fitVsDatCorrelation=0.948203785337702 cont.fitVsDatCorrelation=0.24583994424406 fstatistic=7137.8631121983,70,1106 cont.fstatistic=752.507568913023,70,1106 residuals=-0.999750445595687,-0.122303768207685,0.00336800732242349,0.116921259946694,0.971592730047673 cont.residuals=-1.24274144763844,-0.515977633291772,-0.182513464730172,0.390302277613259,2.17648285098289 predictedValues: Include Exclude Both Lung 139.912887060668 82.0939151037207 99.7294852646656 cerebhem 175.518266863366 80.0073780026172 128.735617761851 cortex 214.136215894988 74.549691297039 150.550689732647 heart 143.966694976269 75.6831052959374 103.105137170639 kidney 138.939051807944 74.227432918532 96.182525937081 liver 126.490050541935 73.0373391349415 88.8284241539931 stomach 134.995203624453 86.1426471280874 88.1838978984925 testicle 155.404301474251 75.3078487217343 107.072848519931 diffExp=57.8189719569477,95.5108888607489,139.586524597949,68.2835896803314,64.7116188894119,53.4527114069939,48.8525564963653,80.0964527525163 diffExpScore=0.998358808225635 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 111.415115790601 134.193514934234 100.519516122313 cerebhem 89.3652954629543 109.932440408749 112.273087424877 cortex 114.62127031934 98.378368299115 110.496585843515 heart 117.910305469207 141.647813731966 100.129526895816 kidney 110.720995497112 100.269180564351 112.869359276483 liver 114.815031512257 110.500417986088 148.36992703205 stomach 95.4782901695277 113.665152414998 113.771553151011 testicle 95.6289451251475 97.6907056157606 110.707518996229 cont.diffExp=-22.7783991436331,-20.5671449457944,16.242902020225,-23.7375082627587,10.4518149327610,4.31461352616981,-18.1868622454706,-2.06176049061317 cont.diffExpScore=2.06448299305276 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=-1,-1,0,-1,0,0,0,0 cont.diffExp1.2Score=0.75 tran.correlation=-0.178299223575820 cont.tran.correlation=0.356637579270083 tran.covariance=-0.00151545282294053 cont.tran.covariance=0.00482043017531704 tran.mean=115.650751865405 cont.tran.mean=109.764552706338 weightedLogRatios: wLogRatio Lung 2.49220721703037 cerebhem 3.75130165676136 cortex 5.10589487443858 heart 2.98883367453491 kidney 2.89665169489731 liver 2.50737686872803 stomach 2.10269750467951 testicle 3.39316427445802 cont.weightedLogRatios: wLogRatio Lung -0.89406237511926 cerebhem -0.952047937405683 cortex 0.712904776696492 heart -0.891718864722694 kidney 0.461808561342832 liver 0.180950427527327 stomach -0.810080721989148 testicle -0.097506437606892 varWeightedLogRatios=0.896956269982616 cont.varWeightedLogRatios=0.466502313059671 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08784656650367 0.103269851777949 39.5841234990186 3.72006149945632e-214 *** df.mm.trans1 1.04066715887051 0.0880168828166444 11.8234948292637 1.84719552534086e-30 *** df.mm.trans2 0.325095766950977 0.076608863226244 4.24357904895276 2.38408460731085e-05 *** df.mm.exp2 -0.0543213675574285 0.0959094914144956 -0.566381561994379 0.571249332369969 df.mm.exp3 -0.082644119210574 0.0959094914144957 -0.861688639901214 0.389045657874712 df.mm.exp4 -0.0860347916779533 0.0959094914144956 -0.897041475344014 0.369892000861148 df.mm.exp5 -0.0715010582008559 0.0959094914144956 -0.745505550559612 0.456124558569413 df.mm.exp6 -0.101994750585822 0.0959094914144956 -1.06344793493928 0.287810993873519 df.mm.exp7 0.135396957356698 0.0959094914144956 1.41171593509497 0.158314884377747 df.mm.exp8 -0.0523174690543939 0.0959094914144956 -0.545487920776178 0.585527977560581 df.mm.trans1:exp2 0.281044496078809 0.0871141216927224 3.22616460589635 0.00129139079098342 ** df.mm.trans2:exp2 0.0285763249671039 0.0574123326883149 0.497738441011299 0.61876735481268 df.mm.trans1:exp3 0.508236460951794 0.0871141216927224 5.83414549875732 7.0938874296416e-09 *** df.mm.trans2:exp3 -0.0137538785164956 0.0574123326883149 -0.239563136916312 0.810713333261902 df.mm.trans1:exp4 0.114596785956807 0.0871141216927224 1.31547886530986 0.188621647395454 df.mm.trans2:exp4 0.00472584945839832 0.0574123326883149 0.0823141864667723 0.93441176754497 df.mm.trans1:exp5 0.0645164253112566 0.0871141216927225 0.740596634134995 0.459095242682516 df.mm.trans2:exp5 -0.0292290421151588 0.0574123326883149 -0.509107377222242 0.610778511568202 df.mm.trans1:exp6 0.00113841015812206 0.0871141216927225 0.0130680323235947 0.989575872333416 df.mm.trans2:exp6 -0.0148983420800203 0.0574123326883149 -0.25949724357834 0.795299901697805 df.mm.trans1:exp7 -0.171177701916834 0.0871141216927224 -1.96498223928181 0.0496662921768558 * df.mm.trans2:exp7 -0.0872562457146534 0.0574123326883149 -1.51981711295999 0.128842754435928 df.mm.trans1:exp8 0.157327592959690 0.0871141216927224 1.80599413622778 0.0711910271312176 . df.mm.trans2:exp8 -0.0339620669237069 0.0574123326883149 -0.591546542936743 0.554275164503176 df.mm.trans1:probe2 -0.0185532102180028 0.0665345094594159 -0.278850935683530 0.780411352350823 df.mm.trans1:probe3 -1.06920942503834 0.0665345094594159 -16.0699978661529 2.19212714432411e-52 *** df.mm.trans1:probe4 -0.351738007362028 0.0665345094594159 -5.28654994558243 1.50115354688850e-07 *** df.mm.trans1:probe5 -0.793190454290223 0.0665345094594159 -11.921489475684 6.53467213339962e-31 *** df.mm.trans1:probe6 -1.01599038956875 0.0665345094594159 -15.2701267030227 6.50015240509268e-48 *** df.mm.trans1:probe7 -0.466971273915968 0.0665345094594159 -7.01848225394684 3.90432225162963e-12 *** df.mm.trans1:probe8 -0.099071963579087 0.0665345094594159 -1.48903124685270 0.136764220490756 df.mm.trans1:probe9 -1.01431362920340 0.0665345094594159 -15.2449253394150 8.94260729749665e-48 *** df.mm.trans1:probe10 -1.08475870672119 0.0665345094594159 -16.3037003734560 1.01769858145648e-53 *** df.mm.trans1:probe11 -1.23439428530003 0.0665345094594159 -18.5526923596390 4.14553762500169e-67 *** df.mm.trans1:probe12 -1.07842110595580 0.0665345094594159 -16.2084475367418 3.5681360794126e-53 *** df.mm.trans1:probe13 -1.05938890162023 0.0665345094594159 -15.9223974179359 1.50251771605198e-51 *** df.mm.trans1:probe14 -1.11192961494861 0.0665345094594159 -16.7120735387229 4.46843124194288e-56 *** df.mm.trans1:probe15 -1.0835602908253 0.0665345094594158 -16.2856884288933 1.29059913385818e-53 *** df.mm.trans1:probe16 -1.23497733593879 0.0665345094594159 -18.5614554908846 3.66074968261715e-67 *** df.mm.trans1:probe17 0.686832035415903 0.0665345094594158 10.3229443035851 6.48785200211684e-24 *** df.mm.trans1:probe18 1.00815930450175 0.0665345094594159 15.1524271042638 2.87536289953672e-47 *** df.mm.trans1:probe19 0.918878065696464 0.0665345094594159 13.8105484381297 3.84133943273037e-40 *** df.mm.trans1:probe20 1.24049587057061 0.0665345094594159 18.6443979319826 1.12643730825313e-67 *** df.mm.trans1:probe21 0.707876547635481 0.0665345094594159 10.6392389962274 3.09581896594305e-25 *** df.mm.trans1:probe22 0.0919963069406255 0.0665345094594159 1.38268558208490 0.167040401145950 df.mm.trans2:probe2 -0.155224645767559 0.0665345094594159 -2.33299451711209 0.019827015221669 * df.mm.trans2:probe3 -0.0242282333795537 0.0665345094594159 -0.364145367214771 0.715819063905854 df.mm.trans2:probe4 0.0406616938932757 0.0665345094594159 0.611136900589582 0.541234531959594 df.mm.trans2:probe5 0.00384586540894478 0.0665345094594159 0.0578025665206212 0.953916334903222 df.mm.trans2:probe6 -0.00217243623044751 0.0665345094594159 -0.0326512699664921 0.97395857532869 df.mm.trans3:probe2 -0.205107122542995 0.0665345094594159 -3.08271788894911 0.00210197977569209 ** df.mm.trans3:probe3 -1.63020636557867 0.0665345094594159 -24.5016665610655 2.96195621410476e-106 *** df.mm.trans3:probe4 -0.406781067511476 0.0665345094594159 -6.11383582469486 1.34659030481925e-09 *** df.mm.trans3:probe5 -0.545269487977839 0.0665345094594159 -8.19528831591428 6.85981908573996e-16 *** df.mm.trans3:probe6 -1.14621549731538 0.0665345094594159 -17.2273833027136 4.23329788346665e-59 *** df.mm.trans3:probe7 -0.3443775919303 0.0665345094594159 -5.17592441468829 2.69200357591896e-07 *** df.mm.trans3:probe8 -1.32090433103997 0.0665345094594159 -19.8529205636616 2.91786063475476e-75 *** df.mm.trans3:probe9 -0.415504691905998 0.0665345094594159 -6.24495010607156 6.03437711771449e-10 *** df.mm.trans3:probe10 -0.73419738755438 0.0665345094594159 -11.0348358095616 6.2174202627924e-27 *** df.mm.trans3:probe11 -1.37983441952900 0.0665345094594159 -20.7386276796804 5.80037054427201e-81 *** df.mm.trans3:probe12 -0.771857687605545 0.0665345094594158 -11.6008623776862 1.91075784448501e-29 *** df.mm.trans3:probe13 -1.4193496220119 0.0665345094594159 -21.3325330500507 7.57923205741785e-85 *** df.mm.trans3:probe14 -1.40835216115048 0.0665345094594159 -21.1672434739981 9.22950077322713e-84 *** df.mm.trans3:probe15 -0.285539384063501 0.0665345094594159 -4.2915982455341 1.92924163542683e-05 *** df.mm.trans3:probe16 -0.630027639845834 0.0665345094594159 -9.46918591516982 1.64503437040074e-20 *** df.mm.trans3:probe17 -1.37625432272483 0.0665345094594159 -20.6848195606568 1.29719011800582e-80 *** df.mm.trans3:probe18 -1.43321384367713 0.0665345094594159 -21.5409094516786 3.20738217777287e-86 *** df.mm.trans3:probe19 -0.882630464663311 0.0665345094594159 -13.2657544458442 2.22306645811916e-37 *** df.mm.trans3:probe20 -0.627544934884165 0.0665345094594159 -9.4318713699535 2.28787629691684e-20 *** df.mm.trans3:probe21 -0.910942479971373 0.0665345094594159 -13.6912782159613 1.57014578036064e-39 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.1541456804421 0.315115843911820 16.3563520528165 5.07733449726251e-54 *** df.mm.trans1 -0.473737857530764 0.268573197595865 -1.76390593615235 0.0780238610693482 . df.mm.trans2 -0.239379672151730 0.233762963450073 -1.02402736780353 0.306046268646192 df.mm.exp2 -0.530529780374917 0.292656567293395 -1.81280668081864 0.0701326592856675 . df.mm.exp3 -0.376724293840189 0.292656567293396 -1.28725726992661 0.198273948508177 df.mm.exp4 0.114609379160618 0.292656567293395 0.391617315205229 0.695416430967988 df.mm.exp5 -0.413553187313263 0.292656567293395 -1.41310065630157 0.157907488402385 df.mm.exp6 -0.553560954878216 0.292656567293395 -1.89150361462163 0.0588182309490413 . df.mm.exp7 -0.444230766067476 0.292656567293395 -1.51792515772292 0.129319042867922 df.mm.exp8 -0.566803797707164 0.292656567293395 -1.93675406962226 0.0530298577433895 . df.mm.trans1:exp2 0.30999918564954 0.265818527878442 1.16620608850600 0.243782543150809 df.mm.trans2:exp2 0.331112879898441 0.175187001380858 1.89005392688125 0.0590120322293975 . df.mm.trans1:exp3 0.405094678167784 0.265818527878442 1.52395200365052 0.127806572185547 df.mm.trans2:exp3 0.0662623398907195 0.175187001380858 0.378237765179076 0.70532655686583 df.mm.trans1:exp4 -0.0579481744887318 0.265818527878442 -0.217999004626312 0.827470100553644 df.mm.trans2:exp4 -0.0605484867812156 0.175187001380858 -0.345622028483624 0.729692515122956 df.mm.trans1:exp5 0.407303662653572 0.265818527878442 1.53226212598631 0.125743736794224 df.mm.trans2:exp5 0.122128663042428 0.175187001380858 0.697133132480069 0.485865985590467 df.mm.trans1:exp6 0.583620359098773 0.265818527878442 2.19555936810267 0.0283306471310859 * df.mm.trans2:exp6 0.35929735902642 0.175187001380858 2.0509361778806 0.0405083696782092 * df.mm.trans1:exp7 0.289866652032054 0.265818527878442 1.09046820154165 0.275744467740735 df.mm.trans2:exp7 0.278204733249987 0.175187001380858 1.58804438147307 0.112562119632033 df.mm.trans1:exp8 0.414016337602151 0.265818527878441 1.55751497424393 0.119634352503966 df.mm.trans2:exp8 0.249327320873072 0.175187001380858 1.42320673855838 0.154958243424484 df.mm.trans1:probe2 0.038058513185116 0.203022254187448 0.187459809947618 0.851334515689039 df.mm.trans1:probe3 -0.19334071506633 0.203022254187448 -0.952312916828423 0.34114637767512 df.mm.trans1:probe4 -0.000158183292205494 0.203022254187448 -0.000779142625711589 0.999378474722608 df.mm.trans1:probe5 0.13225963410165 0.203022254187448 0.651453874507454 0.514888878531308 df.mm.trans1:probe6 0.064756544423648 0.203022254187448 0.318962788994841 0.74981495809465 df.mm.trans1:probe7 0.0600084027622666 0.203022254187448 0.295575492462327 0.767609773066869 df.mm.trans1:probe8 0.274678955728944 0.203022254187448 1.35294998485898 0.176348102104278 df.mm.trans1:probe9 0.0505386048467373 0.203022254187448 0.248931355082264 0.803460098393917 df.mm.trans1:probe10 -0.00174724361272283 0.203022254187448 -0.00860616792831795 0.99313490828385 df.mm.trans1:probe11 0.318359665331647 0.203022254187448 1.56810230782734 0.117143298999815 df.mm.trans1:probe12 0.351286459940471 0.203022254187448 1.73028548691087 0.083858229081753 . df.mm.trans1:probe13 0.0420333685334106 0.203022254187448 0.207038231851183 0.836018111125873 df.mm.trans1:probe14 0.112931442110013 0.203022254187448 0.556251542777889 0.578151425901264 df.mm.trans1:probe15 0.0412706350964183 0.203022254187448 0.203281336135268 0.838952535052257 df.mm.trans1:probe16 -0.0472058019681567 0.203022254187448 -0.232515406535543 0.81618074062962 df.mm.trans1:probe17 0.00426600947729512 0.203022254187448 0.0210125214812972 0.983239457276497 df.mm.trans1:probe18 0.0651141238742376 0.203022254187448 0.320724071037643 0.748480092406247 df.mm.trans1:probe19 -0.0158903044036188 0.203022254187448 -0.0782687812585682 0.937628407697818 df.mm.trans1:probe20 0.225772761849039 0.203022254187448 1.11205918165299 0.266354368984932 df.mm.trans1:probe21 -0.0451338293967726 0.203022254187448 -0.222309763909434 0.824113795876417 df.mm.trans1:probe22 -0.0650861014187952 0.203022254187448 -0.320586044516588 0.748584674728112 df.mm.trans2:probe2 -0.0948771912758686 0.203022254187448 -0.467324095358874 0.64036007490802 df.mm.trans2:probe3 -0.0228193845998559 0.203022254187448 -0.112398439723692 0.910527888390751 df.mm.trans2:probe4 0.0690957084155461 0.203022254187448 0.340335638041684 0.73366841046838 df.mm.trans2:probe5 -0.156289097841393 0.203022254187448 -0.769812641805725 0.441575422176981 df.mm.trans2:probe6 -0.213153972525177 0.203022254187448 -1.04990447169587 0.293991389728237 df.mm.trans3:probe2 0.272950274770921 0.203022254187448 1.34443524855610 0.179083352457319 df.mm.trans3:probe3 0.186003128778209 0.203022254187448 0.916171133665349 0.359776858618404 df.mm.trans3:probe4 0.266327452643005 0.203022254187448 1.31181408515496 0.189855091981544 df.mm.trans3:probe5 0.287844337054758 0.203022254187448 1.41779697110936 0.156531716049950 df.mm.trans3:probe6 0.163400129117674 0.203022254187448 0.804838512761307 0.421085850026859 df.mm.trans3:probe7 0.287025050081084 0.203022254187448 1.41376151708018 0.157713338445652 df.mm.trans3:probe8 0.205130736795103 0.203022254187448 1.01038547530709 0.312531633829289 df.mm.trans3:probe9 -0.0661260119043289 0.203022254187448 -0.325708194744383 0.744706753772699 df.mm.trans3:probe10 0.333682417091074 0.203022254187448 1.64357556971557 0.100548119060865 df.mm.trans3:probe11 0.471097770987197 0.203022254187448 2.32042429472898 0.0204991579466619 * df.mm.trans3:probe12 0.0234180462570426 0.203022254187448 0.115347188665441 0.908190874709822 df.mm.trans3:probe13 0.330361610737548 0.203022254187448 1.62721871087358 0.103975489400699 df.mm.trans3:probe14 -0.0261944490704893 0.203022254187448 -0.129022550632820 0.897363263420524 df.mm.trans3:probe15 0.190616262588087 0.203022254187448 0.938893439790562 0.347990449569456 df.mm.trans3:probe16 0.0860253444788675 0.203022254187448 0.423723718481824 0.671849705818749 df.mm.trans3:probe17 -0.137909126910621 0.203022254187448 -0.679280837771073 0.49710206387282 df.mm.trans3:probe18 0.212277080580569 0.203022254187448 1.04558528044209 0.295981007178614 df.mm.trans3:probe19 0.169981529276790 0.203022254187448 0.837255649421804 0.402629759055935 df.mm.trans3:probe20 0.171012250327445 0.203022254187448 0.842332536459533 0.39978395066834 df.mm.trans3:probe21 0.12887487765485 0.203022254187448 0.634782025106772 0.525701875355363