ArticleBMC bioinformatics2008
A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- A data integration approach unveils a transcriptional signature of type 2 diabetes progression in rat and human islets.PloS one · 2023Observational
- MUREN: a robust and multi-reference approach of RNA-seq transcript normalization.BMC bioinformatics · 2021Article
- The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation.BMC bioinformatics · 2019Article
- A Novel Dual Eigen-Analysis of Mouse Multi-Tissues' Expression Profiles Unveils New Perspectives into Type 2 Diabetes.Scientific reports · 2017Article
- Age-changes in gene expression in primary mixed glia cultures from young vs. old rat cerebral cortex are modified by interactions with neurons.Brain, behavior, and immunity · 2012Article
- Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization.BMC bioinformatics · 2010Article
- Comparative analyses of time-course gene expression profiles of the long-lived sch9Delta mutant.Nucleic acids research · 2010Article
- Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity.Algorithms for molecular biology : AMB · 2009Article
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3 authors.
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Abstract
backgroundMicroarray pre-processing usually consists of normalization and summarization. Normalization aims to remove non-biological variations across different arrays. The normalization algorithms generally require the specification of reference and target arrays. The issue of reference selection has not been fully addressed. Summarization aims to estimate the transcript abundance from normalized intensities. In this paper, we consider normalization and summarization jointly by a new strategy of reference selection.
resultsWe propose a Probe-Treatment-Reference (PTR) model to streamline normalization and summarization by allowing multiple references. We estimate parameters in the model by the Least Absolute Deviations (LAD) approach and implement the computation by median polishing. We show that the LAD estimator is robust in the sense that it has bounded influence in the three-factor PTR model. This model fitting, implicitly, defines an "optimal reference" for each probe-set. We evaluate the effectiveness of the PTR method by two Affymetrix spike-in data sets. Our method reduces the variations of non-differentially expressed genes and thereby increases the detection power of differentially expressed genes.
conclusionOur results indicate that the reference effect is important and should be considered in microarray pre-processing. The proposed PTR method is a general framework to deal with the issue of reference selection and can readily be applied to existing normalization algorithms such as the invariant-set, sub-array and quantile method.
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