chr2.13327_chr2_123279964_123286333_-_2.R fitVsDatCorrelation=0.891644540413298 cont.fitVsDatCorrelation=0.216911309006895 fstatistic=9767.57987740549,53,715 cont.fstatistic=2090.32099474902,53,715 residuals=-0.82385619974643,-0.0922580088617255,0.00184469491750313,0.087306876431698,0.851179584271597 cont.residuals=-0.727041985775946,-0.252906086617130,-0.0457322411998302,0.212211247988924,1.35750922246310 predictedValues: Include Exclude Both chr2.13327_chr2_123279964_123286333_-_2.R.tl.Lung 52.3962483750435 56.2247287726438 71.3127441719035 chr2.13327_chr2_123279964_123286333_-_2.R.tl.cerebhem 47.1547584763707 47.3258420931385 61.9468669452393 chr2.13327_chr2_123279964_123286333_-_2.R.tl.cortex 52.6257889649705 54.6751775852089 64.37345831532 chr2.13327_chr2_123279964_123286333_-_2.R.tl.heart 54.9060109356974 64.4161834468233 68.8685240905777 chr2.13327_chr2_123279964_123286333_-_2.R.tl.kidney 51.7224516655271 59.110734521584 74.0070960553303 chr2.13327_chr2_123279964_123286333_-_2.R.tl.liver 53.4932696686104 58.1569209662136 76.4723005434072 chr2.13327_chr2_123279964_123286333_-_2.R.tl.stomach 56.6576888236152 60.8797076364943 73.3335973604344 chr2.13327_chr2_123279964_123286333_-_2.R.tl.testicle 56.0489465769905 59.0184976612531 86.7450632434556 diffExp=-3.8284803976003,-0.171083616767824,-2.04938862023842,-9.51017251112584,-7.38828285605695,-4.66365129760322,-4.22201881287913,-2.96955108426258 diffExpScore=0.972069090386893 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 65.6667211904554 65.718769448698 59.0023209132781 cerebhem 57.9979505618923 62.9831450501745 65.2748811234025 cortex 66.2491627084257 60.7598737789586 69.1544330460882 heart 64.694840649762 64.9854838464644 62.6560340981538 kidney 60.769210634512 71.1755585676038 63.9862585781854 liver 64.2017493745913 66.8045674645336 65.3895684531794 stomach 62.2044320376427 64.8976383031125 62.2704596398863 testicle 59.2416905433401 69.371686322509 61.9731165952515 cont.diffExp=-0.0520482582425785,-4.98519448828215,5.48928892946705,-0.290643196702391,-10.4063479330918,-2.60281808994229,-2.69320626546977,-10.1299957791689 cont.diffExpScore=1.37413636246259 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=0.850185412386355 cont.tran.correlation=-0.382324913491832 tran.covariance=0.00458750235996285 cont.tran.covariance=-0.000917308795684656 tran.mean=55.3008097606365 cont.tran.mean=64.2326550301673 weightedLogRatios: wLogRatio Lung -0.281670352253551 cerebhem -0.0139620217535464 cortex -0.152138226918540 heart -0.652625230378858 kidney -0.535771678281322 liver -0.336140839610750 stomach -0.292732901706946 testicle -0.209188735633670 cont.weightedLogRatios: wLogRatio Lung -0.00331575988095816 cerebhem -0.338218875645312 cortex 0.358962129448043 heart -0.0187004926348342 kidney -0.661683608775098 liver -0.166192901952389 stomach -0.175966283324972 testicle -0.656756806125644 varWeightedLogRatios=0.0418380768117819 cont.varWeightedLogRatios=0.118016838297346 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75005140533993 0.0753393373481378 49.7754763625171 1.63064753804100e-234 *** df.mm.trans1 0.053048752816338 0.0643252647420794 0.824695444768769 0.409819555295274 df.mm.trans2 0.212726248194890 0.0579435477180269 3.671267234621 0.000259330793048171 *** df.mm.exp2 -0.136902231176940 0.0750725436571474 -1.82359920828266 0.0686300282041986 . df.mm.exp3 0.078798073322899 0.0750725436571474 1.04962572845228 0.294244866288514 df.mm.exp4 0.217671874521362 0.0750725436571474 2.89948713494322 0.00385231765234875 ** df.mm.exp5 2.69001517333473e-05 0.0750725436571474 0.000358322103167291 0.999714200299734 df.mm.exp6 -0.0153444695354801 0.0750725436571475 -0.204395226110327 0.838102826492969 df.mm.exp7 0.129792141358207 0.0750725436571474 1.72888961843309 0.0842605135772673 . df.mm.exp8 -0.0800138550540707 0.0750725436571474 -1.06582048717425 0.286864565959324 df.mm.trans1:exp2 0.031502164382678 0.0675705684483178 0.466211327003603 0.641206241112639 df.mm.trans2:exp2 -0.0353979519232514 0.0527472580887983 -0.671086103919564 0.502382410167721 df.mm.trans1:exp3 -0.0744267820920426 0.0675705684483178 -1.10146745544947 0.271063909789413 df.mm.trans2:exp3 -0.106744932620642 0.0527472580887983 -2.02370580933213 0.0433718963638764 * df.mm.trans1:exp4 -0.170884036056593 0.0675705684483178 -2.52897141434137 0.0116536625698741 * df.mm.trans2:exp4 -0.0816636510925142 0.0527472580887983 -1.54820656184699 0.122015056394286 df.mm.trans1:exp5 -0.0129699375802499 0.0675705684483178 -0.191946551258779 0.847838571773247 df.mm.trans2:exp5 0.0500289670878445 0.0527472580887983 0.948465738325626 0.343212860872977 df.mm.trans1:exp6 0.0360653220693903 0.0675705684483179 0.53374306147765 0.593685198659186 df.mm.trans2:exp6 0.0491326867432555 0.0527472580887983 0.931473758513517 0.351922956490535 df.mm.trans1:exp7 -0.0515994309200222 0.0675705684483178 -0.763637662150032 0.445335100192357 df.mm.trans2:exp7 -0.0502489039778892 0.0527472580887983 -0.952635374777145 0.341096774440679 df.mm.trans1:exp8 0.147404217183347 0.0675705684483178 2.18148552792021 0.0294725290487092 * df.mm.trans2:exp8 0.128508095634925 0.0527472580887983 2.43629906636258 0.0150814973324781 * df.mm.trans1:probe2 -0.0513832750827834 0.0462624057032382 -1.11069180907699 0.267074398339109 df.mm.trans1:probe3 0.0200290194702627 0.0462624057032381 0.432943751320325 0.665186199063144 df.mm.trans1:probe4 0.0090290913844795 0.0462624057032382 0.195171246441417 0.845314334929839 df.mm.trans1:probe5 0.446758238204307 0.0462624057032382 9.65704725928327 8.0207054902781e-21 *** df.mm.trans1:probe6 0.634735574260253 0.0462624057032382 13.7203321922324 3.36039712125684e-38 *** df.mm.trans1:probe7 0.435577470677145 0.0462624057032382 9.4153657609435 6.3346544282574e-20 *** df.mm.trans1:probe8 0.73698467265623 0.0462624057032382 15.9305306642245 4.00361066673660e-49 *** df.mm.trans1:probe9 0.528179610461512 0.0462624057032381 11.4170372775176 7.52333685218913e-28 *** df.mm.trans1:probe10 0.804723789498467 0.0462624057032381 17.3947674632523 8.8215428407841e-57 *** df.mm.trans1:probe11 0.0395874158039263 0.0462624057032382 0.855714595947943 0.392442328197175 df.mm.trans1:probe12 0.175027487586351 0.0462624057032382 3.78336329306152 0.00016767452118905 *** df.mm.trans1:probe13 0.177825835158263 0.0462624057032382 3.8438518804874 0.000131902750073340 *** df.mm.trans1:probe14 0.0690842175517647 0.0462624057032381 1.49331225866036 0.135796598859294 df.mm.trans1:probe15 -0.00931127798846433 0.0462624057032382 -0.20127094228938 0.840543965847197 df.mm.trans1:probe16 0.0322578328573311 0.0462624057032382 0.697279624070072 0.485854472634397 df.mm.trans2:probe2 0.102901447359162 0.0462624057032382 2.22429953209198 0.02643979969304 * df.mm.trans2:probe3 0.0718664255415627 0.0462624057032382 1.55345197572664 0.120757691746336 df.mm.trans2:probe4 0.234441920264903 0.0462624057032381 5.06765518786008 5.13324820106007e-07 *** df.mm.trans2:probe5 0.319438156693564 0.0462624057032382 6.90491883934183 1.11029462402860e-11 *** df.mm.trans2:probe6 0.336616375505193 0.0462624057032382 7.27624018656753 9.05163431180705e-13 *** df.mm.trans3:probe2 0.933357508327677 0.0462624057032382 20.1752912357160 5.36861401735033e-72 *** df.mm.trans3:probe3 0.0826183940942156 0.0462624057032381 1.78586463108279 0.07454487806119 . df.mm.trans3:probe4 -0.231247244501769 0.0462624057032382 -4.99859964017354 7.26993885972031e-07 *** df.mm.trans3:probe5 -0.176264408924496 0.0462624057032382 -3.81010036648738 0.000150861077666639 *** df.mm.trans3:probe6 0.496812374174847 0.0462624057032382 10.7390086317987 4.81484862798507e-25 *** df.mm.trans3:probe7 0.319884880052867 0.0462624057032382 6.91457513266493 1.04166718099078e-11 *** df.mm.trans3:probe8 0.445500794174472 0.0462624057032382 9.62986657097447 1.01392157579273e-20 *** df.mm.trans3:probe9 0.198358697050672 0.0462624057032382 4.28768660071622 2.05328629481468e-05 *** df.mm.trans3:probe10 0.443465457910244 0.0462624057032382 9.58587110136393 1.48016875667582e-20 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19974560599753 0.162446953368238 25.8530278279671 1.44504704816444e-104 *** df.mm.trans1 -0.0376417674998465 0.138698369932164 -0.271393005687498 0.786167117181148 df.mm.trans2 0.0124208219739856 0.124938088460281 0.0994158156816551 0.920835992091556 df.mm.exp2 -0.267732525502005 0.161871691840797 -1.65397990505544 0.098570509754745 . df.mm.exp3 -0.228389795535459 0.161871691840797 -1.41093104630106 0.158699888967868 df.mm.exp4 -0.08621467451618 0.161871691840797 -0.532611190602574 0.594468233124711 df.mm.exp5 -0.0788356715593573 0.161871691840797 -0.487025684743526 0.626389396551315 df.mm.exp6 -0.108960887971386 0.161871691840797 -0.673131211098669 0.501081323776909 df.mm.exp7 -0.120649723739465 0.161871691840797 -0.745341710878796 0.456310109258531 df.mm.exp8 -0.0979964138955942 0.161871691840797 -0.605395624035207 0.545108261119475 df.mm.trans1:exp2 0.143547929903298 0.145695905594006 0.985257130720659 0.324831064161091 df.mm.trans2:exp2 0.225215108371233 0.113733829851176 1.98019453548636 0.0480646199983962 * df.mm.trans1:exp3 0.237220351577502 0.145695905594006 1.62818818147531 0.103925511253854 df.mm.trans2:exp3 0.149934827048940 0.113733829851176 1.31829577220019 0.187826804978359 df.mm.trans1:exp4 0.07130385980028 0.145695905594006 0.489401946537705 0.624707260846881 df.mm.trans2:exp4 0.0749940251906959 0.113733829851176 0.659381868075909 0.509862894830657 df.mm.trans1:exp5 0.00132665784697982 0.145695905594006 0.00910566320701332 0.992737372271244 df.mm.trans2:exp5 0.158600583657557 0.113733829851176 1.39448907915165 0.163603080420653 df.mm.trans1:exp6 0.0863990767404319 0.145695905594006 0.59300964147332 0.553362215831637 df.mm.trans2:exp6 0.125347772552805 0.113733829851176 1.10211511136859 0.270782471545207 df.mm.trans1:exp7 0.0664837051895518 0.145695905594006 0.456318281001077 0.648299694664181 df.mm.trans2:exp7 0.108076388163562 0.113733829851176 0.950257177701512 0.342302679394284 df.mm.trans1:exp8 -0.00497033029957157 0.145695905594006 -0.034114413025592 0.972795436367263 df.mm.trans2:exp8 0.152090651259558 0.113733829851176 1.33725076750316 0.18156605078952 df.mm.trans1:probe2 -0.0155190185223948 0.099751167537475 -0.155577312080728 0.876410120524946 df.mm.trans1:probe3 0.00258692498872776 0.099751167537475 0.0259337815545457 0.979317391503302 df.mm.trans1:probe4 -0.00145729647688290 0.099751167537475 -0.0146093174933057 0.98834794119981 df.mm.trans1:probe5 0.0410988374309087 0.099751167537475 0.412013597890657 0.68045294638554 df.mm.trans1:probe6 -0.000179219579992191 0.099751167537475 -0.00179666648939083 0.998566969465988 df.mm.trans1:probe7 0.112552732876035 0.099751167537475 1.12833499250774 0.25955692863278 df.mm.trans1:probe8 0.0034283612751367 0.099751167537475 0.0343691343146306 0.97259238847151 df.mm.trans1:probe9 0.0921919876568586 0.099751167537475 0.92421963504561 0.355683773720065 df.mm.trans1:probe10 0.0575906665142929 0.099751167537475 0.577343282650371 0.563889325238316 df.mm.trans1:probe11 0.125642661756729 0.099751167537475 1.25956081375716 0.208238837108001 df.mm.trans1:probe12 0.0808070354739947 0.099751167537475 0.810086111960913 0.418160210376939 df.mm.trans1:probe13 0.0484026172738950 0.099751167537475 0.485233591433512 0.627659294518081 df.mm.trans1:probe14 0.0618144440945392 0.099751167537475 0.619686421929011 0.535661677498841 df.mm.trans1:probe15 -0.0484912302511858 0.099751167537475 -0.486121931685345 0.627029667953707 df.mm.trans1:probe16 0.0242297244566365 0.099751167537475 0.242901662755313 0.808151241637029 df.mm.trans2:probe2 -0.0962059608023125 0.099751167537475 -0.96445949633792 0.335141683906698 df.mm.trans2:probe3 -0.175324365672561 0.099751167537475 -1.75761717883346 0.079240568650254 . df.mm.trans2:probe4 0.00253611389491401 0.099751167537475 0.0254244031174997 0.979723540488066 df.mm.trans2:probe5 -0.114529600327432 0.099751167537475 -1.14815298060952 0.251289403341000 df.mm.trans2:probe6 -0.0449859331764359 0.099751167537475 -0.450981520186573 0.652139603900702 df.mm.trans3:probe2 -0.0867557972784251 0.099751167537475 -0.869722123761933 0.384744150201471 df.mm.trans3:probe3 -0.0718647018295132 0.099751167537475 -0.72043970615697 0.471489768836219 df.mm.trans3:probe4 -0.0680191146507988 0.099751167537475 -0.681887904973594 0.495530652651027 df.mm.trans3:probe5 -0.161486877116242 0.099751167537475 -1.61889711271374 0.105910387558459 df.mm.trans3:probe6 -0.173686278526131 0.099751167537475 -1.74119544476389 0.0820794399665551 . df.mm.trans3:probe7 -0.090161509339313 0.099751167537475 -0.903864200942218 0.366371839373366 df.mm.trans3:probe8 -0.123341023253889 0.099751167537475 -1.23648701362369 0.216683599075317 df.mm.trans3:probe9 -0.210218352501361 0.099751167537475 -2.10742748872975 0.0354281417248128 * df.mm.trans3:probe10 0.0160548562311052 0.099751167537475 0.160949055810035 0.87217898889342