Analysis of genetic association using hierarchical clustering and cluster validation indices.

Abstract:

:It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes.

journal_name

Genomics

journal_title

Genomics

authors

Pagnuco IA,Pastore JI,Abras G,Brun M,Ballarin VL

doi

10.1016/j.ygeno.2017.06.009

subject

Has Abstract

pub_date

2017-10-01 00:00:00

pages

438-445

issue

5-6

eissn

0888-7543

issn

1089-8646

pii

S0888-7543(17)30057-5

journal_volume

109

pub_type

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