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to control the false discovery rate are employed to improve the down selection process [33]. However, in the following experiments we did not implement such correction efforts, or other algorithmic optimizations, in order to see the full range of possible solutions and to see where overlap of genes occurs even if the significance assigned by the different methods is quite different. The Bonferroni correction has been applied to the datasets in Chapter 5. The results of the t-test categorized the ProbeSets into two classes: uninformative and differentially expressed. The datasets are available in the Supplementary Materials, in the Data folder. Additionally, within the Data folder is a README file, which details the naming convention and the flags which identify ProbeSets and their classifications, including the Bonferroni correction classifications. Published Candidate Gene Lists In the original report of the Bhattacharjee experiment, two ProbeSet lists were developed, based upon the signal intensity reproducibility across 45 adenocarcinoma replicated samples, where reproducibility was assessed based on whether these ProbeSets had Pearson correlation scores above the 0.8 and 0.85 thresholds [3]. These lists consisted of 675 and 363 ProbeSets, respectively, for the correlations 0.8 and 0.85. The cleansing methods used by the original authors were quite complex, and neither the ProbeSet values nor the exact gene lists could, in fact, be perfectly replicated using the published descriptions [3, 34]. We decided to take those genes from their lists that survived the BaFL pipeline, that is, that had been cleansed of the known extraneous factors leading to variability, and see if the genes remaining had strengths for classification that our own down-selection method missed. That is, the point was to determine whether the original selection methods had merits that our own procedures lack. Of the original ProbeSets lists, 267 and 136 (for 0.8 and 0.85 thresholds respectively) survived the BaFL 72
cleansing process and were assessed for their classification ability. The original report of the Stearman experiment reported a list of 409 genes, which demonstrated concordance in log2 difference (tumor minus normal) between the murine and human model [4]. Surviving the BaFL cleansing process from this list were 178 ProbeSets. The concordances between the original three lists are relatively small, containing 58 and 34 ProbeSets, respectively, for the 0.8 (Bhattacharjee x Stearman) and 0.85 (Bhatacharjee x Stearman) lists, so the size of the concordant lists against BaFL is not unreasonable. We compared the classification abilities of these three lists to that of the 325 ProbeSets whose aggregate was classified as differentially expressed for both the BaFL cleansed Bhattacharjee and Stearman data. ProbeSet values were again generated by each of the three data cleansing pipelines as part of the comparison. Complete ProbeSet lists are given in the Supplementary Materials, in the Data folder. Classification The classification performance of supervised learning methods was assessed, using the various candidate gene lists as training sets. The area under the receiver operating curve (AUC) was the performance metric for all the classification experiments [13, 14]. For each algorithm, the base model included all of the original surviving ProbeSets, either the 12,625 (for RMA and dCHIP) or the intersecting 4,200 (from BaFL). A requirement of the classification experiments is that the same ProbeSets need to be present in both datasets. This only affects the BaFL data because RMA and dCHIP give complete gene value matrices [2, 18]. Therefore, for the BaFL ProbeSet lists, subsets that consisted of candidate gene list intersections were used. The values in the resulting ProbeSet lists were then used, in turn, to train three different classification algorithms: k nearest neighbors (kNN) [7, 23, 35], linear discriminant analysis (LDA) [8, 9, 20] and random forest (RF) [10-12]. The R implementations of these algorithms were used [16]. The parameters 73
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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
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hybridization and multiple dye stra
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classical reaction equations, modif
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prevent the hybridization of probes
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platforms are used to deposit them
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and chip layout [64]. In particular
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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 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: are significant assume such a data
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
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
Page 129 and 130: averaged to give a final approximat
Page 131 and 132: 119 Table 5.1: : NSCLC candidate ge
Page 133 and 134: 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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