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Bioinformatics Advance Access published online on September 1, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl455
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 11, 2006
Revised August 18, 2006
Accepted August 18, 2006

Article

A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

A. Callegaro 1, D. Basso 1, and S. Bicciato 1 *

1 Department of Chemical Process Engineering, University of Padua, Via Marzolo 9, I-35131, Padua, Italy

* To whom correspondence should be addressed.
S. Bicciato, E-mail: silvio.bicciato{at}unipd.it


  Abstract

Motivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations, and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.

Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors.

Availability: Functions in R for implementing the LAP method are available at http://www.dpci.unipd.it/Bioeng/Publications/LAP.htm.

Supplementary Information: http://www.dpci.unipd.it/Bioeng/Publications/LAP.htm.

Associate Editor: John Quackenbush
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