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

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as purely DE (differentially expressed). The NME1, non-metastatic protein 1, gene is interesting since it has been associated with metastatic progression in many forms of cancer [62-69]. Closer inspection of the component probes in the two Signal ProbeSets shows that probes specific to a particular region of the transcript are the probes giving discrepant signals. The probe aligning to the 46,593,573 rd (start position) nucleotide within the gene gives similar expression between the two tissues states, but as the probes traverse the region, the direction of differential expression (adenocarcinoma > normal) inverts and, finally, the direction of difference is restored with the probe that aligns to the 586 th nucleotide. The alignments of these 5 probes are demonstrated in Figure 2.9. These alignments indicate an 18 nucleotide stretch of the transcript that correlates with the discrepancy, which could be evaluated for structural variations in the transcript. Figure 2.9: Examplar transcript region of interest for NME1. The 5 probe alignments against the transcript, for two NME1 ProbeSets (indices not shown but starting position given). The expression patterns are depicted (disease: normal change) with $,!,"symbols. With the exception of the final 1985_s_at probe, those shown have different responses compared to the remaining probes in the three NME1 ProbeSets. The overlapping region (12-18 nucleotides beginning at transcript nucleotide 580) is color coded into 3 sections of 6 nucleotides each. The 6 nucleotides shown in light blue could contain a splice junction or SNP that is not present in the 3 rd probe of 1985_s_at which aligns at the 46,593,586 th transcript nucleotide. Similarly, the six red nucleotides could represent similar transcript phenomenon, however the presence for which may be sterically masked by the plating properties for the same third 1985_s_at probe, which retains the up-regulated expression levels. The final six nucleotides (light green) have the potential to form a hairpin loop downstream of the red region, thereby disrupting binding of the target in the final probe. 62
A Priori Prediction We demonstrated that a priori probe prediction is a feasible approach based upon the cleansing results provided by the BaFL pipeline. The probe prediction implemented in this study included the incorporation of the linear range filter. The linear range filter is the only filter which affects samples differently based upon the biological and laboratory variation. Therefore it is paramount that the cross experiment probe extraction is performed upon similar disease studies and similar tissues. This is apparent with the squamous cell cancer data models which generated the lowest probe true positive rates (probes predicted to be thereafter cleansing and actually were). While the false positive rates for ProbeSet prediction for the adenocarcinoma models seem high, we can partially explain some of the false positives by considering that ~5600 probes were removed due to the batch 10 localized bare spot effect, presented in Figure 2.1. These probes would likely not have been removed via the BaFL pipeline for this third dataset. Their presence would therefore be accounted for in the false positive rate (10%, data not shown) observed at the probe level. Removal of these probes may have additionally eliminated what would otherwise have been reliable ProbeSets since the affected ProbeSets may not have met the requirement of 4 constituent probes for the Bhattacharjee dataset. This effect again would be observed via the 25.2% ProbeSet presence false positive rate as indicated in Table 2.7. Modified CDFs for Computational Efficiency Since the probe characteristics are universal to an array design, one can easily construct a modified CDF, which decreases the total number of probes that must be considered in an analysis to those that are usable, and thus improves the computational requirements for an analysis. We expect that different investigators will have preferred CDFs: for example, the cross-hybridization 63
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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
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 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: Table 2.4: ProbeSet behavior of pro
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
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
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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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