ArticleAlgorithms for molecular biology : AMB2008
"Hook"-calibration of GeneChip-microarrays: chip characteristics and expression measures.
Article in Algorithms for molecular biology : AMB, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Variation of RNA Quality and Quantity Are Major Sources of Batch Effects in Microarray Expression Data.Microarrays (Basel, Switzerland) · 2014Article
- Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes.Biology · 2013Article
- How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis.BMC bioinformatics · 2013Article
- Physico-chemical foundations underpinning microarray and next-generation sequencing experiments.Nucleic acids research · 2013Review
- Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity.BMC genomics · 2013Article
- Article
- Mining SOM expression portraits: feature selection and integrating concepts of molecular function.BioData mining · 2012Article
- Estimating RNA-quality using GeneChip microarrays.BMC genomics · 2012Article
- Expression cartography of human tissues using self organizing maps.BMC bioinformatics · 2011Article
- Washing scaling of GeneChip microarray expression.BMC bioinformatics · 2010Article
- G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration.BMC bioinformatics · 2010Article
- Article
- Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity.Algorithms for molecular biology : AMB · 2009Article
- "Hook"-calibration of GeneChip-microarrays: theory and algorithm.Algorithms for molecular biology : AMB · 2008Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.
resultsIn this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.
conclusionThe single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.
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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.