Dataset Information

Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

ABSTRACT: BACKGROUND: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global "omic" scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. METHODOLOGY/PRINCIPAL FINDINGS: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. CONCLUSIONS/SIGNIFICANCE: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/.

SUBMITTER: Prieto C

PROVIDER: S-EPMC2597745 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

Prieto Carlos C Risueño Alberto A Fontanillo Celia C De las Rivas Javier J

PloS one 20081215 12

<h4>Background</h4>Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global "omic" scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well repo ...[more]

PMID: 19081792

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Project description:BACKGROUND:Jute (Corchorus spp.), belonging to the Malvaceae family, is an important natural fiber crop, second only to cotton, and a multipurpose economic crop. Corchorus capsularis L. is one of the only two commercially cultivated species of jute. Gene expression is spatiotemporal and is influenced by many factors. Therefore, to understand the molecular mechanisms of tissue development, it is necessary to study tissue-specific gene expression and regulation. We used weighted gene coexpression network analysis, to predict the functional roles of gene coexpression modules and individual genes, including those underlying the development of different tissue types. Although several transcriptome studies have been conducted on C. capsularis, there have not yet been any systematic and comprehensive transcriptome analyses for this species. RESULTS:There was significant variation in gene expression between plant tissues. Comparative transcriptome analysis and weighted gene coexpression network analysis were performed for different C. capsularis tissues at different developmental stages. We identified numerous tissue-specific differentially expressed genes for each tissue, and 12 coexpression modules, comprising 126 to 4203 genes, associated with the development of various tissues. There was high consistency between the genes in modules related to tissues, and the candidate upregulated genes for each tissue. Further, a gene network including 21 genes directly regulated by transcription factor OMO55970.1 was discovered. Some of the genes, such as OMO55970.1, OMO51203.1, OMO50871.1, and OMO87663.1, directly involved in the development of stem bast tissue. CONCLUSION:We identified genes that were differentially expressed between tissues of the same developmental stage. Some genes were consistently up- or downregulated, depending on the developmental stage of each tissue. Further, we identified numerous coexpression modules and genes associated with the development of various tissues. These findings elucidate the molecular mechanisms underlying the development of each tissue, and will promote multipurpose molecular breeding in jute and other fiber crops.

Project description:THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.

Dataset Information

Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

Publications

Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

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