fitVsDatCorrelation=0.742022672295726 cont.fitVsDatCorrelation=0.257418009093937 fstatistic=10360.9488328140,58,830 cont.fstatistic=4979.24891874913,58,830 residuals=-0.539712440570524,-0.0893207258780171,-0.00694159916034029,0.0772939939812125,0.824242760961626 cont.residuals=-0.450850860005619,-0.146479698242170,-0.0246543841376226,0.113707444344158,1.06790367022014 predictedValues: Include Exclude Both Lung 53.7421508303495 48.0736538536382 54.1723310434569 cerebhem 64.0491529121806 57.5259273229409 62.2245890246404 cortex 59.0907048176829 48.972601673393 56.6687326606524 heart 56.1845187826221 47.5497453257581 53.1001850636133 kidney 56.0755901251676 47.2688889991102 57.0823485684813 liver 58.3503134313966 54.4683020356106 54.5418184200632 stomach 54.3909954194551 51.0610238438331 52.9083626955782 testicle 57.4855281673296 51.3484279875928 55.3438317712448 diffExp=5.66849697671135,6.52322558923974,10.1181031442899,8.63477345686406,8.80670112605745,3.88201139578602,3.329971575622,6.13710017973683 diffExpScore=0.981515842655174 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=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 52.8432231633024 60.5185254972083 52.6301222643227 cerebhem 53.5666242469145 58.3439679218387 53.7910032737518 cortex 54.2742499966503 58.3145012739698 57.7913536055104 heart 53.139805908001 60.1849331639733 57.0999850913198 kidney 55.3198632578543 58.258221918368 52.2534346525482 liver 57.2220046823511 50.8616179794478 52.2549954056364 stomach 55.9633089095004 58.765037976273 55.1025839037451 testicle 53.6369479874457 55.903382950655 54.1213099816423 cont.diffExp=-7.6753023339059,-4.77734367492417,-4.04025127731956,-7.04512725597228,-2.93835866051371,6.36038670290336,-2.80172906677262,-2.26643496320939 cont.diffExpScore=1.4476283817656 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=0.761598018753631 cont.tran.correlation=-0.72095901747243 tran.covariance=0.00287988767282716 cont.tran.covariance=-0.00111376263614162 tran.mean=54.1023453455038 cont.tran.mean=56.0697635521096 weightedLogRatios: wLogRatio Lung 0.437879917717508 cerebhem 0.441040089123416 cortex 0.748464797614714 heart 0.658316561035072 kidney 0.673360239267364 liver 0.277589192821375 stomach 0.250472768650235 testicle 0.451040603207901 cont.weightedLogRatios: wLogRatio Lung -0.5472458143162 cerebhem -0.343738787501122 cortex -0.289354064881662 heart -0.502362047969266 kidney -0.209031606825932 liver 0.46990861411572 stomach -0.197802865323359 testicle -0.165668276388133 varWeightedLogRatios=0.0339767582214848 cont.varWeightedLogRatios=0.0980568992504717 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.64034379839345 0.072311149812082 50.3427729728231 1.79737660870632e-254 *** df.mm.trans1 0.365789736483131 0.0626032564657437 5.84298257205342 7.36142086887698e-09 *** df.mm.trans2 0.243890529076600 0.055462781379493 4.39737285095437 1.23811982103421e-05 *** df.mm.exp2 0.216374680757943 0.0716838605153539 3.01845742127125 0.00261835744578994 ** df.mm.exp3 0.0683503842694077 0.0716838605153539 0.953497534563834 0.340615642357317 df.mm.exp4 0.0534756374148927 0.0716838605153539 0.745992710638664 0.455883040513734 df.mm.exp5 -0.026703626188505 0.0716838605153539 -0.372519364840645 0.709601296683246 df.mm.exp6 0.200354296181703 0.0716838605153539 2.79497078897961 0.00531019450495692 ** df.mm.exp7 0.0958970321668272 0.0716838605153538 1.33777717156133 0.181335509559968 df.mm.exp8 0.111840707153705 0.0716838605153539 1.56019369422424 0.119095304692941 df.mm.trans1:exp2 -0.0409215031984125 0.066452717237893 -0.615798794982577 0.538196147452827 df.mm.trans2:exp2 -0.0368732147382422 0.0498898554959332 -0.739092434157239 0.460059829591754 df.mm.trans1:exp3 0.0265256236034751 0.066452717237893 0.399165372102339 0.689874037310276 df.mm.trans2:exp3 -0.0498236821822088 0.0498898554959332 -0.998673611838186 0.318243884192388 df.mm.trans1:exp4 -0.00903201024543277 0.066452717237893 -0.135916342037591 0.891920373121252 df.mm.trans2:exp4 -0.064433494481453 0.0498898554959332 -1.29151495511358 0.196884712509179 df.mm.trans1:exp5 0.0692066051134411 0.066452717237893 1.04144131331289 0.297974031580002 df.mm.trans2:exp5 0.00982167730113913 0.0498898554959332 0.196867222875395 0.843979639489245 df.mm.trans1:exp6 -0.118087191169578 0.066452717237893 -1.77701072398349 0.0759326448545677 . df.mm.trans2:exp6 -0.0754696678594906 0.0498898554959332 -1.51272572568671 0.130729997251818 df.mm.trans1:exp7 -0.0838960426643649 0.066452717237893 -1.26249228250557 0.207126297707471 df.mm.trans2:exp7 -0.0356098588940193 0.0498898554959332 -0.713769533706548 0.475570358263707 df.mm.trans1:exp8 -0.0445051003555206 0.066452717237893 -0.669725817173096 0.503218856738049 df.mm.trans2:exp8 -0.0459406750450068 0.0498898554959332 -0.920842014640665 0.357400477647323 df.mm.trans1:probe2 0.128705705973085 0.0445778379100502 2.88721283954571 0.0039876625710377 ** df.mm.trans1:probe3 0.170497129974635 0.0445778379100502 3.8247061312993 0.000140737927499700 *** df.mm.trans1:probe4 0.0649655400017377 0.0445778379100502 1.45735062639929 0.145397844721978 df.mm.trans1:probe5 0.413382901648467 0.0445778379100502 9.27328289188446 1.52438370855007e-19 *** df.mm.trans1:probe6 -0.168141766645623 0.0445778379100502 -3.77186903916025 0.000173532225061657 *** df.mm.trans1:probe7 0.204453139402248 0.0445778379100502 4.58643014079768 5.20394644780135e-06 *** df.mm.trans1:probe8 -0.255858468945498 0.0445778379100502 -5.7395890186907 1.33032095510610e-08 *** df.mm.trans1:probe9 -0.250991847434414 0.0445778379100502 -5.63041769636447 2.46020578965677e-08 *** df.mm.trans1:probe10 -0.0640684088656331 0.0445778379100502 -1.43722557821021 0.151030769484789 df.mm.trans1:probe11 0.0823403307678229 0.0445778379100502 1.84711360236830 0.065086416411016 . df.mm.trans1:probe12 0.0358414157214876 0.0445778379100502 0.80401870978599 0.421616383325739 df.mm.trans1:probe13 -0.193489390570163 0.0445778379100502 -4.34048396336737 1.59708308284459e-05 *** df.mm.trans1:probe14 -0.0413449204831662 0.0445778379100502 -0.927477024942138 0.353948534465275 df.mm.trans1:probe15 -0.208865339790917 0.0445778379100502 -4.68540758330110 3.26405748965651e-06 *** df.mm.trans1:probe16 -0.235658774502292 0.0445778379100502 -5.28645590613452 1.59556914780703e-07 *** df.mm.trans1:probe17 0.135164925671709 0.0445778379100502 3.03211039405828 0.00250402914666461 ** df.mm.trans1:probe18 -0.0308904439597537 0.0445778379100502 -0.692955185984679 0.488531520123295 df.mm.trans1:probe19 -0.0754799142316294 0.0445778379100502 -1.69321613093784 0.0907895721420874 . df.mm.trans1:probe20 -0.105709494603495 0.0445778379100502 -2.37134638106041 0.017950770991092 * df.mm.trans1:probe21 -0.177016242787713 0.0445778379100502 -3.97094724838155 7.77959379427625e-05 *** df.mm.trans1:probe22 -0.107849273805809 0.0445778379100502 -2.41934734527567 0.0157623909130751 * df.mm.trans2:probe2 -0.113764749879522 0.0445778379100502 -2.5520472775974 0.0108872999374039 * df.mm.trans2:probe3 -0.0840701824558614 0.0445778379100502 -1.88591879726198 0.0596548543244267 . df.mm.trans2:probe4 0.133829408591000 0.0445778379100502 3.00215117792483 0.00276115944821848 ** df.mm.trans2:probe5 -0.0673325219081495 0.0445778379100502 -1.51044835426998 0.1313096517784 df.mm.trans2:probe6 -0.0411625146214923 0.0445778379100502 -0.923385174143047 0.356074866590159 df.mm.trans3:probe2 -0.293810321046141 0.0445778379100502 -6.5909504547752 7.76305405919738e-11 *** df.mm.trans3:probe3 -0.0932233241273245 0.0445778379100502 -2.09124821879948 0.0368093257614428 * df.mm.trans3:probe4 -0.272998019914703 0.0445778379100502 -6.12407493754099 1.40661555889519e-09 *** df.mm.trans3:probe5 -0.426026464878854 0.0445778379100502 -9.55691179411831 1.32966703360518e-20 *** df.mm.trans3:probe6 -0.369115915758849 0.0445778379100502 -8.28025613318563 4.89480547022671e-16 *** df.mm.trans3:probe7 -0.312792450456094 0.0445778379100502 -7.0167703307471 4.71655210255817e-12 *** df.mm.trans3:probe8 -0.189338616844744 0.0445778379100502 -4.24737101935706 2.40762187005382e-05 *** df.mm.trans3:probe9 -0.363378935853451 0.0445778379100502 -8.15156034679569 1.31978492411531e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15487821867744 0.104231656588711 39.8619608922866 5.54079043103379e-195 *** df.mm.trans1 -0.242365083971215 0.0902383815805694 -2.68583145803451 0.0073794669918129 ** df.mm.trans2 -0.0283728707516314 0.0799458672310608 -0.354901031589635 0.722753894198362 df.mm.exp2 -0.0448144383668355 0.103327461278191 -0.433712759536213 0.664609743018214 df.mm.exp3 -0.103928856586414 0.103327461278191 -1.00582028534122 0.314795161561333 df.mm.exp4 -0.081445905001028 0.103327461278191 -0.788230969710452 0.43078667633722 df.mm.exp5 0.0149212639732194 0.103327461278191 0.144407534924782 0.885213723017424 df.mm.exp6 -0.0870787771864836 0.103327461278191 -0.842745733895842 0.399613443683135 df.mm.exp7 -0.0179436247034498 0.103327461278191 -0.173657849341132 0.86217670872642 df.mm.exp8 -0.0923553318038385 0.103327461278191 -0.893812067589541 0.371681544506791 df.mm.trans1:exp2 0.0584111541938317 0.0957871202508448 0.609801756654403 0.542160039761093 df.mm.trans2:exp2 0.008220889507257 0.0719128695758016 0.114317361492459 0.909013867308248 df.mm.trans1:exp3 0.130649277334479 0.0957871202508448 1.36395453785789 0.172951633196787 df.mm.trans2:exp3 0.0668301296853488 0.0719128695758016 0.929320858416098 0.352993018118903 df.mm.trans1:exp4 0.0870427165185756 0.0957871202508448 0.90871002584304 0.363766987821978 df.mm.trans2:exp4 0.0759184215918432 0.0719128695758016 1.05570007204092 0.291412452301592 df.mm.trans1:exp5 0.0308812946387795 0.0957871202508448 0.322395062696408 0.747234612571948 df.mm.trans2:exp5 -0.052985557405342 0.0719128695758016 -0.73680215680298 0.461450879098585 df.mm.trans1:exp6 0.166687822496842 0.0957871202508448 1.74019035190038 0.0821962308719776 . df.mm.trans2:exp6 -0.0867621756406974 0.0719128695758016 -1.20649024510479 0.227972283641321 df.mm.trans1:exp7 0.0753104263156418 0.0957871202508448 0.786227063914448 0.431958877390693 df.mm.trans2:exp7 -0.0114588142028521 0.0719128695758016 -0.159343025392328 0.87343738979073 df.mm.trans1:exp8 0.107264014184795 0.0957871202508448 1.11981667163492 0.263115831034851 df.mm.trans2:exp8 0.0130307033040031 0.0719128695758016 0.181201270104620 0.856253835845693 df.mm.trans1:probe2 0.130926857725180 0.0642559536749507 2.03758329364304 0.0419083093617496 * df.mm.trans1:probe3 0.0733900178654609 0.0642559536749507 1.14215125086644 0.253720532561126 df.mm.trans1:probe4 0.101892003843478 0.0642559536749507 1.58572082454671 0.113183542615842 df.mm.trans1:probe5 0.09643595007983 0.0642559536749507 1.50080956805446 0.133785119991111 df.mm.trans1:probe6 0.0571985570477688 0.0642559536749507 0.890167428486348 0.373633932638026 df.mm.trans1:probe7 0.0462915353091814 0.0642559536749507 0.720424064412065 0.471466775217795 df.mm.trans1:probe8 0.0797618786824192 0.0642559536749507 1.24131499294070 0.21484010413792 df.mm.trans1:probe9 0.134750073572857 0.0642559536749507 2.09708308516456 0.0362882519281275 * df.mm.trans1:probe10 0.0944016769880146 0.0642559536749507 1.46915066369664 0.142170835332065 df.mm.trans1:probe11 0.0654141483500795 0.0642559536749507 1.01802470602160 0.308962861967606 df.mm.trans1:probe12 0.0524134547678291 0.0642559536749507 0.815698029056906 0.414906883178867 df.mm.trans1:probe13 0.0137505316967517 0.0642559536749507 0.213996227747408 0.830602557319093 df.mm.trans1:probe14 0.0428101919309980 0.0642559536749507 0.666244752160406 0.505439805557348 df.mm.trans1:probe15 0.0432086971979131 0.0642559536749507 0.672446594077358 0.501486580592576 df.mm.trans1:probe16 0.155837009825024 0.0642559536749507 2.42525401791329 0.0155100499639583 * df.mm.trans1:probe17 0.103977633650432 0.0642559536749507 1.61817898114811 0.106003953631703 df.mm.trans1:probe18 0.032756136243009 0.0642559536749507 0.509775894210695 0.610343993143262 df.mm.trans1:probe19 0.0479107303696863 0.0642559536749507 0.745623208894394 0.456106159751598 df.mm.trans1:probe20 0.0784041334494862 0.0642559536749507 1.22018472943544 0.222741420310036 df.mm.trans1:probe21 0.0612505973241834 0.0642559536749507 0.953228359725693 0.340751896184860 df.mm.trans1:probe22 0.186524774798278 0.0642559536749507 2.90284034599882 0.00379591636190405 ** df.mm.trans2:probe2 -0.0890488543894563 0.0642559536749507 -1.38584596907431 0.166166170935728 df.mm.trans2:probe3 -0.072017323649497 0.0642559536749507 -1.12078833992268 0.26270214737908 df.mm.trans2:probe4 -0.0609111736735174 0.0642559536749507 -0.947945990836064 0.343432858239742 df.mm.trans2:probe5 -0.0665430695260888 0.0642559536749507 -1.03559383559550 0.300693290429427 df.mm.trans2:probe6 -0.0648169266901226 0.0642559536749507 -1.00873028852719 0.313397977462666 df.mm.trans3:probe2 0.0876094275839706 0.0642559536749507 1.36344451484071 0.173112157637199 df.mm.trans3:probe3 0.0964598604587526 0.0642559536749507 1.50118167954849 0.133688886465026 df.mm.trans3:probe4 0.0186094987112067 0.0642559536749507 0.289615166329114 0.772182993215162 df.mm.trans3:probe5 0.130654226365886 0.0642559536749507 2.03334039716883 0.0423358384240999 * df.mm.trans3:probe6 0.117610499911177 0.0642559536749507 1.83034400992831 0.0675569688468733 . df.mm.trans3:probe7 0.173133734481017 0.0642559536749507 2.69443879639297 0.00719319159333388 ** df.mm.trans3:probe8 0.0518469101911674 0.0642559536749507 0.80688103165418 0.419966174638245 df.mm.trans3:probe9 0.0364110687988235 0.0642559536749507 0.566656733211289 0.571100617224751