Comparison of Affymetrix GeneChip expression measures
By Rafael A. Irizarry, Zhijin Wu, Harris A. Jaffee. Bioinform., 2006
358 citations recorded by the source corpus.
MOTIVATION In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT rafa@jhu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/59e033f2ba19f613cdbaac1942b3c2a66de3ea43
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Citations in the corpus
- The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements (2006, methodology): “…be used to compare normalization methods 23 and data analysis algorithms 26 (see Figure S2 in Supplementary Data online), similar to a currently available website (http://affycomp.biostat.jhsph.edu) which illustrates the impact of the different data analysis methods on expression results 30–34 .”
- Context-Specific Metabolic Networks Are Consistent with Experiments (2008, methodology): “The gene expression data used to construct the models consists of CEL files containing the data described in [17], normalized using GCRMA [ 14 ].”
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “We can obtain meaningful estimates of specificity from looking at datasets where we believe all genes to fall under the null hypothesis of no differential expression [38].”