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Weighted Analysis of Paired Microarray Experiments
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Erik Kristiansson
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October 19, 2005
In microarray experiments quality often varies, for example between samples and between arrays. The need for quality control is therefore strong. A statistical model and a corresponding analysis method is suggested for experiments with pairing, including designs with individuals observed before and after treatment and many experiments with two-colour spotted arrays. The model is of mixed type with some parameters estimated by an empirical Bayes method. Differences in quality are modelled by individual variances and correlations between repetitions. The method is applied to three real and several simulated datasets. Two of the real datasets are of Affymetrix type with patients profiled before and after treatment, and the third dataset is of two-colour spotted cDNA type. In all cases, the patients or arrays had different estimated variances, leading to distinctly unequal weights in the analysis. We suggest also plots which illustrate the variances and correlations that affect the weights computed by our analysis method. For simulated data the improvement relative to previously published methods without weighting is shown to be substantial.
Keywords: Quality control; QC; Quality Assurance; QA; Quality Assessment; Empirical Bayes; DNA Microarray
Published Online: 2005-10-19
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
Quality control;
QC;
Quality Assurance;
QA;
Quality Assessment;
Empirical Bayes;
DNA Microarray
Articles in the same Issue
- Article
- Estimating Motifs Under Order Restrictions
- Reproducible Research: A Bioinformatics Case Study
- Generalized Rank Tests for Replicated Microarray Data
- Stepwise Normalization of Two-Channel Spotted Microarrays
- Comparing Automatic and Manual Image Processing in FLARE Assay Analysis for Colon Carcinogenesis
- Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays
- Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels
- Multiple Testing and Data Adaptive Regression: An Application to HIV-1 Sequence Data.
- Early Diagnostic Marker Panel Determination for Microarray Based Clinical Studies
- Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors
- Combined Association and Linkage Analysis for General Pedigrees and Genetic Models
- Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data
- The Relative Inefficiency of Sequence Weights Approaches in Determining a Nucleotide Position Weight Matrix
- A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes
- Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction
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- A General Framework for Weighted Gene Co-Expression Network Analysis
- Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm
- Comparing Bacterial DNA Microarray Fingerprints
- Continuous Covariates in Genetic Association Studies of Case-Parent Triads: Gene and Gene-Environment Interaction Effects, Population Stratification, and Power Analysis
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- Test on the Structure of Biological Sequences via Chaos Game Representation
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- A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
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- Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
- Bayesian Statistical Studies of the Ramachandran Distribution
- On Reference Designs For Microarray Experiments
- Computing Asymptotic Power and Sample Size for Case-Control Genetic Association Studies in the Presence of Phenotype and/or Genotype Misclassification Errors
