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

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the energy of the excitation stage, but this raises the lower limit of detection to unacceptable levels [39]. Using two excitation gains successively gives poor results because of photo-bleaching of the dye and the non-linear relation between the gain settings [38]. The effect of saturation is that the scanner makes a stochastic estimation of the relative amount of fluorescence – although a numerical value is supplied the true value should be ‘very big’ [59]. Scanner manufacturers provide instrument specifications that supply the required sensitivity limits but no pipelines make use of these values. Corrections for individual scanners could be found by the use of either internal or external standards [38], but no ‘standard’ calibration reagents are available, and indeed almost no one is aware of the problem. Most analysis pipelines do enforce at least lower and sometime upper bounds on acceptable measurement values, but these are typically arise from purely statistical assessments of variance. The better strategy is to remove all data from the stochastic response regions, as in done with ELISAs, and then look at the statistical variation of properly quantified spots. Microarray Platforms Published reviews that describe Microarray experiments generally designate two categories: oligonucleotide or cDNA probes [14, 15]. The difference in these array designs is the length of the probe sequence and this difference arises from the production methods. The longer, cDNA, probes are derived from either the excised inserts from clones or (usually) PCR products of those inserts [15]. These probes range from 100 to 1000 nucleotides long and, because of their size (length and tertiary structure), have limited spotting concentration and spot density [5]. Robotic 14
platforms are used to deposit them at their designated spots. The use of such cloned inserts offers investigators immediate access to reagents and thus ease in modifying chip designs and thereby flexibility in their research. The investment in the robotics is prohibitively expensive to individual laboratories and therefore often research parks and academic centers start core laboratories to provide these services for their community of investigators [60]. The established protocols of these core laboratories present the issue of reproducibility of cDNA Microarray experiments, since quality control and laboratory protocol implementations can vary per core facility [61]. In general our lab does not analyze cDNA array data due to the lack of complete probe characterization and quality control steps in the manufacturing steps. Parallel to the development of cDNA Microarrays has been the private sector’s development of mass-produced, short (25-70) oligonucleotide Microarray platforms [8, 11, 12]. The major companies producing these Microarrays include: Agilent [12], NimbleGen [11], and Affymetrix [8]. The latter two use in situ, or on the surface, production of probes by photolithographic methods [5, 10, 62]. The computer-chip derived production methods of these Microarrays allows for extremely high density spotting of probes, and indeed the spotting density has followed Moore’s Law [63]. Where Moore’s Law has predicted the increase in computational memory over time, the increases in memory are directly proportional to the spotting density of electrical circuits. The differences in manufacturing designs are in the production approaches. NimbleGen and Affymetrix both make use of UV irradiation of reactive subunits at the surface, while Agilent uses piezo-electric delivery of either reactive subunits or pre-manufactured and purified oligonucleotides [8, 11, 12]. There are inherent chemistry limitations to the photolithographic methods, which limits the probe length to a maximal 25 nucleotides, after which the failure sequences begin to accumulate [5]. Affymetrix directly builds the probe on the Microarray chip 15
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: prevent the hybridization of probes
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 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
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cleansing process and were assessed
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sample selection, with replacement,
Page 89 and 90:
Figure 3.1: P value distributions.
Page 91 and 92:
Figure 3.2: Down selection models-
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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
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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
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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
Page 167 and 168:
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
Page 173 and 174:
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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