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Chapter 4: Data Mining Adenocarcinoma is a non small cell (NSCLC) lung cancer sub-type, and the most frequent type of lung cancer found in the world today [1, 2]. Adenocarcinomas are peripherally located in the lungs and develop from clara cells, alveoli, and mucin producing cells [1]. While, tobacco smoking has been well established as an initiating condition for lung cancer, with 80-90% of lung cancer cases arising in tobacco smokers, adenocarcinoma in particular is most common among women, non-smokers, and the young [1]. Given that the incidence rate of adenocarcinoma is increasing and affecting non-traditional patients, understanding the disease is of immediate concern [1, 2]. Using the methods described in the previous chapters, we have created two 2-class datasets, one from a subset of the Bhattacharjee dataset and the other from the original Stearman (human subset) experiments [3, 4]. In this chapter we begin with the down-selected ProbeSet list, presented in Chapter 3, consisting of those 325 differentially expressed ProbeSets common to both datasets. The values that the BaFL pipeline yields for these ProbeSets lead to datasets with considerable latent structure; we will demonstrate that this latent structure is superior to that of RMA and dCHIP supplied values using two widely-accepted dimensionality reduction methods: Principal Components Analysis [5, 6], which is linear , and a Laplacian method which is non- linear [7]. In validating the results of these analyses, we use sample correlation to explore the gene/ProbeSet clusters. 92
Although a list of 325 candidate genes is not large by the standards of Microarray experiments, and may reflect real biological contributions to a complex phenotype, to produce a practically useful diagnostic test one would like the smallest possible list of genes that have strong effects. There are many ways to additionally down-select features, one of which was used by the investigators of the Stearman experiment, comparative genomics [4]. Here we chose to use a more traditional statistical approach, in which feature selection from the 325 ProbeSets was accomplished by incorporating the Bonferroni correction [8, 9]. The Bonferroni is a stringent correction to accommodate the multiple hypothesis tests. The underlying assumption of the Bonferroni correction is that all null hypotheses are true (the mean expressions are equal) [8, 9]. Thereby, only the ProbeSets with extreme differences in the mean ProbeSet intensity will survive the correction. There has been considerable debate as to whether the extreme rigor of the Bonferroni method is appropriate for expression Microarray experiments, and one of the questions is how much potentially valuable information we lose by applying this technique [8]. As before the performance of the candidate gene list in classifying samples is assessed using kNN, LDA and RF classifiers [10-13] to build models that are then judged by AUC scores [14, 15]. Materials and Methods As previously described, the ‘Bhattacharjee’, dataset [3], consists of 17 normal and 237 diseased samples, including 51 adenocarcinoma replicates, with disease category assigned after histopathological examination. From this study we used 125 of the 190 adenocarcinoma array results and 13 of the 17 normal results; the selection criteria are described below. The second, ‘Stearman’, dataset (http:/www.ncbi.nlm.nih.gov/geo/; accession number GSE2514) consists of 39 tissue samples, all replicated, from 5 male and 5 female patients (four samples were taken 93
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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
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The process of determining a candid
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implemented in order to demonstrate
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affecting probe-target duplex forma
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It has long been known that the var
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probes can thus be reincorporated i
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(Carr, ms in review). This ProbeFAT
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have specific attributes which can
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elow saturation. The investigator m
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a. A filter based on how many probe
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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
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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: though in Chapter 2 we showed that
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
Page 135 and 136: Figure 5.3: NSCLC refined average p
Page 137 and 138: statistical criterion, but it appea
Page 139 and 140: lacking p53 mutations [30]. While,
Page 141 and 142: Kruppel-like factor 5 (KLF5) has al
Page 143 and 144: Appendix A # This is the main drive
Page 145 and 146: def Driver(usr, pswd, db, logfile,
Page 147 and 148: def DriverXH(usr, pswd, db, logfile
Page 149 and 150: fp.write('\tTable '), fp.write(msk[
Page 151 and 152: #determine outliers lowrs1=nonzero(
Page 153 and 154: 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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