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Chapter 1: BIBLIOGRAPHY 1. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC et al: Minimum information about a Microarray experiment (MIAME)-toward standards for Microarray data. Nature genetics 2001, 29(4):365-371. 2. Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ: High density synthetic oligonucleotide arrays. Nature genetics 1999, 21(1 Suppl):20-24. 3. Fixman M, Freire JJ: Theory of DNA melting curves. Biopolymers 1977, 16(12):2693- 2704. 4. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H et al: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14(13):1675-1680. 5. Southern EM: DNA Microarrays. History and overview. In: Methods in Molecular Biology. Edited by Rampal JB, vol. 170: Humana Press; 2001: 15. 6. Southern EM: DNA Microarrays. In: DNA Arrays Methods and Protocols. Edited by Rampal JB, vol. 170. Totoja, NJ: Humana Press: 1-15. 7. Yalow RS, Berson SA: Immunoassay of endogenous plasma insulin in man. J Clin Invest 1960, 39:1157-1175. 8. Affymetrix.com [http://www.affymetrix.com] 9. GeneChip® Expression Analysis Technical Manual [http://www.affymetrix.com/support/technical/manual/expression_manual.affx] 10. Southern E, Mir K, Shchepinov M: Molecular interactions on Microarrays. Nature genetics 1999, 21(1 Suppl):5-9. 11. Nimblegen.com [http://www.nimblegen.com/] 12. Agilent.com [http://www.agilent.com] 150
13. Bowtell DD: Options available--from start to finish--for obtaining expression data by Microarray. Nature genetics 1999, 21(1 Suppl):25-32. 14. Cheung VG, Morley M, Aguilar F, Massimi A, Kucherlapati R, Childs G: Making and reading Microarrays. Nature genetics 1999, 21(1 Suppl):15-19. 15. Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM: Expression profiling using cDNA Microarrays. Nature genetics 1999, 21(1 Suppl):10-14. 16. Brown PO, Botstein D: Exploring the new world of the genome with DNA Microarrays. Nature genetics 1999, 21(1 Suppl):33-37. 17. Flikka K, Yadetie F, Laegreid A, Jonassen I: XHM: a system for detection of potential cross hybridizations in DNA Microarrays. BMC Bioinformatics 2004, 5:117. 18. Kumari S, Verma LK, Weller JW: AffyMAPSDetector: a software tool to characterize Affymetrix GeneChip expression arrays with respect to SNPs. BMC Bioinformatics 2007, 8:276. 19. Seman Kachalo ZAaJL: Method of Microarray Data Analysis III. Paper from Camda '02 2002:185-199. 20. Wren JD, Kulkarni A, Joslin J, Butow RA, Garner HR: Cross-hybridization on PCRspotted Microarrays. IEEE Eng Med Biol Mag 2002, 21(2):71-75. 21. Bevilacqua PC, SantaLucia J, Jr.: The biophysics of RNA. ACS Chem Biol 2007, 2(7):440-4 22. SantaLucia J, Jr., Hicks D: The thermodynamics of DNA structural motifs. Annu Rev Biophys Biomol Struct 2004, 33:415-440. 23. Watkins NE, Jr., SantaLucia J, Jr.: Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes. Nucleic Acids Res 2005, 33(19):6258-6267. 24. Weckx S, Carlon E, DeVuyst L, Van Hummelen P: Thermodynamic behavior of short oligonucleotides in Microarray hybridizations can be described using Gibbs free energy in a nearest-neighbor model. J Phys Chem B 2007, 111(48):13583-13590. 25. SantaLucia J, Jr., Allawi HT, Seneviratne PA: Improved nearest-neighbor parameters for predicting DNA duplex stability. Biochemistry 1996, 35(11):3555-3562. 26. SantaLucia J, Jr., Turner DH: Measuring the thermodynamics of RNA secondary structure formation. Biopolymers 1997, 44(3):309-319. 27. Wu P, Nakano S, Sugimoto N: Temperature dependence of thermodynamic properties for DNA/DNA and RNA/DNA duplex formation. Eur J Biochem 2002, 269(12):2821-2830. 151
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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
Page 111 and 112: for the RMA and dCHIP interpretatio
Page 113 and 114: 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: BIBLIOGRAPHY 149
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 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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