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

More documents

Recommendations

Info

interpretation to cover the remaining aspects [45-49]. Thus, some Microarray platforms implement multiple probes per target assessment in order to improve the specificity of transcript recognition [8, 12]. In this respect, standard ELISAs are more akin to the single-probe-per-gene Microarrays, since ELISAs assay one specific epitope of a protein which is then taken to be representative of the protein in its entirety. However, ELISAs have been modified to compare abnormalities in protein sub-units [50], similar to Microarray exons studies, and the lessons of the suite of controls developed for such assays should be considered by the Microarray standards groups as a conceptual guideline for improved platforms. Background Contributions Background contributions to the signal are usually derived at the same time that individual spots are identified and extracted, and algorithms for incorporating the values are often incorporated into the image analysis packages that are optimized for the output of a particular scanner [51]. Common adjustments include subtracting the immediately adjacent fluorescence surrounding individual spots [52] and/or a global adjustment of selected background regions to blocks of spots [52]. Such adjustments assume that any intensity is meaningful: they do not identify the minimal signal that is meaningful as a target response. The best remedy for the uncertainty in the noise level would be the incorporation of a quantifiable nucleic standard(s) on every array, or a calibration standard [7]. Background is contributed from both specific and non-specific sources, and can be truly random (noise, such as from scattered light in the scanner) or systematic (error, such as the effect of SNPs in the sample population) in its contributions to the signal. These effects are dissected in more detail below. 1) Specificity. Probe specificity can be estimated for each set of complementary and near-complementary sequences under the prevailing reaction conditions using 10
classical reaction equations, modified with nearest neighbor parameters and surface attachment parameters [25]. However, any target genome not exactly similar to the reference genome used in probe design is likely to diverge in unknown ways from the sequence set expected [31]. Thus the precise interpretation of the values is an open ended issue. Common problems that relate to managing specificity include: a. Cross hybridization. Targets with near perfect sequence similarity to probes have the potential to form sub-optimal but still stable duplexes that compete with perfectly matched targets [29, 53]. The problem is exacerbated with longer oligo probes because the greater stability of longer duplexes can overcome a larger number of mismatches, but it is clear that sequence composition effects can render this a problem even for quite short probes [10]. The heading of cross-hybridization is understood to mean sequences from other sites in the genome than the particular gene that the probe was designed to target, and not variant alleles or alternate transcripts of the intended gene. A complication when the probes are directly attached to the array surface (absence of a spacer) is to determine which of the nucleotides nearest the surface can actually bind the target. Evidence from Affymetrix arrays suggests that the first 6 nucleotides are inaccessible to molecules in the solution phase [10]. This is relevant to the cross-hybridization problem because it results in a shorter string of nucleotides that must be matched, and so a likelier cross-hybridization event. b. SNPs. Alternate alleles are within-gene sequence variants, and their significance to a DNA Microarray assay is that the intended probe may 11
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: hybridization and multiple dye stra
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
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
Page 101 and 102:
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
Page 111 and 112:
for the RMA and dCHIP interpretatio
Page 113 and 114:
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
Page 119 and 120:
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
Page 129 and 130:
averaged to give a final approximat
Page 131 and 132:
119 Table 5.1: : NSCLC candidate ge
Page 133 and 134:
Figure 5.1: NSCLC ProbeSet gain sel
Page 135 and 136:
Figure 5.3: NSCLC refined average p
Page 137 and 138:
statistical criterion, but it appea
Page 139 and 140:
lacking p53 mutations [30]. While,
Page 141 and 142:
Kruppel-like factor 5 (KLF5) has al
Page 143 and 144:
Appendix A # This is the main drive
Page 145 and 146:
def Driver(usr, pswd, db, logfile,
Page 147 and 148:
def DriverXH(usr, pswd, db, logfile
Page 149 and 150:
fp.write('\tTable '), fp.write(msk[
Page 151 and 152:
#determine outliers lowrs1=nonzero(
Page 153 and 154:
tmp="update sample_mask set exclude
Page 155 and 156:
fp.write('\n'+msk[ptr[j]]+' exclude
Page 157 and 158:
cur, conn=UpdateWorkReg(cur, conn,
Page 159 and 160:
Appendix E # The result of permutin
Page 161 and 162:
BIBLIOGRAPHY 149
Page 163 and 164:
13. Bowtell DD: Options available--
Page 165 and 166:
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
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?