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

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is differentially expressed. A python script determined the pattern of expression, µ1 > µ2 or vice versa, and adjusted all probes in the down-regulated class by an increment of 1/50 of each probe mean. This perturbation was decided upon by trial and error. This was sufficient to create pattern inversions in cases of similar expression probes and exaggerate existing pattern inversions, without inverting those probes having differential expression. The ProbeSets which possessed probes demonstrating the pattern inversion after the minor perturbation were reassigned to one of four categories: unique or singular exception, statistical exception, specific transcript region event, and multiple transcript region events. A Priori Prediction Candidate ProbeSets were selected from the intersection of the BaFL-validated ProbeSets in the adenocarcinoma stage I and squamous (unknown stage progression) samples in the Bhattacharjee dataset [47]. This set includes 4,257 ProbeSets (from a comparison of 125 adenocarcinoma and 17 squamous samples). Classification results (using kNN, LDA, and randomForest [2, 52-57]), using DE ProbeSets trained on the Bhattacharjee dataset, demonstrated that there is a significant impact of the stage of disease on the expression profiles (data not shown). Therefore, the training set was subdivided, to create a stage I adenocarcinoma group (72), which was intersected with the 17 squamous samples (from multiple stages but not labeled so subdivision was not possible). This yielded 5174 ProbeSets, of which ~4000 were classified as DE, for the (Ad x Sq) comparison. Restriction to the Stage I samples eliminated 16 samples in addition to the batch 3 samples; only 3 of those 16 samples had been originally included in the full adenocarcinoma training set. The intersection of the above BaFL (AdI x SqI) output with the Lu dataset resulted in ~ 400 DE ProbeSets. The constituent probe intensities were recovered, by their x and y location identifiers, 40
for the 24 samples in the Lu, et al. raw data files [33] (filtered as described above), and the probe and ProbeSet presence were then predicted. Results The results reported here are divided into three sections: (1) providing evidence for the BaFl cleansing process, including details on the number of probes removed for each probe sequence filter, evidence of sample batch processing variability, and exemplar ProbeSets affected by such confounding factors. (2) the stages and effects of the BaFL probe cleansing pipeline, including graphic representation of each filtering step and the final profile consistency across the two datasets. (3) the research enrichment the BaFL pipeline facilitates, including elucidating potential transcript regions of interest and a priori predictions of independent datasets. For the established algorithms we have used the validated samples from the datasets as input, and validated ProbeSets from the author’s lists to compare their classification performance to each other and to the results of our method. The classification performance of the author’s original gene lists is used to see whether the sample cleansing protocol alone has a significant effect. We show that while previous efforts have identified informative genes, the methods are over-tuned to technical properties (lab specific) or non-primary factor biological properties (such as SNPs), rather than the desired biological response to the principle factor (here, disease state specific). 41
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 and 50: a. A filter based on how many probe
Page 51: means. The remaining sets possess s
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
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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
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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
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73. Alter O, Brown PO, Botstein D:
Page 169 and 170:
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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