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Stearman data, since 4 probes were retained by the Stearman normal samples. By comparison 13 probes were retained by the Stearman adenocarcinoma samples. Table 4.1: Final candidate list. The candidate list of 31 genes from the Bhattacharjee and Stearman datasets, as down selected using the Bonferroni correction. This list includes the osteopontin ProbeSet. Note that ProbeSet 34320_at does not align to a specific gene, under the Ensembl genome build used in these studies, although it does align to one transcript region. ProbeSet Hugo Id 37398_at PECAM1 40282_s_at CFD 40841_at TACC1 36627_at SPARCL1 37027_at AHNAK 39066_at MFAP4 39145_at MYL9 32562_at ENG 31856_at LRRC32 33904_at CLDN3 33137_at LTBP4 38995_at CLDN5 1597_at GAS6 38704_at MACF1 895_at MIF 37658_at GAS6 36569_at CLEC3B 39631_at EMP2 32052_at KRT121P 39760_at QKI 770_at GPX3 34320_at 33756_at AOC3 37009_at CAT 40202_at KLF9 36155_at SPOCK2 32847_at MYLK 38044_at FAM107A 41338_at AES 35152_at RAMP3 34342_s_at* SPP1* 106
The candidate gene list values were used as input for cross dataset validation of the classification performance for the three algorithms described in Chapter 3: LDA, kNN, and RF. As described there, the validation was carried out over 100 random permutations of both the training and test datasets [10, 21]. The classification performance of the BaFL interpreted genes is benchmarked against the RMA and dCHIP values input for the same classifiers. Model performance was assessed by the AUC for the classification success [10, 14, 15]; results are presented in Figure 4.8. Figure 4.8: Candidate list validation. Classification performance for the candidate list of 31 ProbeSets. These ProbeSets were elucidated using the Bonferroni correction of the t-test results and are the set of genes lying in the intersection between the two datasets along with the osteopontin Probeset. The BaFL based values for these ProbeSets demonstrates the best performance for all three classification algorithms and both reciprocal training and test scenarios. 107
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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
Page 67 and 68: Figure 2.6: Probe-Transcript region
Page 69 and 70: Table 2.3: ProbeSet behavior predic
Page 71 and 72: cleansing process. This is demonstr
Page 73 and 74: Table 2.4: ProbeSet behavior of pro
Page 75 and 76: A Priori Prediction We demonstrated
Page 77 and 78: Conclusion We have presented a comp
Page 79 and 80: mind that they are most aware of th
Page 81 and 82: Probe Cleansing Methods The BaFL pr
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Page 89 and 90: Figure 3.1: P value distributions.
Page 91 and 92: Figure 3.2: Down selection models-
Page 93 and 94: cleansing methods were used to gene
Page 95 and 96: Discussion A striking result from t
Page 97 and 98: Figure 3.6: Concordance summary. Th
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Page 103 and 104: though in Chapter 2 we showed that
Page 105 and 106: Although a list of 325 candidate ge
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Page 109 and 110: 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: Figure 4.7: Low grade tumor surviva
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:
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11. Kumari S, Verma LK, Weller JW:
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38. Michael Stonebraker LAR, Michae
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64. Cropp CS, Lidereau R, Leone A,
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15. Irizarry RA: affy. In.: Biocond
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Chapter 4: 1. Minna JD, Roth JA, Ga
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30. Delaval B, Ferrand A, Conte N,
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27. Bai J, Cederbaum AI: Catalase p
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51. Shouse GP, Cai X, Liu X: Serine
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