Dataset Information

Identification of metagenes and their interactions through large-scale analysis of Arabidopsis gene expression data.

ABSTRACT: Many plant genes have been identified through whole genome and deep transcriptome sequencing and other methods; yet our knowledge on the function of many of these genes remains limited. The integration and analysis of large gene-expression datasets gives researchers the ability to formalize hypotheses concerning the functionality and interaction between different groups of correlated genes.We applied the non-negative matrix factorization (NMF) algorithm to the AtGenExpress dataset which consists of 783 microarray samples (29 separate experimental series) conducted on the model plant Arabidopsis thaliana. We identified 15 metagenes, which are groups of genes with correlated expression. Functional roles of these metagenes are established by observing the enriched gene ontology (GO) categories using gene set enrichment analyses (GSEA). Activity levels of these metagenes in various experimental conditions are also analyzed to associate metagenes with stimuli/conditions. A metagene correlation network, constructed based on the results of NMF analysis, revealed many new interactions between the metagenes. Comparison of these metagenes with an earlier large-scale clustering analysis indicates many statistically significant overlaps.This study identifies a network of correlated metagenes composed of Arabidopsis genes acting in a highly correlated fashion across a broad spectrum of experimental stimuli, which may shed some light on the function of many of the un-annotated genes.

SUBMITTER: Wilson TJ

PROVIDER: S-EPMC3536586 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Identification of metagenes and their interactions through large-scale analysis of Arabidopsis gene expression data.

Wilson Tyler J TJ Lai Liming L Ban Yuguang Y Ge Steven X SX

BMC genomics 20120613

<h4>Background</h4>Many plant genes have been identified through whole genome and deep transcriptome sequencing and other methods; yet our knowledge on the function of many of these genes remains limited. The integration and analysis of large gene-expression datasets gives researchers the ability to formalize hypotheses concerning the functionality and interaction between different groups of correlated genes.<h4>Results</h4>We applied the non-negative matrix factorization (NMF) algorithm to the ...[more]

PMID: 22694750

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Project description:BACKGROUND: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. RESULTS: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. CONCLUSION: Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

Dataset Information

Identification of metagenes and their interactions through large-scale analysis of Arabidopsis gene expression data.

Publications

Identification of metagenes and their interactions through large-scale analysis of Arabidopsis gene expression data.

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