Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

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emains in the significant pool according to the Bhattacharjee data [47], as observed by the actual cleansing of the Lu dataset [33]. Table 2.2: Apriori predictions. True positive and false negative rates for presence of predicted ProbeSets in the observed BaFL cleansed Lu ProbeSet stage I dataset. Bhattacharjee BaFL cleansed predictors TP rate FP rate Adenocarcinomas 99.11% 28.30% Stage I Adenocarcinomas 98.64% 25.20% Squamous 74.96% 1.28% Stage I-Squamous Model 91.64% 13.24% Table 2.3 presents similar results as Table 2.2, but now examined at the ProbeSet level. This ProbeSet analysis did not include the requirement that identical probes be in the set for every sample, so a follow-on constituent probe analysis was performed. In 25% of the ProbeSets all probes were identical between the Bhattacharjee prediction set and the Lu observation set, while in 20% of the cases the selected Lu ProbeSets contained a complete subset of those in the Bhattacharjee ProbeSets. Conversely, 31% of the probes in the Bhattacharjee predicted ProbeSets were a complete subset of the probes in the observed Lu ProbeSets, and finally, for 22% of the cases, both ProbeSets had at least one unique probe that did not map to the other set. 56
Table 2.3: ProbeSet behavior predictions. Confusion matrices for classification of the informative nature, comparing the classification of the predicted Lu probes and the observed BaFL cleansed Lu probes. TP is true positive, FP is false positive. The first 3 confusion matrices show the result when t-test classification is done at the probe level, while the bottom matrix is the equivalent comparison performed at the ProbeSet level. ProbeSet level analysis was done for the predictive ProbeSets that are present in the cleansed Lu output (True positive prediction rate for the presence of a ProbeSets was 91.64% and the False positive error rate was 13.24% as shown in Table 2.2). Level Confusion Matrix Class TP Rate FP Rate Probe Level Probe Level Probe Level 3904 51 Noise 77 564 not Noise 540 79 Signal 55 3922 not Signal 18 4 DE 2 4572 not DE ProbeSet Level 4444 13 Signal DE 98.71% 12.01% 87.24% 1.38% 81.82% 0.04% 99.71% 6.47% While the characteristic call may have a high level of sensitivity and specificity, as presented above, the influence of a probe within a ProbeSet might either change the overall ProbeSet mean expression and even the direction of relative expression (µ1 > µ2 ! µ1 < µ2), or make so minor a contribution as to be irrelevant. To test the importance of such contributions, ProbeSet averages were calculated for the probes predicted to be present in the Lu data, as derived from the BaFL cleansed Bhattacharjee Stage I adenocarcinoma-squamous model. Similarly, the BaFL cleansed ProbeSets were aggregated for the BaFL cleansed Lu dataset. Figure 2.7 shows the results of an analysis, for the common ProbeSets, using fold change to summarize disease to normal relationships, for the two sample types in the Lu dataset. The data generated by the BAFL method demonstrates that the aggregation of the predictive individual probes closely mirrors that of the BaFL cleansed probes for the Lu dataset. The consistency lies not only in selecting good ProbeSets, but in handling the constituent probes correctly as well. 57
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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
Page 17 and 18: Factors influencing Signal Interpre
Page 19 and 20: likelihood that no such structural
Page 21 and 22: hybridization and multiple dye stra
Page 23 and 24: classical reaction equations, modif
Page 25 and 26: prevent the hybridization of probes
Page 27 and 28: platforms are used to deposit them
Page 29 and 30: and chip layout [64]. In particular
Page 31 and 32: whether analyses based upon individ
Page 33 and 34: The process of determining a candid
Page 35 and 36: implemented in order to demonstrate
Page 37 and 38: affecting probe-target duplex forma
Page 39 and 40: It has long been known that the var
Page 41 and 42: probes can thus be reincorporated i
Page 43 and 44: (Carr, ms in review). This ProbeFAT
Page 45 and 46: have specific attributes which can
Page 47 and 48: elow saturation. The investigator m
Page 49 and 50: a. A filter based on how many probe
Page 51 and 52: means. The remaining sets possess s
Page 53 and 54: for the 24 samples in the Lu, et al
Page 55 and 56: Table 2.1: Probe Numbers per filter
Page 57 and 58: the effect can be. Both the type of
Page 59 and 60: described above. In Figure 2.3, the
Page 61 and 62: various data processing stages are
Page 63 and 64: position of the probe on the transc
Page 65 and 66: Figure 2.5: BaFL consistency. Demon
Page 67: Figure 2.6: Probe-Transcript region
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
Page 83 and 84: are significant assume such a data
Page 85 and 86: cleansing process and were assessed
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
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The candidate gene list values were
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ejecting the null hypothesis [8], a
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Figure 4.9: GO connectivity of cand
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under linear and non-linear dimensi
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sample cleansing process and these
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averaged to give a final approximat
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119 Table 5.1: : NSCLC candidate ge
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Figure 5.1: NSCLC ProbeSet gain sel
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Figure 5.3: NSCLC refined average p
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statistical criterion, but it appea
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lacking p53 mutations [30]. While,
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Kruppel-like factor 5 (KLF5) has al
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Appendix A # This is the main drive
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def Driver(usr, pswd, db, logfile,
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def DriverXH(usr, pswd, db, logfile
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fp.write('\tTable '), fp.write(msk[
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#determine outliers lowrs1=nonzero(
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tmp="update sample_mask set exclude
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fp.write('\n'+msk[ptr[j]]+' exclude
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cur, conn=UpdateWorkReg(cur, conn,
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Appendix E # The result of permutin
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BIBLIOGRAPHY 149
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13. Bowtell DD: Options available--
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method addressing dye, intensity-de
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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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