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

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In an independent QC test of the arrays, we performed a parallel analysis of the datasets with the R-Bioconductor affy package [19] using mock .CEL files, where probes had been aggregated by batch. The results of this widely accepted algorithm were compared with ours for both batch and sample analysis effects: that is, with and without the ‘white box’ probe cleansing approach. At each stage of the above-described probe filtering process graphics of the output were generated in order to monitor batch-specific behavior. Set of probes and ProbeSet Behavior One goal of this research is to assess the effects that this type of probe and measurement filtering has on the reliability of the set-of-probe patterns across experiments. Data for the first goal was collected using the x and y values to find probes present in all samples, and from there we enforced the requirement that there be at least four probes in a probeset. Set-of-probe profiles (mean and variance per class) are then shown graphically. Next the values in the set are aggregated by taking the average for each sample, which is called a ProbeSet value, despite possible confusion with the Affymetrix ProbeSet [36]. Subsequent down selection used component probe and overall ProbeSets’ behaviors, across and between samples, as selection criteria. Within each experiment and for each probe in a set, Welch’s t- test analysis (" = 0.05) [51] was performed: the variance and sample means in each class were compared and used to define three set-of-probe categories. In the first category, all of the probes in the probe-set demonstrate means which are not significantly different between the sample classes: this class was designated ‘Uninformative’. The associated ProbeSets are also ‘Uninformative’, since the aggregated values must also fail to demonstrate a statistical significance in difference of class 38
means. The remaining sets possess some number of probes that do show a significant difference in the class means and thus they are considered to be informative. One group contains sets of probes in which every probe demonstrates a significant difference in class means. This maximally and consistently informative sub-group is labeled ‘Differentially Expressed’ (DE), and here the associated ProbeSet must also show differential expression. The consistency in individual probe and aggregated set behavior is because there is strong concordance in the pattern of differential expression in the probes belonging to the set. The remaining group of informative probes is labeled as the ‘Signal’ category. In this group, one or more probes have a significant difference in expression from the class mean, that is, does contain a signal, but others in the set do not. Because of this, the associated ProbeSet mean may or may not show a statistical difference from the class mean, depending on the respective values of the component probes. The underlying goal of the BaFL pipeline is to eliminate probes which may produce variant results because of underlying transcript variation or probe design issues. The resulting measurements should be more reliable and thereby a better interpretation of the mRNA transcript concentration. However, there still exists uncharacterized transcript events which may undermine the signal intensity interpretation [20]. Analysis for differential expression at the probe level allows the investigator the opportunity to identify probes which demonstrate inconsistent measurements with the remaining probe within the ProbeSet. Three hundred and twenty-five ProbeSets were identified to further classify whether uncharacterized transcript events may be occurring. These ProbeSets were considered “Signal’ since one or more probes, but not every probe, demonstrated a significant difference in class means. However, after aggregation, the ProbeSet values did demonstrate significant differences in the class means. These ProbeSets appear to represent transcript regions of most importance to the disease state, given the aggregate 39
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 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: a. A filter based on how many probe
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-
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
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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
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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
Page 125 and 126:
under linear and non-linear dimensi
Page 127 and 128:
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
Page 167 and 168:
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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