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Discussion In this chapter we have presented latent structure associated with the genes in our candidate ProbeSet list when BaFL-provided values are used, based upon correlation of the disease samples (Figures 4.1-3). This is the reverse of the normal strategy, in which an analyst uses gene correlations to explore the structure between samples. There is an improvement in the Bhattacharjee data model when the BaFL pipeline is implemented to supply the Probeset values as compared to the RMA and dCHIP methods. The impact of osteopontin expression levels (Figure 4.4-7) is a well known feature in the progression of lung cancer, as is the difference among lung cancer sub-types [19, 20, 22, 23]. However, a novel result of the analysis is the demonstration that the significance of the expression levels is not stage specific (Figures 4.6-7), but rather pathology specific, for BaFL- supplied values. This change in perspective suggests why our data fails to demonstrate the same survival rates as the Donati study which suggested long term survival of stage I patients with elevated levels of opsteopontin [20]. An attempt to contact this group to obtain supplementary data in order to determine whether their stage I sub-population had a significant proportion of low grade tumor patients has been unsuccessful. Osteopontin is under development as a potential biomarker, and efforts have been made to improve the performance of the biomarker by coupling it with other biomarker results [ref]. Our results suggest that an additional element leading to success would be to incorporate the tumor grade clinical data into the test. In earlier chapters the multiple-test problem was ignored (leading to the candidate list of 325 ProbeSets described above). The Bonferroni correction was implemented for its low tolerance for 108
ejecting the null hypothesis [8], and applied to the two datasets as interpreted by the BaFL pipeline. This correction enabled us to elucidate a small subset of undeniably differentially expressed genes as pertinent to lung cancer. The candidate list of genes when based on BaFL pipeline values yields reasonable classification performance across 3 independent algorithms when an appropriately sized dataset is used (Figure 4.8). The size of the data set is important: when the Stearman dataset was used for training purposes all three models struggled, with the BaFL data performing the best in all cases Statnikov, et al. used the Bhattacharjee multiclass data in their pipeline for the cancer diagnosis and biomarker discovery, in which whey reported the maximum prior probability of a dominant diagnostic category of 68.5% [24]. Their analysis used the data in a training approach for 10 fold leave one out cross validation and reported perfect prediction for the training model [24]. Additional analysis utilizing the full Bhattacharjee multiclass dataset yielded binary classification accuracies of 52-56% for random forest and support vector machines with and without gene down selection. While multiclass classificaiton, with and without gene down selection yielded 77-82% for random forest and 89% for support vector machines [25]. The relevance of these 31 genes is supported by the GO connections identified by the pathway and literature search, PaLS, software (http://pals.bioinfo.cnio.es/), which connect 23 of the 30 genes (one ProbeSet aligns to no defined gene). KEGG pathway connections link 6 of these genes, including opsteopontin, through focal adhesion and extra-cellular matrix receptor intreraction [26]. Other extra cellular matrix genes include MFAP4, SPARCL1, ENG, RAMP3, and LRRC32. SPARCL1 and SPOCK or SPARC like have been investigated for their role in lung cancer [23]. These genes along with SPP1, MIF, and PECAM1 have strong immunological associations and thereby may be essential for angiogenesis and tumorogenesis [19, 20, 27]. The 109
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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
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for the 24 samples in the Lu, et al
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Table 2.1: Probe Numbers per filter
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the effect can be. Both the type of
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described above. In Figure 2.3, the
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various data processing stages are
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position of the probe on the transc
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Figure 2.5: BaFL consistency. Demon
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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
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: The candidate gene list values were
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
Page 155 and 156: fp.write('\n'+msk[ptr[j]]+' exclude
Page 157 and 158: cur, conn=UpdateWorkReg(cur, conn,
Page 159 and 160: Appendix E # The result of permutin
Page 161 and 162: BIBLIOGRAPHY 149
Page 163 and 164: 13. Bowtell DD: Options available--
Page 165 and 166: method addressing dye, intensity-de
Page 167 and 168: 73. Alter O, Brown PO, Botstein D:
Page 169 and 170: 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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