ArticleBMC genomics2010
Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
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- Analyzing the Impact of the Highest Expressed Epstein-Barr Virus-Encoded microRNAs on the Host Cell Transcriptome.International journal of molecular sciences · 2024Article
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- Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers.PloS one · 2022Observational
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- Signaling networks in inflammatory pathways and risk for suicidal behavior.Brain, behavior, & immunity - health · 2020Article
- Shift Work Disrupts Circadian Regulation of the Transcriptome in Hospital Nurses.Journal of biological rhythms · 2019Article
- Early isolated V-lesion may not truly represent rejection of the kidney allograft.Clinical science (London, England : 1979) · 2018Observational
- Exploratory Analysis of Single-Gene Predictive Biomarkers in HERA DASL Cohort Reveals That C8A mRNA Expression Is Prognostic of Outcome and Predictive of Benefit of Trastuzumab.JCO precision oncology · 2018Article
- Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?Computational psychiatry (Cambridge, Mass.) · 2017Article
- DBNorm: normalizing high-density oligonucleotide microarray data based on distributions.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNormalization of microarrays is a standard practice to account for and minimize effects which are not due to the controlled factors in an experiment. There is an overwhelming number of different methods that can be applied, none of which is ideally suited for all experimental designs. Thus, it is important to identify a normalization method appropriate for the experimental setup under consideration that is neither too negligent nor too stringent. Major aim is to derive optimal results from the underlying experiment. Comparisons of different normalization methods have already been conducted, none of which, to our knowledge, comparing more than a handful of methods.
resultsIn the present study, 25 different ways of pre-processing Illumina Sentrix BeadChip array data are compared. Among others, methods provided by the BeadStudio software are taken into account. Looking at different statistical measures, we point out the ideal versus the actual observations. Additionally, we compare qRT-PCR measurements of transcripts from different ranges of expression intensities to the respective normalized values of the microarray data. Taking together all different kinds of measures, the ideal method for our dataset is identified.
conclusionsPre-processing of microarray gene expression experiments has been shown to influence further downstream analysis to a great extent and thus has to be carefully chosen based on the design of the experiment. This study provides a recommendation for deciding which normalization method is best suited for a particular experimental setup.
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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.