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

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ABSTRACT AN ADENOCARCINOMA CASE STUDY OF THE BAFL PROTOCOL: BIOLOGICAL PROBE FILTERING FOR ROBUST MICROARRAY ANALYSIS Kevin Thompson, PhD George Mason University, 2008 Dissertation Director: Dr. Jennifer Weller Microarrays are high throughput data measurement technologies; those that assay gene expression levels allowing investigators to simultaneously estimate the level of thousands of cellular transcripts present in a sample at the time of collection. Many sources of variation have plagued Microarray analysis, leading to apparent inconsistencies between experimental results derived from independent platforms. A rigorous, robust set of methods for identifying all of the currently known sources of variability and consistently applying them across large data sets has been implemented in the Biologically applied Filter Level, BaFL, protocol. This protocol eliminates all probes for which the underlying sequence characteristics are missing, because of which the probe characteristics, including the identification of the measured transcript region, are impossible to derive. The remaining probes are processed through the biophysical software to determine their Gibb’s free energy, as a measure of the solution stability. This measure eliminates any overly stable probes, which would be less assessable to measure the desired transcript region. The filtering process also enforces a range of acceptable signal intensity measurements, the result of scanner characteristics. Measurements outside the linear range
violate the linear correlation relationship between transcript concentration and signal intensity. Probes identified as covering single nucleotide polymorphisms are identified and removed. The Ensembl database is queried to identify probes which measure single specific gene transcript regions, all other probes were excluded. The final step is to enforce a rule that a minimum of four probes are retained, so that any given statistical estimator of concentration has an adequate basis. Samples are subject to many technical steps, so tests for outliers are implemented that included comparisons of representative probe intensities and probe numbers, against the population mean. Samples exceeding ±2 standard deviations of the average probe numbers and probe intensities are removed. ProbeSet constituents at this stage may not be identical across all samples, with differences arising from the linear range filter step. By performing an intersection operation of the remaining probes across all samples, still enforcing a minimum of four probes per ProbeSet, a final, common ProbeSet dataset is derived, which is used as the basis of all further comparisons and analyses. The suggested data models demonstrated improved performance across three classification algorithms, and remarkable latent structure can be seen across the data models. When Bonferonni correction is applied and the intersecting genes identified a final candidate gene list of 30 ProbeSets results. By including on/off genes in the list, an additional ProbeSet is identified. These 31 candidate genes demonstrate notable connectivity in their GO and KEGG associations. Literature review of the genes establishes that these associations arise from properties specific to angiogenesis and tumorogenesis. A multiclass dataset of non small cell lung cancer samples was constructed and information gain calculated from the k-means clustering efficiency. A candidate list of 18 genes is shown to possess an information gain greater than or equal to 0.8. The literature review of these 18 genes provides evidence that abnormal cytokinesis may underlie
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: LIST OF FIGURES Figure Page Figure
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 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
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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
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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:
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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