Abstract
Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.
Highlights
Molecular biological high-throughput techniques have provided a great amount of diverse and informative gene expression data, currently available in genomic databases
We evaluated the performance of the Absolute value of the Pearson Correlation Coefficient (APCC) against two metrics, Non-linear Correlation coefficient based on Mutual Information (NCMI) and Normalized Mean Residue Similarity (NMRS)
We evaluated three similarity measures: the Absolute value of the Pearson Correlation Coefficient (APCC) (Zhang & Horvath, 2005), the Non-linear Correlation coefficient based on Mutual Information (NCMI) (Dionisio, Menezes & Mendes, 2004; Numata, Ebenhoh & Knapp, 2008) and the Normalized Mean Residue Similarity (NMRS) (Mahanta et al, 2012)
Summary
Molecular biological high-throughput techniques have provided a great amount of diverse and informative gene expression data, currently available in genomic databases. These data, if properly analyzed, allow for a better understanding of the biological processes in different organisms. The construction of functional gene networks that are based on gene expression data are termed gene co-expression networks (GCNs), which reflect information based on the relationships between genes (and/or the proteins they encode) that indicate a coordinated participation in a common biological process or pathway. GCNs predict functional annotations for genes whose function is unknown (Ficklin & Feltus, 2011). Some studies have confirmed through experimental validation that the predictions are accurate (Seo et al, 2011)
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