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

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normal looking that are adjacent to the tumor and 2 adenocarcinoma samples); one of the normal samples is missing, presumably for high tumor content. These sample biopsies were harvested using microdissection techniques and then snap-frozen [49]. The most demanding test of a diagnostic assay is whether it is effective in predicting the outcomes of an experiment not included in the development of the diagnostic set. A third, experimentally comparable, dataset was selected from GEO (http://www.ncbi.nlm.nih.gov/geo/): it was published as a meta-analysis of 5 Stage-I non-small cell lung cancers (NSCLC), accession number GSE6253 [33], which will be called the ‘Lu’ data. Four of these datasets were from published studies of lung cancer, including the original Bhattacharjee dataset. The fifth dataset, also from the Affymetrix Tm HG_U95Av2 platform, consisted of samples from Washington University- St. Louis and included 36 adenocarcinoma and squamous lung cancer patients, all of which were described as being in stage I of cancer progression. This fifth dataset was loaded into the ProbeFATE database system and our probe cleansing and sample cleansing methods were applied as an automated pipeline. The final BaFL cleansed dataset included 10 adenocarcinoma and 15 squamous samples, and 5,311 ProbeSets having at least 4 BaFL-validated probes in common. This dataset will serve for an a priori probe selection experiment, based upon the BaFL cleansing of the Bhattacharjee adenocarcinoma and squamous cell carcinoma data. BaFL Pipeline Components Probe Filtering The BaFL pipeline can be divided into two filtering categories, the first, ‘probe sequence’, category uses only the nucleotide sequence for determining filters, and the second category uses a signal measurement assessment as a filter. The probe sequence filters eliminate probes which 32
have specific attributes which can be detrimental to the interpretation of the signal intensity including cross-hybridization, loss of target sequence, SNP presence, and structural accessibility. These filters affect all samples similarly. Conversely, the measurement reliability filter affects each sample individually. I. Unidentifiable Target. The CDF base table for the U95Av2 arrays (Affymetrix NetAffx; http://www.affymetrix.com/products/arrays/specific/hgu95.affx) was queried all 409,600 probes for which the probe sequence annotation was known. There remained 11,432 probes, representing 174 genes, which we eliminated from further consideration. Affymetrix reports the origin/source of these sequences as unknown (personal communication, Affymetrix Technical Support to H. Deshmukh) [13]. II. Cross-Hybridization and Loss of Target Sequence. Probe cross hybridization is the major confounding factor affecting the interpretation of probe responses [9, 10, 20, 21, 25, 50]. We have chosen to follow the Ensembl definitions of cross hybridizations, where 23/25 nucleotides must be in alignment, and we have queried ENSEMBL Biomart (http://www.ensembl.org/biomart/martview/3ee2b94e6eb250f709ffdf9474635fdf) to acquire the list used to perform this filtering step. This process identifies probes that align to a single human genome region, and eliminates those which align to more than one region of the human genome and also those that don’t align at all. We note that this comparison is available only for perfect match (PM) probes and therefore if Mismatch (MM) probes are included in the analysis an equivalent list must be acquired and applied, or the level of filtering is not the same in the two categories of probes. Without such a step, incorporation of any mismatch probes (MM) information, as background for PM probes for example, results in a discrepancy in the reliability of the 33
Page 1 and 2: An Adenocarcinoma Case Study of the
Page 3 and 4: DEDICATION The work presented in th
Page 5 and 6: TABLE OF CONTENTS LIST OF TABLES ..
Page 7 and 8: Conclusion.........................
Page 9 and 10: LIST OF FIGURES Figure Page Figure
Page 11 and 12: violate the linear correlation rela
Page 13 and 14: Chapter 1: An Introduction to Micro
Page 15 and 16: previously stated, rigorous reagent
Page 17 and 18: Factors influencing Signal Interpre
Page 19 and 20: likelihood that no such structural
Page 21 and 22: hybridization and multiple dye stra
Page 23 and 24: classical reaction equations, modif
Page 25 and 26: prevent the hybridization of probes
Page 27 and 28: platforms are used to deposit them
Page 29 and 30: and chip layout [64]. In particular
Page 31 and 32: 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: (Carr, ms in review). This ProbeFAT
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 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
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whole model analysis without permut
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change, apparently the greater vari
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though in Chapter 2 we showed that
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Although a list of 325 candidate ge
Page 107 and 108:
was determined to consist of 30 Pro
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Figure 4.1: Non-traditional PCA ana
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for the RMA and dCHIP interpretatio
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was one of them: it is counted as p
Page 115 and 116:
Figure 4.5: Kaplan-Meyer survival c
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Figure 4.7: Low grade tumor surviva
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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
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averaged to give a final approximat
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119 Table 5.1: : NSCLC candidate ge
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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:
Page 169 and 170:
11. Kumari S, Verma LK, Weller JW:
Page 171 and 172:
38. Michael Stonebraker LAR, Michae
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64. Cropp CS, Lidereau R, Leone A,
Page 175 and 176:
15. Irizarry RA: affy. In.: Biocond
Page 177 and 178:
Chapter 4: 1. Minna JD, Roth JA, Ga
Page 179 and 180:
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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Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

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