Normalization of single-channel DNA array data by principal component analysis

MOTIVATION Detailed comparison and analysis of the output of DNA gene expression arrays from multiple samples require global normalization of the measured individual gene intensities from the different hybridizations. This is needed for accounting for variations in array preparation and sample hybridization conditions. RESULTS Here, we present a simple, robust and accurate procedure for the global normalization of datasets generated with single-channel DNA arrays based on principal component analysis. The procedure makes minimal assumptions about the data and performs well in cases where other standard procedures produced biased estimates. It is also insensitive to data transformation, filtering (thresholding) and pre-screening.

[1]  E. Wolski,et al.  Normalization strategies for cDNA microarrays. , 2000, Nucleic acids research.

[2]  E. Lander.  Array of hope , 1999, Nature Genetics.

[3]  T. Kepler,et al.  Normalization and analysis of DNA microarray data by self-consistency and local regression , 2002, Genome Biology.

[4]  Taesung Park,et al.  Evaluation of normalization methods for microarray data , 2003 .

[5]  Tommi S. Jaakkola,et al.  Maximum-likelihood estimation of optimal scaling factors for expression array normalization , 2001, SPIE BiOS.

[6]  D. Bowtell,et al.  Options available — from start to finish — for obtaining expression data by microarray , 1999, Nature Genetics.

[7]  C. Li,et al.  Feature extraction and normalization algorithms for high‐density oligonucleotide gene expression array data , 2001, Journal of cellular biochemistry. Supplement.

[8]  Partha S. Vasisht.  Computational Analysis of Microarray Data , 2003 .

[9]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[10]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[11]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  R. Lea,et al.  Beta-actin--an unsuitable internal control for RT-PCR. , 2001, Molecular and cellular probes.

[13]  A. Butte,et al.  Further defining housekeeping, or "maintenance," genes Focus on "A compendium of gene expression in normal human tissues". , 2001, Physiological genomics.

[14]  M. Sporn,et al.  N-(4-Hydroxyphenyl)retinamide, a new retinoid for prevention of breast cancer in the rat. , 1979, Cancer research.

[15]  P. Goodfellow,et al.  DNA microarrays in drug discovery and development , 1999, Nature Genetics.

[16]  C Patriotis,et al.  ArrayExplorer, a program in Visual Basic for robust and accurate filter cDNA array analysis. , 2001, BioTechniques.

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

[18]  T R Brown,et al.  NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations. , 2002, Journal of magnetic resonance.

[19]  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.

[20]  Thomas Lengauer,et al.  Centralization: a new method for the normalization of gene expression data , 2001, ISMB.

[21]  C. Li,et al.  Analyzing high‐density oligonucleotide gene expression array data , 2001, Journal of cellular biochemistry.

[22]  M. Bittner,et al.  Expression profiling using cDNA microarrays , 1999, Nature Genetics.

[23]  D. Botstein,et al.  For Personal Use. Only Reproduce with Permission from the Lancet Publishing Group , 2022 .

[24]  William A. Schmitt,et al.  Interactive exploration of microarray gene expression patterns in a reduced dimensional space. , 2002, Genome research.

[25]  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.

[26]  P. Brown,et al.  DNA arrays for analysis of gene expression. , 1999, Methods in enzymology.

[27]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[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]  M. Dugas,et al.  Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis , 2002, Genome Biology.

[30]  D. Botstein,et al.  Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Leif E. Peterson.  Partitioning large-sample microarray-based gene expression profiles using principal components analysis , 2003, Comput. Methods Programs Biomed..