Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data

López‐Kleine, Liliana; Leal, Luis; López, Carolina Martínez

doi:10.1093/bfgp/elt003

Cited by 46 publications

(33 citation statements)

References 65 publications

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“…37,38 To control for the indirect effects, we employed the approach developed by Schafer and Strimmer 36 based on the graphical Gaussian model (GGM) 39 to reconstruct a GGM network. In this network, each link indicated a partial correlation between two genes that remained after removing the effects of other genes.…”

Section: Coordinated Expression Of Mtdna Genesmentioning

confidence: 99%

Population-level expression variability of mitochondrial DNA-encoded genes in humans

et al. 2014

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Human mitochondria contain multiple copies of a circular genome made up of double-stranded DNA (mtDNA) that encodes proteins involved in cellular respiration. Transcript abundance of mtDNA-encoded genes varies between human individuals, yet the level of variation in the general population has not been systematically assessed. In the present study, we revisited large-scale RNA sequencing data generated from lymphoblastoid cell lines of HapMap samples of European and African ancestry to estimate transcript abundance and quantify expression variation for mtDNA-encoded genes. In both populations, we detected up to over 100-fold difference in mtDNA gene expression between individuals. The marked variation was not due to differences in mtDNA copy number between individuals, but was shaped by the transcription of hundreds of nuclear genes. Many of these nuclear genes were co-expressed with one another, resulting in a module-enriched co-expression network. Significant correlations in expression between genes of the mtDNA and nuclear genomes were used to identify factors involved with the regulation of mitochondrial functions. In conclusion, we determined the baseline amount of variability in mtDNA gene expression in general human populations and cataloged a complete set of nuclear genes whose expression levels are correlated with those of mtDNA-encoded genes. Our findings will enable the integration of information from both mtDNA and nuclear genetic systems, and facilitate the discovery of novel regulatory pathways involving mitochondrial functions.

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Section: Coordinated Expression Of Mtdna Genesmentioning

confidence: 99%

Population-level expression variability of mitochondrial DNA-encoded genes in humans

et al. 2014

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“…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). In the gene coexpression network (GCN), the connection of two genes is usually established based on the correlation coefficient of their expression profiles, which does not necessarily indicate a direct physical or regulatory interaction but is instead a reflection of a potential functional association between the two genes (Horvath and Dong, 2008;López-Kleine et al, 2013). 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).…”

Section: Introductionmentioning

confidence: 99%

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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“…Accumulation of publicly available, genome-wide gene expression data from DNA microarrays in plants has proved useful for defining correlated expression patterns between genes using pairwise similarity metrics such as Pearson’s correlation coefficient, r , and subsequent genome-scale reconstruction of gene co-expression networks (GCN) [4,5]. Genes are usually represented as ‘nodes’, whilst the lines linking individual nodes, or ‘edges’, represent pairwise relationships between nodes.…”

Section: Introductionmentioning

confidence: 99%

Annotation of gene function in citrus using gene expression information and co-expression networks

2014

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BackgroundThe genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed.ResultsWe have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit.ConclusionsIntegration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citrus.

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Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data

Cited by 46 publications

References 65 publications

Population-level expression variability of mitochondrial DNA-encoded genes in humans

Population-level expression variability of mitochondrial DNA-encoded genes in humans

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

Annotation of gene function in citrus using gene expression information and co-expression networks

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