fitVsDatCorrelation=0.918343615329077 cont.fitVsDatCorrelation=0.244583843287119 fstatistic=12768.3132609807,59,853 cont.fstatistic=2115.30462593996,59,853 residuals=-0.677953204133903,-0.0866738071364648,0.00415650747417291,0.0907099043654798,0.704416616105492 cont.residuals=-0.746808528046115,-0.240993470525338,-0.0710944089726418,0.193521384654168,1.61040157569525 predictedValues: Include Exclude Both Lung 54.8049471844 55.7791679584917 67.0309109478517 cerebhem 63.5566012536539 54.654080518172 79.6647520850863 cortex 61.4024557340122 52.8344526789326 81.5802360481268 heart 67.1317450451578 53.5788806839677 94.6905233326048 kidney 67.783609880109 57.6496947188047 83.2551385712351 liver 80.6068804236385 61.465296807118 113.299733876249 stomach 53.5455503204526 56.6604828432029 71.026032018681 testicle 53.9104618493226 61.1180840984345 65.425662885728 diffExp=-0.974220774091648,8.90252073548189,8.56800305507958,13.5528643611901,10.1339151613043,19.1415836165206,-3.11493252275026,-7.2076222491119 diffExpScore=1.43185278570017 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=0,0,0,0,0,1,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,1,0,1,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 63.7318340613119 65.4264073873435 69.7678923555317 cerebhem 65.8319059525203 68.1079998651792 64.8130924209977 cortex 66.0025668435554 57.4909019119697 72.3925292093409 heart 65.6884768365245 73.7207266156475 69.1228379507831 kidney 62.8292099636963 65.1025946715337 72.8117502936526 liver 66.3846474445823 76.5876640222608 72.325349375717 stomach 66.3348775079289 69.2874336210117 70.6895319876606 testicle 64.6721262549409 60.5075417017005 65.4111157343442 cont.diffExp=-1.69457332603167,-2.27609391265891,8.51166493158572,-8.03224977912292,-2.27338470783739,-10.2030165776786,-2.95255611308288,4.16458455324035 cont.diffExpScore=2.54563840376977 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=0.232962558151654 cont.tran.correlation=0.352663968276174 tran.covariance=0.00137172377984446 cont.tran.covariance=0.000629825949350935 tran.mean=59.7801494998669 cont.tran.mean=66.1066821663567 weightedLogRatios: wLogRatio Lung -0.0707018385456136 cerebhem 0.615168925943118 cortex 0.607503470094658 heart 0.923184234185203 kidney 0.669660290412507 liver 1.15331505595449 stomach -0.226675131117424 testicle -0.508215431546646 cont.weightedLogRatios: wLogRatio Lung -0.109370590965252 cerebhem -0.142897619712208 cortex 0.568926879390099 heart -0.489428761792648 kidney -0.147800086713183 liver -0.610046839455373 stomach -0.183618604658495 testicle 0.275304861681794 varWeightedLogRatios=0.348892234316331 cont.varWeightedLogRatios=0.144447847239749 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.01881748584842 0.0681196021619412 44.3164286055542 1.71267481110733e-223 *** df.mm.trans1 0.935583017095634 0.0588264221655038 15.9041291762304 4.49805702636413e-50 *** df.mm.trans2 0.997743295063136 0.0519728780925369 19.1973839371887 1.51036254750678e-68 *** df.mm.exp2 -0.0448995472868657 0.0668537390427593 -0.671608618003372 0.502014731678393 df.mm.exp3 -0.137000768684380 0.0668537390427593 -2.04926112803885 0.0407420005729681 * df.mm.exp4 -0.182828980954312 0.0668537390427593 -2.73476074146541 0.00637204707677071 ** df.mm.exp5 0.0287684611412652 0.0668537390427593 0.430319404018151 0.667072011556366 df.mm.exp6 -0.0420071344164874 0.0668537390427593 -0.628343829649078 0.529946923785074 df.mm.exp7 -0.0654638044843035 0.0668537390427593 -0.979209322046044 0.327754196213442 df.mm.exp8 0.0991907117798541 0.0668537390427593 1.48369729502208 0.138258635096209 df.mm.trans1:exp2 0.193049947520018 0.0617943012196193 3.12407363963727 0.00184394192165789 ** df.mm.trans2:exp2 0.0245229599077193 0.0456381160268937 0.537335062062342 0.591176329566014 df.mm.trans1:exp3 0.250670131738792 0.0617943012196193 4.05652506446995 5.43744082331748e-05 *** df.mm.trans2:exp3 0.0827637938576753 0.0456381160268936 1.81347963200112 0.0701091892309394 . df.mm.trans1:exp4 0.385705546612311 0.0617943012196193 6.24176564828362 6.81159748128034e-10 *** df.mm.trans2:exp4 0.142583488692244 0.0456381160268936 3.12421942676648 0.00184303894848672 ** df.mm.trans1:exp5 0.18377149543262 0.0617943012196193 2.97392302858947 0.00302300556886399 ** df.mm.trans2:exp5 0.00421602453221347 0.0456381160268936 0.0923794603994839 0.926418248873718 df.mm.trans1:exp6 0.427810678376401 0.0617943012196193 6.92314129187974 8.6860228834534e-12 *** df.mm.trans2:exp6 0.139079404800736 0.0456381160268936 3.04743966027826 0.00237913970889828 ** df.mm.trans1:exp7 0.0422160370362864 0.0617943012196193 0.683170392788308 0.494684688131779 df.mm.trans2:exp7 0.081140355011789 0.0456381160268936 1.77790763676517 0.0757752067134434 . df.mm.trans1:exp8 -0.115646622276163 0.0617943012196193 -1.87147714261143 0.0616210533091785 . df.mm.trans2:exp8 -0.00778337957346759 0.0456381160268936 -0.170545593268596 0.864621524162797 df.mm.trans1:probe2 -0.186510349944719 0.0423076658790681 -4.40842920708126 1.17410046935290e-05 *** df.mm.trans1:probe3 -0.151477394592735 0.0423076658790681 -3.58037701786046 0.000362485558022389 *** df.mm.trans1:probe4 -0.0834761945932037 0.0423076658790681 -1.97307492291849 0.0488095059328471 * df.mm.trans1:probe5 0.335918751043064 0.0423076658790681 7.93990271179817 6.3635360927552e-15 *** df.mm.trans1:probe6 0.613869227030223 0.0423076658790681 14.5096453390953 8.45378213128827e-43 *** df.mm.trans1:probe7 0.526013260999471 0.0423076658790681 12.4330484811671 9.8073654416301e-33 *** df.mm.trans1:probe8 0.243632683891603 0.0423076658790681 5.75859430742411 1.18334320362249e-08 *** df.mm.trans1:probe9 0.625784845747409 0.0423076658790681 14.7912874119822 3.08143141532882e-44 *** df.mm.trans1:probe10 0.151760261965884 0.0423076658790681 3.58706297812965 0.000353455416122147 *** df.mm.trans1:probe11 -0.096971132864024 0.0423076658790681 -2.29204639039189 0.0221457482032809 * df.mm.trans1:probe12 0.268891984250485 0.0423076658790681 6.35563268886266 3.37646031385174e-10 *** df.mm.trans1:probe13 -0.126822263208557 0.0423076658790681 -2.99761900292644 0.00279989290910853 ** df.mm.trans1:probe14 0.340684578987525 0.0423076658790681 8.05254962448968 2.71814824517612e-15 *** df.mm.trans1:probe15 0.186871579671140 0.0423076658790681 4.41696736958481 1.12968164053182e-05 *** df.mm.trans1:probe16 0.0565195433580186 0.0423076658790681 1.33591731388760 0.181932602154960 df.mm.trans1:probe17 -0.258054966158601 0.0423076658790681 -6.09948482849947 1.61200820483042e-09 *** df.mm.trans1:probe18 -0.125134286930318 0.0423076658790681 -2.95772135688130 0.00318478121463887 ** df.mm.trans1:probe19 -0.221470578686046 0.0423076658790681 -5.2347624026127 2.08137784628274e-07 *** df.mm.trans1:probe20 -0.157853960993151 0.0423076658790681 -3.73109595420271 0.000203269120008415 *** df.mm.trans1:probe21 -0.140021770998187 0.0423076658790681 -3.30960756375508 0.000973448192306978 *** df.mm.trans1:probe22 -0.221994969246577 0.0423076658790681 -5.24715709633156 1.95023975866634e-07 *** df.mm.trans2:probe2 0.0301063041497722 0.0423076658790681 0.711603997153324 0.476904588295791 df.mm.trans2:probe3 0.077473217469931 0.0423076658790681 1.83118628409754 0.0674214013231439 . df.mm.trans2:probe4 -0.0421242267378989 0.0423076658790681 -0.995664163045687 0.319695703090569 df.mm.trans2:probe5 0.0149748004538143 0.0423076658790681 0.353950050012641 0.723463787319198 df.mm.trans2:probe6 -0.00299514026396677 0.0423076658790681 -0.0707942686445538 0.943578087224698 df.mm.trans3:probe2 -0.868694127630918 0.0423076658790681 -20.5327831158066 1.923279310021e-76 *** df.mm.trans3:probe3 -0.649594587149426 0.0423076658790681 -15.3540634694011 3.69082117396748e-47 *** df.mm.trans3:probe4 -1.1120877782927 0.0423076658790681 -26.2857275433601 5.11988197825007e-112 *** df.mm.trans3:probe5 -1.17669634630851 0.0423076658790681 -27.8128401049579 1.09878533399517e-121 *** df.mm.trans3:probe6 -0.890200132460206 0.0423076658790681 -21.0411071838552 1.70268169333943e-79 *** df.mm.trans3:probe7 -0.678538449358702 0.0423076658790681 -16.0381915489791 8.59853360996831e-51 *** df.mm.trans3:probe8 -0.791587579114197 0.0423076658790681 -18.7102635578352 1.01920035140902e-65 *** df.mm.trans3:probe9 -0.99272103735168 0.0423076658790681 -23.4643300859297 2.46922548174379e-94 *** df.mm.trans3:probe10 -0.309779238115903 0.0423076658790681 -7.32205929302205 5.65032876198469e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21718060553467 0.166885668441461 25.2698787434461 1.29131107433018e-105 *** df.mm.trans1 -0.0817391631681012 0.144118381105206 -0.567166814817548 0.570750073623256 df.mm.trans2 -0.0213295000547566 0.127327938302986 -0.167516260288465 0.867003559757082 df.mm.exp2 0.146255427654868 0.163784440511535 0.892975103117735 0.372122357333985 df.mm.exp3 -0.131219003989965 0.163784440511535 -0.801168924106217 0.423256962504582 df.mm.exp4 0.158886088557046 0.163784440511535 0.970092690494957 0.332275277825115 df.mm.exp5 -0.0619290881823746 0.163784440511535 -0.378113378712633 0.705440444320834 df.mm.exp6 0.162290916616083 0.163784440511535 0.990881161294769 0.322024602215945 df.mm.exp7 0.0842456338473923 0.163784440511535 0.514368969263959 0.607127285266582 df.mm.exp8 0.000969859426911756 0.163784440511535 0.00592156021587077 0.995276690694632 df.mm.trans1:exp2 -0.11383500148062 0.151389364259541 -0.751935263335024 0.452297415036341 df.mm.trans2:exp2 -0.106086708721943 0.111808455390721 -0.948825456457802 0.342978124078286 df.mm.trans1:exp3 0.166228449299256 0.151389364259541 1.09801933651214 0.272505887584512 df.mm.trans2:exp3 0.00191975201008252 0.111808455390721 0.0171700074325670 0.986305004412554 df.mm.trans1:exp4 -0.128646756595177 0.151389364259541 -0.849774072468032 0.395689147056571 df.mm.trans2:exp4 -0.0395280590250035 0.111808455390721 -0.353533718777085 0.723775711867321 df.mm.trans1:exp5 0.047664992799136 0.151389364259541 0.314850339931538 0.752952288088397 df.mm.trans2:exp5 0.0569675336622702 0.111808455390721 0.509510067581148 0.610526528237343 df.mm.trans1:exp6 -0.121509287581483 0.151389364259541 -0.80262763620018 0.422413556342682 df.mm.trans2:exp6 -0.00478085658029945 0.111808455390721 -0.0427593473462491 0.96590337723451 df.mm.trans1:exp7 -0.0442140065778568 0.151389364259541 -0.292054906195765 0.770315659536282 df.mm.trans2:exp7 -0.0269080367704474 0.111808455390721 -0.240661913058504 0.809874989599947 df.mm.trans1:exp8 0.0136762464974797 0.151389364259541 0.0903382253064569 0.92803965274858 df.mm.trans2:exp8 -0.0791278055785597 0.111808455390721 -0.707708601304283 0.47931955068805 df.mm.trans1:probe2 0.0741061328778346 0.103649212214146 0.714970536628163 0.474822879649718 df.mm.trans1:probe3 -0.0346492728309564 0.103649212214146 -0.334293643827882 0.73824015965307 df.mm.trans1:probe4 0.0752263884654569 0.103649212214146 0.725778680401681 0.468173536031022 df.mm.trans1:probe5 -0.0162239899967419 0.103649212214146 -0.156527865964114 0.875653994290104 df.mm.trans1:probe6 0.0397098490431069 0.103649212214146 0.383117712087031 0.701727942378732 df.mm.trans1:probe7 0.0888593790929287 0.103649212214146 0.857308774420197 0.391515057221709 df.mm.trans1:probe8 0.0677784526778081 0.103649212214146 0.653921541996607 0.513338600850253 df.mm.trans1:probe9 0.0355342499553803 0.103649212214146 0.34283183823881 0.73180943820471 df.mm.trans1:probe10 0.0876313024995566 0.103649212214146 0.84546038148852 0.398090931598298 df.mm.trans1:probe11 0.0270818129643221 0.103649212214146 0.261283345872125 0.793937080000742 df.mm.trans1:probe12 0.0173962624508733 0.103649212214146 0.167837864651894 0.86675061689596 df.mm.trans1:probe13 0.00484927961455305 0.103649212214146 0.0467854941775545 0.962695141315564 df.mm.trans1:probe14 0.113929575038224 0.103649212214146 1.09918418678222 0.271997867969679 df.mm.trans1:probe15 0.0291050501382317 0.103649212214146 0.280803389784561 0.77892929689287 df.mm.trans1:probe16 0.090013701941355 0.103649212214146 0.868445596628178 0.385394710149553 df.mm.trans1:probe17 -0.0560004824458811 0.103649212214146 -0.540288548746326 0.589139087070134 df.mm.trans1:probe18 -0.0499068550653421 0.103649212214146 -0.481497678556699 0.630286326854863 df.mm.trans1:probe19 -0.0244270874538530 0.103649212214146 -0.235670748788567 0.813744739507604 df.mm.trans1:probe20 -0.0192422806366843 0.103649212214146 -0.185648112760650 0.852764856341077 df.mm.trans1:probe21 0.0565218051380632 0.103649212214146 0.545318231857713 0.585677224344326 df.mm.trans1:probe22 0.00847457175838047 0.103649212214146 0.081762046978914 0.934855117644962 df.mm.trans2:probe2 -0.0483735454299469 0.103649212214146 -0.466704419614921 0.640830540730831 df.mm.trans2:probe3 -0.0878413147179463 0.103649212214146 -0.847486563973686 0.396961696068898 df.mm.trans2:probe4 -0.0634331631743989 0.103649212214146 -0.611998507459387 0.540701832556713 df.mm.trans2:probe5 0.0253901435238858 0.103649212214146 0.244962243142071 0.806544583561498 df.mm.trans2:probe6 -0.0645444535448432 0.103649212214146 -0.622720155474891 0.533634872483545 df.mm.trans3:probe2 0.356502279629294 0.103649212214146 3.43950785552268 0.000611036248116566 *** df.mm.trans3:probe3 -0.0143288189629154 0.103649212214146 -0.138243394781535 0.89008068764781 df.mm.trans3:probe4 0.104379396533305 0.103649212214146 1.00704476477496 0.314198840194520 df.mm.trans3:probe5 0.100475989525138 0.103649212214146 0.969384980153514 0.332627922801318 df.mm.trans3:probe6 0.0768573663527423 0.103649212214146 0.741514235476771 0.458585802439488 df.mm.trans3:probe7 0.139668834823145 0.103649212214146 1.34751467801395 0.178172329927115 df.mm.trans3:probe8 0.0583893936274608 0.103649212214146 0.563336588673963 0.573353756447781 df.mm.trans3:probe9 0.186825868318692 0.103649212214146 1.80248227967905 0.0718224443958081 . df.mm.trans3:probe10 0.30184935735295 0.103649212214146 2.91222046849048 0.00368228328178475 **