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25. Binder H, Preibisch S: Specific and nonspecific hybridization of oligonucleotide probes on Microarrays. Biophys J 2005, 89(1):337-352. 26. 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. 27. Ferrantini A, Carlon E: On the relationship between perfect matches and mismatches in Affymetrix Genechips. Gene 2008, 422(1-2):1-6. 28. SantaLucia J, Jr., Kierzek R, Turner DH: Stabilities of consecutive A.C, C.C, G.G, U.C, and U.U mismatches in RNA internal loops: Evidence for stable hydrogen-bonded U.U and C.C.+ pairs. Biochemistry 1991, 30(33):8242-8251. 29. Shchepinov MS, Case-Green SC, Southern EM: Steric factors influencing hybridisation of nucleic acids to oligonucleotide arrays. Nucleic Acids Res 1997, 25(6):1155-1161. 30. Sugimoto N, Nakano M, Nakano S: Thermodynamics-structure relationship of single mismatches in RNA/DNA duplexes. Biochemistry 2000, 39(37):11270-11281. 31. Futschik ME, Reeve A, Kasabov N: Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue. Artif Intell Med 2003, 28(2):165-189. 32. 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. 33. Lu Y, Lemon W, Liu PY, Yi Y, Morrison C, Yang P, Sun Z, Szoke J, Gerald WL, Watson M et al: A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. PLoS medicine 2006, 3(12):e467. 34. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S: A comprehensive evaluation of multicategory classification methods for Microarray gene expression cancer diagnosis. Bioinformatics 2005, 21(5):631-643. 35. Berrar DP, Downes CS, Dubitzky W: Multiclass cancer classification using gene expression profiling and probabilistic neural networks. Pac Symp Biocomput 2003:5- 16. 36. GeneChip® Expression Analysis Technical Manual [http://www.affymetrix.com/support/technical/manual/expression_manual.affx] 37. Seman Kachalo ZAaJL: Method of Microarray Data Analysis III. Paper from Camda '02 2002:185-199. 158
38. Michael Stonebraker LAR, Michael Hirohama The Design of POSTGRES. In., 8.0.3 edn: IEEE Transactions on Knowledge and Data Engineering 1986. 39. Rossum Gv: Python. In. 40. Gregorio FD: psycopg2. In., 2 2.0.2 edn: Psycopg is a PostgreSQL database adapter for the Python programming language. Its main advantages are that it supports the full Python DBAPI 2.0 and it is thread safe at level 2. It was designed for heavily multithreaded applications that create and destroy lots of cursors and make a conspicuous number of concurrent INSERTs or UPDATEs. The psycopg distribution includes ZPsycopgDA, a Zope Database Adapter. 41. R DCT: R: A Language and Environment for Statistical Computing. In. Vienna, Austria: R Foundation for Statistical Computing. 42. Walter Moreira GW: rpy. In., 1.0 edn: RPy is a very simple, yet robust, Python interface to the R Programming Language. It can manage all kinds of R objects and can execute arbitrary R functions (including the graphic functions). All errors from the R language are converted to Python exceptions. Any module installed for the R system can be used from within Python. 43. Rouillard JM, Zuker M, Gulari E: OligoArray 2.0: design of oligonucleotide probes for DNA Microarrays using a thermodynamic approach. Nucleic Acids Res 2003, 31(12):3057-3062. 44. Andy Dustman JEaMT: MySQLdb. In., 1.2.0 edn: MySQL support for Python. MySQL versions 3.23-25.21; and Python versions 22.23-22.25 are supported. MySQLdb is the Python DB API-22.20 interface. _mysql is a low-level API similiar to the MySQL C API. ZMySQLDA is a Database Adapter for Zope22. 45. Cuff JA, Coates GM, Cutts TJ, Rae M: The Ensembl computing architecture. Genome Res 2004, 14(5):971-975. 46. Affymetrix.com [http://www.affymetrix.com] 47. 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. 48. Bhattacharjee Arindham WGR, Jane Staunton, Cheng Li, Stefano Monti, Priya Vasa, Christine Ladd, Javad Beheshti, Raphael Bueno, Michael Gillette, Massimo Loda, Griffin Weber, Eugene J. Mark, Eric S. Lander, Wing Wong, Bruce E. Johnson, Todd R. Golub, David J. Sugarbaker, and Mathew Meyerson: Classificaton of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. PNAS 2001, 98(24):13790-13795. 159
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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
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 and 166: method addressing dye, intensity-de
Page 167 and 168: 73. Alter O, Brown PO, Botstein D:
Page 169: 11. Kumari S, Verma LK, Weller JW:
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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