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for kNN were (k=3, l=2, with the Euclidean distance), and default settings in R were used for RF and LDA [16]. We chose to use three different methods in order to explore whether the classification performance was specific to the classification algorithm, and these three were selected specifically because they are the most commonly cited in Microarray analysis papers and because their performance requires minimal parameter tuning [6, 11, 12, 20, 21, 23, 27, 28, 34, 35]. Linear discriminant analysis attempts to find the linear combination of features which best separates the data into their distinct classes, by weighting the features based upon their ability to separate the classes [8, 9, 20]. Conversely, kNN and RF classify samples based upon the characteristics of closely neighboring samples [6, 36]. The entire ensemble of features is utilized for the kNN algorithm while RF stochastically builds forests of classification tress based upon the strongest classifying features [10, 11]. After training with values from one experiment, the models were used in tests against the other experiment and the performance was assessed: that is, the Bhattacharjee gene lists were used for training and then the models were used to predict the Stearman sample classes, and vice versa, for each of the types of gene lists described above [6, 36, 37]. This led to 9 comparisons in which the Bhattacharjee data were used as the training set (RMA, dCHIP and BaFL cleansing post t-test, against 3 types of models) and 9 comparisons in which the Stearman data were used as the training set. The same classification algorithms were invoked for the comparison of the author’s lists to the purely BaFL-derived list of 325 DE ProbeSets. This set of experiments is designed to be similar to that of the validation of a final candidate list. Here, we compared the 325 BaFL intersecting DE ProbeSets, to the BaFL-allowed ProbeSets in the author’s published lists. Validation of a candidate list necessitates perturbing the designed models over iterative analysis to approach a reliable performance metric [6, 36, 37]. Perturbation of our models was done through random 74
sample selection, with replacement, for 100 iterations to approach a reliable AUC performance metric [6, 13, 14, 38]. Random sampling in such a manner permutes the data in a fashion that leads to some artificial replication and may omit some samples [12, 30, 31]. Results The results are given for each of the probe cleansing methodologies as independent analyses of Microarray experiments. There are two major sections: the model’s performance before and after down selection, and the validation of a candidate list. Here the candidate list is the 325 intersecting differentially expressed ProbeSets as interpreted by the BaFL process and this list will be compared to the author’s pre-existing published candidate lists. Independent validation is used throughout to demonstrate that the BaFL probe cleansing algorithm facilitates cross experiment analysis. Down Selection Table 3.1 summarizes the number of DE ProbeSets per probe cleansing algorithm that was the result of the t-test for significant differential expression. The number of DE genes predicted for the Bhattacharjee dataset by the BaFL pipeline approaches the expected number (4,200 * .05 = 210) [5, 33]. This is not true for the Stearman data, likely due to the greatly diminished size of the sample. 75
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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
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 and 84: are significant assume such a data
Page 85: cleansing process and were assessed
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
Page 135 and 136: 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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