chr15.8656_chr15_59348888_59351202_-_1.R fitVsDatCorrelation=0.661395346839265 cont.fitVsDatCorrelation=0.243622437060010 fstatistic=13806.6280448396,42,462 cont.fstatistic=8252.65586861813,42,462 residuals=-0.324101134430881,-0.0723510029043928,-0.00118840680876595,0.0694088080806324,0.517698748820425 cont.residuals=-0.334383166656531,-0.0947990175672456,-0.0144495482304293,0.0835781923500928,0.728835741977516 predictedValues: Include Exclude Both chr15.8656_chr15_59348888_59351202_-_1.R.tl.Lung 46.0579199404541 39.7669676061178 46.7713830489285 chr15.8656_chr15_59348888_59351202_-_1.R.tl.cerebhem 49.8254894784621 42.564112628629 54.0735073651784 chr15.8656_chr15_59348888_59351202_-_1.R.tl.cortex 45.3449052942447 42.5088528569329 48.401735732223 chr15.8656_chr15_59348888_59351202_-_1.R.tl.heart 42.8107766349913 43.216047573105 46.5662747556972 chr15.8656_chr15_59348888_59351202_-_1.R.tl.kidney 44.7125627561239 40.9812500194506 46.2868914239393 chr15.8656_chr15_59348888_59351202_-_1.R.tl.liver 47.5394509290341 45.6486596110591 48.2605311856103 chr15.8656_chr15_59348888_59351202_-_1.R.tl.stomach 44.4971285188306 42.8125929849959 48.5268542193562 chr15.8656_chr15_59348888_59351202_-_1.R.tl.testicle 48.3338972396797 46.2252303130992 48.8341152414377 diffExp=6.29095233433629,7.2613768498331,2.83605243731181,-0.405270938113652,3.73131273667335,1.89079131797497,1.68453553383470,2.10866692658051 diffExpScore=0.992823125631034 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 48.8188453185731 46.6423202844016 44.2753582975975 cerebhem 47.9532068119498 42.5375000451845 47.1649624541186 cortex 50.4154618850958 46.10456909509 45.2567004934856 heart 47.1658859783915 46.6958834072105 47.0839170857217 kidney 45.9667244311194 47.0920690807031 46.0871966369305 liver 47.0789393697988 44.3279284397217 45.9588707713361 stomach 46.4255025513056 46.1266470539935 46.4845567552943 testicle 47.1673534509161 45.6956085943984 46.0693133208680 cont.diffExp=2.17652503417150,5.41570676676531,4.31089279000581,0.47000257118102,-1.12534464958373,2.75101093007709,0.298855497312104,1.47174485651774 cont.diffExpScore=1.07458166433139 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=0.355942361985340 cont.tran.correlation=-0.0867870420715478 tran.covariance=0.00085422418131238 cont.tran.covariance=-9.244767756878e-05 tran.mean=44.5528652740756 cont.tran.mean=46.6384028623658 weightedLogRatios: wLogRatio Lung 0.551686656606818 cerebhem 0.603246912910631 cortex 0.244262582430708 heart -0.0354409983579535 kidney 0.327356977958322 liver 0.155900389920852 stomach 0.145729752854180 testicle 0.171998375853746 cont.weightedLogRatios: wLogRatio Lung 0.17628964215812 cerebhem 0.456625668923713 cortex 0.346424260078272 heart 0.0385438468693703 kidney -0.0928777718735315 liver 0.230108978870326 stomach 0.0247644428905650 testicle 0.121659097214059 varWeightedLogRatios=0.0465300918567188 cont.varWeightedLogRatios=0.0323315531963647 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.59344822095394 0.0569965485760837 63.0467688084149 4.46851509839471e-229 *** df.mm.trans1 0.257298876311538 0.0457911330262051 5.61896723901312 3.32514895017767e-08 *** df.mm.trans2 0.0714857072636269 0.0457911330262050 1.56112553980085 0.119178840261069 df.mm.exp2 0.00152899422525371 0.0614833043374002 0.0248684458607347 0.980170633499473 df.mm.exp3 0.0168097107312737 0.0614833043374003 0.273402851594109 0.784665738728476 df.mm.exp4 0.0144603988246588 0.0614833043374002 0.235192284808001 0.814163703841103 df.mm.exp5 0.0108455561789392 0.0614833043374002 0.176398394585664 0.860058262911534 df.mm.exp6 0.138255290585442 0.0614833043374002 2.24866395967827 0.0250043630070947 * df.mm.exp7 0.00247480835566963 0.0614833043374002 0.0402517135723333 0.967909837785064 df.mm.exp8 0.1555649299331 0.0614833043374002 2.53019793925536 0.0117309693702206 * df.mm.trans1:exp2 0.0770979621873392 0.0486068199448736 1.58615524065920 0.113388269998956 df.mm.trans2:exp2 0.0664458694134844 0.0486068199448736 1.36700712963412 0.172287846580999 df.mm.trans1:exp3 -0.0324116159850573 0.0486068199448736 -0.666812106239747 0.505225030242776 df.mm.trans2:exp3 0.0498660379852915 0.0486068199448736 1.02590620085507 0.305472636590145 df.mm.trans1:exp4 -0.0875702709847321 0.0486068199448736 -1.80160461194639 0.0722594585083058 . df.mm.trans2:exp4 0.0687148910086475 0.0486068199448736 1.41368826610297 0.158126899379241 df.mm.trans1:exp5 -0.040490781638235 0.0486068199448736 -0.833026758058988 0.405260068997724 df.mm.trans2:exp5 0.0192324812875562 0.0486068199448736 0.395674543394698 0.692527785005576 df.mm.trans1:exp6 -0.106595111973905 0.0486068199448736 -2.19300732067636 0.0288046430160116 * df.mm.trans2:exp6 -0.000317654312278333 0.0486068199448736 -0.00653517989118798 0.994788538875102 df.mm.trans1:exp7 -0.0369498825232915 0.0486068199448736 -0.760178974168593 0.447535361750574 df.mm.trans2:exp7 0.07132087153881 0.0486068199448736 1.46730174118976 0.142974388276373 df.mm.trans1:exp8 -0.107331542811927 0.0486068199448736 -2.20815809249927 0.0277236704159938 * df.mm.trans2:exp8 -0.00507577827434815 0.0486068199448736 -0.104425228396030 0.916877220381824 df.mm.trans1:probe2 -0.0419168973481322 0.032606446070049 -1.28554020447618 0.199247580630947 df.mm.trans1:probe3 -0.0586886107131969 0.032606446070049 -1.79990823247388 0.0725273880083276 . df.mm.trans1:probe4 -0.114892666388221 0.032606446070049 -3.52361818707242 0.000468069659409194 *** df.mm.trans1:probe5 -0.0834667255473629 0.032606446070049 -2.55982284509173 0.0107897032169227 * df.mm.trans1:probe6 -0.0137455551189040 0.032606446070049 -0.421559439178813 0.673542686777196 df.mm.trans2:probe2 0.0219912404222184 0.032606446070049 0.674444567646968 0.500366051496906 df.mm.trans2:probe3 0.0556706204194661 0.032606446070049 1.70735014481087 0.088428844966691 . df.mm.trans2:probe4 0.0321831382753464 0.032606446070049 0.98701766534773 0.324150604537835 df.mm.trans2:probe5 0.0307279334441765 0.032606446070049 0.942388305004575 0.346486387074455 df.mm.trans2:probe6 0.130967264744071 0.032606446070049 4.01660654653108 6.89032865952972e-05 *** df.mm.trans3:probe2 -0.138579588993534 0.032606446070049 -4.25006726264558 2.58654032813345e-05 *** df.mm.trans3:probe3 0.00740431189512782 0.032606446070049 0.227081230478814 0.820461025536384 df.mm.trans3:probe4 -0.192665180359565 0.032606446070049 -5.90880649628785 6.69496193912677e-09 *** df.mm.trans3:probe5 -0.0321806735597235 0.032606446070049 -0.986942075520565 0.324187621795551 df.mm.trans3:probe6 -0.118571875469968 0.032606446070049 -3.63645504987689 0.000307521129962461 *** df.mm.trans3:probe7 -0.205482436314355 0.032606446070049 -6.30189613038212 6.8673768422847e-10 *** df.mm.trans3:probe8 0.108552452077057 0.032606446070049 3.32917153386946 0.000940744661790847 *** df.mm.trans3:probe9 -0.121128366780472 0.032606446070049 -3.71485952563644 0.000228192824380648 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95126733385924 0.0737019867517716 53.6114087014657 1.83305165175187e-200 *** df.mm.trans1 -0.048384303356269 0.0592123130954304 -0.817132464970415 0.41427390249541 df.mm.trans2 -0.108641811348744 0.0592123130954303 -1.83478411278495 0.0671804799532065 . df.mm.exp2 -0.173236004119654 0.0795037909300993 -2.17896533099869 0.0298388230571645 * df.mm.exp3 -0.00133719344293608 0.0795037909300993 -0.0168192412876482 0.986588080599998 df.mm.exp4 -0.094801018229786 0.0795037909300993 -1.19240877850889 0.233713078319986 df.mm.exp5 -0.090709252576026 0.0795037909300993 -1.14094248229973 0.254484927190318 df.mm.exp6 -0.124502638053881 0.0795037909300993 -1.56599624492554 0.118034154369531 df.mm.exp7 -0.110076828538226 0.0795037909300993 -1.38454817374692 0.166858984691806 df.mm.exp8 -0.094639309650139 0.0795037909300993 -1.19037480531396 0.234510402209833 df.mm.trans1:exp2 0.155345268586158 0.0628532654892376 2.47155445905604 0.0138121720912190 * df.mm.trans2:exp2 0.0811137553961751 0.0628532654892376 1.29052571516852 0.197513663380182 df.mm.trans1:exp3 0.0335186920324624 0.0628532654892376 0.533284814584562 0.594092880205503 df.mm.trans2:exp3 -0.0102590383867734 0.0628532654892376 -0.163222042751782 0.870414983767402 df.mm.trans1:exp4 0.0603554819392872 0.0628532654892376 0.960260083059994 0.337426610932290 df.mm.trans2:exp4 0.0959487396679576 0.0628532654892376 1.52655138792092 0.127556886585446 df.mm.trans1:exp5 0.0305105924921578 0.0628532654892376 0.485425733327763 0.627604494552192 df.mm.trans2:exp5 0.100305565108926 0.0628532654892376 1.59586879580825 0.111201864425774 df.mm.trans1:exp6 0.0882119790643096 0.0628532654892376 1.40345896713058 0.161151773514767 df.mm.trans2:exp6 0.0736092656690743 0.0628532654892376 1.17112874082379 0.242150695642423 df.mm.trans1:exp7 0.0598093478592443 0.0628532654892376 0.951571050345594 0.341812116354865 df.mm.trans2:exp7 0.0989593491363178 0.0628532654892376 1.57445040231462 0.116067868927362 df.mm.trans1:exp8 0.060224885882301 0.0628532654892376 0.958182290347561 0.338471994336272 df.mm.trans2:exp8 0.0741332212941392 0.0628532654892376 1.17946491271536 0.238820106646769 df.mm.trans1:probe2 -0.0565413283089254 0.0421632522725331 -1.34100965322732 0.180576230246375 df.mm.trans1:probe3 -0.0581188945102164 0.0421632522725331 -1.37842532009983 0.168739129412328 df.mm.trans1:probe4 -0.0427485632334963 0.0421632522725331 -1.01388201643412 0.311169728883668 df.mm.trans1:probe5 -0.0601326291496147 0.0421632522725331 -1.42618574015429 0.154490091951593 df.mm.trans1:probe6 -0.00395784923494049 0.0421632522725331 -0.0938696381711236 0.925253407569343 df.mm.trans2:probe2 0.00268554460397595 0.0421632522725331 0.0636939623778839 0.949241460661232 df.mm.trans2:probe3 0.0236869714437321 0.0421632522725331 0.561791848755528 0.574530274783236 df.mm.trans2:probe4 0.00155945169407201 0.0421632522725331 0.0369860390273524 0.970512112872638 df.mm.trans2:probe5 -0.024258014191662 0.0421632522725331 -0.575335461194124 0.565344538642318 df.mm.trans2:probe6 -0.0054324470165568 0.0421632522725331 -0.128843168488113 0.897537816069268 df.mm.trans3:probe2 -0.0441895328034857 0.0421632522725331 -1.04805797517362 0.295159864311072 df.mm.trans3:probe3 0.00827016697186556 0.0421632522725331 0.196146324728681 0.84458188437171 df.mm.trans3:probe4 5.2959462004501e-05 0.0421632522725331 0.00125605732836224 0.99899835367341 df.mm.trans3:probe5 -0.00389339466162356 0.0421632522725331 -0.0923409474311327 0.926467182681956 df.mm.trans3:probe6 -0.00539781676988066 0.0421632522725331 -0.128021831309181 0.898187402600751 df.mm.trans3:probe7 -0.0227669676015482 0.0421632522725331 -0.539971808967391 0.58947664711706 df.mm.trans3:probe8 0.0529867652867114 0.0421632522725331 1.25670488946673 0.209495527570796 df.mm.trans3:probe9 -0.0193786666076140 0.0421632522725331 -0.459610337512746 0.646012133650582