Propagating uncertainty in microarray data analysis

Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.

[1]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.

[2]  Anne-Mette K. Hein,et al.  BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data. , 2005, Biostatistics.

[3]  Alexander Schliep,et al.  Robust inference of groups in gene expression time-courses using mixtures of HMMs , 2004, ISMB/ECCB.

[4]  Ka Yee Yeung,et al.  Principal component analysis for clustering gene expression data , 2001, Bioinform..

[5]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[7]  Padhraic Smyth,et al.  Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance , 2004, Proc. Natl. Acad. Sci. USA.

[8]  Katherine A. Heller,et al.  Bayesian hierarchical clustering , 2005, ICML.

[9]  Cheng Li,et al.  Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application , 2001, Genome Biology.

[10]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  M Milo,et al.  A probabilistic model for the extraction of expression levels from oligonucleotide arrays. , 2003, Biochemical Society transactions.

[12]  Terence P. Speed,et al.  A benchmark for Affymetrix GeneChip expression measures , 2004, Bioinform..

[13]  G. Church,et al.  Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset , 2005, Genome Biology.

[14]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[15]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16]  T. Poggio,et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.

[17]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[18]  Pierre Baldi,et al.  Bioinformatics - the machine learning approach (2. ed.) , 2000 .

[19]  L. Williams,et al.  Contents , 2020, Ophthalmology (Rochester, Minn.).

[20]  R. Durbin,et al.  Biological sequence analysis: Background on probability , 1998 .

[21]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[22]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[23]  Rainer Breitling,et al.  Biologically valid linear factor models of gene expression , 2004, Bioinform..

[24]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[25]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[26]  Neil D. Lawrence,et al.  A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips , 2005, Bioinform..

[27]  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..

[28]  Shin Ishii,et al.  A Bayesian missing value estimation method for gene expression profile data , 2003, Bioinform..

[29]  D. Slonim.  From patterns to pathways: gene expression data analysis comes of age , 2002, Nature Genetics.

[30]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[31]  Ka Yee Yeung,et al.  Bayesian mixture model based clustering of replicated microarray data , 2004, Bioinform..

[32]  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.

[33]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[34]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[35]  Neil D. Lawrence,et al.  Accounting for probe-level noise in principal component analysis of microarray data , 2005, Bioinform..