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

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Hereafter the pipeline which we present is referred to as BaFL, or Biologically applied Filter Levels. Black Box Strategies A large number of purely statistical approaches have been applied (e.g. dCHIP, RMA, gcRMA) [5, 22-24] to remove variation (sample or technical) unrelated to the factor of interest, but these function as black-box techniques that do not enlighten the investigator about the extent that each factor influences the experimental results. These methods tend to augment the data’s sensitivities to classification algorithms; the outcome has been that the processed data performs well within but not between experiments, using the same or different classifications methods [8]. The implication is that these approaches over-train for the factors that apply in one experiment and that those factors are not consistent in the next experiment. This would be expected if some of the result is due to variables with systematic effects on a subset of particular probes, such as the occurrence of different SNP-responsive probes that will give distinct patterns in different study populations [9, 10, 12, 25-30]. In order to demonstrate that the data inconsistencies are sample/population or platform dependent, an investigator needs to be able to delve into the aggregated signal and identify discordant probes and the likely cause of their behavior, and then perform follow-up assays as needed, such as genotyping samples. A black box method does not allow the investigator to understand which particular type of secondary assay must be performed. Our approach is to identify and remove all problematic probes in a progressive manner, categorizing them as they are removed. Post- BaFL filtering the final set of data from all samples gives a considerably more homogeneous response; in addition the investigator is provided categorizations of the excluded sets that allow examination of each subcategory, and subsets of 28
probes can thus be reincorporated into the analysis, as the investigator deems appropriate. The impact of each variable is study dependent, but, since our interest is to identify diagnostic signatures, there is a requirement for gene patterns that are robust to individual sampling and technical variation, allowing high accuracy in sample classification, whether binary or multistate [31-35]. An advantage of the multi-probe per transcript platforms is that multiple measurements are available per gene-sample, increasing confidence in the measurement [36]. To retain this feature is important, so, after BaFL filters out probes subject to confounding factors, our analysis pipeline currently requires that a minimum of 4 probes, per ProbeSet, per array, must be present. An optional constraint is that this must be exactly the same 4 probes per array. Variation due to the technical complexity of the assay is completely lab dependent [6, 8, 20] and cannot be quantified in the same way as the factors listed above, so simple statistical tests are used. Two simple tests of overall similarity are: the total amount of response, assessed by measuring the total amount of label present, and the total number of genes contributing to that response, assessed from the total number of probes giving signal. This can be determined at both the probe and ProbeSet level. In these tests, care must be taken only to use signals that can be directly compared, so the manufacturer’s specification for the linear range of the scanner is used to set lower and higher bounds for interpretable signal [16, 37]. It is possible that these criteria exclude samples unnecessarily, but in the absence of calibration standards and consistent controls we consider this to be a reasonable, conservative approach [18]. The remaining probes can be collected either as a linked set of values or aggregated into a single transcript- or gene-level value (similar to ‘ProbeSet’ values computed by other algorithms). 29
Page 1 and 2: An Adenocarcinoma Case Study of the
Page 3 and 4: DEDICATION The work presented in th
Page 5 and 6: TABLE OF CONTENTS LIST OF TABLES ..
Page 7 and 8: Conclusion.........................
Page 9 and 10: LIST OF FIGURES Figure Page Figure
Page 11 and 12: violate the linear correlation rela
Page 13 and 14: Chapter 1: An Introduction to Micro
Page 15 and 16: 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: It has long been known that the var
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 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-
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cleansing methods were used to gene
Page 95 and 96:
Discussion A striking result from t
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Figure 3.6: Concordance summary. Th
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whole model analysis without permut
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change, apparently the greater vari
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though in Chapter 2 we showed that
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Although a list of 325 candidate ge
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was determined to consist of 30 Pro
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Figure 4.1: Non-traditional PCA ana
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for the RMA and dCHIP interpretatio
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was one of them: it is counted as p
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Figure 4.5: Kaplan-Meyer survival c
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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:
Page 171 and 172:
38. Michael Stonebraker LAR, Michae
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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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