fitVsDatCorrelation=0.943398774427743 cont.fitVsDatCorrelation=0.256970034315556 fstatistic=4734.98976747595,59,853 cont.fstatistic=544.912910424346,59,853 residuals=-1.08352964712280,-0.111247107493474,0.0061080366839985,0.141421905937597,1.17122952674453 cont.residuals=-1.43361846954828,-0.561049261940155,-0.168619770583867,0.425614849618631,2.61985163632169 predictedValues: Include Exclude Both Lung 139.866441632520 46.8401555364063 100.957896727357 cerebhem 318.345966462032 44.8712537883191 233.877105069773 cortex 423.730307517945 45.7328575924162 342.843894628429 heart 154.265878105108 45.2369196448805 106.288475547804 kidney 128.306171600697 45.3308311891248 84.6472733098893 liver 95.2109580772985 44.0693533200786 66.1010375304899 stomach 138.986039638757 43.3265098777658 96.3504298905158 testicle 136.29057058159 45.6332490477305 93.6693107479713 diffExp=93.026286096114,273.474712673713,377.997449925529,109.028958460228,82.975340411572,51.1416047572199,95.6595297609907,90.6573215338595 diffExpScore=0.999148908068692 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 110.559530904644 155.895024555242 95.648728821957 cerebhem 107.098913443586 100.706706006902 89.6752437164471 cortex 101.036391084754 114.740165387197 101.272643888286 heart 93.2274166799632 108.901267008230 122.938510206441 kidney 110.141587268362 103.967744678691 100.367458230568 liver 113.085499110456 80.6012526638556 124.551780651128 stomach 124.904919051049 95.277123336708 80.9047090327112 testicle 123.856668042356 144.785692147920 94.7032968282751 cont.diffExp=-45.3354936505981,6.39220743668403,-13.7037743024436,-15.6738503282663,6.17384258967142,32.4842464466008,29.6277957143406,-20.9290241055647 cont.diffExpScore=7.75450033243225 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=-1,0,0,0,0,1,0,0 cont.diffExp1.4Score=2 cont.diffExp1.3=-1,0,0,0,0,1,1,0 cont.diffExp1.3Score=1.5 cont.diffExp1.2=-1,0,0,0,0,1,1,0 cont.diffExp1.2Score=1.5 tran.correlation=0.197932539137213 cont.tran.correlation=0.0996393055868541 tran.covariance=0.00275973711355893 cont.tran.covariance=0.0011216123607182 tran.mean=118.502716475792 cont.tran.mean=111.799118835620 weightedLogRatios: wLogRatio Lung 4.80649123323435 cerebhem 9.37244685995356 cortex 10.9888233811404 heart 5.42879345952658 kidney 4.50944377799013 liver 3.21299418988136 stomach 5.07222726275984 testicle 4.77894421717485 cont.weightedLogRatios: wLogRatio Lung -1.67600400850538 cerebhem 0.285731196240549 cortex -0.595128709526047 heart -0.716819218651955 kidney 0.269562236269947 liver 1.54375619084339 stomach 1.27046668472300 testicle -0.76459663089035 varWeightedLogRatios=7.1928251233423 cont.varWeightedLogRatios=1.19926301330503 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.44194119482191 0.126086335041077 43.1604360063999 1.02033561476052e-216 *** df.mm.trans1 0.0535442816684039 0.108885074765918 0.491750423862166 0.623022303273089 df.mm.trans2 -1.60396053939628 0.096199471403929 -16.6732780958999 3.07438705411533e-54 *** df.mm.exp2 -0.0605849592602191 0.123743279058720 -0.489602018962739 0.624541429591634 df.mm.exp3 -0.138086288518291 0.123743279058720 -1.11590940185741 0.264775175385247 df.mm.exp4 0.0117090172361519 0.123743279058720 0.0946234601605767 0.924636137416306 df.mm.exp5 0.0571886054195385 0.123743279058720 0.462155244749903 0.644087846614042 df.mm.exp6 -0.0220500696670575 0.123743279058720 -0.178192058872095 0.858614455216218 df.mm.exp7 -0.0375789501504563 0.123743279058720 -0.303684777357677 0.76144211440304 df.mm.exp8 0.022929870266043 0.123743279058720 0.185301944804308 0.85303626328398 df.mm.trans1:exp2 0.883035716572772 0.114378486073415 7.72029554584229 3.24637826331382e-14 *** df.mm.trans2:exp2 0.0176414622303048 0.0844741103204131 0.208838686354791 0.834624029005504 df.mm.trans1:exp3 1.24649549526537 0.114378486073415 10.897989106669 5.54450841216505e-26 *** df.mm.trans2:exp3 0.114162453276293 0.084474110320413 1.35144901607453 0.176909932808409 df.mm.trans1:exp4 0.0862805987903116 0.114378486073415 0.75434289919637 0.450851541882626 df.mm.trans2:exp4 -0.0465363168134081 0.084474110320413 -0.550894429510939 0.581850298293471 df.mm.trans1:exp5 -0.143457210999232 0.114378486073415 -1.25423246909521 0.210101107313663 df.mm.trans2:exp5 -0.0899420635457806 0.0844741103204131 -1.06472933783650 0.287299725077752 df.mm.trans1:exp6 -0.362542868239492 0.114378486073415 -3.16967710174788 0.00158052227488935 ** df.mm.trans2:exp6 -0.0389261848261466 0.084474110320413 -0.460806094062410 0.645055188829356 df.mm.trans1:exp7 0.0312644650500658 0.114378486073415 0.273342182812232 0.784656359010481 df.mm.trans2:exp7 -0.0403972241335122 0.0844741103204131 -0.478220178706638 0.632616038253679 df.mm.trans1:exp8 -0.0488286942433416 0.114378486073415 -0.426904533532646 0.669556589596707 df.mm.trans2:exp8 -0.0490341329731565 0.084474110320413 -0.580463443618031 0.561755440721706 df.mm.trans1:probe2 -1.29403748595856 0.078309596144625 -16.5246349064128 1.99801361774520e-53 *** df.mm.trans1:probe3 -1.32537236028946 0.078309596144625 -16.9247758326031 1.27118862274625e-55 *** df.mm.trans1:probe4 -0.568062796123448 0.078309596144625 -7.2540636664034 9.08551787325401e-13 *** df.mm.trans1:probe5 -0.536356682945204 0.078309596144625 -6.8491820843341 1.42100909055139e-11 *** df.mm.trans1:probe6 -1.41812244374321 0.078309596144625 -18.1091783582202 2.86725560853938e-62 *** df.mm.trans1:probe7 -1.46622202363142 0.078309596144625 -18.7234016751095 8.55739078485922e-66 *** df.mm.trans1:probe8 -0.0914370911545873 0.078309596144625 -1.16763584102411 0.243279959732133 df.mm.trans1:probe9 -1.20015690163721 0.078309596144625 -15.3257960802239 5.19211483325041e-47 *** df.mm.trans1:probe10 -1.36842751975333 0.078309596144625 -17.4745827730496 1.10857698164239e-58 *** df.mm.trans1:probe11 -0.519192846929946 0.078309596144625 -6.63000286671231 5.95024095354901e-11 *** df.mm.trans1:probe12 -0.640740586467177 0.078309596144625 -8.18214648028365 1.00907771221024e-15 *** df.mm.trans1:probe13 -0.186633795504721 0.078309596144625 -2.38328129237239 0.0173770524630305 * df.mm.trans1:probe14 -0.0198858110540717 0.078309596144625 -0.253938368132379 0.799604369483585 df.mm.trans1:probe15 -0.7301954446367 0.078309596144625 -9.32446954889345 9.3291755916543e-20 *** df.mm.trans1:probe16 0.00896172682557707 0.078309596144625 0.114439701732419 0.908916174148123 df.mm.trans1:probe17 -0.982447932947798 0.078309596144625 -12.5456902003859 2.96325471778244e-33 *** df.mm.trans1:probe18 -1.05143559975580 0.078309596144625 -13.4266507748804 1.99428536960254e-37 *** df.mm.trans1:probe19 -1.06573554574523 0.078309596144625 -13.6092586121500 2.58295135997226e-38 *** df.mm.trans1:probe20 -1.29015023269555 0.078309596144625 -16.4749953545011 3.72584824898005e-53 *** df.mm.trans1:probe21 -0.828301134403501 0.078309596144625 -10.5772622409362 1.18195513299825e-24 *** df.mm.trans1:probe22 -1.17956741326111 0.078309596144625 -15.0628718743823 1.22070047149960e-45 *** df.mm.trans2:probe2 -0.00568594305643589 0.078309596144625 -0.0726085095105699 0.942134676312054 df.mm.trans2:probe3 0.00236888043332554 0.078309596144625 0.0302501934622496 0.97587459433269 df.mm.trans2:probe4 0.0206016546742180 0.078309596144625 0.263079567364518 0.792552783232328 df.mm.trans2:probe5 0.0130874411225860 0.078309596144625 0.167124359809181 0.867311809177918 df.mm.trans2:probe6 0.109791229030267 0.078309596144625 1.40201500755413 0.161274551310538 df.mm.trans3:probe2 0.689765702158053 0.078309596144625 8.8081887293119 7.01265320154996e-18 *** df.mm.trans3:probe3 0.30525918565427 0.078309596144625 3.89810700965060 0.000104552340278958 *** df.mm.trans3:probe4 1.40426337330547 0.078309596144625 17.9322004255011 2.90864555840457e-61 *** df.mm.trans3:probe5 1.14771589492123 0.078309596144625 14.6561334935451 1.51702338916810e-43 *** df.mm.trans3:probe6 0.0273875680249376 0.078309596144625 0.349734507305558 0.726624282419902 df.mm.trans3:probe7 1.03856547736774 0.078309596144625 13.2623015377283 1.23630254577760e-36 *** df.mm.trans3:probe8 0.286551092107736 0.078309596144625 3.65920789041643 0.000268535648011642 *** df.mm.trans3:probe9 0.81824084129745 0.078309596144625 10.4487940377868 3.94890849038521e-24 *** df.mm.trans3:probe10 1.51403715265978 0.078309596144625 19.3339926037110 2.40164603681236e-69 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.076737401224 0.367400521229952 13.8179918314447 2.44447177446074e-39 *** df.mm.trans1 -0.356282439646102 0.317278103214974 -1.12293422091187 0.261781389608936 df.mm.trans2 -0.0856269987049986 0.280313770118981 -0.305468399460553 0.7600839605333 df.mm.exp2 -0.404284090039526 0.360573135939481 -1.12122631927682 0.262507085457701 df.mm.exp3 -0.453719996446374 0.360573135939481 -1.25833000637775 0.208616744355957 df.mm.exp4 -0.780255372479737 0.360573135939481 -2.16393096076546 0.0307465877030667 * df.mm.exp5 -0.457045207112899 0.360573135939481 -1.26755202081835 0.205303884551562 df.mm.exp6 -0.901117757667268 0.360573135939481 -2.49912616290558 0.0126369839312170 * df.mm.exp7 -0.202984069675959 0.360573135939481 -0.562948399211936 0.573617951578715 df.mm.exp8 0.049576272687548 0.360573135939481 0.137492973674747 0.890673593257434 df.mm.trans1:exp2 0.372482805083108 0.333285247661256 1.11760963828105 0.264048422009772 df.mm.trans2:exp2 -0.0326863797614957 0.246147468336241 -0.132791858402726 0.894389311843603 df.mm.trans1:exp3 0.363646639054856 0.333285247661256 1.09109731560774 0.275538162933375 df.mm.trans2:exp3 0.147207275807538 0.246147468336241 0.598045053246092 0.549968618280399 df.mm.trans1:exp4 0.609743104190172 0.333285247661256 1.82949322980506 0.0676746708909695 . df.mm.trans2:exp4 0.421514175733729 0.246147468336241 1.71244570818797 0.08717799381788 . df.mm.trans1:exp5 0.453257785106673 0.333285247661256 1.35996954046809 0.174198886603072 df.mm.trans2:exp5 0.0519430496084645 0.246147468336241 0.211024106644515 0.832918862646428 df.mm.trans1:exp6 0.923707802468685 0.333285247661256 2.77152321907605 0.00570093602421443 ** df.mm.trans2:exp6 0.241449087574362 0.246147468336241 0.980912333595645 0.326914099603814 df.mm.trans1:exp7 0.324982752840128 0.333285247661256 0.975088921938822 0.329792592228322 df.mm.trans2:exp7 -0.289409058632901 0.246147468336241 -1.17575476436574 0.240020938465239 df.mm.trans1:exp8 0.0639946043549122 0.333285247661256 0.19201151207255 0.847778887297843 df.mm.trans2:exp8 -0.123504469971119 0.246147468336241 -0.501749909539636 0.615972928398432 df.mm.trans1:probe2 0.201481409712198 0.228184810284708 0.882974679431152 0.37749872277088 df.mm.trans1:probe3 -0.155641017872463 0.228184810284708 -0.682083166176876 0.495371528405246 df.mm.trans1:probe4 0.044250012430934 0.228184810284708 0.193921814408781 0.846283272359682 df.mm.trans1:probe5 0.231675932964692 0.228184810284708 1.01529954020878 0.310250954667067 df.mm.trans1:probe6 -0.124626329331626 0.228184810284708 -0.54616400266139 0.585096022149876 df.mm.trans1:probe7 -0.0104962766856128 0.228184810284708 -0.0459990157649692 0.963321800879064 df.mm.trans1:probe8 0.047042663947987 0.228184810284708 0.206160365754809 0.836714841018458 df.mm.trans1:probe9 -0.355979212191486 0.228184810284707 -1.56004780400294 0.119119498416278 df.mm.trans1:probe10 0.00794789189180165 0.228184810284708 0.0348309419977825 0.97222269628498 df.mm.trans1:probe11 0.101270114405601 0.228184810284707 0.443807430824365 0.657294369683606 df.mm.trans1:probe12 0.0121639016154576 0.228184810284707 0.0533072363593378 0.957499596174296 df.mm.trans1:probe13 0.244571665116642 0.228184810284707 1.07181395997169 0.284106749986086 df.mm.trans1:probe14 -0.171144443602555 0.228184810284707 -0.750025575273908 0.453446116703068 df.mm.trans1:probe15 -0.197152627242610 0.228184810284707 -0.864004168360821 0.387828485938593 df.mm.trans1:probe16 -0.006303675120166 0.228184810284708 -0.0276253056121521 0.977967460003358 df.mm.trans1:probe17 -0.184172329927054 0.228184810284707 -0.80711914915485 0.419822831661928 df.mm.trans1:probe18 0.0262345131525019 0.228184810284708 0.114970462406192 0.908495591724369 df.mm.trans1:probe19 -0.0775770619276249 0.228184810284708 -0.339974697837387 0.733959279659386 df.mm.trans1:probe20 -0.0403534337056028 0.228184810284708 -0.176845398496305 0.859671808192571 df.mm.trans1:probe21 -0.0355022452154521 0.228184810284708 -0.155585488671028 0.87639657406156 df.mm.trans1:probe22 -0.0345164754726563 0.228184810284708 -0.151265438876452 0.879802096671564 df.mm.trans2:probe2 0.169456966380129 0.228184810284707 0.742630353741321 0.457909961224093 df.mm.trans2:probe3 0.183494947601565 0.228184810284708 0.804150580280156 0.421534065651884 df.mm.trans2:probe4 0.314394313453028 0.228184810284708 1.37780561756392 0.168624656576777 df.mm.trans2:probe5 0.206781711560647 0.228184810284707 0.906202789320835 0.365084429337464 df.mm.trans2:probe6 0.0550314002190828 0.228184810284708 0.241170304677248 0.809481082817447 df.mm.trans3:probe2 -0.120663884423057 0.228184810284707 -0.528798933954035 0.597082501084433 df.mm.trans3:probe3 0.0764346480440233 0.228184810284708 0.334968168777998 0.737731452083298 df.mm.trans3:probe4 -0.136511201914119 0.228184810284708 -0.598248418655883 0.549832998119334 df.mm.trans3:probe5 -0.386741667030951 0.228184810284708 -1.69486157535382 0.0904666665969238 . df.mm.trans3:probe6 0.131613295611931 0.228184810284707 0.576783772099974 0.564237708744382 df.mm.trans3:probe7 0.187005325047434 0.228184810284708 0.819534502818599 0.41271044669349 df.mm.trans3:probe8 -0.239414284287820 0.228184810284708 -1.04921218896692 0.294377644580898 df.mm.trans3:probe9 -0.247907017076580 0.228184810284707 -1.08643084860585 0.277595333380207 df.mm.trans3:probe10 -0.00527078362703457 0.228184810284708 -0.0230987488626354 0.981576905894871