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

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 ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Roel G. W. Verhaak, Frank J. T. Staal, Peter J. M. Valk, Bob Löwenberg, Marcel J. T. Reinders, Dick de Ridder
• Keywords: Affymetrix, BMCBI 2006, Pre-processing, Pre-processing Method