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

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Higher-level analyses are performed on a set characteristic or on the aggregated, transcript-level (ProbeSet) values. The consistency of probe behavior within the remaining set and over the remaining samples can again be assessed. Where differences remain, the investigator has the option of weighting the probes accordingly or classifying the ProbeSet and then handling the separate classes; we have chosen the latter course. The collection of methods discussed in detail below constitutes a ‘white box’ approach to data cleansing. They have been instantiated in a software pipeline, with a database backend, that includes the following steps: upper and lower limits on intensities that reflect scanner limitations, elimination of probes with cross-hybridization potential in the target genome along with those whose target sequence no longer appears, elimination of probes sensitive to regions of transcripts with known SNP variations, and elimination of probes with low binding accessibility scores. However, this only removes known sources of variation and therefore there may still be probes affected by uncharacterized transcript phenomena [20]. The BaFL pipeline in conjunction with the ProbeFATE database system allows investigators to identify potential regions of interest, for further analysis. Materials and Methods Hardware and Software A relational database and associated tools system, called ProbeFATE, was used for data storage, organization and simple transformations, which then became the basis for querying for data used in specific analyses. The information system originated as part of the doctoral thesis of Dr. Deshmukh [13], in collaboration with Drs. Carr and Weller and is described in detail elsewhere 30
(Carr, ms in review). This ProbeFATE system was developed for PostgresSQL 8.0.3 [38] and installed onto an AMD Anthon Tm 64 bit dual core processor running SUSE LINUX Tm 10.0 as the operating system. Python 2.4.1 [39] scripts were developed with the psycopg2 2.0.2 [40] module to automate the cleansing process and modify the existing system. Through this module data could be extracted and manipulated and analyzed in the R 2.3.1 language environment [41], via the python rpy 1.0 module [42]. Additional software and modules included Oligoarrayaux 2.3 [43] for the calculation of probe thermodynamics and the python MySQLdb 1.2.0 [44] module to enable querying of the public domain Ensembl mysql database [45]. Datasets Two independent datasets were used in testing the effects of the filtering algorithms. Both were studies of adenocarcinoma patients in which the assays were performed using the Affymetrix AG-U95Av2 GeneChip TM , so consistency of probe placement along the transcripts in the samples is assured. Using this platform, samples are assayed by 409,600 probes across 12,625 defined genes [46]. The largest, or ‘Bhattacharjee’, dataset (www.genome.wi.mit.edu/MPR/lung) contains measurements taken from 203 snap-frozen lung biopsy tissue samples. The tissues, as described by Bhattacharjee, et al [47], consist of 17 normal and 237 diseased samples, including 51 adenocarcinoma replicates, with disease category assigned after histopathological examination. The diseased samples are sub-classified into 5 states: 190 adenocarcinomas, 21 squamous cell lung carcinomas, 20 pulmonary carcinomas, and 6 small-cell lung carcinomas (SCLC) [48]. From this study we used 125 of the 190 adenocarcinoma array results and 13 of the 17 normal results; the selection criteria are described below. The second, ‘Stearman’, dataset (http:/www.ncbi.nlm.nih.gov/geo/; accession number GSE2514) consists of 39 tissue samples, all replicated, from 5 male and 5 female patients (four samples were taken from each patient: 2 31
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: probes can thus be reincorporated i
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
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
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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
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
Page 117 and 118:
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
Page 123 and 124:
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:
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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
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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