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fp=open(logfile, 'a') fp.write('\n') fp.write('SNP filter:\n') conn.close() snp_filter(usr, pswd, db, msk, msk_tab, fp) def XH_filter(usr, pswd, db, msk, msk_tab, fp): cur, conn= make_connect(usr, pswd, db) exp=get_exp(usr, pswd, db) notes='probes w/o xhybrid' k=0 #print 'Map length ', len(map), map[k] pid_tbles=[] #fp=open(logfile,'a') for i in msk: trl= 'CREATE table tmp as select count(*), probeset_id, xhybrid_list.x, xhybrid_list.y from xhybrid_list inner join ' trl=trl+ msk[k] +'_snp on ( '+ msk[k] +'_snp.x = xhybrid_list.x and ' +msk[k]+ '_snp.y = xhybrid_list.y) ' trl=trl+ 'group by probeset_id, xhybrid_list.x, xhybrid_list.y having count(*) = 1 order by probeset_id, xhybrid_list.x, xhybrid_list.y' cur.execute(trl) conn.commit() trl= 'create table ' +msk[k]+'_XH as select xhybrid_list.* from xhybrid_list inner join tmp on (tmp.x = xhybrid_list.x and tmp.y = xhybrid_list.y) order by probeset_id, xhybrid_list.x, xhybrid_list.y' cur.execute(trl) conn.commit() fp.write('\tTable '), fp.write(msk[k]+ '_XH '), fp.write(' created.\n') pid_tbles.append(msk[k]+ '_XH') trl='drop table tmp' cur.execute(trl) conn.commit() cur, conn=UpdateWorkReg(cur, conn, msk[k]+'_XH', exp, 'xhybrid_filter', usr, msk[k]+'_snp', 'xhybrid_list', notes) k+=1 onto='select * from '+msk[k-1]+'_XH' cur.execute(onto) desc=cur.description conn.close() fp=UpdateOntology2(usr, pswd, db, desc, exp, 'xhybrid_filter', '_XH', notes, '', fp) fp.close() 134
def DriverXH(usr, pswd, db, logfile): # first step when combines chip and cel info cur, conn= make_connect(usr, pswd, db) msk, msk_tab, Lmsk, state=get_mask2(cur) #print len(msk), len(tables), len(msk_tab) t = datetime.datetime.now() EpochSeconds=time.mktime(t.timetuple()) now = datetime.datetime.fromtimestamp(EpochSeconds) fp=open(logfile, 'a') fp.write('\n') fp.write('Xhybrid filter:\n') conn.close() XH_filter(usr, pswd, db, msk, msk_tab, fp) def BioPhy_filter(usr, pswd, db, msk, msk_tab, fp): cur, conn= make_connect(usr, pswd, db) exp=get_exp(usr, pswd, db) notes='probes w/o structural issues' k=0 #print 'Map length ', len(map), map[k] pid_tbles=[] #fp=open(logfile,'a') for i in msk: trl= 'create table tmp as select known_seq_biophysical.* from known_seq_biophysical inner join ' trl=trl+ msk[k]+ '_XH on ( '+msk[k]+'_XH.x = known_seq_biophysical.x and '+msk[k]+'_XH.y = known_seq_biophysical.y) where dgss>-3.6 and dgss
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An Adenocarcinoma Case Study of the
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DEDICATION The work presented in th
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TABLE OF CONTENTS LIST OF TABLES ..
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Conclusion.........................
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LIST OF FIGURES Figure Page Figure
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violate the linear correlation rela
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Chapter 1: An Introduction to Micro
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previously stated, rigorous reagent
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Factors influencing Signal Interpre
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likelihood that no such structural
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hybridization and multiple dye stra
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classical reaction equations, modif
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prevent the hybridization of probes
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platforms are used to deposit them
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and chip layout [64]. In particular
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whether analyses based upon individ
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The process of determining a candid
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implemented in order to demonstrate
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affecting probe-target duplex forma
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It has long been known that the var
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probes can thus be reincorporated i
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(Carr, ms in review). This ProbeFAT
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have specific attributes which can
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elow saturation. The investigator m
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a. A filter based on how many probe
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means. The remaining sets possess s
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for the 24 samples in the Lu, et al
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Table 2.1: Probe Numbers per filter
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the effect can be. Both the type of
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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
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: def Driver(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
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