, India.

Abstract

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.

Keywords: Analysis of rank; co-expression; gene expression; gene module; network.