Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset
By Sung E. Choe, Michael Boutros, Alan M. Michelson, George M. Church, Marc S. Halfon. Genome Biology, 2005
419 citations recorded by the source corpus.
BackgroundAs more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important.ResultsWe present a 'spike-in' experiment for Affymetrix GeneChips that provides a defined dataset of 3,860 RNA species, which we use to evaluate analysis options for identifying differentially expressed genes. The experimental design incorporates two novel features. First, to obtain accurate estimates of false-positive and false-negative rates, 100-200 RNAs are spiked in at each fold-change level of interest, ranging from 1.2 to 4-fold. Second, instead of using an uncharacterized background RNA sample, a set of 2,551 RNA species is used as the constant (1x) set, allowing us to know whether any given probe set is truly present or absent. Application of a large number of analysis methods to this dataset reveals clear variation in their ability to identify differentially expressed genes. False-negative and false-positive rates are minimized when the following options are chosen: subtracting nonspecific signal from the PM probe intensities; performing an intensity-dependent normalization at the probe set level; and incorporating a signal intensity-dependent standard deviation in the test statistic.ConclusionsA best-route combination of analysis methods is presented that allows detection of approximately 70% of true positives before reaching a 10% false-discovery rate. We highlight areas in need of improvement, including better estimate of false-discovery rates and decreased false-negative rates.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/95124346ee368ebdf57e003b1fa6a656beda0344
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Citations in the corpus
- Bgee: Integrating and Comparing Heterogeneous Transcriptome Data Among Species (2008, background): “Although MAS5 classification is efficient [18], the estimation of the background signal can be biased depending on probe sequence affinity [19].”
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- Comparison of Affymetrix GeneChip expression measures (2006, neutral): “Currently, there is another spike-in experiment in the public domain (Choe et al. , 2005).”
- Microarray data analysis: from disarray to consolidation and consensus (2006, methodology): “RMA and a modification of this method, GCRMA, often perform as well or better than competitors, although there is some controversy about which method is bes”