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decrease performance in the weighted AUC metric. This list of candidate genes demonstrate a decrease true positive classification rate for the squamous samples, with coordinating increases in false positive rates for the adenocarcinoma and normal samples (data not shown). Although ProbeSets selected by their average probe gain criteria demonstrate enhanced performance for the kNN algorithm and similar performance for the LDA algorithm, it likely not a suitable approach. We observed little overlap (6) between the 29 ProbeSets selected by their average probe gain (>=0.6) and the 18 ProbeSets selected by their ProbeSe5t gain criteria (>=0.8). This translates to only 1/5 th of th ProbeSets having an enhanced information gain for the aggregated ProbeSet, and in fact 2/3 rd of them demonstrate diminished information gain for the aggregated ProbeSet. Therefore, we believe that the enhanced classification performance for the average probe information gain selected lists is likely the result of the ‘good’ classifiers being more significant in the feature subspace. However the average probe gain suggests probes to investigate for transcript measurement anomalies. For example, the same list of 9 ProbeSets included 6 ProbeSets containing distinct probes demonstrating poor clustering performance, and these have been show to skew the gain distributions and presumably the aggregate’s clustering performance. For example, the KRT17 ProbeSet has 2 probes (index 6 and 10) with low probe performances, 0.033 and 0.06 gain ratios respectively. Selection by the average probe gain per ProbeSet appears to have a high false positive rate; furthermore these ProbeSets appear to have limited connection to the ProbeSets selected by the ProbeSet gain criterion. As a counterexample, ProbeSet 33108_i_at, SOX2, includes five probes all of whose gain values range from 0.73 to 0.99, yet the ProbeSet aggregate gain ratio value is a dismal 0.18. This ProbeSet is up-regulated in the squamous samples, by the 124
statistical criterion, but it appears to be unable to classify the adenocarcinomas because the Euclidean Sum of Squares is two fold larger than for the (smaller N) squamous and normal clusters. The ProbeSets KRT17, EMP2 and MFAP4 which were selected by both criteria, demonstrate that one method by which to avoid such false positives: that is, select an average probe gain threshold criteria of ProbeSets whose aggregate gain improves on the average probe gain. Thirteen of 29 ProbeSets demonstrating an average probe gain had an aggregate gain ratio that improved on the average probe gain ratio. In contrast to the average probe gain list of 29 genes, half of this list (6 of 13) demonstrates enhanced ProbeSet information gain; having a ProbeSet gain ratio greater than 0.8. The 7 additional genes for demonstrate a close relationship to the underlying biology. Such effective down selection, at the ProbeSet level, may be a reasonable endpoint if the goal is to produce an effective and simple diagnostic test. However, this is a limited approach if the goal is to understand the biological mechanisms of disease. Based upon the classification performances, presented in Figure 5.1, 18 Genes were identified were selected as a candidate list (Table 5.1) to explore the biological function with respect to NSCLC. Literature Validation of Biological Process We observed significant down regulation of TACC1 in both forms of NSCLC, which is supported by the literature [15-17]. There are 3 TACC human genes, which appear to be important for cellular division and organization [18], with orthologues reported in Mus. musculus, Drosophila melangaster, and Xenopus laevis [18]. Proper localization of TACC during cytokinesis appears to be dependent upon phosphorylation by aurora kinases [15, 19] and may posses a critical function in cell cycle control [17]. Appropriate cell division is a prerequisite in the propagation 125
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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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Page 87 and 88: sample selection, with replacement,
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
Page 99 and 100: whole model analysis without permut
Page 101 and 102: change, apparently the greater vari
Page 103 and 104: though in Chapter 2 we showed that
Page 105 and 106: Although a list of 325 candidate ge
Page 107 and 108: was determined to consist of 30 Pro
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 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: Figure 5.3: NSCLC refined average p
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 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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