fitVsDatCorrelation=0.889480267820635 cont.fitVsDatCorrelation=0.194305906938299 fstatistic=12332.0085383119,61,899 cont.fstatistic=2664.73259255762,61,899 residuals=-0.707936013985196,-0.085734499105256,-0.00627487905868631,0.0724025356705904,0.780189791918872 cont.residuals=-0.499041785577957,-0.195130209444437,-0.0732724206507705,0.128512630719454,1.75222447521998 predictedValues: Include Exclude Both Lung 56.1980824830877 46.0892247627463 61.2823187396224 cerebhem 55.411696462992 45.9436041271296 64.8716946067452 cortex 53.669539820733 46.805567843337 55.8440851486596 heart 54.7742661148886 46.1464265817497 58.4883552852755 kidney 55.6127131493278 44.1754778555856 61.329085540528 liver 56.2243605837975 44.6554117029793 64.3006016796025 stomach 57.3726046887096 46.0671807741077 59.1578620000505 testicle 56.3403430710663 47.7091645202829 63.4429441179525 diffExp=10.1088577203414,9.46809233586236,6.86397197739593,8.62783953313894,11.4372352937422,11.5689488808182,11.3054239146019,8.63117855078342 diffExpScore=0.987343622259064 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=1,1,0,0,1,1,1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 56.205798930045 52.3376992205039 58.1377648397152 cerebhem 56.8055907583474 56.811233518731 58.4003256791023 cortex 59.1311105687831 57.9427727714169 56.1710094062649 heart 56.7582122912871 59.171438853281 56.111282405487 kidney 59.8771581664922 56.2749876304423 55.0581210516223 liver 55.7907064594495 58.7636089447213 56.6478771250264 stomach 54.4744642007811 58.7197319457845 58.3099791655731 testicle 53.727415714333 56.160561456597 62.3663364885196 cont.diffExp=3.86809970954116,-0.00564276038356581,1.18833779736621,-2.41322656199387,3.60217053604992,-2.97290248527179,-4.24526774500336,-2.43314574226395 cont.diffExpScore=4.69872613670493 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=-0.119755582089210 cont.tran.correlation=-0.0158439197033479 tran.covariance=-6.14995048002718e-05 cont.tran.covariance=-1.70677212107571e-05 tran.mean=50.8247290339075 cont.tran.mean=56.8095307144373 weightedLogRatios: wLogRatio Lung 0.77927919846756 cerebhem 0.734720737796403 cortex 0.535663250437159 heart 0.671466689209322 kidney 0.89870146204528 liver 0.901723781056663 stomach 0.86466011366002 testicle 0.656546862642635 cont.weightedLogRatios: wLogRatio Lung 0.284739140006282 cerebhem -0.000401260488318575 cortex 0.0826184833838917 heart -0.169036951631881 kidney 0.251981386919538 liver -0.210131213693372 stomach -0.302820685121177 testicle -0.177434212462396 varWeightedLogRatios=0.0171403624189755 cont.varWeightedLogRatios=0.0485828155514798 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.6505431647579 0.0663700431931246 55.0028746272696 6.30026140496185e-290 *** df.mm.trans1 0.339766785168706 0.0570902100456051 5.95140191106831 3.8069981693407e-09 *** df.mm.trans2 0.167140736849088 0.050217886221775 3.3283108753513 0.000909231802610385 *** df.mm.exp2 -0.0741765040986355 0.0640990121076748 -1.15721758666159 0.247490851138886 df.mm.exp3 0.0623137047308348 0.0640990121076748 0.972147661592023 0.331238580882059 df.mm.exp4 0.0222418750217517 0.0640990121076748 0.346992477581236 0.728678178921521 df.mm.exp5 -0.0536430048477184 0.0640990121076748 -0.836877247930245 0.402883971350476 df.mm.exp6 -0.0792138206801626 0.0640990121076748 -1.23580407989904 0.216854211925393 df.mm.exp7 0.055487743776282 0.0640990121076748 0.865656769921393 0.386909362250326 df.mm.exp8 0.00242291515502054 0.0640990121076748 0.0377995709348917 0.96985587830107 df.mm.trans1:exp2 0.0600845662588823 0.0589625153968299 1.01902990153152 0.308462921304702 df.mm.trans2:exp2 0.0710119646859628 0.0423197889711843 1.67798484851413 0.0936976236526274 . df.mm.trans1:exp3 -0.108350729614795 0.0589625153968299 -1.83762054392646 0.0664482046109372 . df.mm.trans2:exp3 -0.0468907243882724 0.0423197889711843 -1.10800940950345 0.268154099814480 df.mm.trans1:exp4 -0.047904024605911 0.0589625153968299 -0.812448795366125 0.416749240534811 df.mm.trans2:exp4 -0.0210015341014457 0.0423197889711843 -0.496258006289817 0.619833698060415 df.mm.trans1:exp5 0.0431721969323071 0.0589625153968299 0.732197339983706 0.464238964174757 df.mm.trans2:exp5 0.0112336538096917 0.0423197889711843 0.265446829551553 0.790726121028319 df.mm.trans1:exp6 0.0796813092077467 0.0589625153968299 1.35138924571781 0.176910733548923 df.mm.trans2:exp6 0.0476101368964814 0.0423197889711843 1.12500884465419 0.260885553868828 df.mm.trans1:exp7 -0.034803461411538 0.0589625153968299 -0.590264190347097 0.555161880662379 df.mm.trans2:exp7 -0.0559661476144222 0.0423197889711843 -1.32245809761787 0.186351985113233 df.mm.trans1:exp8 0.000105299773417812 0.0589625153968299 0.00178587654731354 0.998575473621323 df.mm.trans2:exp8 0.0321214060767877 0.0423197889711843 0.75901621576278 0.448041813721583 df.mm.trans1:probe2 0.453762646399143 0.0416927944729146 10.8834788393454 5.32324020665558e-26 *** df.mm.trans1:probe3 -0.135443747486733 0.0416927944729146 -3.24861284063659 0.00120269904359945 ** df.mm.trans1:probe4 0.311027228186898 0.0416927944729146 7.45997556937457 2.03905979449336e-13 *** df.mm.trans1:probe5 0.407128351183659 0.0416927944729146 9.76495714261 1.77755332369254e-21 *** df.mm.trans1:probe6 0.0323899722938801 0.0416927944729146 0.776872183871531 0.437438542422545 df.mm.trans1:probe7 -0.195947073485923 0.0416927944729146 -4.69978268338954 3.01204694943481e-06 *** df.mm.trans1:probe8 0.101270394718281 0.0416927944729146 2.42896634774795 0.0153370250899639 * df.mm.trans1:probe9 -0.124416402753120 0.0416927944729146 -2.98412242033682 0.00292087199451073 ** df.mm.trans1:probe10 0.301278096316820 0.0416927944729146 7.22614303324145 1.06049117450027e-12 *** df.mm.trans1:probe11 0.0731286752711853 0.0416927944729146 1.75398833768969 0.0797732248878979 . df.mm.trans1:probe12 -0.0636529450599315 0.0416927944729146 -1.52671332935678 0.127184038710801 df.mm.trans1:probe13 0.620210106914273 0.0416927944729146 14.875714491078 6.55651245618185e-45 *** df.mm.trans1:probe14 0.0160251492993375 0.0416927944729146 0.384362561970945 0.70080064262539 df.mm.trans1:probe15 0.114336626912432 0.0416927944729146 2.74235940185563 0.00622118959521283 ** df.mm.trans1:probe16 0.218140029578557 0.0416927944729146 5.23207984344322 2.08658158224197e-07 *** df.mm.trans1:probe17 -0.0761183455551898 0.0416927944729146 -1.82569545931107 0.0682277589185865 . df.mm.trans1:probe18 -0.175236307554461 0.0416927944729146 -4.20303579478948 2.89663570525749e-05 *** df.mm.trans1:probe19 -0.143595751549219 0.0416927944729146 -3.44413833048549 0.000599359204165606 *** df.mm.trans1:probe20 -0.182245988530145 0.0416927944729146 -4.3711627113059 1.38016236814608e-05 *** df.mm.trans1:probe21 -0.146424453181726 0.0416927944729146 -3.51198462546926 0.000466909580950365 *** df.mm.trans1:probe22 -0.0941449082379312 0.0416927944729146 -2.25806184085578 0.0241808711664106 * df.mm.trans2:probe2 0.208938407365514 0.0416927944729146 5.01137930443231 6.50727882505592e-07 *** df.mm.trans2:probe3 -0.0189227873385203 0.0416927944729146 -0.453862293898609 0.650037487480474 df.mm.trans2:probe4 0.0541917369634312 0.0416927944729146 1.29978663336266 0.194007280990856 df.mm.trans2:probe5 -0.0136722384806365 0.0416927944729146 -0.327928090536568 0.743042389371812 df.mm.trans2:probe6 0.00158000985288262 0.0416927944729146 0.037896472828394 0.969778638677337 df.mm.trans3:probe2 -0.199661785832935 0.0416927944729146 -4.78887990975619 1.96069814344329e-06 *** df.mm.trans3:probe3 -0.321634299633381 0.0416927944729146 -7.71438575177127 3.22381038620138e-14 *** df.mm.trans3:probe4 -0.288830452176898 0.0416927944729146 -6.92758678875636 8.15368664379647e-12 *** df.mm.trans3:probe5 1.01752543092584 0.0416927944729146 24.4053065713038 2.44072910125423e-101 *** df.mm.trans3:probe6 0.0345229298586333 0.0416927944729146 0.828031085348832 0.407872652417303 df.mm.trans3:probe7 -0.370569183611998 0.0416927944729146 -8.88808697754082 3.34214395840428e-18 *** df.mm.trans3:probe8 -0.132539935180029 0.0416927944729146 -3.17896501915055 0.00152856768475477 ** df.mm.trans3:probe9 -0.200193235019995 0.0416927944729146 -4.80162669715145 1.84282793702865e-06 *** df.mm.trans3:probe10 0.0806270792239382 0.0416927944729146 1.93383725517168 0.0534467434577854 . df.mm.trans3:probe11 -0.168573637593490 0.0416927944729146 -4.04323192351624 5.72392063452306e-05 *** df.mm.trans3:probe12 0.045815205613523 0.0416927944729146 1.09887586554762 0.272116410844388 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9168005109161 0.142470152081544 27.4920778400959 3.03191215907206e-121 *** df.mm.trans1 0.137975566416009 0.122550031855444 1.12587132232459 0.260520452917145 df.mm.trans2 0.042477119946376 0.107797879028216 0.394044115981703 0.693641915486532 df.mm.exp2 0.0881259393625267 0.137595149315846 0.640472718701994 0.522028528214032 df.mm.exp3 0.186890838398157 0.137595149315846 1.35826618400009 0.174719986713300 df.mm.exp4 0.167981001596092 0.137595149315846 1.2208351997242 0.222468518268905 df.mm.exp5 0.190234469701849 0.137595149315846 1.38256668674541 0.167141112501832 df.mm.exp6 0.134354135932271 0.137595149315846 0.97644529331383 0.329106516826803 df.mm.exp7 0.0808131932635904 0.137595149315846 0.587325888052099 0.557132332996276 df.mm.exp8 -0.0448089106244031 0.137595149315846 -0.325657632897693 0.744759183675107 df.mm.trans1:exp2 -0.0775111250202705 0.126569128654220 -0.612401506152633 0.540427009066086 df.mm.trans2:exp2 -0.00610879776904775 0.0908437976036761 -0.0672450726432483 0.946401555468558 df.mm.trans1:exp3 -0.136153582310938 0.126569128654220 -1.07572505048132 0.282338845342804 df.mm.trans2:exp3 -0.0851519290883336 0.0908437976036761 -0.93734444546039 0.348833148177671 df.mm.trans1:exp4 -0.158200581110624 0.126569128654220 -1.24991443642486 0.211656081841977 df.mm.trans2:exp4 -0.0452589657223102 0.0908437976036761 -0.498206447948833 0.618460393521172 df.mm.trans1:exp5 -0.126959305471235 0.126569128654220 -1.00308271709827 0.316090861384303 df.mm.trans2:exp5 -0.117701240510811 0.0908437976036761 -1.29564421144420 0.195430656185498 df.mm.trans1:exp6 -0.141766766707265 0.126569128654220 -1.12007381432296 0.262981449419554 df.mm.trans2:exp6 -0.0185483059166789 0.0908437976036761 -0.204178011113093 0.838260605738232 df.mm.trans1:exp7 -0.112101083556583 0.126569128654220 -0.885690568849827 0.376021131892712 df.mm.trans2:exp7 0.0342456882800877 0.0908437976036761 0.376973323258581 0.706282393420787 df.mm.trans1:exp8 -0.000287618806920299 0.126569128654220 -0.0022724246423949 0.998187373159885 df.mm.trans2:exp8 0.115306729942335 0.0908437976036761 1.26928566378723 0.204667693128464 df.mm.trans1:probe2 -0.0336833910496526 0.0894978891602713 -0.376359614351719 0.706738368952828 df.mm.trans1:probe3 -0.0778278318477394 0.0894978891602713 -0.869605222849074 0.384748296587037 df.mm.trans1:probe4 -0.0453780338101366 0.0894978891602713 -0.507029095723972 0.612258745220403 df.mm.trans1:probe5 -0.125289671499223 0.0894978891602713 -1.39991761453565 0.161882980095594 df.mm.trans1:probe6 -0.0346290171604529 0.0894978891602713 -0.386925518415746 0.698902919978966 df.mm.trans1:probe7 -0.0671621781383936 0.0894978891602713 -0.750433097009927 0.453190262266059 df.mm.trans1:probe8 -0.0390771922056135 0.0894978891602713 -0.43662697044882 0.662486743871129 df.mm.trans1:probe9 -0.0877643679131464 0.0894978891602713 -0.980630590694486 0.327038763731447 df.mm.trans1:probe10 -0.088992857841403 0.0894978891602713 -0.994357058880306 0.320316624130005 df.mm.trans1:probe11 -0.0151271917642841 0.0894978891602713 -0.169022888765506 0.865816659694023 df.mm.trans1:probe12 0.0926482993140562 0.0894978891602713 1.03520094365738 0.300853454428522 df.mm.trans1:probe13 -0.0257401518024148 0.0894978891602713 -0.287606244615666 0.773714448078832 df.mm.trans1:probe14 -0.0715034120507477 0.0894978891602713 -0.798939647869243 0.424536426698579 df.mm.trans1:probe15 -0.0363657369254687 0.0894978891602713 -0.406330666194211 0.684596359770233 df.mm.trans1:probe16 -0.0173087387365071 0.0894978891602713 -0.193398290159793 0.84669073982318 df.mm.trans1:probe17 -0.0385143549127623 0.0894978891602713 -0.430338137291612 0.667052830522284 df.mm.trans1:probe18 0.0195541984555701 0.0894978891602713 0.218487817299833 0.827098636826485 df.mm.trans1:probe19 -0.0684462205763565 0.0894978891602713 -0.764780278267616 0.444603094949799 df.mm.trans1:probe20 -0.0200629009518397 0.0894978891602713 -0.224171778128883 0.822674577911538 df.mm.trans1:probe21 -0.0358503668218879 0.0894978891602713 -0.400572205202379 0.688830286927409 df.mm.trans1:probe22 -0.0591877079567466 0.0894978891602713 -0.661330770055976 0.508569663999944 df.mm.trans2:probe2 -0.039093891732957 0.0894978891602713 -0.436813561747232 0.662351457612451 df.mm.trans2:probe3 0.0801637950227835 0.0894978891602713 0.895705985637578 0.370649576775828 df.mm.trans2:probe4 -0.0822617571621331 0.0894978891602713 -0.919147456258104 0.358264994720928 df.mm.trans2:probe5 0.000250009359099383 0.0894978891602713 0.00279346654368206 0.997771758802536 df.mm.trans2:probe6 0.0128493693197815 0.0894978891602713 0.143571758399476 0.885870817694524 df.mm.trans3:probe2 -0.0096878057389594 0.0894978891602713 -0.108246192506403 0.913824557749421 df.mm.trans3:probe3 -0.0937355248800466 0.0894978891602713 -1.04734900185396 0.295220141933117 df.mm.trans3:probe4 -0.0083846115690386 0.0894978891602713 -0.0936850203698501 0.925380244587757 df.mm.trans3:probe5 -0.112466854851341 0.0894978891602713 -1.25664254103175 0.209209519096254 df.mm.trans3:probe6 0.0285656282030255 0.0894978891602713 0.319176557917144 0.74966677953718 df.mm.trans3:probe7 0.0127604878497086 0.0894978891602713 0.142578645926022 0.886654910916506 df.mm.trans3:probe8 -0.0646587706332112 0.0894978891602713 -0.722461403725638 0.470198735812178 df.mm.trans3:probe9 0.0332692717069047 0.0894978891602713 0.371732473458974 0.710179641624522 df.mm.trans3:probe10 -0.0783281927975076 0.0894978891602713 -0.875195979843042 0.381701040873219 df.mm.trans3:probe11 -0.0615393066959731 0.0894978891602713 -0.687606236005964 0.491878136395548 df.mm.trans3:probe12 -0.0590472886268616 0.0894978891602713 -0.659761801992008 0.509575680261957