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tumorogenesis for both cancer sub-types. The squamous cell carcinomas, in particular, appear to suffering from the production of radical oxidative species. Currently most Microarray analyses implement one of a small number of published probe cleansing algorithms. Occasional efforts to accommodate one of the confounding factors of the probe-transcript interaction have been made, but no method is as inclusive as that presented in this work. Further, no work exists that demonstrates the improved efficacy of removing a factor on subsequent performance with the existing algorithms. Great effort has been taken here to show that analysis of the resulting datasets leads to greatly improved consistency in inter- experimental comparisons, using two independent lung adenocarcinoma datasets, in comparison to the pre-eminent probe cleansing methodologies, RMA and dCHIP.
Chapter 1: An Introduction to Microarrays Gene expression Microarray technology is a high throughput capture detection assay that produces a fairly complete representation of a sample’s genome-wide transcript complement [1, 2]. The assay has been predominately utilized for gene expression experiments, but modifications in probe design allow the use of this platform for single nucleotide polymorphism and comparative genome hybridization experiments [3-5]. The array concept is a capture detection assay as developed from Northern and Southern blots [5, 6], and this detection method has spawned analogous methods such as the immunological assays known as ELISAs (enzyme linked immunosorbent assays) [7]. Unfortunately, for most platforms, rigorous experimental design has been sacrificed in order to rapidly achieve high throughput genome-wide analysis, but we have developed a number of assessment strategies for the individual probes that allow us to ascertain the reliability of individual measurements. Our focus here is upon gene expression analysis, and particularly those performed using the Affymetrix platform [8], which has the benefits of sampling transcripts multiple times, and having probes short enough to be sensitive to individual differences in sequence [8, 9]. For gene expression Microarray analysis, mRNA transcripts are extracted from a collection of cells, which can represent anything from a pure culture to a cryogenically preserved and laser-dissected tissue sample. These mRNA samples can be sheared, transformed or otherwise amplified into cDNA or cRNA ‘targets’, and at some part of the protocol they are labeled, most often with a fluorescent dye [5]. 1
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: violate the linear correlation rela
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 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
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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
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Table 2.3: ProbeSet behavior predic
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cleansing process. This is demonstr
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Table 2.4: ProbeSet behavior of pro
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A Priori Prediction We demonstrated
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Conclusion We have presented a comp
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mind that they are most aware of th
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Probe Cleansing Methods The BaFL pr
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are significant assume such a data
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cleansing process and were assessed
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sample selection, with replacement,
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Figure 3.1: P value distributions.
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Figure 3.2: Down selection models-
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cleansing methods were used to gene
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Discussion A striking result from t
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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
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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
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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
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ejecting the null hypothesis [8], a
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Figure 4.9: GO connectivity of cand
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under linear and non-linear dimensi
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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:
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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,
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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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