A Comparison of Statistical Tests for Detecting Differential Expression Using Affymetrix Oligonucleotide Microarrays
| Autor: | Sujoy Ghosh, Dilip Rajagopalan, Steven J. Blakemore, Richard J. Stephens, Saran Vardhanabhuti, Steven M. Clark |
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| Rok vydání: | 2006 |
| Předmět: |
Quantification methods
Computer science Gene Expression Profiling Affymetrix microarray Computational biology computer.software_genre Biochemistry Oligonucleotide Microarrays Resolver Rat liver Genetics Animals Humans Molecular Medicine Data mining Affymetrix GeneChip Operating Software Differential expression Molecular Biology computer Oligonucleotide Array Sequence Analysis Biotechnology Statistical hypothesis testing |
| Zdroj: | OMICS: A Journal of Integrative Biology. 10:555-566 |
| ISSN: | 1557-8100 1536-2310 |
| Popis: | Signal quantification and detection of differential expression are critical steps in the analysis of Affymetrix microarray data. Many methods have been proposed in the literature for each of these steps. The goal of this paper is to evaluate several signal quantification methods (GCRMA, RSVD, VSN, MAS5, and Resolver) and statistical methods for differential expression (t test, Cyber-T, SAM, LPE, RankProducts, Resolver RatioBuild). Our particular focus is on the ability to detect differential expression via statistical tests. We have used two different datasets for our evaluation. First, we have used the HG-U133 Latin Square spike in dataset developed by Affymetrix. Second, we have used data from an in-house rat liver transcriptomics study following 30 different drug treatments generated using the Affymetrix RAE230A chip. Our overall recommendation based on this study is to use GCRMA for signal quantification. For detection of differential expression, GCRMA coupled with Cyber-T or SAM is the best approach, as measured by area under the receiver operating characteristic (ROC) curve. The integrated pipeline in Resolver RatioBuild combining signal quantification and detection of differential expression is an equally good alternative for detecting differentially expressed genes. For most of the differential expression algorithms we considered, the performance using MAS5 signal quantification was inferior to that of the other methods we evaluated. |
| Databáze: | OpenAIRE |
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