fitVsDatCorrelation=0.945191345796219 cont.fitVsDatCorrelation=0.238272421557263 fstatistic=7308.85729366995,66,1014 cont.fstatistic=812.496599657304,66,1014 residuals=-0.611011138417483,-0.112597481451234,-0.00516995916588612,0.101503979961856,1.28467474348492 cont.residuals=-1.08294429359839,-0.485885657637486,-0.177240208392376,0.419444541374517,2.20271970912023 predictedValues: Include Exclude Both Lung 99.868609267066 341.586141706222 62.0537796272923 cerebhem 109.936007508068 336.863696979590 69.5591794631135 cortex 97.0842298653049 213.898795014908 74.212999705807 heart 87.8620835832944 216.777527282923 58.3434191357153 kidney 99.564237247872 307.134178628276 60.7694529760476 liver 95.1314496690309 264.967084564872 57.9028889673891 stomach 96.9762400905534 234.203470876374 65.1526711075584 testicle 98.1897160495663 221.110791387155 68.0505879478025 diffExp=-241.717532439157,-226.927689471522,-116.814565149604,-128.915443699628,-207.569941380404,-169.835634895841,-137.227230785820,-122.921075337588 diffExpScore=0.999260862974805 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 85.0393057478249 98.0230602422965 83.812573091901 cerebhem 89.5717605216231 86.0604474582104 98.7347141994715 cortex 84.3958271679583 98.0464633094727 115.262589402498 heart 89.7899793643625 79.0774003278455 104.143772368637 kidney 89.4319955416644 113.267326688474 78.3061576927569 liver 93.5570349970661 110.832963144082 98.8548087424107 stomach 84.7587693040906 84.8176688612302 78.9372793231652 testicle 91.9190324177614 76.7181838912966 87.6772698928997 cont.diffExp=-12.9837544944716,3.51131306341273,-13.6506361415144,10.7125790365170,-23.8353311468091,-17.2759281470154,-0.0588995571396396,15.2008485264647 cont.diffExpScore=2.46901376432869 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,-1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.700499929135179 cont.tran.correlation=0.0604933088992842 tran.covariance=0.00850488674829494 cont.tran.covariance=8.6812313678009e-05 tran.mean=182.572141232567 cont.tran.mean=90.9567011865786 weightedLogRatios: wLogRatio Lung -6.41770151196388 cerebhem -5.88980556889193 cortex -3.92634976085871 heart -4.44987871111781 kidney -5.81720081970691 liver -5.19080429222209 stomach -4.42213654066993 testicle -4.05295330425172 cont.weightedLogRatios: wLogRatio Lung -0.641413074539334 cerebhem 0.178957814292907 cortex -0.67622700774371 heart 0.563316782838987 kidney -1.08959650725657 liver -0.783431584603482 stomach -0.00308442927083692 testicle 0.80090283554176 varWeightedLogRatios=0.884079688970265 cont.varWeightedLogRatios=0.474276579728047 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 6.52857833971037 0.0975432379402852 66.9300966173287 0 *** df.mm.trans1 -1.85226801713576 0.083386474972393 -22.2130509503969 2.17254732367751e-89 *** df.mm.trans2 -0.680106465725239 0.0728326880312012 -9.33792894522696 6.03051631570516e-20 *** df.mm.exp2 -0.0320549428338028 0.09178033181143 -0.349257212314969 0.726968732580647 df.mm.exp3 -0.675311284156038 0.09178033181143 -7.35790850640548 3.8521760123491e-13 *** df.mm.exp4 -0.521160493591766 0.09178033181143 -5.67834614787111 1.77531881646599e-08 *** df.mm.exp5 -0.0884533768255507 0.09178033181143 -0.963750893898327 0.335400607020177 df.mm.exp6 -0.233355938776918 0.09178033181143 -2.54254843245028 0.0111524773311455 * df.mm.exp7 -0.455530914546651 0.09178033181143 -4.96327378160471 8.12645119881592e-07 *** df.mm.exp8 -0.544139873913179 0.09178033181143 -5.92871983761356 4.18175247329899e-09 *** df.mm.trans1:exp2 0.128097974691735 0.083728457849583 1.52992158200097 0.126348027858371 df.mm.trans2:exp2 0.0181334390531543 0.0569384660056621 0.318474316665839 0.750190756316009 df.mm.trans1:exp3 0.647034820263604 0.083728457849583 7.72777663510766 2.62429421354436e-14 *** df.mm.trans2:exp3 0.207214374508858 0.0569384660056621 3.6392686534311 0.000287205173997611 *** df.mm.trans1:exp4 0.393073431943527 0.083728457849583 4.69462166196442 3.03762778685527e-06 *** df.mm.trans2:exp4 0.0664322090246885 0.0569384660056621 1.16673689484508 0.243590953292321 df.mm.trans1:exp5 0.0854009984352038 0.083728457849583 1.01997577202037 0.307983238410764 df.mm.trans2:exp5 -0.0178617994519044 0.0569384660056621 -0.313703559385113 0.753810717796435 df.mm.trans1:exp6 0.18476013997438 0.083728457849583 2.20665881970858 0.0275613984310711 * df.mm.trans2:exp6 -0.0206383444564070 0.0569384660056621 -0.362467518080917 0.717078224556382 df.mm.trans1:exp7 0.426141500799742 0.083728457849583 5.08956586260432 4.27559791328905e-07 *** df.mm.trans2:exp7 0.078121293422898 0.0569384660056621 1.37203017403260 0.170357497425095 df.mm.trans1:exp8 0.527185944517753 0.083728457849583 6.29637709874981 4.53014015988749e-10 *** df.mm.trans2:exp8 0.109203875990926 0.0569384660056621 1.91792796068771 0.0554007784981225 . df.mm.trans1:probe2 -0.827437971446248 0.062339636646789 -13.2730637513086 3.4508123835423e-37 *** df.mm.trans1:probe3 -0.769913429668069 0.062339636646789 -12.3503034518846 9.6117887263004e-33 *** df.mm.trans1:probe4 -0.322529011743792 0.062339636646789 -5.17373903815342 2.76483350453502e-07 *** df.mm.trans1:probe5 -0.807348278000615 0.062339636646789 -12.9508017920441 1.30607377398621e-35 *** df.mm.trans1:probe6 -0.793578869193168 0.062339636646789 -12.7299245212082 1.51968237380328e-34 *** df.mm.trans1:probe7 -0.596933356395956 0.062339636646789 -9.5755026577734 7.48053239914431e-21 *** df.mm.trans1:probe8 -0.369380993758077 0.062339636646789 -5.92529911348308 4.26676141111392e-09 *** df.mm.trans1:probe9 -0.687750697775266 0.062339636646789 -11.0323180366290 8.32799790500966e-27 *** df.mm.trans1:probe10 -0.307228095365913 0.0623396366467891 -4.92829461144665 9.68394294416791e-07 *** df.mm.trans1:probe11 -0.169175595933183 0.062339636646789 -2.71377256963683 0.00676498476486477 ** df.mm.trans1:probe12 -0.345983929628855 0.062339636646789 -5.54998309645546 3.64668430839663e-08 *** df.mm.trans1:probe13 -0.334467223531395 0.062339636646789 -5.36524178712265 1.00162736475690e-07 *** df.mm.trans1:probe14 -0.432781228235087 0.062339636646789 -6.9423123315137 6.87675803266712e-12 *** df.mm.trans1:probe15 0.063447261531572 0.062339636646789 1.01776758647245 0.309031190016716 df.mm.trans1:probe16 -0.0253955801695275 0.062339636646789 -0.407374529842332 0.683818915280826 df.mm.trans1:probe17 0.75410660624376 0.062339636646789 12.0967436899971 1.45782735863146e-31 *** df.mm.trans1:probe18 0.60612542711566 0.062339636646789 9.72295412226917 2.00468820068984e-21 *** df.mm.trans1:probe19 0.486370805792319 0.062339636646789 7.80195124569066 1.51052595564644e-14 *** df.mm.trans1:probe20 0.633271034565135 0.062339636646789 10.1584011173051 3.73396111947866e-23 *** df.mm.trans1:probe21 0.680117514665551 0.062339636646789 10.9098729355617 2.79448988034262e-26 *** df.mm.trans1:probe22 0.740724204823083 0.062339636646789 11.8820744660409 1.41055646804895e-30 *** df.mm.trans2:probe2 -0.0624211783099384 0.062339636646789 -1.00130802275302 0.316916798292794 df.mm.trans2:probe3 -0.529503340484883 0.062339636646789 -8.49384707653981 7.04492215724966e-17 *** df.mm.trans2:probe4 -0.0497137488426169 0.062339636646789 -0.797466130967216 0.425367063531107 df.mm.trans2:probe5 0.130667054553657 0.062339636646789 2.09605094899742 0.0363250711112708 * df.mm.trans2:probe6 0.168915634688817 0.062339636646789 2.70960249007992 0.00684998775818562 ** df.mm.trans3:probe2 -0.0098797659125393 0.062339636646789 -0.158482892168865 0.874107856898726 df.mm.trans3:probe3 0.07157608843573 0.062339636646789 1.14816338826730 0.251171912465111 df.mm.trans3:probe4 -0.0226001507020896 0.062339636646789 -0.36253260233357 0.717029612039734 df.mm.trans3:probe5 -0.051274911435755 0.062339636646789 -0.822508987761258 0.410980646109764 df.mm.trans3:probe6 0.094223077139257 0.062339636646789 1.51144732641155 0.13098608604498 df.mm.trans3:probe7 -0.0460379063665039 0.062339636646789 -0.738501358731856 0.460380731788932 df.mm.trans3:probe8 -0.054206305962611 0.062339636646789 -0.869531952355436 0.384762015246710 df.mm.trans3:probe9 0.0715184534006909 0.062339636646789 1.14723885552796 0.251553530406091 df.mm.trans3:probe10 -0.0234539015627573 0.062339636646789 -0.37622775531473 0.706826285711759 df.mm.trans3:probe11 0.539886205932251 0.062339636646789 8.66040026815041 1.82720739081861e-17 *** df.mm.trans3:probe12 0.100353653600705 0.062339636646789 1.60978887588485 0.107755200628732 df.mm.trans3:probe13 -0.00947699696754696 0.062339636646789 -0.15202201163351 0.879199815806237 df.mm.trans3:probe14 0.803862336616084 0.062339636646789 12.8948832533417 2.43789095561431e-35 *** df.mm.trans3:probe15 -0.138639435634908 0.062339636646789 -2.22393717853101 0.0263722977411227 * df.mm.trans3:probe16 0.571404435880844 0.062339636646789 9.1659891942966 2.65889635304725e-19 *** df.mm.trans3:probe17 0.343303737950388 0.062339636646789 5.50698971659903 4.62585129730719e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.59204376168574 0.290134500271949 15.8272930567772 1.36927367113077e-50 *** df.mm.trans1 -0.193354830532806 0.248026349713401 -0.77957374591946 0.435823724714901 df.mm.trans2 -0.0110141394044928 0.216634960983473 -0.0508419294581593 0.959461492327184 df.mm.exp2 -0.242079871181552 0.272993200422613 -0.886761541338008 0.375417560756381 df.mm.exp3 -0.325986767174022 0.272993200422613 -1.19412046406054 0.232710114488829 df.mm.exp4 -0.377605163194098 0.272993200422613 -1.38320354722952 0.166906931738735 df.mm.exp5 0.262869718981106 0.272993200422613 0.962916726768888 0.33581888434474 df.mm.exp6 0.0532099897368819 0.272993200422613 0.194913241994706 0.84549991302432 df.mm.exp7 -0.0880738073128693 0.272993200422613 -0.322622714325942 0.747047498218334 df.mm.exp8 -0.212349116647426 0.272993200422613 -0.777854966052979 0.436835969462452 df.mm.trans1:exp2 0.294006398565803 0.249043550221301 1.18054211122733 0.238061519613391 df.mm.trans2:exp2 0.111927038368756 0.169358878479279 0.66088674756104 0.508835064353027 df.mm.trans1:exp3 0.318391156246224 0.249043550221301 1.27845573982261 0.201381322936030 df.mm.trans2:exp3 0.326225489305473 0.169358878479279 1.92623789337024 0.0543539569807593 . df.mm.trans1:exp4 0.431964973736156 0.249043550221301 1.73449572716222 0.0831340511719318 . df.mm.trans2:exp4 0.162829527419062 0.169358878479279 0.96144665624356 0.336556841548098 df.mm.trans1:exp5 -0.212504779034286 0.249043550221301 -0.85328360780857 0.393703561685589 df.mm.trans2:exp5 -0.118321730863509 0.169358878479279 -0.698644983516383 0.484934123334192 df.mm.trans1:exp6 0.0422476903825035 0.249043550221301 0.169639769208888 0.865327281182057 df.mm.trans2:exp6 0.0696114820472318 0.169358878479279 0.411029422680954 0.681137808647489 df.mm.trans1:exp7 0.0847694506656225 0.249043550221301 0.340380028273353 0.733640858368188 df.mm.trans2:exp7 -0.0566250719651278 0.169358878479279 -0.334349592259823 0.73818491270089 df.mm.trans1:exp8 0.290143653611405 0.249043550221301 1.16503179204434 0.244280121237389 df.mm.trans2:exp8 -0.0327148845492630 0.169358878479279 -0.193168996175572 0.846865302952827 df.mm.trans1:probe2 0.00408358167461201 0.185424225272526 0.0220229135034012 0.982434010434764 df.mm.trans1:probe3 0.0220342894340289 0.185424225272526 0.118831772933899 0.905432172480063 df.mm.trans1:probe4 0.0406458130690379 0.185424225272526 0.219204437873741 0.826534913409864 df.mm.trans1:probe5 0.139091071804755 0.185424225272526 0.750123515955519 0.453354328050023 df.mm.trans1:probe6 0.0370537195391255 0.185424225272526 0.199832138894829 0.841651931992536 df.mm.trans1:probe7 0.181816559528925 0.185424225272526 0.980543719471936 0.327051686053469 df.mm.trans1:probe8 0.0681380823580748 0.185424225272526 0.367471306718037 0.713344172792181 df.mm.trans1:probe9 0.109921734306805 0.185424225272526 0.592812153564337 0.553439053799325 df.mm.trans1:probe10 -0.0808766937427581 0.185424225272526 -0.436171129332698 0.662805418691243 df.mm.trans1:probe11 0.199943316397566 0.185424225272526 1.07830201853993 0.281155438593315 df.mm.trans1:probe12 0.0332191532249516 0.185424225272526 0.179152174836530 0.857854004889984 df.mm.trans1:probe13 0.469313104074973 0.185424225272526 2.53102367495511 0.0115232566560093 * df.mm.trans1:probe14 -0.165349567210387 0.185424225272526 -0.89173659465135 0.37274568612091 df.mm.trans1:probe15 0.220318021167808 0.185424225272526 1.18818358736027 0.235039305635856 df.mm.trans1:probe16 -0.141319104600598 0.185424225272526 -0.76213938277426 0.446154055515553 df.mm.trans1:probe17 0.017783456253143 0.185424225272526 0.0959068656051059 0.923613492092892 df.mm.trans1:probe18 0.223273730136029 0.185424225272526 1.20412383984819 0.228822715362907 df.mm.trans1:probe19 -0.0524055626629507 0.185424225272526 -0.282625221089251 0.777521824614255 df.mm.trans1:probe20 0.274327547811854 0.185424225272526 1.47945904807564 0.139328261016937 df.mm.trans1:probe21 0.0347764405770733 0.185424225272526 0.187550685601953 0.851266408899033 df.mm.trans1:probe22 0.0967722285049763 0.185424225272526 0.52189636150695 0.601856471735501 df.mm.trans2:probe2 -0.00860163086928086 0.185424225272526 -0.0463889271029105 0.9630093960742 df.mm.trans2:probe3 -0.0854242949227459 0.185424225272526 -0.460696517929058 0.645115139360479 df.mm.trans2:probe4 0.149263961783303 0.185424225272526 0.804986304049123 0.421016289355078 df.mm.trans2:probe5 0.0569797825459023 0.185424225272526 0.307294165377564 0.758682575203297 df.mm.trans2:probe6 -0.0162356605575443 0.185424225272526 -0.08755954370947 0.930244046035072 df.mm.trans3:probe2 0.169615416389755 0.185424225272526 0.914742483839231 0.360544302403327 df.mm.trans3:probe3 0.141974588156456 0.185424225272526 0.765674430877572 0.44404822871539 df.mm.trans3:probe4 0.0170657456703467 0.185424225272526 0.092036224744984 0.926687437573523 df.mm.trans3:probe5 0.088271606954879 0.185424225272526 0.47605218155903 0.634139752163393 df.mm.trans3:probe6 0.041387379901624 0.185424225272526 0.223203736409281 0.82342189973798 df.mm.trans3:probe7 0.159592507754853 0.185424225272526 0.860688550917732 0.389613060105101 df.mm.trans3:probe8 0.0912193928178551 0.185424225272526 0.491949704434715 0.622861380129457 df.mm.trans3:probe9 -0.0191217750889194 0.185424225272526 -0.103124470714737 0.917884574561572 df.mm.trans3:probe10 0.118581113676797 0.185424225272526 0.639512520559348 0.522634018846025 df.mm.trans3:probe11 0.0885352157336586 0.185424225272526 0.477473833872217 0.633127648485748 df.mm.trans3:probe12 -0.00409625619610638 0.185424225272526 -0.0220912676867650 0.982379498407766 df.mm.trans3:probe13 -0.145584326781680 0.185424225272526 -0.785141890536196 0.432553782795197 df.mm.trans3:probe14 -0.0764885677631977 0.185424225272526 -0.412505796644312 0.680055930791892 df.mm.trans3:probe15 0.0928494197895786 0.185424225272526 0.500740502774726 0.616662432933759 df.mm.trans3:probe16 -0.128867893295099 0.185424225272526 -0.694989519873665 0.487221090022747 df.mm.trans3:probe17 0.060506535762664 0.185424225272526 0.326314081527023 0.744254070409766