Cited by 230Gene expression and cancer classificationGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

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Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips

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Shi, Wei, Oshlack, Alicia, Smyth, Gordon K.·2010·Nucleic Acids Research

DOI 10.1093/nar/gkq871PMC PMC3001098

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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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Figure 1. Operating characteristics of each pre-processing strategy. Shown are boxplots of normalized intensities (a1–a4), sample variances (b1–b4) and absolute fold changes (c1–c4) for each pre-processing strategy applied to each data set. Each boxplot shows the spread of values across microarray probes for a particular strategy applied to a particular data set. The vertical axis has been truncated in some cases to better show the main body of the boxplots. The first and second rows show results for mixture data sets M1 and M2, respectively. Panels c1-c2 show fold changes for comparing pure samples. The third and fourth rows show results for spike-in data sets S1 and S2, respectively. Only results for spike-in probes are shown (note that spike-in probes and array probes were normalized together). Panels a3–a4 show intensities for probes for which the spike-in concentration is actually 0. Panels c3–c4 show fold changes from the comparison between spike-in concentrations 1000 pM and 0.01 pM. In the left column (a1–a4), longer boxes indicate a good range of normalized values. In the middle column (b1–b4), lower boxes indicate higher precision. In the third column (c1–c4), longer and higher boxes indicate a greater range of fold changes.CC BY-NC · PubMed Central

Figure 2. Operating characteristics of pre-processing strategies when total offsets are forced equal. Shown are boxplots of (a) normalized intensities, (b) log variances and (c) absolute fold changes. Results shown are for data set M1, with added offsets of 73, 237 and 256 for the logq, neq and neqc strategies, respectively.CC BY-NC · PubMed Central

Figure 3. Operating characteristics of the logq pre-processing strategy for different added offsets. Shown are boxplots of (a) normalized intensities, (b) variances and (c) absolute fold changes for comparing pure samples. Results shown are for data M1.CC BY-NC · PubMed Central

Figure 4. AUC as a function of added offset for the logq, neq and neqc strategies. Panel (a) is for data set M1, (b) for data set M2.CC BY-NC · PubMed Central

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Authors: Shi, W., Oshlack, A., & Smyth, G. K.
Affiliations: The University of Melbourne (AU)
Corresponding author: Shi, Wei
Smyth, Gordon K.
ORCID: Shi, Wei
Oshlack, Alicia
Smyth, Gordon K.
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2010-10-06
Volume: 38
Issue: 22
Pages: e204-e204
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gkq871
PMC: PMC3001098
OpenAlex: W2118373398
Cited by: 225
References: 32
Citations by year: 2026: 5 · 2025: 4 · 2024: 8 · 2023: 10 · 2022: 13 · 2021: 15 · 2020: 11 · 2019: 20 · 2018: 14 · 2017: 19 · 2016: 28 · 2015: 23 · 2014: 15 · 2013: 20 · 2012: 9
FWCI: 5.21
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/2.5 (vor, 2190 d)
Locations: Nucleic Acids Research (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
HighWire Press Open Archive (submittedVersion)
Funders: Medical Research Council
National Health and Medical Research Council (490037)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: variance stabilizing transformation, background correction, false discovery rate, normalization, gene expression data
MeSH terms: Algorithms, Humans, Reproducibility of Results, Oligonucleotide Probes, Genome, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling

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Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips

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