chr19.11913_chr19_5213702_5214316_+_1.R fitVsDatCorrelation=0.865528744172934 cont.fitVsDatCorrelation=0.280379579994853 fstatistic=6920.70894129306,38,370 cont.fstatistic=1877.16956254623,38,370 residuals=-0.430234473613912,-0.0914056630805408,-0.00211359581247771,0.090379943092867,1.03389760276325 cont.residuals=-0.797304827719099,-0.226662201736184,-0.0462968469982606,0.213494921161954,1.10767080886157 predictedValues: Include Exclude Both chr19.11913_chr19_5213702_5214316_+_1.R.tl.Lung 62.9833067532815 61.4767727466447 100.144391342727 chr19.11913_chr19_5213702_5214316_+_1.R.tl.cerebhem 72.6722252525946 72.7697296467305 104.365453125013 chr19.11913_chr19_5213702_5214316_+_1.R.tl.cortex 80.769676318092 69.1564689904648 119.692728447395 chr19.11913_chr19_5213702_5214316_+_1.R.tl.heart 68.7937379357779 65.266429559876 86.9969208297798 chr19.11913_chr19_5213702_5214316_+_1.R.tl.kidney 60.9927269218239 59.7955650879144 88.337238973735 chr19.11913_chr19_5213702_5214316_+_1.R.tl.liver 60.970841121303 65.6760587525773 79.6446420470762 chr19.11913_chr19_5213702_5214316_+_1.R.tl.stomach 66.5046308688997 70.331944037543 83.1297582314581 chr19.11913_chr19_5213702_5214316_+_1.R.tl.testicle 65.9814223657932 66.7272488234863 91.40721854171 diffExp=1.50653400663675,-0.0975043941359104,11.6132073276272,3.52730837590195,1.19716183390948,-4.70521763127434,-3.82731316864336,-0.745826457693042 diffExpScore=2.87484868064241 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 72.806780421699 70.255048481349 80.425211585848 cerebhem 74.5461190843453 78.7262497786125 74.1345195388049 cortex 75.3043632034218 69.2099513387816 82.5568481144042 heart 77.7995838796973 76.0110449186067 64.584525054229 kidney 72.6068301312605 67.6685885298292 72.8761244055219 liver 78.4117816855029 77.2129922085133 74.0332938003422 stomach 87.5453687744822 69.721503138052 81.7039445659568 testicle 72.6894373024708 72.8550500719063 88.4096708504161 cont.diffExp=2.55173194034998,-4.18013069426711,6.09441186464018,1.78853896109058,4.9382416014313,1.19878947698955,17.8238656364301,-0.165612769435484 cont.diffExpScore=1.24771425276247 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=0,0,0,0,0,0,1,0 cont.diffExp1.2Score=0.5 tran.correlation=0.6535628180417 cont.tran.correlation=0.00316420937704486 tran.covariance=0.00425271611664379 cont.tran.covariance=8.80006701793142e-05 tran.mean=66.9292990739252 cont.tran.mean=74.5856683092831 weightedLogRatios: wLogRatio Lung 0.100007027011489 cerebhem -0.00574752025860662 cortex 0.669660132363317 heart 0.221319036733706 kidney 0.0812915470688104 liver -0.308324684702157 stomach -0.236421725041728 testicle -0.0471524567818023 cont.weightedLogRatios: wLogRatio Lung 0.152339393676234 cerebhem -0.236713512094011 cortex 0.361148251805452 heart 0.100995539369248 kidney 0.299345612374534 liver 0.0670838881119484 stomach 0.992167436600675 testicle -0.00975698318829286 varWeightedLogRatios=0.0912989851218923 cont.varWeightedLogRatios=0.132260085819197 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.16770162679188 0.0903313203556788 35.0675891188027 1.10332441552849e-119 *** df.mm.trans1 0.941410822822322 0.0743914385742787 12.6548275025269 9.53205470117847e-31 *** df.mm.trans2 0.913337432733667 0.0743914385742787 12.2774535650593 2.67306282130515e-29 *** df.mm.exp2 0.270444535059795 0.101691032343202 2.65947280530165 0.00816647690518138 ** df.mm.exp3 0.188129257575041 0.101691032343202 1.85000833642946 0.0651094747842058 . df.mm.exp4 0.288801719990756 0.101691032343202 2.83999201636646 0.00476076659048287 ** df.mm.exp5 0.065608285241476 0.101691032343202 0.64517277216787 0.519215078050484 df.mm.exp6 0.262639340940614 0.101691032343202 2.58271879917809 0.0101858164378667 * df.mm.exp7 0.375178849042949 0.101691032343202 3.68939955075627 0.000258522864802734 *** df.mm.exp8 0.219746082167714 0.101691032343202 2.16091898276814 0.0313427541990013 * df.mm.trans1:exp2 -0.127354988672664 0.0843177497065739 -1.51041730971071 0.131790488465925 df.mm.trans2:exp2 -0.101803892439973 0.0843177497065738 -1.20738388766602 0.228055592676866 df.mm.trans1:exp3 0.060602625308941 0.0843177497065739 0.71874101858551 0.472754149136701 df.mm.trans2:exp3 -0.0704170782821142 0.0843177497065739 -0.835139440119855 0.404178128722596 df.mm.trans1:exp4 -0.200558716679272 0.0843177497065739 -2.37860613426256 0.0178850576773970 * df.mm.trans2:exp4 -0.228983336019142 0.0843177497065739 -2.7157192502884 0.00692336313391464 ** df.mm.trans1:exp5 -0.0977233780765619 0.0843177497065739 -1.15898939922661 0.247207949585419 df.mm.trans2:exp5 -0.0933362140019972 0.0843177497065739 -1.10695807616792 0.269031198609749 df.mm.trans1:exp6 -0.295113324567548 0.0843177497065739 -3.50001423893005 0.000521983391341295 *** df.mm.trans2:exp6 -0.196564308966564 0.0843177497065739 -2.33123286200839 0.0202775140779913 * df.mm.trans1:exp7 -0.320776985789451 0.0843177497065739 -3.80438267038383 0.000166276147260566 *** df.mm.trans2:exp7 -0.240612182008283 0.0843177497065739 -2.85363618983683 0.00456530061353727 ** df.mm.trans1:exp8 -0.173242578180256 0.0843177497065739 -2.05463948911280 0.0406156639702603 * df.mm.trans2:exp8 -0.137792109140246 0.0843177497065739 -1.63420050487309 0.103067274037667 df.mm.trans1:probe2 0.0205644588262082 0.0492309593404825 0.417713956861654 0.676398562500066 df.mm.trans1:probe3 0.157219520768885 0.0492309593404825 3.19350918355158 0.0015260262716356 ** df.mm.trans1:probe4 -0.0520197317675524 0.0492309593404825 -1.05664672117768 0.291362259508845 df.mm.trans1:probe5 -0.207958746103587 0.0492309593404825 -4.22414571825302 3.02338188738718e-05 *** df.mm.trans1:probe6 0.453524462730540 0.0492309593404825 9.21218007542682 2.43420735262856e-18 *** df.mm.trans2:probe2 -0.00977478095305956 0.0492309593404825 -0.198549471389679 0.84272423289457 df.mm.trans2:probe3 0.364614299690065 0.0492309593404825 7.4061993626487 8.86974839710816e-13 *** df.mm.trans2:probe4 -0.212281194389366 0.0492309593404825 -4.31194511001145 2.07803858987865e-05 *** df.mm.trans2:probe5 0.0258180120617243 0.0492309593404825 0.52442634487714 0.600296069034818 df.mm.trans2:probe6 0.245447678941866 0.0492309593404825 4.98563672595418 9.50961015712262e-07 *** df.mm.trans3:probe2 -0.808965176657177 0.0492309593404825 -16.4320416968183 5.96871608360418e-46 *** df.mm.trans3:probe3 -0.629803842555223 0.0492309593404825 -12.7928411510222 2.78901908997155e-31 *** df.mm.trans3:probe4 -0.317959518345529 0.0492309593404825 -6.45852777611978 3.32896323121894e-10 *** df.mm.trans3:probe5 -0.322455584307682 0.0492309593404825 -6.54985376331124 1.93015939645775e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19546612998454 0.173118613098257 24.2346334394634 2.21242080745264e-78 *** df.mm.trans1 0.111462420388434 0.142570070067106 0.781807993332475 0.434827418125588 df.mm.trans2 0.0354663695263511 0.142570070067106 0.248764481280381 0.803680990433898 df.mm.exp2 0.218899794825484 0.194889329796875 1.12320051104714 0.262080606256021 df.mm.exp3 -0.00741794363446453 0.194889329796875 -0.0380623384676624 0.969658506798901 df.mm.exp4 0.364426353643159 0.194889329796875 1.86991434586484 0.0622852048101629 . df.mm.exp5 0.05830645975782 0.194889329796875 0.29917728086289 0.764972757521198 df.mm.exp6 0.251413465458323 0.194889329796875 1.29003196696485 0.197845234522111 df.mm.exp7 0.160950102832876 0.194889329796875 0.825853847414979 0.409419186809926 df.mm.exp8 -0.0599269829576918 0.194889329796875 -0.307492375391466 0.75864163079092 df.mm.trans1:exp2 -0.195290901385492 0.161593695645016 -1.20853044796067 0.227615079079830 df.mm.trans2:exp2 -0.105055322854957 0.161593695645016 -0.650120182199058 0.516018036381566 df.mm.trans1:exp3 0.0411469325038592 0.161593695645016 0.254632040808383 0.79914877208685 df.mm.trans2:exp3 -0.00756956874211899 0.161593695645016 -0.0468432181831374 0.962663456473522 df.mm.trans1:exp4 -0.298099359589423 0.161593695645016 -1.84474622230485 0.065873540198734 . df.mm.trans2:exp4 -0.285679866174411 0.161593695645016 -1.76788992314393 0.0779033999397004 . df.mm.trans1:exp5 -0.0610565519355313 0.161593695645016 -0.377839937949426 0.705766161925255 df.mm.trans2:exp5 -0.0958165380037253 0.161593695645016 -0.592947253426347 0.553578690002281 df.mm.trans1:exp6 -0.177248361317853 0.161593695645016 -1.09687671050749 0.273408653642842 df.mm.trans2:exp6 -0.15697789986577 0.161593695645016 -0.97143579295701 0.331965929156073 df.mm.trans1:exp7 0.0233979679093678 0.161593695645016 0.144795054138546 0.884951526311326 df.mm.trans2:exp7 -0.168573492909075 0.161593695645016 -1.04319349982187 0.297539787796836 df.mm.trans1:exp8 0.0583139768396573 0.161593695645016 0.360867895290666 0.718404149064332 df.mm.trans2:exp8 0.0962666645700332 0.161593695645016 0.595732798769011 0.551717969961544 df.mm.trans1:probe2 -0.0410681237925149 0.0943503910821026 -0.435272427824681 0.66361848710386 df.mm.trans1:probe3 -0.0348763555269267 0.0943503910821026 -0.369647175034788 0.711856868441023 df.mm.trans1:probe4 -0.101510403313168 0.0943503910821026 -1.07588746743863 0.282678624612922 df.mm.trans1:probe5 0.0160949644081752 0.0943503910821026 0.170587150976084 0.864641644164033 df.mm.trans1:probe6 -0.0489541620890244 0.0943503910821026 -0.518854893207862 0.604172115306763 df.mm.trans2:probe2 0.0389892226543826 0.0943503910821026 0.413238590823165 0.67967120273373 df.mm.trans2:probe3 0.0307625845604471 0.0943503910821026 0.326046179646228 0.744573743092571 df.mm.trans2:probe4 0.128424609956448 0.0943503910821026 1.36114549694547 0.174296102129975 df.mm.trans2:probe5 0.0819703936949713 0.0943503910821026 0.868787005065423 0.385526828352890 df.mm.trans2:probe6 -0.0469504340020179 0.0943503910821026 -0.49761779960363 0.619049056221306 df.mm.trans3:probe2 0.0982202248869966 0.0943503910821026 1.04101555659188 0.298548074885103 df.mm.trans3:probe3 -0.0263745339868382 0.0943503910821026 -0.279538152246633 0.779988059525972 df.mm.trans3:probe4 -0.0171690876732106 0.0943503910821026 -0.181971558106954 0.855704699168834 df.mm.trans3:probe5 0.169987317118196 0.0943503910821026 1.80165991013513 0.0724128493170265 .