ArticleBMC bioinformatics2008
Using generalized procrustes analysis (GPA) for normalization of cDNA microarray data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- CD28 signaling complexes are correlated with patient outcomes in anti-CD19 41BB-costimulation CAR T cell therapy.Journal for immunotherapy of cancer · 2025Article
- Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition.Sensors (Basel, Switzerland) · 2020Article
- Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review.Frontiers in bioengineering and biotechnology · 2019Review
- Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids.Experimental and therapeutic medicine · 2018Article
- Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data.Bioinformatics (Oxford, England) · 2015Article
- miRNA-Seq normalization comparisons need improvement.RNA (New York, N.Y.) · 2013Article
- Article
- Quality Visualization of Microarray Datasets Using Circos.Microarrays (Basel, Switzerland) · 2012Article
- Evaluation of normalization methods in mammalian microRNA-Seq data.RNA (New York, N.Y.) · 2012Article
- Meta-type analysis of dopaminergic effects on gene expression in the neuroendocrine brain of female goldfish.Frontiers in endocrinology · 2012Article
- Article
- Normalization of high dimensional genomics data where the distribution of the altered variables is skewed.PloS one · 2011Article
- Assessment and optimisation of normalisation methods for dual-colour antibody microarrays.BMC bioinformatics · 2010Article
- Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data.BMC cancer · 2010Article
- Article
- Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database.BMC bioinformatics · 2010Article
- Application of chicken microarrays for gene expression analysis in other avian species.BMC genomics · 2009Article
- SPRINT: a new parallel framework for R.BMC bioinformatics · 2008Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNormalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides (Global, Lowess) and across slides (Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice.
resultsIn this study, we propose a novel assumption-free normalization method based on the Generalized Procrustes Analysis (GPA) algorithm. Using experimental and simulated normal microarray data and boutique array data, we systemically evaluate the ability of the GPA method in normalization compared with six other popular normalization methods including Global, Lowess, Scale, Quantile, VSN, and one boutique array-specific housekeeping gene method. The assessment of these methods is based on three different empirical criteria: across-slide variability, the Kolmogorov-Smirnov (K-S) statistic and the mean square error (MSE). Compared with other methods, the GPA method performs effectively and consistently better in reducing across-slide variability and removing systematic bias.
conclusionThe GPA method is an effective normalization approach for microarray data analysis. In particular, it is free from the statistical and biological assumptions inherent in other normalization methods that are often difficult to validate. Therefore, the GPA method has a major advantage in that it can be applied to diverse types of array sets, especially to the boutique array where the majority of genes may be differentially expressed.
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