ArticleBiology direct2007
How high is the level of technical noise in microarray data?
Article in Biology direct, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data.Frontiers in genetics · 2018Article
- Gene expression prediction using low-rank matrix completion.BMC bioinformatics · 2016Article
- CrossNorm: a novel normalization strategy for microarray data in cancers.Scientific reports · 2016Article
- A network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studies.Oncotarget · 2015Article
- A glance at the applications of Singular Spectrum Analysis in gene expression data.Biomolecular detection and quantification · 2015Review
- An algebra-based method for inferring gene regulatory networks.BMC systems biology · 2014Article
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- MicroRNA-146a regulates human foetal femur derived skeletal stem cell differentiation by down-regulating SMAD2 and SMAD3.PloS one · 2014Article
- Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors.PloS one · 2014Article
- RNA-seq and microarray complement each other in transcriptome profiling.BMC genomics · 2012Article
- Cardiovascular genomics: a biomarker identification pipeline.IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society · 2012Review
- Regulatory component analysis: a semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge.Signal processing · 2012Article
- Cellular transcripts of chicken brain tissues in response to H5N1 and Newcastle disease virus infection.Virology journal · 2012Article
- Microarrays, deep sequencing and the true measure of the transcriptome.BMC biology · 2011Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundMicroarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data.
resultsIn the present report, the MAQC study is reanalyzed in order to quantitatively assess measurement errors inherent in high-density oligonucleotide array technology (Affymetrix platform). The level of noise is directly estimated from technical replicates of gene expression measurements in the absence of biological variability. For each probe set, the magnitude of random fluctuations across technical replicates is characterized by the standard deviation of the corresponding log-expression signal. The resultant standard deviations appear to be uniformly small and symmetrically distributed across probe sets. The observed noise level does not cause any tangible bias in estimated pair-wise correlation coefficients, the latter being particularly prone to its presence in microarray data.
conclusionThe reported analysis strongly suggests that, contrary to popular belief, the random fluctuations of gene expression signals caused by technical noise are quite low and the effect of such fluctuations on the results of statistical inference from Affymetrix GeneChip microarray data is negligibly small.
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