By linking differential gene expression to the chromosomal localization of genes, one can investigate microarray data for characteristic patterns of expression phenomena involving sizeable parts of specific chromosomes. We have implemented a statistical approach for identifying significantly differentially expressed chromosome regions. We demonstrate the applicability of the approach on a publicly available data set on acute lymphocytic leukemia

Georgi, B.

Heinig, M.

Roepcke, S.

Schmeier, S.

Toedling, J.

English

Toedling, Joern

Schmeier, Sebastian

Heinig, Matthias

Georgi, Benjamin

Roepcke, Stefan

Max Planck Society eDoc Server

MACAT—microarray chromosome analysis tool

By linking differential gene expression to the chromosomal localization of genes, one can investigate microarray data for characteristic patterns of expression phenomena involving sizeable parts of specific chromosomes. We have implemented a statistical approach for identifying significantly differentially expressed chromosome regions. We demonstrate the applicability of the approach on a publicly available data set on acute lymphocytic leukemia. Availability: The R-package MACAT can be obtained from http://www.compdiag.molgen.mpg.de/software/macat.shtm

MPG.PuRe

MACAT--microarray chromosome analysis tool

BIOINFORMATICS APPLICATIONS NOTE Vol. 21 no. 9 2005, pages 2112–2113doi:10.1093/bioinformatics/bti183Gene expressionMACAT—microarray chromosome analysis toolJoern Toedling, Sebastian Schmeier, Matthias Heinig, Benjamin Georgi andStefan Roepcke∗Freie Universitaet Berlin, Bioinformatics programme and Max Planck Institute for Molecular Genetics, Ihnestr. 73,D-14195 Berlin, GermanyReceived on October 4, 2004; revised on November 15, 2004; accepted on November 24, 2004Advance Access publication November 30, 2004ABSTRACTSummary: By linking differential gene expression to the chromosomallocalization of genes, one can investigate microarray data forcharacteristic patterns of expression phenomena involving sizeableparts of specific chromosomes. We have implemented a statisticalapproach for identifying significantly differentially expressed chromo-some regions. We demonstrate the applicability of the approach on apublicly available data set on acute lymphocytic leukemia.Availability: The R-package MACAT can be obtained fromhttp://www.compdiag.molgen.mpg.de/software/macat.shtmlContact: roepcke@molgen.mpg.deSupplementary information: http://www.compdiag.molgen.mpg.de/software/macat.shtmlMicroarray data analysts have defined tumor subtypes by specificgene expression profiles, consisting of genes that show differentialexpression between subtypes (Yeoh et al., 2002). However, tumorsubtypes have also been characterized by phenomena involving largechromosomal regions. For instance, Christiansen et al. (2004) reporton a subtype of acute myeloid leukemia, showing mutations in theAML1 gene on chromosome 21 along with deletions or loss of chro-mosome arm 7q. A natural approach to bridging the gap betweenthese two paradigms is to link scoring for differential gene expressionto the chromosomal localization of genes. Tumor subtypes can bedefined by differential expression patterns affecting sizeable regionsof certain chromosomes.To assist in the identification of significantly differentiallyexpressed chromosome regions, we provide the implementation ofa statistical approach. MACAT is written in the R statistical pro-gramming language and is part of the developmental branch of thepopular Bioconductor package (Gentleman et al., 2004). We assumenormalized expression data, which can be provided as a matrix orexpression set in R or as a delimited text file. In a preprocessing stepthe expression data is integrated with gene location data into onecommon data format. To date, this step has only been implementedfor commercial Affymetrix® oligo-nucleotide microarrays.For each gene, we compute a statistic denoting the degree of differ-ential expression between two groups of samples. This statistic is theregularized t-score introduced in Tusher et al. (2001). In essence, itis Student’s t-statistic augmented by a fudge factor s0 in the denom-inator, which prevents a high statistic for genes with a low variance.∗To whom correspondence should be addressed.We set s0 to the median over all gene standard deviations, analogousto Tibshirani et al. (2002).The distribution of measured genes is not uniform over the lengthof the chromosome. Since we want to evaluate differential expressionover the whole chromosome, we interpolate the statistic for posi-tions between measured genes. This interpolation, however, doesnot aim to assign statistics to non-coding regions, but to provide asmooth estimate of differential expression over large chromosomalregions.The following kernel functions are used for interpolation:• k-nearest neighbor: For every chromosomal coordinate computethe average of the k nearest genes.• Radial basis function (rbf ): For every coordinate compute theaverage over all genes weighted by distance from the coordinate.• Base-pair distance: Similar to the k-nearest-neighbors, but theaverage is taken over all genes within a certain radius of thecoordinate.The free parameters of the kernels determine the degree of smooth-ing. By default, optimal parameter settings are estimated from thedata by cross-validation (for details see the package’s vignette).To judge the significance of differential expression, we investigaterandom permutations of the class labels. To obtain a reliable sim-ulation of the empirical distribution, we suggest observing at leastB ≥ 1000 permutations. For each permutation, the regularized t-statistic is computed for each gene. Thus, for each gene we obtain Bpermutation statistics and consequently an empirical p-value, denot-ing the proportion of the permutation statistics being greater or equalthan the gene’s actual statistic that is based on the true class labels.The permutation statistics also provide upper and lower significanceborders, which are smoothed using the same kernel function as forthe original statistics.Optionally, to judge the significance of differential expression overchromosomal regions, one can instead investigate permutations ofthe ordering of genes on chromosomes.Meaningful and concise visualization facilitates a better under-standing of both the data and the statistical analysis. MACAT includesfunctions for plotting expression levels and statistical scores versusbase-pair coordinate on the chromosome. Regions showing signi-ficant differential expression are highlighted in these plots. Onecan also generate HTML-pages, which contain additional inform-ation on genes located within the highlighted chromosomal regions(Fig. 1). For each gene comprehensive annotation, a LocusLink ID,2112 © The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.orgMACAT—microarray chromosome analysis toolFig. 1. Excerpt of a generated HTML-page for the T-versus B-lymphocyte ALL analysis.with a hyperlink to the NCBI website, and the empirical p-value areprovided. In addition, MACAT includes functions for writing geneexpression levels and statistics into text files, which can be used withother programs for further analyses.As an example, we present the results of an analysis on T-versusB-lymphocyte ALL within the publicly available data set described inYeoh et al. (2002). A region on the p-arm of chromosome 6 could beidentified as significantly under-expressed (Fig. 1). Among the geneswithin that region are the MHC class II genes, which are known to beexpressed by B-lymphocytes, but not by T-lymphocytes. Since thesegenes are distributed over a large part of the p-arm of chromosome 6,it makes sense to assume that all genes in this region are significantlyless transcribed in T-lymphocytes compared to B-lymphocytes. Thisgives an indication that chromosomal regions highlighted by ourmethod are indeed biologically meaningful.The method which we have described can detect significant dif-ferential expression for chromosomal regions. However, the reasonfor the differential expression, be it a mutation, translocation,hypermethylation, loss of heterozygosity, or another event, remainsto be investigated.REFERENCESChristiansen,D.H., Andersen,M.K. and Pedersen-Bjergaard,J. (2004) Mutations ofAML1 are common in therapy-related myelodysplasia following therapy with alkylat-ing agents and are significantly associated with deletion or loss of chromosome arm 7qand with subsequent leukemic transformation. Blood, 104, 1474–1481.Gentleman,R.C., Carey,V.J., Bates,D.M., Bolstad,B., Dettling,M., Dudoit,S., Ellis,B.,Gautier,L., Ge,Y., Gentry,J. et al. (2004) Bioconductor: Open software developmentfor computational biology and bioinformatics. Genome Biol., 5, R80.Tibshirani,R., Hastie,T., Narasimhan,B. and Chu,G. (2002) Diagnosis of multiple cancertypes by shrunken centroids of gene expression. Proc. Natl Acad. Sci. USA, 99,6567–6572.Tusher,V., Tibshirani,R. and Chu,G. (2001) Significance analysis of microarrays appliedto ionizing radiation response. Proc. Natl Acad. Sci., 98, 5116–5121.Yeoh,E.J., Ross,M.E., Shurtleff,S.A., Williams,W.K., Patel,D., Mahfouz,R., Behm,F.G.,Raimondi,S.C., Relling,M.V., Patel,A. et al. (2002) Classification, subtype discov-ery, and prediction of outcome in pediatric acute lymphoblastic leukemia by geneexpression profiling. Cancer Cell, 1, 133–143.2113

https://pure.mpg.de/pubman/item/item_1585571_1/component/file_1585570/Toedling%20et%20al.%20-%20Bioinformatics.pdf

MACAT—microarray chromosome analysis tool

Abstract

Similar works

Full text

Available Versions

Max Planck Society eDoc Server

MPG.PuRe

MPG.PuRe

MPG.PuRe