Preprocessing differential methylation hybridization microarray data

BackgroundDNA methylation plays a very important role in the silencing of tumor suppressor genes in various tumor types. In order to gain a genome-wide understanding of how changes in methylation affect tumor growth, the differential methylation hybridization (DMH) protocol has been developed and large amounts of DMH microarray data have been generated. However, it is still unclear how to preprocess this type of microarray data and how different background correction and normalization methods used for two-color gene expression arrays perform for the methylation microarray data. In this paper, we demonstrate our discovery of a set of internal control probes that have log ratios (M) theoretically equal to zero according to this DMH protocol. With the aid of this set of control probes, we propose two LOESS (or LOWESS, locally weighted scatter-plot smoothing) normalization methods that are novel and unique for DMH microarray data. Combining with other normalization methods (global LOESS and no normalization), we compare four normalization methods. In addition, we compare five different background correction methods.ResultsWe study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.ConclusionsIt is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.

[1]  R. Koenker.  Quantile Regression: Name Index , 2005 .

[2]  Alicia Oshlack,et al.  Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes , 2007, Genome Biology.

[3]  M. Pellegrini,et al.  Genome-wide High-Resolution Mapping and Functional Analysis of DNA Methylation in Arabidopsis , 2006, Cell.

[4]  A. Mhashilkar,et al.  Housekeeping genes in cancer: normalization of array data. , 2005, BioTechniques.

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

[6]  Giovanni Parmigiani,et al.  Pre-processing Agilent microarray data , 2007, BMC Bioinformatics.

[7]  T. Huang,et al.  Methylation profiling of CpG islands in human breast cancer cells. , 1999, Human molecular genetics.

[8]  T. Mikkelsen,et al.  Genome-scale DNA methylation maps of pluripotent and differentiated cells , 2008, Nature.

[9]  Ken W. Y. Cho,et al.  Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals. , 2002, Nucleic acids research.

[10]  Pearlly Yan,et al.  Identifying differentially methylated genes using mixed effect and generalized least square models , 2009, BMC Bioinformatics.

[11]  E. Levanon,et al.  Human housekeeping genes are compact. , 2003, Trends in genetics : TIG.

[12]  Jörg Rahnenführer,et al.  Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit (2005): Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2009 .

[13]  Michael B. Stadler,et al.  Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome , 2007, Nature Genetics.

[14]  Charles L. Kooperberg,et al.  Improved Background Correction for Spotted DNA Microarrays , 2002, J. Comput. Biol..

[15]  Peter J. Park,et al.  Normalization and experimental design for ChIP-chip data , 2007, BMC Bioinformatics.

[16]  Pearlly S Yan,et al.  High-throughput methylation profiling by MCA coupled to CpG island microarray. , 2007, Genome research.

[17]  Timothy E. Reddy,et al.  Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. , 2009, Genome research.

[18]  Tim Hui-Ming Huang,et al.  Applications of CpG island microarrays for high-throughput analysis of DNA methylation. , 2002, The Journal of nutrition.

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

[20]  Kenny Q. Ye,et al.  Comparative isoschizomer profiling of cytosine methylation: the HELP assay. , 2006, Genome research.

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

[22]  G. Smyth,et al.  Microarray background correction: maximum likelihood estimation for the normal–exponential convolution , 2008, Biostatistics.

[23]  Paul C Boutros,et al.  Evaluation of various housekeeping genes for their applicability for normalization of mRNA expression in dioxin-treated rats. , 2006, Chemico-biological interactions.

[24]  A. Feinberg,et al.  Comprehensive High‐Throughput Arrays for Relative Methylation (CHARM) , 2010, Current protocols in human genetics.

[25]  Alexander Dobrovic,et al.  Variable promoter region CpG island methylation of the putative tumor suppressor gene Connexin 26 in breast cancer. , 2002, Carcinogenesis.

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

[27]  I. Simon,et al.  Evidence for an instructive mechanism of de novo methylation in cancer cells , 2006, Nature Genetics.

[28]  Martin Widschwendter,et al.  DNA methylation and breast carcinogenesis , 2002, Oncogene.

[29]  Shili Lin,et al.  Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray. , 2009, Methods in molecular biology.

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

[31]  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.

[32]  Rafael A Irizarry,et al.  Comprehensive high-throughput arrays for relative methylation (CHARM). , 2008, Genome research.

[33]  W. Lam,et al.  Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells , 2005, Nature Genetics.

[34]  Gordon K. Smyth,et al.  A comparison of background correction methods for two-colour microarrays , 2007, Bioinform..

[35]  Susan J Clark,et al.  DNA methylation changes in ovarian cancer: implications for early diagnosis, prognosis and treatment. , 2008, Gynecologic oncology.

[36]  P. M. Nissom,et al.  A novel normalization method for effective removal of systematic variation in microarray data , 2006, Nucleic acids research.

[37]  N. Davidson,et al.  DNA methylation in breast cancer. , 2001, Endocrine-related cancer.

[38]  Wotao Yin,et al.  Background correction for cDNA microarray images using the TV+L1 model , 2005, Bioinform..

[39]  Martin Widschwendter,et al.  Breast cancer DNA methylation profiles in cancer cells and tumor stroma: association with HER-2/neu status in primary breast cancer. , 2006, Cancer research.