fitVsDatCorrelation=0.94177485801666 cont.fitVsDatCorrelation=0.256597669133495 fstatistic=9069.82399191465,50,646 cont.fstatistic=1086.35495839222,50,646 residuals=-0.588200514418306,-0.0984606840053374,-0.00613383678746909,0.0944270024298443,0.853775453300616 cont.residuals=-1.03617844381431,-0.363476611557979,-0.0985766239635256,0.29044071885549,1.58588300239656 predictedValues: Include Exclude Both Lung 72.59315166456 290.710007789698 88.2176138444811 cerebhem 59.6278005418049 128.056010971557 68.9880597047268 cortex 57.91992048373 174.421756536365 77.1561415725065 heart 64.8607322638073 219.003603967004 84.5503941582262 kidney 61.7430806727998 309.112808931514 80.3137819252419 liver 57.2534880679506 314.701267096766 78.0125456401211 stomach 58.4360989819393 196.888296812856 81.0275341787193 testicle 61.2356254839901 288.863505793969 79.640080414587 diffExp=-218.116856125138,-68.4282104297521,-116.501836052635,-154.142871703197,-247.369728258714,-257.447779028815,-138.452197830917,-227.627880309979 diffExpScore=0.999300252715004 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 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 90.7803413420544 102.147933580315 103.578874672759 cerebhem 86.0368314297186 87.4566875963338 85.9057314160663 cortex 102.174509584645 80.246380837558 96.1566038777117 heart 83.8092537694552 93.0660024460176 75.9205839321364 kidney 95.3083275593635 101.208015986263 81.2671813231344 liver 88.4697215191884 111.917158912832 92.4441914546807 stomach 90.2359551398205 83.3033576152366 88.1590762273035 testicle 101.703412712963 95.6148833685997 94.390934895556 cont.diffExp=-11.3675922382608,-1.41985616661522,21.9281287470867,-9.2567486765624,-5.89968842690001,-23.4474373936438,6.93259752458393,6.08852934436312 cont.diffExpScore=4.95013447101956 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=0,0,1,0,0,-1,0,0 cont.diffExp1.2Score=2 tran.correlation=0.304893055716231 cont.tran.correlation=-0.214538825058907 tran.covariance=0.00808923908012982 cont.tran.covariance=-0.00172754130522627 tran.mean=150.964197253769 cont.tran.mean=93.3424233375228 weightedLogRatios: wLogRatio Lung -6.907584472954 cerebhem -3.41685124582747 cortex -5.08242893888812 heart -5.81733245555956 kidney -7.93820395978578 liver -8.34950649122294 stomach -5.67908161082928 testicle -7.58602101069342 cont.weightedLogRatios: wLogRatio Lung -0.538862638579566 cerebhem -0.0730505921567395 cortex 1.08853593244549 heart -0.469446548223518 kidney -0.275507632274605 liver -1.08150276345833 stomach 0.356724942596783 testicle 0.283425316719808 varWeightedLogRatios=2.75756636430455 cont.varWeightedLogRatios=0.441596961188538 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.66374097221477 0.084745304057873 66.8325051774783 2.18843717073139e-292 *** df.mm.trans1 -1.65292032589864 0.0721326319309597 -22.9150147672512 1.65771673187212e-85 *** df.mm.trans2 0.140739007378337 0.0657549739743352 2.14035530503394 0.0326998765679488 * df.mm.exp2 -0.770733945241652 0.0858499237868138 -8.97768933558486 2.99106270744386e-18 *** df.mm.exp3 -0.602683726216799 0.0858499237868138 -7.02020106288515 5.62165462457967e-12 *** df.mm.exp4 -0.353407334947722 0.0858499237868138 -4.11657133004939 4.34410602225918e-05 *** df.mm.exp5 -0.00664320826671318 0.0858499237868138 -0.0773816443123453 0.938343903077302 df.mm.exp6 -0.0351474319989482 0.0858499237868138 -0.409405511951622 0.682377803730709 df.mm.exp7 -0.521608820914031 0.0858499237868138 -6.07582159547762 2.10744623151560e-09 *** df.mm.exp8 -0.0742242494205283 0.0858499237868138 -0.864581424729568 0.387589495593678 df.mm.trans1:exp2 0.573985274909556 0.0765680519503968 7.49640692545505 2.17107104139866e-13 *** df.mm.trans2:exp2 -0.049124534329552 0.0620693923485203 -0.791445388311798 0.428974624420589 df.mm.trans1:exp3 0.376874513737371 0.0765680519503967 4.92208570203043 1.08824579244589e-06 *** df.mm.trans2:exp3 0.0918337471994545 0.0620693923485203 1.47953353053317 0.139485247983746 df.mm.trans1:exp4 0.240779137984768 0.0765680519503967 3.14464233908879 0.00173918026178581 ** df.mm.trans2:exp4 0.0701692876057554 0.0620693923485202 1.13049741508269 0.258686230455055 df.mm.trans1:exp5 -0.155245464077660 0.0765680519503968 -2.02754882908909 0.0430163325071660 * df.mm.trans2:exp5 0.0680232624947669 0.0620693923485203 1.09592280383245 0.273520949281323 df.mm.trans1:exp6 -0.202234588163741 0.0765680519503967 -2.64123982538768 0.00846013964193742 ** df.mm.trans2:exp6 0.114445028931849 0.0620693923485203 1.84382389776331 0.0656665954977954 . df.mm.trans1:exp7 0.304672065342762 0.0765680519503967 3.97910169557583 7.70126187440672e-05 *** df.mm.trans2:exp7 0.131919134086734 0.0620693923485203 2.12534921150841 0.0339363262684659 * df.mm.trans1:exp8 -0.09591720236554 0.0765680519503967 -1.25270527226784 0.210766263516843 df.mm.trans2:exp8 0.0678522941808274 0.0620693923485202 1.09316833327183 0.274727372924329 df.mm.trans1:probe2 -0.00390537025777763 0.0513634107197467 -0.0760340912539168 0.939415516499958 df.mm.trans1:probe3 0.26838867810981 0.0513634107197467 5.22528925452818 2.34957605801333e-07 *** df.mm.trans1:probe4 0.372814165742967 0.0513634107197467 7.25836077703535 1.12860139245033e-12 *** df.mm.trans1:probe5 0.488099595971654 0.0513634107197467 9.5028657390932 3.91935190789071e-20 *** df.mm.trans1:probe6 0.268187975373581 0.0513634107197468 5.22138175046221 2.39764997501055e-07 *** df.mm.trans1:probe7 0.299070634519757 0.0513634107197467 5.82263970263912 9.13104500260385e-09 *** df.mm.trans1:probe8 0.408543609016353 0.0513634107197468 7.95398131260172 8.10342421846169e-15 *** df.mm.trans1:probe9 1.01519922652490 0.0513634107197467 19.7650275224929 2.30429642579716e-68 *** df.mm.trans1:probe10 1.00973705129749 0.0513634107197467 19.6586838208020 8.55954938972714e-68 *** df.mm.trans1:probe11 0.73263000697875 0.0513634107197467 14.2636557174169 2.50132021881015e-40 *** df.mm.trans1:probe12 0.393378220003264 0.0513634107197467 7.65872465420271 6.88439381427916e-14 *** df.mm.trans1:probe13 0.342700509864504 0.0513634107197467 6.67207463566574 5.4276591346504e-11 *** df.mm.trans1:probe14 0.708304348503546 0.0513634107197467 13.7900567461973 4.17511748348589e-38 *** df.mm.trans2:probe2 -0.321333217217597 0.0513634107197467 -6.2560724203243 7.18636046345155e-10 *** df.mm.trans2:probe3 -0.130038475296993 0.0513634107197467 -2.5317336499813 0.0115859448525574 * df.mm.trans2:probe4 -0.421528226273115 0.0513634107197467 -8.20678028126075 1.23317785725323e-15 *** df.mm.trans2:probe5 -0.719114328840053 0.0513634107197467 -14.0005174649274 4.34383992971758e-39 *** df.mm.trans2:probe6 -0.390291943867825 0.0513634107197467 -7.59863759821885 1.05564350237709e-13 *** df.mm.trans3:probe2 0.0983143320905945 0.0513634107197467 1.91409275032426 0.0560498403612504 . df.mm.trans3:probe3 0.209660308821961 0.0513634107197467 4.08190005071757 5.02685255179201e-05 *** df.mm.trans3:probe4 0.315974714123913 0.0513634107197467 6.1517471230242 1.34383010784945e-09 *** df.mm.trans3:probe5 0.180855171875545 0.0513634107197467 3.52108961109186 0.000460023571380944 *** df.mm.trans3:probe6 0.474011995984026 0.0513634107197467 9.22859267602708 3.85786847310734e-19 *** df.mm.trans3:probe7 0.680270011242983 0.0513634107197467 13.244253092045 1.35413207225024e-35 *** df.mm.trans3:probe8 0.51958582556216 0.0513634107197467 10.1158746718976 1.98338784954175e-22 *** df.mm.trans3:probe9 0.47555057579009 0.0513634107197467 9.25854745871197 3.01252941955765e-19 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.60119448040635 0.243596481716587 18.8885916905796 1.08563036261353e-63 *** df.mm.trans1 -0.112657609575369 0.207341935351837 -0.543342133776274 0.587081694184108 df.mm.trans2 0.0484313942208102 0.189009650665425 0.256237679135977 0.797848891829894 df.mm.exp2 -0.0218628521064819 0.246771660360361 -0.0885954735424463 0.929430863318149 df.mm.exp3 -0.0487257129832758 0.246771660360361 -0.197452628523557 0.843535459517501 df.mm.exp4 0.137633075394654 0.246771660360361 0.557734527512878 0.577218892532893 df.mm.exp5 0.282021438883364 0.246771660360361 1.14284370608654 0.253526855796502 df.mm.exp6 0.179282729680217 0.246771660360361 0.726512636898459 0.467787721787772 df.mm.exp7 -0.0487575000420342 0.246771660360361 -0.197581440149301 0.843434710079146 df.mm.exp8 0.140412858670716 0.246771660360361 0.56899912439569 0.569554501041841 df.mm.trans1:exp2 -0.0318044280070808 0.220091346350851 -0.144505581588751 0.885146319266521 df.mm.trans2:exp2 -0.133415567772812 0.178415615667158 -0.747779656359811 0.454865193693811 df.mm.trans1:exp3 0.166965185540107 0.220091346350851 0.758617675380772 0.448357980808871 df.mm.trans2:exp3 -0.1925947164124 0.178415615667158 -1.07947230791554 0.280780097824514 df.mm.trans1:exp4 -0.217532404283264 0.220091346350851 -0.988373272688754 0.323339917773705 df.mm.trans2:exp4 -0.230746222032795 0.178415615667158 -1.29330732161506 0.196367067244476 df.mm.trans1:exp5 -0.233347006589393 0.220091346350851 -1.06022799377769 0.289437123299646 df.mm.trans2:exp5 -0.291265567607885 0.178415615667158 -1.63251163032306 0.103059054433647 df.mm.trans1:exp6 -0.205065123055938 0.220091346350851 -0.931727332564185 0.351825531155407 df.mm.trans2:exp6 -0.0879458764184592 0.178415615667158 -0.492927012524093 0.622231665003664 df.mm.trans1:exp7 0.0427427063020755 0.220091346350851 0.194204392906656 0.84607689653126 df.mm.trans2:exp7 -0.155175735883074 0.178415615667158 -0.869743017183883 0.384763902334318 df.mm.trans1:exp8 -0.0267947566361063 0.220091346350851 -0.121743799019669 0.903139784604177 df.mm.trans2:exp8 -0.20650645875028 0.178415615667158 -1.15744610121754 0.247517924429212 df.mm.trans1:probe2 0.00722757037134976 0.147641763509986 0.0489534275365177 0.960971541774175 df.mm.trans1:probe3 0.00803928529058951 0.147641763509986 0.0544512954835153 0.956592446562155 df.mm.trans1:probe4 0.05076425774969 0.147641763509986 0.343833997527783 0.731083012987698 df.mm.trans1:probe5 0.260655010662442 0.147641763509986 1.76545581999102 0.077959560926334 . df.mm.trans1:probe6 0.0704600982674519 0.147641763509986 0.477236905008158 0.633354845955829 df.mm.trans1:probe7 0.107433304132143 0.147641763509986 0.72766202176173 0.467084146494179 df.mm.trans1:probe8 0.127479440971808 0.147641763509986 0.863437539224368 0.388217401023737 df.mm.trans1:probe9 -0.0580624726601005 0.147641763509986 -0.393265911214705 0.694252843227577 df.mm.trans1:probe10 0.0214034942868001 0.147641763509986 0.144969104797725 0.884780482366675 df.mm.trans1:probe11 -0.071130921328689 0.147641763509986 -0.481780491086305 0.630125060215497 df.mm.trans1:probe12 -0.0209075358420749 0.147641763509986 -0.141609903221325 0.887432293465075 df.mm.trans1:probe13 -0.141817683829346 0.147641763509986 -0.960552627236492 0.337136538299791 df.mm.trans1:probe14 0.0962915366488187 0.147641763509986 0.652197145032788 0.51450598872919 df.mm.trans2:probe2 -0.145854236866424 0.147641763509986 -0.987892811620059 0.323575009982798 df.mm.trans2:probe3 -0.0622347435423517 0.147641763509986 -0.421525333095486 0.673511739369856 df.mm.trans2:probe4 0.165079329677903 0.147641763509986 1.11810727366947 0.263936800895369 df.mm.trans2:probe5 -0.141509728285006 0.147641763509986 -0.958466797746119 0.338185996930109 df.mm.trans2:probe6 -0.1635373635202 0.147641763509986 -1.10766330360948 0.268419502402946 df.mm.trans3:probe2 0.186076409265817 0.147641763509986 1.26032367022784 0.208007679692510 df.mm.trans3:probe3 0.168000142983751 0.147641763509986 1.13789038406053 0.255588100446363 df.mm.trans3:probe4 0.0807810882788608 0.147641763509986 0.547142531749814 0.584469771622045 df.mm.trans3:probe5 0.0710259456206853 0.147641763509986 0.481069474734915 0.630630017318832 df.mm.trans3:probe6 0.231814830815481 0.147641763509986 1.57011691884730 0.116877562961842 df.mm.trans3:probe7 -0.06445780619147 0.147641763509986 -0.436582472730423 0.662560055328469 df.mm.trans3:probe8 0.152463998790775 0.147641763509986 1.0326617290809 0.302148669898191 df.mm.trans3:probe9 0.104581574435758 0.147641763509986 0.70834682510877 0.478985416124443