Weighted Analysis of Paired Microarray Experiments

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.

[1]  Pierre Baldi,et al.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..

[2]  Ernst Wit,et al.  Weighted analysis of microarray gene expression using maximum-likelihood , 2005, Bioinform..

[3]  Christian P. Robert,et al.  The Bayesian choice , 1994 .

[4]  X. Wang,et al.  Quantitative quality control in microarray image processing and data acquisition. , 2001, Nucleic acids research.

[5]  Sandrine Dudoit,et al.  Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data , 2003 .

[6]  J. S. Maritz,et al.  Empirical Bayes Methods , 1974 .

[7]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[8]  Weida Tong,et al.  QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies , 2004, Expert review of molecular diagnostics.

[9]  W. Huber,et al.  Analysis of microarray gene expression data , 2003 .

[10]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

[11]  Kimberly F. Johnson,et al.  QA/QC as a pressing need for microarray analysis: meeting report from CAMDA'02. , 2003, BioTechniques.

[12]  Jaakko Astola,et al.  A novel strategy for microarray quality control using Bayesian networks , 2003, Bioinform..

[13]  Ben Bolstad,et al.  Low-level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization , 2003 .

[14]  Taesung Park,et al.  Diagnostic plots for detecting outlying slides in a cDNA microarray experiment. , 2005, BioTechniques.

[15]  Dung-Tsa Chen,et al.  A Graphical Approach for Quality Control of Oligonucleotide Array Data , 2004, Journal of biopharmaceutical statistics.

[16]  P. R. Nelson.  Continuous Univariate Distributions Volume 2 , 1996 .

[17]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[18]  Weida Tong,et al.  Development of public toxicogenomics software for microarray data management and analysis. , 2004, Mutation research.

[19]  Ingrid Lönnstedt.  Replicated microarray data , 2001 .

[20]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[21]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[22]  H. Robbins.  An Empirical Bayes Approach to Statistics , 1956 .

[23]  C. Li,et al.  Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Xinqiang Han,et al.  Genomic profiling of the human heart before and after mechanical support with a ventricular assist device reveals alterations in vascular signaling networks. , 2004, Physiological genomics.

[25]  Sabine Bahn,et al.  Application and optimization of microarray technologies for human postmortem brain studies , 2004, Biological Psychiatry.

[26]  Mats Rudemo,et al.  Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in glucocorticoid-treated nasal polyps. , 2004, The Journal of allergy and clinical immunology.

[27]  Huda Akil,et al.  Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain , 2004, Biological Psychiatry.

[28]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[29]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[30]  Kellie J Archer,et al.  Evaluation of quality-control criteria for microarray gene expression analysis. , 2004, Clinical chemistry.

[31]  Yan Wu,et al.  Quantitative Quality Control in Microarray Experiments and the Application in Data Filtering, Normalization and False Positive Rate Prediction , 2003, Bioinform..