chr5.17918_chr5_29332424_29332886_+_1.R fitVsDatCorrelation=0.885014889323429 cont.fitVsDatCorrelation=0.319503591429044 fstatistic=7290.94773302501,37,347 cont.fstatistic=1752.85019699325,37,347 residuals=-0.425629373615678,-0.0914688042260528,-0.000562999365408416,0.0704701114599596,1.12727928596804 cont.residuals=-0.728497009212953,-0.241361323742949,-0.0214722453168402,0.21294498683917,1.12158525304862 predictedValues: Include Exclude Both chr5.17918_chr5_29332424_29332886_+_1.R.tl.Lung 57.9291268851567 87.9275727491458 72.560367840599 chr5.17918_chr5_29332424_29332886_+_1.R.tl.cerebhem 52.8964557867887 71.1034190790999 64.9038263596692 chr5.17918_chr5_29332424_29332886_+_1.R.tl.cortex 54.3395348355489 97.6916058240105 63.1312955139792 chr5.17918_chr5_29332424_29332886_+_1.R.tl.heart 59.0606474566056 117.141473487062 80.9038280711176 chr5.17918_chr5_29332424_29332886_+_1.R.tl.kidney 61.6376294777977 82.9505143933977 69.855537483018 chr5.17918_chr5_29332424_29332886_+_1.R.tl.liver 70.001454209976 102.909868521459 65.1356580484375 chr5.17918_chr5_29332424_29332886_+_1.R.tl.stomach 59.7541290477231 147.259545053383 69.7405768025211 chr5.17918_chr5_29332424_29332886_+_1.R.tl.testicle 57.8530322319805 98.7559567633232 70.6027656532144 diffExp=-29.9984458639891,-18.2069632923111,-43.3520709884615,-58.080826030456,-21.3128849155999,-32.9084143114827,-87.5054160056604,-40.9029245313427 diffExpScore=0.996999411397992 diffExp1.5=-1,0,-1,-1,0,0,-1,-1 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,-1,-1,0,-1,-1,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 81.1794396227346 85.195434667516 86.578779224072 cerebhem 67.7227888423 70.1905592533964 79.1599743091211 cortex 67.6267188921506 72.2469435682002 71.0330427523465 heart 69.4672455808765 76.3795140401708 79.5828777631485 kidney 74.2326034771464 77.1629391733004 72.631960806731 liver 69.9996498219786 79.618660045351 69.7339690799241 stomach 69.4777997594983 77.4561552709846 79.8725689973099 testicle 74.1052212384282 90.77880478257 79.36889682253 cont.diffExp=-4.01599504478143,-2.46777041109645,-4.62022467604957,-6.91226845929428,-2.93033569615400,-9.61901022337246,-7.97835551148637,-16.6735835441418 cont.diffExpScore=0.982211958464188 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,-1 cont.diffExp1.2Score=0.5 tran.correlation=0.26364273073637 cont.tran.correlation=0.714687619331789 tran.covariance=0.00622255610043596 cont.tran.covariance=0.00379067430486803 tran.mean=79.9507478626536 cont.tran.mean=75.1775298772877 weightedLogRatios: wLogRatio Lung -1.78095150808908 cerebhem -1.21757881018463 cortex -2.5154978216022 heart -3.02756027361611 kidney -1.26799725966066 liver -1.71135515524920 stomach -4.0959890086826 testicle -2.31292491172011 cont.weightedLogRatios: wLogRatio Lung -0.213462280320125 cerebhem -0.151515033343976 cortex -0.280674006028002 heart -0.406783126302227 kidney -0.167506395374726 liver -0.555317505566143 stomach -0.466926058564793 testicle -0.894346923032573 varWeightedLogRatios=0.946639520613507 cont.varWeightedLogRatios=0.0624795786398219 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25453325441604 0.0888431591252437 47.8881356348253 5.66169800347431e-155 *** df.mm.trans1 -0.259903497969032 0.0740185436605028 -3.51132952792369 0.000504744897041903 *** df.mm.trans2 0.230665892515077 0.0740185436605028 3.11632573552190 0.00198396495143494 ** df.mm.exp2 -0.191749694015513 0.101982003915084 -1.88023069418379 0.0609141103367528 . df.mm.exp3 0.180536191158078 0.101982003915084 1.77027499193292 0.0775593447614787 . df.mm.exp4 0.197371200390870 0.101982003915084 1.93535322717538 0.053759169445226 . df.mm.exp5 0.0417725342111549 0.101982003915084 0.409606916980539 0.682347057116173 df.mm.exp6 0.454582540069829 0.101982003915084 4.4574780119867 1.12093571790414e-05 *** df.mm.exp7 0.586337747886708 0.101982003915084 5.749423676504 1.96420694990216e-08 *** df.mm.exp8 0.142173397516227 0.101982003915084 1.39410280302600 0.164178590304611 df.mm.trans1:exp2 0.100865719175357 0.0861905245145736 1.17026459397288 0.242697608590923 df.mm.trans2:exp2 -0.0206283206736815 0.0861905245145736 -0.239333973077210 0.81098792435711 df.mm.trans1:exp3 -0.244504460555631 0.0861905245145736 -2.83679049330172 0.00482457188104869 ** df.mm.trans2:exp3 -0.0752339923272435 0.0861905245145735 -0.872880084568026 0.383332056963272 df.mm.trans1:exp4 -0.178026674543450 0.0861905245145736 -2.06550169576179 0.0396176558873031 * df.mm.trans2:exp4 0.0894977403907166 0.0861905245145736 1.03837099141430 0.299820374854234 df.mm.trans1:exp5 0.0202797048698508 0.0861905245145735 0.235289261598841 0.814123022577645 df.mm.trans2:exp5 -0.100041755003247 0.0861905245145735 -1.16070479402097 0.246559864754778 df.mm.trans1:exp6 -0.265286836908566 0.0861905245145736 -3.07791185171069 0.00225051262660273 ** df.mm.trans2:exp6 -0.297242436485518 0.0861905245145735 -3.44866721904284 0.000632694526584327 *** df.mm.trans1:exp7 -0.555319767162071 0.0861905245145736 -6.4429329127493 3.91605180812697e-10 *** df.mm.trans2:exp7 -0.070654544017412 0.0861905245145736 -0.819748393635375 0.412922253467709 df.mm.trans1:exp8 -0.143487842931869 0.0861905245145736 -1.6647751448317 0.0968602425196578 . df.mm.trans2:exp8 -0.0260351125561921 0.0861905245145735 -0.302064672454684 0.762783725823742 df.mm.trans1:probe2 0.0875587616907216 0.0472084945198339 1.85472471811053 0.064483645960968 . df.mm.trans1:probe3 0.0354741218495983 0.0472084945198340 0.751435143408235 0.452900146069641 df.mm.trans1:probe4 0.300857701048044 0.0472084945198340 6.37295690337346 5.90222285721392e-10 *** df.mm.trans1:probe5 0.100598324913421 0.0472084945198340 2.13093694125654 0.0337969109398254 * df.mm.trans1:probe6 0.121416657033487 0.0472084945198340 2.57192393590259 0.0105290861529149 * df.mm.trans2:probe2 0.0136825314307297 0.0472084945198340 0.289831979814166 0.772117889776861 df.mm.trans2:probe3 0.03426881555333 0.0472084945198339 0.725903587942896 0.468387186086267 df.mm.trans2:probe4 -0.0701187346029707 0.0472084945198340 -1.48529910381936 0.138372498402042 df.mm.trans2:probe5 0.0173384265964960 0.0472084945198339 0.367273448832635 0.713638943604318 df.mm.trans2:probe6 -0.0820281217305334 0.0472084945198340 -1.73757122663741 0.0831736172468302 . df.mm.trans3:probe2 -0.323900977581907 0.0472084945198340 -6.8610740689014 3.15121810893823e-11 *** df.mm.trans3:probe3 0.48262268896264 0.0472084945198339 10.2232171110619 1.27894339645328e-21 *** df.mm.trans3:probe4 0.0777711938190953 0.0472084945198340 1.64739830426960 0.100381405273187 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.18840120426363 0.180827302517840 23.1624381160605 2.08656172218802e-72 *** df.mm.trans1 0.161024929727648 0.150653958258725 1.06883968790991 0.285884877988348 df.mm.trans2 0.300002566398180 0.150653958258725 1.99133544093791 0.0472276927934419 * df.mm.exp2 -0.285389354631418 0.207569506250129 -1.37490983038478 0.170046513184525 df.mm.exp3 -0.149607097508653 0.207569506250129 -0.720756628521201 0.471544539884021 df.mm.exp4 -0.180784207214692 0.207569506250129 -0.870957446884515 0.384379536215371 df.mm.exp5 -0.0128374388216776 0.207569506250129 -0.061846458343491 0.950720712868186 df.mm.exp6 0.000496052010084325 0.207569506250129 0.00238981158189278 0.998094581325712 df.mm.exp7 -0.170268280929683 0.207569506250129 -0.820295254373748 0.412610933310767 df.mm.exp8 0.0592501436635157 0.207569506250129 0.285447244799614 0.775471824162262 df.mm.trans1:exp2 0.104150084519081 0.175428251359194 0.593690490055853 0.553105945965092 df.mm.trans2:exp2 0.0916553243645945 0.175428251359194 0.522466157272057 0.601679195058842 df.mm.trans1:exp3 -0.0330517561380589 0.175428251359194 -0.188406119778191 0.850668437703931 df.mm.trans2:exp3 -0.0152507286329604 0.175428251359194 -0.0869342794834918 0.930773894830976 df.mm.trans1:exp4 0.0249775536000506 0.175428251359194 0.142380451304325 0.886862136084065 df.mm.trans2:exp4 0.0715508779105126 0.175428251359194 0.40786405471266 0.683625015649742 df.mm.trans1:exp5 -0.0766211160401945 0.175428251359194 -0.436766116327015 0.662552629455421 df.mm.trans2:exp5 -0.0861911306215221 0.175428251359194 -0.491318416239831 0.623511864428789 df.mm.trans1:exp6 -0.148667821005258 0.175428251359194 -0.847456551914528 0.397324824961968 df.mm.trans2:exp6 -0.0681954126371714 0.175428251359194 -0.388736774771468 0.697709290442338 df.mm.trans1:exp7 0.0146135460627201 0.175428251359194 0.0833021246549307 0.933659344559649 df.mm.trans2:exp7 0.075032470074692 0.175428251359194 0.427710300327061 0.669127360558129 df.mm.trans1:exp8 -0.150426160275604 0.175428251359194 -0.857479676791639 0.391771677116617 df.mm.trans2:exp8 0.00422783850554433 0.175428251359194 0.0241001005983222 0.980786615907656 df.mm.trans1:probe2 0.103261007154897 0.0960860104931168 1.07467264615273 0.283268010056527 df.mm.trans1:probe3 0.201740258100357 0.0960860104931169 2.09957991871053 0.0364876127030394 * df.mm.trans1:probe4 0.0593791817501064 0.0960860104931169 0.617979469075365 0.536994245190015 df.mm.trans1:probe5 0.0190006014427691 0.0960860104931169 0.197745762835374 0.843359771144532 df.mm.trans1:probe6 0.088977696527692 0.0960860104931169 0.926021343492724 0.355078465586937 df.mm.trans2:probe2 -0.122665093510096 0.0960860104931169 -1.27661761457858 0.202590964762321 df.mm.trans2:probe3 -0.154992895899436 0.0960860104931168 -1.61306411936562 0.107639605763042 df.mm.trans2:probe4 -0.0583862698645995 0.0960860104931169 -0.607645895224072 0.543819806760752 df.mm.trans2:probe5 -0.124950999351367 0.0960860104931168 -1.30040781909993 0.194324240721119 df.mm.trans2:probe6 0.0264360388575572 0.0960860104931169 0.275128905049617 0.783380952018054 df.mm.trans3:probe2 -0.238961746594495 0.0960860104931169 -2.48695668982545 0.0133534949889417 * df.mm.trans3:probe3 -0.236021198446825 0.0960860104931168 -2.45635339874719 0.0145247799898481 * df.mm.trans3:probe4 -0.278512888763055 0.0960860104931169 -2.89857896413554 0.00398699392313395 **