ArticleBMC bioinformatics2009
A comparison on effects of normalisations in the detection of differentially expressed genes.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments.Frontiers in genetics · 2019Article
- A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns.PloS one · 2015Article
- Article
- MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples.Bioinformatics (Oxford, England) · 2014Article
- How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis.BMC bioinformatics · 2013Article
- β-empirical Bayes inference and model diagnosis of microarray data.BMC bioinformatics · 2012Article
- A novel approach to the clustering of microarray data via nonparametric density estimation.BMC bioinformatics · 2011Article
- A comprehensive and universal method for assessing the performance of differential gene expression analyses.PloS one · 2010Article
- Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions.Nucleic acids research · 2009Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundVarious normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.
resultsThrough three precise simulation plans, we quantify the impact of normalisations: (a) on the sensitivity and specificity of a specified test statistic for the identification of deregulated genes, (b) on the gene ranking induced by the statistic.
conclusionAlthough we found a limited difference of sensitivities and specificities for the test after each normalisation, the study highlights a strong impact in terms of gene ranking agreement, resulting in different levels of agreement between competing normalisations. However, we show that the combination of two normalisations, such as glog and lowess, that handle different aspects of microarray data, is able to outperform other individual techniques.
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