Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

Elo, Laura L.; Järvenpää, Henna; Orešič, Matej; Lahesmaa, Riitta; Aittokallio, Tero

doi:10.1093/bioinformatics/btm309

Cited by 92 publications

(104 citation statements)

References 30 publications

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“…Previous DN analytical studies usually considered a single network feature to identify genes that show important changes in two networks (Choi et al, 2005;Elo et al, 2007;Fuller et al, 2007;Yu et al, 2011). However, whether a single feature is sufficient to detect all of the genes of interest remains unevaluated, considering that genes with different functional roles might have different connection patterns with other genes in a network.…”

Section: Multiple Features Versus a Single Feature For Dn Analysismentioning

confidence: 99%

“…Among the network analytical techniques that have recently been applied in biology, differential network (DN) analysis has shown robustness, which is evident in its ability to identify the DNA damage response genes in yeast (Bandyopadhyay et al, 2010;Califano, 2011), body weight-related genes in mice (Fuller et al, 2007;Gill et al, 2010), T cell differentiation-related genes in human (Elo et al, 2007), and human disease-relevant genes (Hudson et al, 2009;Amar et al, 2013). In contrast with DE analysis, which is a gene-centric analytic approach that assesses expression changes in individual genes, DN analysis is a networkcentric analytic approach that focuses on detecting the changes in a gene's associations with other genes via a comparison of two or more networks that were constructed under different experimental conditions (de la Fuente, 2010;Hudson et al, 2012;Ideker and Krogan, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast with DE analysis, which is a gene-centric analytic approach that assesses expression changes in individual genes, DN analysis is a networkcentric analytic approach that focuses on detecting the changes in a gene's associations with other genes via a comparison of two or more networks that were constructed under different experimental conditions (de la Fuente, 2010;Hudson et al, 2012;Ideker and Krogan, 2012). DN analysis has been validated to be complementary to traditional DE analysis and is especially effective in detecting biologically important genes that have less dramatic expression changes for certain experiments (Elo et al, 2007;Hudson et al, 2009;Southworth et al, 2009;de la Fuente, 2010). Currently, many methods and software systems have been developed for network inference based on gene expression data, but many technical issues have not been solved (Usadel et al, 2009;De Smet and Marchal, 2010;Marbach et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, how to distill millions of edges in a GCN (even in a small network constructed from a thousand genes) and select biologically significant associations has been regarded as a critical step (Usadel et al, 2009;Friedel et al, 2012;Alipanahi and Frey, 2013). Moreover, most DN analysis studies examine only one network characteristic (Carter et al, 2004;Elo et al, 2007;Liu et al, 2010;Yu et al, 2011), such as the "degree," which represents the number of connections of a gene to its directly connected genes; however, whether one characteristic is sufficient to identify all of the genes of interest remains to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

et al. 2014

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ORCID IDs: 0000-0001-9612-7898 (C.M.); 0000-0002-3306-5594 (M.X.); 0000-0002-6406-5597 (X.W.)Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stressresponsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

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Section: Multiple Features Versus a Single Feature For Dn Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

et al. 2014

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“…Aoki et al (2007) showed that the Arabidopsis coexpression network had a minimal network density at a cutoff r value ranging from 0.55 to 0.60. Using simulated data sets, Elo et al (2007) demonstrated that a cutoff value of r = 0.6 yielded both low error and high reproducibility.…”

Section: Discussionmentioning

confidence: 99%

Exploring Tomato Gene Functions Based on Coexpression Modules Using Graph Clustering and Differential Coexpression Approaches

Fukushima

Nishizawa²,

Hayakumo³

et al. 2012

Plant Physiology

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Gene-to-gene coexpression analysis provides fundamental information and is a promising approach for predicting unknown gene functions in plants. We investigated various associations in the gene expression of tomato (Solanum lycopersicum) to predict unknown gene functions in an unbiased manner. We obtained more than 300 microarrays from publicly available databases and our own hybridizations, and here, we present tomato coexpression networks and coexpression modules. The topological characteristics of the networks were highly heterogenous. We extracted 465 total coexpression modules from the data set by graph clustering, which allows users to divide a graph effectively into a set of clusters. Of these, 88% were assigned systematically by Gene Ontology terms. Our approaches revealed functional modules in the tomato transcriptome data; the predominant functions of coexpression modules were biologically relevant. We also investigated differential coexpression among data sets consisting of leaf, fruit, and root samples to gain further insights into the tomato transcriptome. We now demonstrate that (1) duplicated genes, as well as metabolic genes, exhibit a small but significant number of differential coexpressions, and (2) a reversal of gene coexpression occurred in two metabolic pathways involved in lycopene and flavonoid biosynthesis. Independent experimental verification of the findings for six selected genes was done using quantitative real-time polymerase chain reaction. Our findings suggest that differential coexpression may assist in the investigation of key regulatory steps in metabolic pathways. The approaches and results reported here will be useful to prioritize candidate genes for further functional genomics studies of tomato metabolism.

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Computational Imaging and Modeling for Systems Biology

Zhou

Wong

2010

Elements of Computational Systems Biology

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Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 92 publications

References 30 publications

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

Exploring Tomato Gene Functions Based on Coexpression Modules Using Graph Clustering and Differential Coexpression Approaches

Computational Imaging and Modeling for Systems Biology

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