fitVsDatCorrelation=0.886368840351924 cont.fitVsDatCorrelation=0.22890265440627 fstatistic=2734.74779109549,69,1083 cont.fstatistic=606.461476302052,69,1083 residuals=-1.75465950269951,-0.151396120470991,0.00514637837031829,0.146928692784280,1.41447606280180 cont.residuals=-1.17622954794483,-0.462097617882197,-0.261202933340579,0.24131380986688,2.85150906518583 predictedValues: Include Exclude Both Lung 52.2902004614464 51.1843835816621 59.6857736718686 cerebhem 161.663118063435 66.3310605157035 158.637858746410 cortex 307.630487568942 55.4814449847527 240.307090803087 heart 50.845321622425 46.4466296288120 59.400440219039 kidney 53.8471106777197 49.1360232930596 62.6238144736587 liver 56.6311364657992 53.5244547488322 63.8757906859305 stomach 54.6336412231732 51.7173128135857 66.0259490357948 testicle 54.0276820937398 54.0659368001241 60.8183751720866 diffExp=1.10581687978428,95.3320575477315,252.149042584189,4.39869199361298,4.71108738466005,3.10668171696693,2.91632840958754,-0.0382547063842509 diffExpScore=0.997467678757316 diffExp1.5=0,1,1,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,1,1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,1,1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,1,1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 83.8858470609913 64.4736340409859 81.8154627460313 cerebhem 78.297379506844 64.8419749420753 86.1399506670102 cortex 76.1168091535872 86.2845491402424 67.378267481995 heart 80.0422027423607 79.2386720316782 83.8004146705256 kidney 81.650442529772 61.4524348395277 77.4648113859623 liver 76.2107579014122 62.158950444433 88.1100044015323 stomach 80.6720266261197 71.1755925742911 79.7612319842805 testicle 73.4487843160495 77.9427157823948 82.8077087953005 cont.diffExp=19.4122130200054,13.4554045647687,-10.1677399866553,0.803530710682494,20.1980076902443,14.0518074569792,9.49643405182864,-4.49393146634522 cont.diffExpScore=1.44424782940376 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=1,0,0,0,1,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=1,1,0,0,1,1,0,0 cont.diffExp1.2Score=0.8 tran.correlation=0.499126413523192 cont.tran.correlation=-0.460619633358581 tran.covariance=0.0462305995002199 cont.tran.covariance=-0.00253425130484987 tran.mean=76.2159965339508 cont.tran.mean=74.8682983520478 weightedLogRatios: wLogRatio Lung 0.0843463966281106 cerebhem 4.13365062841709 cortex 8.3458205238895 heart 0.351399597544004 kidney 0.360765789120649 liver 0.226152244146406 stomach 0.217959935850483 testicle -0.00282404392448357 cont.weightedLogRatios: wLogRatio Lung 1.13119821119534 cerebhem 0.804445196977533 cortex -0.551046315977712 heart 0.0441671727420713 kidney 1.21072389104676 liver 0.862432875080335 stomach 0.542018349429002 testicle -0.256919640568896 varWeightedLogRatios=9.08267918869191 cont.varWeightedLogRatios=0.429869833261252 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.88096980636566 0.155507430439943 18.5262517566856 8.94446927703709e-67 *** df.mm.trans1 0.913920107199855 0.132624974499041 6.89101061585096 9.38395496764596e-12 *** df.mm.trans2 1.06624665984784 0.115522724065551 9.22975690257115 1.38427080024722e-19 *** df.mm.exp2 0.410399173092993 0.14483408702619 2.83358138625738 0.00468840084658808 ** df.mm.exp3 0.459880572729797 0.14483408702619 3.1752233343152 0.00153946231084297 ** df.mm.exp4 -0.120359383303479 0.14483408702619 -0.831015583242606 0.406147995229959 df.mm.exp5 -0.0595542079044511 0.14483408702619 -0.411189169119297 0.681015180736153 df.mm.exp6 0.0566074045268113 0.14483408702619 0.390843106682304 0.695990081068484 df.mm.exp7 -0.0467551673486408 0.14483408702619 -0.322818808117913 0.746894781238686 df.mm.exp8 0.0686591694399739 0.14483408702619 0.474053938887732 0.635556999997255 df.mm.trans1:exp2 0.718306496971846 0.131678767591489 5.45499103697771 6.06683578339162e-08 *** df.mm.trans2:exp2 -0.151175378556328 0.0874013235232894 -1.72966921394554 0.083974313953539 . df.mm.trans1:exp3 1.31220979248301 0.131678767591489 9.96523446022758 1.93301117785510e-22 *** df.mm.trans2:exp3 -0.379266409825734 0.0874013235232894 -4.33936689442321 1.56252414352676e-05 *** df.mm.trans1:exp4 0.0923385162327869 0.131678767591489 0.701240738516412 0.483303435390581 df.mm.trans2:exp4 0.0232288094590616 0.0874013235232894 0.265771827275269 0.790465525413828 df.mm.trans1:exp5 0.0888939732169713 0.131678767591489 0.675082056453851 0.499767730091491 df.mm.trans2:exp5 0.0187121683052821 0.0874013235232894 0.214094793430628 0.830513436961459 df.mm.trans1:exp6 0.0231425618211481 0.131678767591489 0.175750139862668 0.860523097322585 df.mm.trans2:exp6 -0.0119032343037015 0.0874013235232894 -0.136190549797907 0.891695956279663 df.mm.trans1:exp7 0.090596018141428 0.131678767591489 0.688007791981214 0.491595203971384 df.mm.trans2:exp7 0.0571132861249768 0.0874013235232894 0.653460197427766 0.513598349387669 df.mm.trans1:exp8 -0.0359716047012652 0.131678767591489 -0.273176954487157 0.78476927701409 df.mm.trans2:exp8 -0.0138892932494183 0.0874013235232894 -0.158913992254559 0.873766265100226 df.mm.trans1:probe2 0.644349401590839 0.100017203811722 6.44238568000563 1.76576716333695e-10 *** df.mm.trans1:probe3 0.553670253341062 0.100017203811722 5.53575017337342 3.88693099286435e-08 *** df.mm.trans1:probe4 0.588250118431835 0.100017203811722 5.88148934396519 5.41217201820411e-09 *** df.mm.trans1:probe5 0.675294357510175 0.100017203811722 6.75178201123668 2.37690066531076e-11 *** df.mm.trans1:probe6 0.70815913188506 0.100017203811722 7.08037322477183 2.58128866436382e-12 *** df.mm.trans1:probe7 0.911007158894143 0.100017203811722 9.10850457896301 3.93076453491264e-19 *** df.mm.trans1:probe8 0.294121046792857 0.100017203811722 2.94070455465370 0.00334427529533089 ** df.mm.trans1:probe9 0.464855498081771 0.100017203811722 4.64775538973118 3.76844758275149e-06 *** df.mm.trans1:probe10 0.340233425872314 0.100017203811722 3.40174902822509 0.000694016871108046 *** df.mm.trans1:probe11 0.240872368470842 0.100017203811722 2.40830936369981 0.0161928603017636 * df.mm.trans1:probe12 0.232186943050209 0.100017203811722 2.32147004916566 0.0204463406546919 * df.mm.trans1:probe13 0.334691494024653 0.100017203811722 3.34633924234371 0.000846978475189373 *** df.mm.trans1:probe14 0.166664795736308 0.100017203811722 1.66636127970592 0.0959306425118088 . df.mm.trans1:probe15 0.399252677244254 0.100017203811722 3.99184002380062 6.99794980995888e-05 *** df.mm.trans1:probe16 0.139434251048041 0.100017203811722 1.39410267168157 0.163572549759204 df.mm.trans1:probe17 -0.0202543111816454 0.100017203811722 -0.202508272674502 0.839557427510124 df.mm.trans1:probe18 -0.0700723065473864 0.100017203811722 -0.700602535132804 0.483701581153157 df.mm.trans1:probe19 -0.00150436811779502 0.100017203811722 -0.0150410935365373 0.988002166502612 df.mm.trans1:probe20 0.066491126782834 0.100017203811722 0.664796897421777 0.506321937753091 df.mm.trans1:probe21 0.0780172976811524 0.100017203811722 0.780038780408388 0.435538488372386 df.mm.trans1:probe22 0.054880506959572 0.100017203811722 0.548710670445081 0.58331712046325 df.mm.trans2:probe2 -0.0984001798112056 0.100017203811722 -0.983832541413976 0.325417591716118 df.mm.trans2:probe3 -0.0368024500003016 0.100017203811722 -0.367961196651535 0.712974070502503 df.mm.trans2:probe4 -0.108125685496934 0.100017203811722 -1.08107086957236 0.279906259181693 df.mm.trans2:probe5 -0.107055678294897 0.100017203811722 -1.0703726380556 0.284690097869166 df.mm.trans2:probe6 0.0440522831612862 0.100017203811722 0.440447057930283 0.659701243429641 df.mm.trans3:probe2 -0.812379680100698 0.100017203811722 -8.12239943870028 1.23629039689117e-15 *** df.mm.trans3:probe3 -0.59275985778981 0.100017203811722 -5.92657898040878 4.15256976149067e-09 *** df.mm.trans3:probe4 -1.22980781390669 0.100017203811722 -12.2959627647835 1.27480269319082e-32 *** df.mm.trans3:probe5 -1.16749400306476 0.100017203811722 -11.6729318414312 9.72590736182484e-30 *** df.mm.trans3:probe6 -0.807348480156105 0.100017203811722 -8.07209609334716 1.82551783198272e-15 *** df.mm.trans3:probe7 -1.06734920288550 0.100017203811722 -10.6716560972324 2.3859611206321e-25 *** df.mm.trans3:probe8 -0.374926907942721 0.100017203811722 -3.74862417318231 0.000187202991055738 *** df.mm.trans3:probe9 -0.960132220706827 0.100017203811722 -9.59967069779553 5.34296171472078e-21 *** df.mm.trans3:probe10 -1.01206755646625 0.100017203811722 -10.1189347221846 4.64580130712619e-23 *** df.mm.trans3:probe11 -0.447733759854913 0.100017203811722 -4.47656745831202 8.38786887803603e-06 *** df.mm.trans3:probe12 -0.779863126910105 0.100017203811722 -7.79728983803793 1.47944410062623e-14 *** df.mm.trans3:probe13 -0.0371978676930717 0.100017203811722 -0.371914693427095 0.710029120634179 df.mm.trans3:probe14 -0.562283023260984 0.100017203811722 -5.62186305787412 2.40245147049812e-08 *** df.mm.trans3:probe15 -1.05304544423857 0.100017203811722 -10.5286431144475 9.53577822151948e-25 *** df.mm.trans3:probe16 -1.22711627763826 0.100017203811722 -12.2690520317711 1.70711482217195e-32 *** df.mm.trans3:probe17 -1.13588579627470 0.100017203811722 -11.3569041423409 2.54904484195356e-28 *** df.mm.trans3:probe18 -0.758655734571015 0.100017203811722 -7.58525239316983 7.12283274862717e-14 *** df.mm.trans3:probe19 -0.956765176159632 0.100017203811722 -9.56600604392725 7.21622956619492e-21 *** df.mm.trans3:probe20 -0.458045637692568 0.100017203811722 -4.57966849937957 5.19696564968138e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16569791524731 0.326965901942244 12.7404658727476 9.5745327877561e-35 *** df.mm.trans1 0.244674903632647 0.278853841803355 0.877430635526944 0.380447378078483 df.mm.trans2 -0.0407169189835691 0.242895092292746 -0.167631707167206 0.866904321538422 df.mm.exp2 -0.114753036582325 0.304524406084817 -0.376827059800139 0.706375948846876 df.mm.exp3 0.388350123501236 0.304524406084817 1.27526764929663 0.202488015567434 df.mm.exp4 0.13533351706394 0.304524406084817 0.444409427815276 0.65683538856099 df.mm.exp5 -0.0203601089067025 0.304524406084817 -0.0668587098435444 0.946706524519104 df.mm.exp6 -0.206635649980516 0.304524406084817 -0.678552017019494 0.497566718437066 df.mm.exp7 0.0852572482507464 0.304524406084817 0.279968523202703 0.779555118190346 df.mm.exp8 0.0447943168279375 0.304524406084817 0.147095982892948 0.883083638450917 df.mm.trans1:exp2 0.0458102606182107 0.276864371627708 0.165461017424844 0.868612010741107 df.mm.trans2:exp2 0.120449826013166 0.183767762710057 0.655445896695212 0.512319911312308 df.mm.trans1:exp3 -0.485537911641087 0.276864371627708 -1.75370311747434 0.0797642473522683 . df.mm.trans2:exp3 -0.0969559437267398 0.183767762710057 -0.52760039245683 0.597884789616065 df.mm.trans1:exp4 -0.182236398263126 0.276864371627708 -0.658215418588327 0.510539609538412 df.mm.trans2:exp4 0.0708745801409614 0.183767762710057 0.385674718436797 0.69981333132193 df.mm.trans1:exp5 -0.00664956290880377 0.276864371627708 -0.0240174019853492 0.980843152315164 df.mm.trans2:exp5 -0.0276327984379685 0.183767762710057 -0.150368040783991 0.88050225422555 df.mm.trans1:exp6 0.110681371461459 0.276864371627708 0.399767477522495 0.689406616104752 df.mm.trans2:exp6 0.170074105483016 0.183767762710057 0.925483898671355 0.354920429489832 df.mm.trans1:exp7 -0.124322278246533 0.276864371627708 -0.44903675223949 0.653494983006432 df.mm.trans2:exp7 0.0136363448023643 0.183767762710057 0.0742042271248592 0.940861585472969 df.mm.trans1:exp8 -0.177662876577305 0.276864371627708 -0.6416964217274 0.521206100394804 df.mm.trans2:exp8 0.144923461329787 0.183767762710057 0.788622874831659 0.430505048591284 df.mm.trans1:probe2 0.058546660966028 0.210293586367697 0.278404405846499 0.780755083041076 df.mm.trans1:probe3 0.102271621479566 0.210293586367697 0.486327820291889 0.626833149544402 df.mm.trans1:probe4 -0.145449872684603 0.210293586367697 -0.691651491597493 0.489304458718744 df.mm.trans1:probe5 0.0347542517917306 0.210293586367697 0.165265391075518 0.868765940951011 df.mm.trans1:probe6 0.106909632813192 0.210293586367697 0.508382755079659 0.611288480950402 df.mm.trans1:probe7 -0.170902108779041 0.210293586367697 -0.812683409565424 0.416578202639083 df.mm.trans1:probe8 0.0486162260934711 0.210293586367697 0.231182638202128 0.817216585191367 df.mm.trans1:probe9 0.230939261951699 0.210293586367697 1.09817548856627 0.272371953817459 df.mm.trans1:probe10 -0.0697581316765196 0.210293586367697 -0.331717827830223 0.740166518915981 df.mm.trans1:probe11 0.094318467612077 0.210293586367697 0.44850853153059 0.653875948937393 df.mm.trans1:probe12 0.176685525508474 0.210293586367697 0.840185041114571 0.400990086482643 df.mm.trans1:probe13 -0.0488564993876903 0.210293586367697 -0.232325199410814 0.81632933555519 df.mm.trans1:probe14 -0.0196199602403804 0.210293586367697 -0.0932979487357028 0.925684117922482 df.mm.trans1:probe15 0.123839254519441 0.210293586367697 0.588887453290701 0.556059578594749 df.mm.trans1:probe16 0.0714015108179048 0.210293586367697 0.339532517615919 0.734274435746746 df.mm.trans1:probe17 0.0616336195241932 0.210293586367697 0.293083686425069 0.769514332839087 df.mm.trans1:probe18 -0.0468654860679179 0.210293586367697 -0.222857419845292 0.82368850056214 df.mm.trans1:probe19 0.172199646102500 0.210293586367697 0.818853532705514 0.413050128969518 df.mm.trans1:probe20 0.064337103730395 0.210293586367697 0.305939447995823 0.759709548436395 df.mm.trans1:probe21 0.035911125636235 0.210293586367697 0.170766623255189 0.864439171655495 df.mm.trans1:probe22 -0.0793799783469948 0.210293586367697 -0.377472179337888 0.705896694473159 df.mm.trans2:probe2 0.071655698320585 0.210293586367697 0.340741244458571 0.733364472899 df.mm.trans2:probe3 0.123346190425337 0.210293586367697 0.586542806919783 0.557633036683205 df.mm.trans2:probe4 0.0938862761027948 0.210293586367697 0.446453349930679 0.655359055772763 df.mm.trans2:probe5 0.0322898181967772 0.210293586367697 0.153546376541977 0.877996025622241 df.mm.trans2:probe6 0.751981621576406 0.210293586367697 3.57586569597787 0.000364473174974607 *** df.mm.trans3:probe2 0.104131333145378 0.210293586367697 0.495171226778667 0.62057970681762 df.mm.trans3:probe3 0.161290550464292 0.210293586367697 0.766977981830968 0.443261767823723 df.mm.trans3:probe4 -0.00977675634330237 0.210293586367697 -0.0464909867779219 0.96292748896695 df.mm.trans3:probe5 0.0543997576411024 0.210293586367697 0.258684815741289 0.795927591398356 df.mm.trans3:probe6 -0.110740409066036 0.210293586367697 -0.526599079785566 0.598579862954103 df.mm.trans3:probe7 0.0364393380678523 0.210293586367697 0.173278408995976 0.862464973793782 df.mm.trans3:probe8 -0.156759189791892 0.210293586367697 -0.745430198321882 0.456173439845911 df.mm.trans3:probe9 0.123305821152701 0.210293586367697 0.586350840662832 0.557761958483547 df.mm.trans3:probe10 0.0467813592922773 0.210293586367697 0.222457375425994 0.82399979615215 df.mm.trans3:probe11 0.2430080906827 0.210293586367697 1.15556586808027 0.248113648838480 df.mm.trans3:probe12 0.0439428196448159 0.210293586367697 0.208959390554034 0.834519235731784 df.mm.trans3:probe13 -0.163766052726578 0.210293586367697 -0.77874963071976 0.436297329939839 df.mm.trans3:probe14 -0.0826980655055972 0.210293586367697 -0.393250535758139 0.694211846334884 df.mm.trans3:probe15 0.0715021649126108 0.210293586367697 0.340011153681072 0.733914060566104 df.mm.trans3:probe16 0.354964645012433 0.210293586367697 1.68794803086281 0.0917091228842714 . df.mm.trans3:probe17 0.310669157594966 0.210293586367697 1.47731161449576 0.139882854779908 df.mm.trans3:probe18 -0.0867756173077116 0.210293586367697 -0.412640341565078 0.679951813000903 df.mm.trans3:probe19 -0.236442120110039 0.210293586367697 -1.12434299207120 0.261116685821699 df.mm.trans3:probe20 -0.00726968729479677 0.210293586367697 -0.0345692297152885 0.97242960811672