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

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platform: only the CDF and probe sequence files are required in order to flag problematic probes. Thus the order of the first four steps is irrelevant and can be set to optimize the computational efficiency. Using our data the cross hybridization filter (II), implemented here only for the PM probes, reduces the dataset most drastically, so if it is applied first the succeeding steps will be accomplished more quickly. Once steps (I)-(IV) have been completed the results are applicable to any future experiments using the same chip design and sequence files. The last two steps described above, (V) and (VI), are experiment/measurement dependent, and it is here that an investigator’s choices will affect what appears in the final gene list. Scanner response limits can be re-set in the code, to reflect the behavior of individual instruments Batch and Sample Filtering Technical steps will cause the amount of target, the labeling of that target and the effective length of the target to vary independently of the biological factors. Similarly, biological factors, such as secondary infections in cancer patients that lead to dramatic gene expression differences compared to uninfected cancer patients, may obscure the effect of interest. Technical differences tend to be seen in ‘batch’ effects, i.e. in groups of samples processed in parallel, while biological effects must be screened by comparing an array to the set of all arrays in its class (which may include multiple batches) [19]. The Bhattacharjee data set was explicitly batch annotated [47], while for the Stearman dataset the scan date was used as a proxy for batch annotation: there were 4 dates but in 2-day pairs one month apart, so our assumption is that this reflects only two technical batches. In the following discussion, both individual probe and aggregated ProbeSet values were used to compare individual array to batch and sample class trends, as follows: I. Probes-per-Sample 36
a. A filter based on how many probes contribute to the overall intensity, compared to the group mean, using only those probes that survived the above pipeline. Arrays for which this mean exceeded ±2 standard deviations of the group (or class) mean were excluded from further analysis. b. A filter based on the mean signal per probe, relative to the dataset mean. Arrays for which this value exceeded ±2 standard deviations of the dataset mean were excluded from further analysis. II. ProbeSets-per-Sample a. This filter determines how many ProbeSets contribute to the overall sample intensity, compared to the group mean, using only those probes that survived the above pipeline. Arrays where this mean exceeded -1.5 standard deviations of the dataset mean were excluded from further analysis. Samples possessing the lowest surviving ProbeSets were removed more aggressively, since these samples will most limit the population of ProbeSets in the final dataset. b. The second filter determines the mean signal per ProbeSet, relative to the dataset mean. Arrays in which this value exceeded ±2 standard deviations of the group (or class) mean were excluded from further analysis. The above two filters were performed in parallel, not sequentially, so there is no order of operations dependence: failing either test was sufficient to eliminate the sample from the pool. Probeset aggregations had the statistical rigor of 4 probes per probeset enforced per individual sample described in the previous section. The filter in IIa is less rigorous than the others, in part because of a desire to retain more samples for the final comparison, accepting that later pruning might be required. 37
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 and 44: (Carr, ms in review). This ProbeFAT
Page 45 and 46: have specific attributes which can
Page 47: elow saturation. The investigator m
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
Page 99 and 100:
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
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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:
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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,
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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