The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies
<p>Abstract</p> <p>Background</p> <p>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. Vario...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2006-03-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/105 |
| _version_ | 1819027782871547904 |
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| author | Reinders Marcel JT Valk Peter JM Lowenberg Bob Staal Frank JT Verhaak Roel GW de Ridder Dick |
| author_facet | Reinders Marcel JT Valk Peter JM Lowenberg Bob Staal Frank JT Verhaak Roel GW de Ridder Dick |
| author_sort | Reinders Marcel JT |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T05:47:57Z |
| publishDate | 2006-03-01 |
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| series | BMC Bioinformatics |
| spelling | doaj.art-a60f012bb41c45b7b2d82f847008ff6b2022-12-21T19:14:05ZengBMCBMC Bioinformatics1471-21052006-03-017110510.1186/1471-2105-7-105The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studiesReinders Marcel JTValk Peter JMLowenberg BobStaal Frank JTVerhaak Roel GWde Ridder Dick<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1471-2105/7/105 |
| spellingShingle | Reinders Marcel JT Valk Peter JM Lowenberg Bob Staal Frank JT Verhaak Roel GW de Ridder Dick The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies BMC Bioinformatics |
| title | The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies |
| title_full | The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies |
| title_fullStr | The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies |
| title_full_unstemmed | The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies |
| title_short | The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies |
| title_sort | effect of oligonucleotide microarray data pre processing on the analysis of patient cohort studies |
| url | http://www.biomedcentral.com/1471-2105/7/105 |
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