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

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In the above ProbeSet group it is clear that the structure depends greatly on the associated value, since for a large majority of the ProbeSets the direction of change is the same regardless of the method for generating the value. However, the selection of the ProbeSets was based on BaFL values: it is possible that an intersection of RMA/dCHIP DE ProbeSets would have equally meaningful structure. To assess this, a list of predicted DE ProbeSets shared by RMA and dCHIP in both experiments was derived. As a final step, that list was filtered for those retained (not necessarily DE) in the BaFL protocol. The resulting list has 940 ProbeSets. The Laplacian dimension reduction method was again employed using values supplied by each of the three methods; results are presented in the Appendix F. Similarly these 940 ProbeSets demonstrated the same consistent latent structure for the log transformed data, while The RMA and dCHIP produced data lacked any structure. Additionally, these 940 ProbeSets possessed a marked increase in the number of pattern discondant ProbeSets for the RMA and dCHIP data. We note that for one ProbeSet the BaFL interpretation of the Bhattacharjee and Stearman results disagreed as to the direction of differential expression. This ProbeSet, 34532_at / CUGBP2, is the overall pattern is reflected across all of the individual probes as well, and is not the result of a single highly variant probe in the set (data not shown). The ‘On’ and ’Off’ Category of ProbeSets Because the BaFL-based pipeline requires that a minimum of 4 probes be present before a ProbeSet is considered it will miss any genes that are always present in one sample class and never present in the other. For the goal of finding the smallest number of ProbeSets with the greatest difference between classes (the ideal diagnostic) this is a flaw, if such ProbeSets exist. In fact, we have identified only two such Probesets in these experiments. Osteopontin (34342_s_at) 100
was one of them: it is counted as present in the adenocarcinoma samples and not detectable in the normal samples. The source of the discrepancy is the linear range filter: the 7 probes with reliable values in the diseased sample fall below that threshold in the normal samples. This ProbeSet alone can classify the Bhattacharjeedata with a 98.9% AUC, as even if you allow aggregation of probe values below 200 fluorescent units, only three of the normal samples have ProbeSet values greater than 200 f.u. Figure 4.4 presents boxplots of the BaFL-based ProbeSet means of the osteopontin log-transformed expression values for each of the five tissue classes in the Bhattacharee experiment. This figure mirrors that of Hu’s 2005 plasma protein study on lung cancer [19]. The aggregated ProbeSet values used only the 7 probes which survived the cleansing for the adenocarcinoma samples, although it should be noted that in the squamous carcinoma and small cell cancer samples additional probes were retained. 101
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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
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
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: for the RMA and dCHIP interpretatio
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
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
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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,
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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