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Bioinformatics Advance Access originally published online on February 24, 2006
Bioinformatics 2006 22(9):1103-1110; doi:10.1093/bioinformatics/btl053
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes

C. Prieto 1, M.J. Rivas 2, J.M. Sánchez 2, J. López-Fidalgo 2 and J. De Las Rivas 1,*

1 Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC) Salamanca, Spain
2 Department of Statistics, Faculty of Science (USAL) Salamanca, Spain

*To whom correspondence should be addressed.

Motivation: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better.

Results: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.

Availability: The method is implemented in an R package called AlteredExpression available in http://bioinfow.dep.usal.es/Altered-Expression/ and will be included in the Bioconductor project.

Contact: jrivas{at}usal.es

Received on July 21, 2005; revised on December 30, 2005; accepted on February 8, 2006

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J. W.K. Ho, M. Stefani, C. G. dos Remedios, and M. A. Charleston
Differential variability analysis of gene expression and its application to human diseases
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[Abstract] [Full Text] [PDF]


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