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Bioinformatics Advance Access published online on February 24, 2006
Bioinformatics, 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
Received July 21, 2005
Revised December 30, 2005
Accepted February 8, 2006

Article

Algorithm to find gene expression profiles of de-egulation 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


  Abstract

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 de-regulation states in the genomic expression profiles will help to better identify disease-altered genes.

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 APL.

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

Associate Editor: Alfonso Valencia
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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
Bioinformatics, July 1, 2008; 24(13): i390 - i398.
[Abstract] [Full Text] [PDF]


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