Evaluation of normalization methods for microarray data

BackgroundMicroarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.ResultsIn this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.ConclusionsOur results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

[1]  Jonathan Pevsner,et al.  SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis , 2002, Bioinform..

[2]  Robert Clarke,et al.  Iterative normalization of cDNA microarray data , 2002, IEEE Transactions on Information Technology in Biomedicine.

[3]  M. Oh,et al.  Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. , 2001, Nucleic acids research.

[4]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[5]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[6]  David Edwards,et al.  Non-linear Normalization and Background Correction in One-channel CDNA Microarray Studies , 2003, Bioinform..

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

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

[9]  William C Reinhold,et al.  Transcriptional regulation of mitotic genes by camptothecin-induced DNA damage: microarray analysis of dose- and time-dependent effects. , 2002, Cancer research.

[10]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[11]  S. Knudsen,et al.  A new non-linear normalization method for reducing variability in DNA microarray experiments , 2002, Genome Biology.

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

[13]  L. K. Buehler,et al.  Normalizing DNA microarray data. , 2002, Current issues in molecular biology.

[14]  Pierre R. Bushel,et al.  Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models , 2001, J. Comput. Biol..

[15]  Jin Hyuk KIm,et al.  A novel method using edge detection for signal extraction from cDNA microarray image analysis , 2001, Experimental & Molecular Medicine.

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

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

[18]  Terence P. Speed,et al.  Normalization for cDNA microarry data , 2001, SPIE BiOS.

[19]  James J. Chen,et al.  Normalization Methods for Analysis of Microarray Gene-Expression Data , 2003, Journal of biopharmaceutical statistics.

[20]  Taesung Park,et al.  Statistical tests for identifying differentially expressed genes in time-course microarray experiments , 2003, Bioinform..

[21]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[22]  John Quackenbush.  Microarray data normalization and transformation , 2002, Nature Genetics.

[23]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.