Dataset Information

Detecting genetic variation in microarray expression data.

ABSTRACT: The use of high-density oligonucleotide arrays to measure the expression levels of thousands of genes in parallel has become commonplace. To take further advantage of the growing body of data, we developed a method, termed "GeSNP," to mine the detailed hybridization patterns in oligonucleotide array expression data for evidence of genetic variation. To demonstrate the performance of the algorithm, the hybridization patterns in data obtained previously from SAMP8/Ta, SAMP10/Ta, and SAMR1/Ta inbred mice and from humans and chimpanzees were analyzed. Genes with consistent strain-specific and species-specific hybridization pattern differences were identified, and approximately 90% of the candidate genes were independently confirmed to harbor sequence differences. Importantly, the quality of gene expression data was also improved by masking the probes of regions with putative sequence differences between species and strains. To illustrate the application to human disease groups, data from an inflammatory bowel disease study were analyzed. GeSNP identified sequence differences in candidate genes previously discovered in independent association and linkage studies and uncovered many promising new candidates. This approach enables the opportunistic extraction of genetic variation information from new or pre-existing gene expression data obtained with high-density oligonucleotide arrays.

SUBMITTER: Greenhall JA

PROVIDER: S-EPMC1933513 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

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Detecting genetic variation in microarray expression data.

Greenhall Jennifer A JA Zapala Matthew A MA Cáceres Mario M Libiger Ondrej O Barlow Carrolee C Schork Nicholas J NJ Lockhart David J DJ

Genome research 20070703 8

The use of high-density oligonucleotide arrays to measure the expression levels of thousands of genes in parallel has become commonplace. To take further advantage of the growing body of data, we developed a method, termed "GeSNP," to mine the detailed hybridization patterns in oligonucleotide array expression data for evidence of genetic variation. To demonstrate the performance of the algorithm, the hybridization patterns in data obtained previously from SAMP8/Ta, SAMP10/Ta, and SAMR1/Ta inbre ...[more]

PMID: 17609390

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Project description:BackgroundIdentification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip(R) system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.ResultsWe introduce a new fold-change (FC)-based method, the weighted average difference method (WAD), for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different conditions. The idea is based on our observation that known or potential marker genes (or proteins) tend to have high expression levels. We compared WAD with seven other methods; average difference (AD), FC, rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT). The evaluation was performed using a total of 38 different binary (two-class) probe-level datasets: two artificial "spike-in" datasets and 36 real experimental datasets. The results indicate that WAD outperforms the other methods when sensitivity and specificity are considered simultaneously: the area under the receiver operating characteristic curve for WAD was the highest on average for the 38 datasets. The gene ranking for WAD was also the most consistent when subsets of top-ranked genes produced from three different preprocessed data (MAS, RMA, and DFW) were compared. Overall, WAD performed the best for MAS-preprocessed data and the FC-based methods (AD, WAD, FC, or RP) performed well for RMA and DFW-preprocessed data.ConclusionWAD is a promising alternative to existing methods for ranking DEGs with two classes. Its high performance should increase researchers' confidence in microarray analyses.

Dataset Information

Detecting genetic variation in microarray expression data.

Publications

Detecting genetic variation in microarray expression data.

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