ArticleBMC bioinformatics2007
Orthogonal projections to latent structures as a strategy for microarray data normalization.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Differential Metabolic Pathways and Metabolites in a C57BL/6J Mouse Model of Alcoholic Liver Disease.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm.Bioinformatics (Oxford, England) · 2019Article
- Alteration of specific cytokine expression patterns in patients with breast cancer.Scientific reports · 2019Article
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- Aerobic Variables for Prediction of Alpine Skiing Performance - A Novel Approach.Sports medicine international open · 2018Article
- Metabolomics reveals perturbations in endometrium and serum of minimal and mild endometriosis.Scientific reports · 2018Article
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- Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells.PloS one · 2014Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.
resultsWe introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dual-channel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods.
conclusionThe OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are non-correlated (orthogonal) to the within-sample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, bias-related sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be non-differentially expressed, making it applicable to specialized boutique arrays.
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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.