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Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.

ABSTRACT: We examined how well differentially expressed genes and multigene outcome classifiers retain their class-discriminating values when tested on data generated by different transcriptional profiling platforms. RNA from 33 stage I-III breast cancers was hybridized to both Affymetrix GeneChip and Millennium Pharmaceuticals cDNA arrays. Only 30% of all corresponding gene expression measurements on the two platforms had Pearson correlation coefficient r >or= 0.7 when UniGene was used to match probes. There was substantial variation in correlation between different Affymetrix probe sets matched to the same cDNA probe. When cDNA and Affymetrix probes were matched by basic local alignment tool (BLAST) sequence identity, the correlation increased substantially. We identified 182 genes in the Affymetrix and 45 in the cDNA data (including 17 common genes) that accurately separated 91% of cases in supervised hierarchical clustering in each data set. Cross-platform testing of these informative genes resulted in lower clustering accuracy of 45 and 79%, respectively. Several sets of accurate five-gene classifiers were developed on each platform using linear discriminant analysis. The best 100 classifiers showed average misclassification error rate of 2% on the original data that rose to 19.5% when tested on data from the other platform. Random five-gene classifiers showed misclassification error rate of 33%. We conclude that multigene predictors optimized for one platform lose accuracy when applied to data from another platform due to missing genes and sequence differences in probes that result in differing measurements for the same gene.

SUBMITTER: Stec J

PROVIDER: S-EPMC1867535 | biostudies-literature | 2005 Aug

REPOSITORIES: biostudies-literature

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Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.

Stec James J Wang Jing J Coombes Kevin K Ayers Mark M Hoersch Sebastian S Gold David L DL Ross Jeffrey S JS Hess Kenneth R KR Tirrell Stephen S Linette Gerald G Hortobagyi Gabriel N GN Fraser Symmans W W Pusztai Lajos L

The Journal of molecular diagnostics : JMD 20050801 3

We examined how well differentially expressed genes and multigene outcome classifiers retain their class-discriminating values when tested on data generated by different transcriptional profiling platforms. RNA from 33 stage I-III breast cancers was hybridized to both Affymetrix GeneChip and Millennium Pharmaceuticals cDNA arrays. Only 30% of all corresponding gene expression measurements on the two platforms had Pearson correlation coefficient r >or= 0.7 when UniGene was used to match probes. T ...[more]

PMID: 16049308

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Project description:BackgroundAffymetrix GeneChips are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).ResultsWe studied probe-, exon- and gene-level reproducibility of technical and biological replicates from each of the 3 platforms. The U133 array has larger feature sizes so it is no surprise that probe-level variances are smaller, however the larger number of probes per gene on the HuGene array seems to produce gene-level summaries that have similar variances. The gene-level summaries of the HuEx array are less reproducible than the other two, despite having the largest average number of probes per gene. Greater than 80% of the content on the HuEx arrays is expressed at or near background. Biological variation seems to have a smaller effect on U133 data. Comparing the overlap of differentially expressed genes, we see a high overall concordance among all 3 platforms, with HuEx and HuGene having greater overlap, as expected given their design. We performed an analysis of detection rates and area under ROC curves using an experiment made up of several mixtures of 2 human tissues. Though it appears that the HuEx array has worse performance in terms of detection rates, all arrays have similar ability to separate differentially expressed and non-differentially expressed genes.ConclusionDespite noticeable differences in the probe-level reproducibility, gene-level reproducibility and differential expression detection are quite similar across the three platforms. The HuEx array, an all-encompassing array, has the flexibility of measuring all known or predicted exonic content. However, the HuEx array induces poorer reproducibility for genes with fewer exons. The HuGene measures just the well-annotated genome content and appears to perform well. The U133 array, though not able to measure across the full length of a transcript, appears to perform as well as the newer designs on the set of genes common to all 3 platforms.

Dataset Information

Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.

Publications

Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.

Similar Datasets

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