微陣列資料分類近年來被廣泛的應用在生物與醫學領域上，也因此有越來越多公開的微陣列資料可以取得，本論文研究是否不同實驗室所產生的微陣列資料集可以進一步結合，期望透過更大的資料集得到更準確的分類模型，並達到樣本共享的目的。當考慮將多樣性的微陣列資料加以合併用於未來的分類工作之時，資料集數值的取得與使用都必須更加小心，以避免產生錯誤的預測結果。完整的微陣列資料分類工作包含數個部分：1、原始資料轉換為基因表現值；2、樣本正規化；3、基因對應關係建立；4、特徵基因選取；5、屬性正規化。本論文研究何種基因表現值轉換演算法於多樣性微陣列資料分類有較佳的表現，並探討樣本與屬性正規化在多樣性樣本分類效果上的影響。本論文使用三組多樣性微陣列資料以評估此些因素於樣本分類的影響力與交互的關係。

Microarray experiments have been widely used in biological and medical research. When more and more public data sources available in the world, this thesis aims to investigate the possibility and develop a correct procedure of combining these heterogeneous data sources together for classification analysis. Sample classification includes several parts: 1. translating raw data expression values; 2. sample-wise normalization; 3. gene mapping; 4. gene selection; and 5. gene-wise normalization. This work studies which algorithm of translating expression values performs better in cross-generation and cross-laboratory analysis. In addition, the effect of sample- or feature-wise normalization on the performance of classification is examined. Eight data sets from heterogeneous sources are employed in this study to validate the proposed methodology.

Contents
書名頁...........i
摘要...........ii
Abstract...........iii
誌謝...........iv
Contents...........v
List of Tables...........vii
List of Figures...........viii
Chapter 1 Background...........1
Chapter 2 Related Work...........4
2.1 Raw Data to Expression Values...........4
2.1.1 Affy Microarray Suite (MAS5)...........4
2.1.2 dChip...........5
2.1.3 Robust Multi-array Analysis (RMA)...........5
2.2 Sample-wise Normalization...........5
2.3 Gene Mapping...........6
2.4 Gene Selection...........7
2.5 Gene-wise Normalization...........7
2.6 Cross-validation...........8
Chapter 3 Methods...........9
3.1 Microarray Data Collection and Preprocessing...........11
3.2 Gene Mapping (Common Probe Sets Identification)...........13
3.3 Sample-wise Normalization and Gene-wise Normalization...........13
3.4 Predictions...........14
Chapter 4 Results and Discussions...........15
4.1 A Comparison between Using Expression Values and Rank Information...........15
4.2 Additional Normalization for Expression Values and Rank Levels...........21
4.3 Pitfalls of Reporting Cross-validation Accuracy...........21
4.4 Effects of the Algorithms Translating Raw Data to Expression Values...........25
Chapter 5 Conclusions...........28
References...........29
Appendix...........34
Gene Mapping...........34
Breast Cancer Prediction...........39

[1] Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics 2001, 29:365-371.
[2] Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18(3):405-412.
[3] Mah N, Thelin A, Nikolaus TLS, Kiihbacher T, Gurbuz Y, Eickhoff H, Kloppel G, Lehrach H, Mellgard B, Costello CM, Schreiber S: A comparison of oligonucleotide and cDNA-based microarray systems. Physiological Genomics 2004, 16:361-370.
[4] Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JGN, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W: Multiple-laboratory comparison of microarray platforms. Nature Methods 2005, 2:345-350.
[5] Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J: Independence and reproducibility across microarray platforms. Nature Methods 2005, 2(5):337-344.
[6] Bammler T, Beyer R, Bhattacharya S, Boorman G, Boyles A, Bradford B, Bumgar-ner R, Bushel P, Chaturvedi K, Choi D: Standardizing global gene expression analysis between laboratories and across platforms. Nature Methods 2005, 2(5):351-356.
[7] Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(25):9309-9314.
[8] Nimgaonkar A, Sanoudou D, Butte1 AJ, Haslett JN, Kunkel LM, Beggs AH, Kohane IS: Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics 2003, 4:27.
[9] Hwang KB, Kong SW, Greenberg SA, Park PJ: Combining gene expression data from different generations of oligonucleotide arrays. BMC Bioinformatics 2004, 5:159.
[10] Kong SW, Hwang KB, Kim RD, Zhang BT, Greenberg SA, Kohane IS, Park PJ: CrossChip: a system supporting comparative analysis of different generations of Affymetrix arrays. Bioinformatics 2005, 21(9):2116-2117.
[11] Elo LL, Lahti L, Skottman H, Kylaniemi M, Lahesmaa R, Aittokallio T: Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Research 2005, 33(22):e193.
[12] Bhattacharya S, Mariani TJ: Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling. Nucleic Acids Research 2005, 33(18):e157.
[13] Bloom G, Yang IV, Boulware D, Kwong KY, Coppola D, Eschrich S, Quackenbush J, Yeatman TJ: Multi-platform, multi-site, microarray-based human tumor classification. American Journal of Pathology 2004, 164:9-16.
[14] Jiang H, Deng Y, Chen HS, Tao L, Sha Q, Chen J, Tsai CJ, Zhang S: Joint analysis of two microarray gene-expression data sets to select lung ade-nocarcinoma marker genes. BMC Bioinformatics 2004, 5:81.
[15] Xu L, Tan AC, Naiman DQ, Geman D, Winslow RL: Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 2005, 21(20):3905-3911.
[16] Tothill RW, Kowalczyk A, Rischin D: An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Research 2005, 65(10):4031-4040.
[17] Warnat P, Eils R, Brors B: Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics 2005, 6:265.
[18] Todling J, Spang R: Assessment of five microarray experiments on gene expression profiling of breast cancer. Poster Presentation RECOMB 2003, [http://citeseer.ist.psu.edu/611350.html].
[19] Li C, Hung Wong W: Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol 2001, 2(8).
[20] Li C, Hung Wong W: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 2001, 98:31-6.
[21] Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003, 31(4).
[22] Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4(2):249-64.
[23] Tsodikov A, Szabo A, Jones D: Adjustments and measures of differential expression for microarray data. Bioinformatics 2002, 18(2):261-60.
[24] Szabo A, Boucher K, Carroll W, Klebanov L, Tsodikov A, Yakovlev A: Variable selection and pattern recognition with gene expression data generated by the microarray technology. Mathematical Biosciences 2002, 176:71-98.
[25] Bolstad B, Irizarry R, Astrand M, Speed T: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19(2):185-93.
[26] Qiu X, Brooks AI, Klebanov L, Yakovlev A: The effects of normalization on the correlation structure of microarray data. BMC Bioinformatics 2005, 6:120.
[27] Pontius J, Wagner L, Schuler G: UniGene: a unified view of the transcriptome. In NCBI Handbook, Bethesda (MD): National Center for Biotechnology Information 2003.
[28] Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research 2005, 33:D54-D58.
[29] Affymetrix: User''s guide to product comparison spreadsheets 2003, [http://www. affymetrix.com/support/technical/manual/].
[30] Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 2001, 98:5116-5121.
[31] Varma S, Simon R: Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 2006, 7:91.
[32] Cover T, Hart P: Nearest neighbor pattern classification. IEEE Trans Information Theory 1967, 13:21-27.
[33] R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 2005, [http: //www.R-proj ect.org].
[34] Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A, Cheng C, Campana D, Wilkins D, Zhou X, Liu JLH, Pui CH, Evans WE, Naeve C, Wong L, Downing JR: Pediatric lymphoblas-tic leukemia by gene expression profiling. Cancer Cell 2002, 1:133 143.
[35] Ross ME, Zhou X, Song G, Shurtleff SA, Girtman K, Williams WK, Liu HC, Mahfouz R, Raimondi SC, Lenny N, Patel A, Downing JR: Classification of pediatric acute lymphoblastic leukemia by gene expression profile. Blood 2003, 102:2951-2959.
[36] Ross ME, Mahfouz R, Onciu M, Liu HC, Zhou X, Song G, Shurtleff SA, Pounds S, Ma CCJ, Ribeiro RC, Rubnitz JE, Girtman K, Williams WK, Raimondi SC, Liang DC, Shih LY, Pui CH, Downing JR: Gene expression profiling of pediatric acute myelogenous leukemia. Blood 2004, 104:3679-3687.
[37] Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, van Waalwijk van Doorn-Khosrovani SB, Boer JM, Beverloo HB, Moorhouse MJ, van der Spek PJ, Lowen-berg B, Delwel R: Prognostically useful gene-expression profiles in acute myeloid leukemia. New England Journal of Medicine 2004, 350:1617-1628.
[38] Gutierrez NC, Lopez-Perez R, Hernandez JM, Isidro I, Gonzalez B, Delgado M, Ferminan E, Garcia JL, Vazquez L, Gonzalez M, Miguel JFS: Gene expression profile reveals deregulation of genes with relevant functions in the different subclasses of acute myeloid leukemia. Leukemia 2005, 19:402 409.
[39] West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, John A Olson J, Marks JR, Nevins JR: Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(20):11462-11467.
[40] Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM, West M, Nevins JR, Huang AT: Gene expression predictors of breast cancer outcomes. Lancet 2003, 361(9369):1590-1596.
[41] Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Tim-mermans M, van Gelder MEM, Yu J, Jatkoe T, Berns EMJJ, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365(9460):671-679.