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57. Ioannidis JP: Microarrays and molecular research: noise discovery? Lancet 2005, 365(9458):454-455. 58. Cole KA, Krizman DB, Emmert-Buck MR: The genetics of cancer--a 3D model. Nature genetics 1999, 21(1 Suppl):38-41. 59. Kachalo S. AZ, and Liang J.: Assessing the potential effect of cross-hybridization on oligonucleotide Microarrays. In: Methods of Microarray Data Analysis III. Edited by Kimberly F. Johnson SML. Norwell: Kluwer Academic Publishers; 2003. 60. Lander ES: Array of hope. Nature genetics 1999, 21(1 Suppl):3-4. 61. [http://www.abrf.org/] 62. Hacia JG: Resequencing and mutational analysis using oligonucleotide Microarrays. Nature genetics 1999, 21(1 Suppl):42-47. 63. Brown VM, Ossadtchi A, Khan AH, Cherry SR, Leahy RM, Smith DJ: Highthroughput imaging of brain gene expression. Genome Res 2002, 12(2):244-254. 64. Netaffx Analysis Center [https://www.affymetrix.com/site/login/login.affx] 65. Carlon E, Heim T, Wolterink JK, Barkema GT: Comment on "Solving the riddle of the bright mismatches: labeling and effective binding in oligonucleotide arrays". Phys Rev E Stat Nonlin Soft Matter Phys 2006, 73(6 Pt 1):063901; author reply 063902. 66. Lincoln SE: CHI Total Microarray Data Analysis and Interpretation. In. Edited by Panel. Washington D.C.; 2006: Stated while addressing the morning panel. 67. Ho J, Hwang WL: Automatic Microarray spot segmentation using a Snake-Fisher model. IEEE Trans Med Imaging 2008, 27(6):847-857. 68. Shields R: The emperor's new clothes revisited. Trends Genet 2006, 22(9):463. 69. Fridlyand SDaJ: Introduction to Classification in Microarray Experiments. In: DNA Arrays Methods and Protocols. Edited by Rampal JB, vol. 170. Totoja, NJ: Humana Press: 132-149. 70. Statnikov A, Tsamardinos I, Dosbayev Y, Aliferis CF: GEMS: a system for automated cancer diagnosis and biomarker discovery from Microarray gene expression data. International journal of medical informatics 2005, 74(7-8):491-503. 71. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S: A comprehensive evaluation of multicategory classification methods for Microarray gene expression cancer diagnosis. Bioinformatics (Oxford, England) 2005, 21(5):631-643. 72. Ringner M, Peterson C, Khan J: Analyzing array data using supervised methods. Pharmacogenomics 2002, 3(3):403-415. 154
73. Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000, 97(18):10101-10106. 74. Brody JP, Williams BA, Wold BJ, Quake SR: Significance and statistical errors in the analysis of DNA Microarray data. Proc Natl Acad Sci U S A 2002, 99(20):12975- 12978. 75. Fathallah-Shaykh HM: Microarrays: applications and pitfalls. Arch Neurol 2005, 62(11):1669-1672. 76. van Steensel B: Mapping of genetic and epigenetic regulatory networks using Microarrays. Nature genetics 2005, 37 Suppl:S18-24. 77. Alpaydin E: Introduction to Machine Learning. Cambridge: The MIT Press; 2004. 78. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M et al: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001, 98(24):13790-13795. 79. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537. 80. Li L, Weinberg CR, Darden TA, Pedersen LG: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics (Oxford, England) 2001, 17(12):1131-1142. 81. Ntzani EE, Ioannidis JP: Predictive ability of DNA Microarrays for cancer outcomes and correlates: an empirical assessment. Lancet 2003, 362(9394):1439-1444. 82. Ringner M, Peterson C: Microarray-based cancer diagnosis with artificial neural networks. Biotechniques 2003, Suppl:30-35. 83. Statnikov A, Wang L, Aliferis CF: A comprehensive comparison of random forests and support vector machines for Microarray-based cancer classification. BMC Bioinformatics 2008, 9:319. 84. Manly BFJ: Multivariate Statistical Methods, 3rd edn. Washington D.C.: Chapman & Hall/CRC; 2005. 85. Edward Keedwell AN: Intelligent Bioinformatics The application of artificial intelligence techniques to bioinformatics problems. Hoboken: John Wiley & Sons; 2005. 86. Rosner B: Fundamentals of Biostatistics, 5th edn. Pacific Grove: Duxbury; 2000. 155
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An Adenocarcinoma Case Study of the
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DEDICATION The work presented in th
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TABLE OF CONTENTS LIST OF TABLES ..
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Conclusion.........................
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LIST OF FIGURES Figure Page Figure
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violate the linear correlation rela
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Chapter 1: An Introduction to Micro
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previously stated, rigorous reagent
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Factors influencing Signal Interpre
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likelihood that no such structural
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hybridization and multiple dye stra
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classical reaction equations, modif
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prevent the hybridization of probes
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platforms are used to deposit them
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and chip layout [64]. In particular
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whether analyses based upon individ
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The process of determining a candid
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implemented in order to demonstrate
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affecting probe-target duplex forma
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It has long been known that the var
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probes can thus be reincorporated i
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(Carr, ms in review). This ProbeFAT
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have specific attributes which can
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elow saturation. The investigator m
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a. A filter based on how many probe
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means. The remaining sets possess s
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for the 24 samples in the Lu, et al
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Table 2.1: Probe Numbers per filter
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the effect can be. Both the type of
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described above. In Figure 2.3, the
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various data processing stages are
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position of the probe on the transc
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Figure 2.5: BaFL consistency. Demon
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Figure 2.6: Probe-Transcript region
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Table 2.3: ProbeSet behavior predic
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cleansing process. This is demonstr
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Table 2.4: ProbeSet behavior of pro
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A Priori Prediction We demonstrated
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Conclusion We have presented a comp
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mind that they are most aware of th
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Probe Cleansing Methods The BaFL pr
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are significant assume such a data
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cleansing process and were assessed
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sample selection, with replacement,
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Figure 3.1: P value distributions.
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Figure 3.2: Down selection models-
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cleansing methods were used to gene
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Discussion A striking result from t
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Figure 3.6: Concordance summary. Th
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whole model analysis without permut
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change, apparently the greater vari
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though in Chapter 2 we showed that
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Although a list of 325 candidate ge
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was determined to consist of 30 Pro
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Figure 4.1: Non-traditional PCA ana
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for the RMA and dCHIP interpretatio
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was one of them: it is counted as p
Page 115 and 116: Figure 4.5: Kaplan-Meyer survival c
Page 117 and 118: Figure 4.7: Low grade tumor surviva
Page 119 and 120: The candidate gene list values were
Page 121 and 122: ejecting the null hypothesis [8], a
Page 123 and 124: Figure 4.9: GO connectivity of cand
Page 125 and 126: under linear and non-linear dimensi
Page 127 and 128: sample cleansing process and these
Page 129 and 130: averaged to give a final approximat
Page 131 and 132: 119 Table 5.1: : NSCLC candidate ge
Page 133 and 134: Figure 5.1: NSCLC ProbeSet gain sel
Page 135 and 136: Figure 5.3: NSCLC refined average p
Page 137 and 138: statistical criterion, but it appea
Page 139 and 140: lacking p53 mutations [30]. While,
Page 141 and 142: Kruppel-like factor 5 (KLF5) has al
Page 143 and 144: Appendix A # This is the main drive
Page 145 and 146: def Driver(usr, pswd, db, logfile,
Page 147 and 148: def DriverXH(usr, pswd, db, logfile
Page 149 and 150: fp.write('\tTable '), fp.write(msk[
Page 151 and 152: #determine outliers lowrs1=nonzero(
Page 153 and 154: tmp="update sample_mask set exclude
Page 155 and 156: fp.write('\n'+msk[ptr[j]]+' exclude
Page 157 and 158: cur, conn=UpdateWorkReg(cur, conn,
Page 159 and 160: Appendix E # The result of permutin
Page 161 and 162: BIBLIOGRAPHY 149
Page 163 and 164: 13. Bowtell DD: Options available--
Page 165: method addressing dye, intensity-de
Page 169 and 170: 11. Kumari S, Verma LK, Weller JW:
Page 171 and 172: 38. Michael Stonebraker LAR, Michae
Page 173 and 174: 64. Cropp CS, Lidereau R, Leone A,
Page 175 and 176: 15. Irizarry RA: affy. In.: Biocond
Page 177 and 178: Chapter 4: 1. Minna JD, Roth JA, Ga
Page 179 and 180: 30. Delaval B, Ferrand A, Conte N,
Page 181 and 182: 27. Bai J, Cederbaum AI: Catalase p
Page 183 and 184: 51. Shouse GP, Cai X, Liu X: Serine
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