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Chapter 3: messenger RNA expression is a prognostic marker in curatively resected non-small cell lung cancer. Clin Cancer Res 2004, 10(5):1588-1596. 1. 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):e15. 2. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A 2001, 98(1):31-36. 3. 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. 4. Stearman RS, Dwyer-Nield L, Zerbe L, Blaine SA, Chan Z, Bunn PA, Jr., Johnson GL, Hirsch FR, Merrick DT, Franklin WA et al: Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol 2005, 167(6):1763-1775. 5. Rosner B: Fundamentals of Biostatistics, 5th edn. Pacific Grove: Duxbury; 2000. 6. Alpaydin E: Introduction to Machine Learning. Cambridge: The MIT Press; 2004. 7. Manly BFJ: Multivariate Statistical Methods, 3rd edn. Washington D.C.: Chapman & Hall/CRC; 2005. 8. Linear Discriminant Analysis, A Brief tutorial [http://www.music.mcgill.ca/~ich/classes/mumt611/classifiers/lda_theory.pdf] 9. Fisher Linear Discriminant Analysis [http://www.ics.uci.edu/~welling/classnotes/papers_class/Fisher-LDA.pdf] 10. Random Forest [http://oz.berkeley.edu/users/breiman/randomforest2001.pdf] 11. Looking Inside the Black Box [http://stat-www.berkeley.edu/users/breiman/wald2002- 2.pdf] 12. Diaz-Uriarte R, Alvarez de Andres S: Gene selection and classification of Microarray data using random forest. BMC Bioinformatics 2006, 7:3. 13. Davis JG, M.: The Relationship between Precision-Recall and ROC Curves. ICML Proceedings of the 23rd International Conference on Machine Learning 2006:8. 14. Everson Richard M. JF: Multi-class ROC analysis from a multi-objective optimisation perspective. Pattern Recognition Letters 2006, 27(8):22. 162
15. Irizarry RA: affy. In.: Bioconductor. 16. R DCT: R: A Language and Environment for Statistical Computing. In. Vienna, Austria: R Foundation for Statistical Computing. 17. Li C, Hung Wong W: Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome biology 2001, 2(8):RESEARCH0032. 18. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4(2):249-264. 19. Berrar DP, Downes CS, Dubitzky W: Multiclass cancer classification using gene expression profiling and probabilistic neural networks. Pac Symp Biocomput 2003:5- 16. 20. 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. 21. G. Parmigiani ESG, R. A. Irizarry, S. L. Zeger: The Analysis of Gene Expression Data. New York: Springer; 2003. 22. Kostka D, Spang R: Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Comput Biol 2008, 4(2):e22. 23. 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. 24. Ringner M, Peterson C: Microarray-based cancer diagnosis with artificial neural networks. Biotechniques 2003, Suppl:30-35. 25. Ringner M, Peterson C, Khan J: Analyzing array data using supervised methods. Pharmacogenomics 2002, 3(3):403-415. 26. 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. 27. 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. 28. 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. 163
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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
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Figure 4.5: Kaplan-Meyer survival c
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Figure 4.7: Low grade tumor surviva
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The candidate gene list values were
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ejecting the null hypothesis [8], a
Page 123 and 124: Figure 4.9: GO connectivity of cand
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Page 127 and 128: sample cleansing process and these
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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 and 166: method addressing dye, intensity-de
Page 167 and 168: 73. Alter O, Brown PO, Botstein D:
Page 169 and 170: 11. Kumari S, Verma LK, Weller JW:
Page 171 and 172: 38. Michael Stonebraker LAR, Michae
Page 173: 64. Cropp CS, Lidereau R, Leone A,
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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