ArticleNucleic acids research2010
Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers, 3 of them syntheses that pooled it.
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Who cites it
148 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart.PloS one · 2014Trial
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88 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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Abstract
Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background corrections that either use or ignore the control probes. Four calibration data sets are used to evaluate precision, bias and false discovery rate (FDR). The original algorithms are shown to have operating characteristics that are not easily comparable. Some tend to minimize noise while others minimize bias. Each original algorithm is shown to have an innate intensity offset, by which unlogged intensities are bounded away from zero, and the size of this offset determines its position on the noise-bias spectrum. By adding extra offsets, a continuum of related algorithms with different noise-bias trade-offs is generated, allowing direct comparison of the performance of the strategies on equivalent terms. Adding a positive offset is shown to decrease the FDR of each original algorithm. The potential of each strategy to generate an algorithm with an optimal noise-bias trade-off is explored by finding the offset that minimizes its FDR. The use of control probes as part of the background correction and normalization strategy is shown to achieve the lowest FDR for a given bias.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.