Research article
The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies
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* Corresponding author: Roel GW Verhaak r.verhaak@erasmusmc.nl
1 Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands
2 Department of Immunology, Erasmus Medical Center, Rotterdam, The Netherlands
3 Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
BMC Bioinformatics 2006, 7:105 doi:10.1186/1471-2105-7-105
Published: 2 March 2006Abstract
Background
Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.
Results
Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing.
Conclusion
Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.