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2011 | 58 | 4 | 573-580
Article title

Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods

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Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.
Physical description
  • Department of Biotechnology and Food Microbiology, Poznan University of Life Sciences, Poznań, Poland
  • Institute of Bioorganic Chemistry PAS, Poznań, Poland
  • Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Poznań, Poland
  • Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Poznań, Poland
  • Department of Biotechnology and Food Microbiology, Poznan University of Life Sciences, Poznań, Poland
  • Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Poznań, Poland
  • Poznan University of Medical Sciences, Department of Hematology, Poznań, Poland and Institute of Computing Science, Poznan University of Technology, Poznań, Poland
  • Baker SG (2008) Using microarrays to study the microenvironment in tumor biology: the crucial role of statistics. Semin Cancer Biol 18: 305-310.
  • Belcher CE, Drenkow J, Kehoe B, Gingeras TR, McNamara N, Lemjabbar H, Basbaum C, Relman DA (2000) The transcriptional responses of respiratory epithelial cells to Bordetella pertussis reveal host defensive and pathogen counter-defensive strategies. Proc Natl Acad Sci USA 97: 13847-13852.
  • Berger JA, Hautaniemi S, Järvinen AK, Edgren H, Mitra SK, Astola J (2004) Optimized LOWESS normalization parameter selection for DNA microarray data. BMC Bioinformatics 5: 194.
  • Chua SW, Vijayakumar P, Nissom PM, Yam CY, Wong VV, Yang H (2006) A novel normalization method for effective removal of systematic variation in microarray data. Nucleic Acids Res 34: e38.
  • Churchill G A (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32: 490-495.
  • Cowell JK, Hawthorn L (2007) The application of microarray technology to the analysis of the cancer genome. Curr Mol Med 7: 103-120.
  • Delgado S, O'Sullivan E, Fitzgerald G, Mayo B (2008) In vitro evaluation of the probiotic properties of human intestinal Bifidobacterium species and selection of new probiotic candidates. J Appl Microbiol 104: 1119-1127.
  • Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiment. Statistica Sinica 12: 111-139.
  • Eckmann L, Smith JR, Housley MP, Dwinell MB, Kagnoff MF (2000) Analysis by high density cDNA arrays of altered gene expression in human intestinal epithelial cells in response to infection with the invasive enteric bacteria Salmonella. J Biol Chem 275: 14084-14094.
  • Fukushima K, Ogawa H, Takahashi K, Naito H, Funayama Y, Kitayama T, Yonezawa H, Sasaki I (2003) Non-pathogenic bacteria modulate colonic epithelial gene expression in germ-free mice. Scand J Gastroenterol 38: 626-634.
  • Gentleman R, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.
  • Gentleman R, Irizarry RA, Carey VJ, Dudoit S, Hubrer W (2005) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, New York.
  • Gopal PK, Prasad J, Smart J, Gill HS (2001) In vitro adherence properties of Lactobacillus rhamnosus DR20 and Bifidobacterium lactis DR10 strains and their antagonistic activity against an enterotoxigenic Escherichia coli. Int J Food Microbiol 67: 207-216.
  • Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P, Phillips KL, Xu J, Deng XT, Sun YA, Tong W, Dragan YP, Shi L (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology 24: 1162-1169.
  • Hahne F, Huber W, Gentleman R, Falcon S (2008) Bioconductor Case Studies. Springer, New York.
  • Heczko PB, Strus M, Kochan P (2006) Critical evaluation of probiotic activity of lactic acid bacteria and their effects. J Physiol Pharmacol 57 (Suppl 9): 5-12.
  • Howbrook DN, van der Valk AM, O'Shaughnessy MC, Sarker DK, Baker SC, Lloyd AW (2003) Developments in microarray technologies. Drug Discov Today 8: 642-651.
  • Hsu HH, Lu MD (2008) Feature Selection for Cancer Classification on Microarray Expression Data. Eighth International Conference on Intelligent Systems Design and Applications, IEEE 2008: 153-158.
  • Jeffery IB, Higgins DG, Culhane AC (2006) Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 7: 359.
  • Jirapech-Umpai T, Aitken S (2005) Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 6: 148-158.
  • Jung K, Becker B, Brunner E, Beissbarth T (2011) Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes. Bioinformatics 27: 1377-1383.
  • Knapen D, Vergauwen L, Laukens K, Blust R (2009) Best practices for hybridization design in two-colour microarray analysis. Trends Biotechnol 27: 406-414.
  • Ness SA (2007) Microarray analysis: basic strategies for successful experiments. Mol Biotechnol 36: 205-219.
  • Panigrahi P, Braileanu GT, Chen H, Stine OC (2007) Probiotic bacteria change Escherichia coli-induced gene expression in cultured colonocytes: Implications in intestinal pathophysiology. World J Gastroenterol 13: 6370-6378.
  • Pedron T, Thibault C, Sansonetti PJ (2003) The invasive phenotype of Shigella flexneri directs a distinct gene expression pattern in the human intestinal epithelial cell line Caco-2. J Biol Chem 278: 33878-33886.
  • Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32 (Suppl): 496-501.
  • R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. URL
  • Rosenberger CM, Scott MG, Gold MR, Hancock RE, Finlay BB (2000) Salmonella typhimurium infection and lipopolysaccharide stimulation induce similar changes in macrophage gene expression. J Immunol 164: 5894-5904.
  • Sambuy Y, De Angelis I, Ranaldi G, Scarino ML, Stammati A, Zucco F (2005) The Caco-2 cell line as a model of the intestinal barrier: influence of cell and culture-related factors on Caco-2 cell functional characteristics. Cell Biol Toxicol 21: 1-26.
  • Shi LM, Tong WD, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su ZQ, Han T, Fuscoe JC, Xu ZA, Patterson TA, Hong HX, Xie Q, Perkins RG, Chen JJ, Casciano DA (2005) Cross-platform comparability of microarray technology: Intraplatform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics 6: S12.
  • Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y (2007) Design and Analysis of DNA Microarray Investigations. Springer, New York.
  • Smyth GK (2005) Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor. Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S, eds. pp 397-420. Springer, New York.
  • Smyth GK, Speed TP (2003) Normalization of cDNA microarray data. Methods 31: 265-273.
  • Trevino V, Falciani F, Barrera-Saldana HA (2007) DNA microarrays: a powerful genomic tool for biomedical and clinical research. Mol Med 13: 527-541.
  • Venkatasubbarao S (2004) Microarrays - status and prospects. Trends Biotechnol 22: 630-637.
  • Yang YH, Dudoit S, Luu P, Speed TP (2001) Normalization for cDNA microarray data. In Microarrays: Optical Technologies and Informatics. Bittner ML, Chen Y, Dorsel AN, Dougherty ER eds. Proceedings of SPIE 4266: 141-152.
  • Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl Acids Res 30: e15.
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