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

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Figure 2.7: Fold change concordance. A comparison of the fold change for our predicted versus cleansed ProbeSets, with a near perfect slope and y intercept. Discussion The BaFL filtering process enriches the performance of the standard Affymetrix Tm Microarray experiment beyond that of a single ProbeSet measurement. The BaFL approach allows an investigator to delve into the standard Microarray black box and assess individual probe performance. The ability to evaluate probe performance can facilitate the investigator’s identification of transcript regions of interest, which may prove to be correlated to the phenotype of the disease state. Additionally, the BaFL approach allows the investigator to identify entire ProbeSets for which one tissue state demonstrates negligible transcript concentrations in contrast to the second tissue state. Finally, modified CDF files can be constructed to facilitate the 58
cleansing process. This is demonstrated by an a priori extraction of reliable probe and ProbeSets from the third stage 1 dataset, shown below. Inside the Black Box The traditional blackbox approach to Microarray data analysis uses a statistical comparison of probes across samples in classes of the experiment at hand, discards (in some cases) or weights component probes according to some ‘fitness to a model’ scheme, and then aggregates the measurements to give a single ProbeSet value. Thereafter the ProbeSet value is the only factor used as input to machine learning and statistical algorithm development [22-24, 58]. For diagnostic purposes, if the predictive results of these methods are acceptable then the goal has been achieved. However, biological investigators are often motivated by the desire to understand the mechanisms that cause a gene to appear on such a list [7, 8, 20, 21, 50]. Being able to target specific mechanisms may allow an investigator to select a ‘discarded’ probe for further study: here we are thinking particularly of those probes that are discarded because they respond to SNPs in the coding region, which may in fact be extremely important to the phenotype, if the investigator can apply a follow-up test to qualify the samples. Despite our attempt to identify all such factors, it is clear that we have not done so, since we end up with three response classes and not two in the last analysis stage. We propose that, by doing Welch’s T-test at the probe level during the aggregation process, an estimate of the presence of such factors is produced, and the resulting probeset value can be annotated, i.e. affixed with a numerical or categorical denotation (such as our ‘U’, ‘DE’ and ‘S’ labels), based upon the agreement of T-tests results. Uninformative probesets thus are comprised only of probes showing no difference in the means between classes (for a given allowed variation) while the DE probesets are comprised only of probes all of which show a difference in means between classes, as depicted in 2.8. These 59
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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
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 and 68: Figure 2.6: Probe-Transcript region
Page 69: Table 2.3: ProbeSet behavior predic
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
Page 119 and 120: 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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