Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/86020
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Type: Journal article
Title: Building gene co-expression networks using transcriptomics data for systems biology investigations: comparison of methods using microarray data
Author: Kadarmideen, H.
Watson-Haigh, N.
Citation: Bioinformation, 2012; 8(18):855-861
Publisher: Biomedical Informatics
Issue Date: 2012
ISSN: 0973-8894
0973-2063
Statement of
Responsibility: 
Haja N Kadarmideen, Nathan S Watson-haigh
Abstract: Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended.
Rights: © 2012 Biomedical Informatics. This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
RMID: 0030007893
DOI: 10.6026/97320630008855
Appears in Collections:Australian Centre for Plant Functional Genomics publications

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