The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

• Published in: BMC bioinformatics Vol. 7; no. 1; p. 105
• Main Authors: Verhaak, Roel G W, Staal, Frank J T, Valk, Peter J M, Lowenberg, Bob, Reinders, Marcel J T, de Ridder, Dick
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.03.2006; BioMed Central; BMC
• Subjects: Algorithms; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Clinical Trials as Topic; Cohort Studies; Databases, Genetic; Diagnosis, Computer-Assisted - methods; Gene Expression Profiling - methods; Humans; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasms - diagnosis; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; Reproducibility of Results; Sensitivity and Specificity; Software; Software Validation
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-105

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. 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. 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.