fitVsDatCorrelation=0.935023316666302 cont.fitVsDatCorrelation=0.248781741159975 fstatistic=4343.94895332096,56,784 cont.fstatistic=570.081170200928,56,784 residuals=-1.48093650717427,-0.151124461291322,0.0206688165693018,0.171631288804003,1.9386244467321 cont.residuals=-1.78294489968476,-0.632770135326174,-0.0900041154456215,0.541280949515437,2.62791748027549 predictedValues: Include Exclude Both Lung 325.760513549937 107.011839446103 361.931290498744 cerebhem 491.942648486184 58.6465256108882 513.153206422261 cortex 718.799389746967 66.4003900277567 609.433572665934 heart 299.650390673314 191.129263018210 332.328127449788 kidney 203.713874548972 117.603746787306 236.238098666249 liver 171.81627695725 76.033980896271 147.015358841143 stomach 268.86252604709 100.400733571251 229.166855832931 testicle 237.261882610056 76.3677174817781 222.28802148043 diffExp=218.748674103834,433.296122875295,652.39899971921,108.521127655103,86.1101277616658,95.7822960609791,168.461792475839,160.894165128278 diffExpScore=0.999480577036842 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 264.190373440255 263.651366238521 181.864318956935 cerebhem 274.193174614425 370.912117490186 278.402390124290 cortex 296.520774654543 258.677337612426 220.386060100469 heart 251.034140646050 266.621607663652 306.696912062684 kidney 292.391458732186 294.949146734854 259.466410188418 liver 269.028310553645 265.299214320538 242.533597018734 stomach 307.445292647477 256.326296468972 205.836230718878 testicle 279.833925366994 219.606631466487 235.842521783836 cont.diffExp=0.539007201733284,-96.7189428757605,37.8434370421174,-15.5874670176021,-2.55768800266867,3.72909623310738,51.118996178505,60.2272939005068 cont.diffExpScore=6.77687882465025 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,-1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,0,0,0,1 cont.diffExp1.2Score=2 tran.correlation=-0.347371036615425 cont.tran.correlation=-0.09717712700974 tran.covariance=-0.0645650828324293 cont.tran.covariance=-0.000905864848542247 tran.mean=219.462606216208 cont.tran.mean=276.917573040701 weightedLogRatios: wLogRatio Lung 5.82165650300619 cerebhem 10.9211751319120 cortex 12.8303329555910 heart 2.46317538994049 kidney 2.7700625576904 liver 3.86328734921891 stomach 5.02531609872283 testicle 5.55733975836783 cont.weightedLogRatios: wLogRatio Lung 0.0113871855026523 cerebhem -1.74176208121843 cortex 0.767856763106133 heart -0.334684314110254 kidney -0.0494909475629275 liver 0.0779968127073443 stomach 1.02513356943597 testicle 1.33612598495989 varWeightedLogRatios=14.1943278693772 cont.varWeightedLogRatios=0.918705003239826 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.7943055081465 0.153156558971826 37.8325652328895 5.14399104572527e-179 *** df.mm.trans1 0.0361887317013511 0.131626091635373 0.274935852396197 0.78343795793048 df.mm.trans2 -1.17877610117962 0.117254925657814 -10.0531051857016 1.90235295356293e-22 *** df.mm.exp2 -0.538331552226397 0.151297543312434 -3.55809843597214 0.000396007506642914 *** df.mm.exp3 -0.206892299281449 0.151297543312434 -1.36745313077695 0.171875204114234 df.mm.exp4 0.58179600381306 0.151297543312434 3.84537640913068 0.000130131731747890 *** df.mm.exp5 0.0515495606786666 0.151297543312434 0.340716442250587 0.733408338505908 df.mm.exp6 -0.0805785363156434 0.151297543312434 -0.532583243266855 0.594473013232155 df.mm.exp7 0.201271761574452 0.151297543312434 1.33030422813159 0.183804883415576 df.mm.exp8 -0.166896932415511 0.151297543312434 -1.10310404757111 0.270320464992523 df.mm.trans1:exp2 0.950531203418596 0.138266820511262 6.87461532639477 1.26912933503373e-11 *** df.mm.trans2:exp2 -0.0630795911696423 0.104605810443036 -0.603021867547147 0.546668538582948 df.mm.trans1:exp3 0.998312114813849 0.138266820511262 7.2201856607568 1.22922293292572e-12 *** df.mm.trans2:exp3 -0.270344247711174 0.104605810443036 -2.58440947559402 0.00993427400030462 ** df.mm.trans1:exp4 -0.665342063532281 0.138266820511262 -4.81201535604913 1.79241179338282e-06 *** df.mm.trans2:exp4 -0.00178551231269838 0.104605810443036 -0.0170689592206802 0.986385945008702 df.mm.trans1:exp5 -0.520995617492836 0.138266820511262 -3.76804511426804 0.000176865220856737 *** df.mm.trans2:exp5 0.0428318573448444 0.104605810443036 0.40945963865142 0.682314222617978 df.mm.trans1:exp6 -0.559158205370694 0.138266820511262 -4.04405195189362 5.77265032805108e-05 *** df.mm.trans2:exp6 -0.261180583638632 0.104605810443036 -2.49680761070974 0.0127358477444240 * df.mm.trans1:exp7 -0.39323405858106 0.138266820511262 -2.84402329587836 0.00457074890467501 ** df.mm.trans2:exp7 -0.265041725226713 0.104605810443036 -2.53371895981862 0.0114796251932224 * df.mm.trans1:exp8 -0.150101036973510 0.138266820511262 -1.08558970560319 0.277994239160045 df.mm.trans2:exp8 -0.170482484129534 0.104605810443036 -1.62976113284235 0.103553670387975 df.mm.trans1:probe2 -0.28301888894132 0.0946648206856702 -2.98969444923019 0.00287988959688759 ** df.mm.trans1:probe3 -0.130960185220926 0.0946648206856702 -1.38340921445119 0.166933212921034 df.mm.trans1:probe4 -0.0131175220776848 0.0946648206856703 -0.138568076109718 0.889827054853272 df.mm.trans1:probe5 -0.38856988828917 0.0946648206856703 -4.10469153667334 4.4728212892931e-05 *** df.mm.trans1:probe6 -0.167838484366725 0.0946648206856703 -1.77297630895034 0.0766208429752436 . df.mm.trans1:probe7 -0.442442943101042 0.0946648206856703 -4.67378419878015 3.48014730789521e-06 *** df.mm.trans1:probe8 -0.663227160180222 0.0946648206856703 -7.00605732284049 5.28220204593514e-12 *** df.mm.trans1:probe9 -0.330269769717376 0.0946648206856702 -3.488833204618 0.000512192142727567 *** df.mm.trans1:probe10 0.191330642309433 0.0946648206856703 2.0211377460349 0.0436048947577417 * df.mm.trans1:probe11 -0.287151567898805 0.0946648206856702 -3.03335035992174 0.00249833433806196 ** df.mm.trans1:probe12 -0.0610176133922589 0.0946648206856702 -0.644564823028237 0.519397781420043 df.mm.trans1:probe13 0.119082076545496 0.0946648206856703 1.25793378873977 0.208790230105261 df.mm.trans1:probe14 0.302004065007933 0.0946648206856703 3.19024599445154 0.00147808728352548 ** df.mm.trans1:probe15 -0.0556954059052233 0.0946648206856703 -0.588343225094749 0.556471382783624 df.mm.trans1:probe16 0.334850719183787 0.0946648206856702 3.53722445950267 0.000428125506195566 *** df.mm.trans1:probe17 0.290657901323315 0.0946648206856703 3.07038981554119 0.00221156465473865 ** df.mm.trans1:probe18 0.132141085992023 0.0946648206856702 1.39588376162239 0.163144658124786 df.mm.trans1:probe19 0.167622204393204 0.0946648206856703 1.77069161679168 0.0770005178417208 . df.mm.trans2:probe2 0.16825242840164 0.0946648206856702 1.77734904247391 0.0758984377893805 . df.mm.trans2:probe3 0.228659554475927 0.0946648206856702 2.41546493005231 0.0159428680463634 * df.mm.trans2:probe4 0.225642369002564 0.0946648206856702 2.38359263101336 0.0173816808820076 * df.mm.trans2:probe5 0.137520162324615 0.0946648206856702 1.45270609851197 0.146705475443146 df.mm.trans2:probe6 0.158486612241262 0.0946648206856702 1.67418700097165 0.0944926984726813 . df.mm.trans3:probe2 2.43768533325181 0.0946648206856703 25.7506993157049 1.90587713590090e-106 *** df.mm.trans3:probe3 -0.288277688512555 0.0946648206856703 -3.04524623217496 0.00240271665131498 ** df.mm.trans3:probe4 2.22634390831952 0.0946648206856703 23.5181759411131 5.76247985956737e-93 *** df.mm.trans3:probe5 0.193768252017807 0.0946648206856703 2.04688764648068 0.0410020363857164 * df.mm.trans3:probe6 1.56259217666569 0.0946648206856703 16.5065772622566 9.28311229427153e-53 *** df.mm.trans3:probe7 1.73315250686171 0.0946648206856702 18.3083060244370 1.30660666254527e-62 *** df.mm.trans3:probe8 2.019276875935 0.0946648206856703 21.3308054809495 5.73748812807056e-80 *** df.mm.trans3:probe9 0.133547418649913 0.0946648206856702 1.41073967797764 0.158718042976315 df.mm.trans3:probe10 2.37927371571264 0.0946648206856703 25.1336631546887 1.04770970944843e-102 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 6.52923603073628 0.418350282873349 15.6071031813141 5.07407309487456e-48 *** df.mm.trans1 -0.795567245802393 0.359539369641369 -2.2127402809766 0.0272029513714541 * df.mm.trans2 -0.888487102426457 0.320284234945913 -2.7740581817163 0.0056678037514112 ** df.mm.exp2 -0.047306178300831 0.413272343461588 -0.114467321729280 0.908896657420034 df.mm.exp3 -0.0957187544521004 0.413272343461588 -0.231611807483549 0.816899972115155 df.mm.exp4 -0.562477294898388 0.413272343461588 -1.36103299385353 0.173894337480115 df.mm.exp5 -0.141767473592472 0.413272343461588 -0.343036440341063 0.731663004395592 df.mm.exp6 -0.263502021715407 0.413272343461588 -0.637598972891102 0.523920924130027 df.mm.exp7 -0.000369160267494147 0.413272343461588 -0.000893261485639335 0.99928750777957 df.mm.exp8 -0.385166785039949 0.413272343461588 -0.931992646335283 0.351627236797841 df.mm.trans1:exp2 0.0844690983357955 0.377678656801931 0.223653354020728 0.823085309000454 df.mm.trans2:exp2 0.388643684788323 0.285732917237258 1.36016420000225 0.174168931372175 df.mm.trans1:exp3 0.211166081937414 0.377678656801931 0.559115740681523 0.576242404653445 df.mm.trans2:exp3 0.0766725907935222 0.285732917237258 0.268336569460974 0.788510847802757 df.mm.trans1:exp4 0.511396288504202 0.377678656801931 1.35405133251254 0.176110163823428 df.mm.trans2:exp4 0.573680100380584 0.285732917237258 2.00774942532866 0.0450125922588647 * df.mm.trans1:exp5 0.243191035352764 0.377678656801931 0.643909924410429 0.519822165795202 df.mm.trans2:exp5 0.253942783143761 0.285732917237257 0.888741785857668 0.374414462174011 df.mm.trans1:exp6 0.281648684686916 0.377678656801931 0.745736301521065 0.456050248631729 df.mm.trans2:exp6 0.269732673400106 0.285732917237257 0.944002798166003 0.345459035120527 df.mm.trans1:exp7 0.151996366810237 0.377678656801931 0.402448918075743 0.687463377897844 df.mm.trans2:exp7 -0.0278072592574584 0.285732917237257 -0.0973190611929698 0.922497892274457 df.mm.trans1:exp8 0.442693133766003 0.377678656801931 1.17214231144168 0.241495981266227 df.mm.trans2:exp8 0.202377044175135 0.285732917237258 0.70827346786612 0.478985971454207 df.mm.trans1:probe2 -0.455764038593485 0.258578899773337 -1.7625724256426 0.0783622303732024 . df.mm.trans1:probe3 -0.222925589620748 0.258578899773337 -0.8621182541041 0.388886063398339 df.mm.trans1:probe4 -0.0466278156532275 0.258578899773337 -0.180323358534282 0.856945318269426 df.mm.trans1:probe5 -0.353264603903765 0.258578899773337 -1.36617722564922 0.172275070397615 df.mm.trans1:probe6 -0.0557010120862044 0.258578899773337 -0.215412054637986 0.82950209775279 df.mm.trans1:probe7 -0.0813365206917732 0.258578899773337 -0.31455204103301 0.75318548722876 df.mm.trans1:probe8 -0.312160931009806 0.258578899773337 -1.20721733785486 0.227712543553831 df.mm.trans1:probe9 -0.0888448871455082 0.258578899773337 -0.343589083345111 0.73124745585584 df.mm.trans1:probe10 -0.537865471861446 0.258578899773337 -2.08008260663544 0.0378422534138817 * df.mm.trans1:probe11 -0.485014040919413 0.258578899773337 -1.87569071314234 0.0610691603674841 . df.mm.trans1:probe12 -0.350083630590614 0.258578899773337 -1.35387547436193 0.176166248421464 df.mm.trans1:probe13 -0.306037752055104 0.258578899773337 -1.18353721948453 0.236955040395966 df.mm.trans1:probe14 -0.166713998553444 0.258578899773337 -0.644731641675251 0.519289709010465 df.mm.trans1:probe15 -0.312911244949240 0.258578899773337 -1.21011902062979 0.226597949498835 df.mm.trans1:probe16 -0.285389225397672 0.258578899773337 -1.10368334635129 0.270069160420506 df.mm.trans1:probe17 -0.126199541553561 0.258578899773337 -0.488050423542616 0.625650586872262 df.mm.trans1:probe18 0.000363787412183756 0.258578899773337 0.00140687199343273 0.998877836813402 df.mm.trans1:probe19 -0.366489557102918 0.258578899773337 -1.41732197570711 0.156786086900067 df.mm.trans2:probe2 -0.182385395390974 0.258578899773337 -0.7053375025992 0.480809730753179 df.mm.trans2:probe3 0.105210774668535 0.258578899773337 0.406880742244477 0.684206634708941 df.mm.trans2:probe4 -0.199168695046689 0.258578899773337 -0.770243415921697 0.441387543081371 df.mm.trans2:probe5 -0.231149953983523 0.258578899773337 -0.89392426909598 0.371636739796682 df.mm.trans2:probe6 -0.550447215539492 0.258578899773337 -2.12873987793280 0.0335869435528501 * df.mm.trans3:probe2 0.257847781806868 0.258578899773337 0.997172553649544 0.318988425041084 df.mm.trans3:probe3 0.240114929367265 0.258578899773337 0.928594442847978 0.353385102839451 df.mm.trans3:probe4 0.423287688232447 0.258578899773337 1.63697690957572 0.102036598637967 df.mm.trans3:probe5 0.450203417186943 0.258578899773337 1.74106788133749 0.082063955178128 . df.mm.trans3:probe6 0.389806100950951 0.258578899773337 1.50749384923768 0.13208694834535 df.mm.trans3:probe7 0.701108862169479 0.258578899773337 2.71139239429068 0.00684677357608787 ** df.mm.trans3:probe8 0.0506868228120978 0.258578899773337 0.196020722713758 0.844644741820283 df.mm.trans3:probe9 0.370932921063320 0.258578899773337 1.43450575970610 0.151826738238296 df.mm.trans3:probe10 0.696803739503176 0.258578899773337 2.69474322968338 0.00719507541054736 **