fitVsDatCorrelation=0.883238326711648 cont.fitVsDatCorrelation=0.25515742346375 fstatistic=14379.5956431839,61,899 cont.fstatistic=3370.85303000892,61,899 residuals=-0.566111300562154,-0.0782264824374943,-0.00205208307896210,0.0760836904748719,0.832500072460267 cont.residuals=-0.519023935377466,-0.177878346854899,-0.0563175296710188,0.111813582310741,1.08316565736420 predictedValues: Include Exclude Both Lung 52.126959485667 50.2872065993539 65.7585822192047 cerebhem 55.2934475464973 46.9142426382198 63.2423770619727 cortex 50.6740363100997 53.3193510804677 72.4584214486551 heart 53.9010314286711 52.9829913108972 70.0334965189844 kidney 52.6742905003002 54.7872879216325 72.4852196026937 liver 56.1258690642027 55.2138651521446 67.796844212069 stomach 53.948629447654 52.1038226387888 65.7780536558805 testicle 53.5724417407677 49.1816109870044 69.4627070801936 diffExp=1.83975288631311,8.37920490827745,-2.64531477036796,0.918040117773941,-2.11299742133227,0.912003912058083,1.84480680886522,4.39083075376329 diffExpScore=1.58628889938031 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 58.961298503667 49.3708086536919 57.6582609268039 cerebhem 57.6230929770637 55.4808095299202 53.7638619710482 cortex 59.7068525859141 57.5331355747977 56.4985493322239 heart 58.8097234538978 65.6790853393938 61.6219852496827 kidney 57.8579367787074 54.4687695267792 54.2670075839998 liver 59.0456943959158 51.3968403804857 57.5948745147774 stomach 56.5502392969274 56.6165376271676 57.7262757604806 testicle 55.7615744319839 56.9357431536324 53.5617084904075 cont.diffExp=9.59048984997511,2.1422834471435,2.17371701111642,-6.86936188549593,3.38916725192816,7.64885401543017,-0.0662983302402509,-1.17416872164852 cont.diffExpScore=1.85337419137266 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=-0.117358116235334 cont.tran.correlation=-0.0713788431740952 tran.covariance=-0.000247220879528504 cont.tran.covariance=-0.000199335146085213 tran.mean=52.694192740773 cont.tran.mean=56.9873838881216 weightedLogRatios: wLogRatio Lung 0.141416467220683 cerebhem 0.645909262460017 cortex -0.201041900519048 heart 0.0683463753549991 kidney -0.156685379451756 liver 0.0658488278719046 stomach 0.138154165612947 testicle 0.336781766903487 cont.weightedLogRatios: wLogRatio Lung 0.707978850914074 cerebhem 0.152870152728081 cortex 0.150972504095739 heart -0.45620447511412 kidney 0.243131198163448 liver 0.556180571956627 stomach -0.00472861774987339 testicle -0.0840097613731513 varWeightedLogRatios=0.072687354231458 cont.varWeightedLogRatios=0.132949055915624 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90075630088026 0.0613522341462441 63.5796944506063 0 *** df.mm.trans1 0.156748383205389 0.0527739890719111 2.97018258354130 0.00305537402761906 ** df.mm.trans2 -0.0204140935642320 0.0464212371361987 -0.439757637314525 0.660218346696918 df.mm.exp2 0.0285583393762214 0.0592529010103219 0.481973690558148 0.629941948294061 df.mm.exp3 -0.0667426837150875 0.0592529010103219 -1.12640364567907 0.260295288505821 df.mm.exp4 0.0227041241209045 0.0592529010103219 0.38317320728228 0.701681928359155 df.mm.exp5 -0.00123977216814722 0.0592529010103219 -0.0209234003231547 0.983311402826406 df.mm.exp6 0.136852500430209 0.0592529010103219 2.30963375795506 0.0211342446102149 * df.mm.exp7 0.0695415692129703 0.0592529010103219 1.17363990669176 0.240850123312370 df.mm.exp8 -0.0496782210141369 0.0592529010103219 -0.83840993718574 0.402023368579351 df.mm.trans1:exp2 0.0304138014546674 0.054504741543585 0.558002856143201 0.576981361764952 df.mm.trans2:exp2 -0.0979877318343648 0.0391202638579675 -2.50478197667902 0.01242867649417 * df.mm.trans1:exp3 0.0384740873411646 0.054504741543585 0.705885144146562 0.480442441940435 df.mm.trans2:exp3 0.125291305873598 0.0391202638579676 3.20272139084971 0.00140922622535657 ** df.mm.trans1:exp4 0.0107632181153140 0.054504741543585 0.197473060333790 0.843501999830409 df.mm.trans2:exp4 0.0295161164693499 0.0391202638579676 0.754496865780684 0.450748515658999 df.mm.trans1:exp5 0.0116849909220629 0.054504741543585 0.214384851503588 0.830295570688393 df.mm.trans2:exp5 0.0869472641852763 0.0391202638579676 2.22256333702023 0.0264933633454945 * df.mm.trans1:exp6 -0.0629379415111036 0.054504741543585 -1.15472415295787 0.248510247346899 df.mm.trans2:exp6 -0.0433891011893089 0.0391202638579676 -1.10912087267203 0.267674648956612 df.mm.trans1:exp7 -0.0351915534164181 0.054504741543585 -0.645660403476584 0.518664067441913 df.mm.trans2:exp7 -0.0340539547533541 0.0391202638579675 -0.870493994544426 0.384262875756073 df.mm.trans1:exp8 0.0770307387811587 0.054504741543585 1.41328509409701 0.157918055533366 df.mm.trans2:exp8 0.0274473114256846 0.0391202638579676 0.701613657958353 0.483101716057846 df.mm.trans1:probe2 -0.247431471289483 0.0385406723522891 -6.42000920554224 2.20356001600414e-10 *** df.mm.trans1:probe3 -0.134679201723570 0.0385406723522891 -3.49446943977797 0.000498200335246221 *** df.mm.trans1:probe4 -0.329057598828883 0.0385406723522891 -8.53793093750578 5.77395304009684e-17 *** df.mm.trans1:probe5 0.00861196537897515 0.0385406723522891 0.223451352904684 0.823235005025987 df.mm.trans1:probe6 -0.196692761254743 0.0385406723522891 -5.10351141404155 4.06880617569727e-07 *** df.mm.trans1:probe7 -0.255443227973376 0.0385406723522891 -6.62788717431922 5.86329117439934e-11 *** df.mm.trans1:probe8 -0.243798885917446 0.0385406723522891 -6.32575591024857 3.96805080982957e-10 *** df.mm.trans1:probe9 -0.253252930399938 0.0385406723522891 -6.57105636572778 8.4510576438207e-11 *** df.mm.trans1:probe10 -0.0446402551227612 0.0385406723522891 -1.15826352780558 0.247064110877673 df.mm.trans1:probe11 -0.061735332552051 0.0385406723522891 -1.60182292586248 0.109546063971032 df.mm.trans1:probe12 -0.213463907102012 0.0385406723522891 -5.53866588394725 4.00236284462000e-08 *** df.mm.trans1:probe13 0.0545578486704333 0.0385406723522891 1.41559151256459 0.157241466518459 df.mm.trans1:probe14 -0.192736869718569 0.0385406723522891 -5.00086941807392 6.8620728170671e-07 *** df.mm.trans1:probe15 -0.133912158961655 0.0385406723522891 -3.47456727629459 0.000536128104253835 *** df.mm.trans1:probe16 -0.0685511363871453 0.0385406723522891 -1.77866996612149 0.0756317479165972 . df.mm.trans1:probe17 -0.274760270262892 0.0385406723522891 -7.12909903987632 2.07473052566504e-12 *** df.mm.trans1:probe18 -0.284395116704736 0.0385406723522891 -7.37909069424536 3.6251553689073e-13 *** df.mm.trans1:probe19 -0.286142867601798 0.0385406723522891 -7.42443891446027 2.62728568149819e-13 *** df.mm.trans1:probe20 -0.203486390949505 0.0385406723522891 -5.27978311041112 1.62260447089003e-07 *** df.mm.trans1:probe21 0.131360716941150 0.0385406723522891 3.40836599165734 0.000682565609957489 *** df.mm.trans1:probe22 -0.300312177390803 0.0385406723522891 -7.79208454501616 1.81633201333026e-14 *** df.mm.trans2:probe2 0.0556787889770719 0.0385406723522891 1.44467611950638 0.14889731883155 df.mm.trans2:probe3 0.0463861814772405 0.0385406723522891 1.20356440731593 0.229074808248549 df.mm.trans2:probe4 0.194364358203808 0.0385406723522891 5.04309723575083 5.54065274269251e-07 *** df.mm.trans2:probe5 0.126640012225143 0.0385406723522891 3.28587968231491 0.00105599348509803 ** df.mm.trans2:probe6 0.250283577722711 0.0385406723522891 6.49401171403917 1.38120720905947e-10 *** df.mm.trans3:probe2 -0.114506640345580 0.0385406723522891 -2.97105974952664 0.00304674645840675 ** df.mm.trans3:probe3 -0.211094831831941 0.0385406723522891 -5.47719639923208 5.61006189815149e-08 *** df.mm.trans3:probe4 -0.0985960391264192 0.0385406723522891 -2.55823350005889 0.0106834363193393 * df.mm.trans3:probe5 0.436761266569013 0.0385406723522891 11.3324765737532 6.40430648162625e-28 *** df.mm.trans3:probe6 0.333309742072223 0.0385406723522891 8.64825966255947 2.37670647787354e-17 *** df.mm.trans3:probe7 0.719316615428826 0.0385406723522891 18.6638315194339 5.89800494037419e-66 *** df.mm.trans3:probe8 0.220872677227924 0.0385406723522891 5.73089839245643 1.36284185375868e-08 *** df.mm.trans3:probe9 -0.12954077485243 0.0385406723522891 -3.36114465436242 0.000808915968356193 *** df.mm.trans3:probe10 -0.028628659020532 0.0385406723522891 -0.742816802956776 0.457786653265249 df.mm.trans3:probe11 -0.0606466916378881 0.0385406723522891 -1.57357637883258 0.115937256922167 df.mm.trans3:probe12 0.719548536322656 0.0385406723522891 18.6698490816525 5.43811036734707e-66 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88985251274483 0.126505361339866 30.748519047303 1.88426721990159e-142 *** df.mm.trans1 0.239508493490835 0.108817431830997 2.20101218583079 0.0279886974435506 * df.mm.trans2 0.0196044294211539 0.0957183623298899 0.204813673614556 0.837764064636548 df.mm.exp2 0.163651991156796 0.122176637200831 1.33947041681781 0.180756097673563 df.mm.exp3 0.185885816129099 0.122176637200831 1.5214514033771 0.128498232366885 df.mm.exp4 0.216361963091972 0.122176637200831 1.77089473117779 0.0769170030827747 . df.mm.exp5 0.139994622364361 0.122176637200831 1.14583790789921 0.252167140805573 df.mm.exp6 0.0427476734192781 0.122176637200831 0.349884187342712 0.726507564790157 df.mm.exp7 0.094011033487045 0.122176637200831 0.769468170354959 0.441817552855019 df.mm.exp8 0.160466830058074 0.122176637200831 1.31340028449385 0.189383204305533 df.mm.trans1:exp2 -0.186609855167567 0.112386160335603 -1.66043447529767 0.0971757904565159 . df.mm.trans2:exp2 -0.0469741360253741 0.0806641059438268 -0.582342486484462 0.560482028348318 df.mm.trans1:exp3 -0.173320289740494 0.112386160335603 -1.54218534758134 0.123380465436772 df.mm.trans2:exp3 -0.0328840950316405 0.0806641059438268 -0.407667011824819 0.683615218940901 df.mm.trans1:exp4 -0.218936028102906 0.112386160335603 -1.94806929473459 0.0517175681181926 . df.mm.trans2:exp4 0.0690592443495304 0.0806641059438268 0.85613351244012 0.392152063981951 df.mm.trans1:exp5 -0.158885253412630 0.112386160335603 -1.41374394265426 0.157783276367563 df.mm.trans2:exp5 -0.0417264527406458 0.0806641059438268 -0.517286496297417 0.605083426085406 df.mm.trans1:exp6 -0.0413173190891828 0.112386160335603 -0.367637073513346 0.713230399700819 df.mm.trans2:exp6 -0.00253030563596745 0.0806641059438268 -0.0313684210140446 0.974982687644857 df.mm.trans1:exp7 -0.135762870441262 0.112386160335603 -1.20800345910789 0.227363610587913 df.mm.trans2:exp7 0.0429307616785693 0.0806641059438268 0.532216419884027 0.594707615052988 df.mm.trans1:exp8 -0.216263099243174 0.112386160335603 -1.92428586044205 0.0546341407077876 . df.mm.trans2:exp8 -0.0179028428046448 0.0806641059438268 -0.221943113298895 0.824408574210528 df.mm.trans1:probe2 0.0640127668853695 0.0794690160848235 0.805505969987645 0.420740758765702 df.mm.trans1:probe3 -0.156369049751398 0.0794690160848235 -1.96767315685012 0.0494127088388167 * df.mm.trans1:probe4 -0.0276518131506653 0.0794690160848235 -0.347957160072932 0.727953812710385 df.mm.trans1:probe5 -0.117119398158077 0.0794690160848235 -1.47377435795941 0.140892410366269 df.mm.trans1:probe6 -0.053421967334237 0.0794690160848235 -0.672236425794118 0.501605922534934 df.mm.trans1:probe7 -0.0786040846134255 0.0794690160848235 -0.98911611702258 0.322872469740618 df.mm.trans1:probe8 -0.0241679432931360 0.0794690160848235 -0.304117811995302 0.76110851407831 df.mm.trans1:probe9 -0.0830181632506524 0.0794690160848235 -1.04466076643054 0.296460595335611 df.mm.trans1:probe10 -0.084263900147539 0.0794690160848235 -1.06033652231956 0.289276313663229 df.mm.trans1:probe11 0.0333088598662721 0.0794690160848235 0.419142723885230 0.675211955769618 df.mm.trans1:probe12 -0.0636671739391517 0.0794690160848235 -0.801157194033896 0.423252335778246 df.mm.trans1:probe13 -0.172200402870861 0.0794690160848235 -2.16688731476250 0.0305054735247245 * df.mm.trans1:probe14 -0.119006013101703 0.0794690160848235 -1.49751461594389 0.134610413963162 df.mm.trans1:probe15 -0.170096525704947 0.0794690160848235 -2.14041313313090 0.0325898930846652 * df.mm.trans1:probe16 -0.0767641684314423 0.0794690160848235 -0.965963493866665 0.334322223577927 df.mm.trans1:probe17 -0.0928202604428726 0.0794690160848235 -1.16800565825300 0.243114143390231 df.mm.trans1:probe18 -0.104521441857312 0.0794690160848235 -1.31524771548383 0.188762020447132 df.mm.trans1:probe19 -0.0655519754215625 0.0794690160848235 -0.824874632291832 0.409661581467564 df.mm.trans1:probe20 -0.104465999298936 0.0794690160848235 -1.31455005290906 0.188996426797382 df.mm.trans1:probe21 -0.163349444076652 0.0794690160848235 -2.05551109255331 0.0401180410969619 * df.mm.trans1:probe22 -0.124572896299998 0.0794690160848235 -1.56756560527982 0.117334440553091 df.mm.trans2:probe2 -0.0292861746991451 0.0794690160848235 -0.368523182266227 0.712569925218905 df.mm.trans2:probe3 0.0137242921562195 0.0794690160848235 0.172699912901533 0.86292618433987 df.mm.trans2:probe4 0.0156565196942624 0.0794690160848235 0.197014137906916 0.843861005437907 df.mm.trans2:probe5 -0.125591916773329 0.0794690160848235 -1.58038847038543 0.114369679628697 df.mm.trans2:probe6 -0.0562597107168897 0.0794690160848235 -0.707945228072778 0.479162769400716 df.mm.trans3:probe2 -0.0706272020941137 0.0794690160848235 -0.888738851613914 0.374381177364285 df.mm.trans3:probe3 -0.0724088220606835 0.0794690160848235 -0.91115790314298 0.362456425287582 df.mm.trans3:probe4 -0.158164290618298 0.0794690160848235 -1.99026360725892 0.0468643717557881 * df.mm.trans3:probe5 -0.0315062029096750 0.0794690160848235 -0.39645895295905 0.691860567889901 df.mm.trans3:probe6 -0.118349577680278 0.0794690160848235 -1.48925434730378 0.136771119016747 df.mm.trans3:probe7 -0.0964213815456668 0.0794690160848235 -1.21332043978937 0.225326020510945 df.mm.trans3:probe8 -0.141179115634533 0.0794690160848235 -1.77653030816239 0.0759836724969704 . df.mm.trans3:probe9 -0.109775284053141 0.0794690160848235 -1.38135954692039 0.167511659558995 df.mm.trans3:probe10 -0.0893100313867862 0.0794690160848235 -1.12383461865765 0.261383192594805 df.mm.trans3:probe11 -0.133795230469273 0.0794690160848235 -1.68361503716698 0.092603254890812 . df.mm.trans3:probe12 -0.111645718270000 0.0794690160848235 -1.40489619439646 0.160397578575062