chr6.20157_chr6_88531077_88535882_-_0.R fitVsDatCorrelation=0.576309323399347 cont.fitVsDatCorrelation=0.339981546355857 fstatistic=12197.5259723488,43,485 cont.fstatistic=9208.2473798595,43,485 residuals=-0.339848420322404,-0.0739614573304088,-0.00554228422855374,0.0746801288032427,0.5570463585324 cont.residuals=-0.379032247148474,-0.0889397816110294,-0.0125890623647820,0.0810937489861505,0.683012122166623 predictedValues: Include Exclude Both chr6.20157_chr6_88531077_88535882_-_0.R.tl.Lung 45.0436048411929 48.2778902215429 48.8743024153767 chr6.20157_chr6_88531077_88535882_-_0.R.tl.cerebhem 48.1028002786041 48.6926366233602 52.3275203155598 chr6.20157_chr6_88531077_88535882_-_0.R.tl.cortex 45.6028361820716 45.8026432046332 48.5815171779176 chr6.20157_chr6_88531077_88535882_-_0.R.tl.heart 48.5145408488046 46.2260955875858 47.7720991582023 chr6.20157_chr6_88531077_88535882_-_0.R.tl.kidney 44.5303242321140 45.7986513967044 47.5847378898953 chr6.20157_chr6_88531077_88535882_-_0.R.tl.liver 52.1655521344685 48.8073644526562 50.6734318867005 chr6.20157_chr6_88531077_88535882_-_0.R.tl.stomach 46.276241916865 45.9357704003633 48.0333679458907 chr6.20157_chr6_88531077_88535882_-_0.R.tl.testicle 45.5042034708524 51.7381753588749 48.9190361117424 diffExp=-3.23428538035005,-0.589836344756158,-0.199807022561551,2.28844526121875,-1.26832716459040,3.35818768181237,0.340471516501658,-6.23397188802256 diffExpScore=2.67823855694551 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 48.8205581647722 52.6506991745808 48.6722478064929 cerebhem 56.7247906717768 50.7127211771872 47.4164327782629 cortex 47.4059478291327 47.8942693713554 45.9690011337325 heart 47.6803796483332 48.648897169754 49.6369037361077 kidney 51.5520089621463 47.5772980558351 47.6259622478777 liver 49.583112255809 48.8566276021841 50.2490052570888 stomach 50.1811708418333 46.0587927777417 45.9802928589213 testicle 48.4447673898261 48.4010271140345 48.1067215435058 cont.diffExp=-3.83014100980858,6.0120694945896,-0.488321542222657,-0.96851752142075,3.97471090631114,0.726484653624809,4.12237806409153,0.0437402757916558 cont.diffExpScore=1.90385154877573 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.160744185025550 cont.tran.correlation=0.216020464524394 tran.covariance=0.000387783623376824 cont.tran.covariance=0.000492863979413973 tran.mean=47.3137081969184 cont.tran.mean=49.4495667628939 weightedLogRatios: wLogRatio Lung -0.266435540479747 cerebhem -0.0472803187745069 cortex -0.0167100441454422 heart 0.186400732907753 kidney -0.107006940799329 liver 0.260917924981638 stomach 0.0282898208983798 testicle -0.498414696946214 cont.weightedLogRatios: wLogRatio Lung -0.296516625580526 cerebhem 0.446143322149088 cortex -0.0395975664280762 heart -0.07791441876073 kidney 0.313116771669862 liver 0.0575099421992801 stomach 0.331979245415282 testicle 0.00350476936356061 varWeightedLogRatios=0.0586452083310696 cont.varWeightedLogRatios=0.062579215138747 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79000291764704 0.0617178705253198 61.4085172639292 6.9876937152145e-231 *** df.mm.trans1 -0.00487648910203736 0.0494083829251238 -0.098697605817771 0.921419147946146 df.mm.trans2 0.0964268251428698 0.0494083829251238 1.95162884178987 0.0515574869265433 . df.mm.exp2 0.00599281132632766 0.0661611907729471 0.0905789520459792 0.927864552544635 df.mm.exp3 -0.0342844088442964 0.066161190772947 -0.518195160089456 0.604558420301883 df.mm.exp4 0.053613304888187 0.066161190772947 0.81034371149966 0.418140087194876 df.mm.exp5 -0.0374399997065016 0.066161190772947 -0.565890656880538 0.571729832693281 df.mm.exp6 0.121548853132327 0.066161190772947 1.83716241670226 0.0667975746504029 . df.mm.exp7 -0.00537608212799119 0.066161190772947 -0.0812573362901056 0.935270823109432 df.mm.exp8 0.0784810407919814 0.0661611907729471 1.18620961737696 0.236120300691671 df.mm.trans1:exp2 0.0597165648739375 0.0519011081221218 1.15058362016908 0.250470537775348 df.mm.trans2:exp2 0.00256131216520341 0.0519011081221219 0.0493498550970572 0.960660805321026 df.mm.trans1:exp3 0.0466233033384867 0.0519011081221219 0.898310364179188 0.369465844091905 df.mm.trans2:exp3 -0.0183474862756463 0.0519011081221219 -0.353508565413964 0.723860818016219 df.mm.trans1:exp4 0.0206192423691244 0.0519011081221218 0.397279424566580 0.691336193430173 df.mm.trans2:exp4 -0.0970425231705465 0.0519011081221218 -1.86975821291153 0.0621196637878968 . df.mm.trans1:exp5 0.0259793831525934 0.0519011081221219 0.500555461965565 0.616911077450283 df.mm.trans2:exp5 -0.0152790515600587 0.0519011081221219 -0.294387771530957 0.768587564819383 df.mm.trans1:exp6 0.0252424860498178 0.0519011081221218 0.486357362359643 0.626933500889063 df.mm.trans2:exp6 -0.110641337210415 0.0519011081221218 -2.13177215696589 0.0335275379644602 * df.mm.trans1:exp7 0.0323737609098221 0.0519011081221219 0.62375856857714 0.533079402172477 df.mm.trans2:exp7 -0.0443534892226974 0.0519011081221219 -0.854576921909545 0.393207327582172 df.mm.trans1:exp8 -0.068307352194689 0.0519011081221219 -1.31610585334640 0.18876020065814 df.mm.trans2:exp8 -0.0092588267026189 0.0519011081221219 -0.178393622749502 0.858488347301123 df.mm.trans1:probe2 0.195173059453476 0.0355342595975009 5.49253204271638 6.40500457901456e-08 *** df.mm.trans1:probe3 0.0321649839264955 0.0355342595975009 0.905182330821875 0.365818231858991 df.mm.trans1:probe4 0.084596011680476 0.0355342595975009 2.38068873922521 0.0176655241238476 * df.mm.trans1:probe5 -0.00475537725346732 0.0355342595975009 -0.133825139663294 0.893596358869823 df.mm.trans1:probe6 0.0528947385489635 0.0355342595975009 1.48855609060400 0.137254212654942 df.mm.trans2:probe2 -0.0833243186211044 0.0355342595975009 -2.34490093686839 0.0194344318467763 * df.mm.trans2:probe3 0.0537655062193605 0.0355342595975009 1.51306110858552 0.130915475286188 df.mm.trans2:probe4 -0.0386659473149292 0.0355342595975009 -1.08813150331261 0.277077481670005 df.mm.trans2:probe5 0.0155119642463317 0.0355342595975009 0.436535456824958 0.662642546495404 df.mm.trans2:probe6 -0.0985839457063902 0.0355342595975009 -2.77433515776205 0.00574485807071887 ** df.mm.trans3:probe2 -0.0470523338344199 0.0355342595975009 -1.32413998117268 0.186080246350478 df.mm.trans3:probe3 0.120086644347726 0.0355342595975009 3.37946099645684 0.000784836502504773 *** df.mm.trans3:probe4 0.00408808136269643 0.0355342595975009 0.115046195108676 0.908456120594804 df.mm.trans3:probe5 -0.0317926386968223 0.0355342595975009 -0.894703845160693 0.371389213106511 df.mm.trans3:probe6 0.148456934282186 0.0355342595975009 4.17785359716984 3.49047069874172e-05 *** df.mm.trans3:probe7 -0.105375518534650 0.0355342595975009 -2.96546261912437 0.0031712487967257 ** df.mm.trans3:probe8 0.0950930557836096 0.0355342595975009 2.67609503787993 0.00770061448801053 ** df.mm.trans3:probe9 -0.0405947508625634 0.0355342595975009 -1.14241161409814 0.25384661966054 df.mm.trans3:probe10 -0.0659245570014778 0.0355342595975009 -1.85523935909204 0.0641686021316373 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98183893119936 0.0710220502190617 56.0648266125478 4.52103005248397e-214 *** df.mm.trans1 -0.0850296500367715 0.0568568653371016 -1.49550365699261 0.135433428087944 df.mm.trans2 -0.0104991387359699 0.0568568653371016 -0.184659120296573 0.853573600290187 df.mm.exp2 0.138697269884056 0.0761352161640354 1.82172294073782 0.0691128363196795 . df.mm.exp3 -0.0669457273096787 0.0761352161640354 -0.879300416845764 0.379673852355185 df.mm.exp4 -0.122307467910073 0.0761352161640354 -1.60645065545697 0.108825759944898 df.mm.exp5 -0.0251531499693285 0.0761352161640354 -0.330374710109647 0.741259511085631 df.mm.exp6 -0.0911724207827675 0.0761352161640354 -1.19750655972834 0.2316941048122 df.mm.exp7 -0.0493763395614097 0.0761352161640354 -0.648534831174932 0.516946027807707 df.mm.exp8 -0.0801985499791868 0.0761352161640355 -1.05336996491081 0.292695959585009 df.mm.trans1:exp2 0.0113625725298752 0.0597253773680033 0.190246977593188 0.849195202222842 df.mm.trans2:exp2 -0.176199998128081 0.0597253773680033 -2.95016969156023 0.00332956912023760 ** df.mm.trans1:exp3 0.0375419317493591 0.0597253773680033 0.62857588187415 0.529922657223255 df.mm.trans2:exp3 -0.0277379309327546 0.0597253773680033 -0.464424540373262 0.642552015046932 df.mm.trans1:exp4 0.0986759550421007 0.0597253773680033 1.65216126528762 0.0991488907901051 . df.mm.trans2:exp4 0.0432570894654812 0.0597253773680033 0.7242664905899 0.469251366034543 df.mm.trans1:exp5 0.0795928327941378 0.0597253773680033 1.33264679607999 0.183273477567413 df.mm.trans2:exp5 -0.076170652322411 0.0597253773680033 -1.27534819668160 0.202796568127900 df.mm.trans1:exp6 0.106671219807620 0.0597253773680033 1.78602839376562 0.0747192800578178 . df.mm.trans2:exp6 0.016382944419361 0.0597253773680033 0.274304577741150 0.783967360831931 df.mm.trans1:exp7 0.0768647150494637 0.0597253773680033 1.28696909817505 0.198718943139338 df.mm.trans2:exp7 -0.0843844943259677 0.0597253773680033 -1.41287502975536 0.158333874312398 df.mm.trans1:exp8 0.0724713842570698 0.0597253773680033 1.21341023616361 0.225563620347223 df.mm.trans2:exp8 -0.00395993332001006 0.0597253773680033 -0.0663023574654133 0.947164427908225 df.mm.trans1:probe2 -0.0148921807661123 0.0408911705499549 -0.364190620268969 0.715874390439981 df.mm.trans1:probe3 -0.0244692323162430 0.0408911705499549 -0.598398920528577 0.549853134033901 df.mm.trans1:probe4 -0.0732177016109593 0.0408911705499549 -1.79055039575139 0.0739890364254789 . df.mm.trans1:probe5 -0.00317614511268689 0.0408911705499549 -0.0776731277185311 0.938120103558838 df.mm.trans1:probe6 -0.0227692705319729 0.0408911705499549 -0.556826088022028 0.577903104126567 df.mm.trans2:probe2 0.0118914704770964 0.0408911705499549 0.290807778724973 0.771322649182101 df.mm.trans2:probe3 -0.0281948654928568 0.0408911705499549 -0.689509865177676 0.490832221661993 df.mm.trans2:probe4 -0.0278812042661496 0.0408911705499549 -0.681839230600855 0.495666084236051 df.mm.trans2:probe5 -0.0508595071363911 0.0408911705499549 -1.24377723729523 0.214182379516698 df.mm.trans2:probe6 -0.0275202824205034 0.0408911705499549 -0.673012830163008 0.501259674297302 df.mm.trans3:probe2 0.0124746043051625 0.0408911705499549 0.305068408103478 0.760444925428677 df.mm.trans3:probe3 0.00116787351686195 0.0408911705499549 0.0285605303334422 0.977226839608289 df.mm.trans3:probe4 -0.0307075756168164 0.0408911705499549 -0.750958586018034 0.453041777599212 df.mm.trans3:probe5 0.0160223711568117 0.0408911705499549 0.391829603831906 0.695356226980076 df.mm.trans3:probe6 -0.00677140947300897 0.0408911705499549 -0.165595882483644 0.86854399081393 df.mm.trans3:probe7 -0.0591685334405313 0.0408911705499549 -1.44697578095124 0.148549707438666 df.mm.trans3:probe8 0.0397610714135477 0.0408911705499549 0.972363248075115 0.331354560543703 df.mm.trans3:probe9 -0.0228192611938665 0.0408911705499549 -0.558048617512409 0.577068688367572 df.mm.trans3:probe10 0.0380295092676975 0.0408911705499549 0.930017623761553 0.352824620037891