Dataset Information

A new gene selection procedure based on the covariance distance.

ABSTRACT:

Motivation

Very little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance.

Results

We apply the proposed method, along with two alternative methods, to several simulated datasets and find out that our method is much more powerful than the other two. For biological data, we demonstrate that the analysis of differentially associated genes complements the analysis of differentially expressed genes. Combining both procedures provides a more comprehensive functional interpretation of the experimental results.

Availability

The code is downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hu R

PROVIDER: S-EPMC2815661 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Publications

A new gene selection procedure based on the covariance distance.

Hu Rui R Qiu Xing X Glazko Galina G

Bioinformatics (Oxford, England) 20091208 3

<h4>Motivation</h4>Very little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance.<h4>Results</h4>We apply the proposed method, along with two alternative metho ...[more]

PMID: 19996162

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Project description:The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalised normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices (ECMs) that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.

Dataset Information

A new gene selection procedure based on the covariance distance.

Motivation

Results

Availability

Supplementary information

Publications

A new gene selection procedure based on the covariance distance.

Similar Datasets

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