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Linking co-expression modules with phenotypes


Rakesh Kumar1, Niharika1, Krishna Kumar Ojha1, Harlokesh Narayan Yadav2 & Vijay Kumar Singh1,*



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



Vijay Kumar Singh - Email: vksingh@cub.ac.in


Article Type

Research Article



Received March 1, 2022; Revised April 30, 2022; Accepted April 30, 2022, Published April 30, 2022



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.



Analysis of rank, co-expression, gene module, gene expression, network



Kumar et al. Bioinformation 18(4): 438-441 (2022)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.