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

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probesets can be reliably trusted to demonstrate the same t-test results for the aggregate as the component probes. In the remaining ProbeSets the component probes do not show a consistent response pattern: in the few we have looked at in detail the reason seems to be the probable presence of alternate transcripts, but we do not suggest that this will always be the case. Figure 2.8: Analysis schematic. Schematic depiction of down selection for the white box analysis and the standard black box analysis. This type of categorical grouping of the ProbeSets facilitates targeted down selection of the dataset, or alternatively a rescue of specific probes if additional assays can be performed. Also, in comparison to the typical blackbox approach, this type of down selection is more stringent, since the criteria for reaching concordance are more exacting. Table 2.4 provides the results of such down selection for the two datasets. 60
Table 2.4: ProbeSet behavior of probe level analysis. Number of ProbeSets in each response type, for each experiment, and in common, following use of the BaFL protocol.. Note that the ‘informative’ category is based upon the probe behavior within a ProbeSet, but for the Signal class the two groups may not agree as to which probes in the set differ, a not unexpected outcome if transcript isoforms vary by individual Bhattacharjee Stearman Intersect Cleansed ProbeSets 4,253 6,506 4,200 Uninformative 2,810 1,233 1,225 Informative 1,443 5,273 1,219 Signal 1,288 4,536 937 DE 155 737 79 Transcript Regions Identified by Signal Probesets Individually, there were 75 ProbeSets failing the statistical power test, 104 ProbeSets having single probes in which a measurement issue, 18 ProbeSets conforming to the single transcript region event criterion, and 128 ProbeSets suggesting distinct transcript region events. More importantly, all 325 probes required additional analysis in order to understand the nature of the information in the signal, and the BaFL approach allows the investigator to assess and prioritize the ProbeSets to evaluate. In addition to identifying the rule under which particular classes of probes were excluded, our method provides a category for sets containing probes with variable behavior (some of which might have been excluded by the two statistical methods, depending on cutoffs of variability chosen). To highlight why this is important we provide two specific examples. For the two ProbeSets (1985_s_at and 39073_at) mapping to the NME1 gene, both were categorized as Signal sets, where not every probe within the accepted probe-set is differentially expressed, while a third ProbeSet that maps to the same gene (1521_at) is classified 61
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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
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 and 68: Figure 2.6: Probe-Transcript region
Page 69 and 70: Table 2.3: ProbeSet behavior predic
Page 71: cleansing process. This is demonstr
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
Page 119 and 120: The candidate gene list values were
Page 121 and 122: 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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