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 (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.
|Keywords||Algorithms, Clinical Trials as Topic, Cohort Studies, Databases, Genetic, Diagnosis, Computer-Assisted/*methods, Gene Expression Profiling/*methods, Humans, Neoplasm Proteins/genetics/*metabolism, Neoplasms/*diagnosis/genetics/*metabolism, Oligonucleotide Array Sequence Analysis/*methods, Reproducibility of Results, Sensitivity and Specificity, Software, Software Validation, Tumor Markers, Biological/genetics/*metabolism|
|Persistent URL||dx.doi.org/10.1186/1471-2105-7-105, hdl.handle.net/1765/13984|
Verhaak, R.G.W., Staal, F.J.T., Valk, P.J.M., Löwenberg, B., Reinders, M.J., & de Ridder, D.. (2006). The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.. B M C Bioinformatics, 7(105), 1–15. doi:10.1186/1471-2105-7-105