ArticleNucleic acids research2003
A model-based analysis of microarray experimental error and normalisation.
Article in Nucleic acids research, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens.BMC bioinformatics · 2014Article
- Microarray gene expression profiling of neural tissues in bovine spastic paresis.BMC veterinary research · 2013Article
- Activation of silenced tumor suppressor genes in prostate cancer cells by a novel energy restriction-mimetic agent.The Prostate · 2012Article
- Identification and characterization of gene expression involved in the coloration of cichlid fish using microarray and qRT-PCR approaches.Journal of molecular evolution · 2011Article
- Hif1α down-regulation is associated with transposition of great arteries in mice treated with a retinoic acid antagonist.BMC genomics · 2010Article
- The power of the web in cancer drug discovery and clinical trial design: research without a laboratory?Cancer informatics · 2010Article
- ExprAlign--the identification of ESTs in non-model species by alignment of cDNA microarray expression profiles.BMC genomics · 2009Article
- Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.Journal of biomedical informatics · 2009Article
- Article
- Effects of dutasteride on the expression of genes related to androgen metabolism and related pathway in human prostate cancer cell lines.Investigational new drugs · 2007Article
- Gene expression profiling in a mouse model for African trypanosomiasis.Genes and immunity · 2006Article
- Whole-genome comparison between Photorhabdus strains to identify genomic regions involved in the specificity of nematode interaction.Journal of bacteriology · 2006Article
- Evaluation of normalization methods for cDNA microarray data by k-NN classification.BMC bioinformatics · 2005Article
- Identification of novel genes in intestinal tissue that are regulated after infection with an intestinal nematode parasite.Infection and immunity · 2005Article
- A generally applicable validation scheme for the assessment of factors involved in reproducibility and quality of DNA-microarray data.BMC genomics · 2005Article
- Coping with cold: An integrative, multitissue analysis of the transcriptome of a poikilothermic vertebrate.Proceedings of the National Academy of Sciences of the United States of America · 2004Article
- Biphenyl and benzoate metabolism in a genomic context: outlining genome-wide metabolic networks in Burkholderia xenovorans LB400.Applied and environmental microbiology · 2004Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. Through analysis of the model and two-dye microarray data sets, this study found the following. The systematic error introduced by microarray experiments mainly involves spot intensity-dependent, feature-specific and spot position-dependent contributions. It is difficult to remove all these errors effectively without a suitable combined normalisation operation. Adaptive normalisation using a suitable regression technique is more effective in removing spot intensity-related dye bias than self-normalisation, while regional normalisation (block normalisation) is an effective way to correct spot position-dependent errors. However, dye-flip replicates are necessary to remove feature-specific errors, and also allow the analyst to identify the experimentally introduced dye bias contained in non-self-self data sets. In this case, the bias present in the data sets may include both experimentally introduced dye bias and the biological difference between two samples. Self-normalisation is capable of removing dye bias without identifying the nature of that bias. The performance of adaptive normalisation, on the other hand, depends on its ability to correctly identify the dye bias. If adaptive normalisation is combined with an effective dye bias identification method then there is no systematic difference between the outcomes of the two methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.