fitVsDatCorrelation=0.927435355373136 cont.fitVsDatCorrelation=0.273638917206723 fstatistic=9533.58532286567,53,715 cont.fstatistic=1429.8751331802,53,715 residuals=-0.904610183004422,-0.0772114707431274,-0.00291908776729441,0.0744233181428724,0.702756731268218 cont.residuals=-0.843377773535034,-0.275031399131737,-0.0932147895095696,0.158134601809929,1.84951544086183 predictedValues: Include Exclude Both Lung 72.2101395133818 51.5330547163926 60.3648000146584 cerebhem 66.5441463196945 54.853297877906 62.1271020424356 cortex 66.325253036852 50.2664792369663 57.9703681596104 heart 69.6759248913433 49.9630685428256 63.4940411163724 kidney 83.754388043564 53.6734717539764 72.6639009994117 liver 103.898100162262 50.5191877530204 86.544093911926 stomach 71.2150905818964 48.9487805464388 62.893829086913 testicle 75.62458852576 48.6302160662578 64.6665632679933 diffExp=20.6770847969892,11.6908484417885,16.0587737998857,19.7128563485177,30.0809162895876,53.3789124092418,22.2663100354576,26.9943724595021 diffExpScore=0.995046073365048 diffExp1.5=0,0,0,0,1,1,0,1 diffExp1.5Score=0.75 diffExp1.4=1,0,0,0,1,1,1,1 diffExp1.4Score=0.833333333333333 diffExp1.3=1,0,1,1,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 65.1429413601888 70.5924824906028 92.2169620787072 cerebhem 70.7978009965462 70.801305772241 68.3251138425897 cortex 67.0149551442073 64.8265182661192 78.1805378739758 heart 69.7522416147224 73.3125984878256 66.4658968510962 kidney 70.3355596104721 67.9894070610743 110.589256075328 liver 72.7986895070727 70.0793737480187 60.48664042967 stomach 69.878971707432 73.3892154173817 72.0957386052225 testicle 63.5931220339411 75.8217442590679 72.060687158324 cont.diffExp=-5.449541130414,-0.00350477569482166,2.18843687808811,-3.56035687310322,2.3461525493978,2.71931575905401,-3.51024370994972,-12.2286222251268 cont.diffExpScore=1.73021650552047 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=-0.0354312863167328 cont.tran.correlation=-0.222973434990871 tran.covariance=-0.000219282058494116 cont.tran.covariance=-0.000479414180566501 tran.mean=63.6021992230336 cont.tran.mean=69.7579329673071 weightedLogRatios: wLogRatio Lung 1.38684168123473 cerebhem 0.792377611792906 cortex 1.12444152241313 heart 1.35608031532428 kidney 1.87127720001322 liver 3.08820366926108 stomach 1.52905553895549 testicle 1.81251281417389 cont.weightedLogRatios: wLogRatio Lung -0.338772757913614 cerebhem -0.000210874609636224 cortex 0.139056479411365 heart -0.212565118509322 kidney 0.143719446210487 liver 0.162505633391547 stomach -0.209344493263986 testicle -0.745808634370681 varWeightedLogRatios=0.474092524278624 cont.varWeightedLogRatios=0.097940002748394 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20973861562596 0.0847844596725433 49.6522432517105 6.45084277042867e-234 *** df.mm.trans1 -0.0808771783573068 0.0752902410471131 -1.07420533169362 0.283093041389707 df.mm.trans2 -0.243731785729048 0.0684862650087035 -3.55884184512141 0.000397114080144149 *** df.mm.exp2 -0.0480521869901803 0.0923296624891262 -0.520441488626036 0.602917012688105 df.mm.exp3 -0.0694205924836439 0.0923296624891263 -0.751877463992891 0.452372177488044 df.mm.exp4 -0.117204869400869 0.0923296624891263 -1.26941728412223 0.204705301429592 df.mm.exp5 0.00356496167305111 0.0923296624891262 0.0386112282547438 0.96921112803144 df.mm.exp6 -0.0162879675568808 0.0923296624891263 -0.176410994232748 0.860021022361408 df.mm.exp7 -0.106366612994359 0.0923296624891263 -1.15203077891556 0.249693471255535 df.mm.exp8 -0.0806154882205996 0.0923296624891262 -0.87312664259001 0.382887186002408 df.mm.trans1:exp2 -0.0336627036823311 0.0876713462026966 -0.383964717554385 0.701118752256979 df.mm.trans2:exp2 0.110491056366493 0.0738258802680925 1.49664394065136 0.134927206279405 df.mm.trans1:exp3 -0.0155891649065704 0.0876713462026967 -0.177813682369244 0.858919664950615 df.mm.trans2:exp3 0.0445355897425826 0.0738258802680926 0.603251726641868 0.546532549066724 df.mm.trans1:exp4 0.081479244533296 0.0876713462026966 0.929371431629618 0.353010278122223 df.mm.trans2:exp4 0.0862655318323317 0.0738258802680925 1.16849987455707 0.242994639720929 df.mm.trans1:exp5 0.144743129697660 0.0876713462026966 1.65097418902424 0.099182970719802 . df.mm.trans2:exp5 0.0371304685413324 0.0738258802680925 0.502946506110001 0.615156767423208 df.mm.trans1:exp6 0.380118107730422 0.0876713462026966 4.33571656184663 1.66147018555094e-05 *** df.mm.trans2:exp6 -0.00358225375355386 0.0738258802680926 -0.0485230076572769 0.961312979086559 df.mm.trans1:exp7 0.0924908828178437 0.0876713462026966 1.05497276845737 0.291794126467610 df.mm.trans2:exp7 0.054917628500262 0.0738258802680926 0.743880442750337 0.457193189354236 df.mm.trans1:exp8 0.126816491063182 0.0876713462026966 1.44649873140971 0.148475591246580 df.mm.trans2:exp8 0.0226371147592748 0.0738258802680925 0.306628443535925 0.7592154728152 df.mm.trans1:probe2 -0.0439100622122468 0.0480195739620618 -0.91442007059326 0.360804420314037 df.mm.trans1:probe3 0.233572987725656 0.0480195739620618 4.86412036704431 1.41482427900207e-06 *** df.mm.trans1:probe4 -0.234834002528167 0.0480195739620618 -4.89038079999834 1.24385809022354e-06 *** df.mm.trans1:probe5 0.70013656370163 0.0480195739620618 14.5802327245714 2.38942218066111e-42 *** df.mm.trans1:probe6 -0.136781949681703 0.0480195739620618 -2.84846237473429 0.00451929893639253 ** df.mm.trans1:probe7 0.212777209391793 0.0480195739620618 4.43105158658632 1.08480026183576e-05 *** df.mm.trans1:probe8 -0.130675349515286 0.0480195739620618 -2.72129339628308 0.00666090671801814 ** df.mm.trans1:probe9 0.812587186389893 0.0480195739620618 16.9219990796229 2.81383986857594e-54 *** df.mm.trans1:probe10 1.47557388795765 0.0480195739620618 30.7285918263963 7.81382288075929e-133 *** df.mm.trans1:probe11 0.328090448946796 0.0480195739620618 6.83243148316989 1.78816910959105e-11 *** df.mm.trans1:probe12 0.372167883989513 0.0480195739620618 7.75033706637104 3.15162968464973e-14 *** df.mm.trans1:probe13 0.0678360150346282 0.0480195739620618 1.41267423755618 0.158186654314314 df.mm.trans1:probe14 0.297387279539488 0.0480195739620618 6.19304285736564 9.9510441530204e-10 *** df.mm.trans1:probe15 0.449179816955102 0.0480195739620618 9.35409833727345 1.06294394378727e-19 *** df.mm.trans1:probe16 0.282009960534884 0.0480195739620618 5.87281263173404 6.56218629288397e-09 *** df.mm.trans1:probe17 -0.130575912092522 0.0480195739620618 -2.71922262774936 0.00670231579253697 ** df.mm.trans1:probe18 -0.0445885358907252 0.0480195739620618 -0.928549177174138 0.353436126066144 df.mm.trans1:probe19 -0.0779675003452442 0.0480195739620618 -1.62366080979483 0.104888974511734 df.mm.trans1:probe20 -0.105718095242006 0.0480195739620618 -2.20156254042422 0.0280149293933178 * df.mm.trans1:probe21 -0.154062334877295 0.0480195739620618 -3.20832365149705 0.00139468096335134 ** df.mm.trans1:probe22 -0.253510581456615 0.0480195739620618 -5.2793175894668 1.72183629330427e-07 *** df.mm.trans2:probe2 -0.0718999504767933 0.0480195739620618 -1.49730504759576 0.134755206036025 df.mm.trans2:probe3 -0.119137507974894 0.0480195739620618 -2.48101967895466 0.0133295959079764 * df.mm.trans2:probe4 0.0418750513626306 0.0480195739620618 0.872041292905145 0.383478581795337 df.mm.trans2:probe5 -0.052967046721901 0.0480195739620618 -1.10303033433299 0.270385104594762 df.mm.trans2:probe6 -0.0357044358353017 0.0480195739620618 -0.7435392047316 0.457399546287378 df.mm.trans3:probe2 0.209298482125782 0.0480195739620618 4.35860764385663 1.50091081641426e-05 *** df.mm.trans3:probe3 0.405681070917069 0.0480195739620618 8.4482438606719 1.64829728715919e-16 *** df.mm.trans3:probe4 0.246126456172816 0.0480195739620618 5.12554435337701 3.82219084535806e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74435617365189 0.218053596409147 17.1717239949858 1.34987174520205e-55 *** df.mm.trans1 0.426099732279793 0.193635813664933 2.20052129931458 0.0280889636620772 * df.mm.trans2 0.512306250644859 0.176136953015387 2.90856769050675 0.00374347556526099 ** df.mm.exp2 0.386064458813906 0.237458787126251 1.62581668796550 0.104429302752602 df.mm.exp3 0.108246289785481 0.237458787126251 0.455852954929518 0.648634135508757 df.mm.exp4 0.433629513359832 0.237458787126251 1.82612536098435 0.0682482677370116 . df.mm.exp5 -0.142557057797239 0.237458787126251 -0.600344419856926 0.548466950827344 df.mm.exp6 0.525540415132108 0.237458787126251 2.21318579738509 0.0271998696940906 * df.mm.exp7 0.355183299682359 0.237458787126251 1.49576818773826 0.135155312708776 df.mm.exp8 0.294018266603198 0.237458787126251 1.23818650874720 0.216053300683143 df.mm.trans1:exp2 -0.302820471235553 0.225478258814926 -1.34301405744005 0.179693572766349 df.mm.trans2:exp2 -0.383110673700245 0.189869685585103 -2.01775587566625 0.0439897692913983 * df.mm.trans1:exp3 -0.07991443746299 0.225478258814927 -0.354421920246351 0.723127211101807 df.mm.trans2:exp3 -0.193455196164887 0.189869685585103 -1.01888406023707 0.308602639360771 df.mm.trans1:exp4 -0.365263908597438 0.225478258814926 -1.61995178833294 0.105683569506452 df.mm.trans2:exp4 -0.39582070207628 0.189869685585103 -2.08469667422958 0.0374511255483563 * df.mm.trans1:exp5 0.219250602065551 0.225478258814926 0.972380233987495 0.331190257912443 df.mm.trans2:exp5 0.104985313798309 0.189869685585103 0.552933521087296 0.580481760505481 df.mm.trans1:exp6 -0.41442641463612 0.225478258814927 -1.83798835778789 0.066478823120532 . df.mm.trans2:exp6 -0.532835563314618 0.189869685585103 -2.80632246096911 0.00514737700929609 ** df.mm.trans1:exp7 -0.285002482912672 0.225478258814926 -1.26399096928721 0.206645185771854 df.mm.trans2:exp7 -0.316329962302195 0.189869685585103 -1.66603721561655 0.0961440058059806 . df.mm.trans1:exp8 -0.318096899493696 0.225478258814926 -1.41076528249578 0.158748759245559 df.mm.trans2:exp8 -0.222556810042958 0.189869685585103 -1.17215557268727 0.241525009224082 df.mm.trans1:probe2 -0.144107377391187 0.123499528579923 -1.16686580951544 0.243653584763697 df.mm.trans1:probe3 0.065637013535787 0.123499528579923 0.531475822543806 0.59525416181855 df.mm.trans1:probe4 -0.0297593117511706 0.123499528579923 -0.240967006865227 0.809649768423228 df.mm.trans1:probe5 0.0547902151534767 0.123499528579923 0.443647160304899 0.657431922115104 df.mm.trans1:probe6 -0.052556357788235 0.123499528579923 -0.425559177371458 0.670557150078612 df.mm.trans1:probe7 -0.0841006737273013 0.123499528579923 -0.680979714613851 0.496104800940958 df.mm.trans1:probe8 -0.00416208254505142 0.123499528579923 -0.0337012018823854 0.973124826223246 df.mm.trans1:probe9 0.0157342211430445 0.123499528579923 0.127403086667347 0.89865719495236 df.mm.trans1:probe10 0.170267134124263 0.123499528579923 1.37868651064586 0.168422607110215 df.mm.trans1:probe11 0.074018891113027 0.123499528579923 0.59934553568053 0.549132348257864 df.mm.trans1:probe12 0.0798968398937582 0.123499528579923 0.646940444327709 0.517878116593789 df.mm.trans1:probe13 -0.0127985052870956 0.123499528579923 -0.103632017338536 0.91749043848995 df.mm.trans1:probe14 0.00962792763876626 0.123499528579923 0.0779592258324736 0.937882298376576 df.mm.trans1:probe15 -0.0880459667295012 0.123499528579923 -0.712925528881852 0.476124441406308 df.mm.trans1:probe16 0.0331746394831705 0.123499528579923 0.268621587990122 0.788298384104163 df.mm.trans1:probe17 0.0452991680941683 0.123499528579923 0.366796283476116 0.713879502373128 df.mm.trans1:probe18 -0.00852726816186682 0.123499528579923 -0.0690469693278899 0.944971537692875 df.mm.trans1:probe19 0.00425433899193129 0.123499528579923 0.0344482204980894 0.972529346269668 df.mm.trans1:probe20 0.0226784168145019 0.123499528579923 0.183631606333019 0.854354528889896 df.mm.trans1:probe21 -0.0171260096462114 0.123499528579923 -0.138672672220997 0.88974786052372 df.mm.trans1:probe22 0.0251339777898899 0.123499528579923 0.203514767051312 0.838790610669487 df.mm.trans2:probe2 0.0108391859755478 0.123499528579923 0.0877670230824658 0.930086423089702 df.mm.trans2:probe3 0.00592034620124716 0.123499528579923 0.0479382089091602 0.96177887489842 df.mm.trans2:probe4 -0.187097108019256 0.123499528579923 -1.51496212309973 0.130223925867506 df.mm.trans2:probe5 -0.048313798859844 0.123499528579923 -0.391206342367352 0.695761269014474 df.mm.trans2:probe6 0.221263717001696 0.123499528579923 1.79161588344448 0.0736173013914465 . df.mm.trans3:probe2 -0.27367905762163 0.123499528579923 -2.21603321703789 0.0270033553568375 * df.mm.trans3:probe3 -0.195340712631408 0.123499528579923 -1.58171221281215 0.114157508239964 df.mm.trans3:probe4 -0.165452519545348 0.123499528579923 -1.33970162840156 0.180768006848044