fitVsDatCorrelation=0.92268801172531 cont.fitVsDatCorrelation=0.228479564057523 fstatistic=9334.67198140307,56,784 cont.fstatistic=1452.18986212645,56,784 residuals=-0.720956204064691,-0.0945777105153471,-0.00257400099697492,0.0851298474118678,0.832580910565657 cont.residuals=-0.832745700031217,-0.292361092679047,-0.111384434180616,0.213735699015593,1.94835220874119 predictedValues: Include Exclude Both Lung 81.3593559717253 67.3590009969931 133.395719513626 cerebhem 57.0242598971402 53.9595567750258 61.6053711098908 cortex 54.8965904399784 54.5337866045436 69.8446653971985 heart 71.5901929227992 60.5750249337321 109.803872207985 kidney 60.3251753771398 74.6476826672019 69.3484073556687 liver 156.571388105651 69.4532284346006 240.392772817499 stomach 54.9807609351393 58.2091537673486 65.4320069914547 testicle 57.940229314418 63.6499386713462 70.8856746684119 diffExp=14.0003549747322,3.06470312211444,0.362803835434796,11.0151679890670,-14.3225072900621,87.11815967105,-3.22839283220938,-5.70970935692822 diffExpScore=1.4878985629368 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,1,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,0,0,-1,1,0,0 diffExp1.2Score=1.5 cont.predictedValues: Include Exclude Both Lung 76.5250586475052 77.9248689485717 89.9911070466684 cerebhem 84.7622211846287 68.313942183754 79.2036760230426 cortex 78.5207671400751 74.7022168951744 67.878612237838 heart 74.5772400342374 89.2874861396326 74.629496239093 kidney 77.6063960401497 70.4679359297887 71.8658619081155 liver 78.0483959077462 81.2475368555192 73.8091819900698 stomach 75.595897046286 85.4906212748215 78.6496434784072 testicle 83.2389758240883 76.1178437644138 69.2879310713619 cont.diffExp=-1.39981030106655,16.4482790008746,3.81855024490073,-14.7102461053952,7.13846011036101,-3.19914094777302,-9.89472422853552,7.12113205967455 cont.diffExpScore=10.0799279844237 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.454723701365931 cont.tran.correlation=-0.723191397727697 tran.covariance=0.0208555639601903 cont.tran.covariance=-0.00300511888798649 tran.mean=68.5672078634239 cont.tran.mean=78.2767127385245 weightedLogRatios: wLogRatio Lung 0.812850479998484 cerebhem 0.221843794517492 cortex 0.0265373398784210 heart 0.699615002641005 kidney -0.896060367205122 liver 3.77742078836960 stomach -0.230263548369201 testicle -0.3859462841383 cont.weightedLogRatios: wLogRatio Lung -0.0787918326178409 cerebhem 0.934564714680645 cortex 0.216285679274788 heart -0.792447055184467 kidney 0.415244257339994 liver -0.17584692679608 stomach -0.539609709673373 testicle 0.391448092666733 varWeightedLogRatios=2.06397601383255 cont.varWeightedLogRatios=0.312134344934706 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.12703821342519 0.0834152066540028 37.4876277223146 5.26140678761801e-177 *** df.mm.trans1 1.06216695116707 0.0727038303080804 14.6095047078836 6.2535686499027e-43 *** df.mm.trans2 1.013838660902 0.0648803228380346 15.6262887814680 4.03372230803557e-48 *** df.mm.exp2 0.195370217208119 0.0848836097694028 2.30162475110174 0.0216177011167749 * df.mm.exp3 0.0424056488530240 0.0848836097694028 0.499574051671746 0.617515199675183 df.mm.exp4 -0.0394473514915586 0.0848836097694028 -0.46472283163643 0.642258988698412 df.mm.exp5 0.45779348881244 0.0848836097694028 5.39319062957024 9.1698297676675e-08 *** df.mm.exp6 0.0962991452280855 0.0848836097694028 1.13448456645157 0.256938089647472 df.mm.exp7 0.174422074590491 0.0848836097694028 2.05483809023121 0.0402255277436468 * df.mm.exp8 0.236149618704955 0.0848836097694028 2.78204024718654 0.00553161359793469 ** df.mm.trans1:exp2 -0.55076926361412 0.0792628957989068 -6.94863918435991 7.76003753542987e-12 *** df.mm.trans2:exp2 -0.417171938875961 0.0618246323478427 -6.74766550213893 2.91994399819338e-11 *** df.mm.trans1:exp3 -0.435830243108442 0.0792628957989068 -5.49854050518367 5.18409563019545e-08 *** df.mm.trans2:exp3 -0.253621740587129 0.0618246323478427 -4.10227656769848 4.51877935006149e-05 *** df.mm.trans1:exp4 -0.0884703901989904 0.0792628957989067 -1.1161639920833 0.264693918907734 df.mm.trans2:exp4 -0.0667065092077618 0.0618246323478427 -1.07896329787215 0.280935923483078 df.mm.trans1:exp5 -0.75691980602449 0.0792628957989068 -9.5494846408946 1.61565283048257e-20 *** df.mm.trans2:exp5 -0.35505054696471 0.0618246323478427 -5.74286548065658 1.33127303596933e-08 *** df.mm.trans1:exp6 0.55833707885133 0.0792628957989067 7.04411658473662 4.08740507771576e-12 *** df.mm.trans2:exp6 -0.0656821303881315 0.0618246323478427 -1.06239419295833 0.288383903646339 df.mm.trans1:exp7 -0.566314587655896 0.0792628957989068 -7.14476277895095 2.06295666226484e-12 *** df.mm.trans2:exp7 -0.320415990013932 0.0618246323478427 -5.18265904455657 2.78505234932330e-07 *** df.mm.trans1:exp8 -0.57561350450434 0.0792628957989067 -7.26208017891115 9.20180422531146e-13 *** df.mm.trans2:exp8 -0.292787796250880 0.0618246323478427 -4.73577901124544 2.58979908442417e-06 *** df.mm.trans1:probe2 0.122543238214543 0.0503706911993846 2.43282820419268 0.0152040564161145 * df.mm.trans1:probe3 0.0123378961182365 0.0503706911993846 0.244941965743510 0.806565463266674 df.mm.trans1:probe4 0.221158373844346 0.0503706911993845 4.39061622102672 1.28530817361446e-05 *** df.mm.trans1:probe5 0.147875922193389 0.0503706911993846 2.93575328573605 0.00342468948180346 ** df.mm.trans1:probe6 0.183355888873381 0.0503706911993845 3.64013049071721 0.000290379163484121 *** df.mm.trans1:probe7 0.0077639249254681 0.0503706911993846 0.154135763091592 0.87754235787495 df.mm.trans1:probe8 -0.0288044789110466 0.0503706911993845 -0.571849983098872 0.567587526887352 df.mm.trans1:probe9 0.116615738526370 0.0503706911993846 2.31515065109520 0.0208620468835624 * df.mm.trans1:probe10 0.182935124061641 0.0503706911993846 3.63177712486653 0.000299782038603182 *** df.mm.trans1:probe11 0.298471793631405 0.0503706911993846 5.9255052198897 4.66049233525529e-09 *** df.mm.trans1:probe12 0.29286277955943 0.0503706911993845 5.81415050272346 8.86793048153702e-09 *** df.mm.trans1:probe13 0.268773558314781 0.0503706911993846 5.33591165646075 1.24531892877788e-07 *** df.mm.trans1:probe14 0.373231209810848 0.0503706911993846 7.40969005832123 3.28040276617469e-13 *** df.mm.trans1:probe15 0.278533853054123 0.0503706911993845 5.52968097959169 4.37181760730698e-08 *** df.mm.trans1:probe16 0.262474040405340 0.0503706911993845 5.21084849454173 2.40547990803013e-07 *** df.mm.trans1:probe17 0.512303830094112 0.0503706911993846 10.1706730222588 6.57936857687073e-23 *** df.mm.trans1:probe18 0.467603285518302 0.0503706911993846 9.28324139264572 1.57495756993278e-19 *** df.mm.trans1:probe19 0.657032959502918 0.0503706911993846 13.0439536138616 2.45267826070922e-35 *** df.mm.trans1:probe20 0.792036912937502 0.0503706911993846 15.7241620886707 1.24843718292396e-48 *** df.mm.trans1:probe21 0.440622628902171 0.0503706911993845 8.74759941566089 1.31870051281652e-17 *** df.mm.trans1:probe22 0.470720989039795 0.0503706911993846 9.3451365830284 9.31764171285996e-20 *** df.mm.trans2:probe2 0.120084075733801 0.0503706911993845 2.38400690708149 0.0173622705000587 * df.mm.trans2:probe3 0.22212636051606 0.0503706911993846 4.40983348107746 1.17889631550372e-05 *** df.mm.trans2:probe4 0.112467201749296 0.0503706911993846 2.23279051907610 0.0258443393945490 * df.mm.trans2:probe5 0.228131912408797 0.0503706911993845 4.52906059013111 6.84612252617588e-06 *** df.mm.trans2:probe6 0.216266090590200 0.0503706911993845 4.29349062799523 1.97932170373994e-05 *** df.mm.trans3:probe2 0.076388769682529 0.0503706911993846 1.51653209165139 0.129787947098028 df.mm.trans3:probe3 -0.569663992144626 0.0503706911993846 -11.3094336920988 1.41197623728657e-27 *** df.mm.trans3:probe4 -0.289840417547656 0.0503706911993846 -5.75414810966893 1.24872248686533e-08 *** df.mm.trans3:probe5 -0.372414260625844 0.0503706911993846 -7.39347131751081 3.67717512789972e-13 *** df.mm.trans3:probe6 -0.66414638986069 0.0503706911993845 -13.1851752288205 5.32389335292942e-36 *** df.mm.trans3:probe7 -0.348390170424654 0.0503706911993846 -6.91652550578681 9.61138404241698e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25121433865643 0.210632355677639 20.1831020926466 2.8183575395033e-73 *** df.mm.trans1 0.160658428240523 0.183584979991696 0.875117497345313 0.381777990619204 df.mm.trans2 0.127178914223656 0.163829783377335 0.776286897302036 0.437813731664552 df.mm.exp2 0.0982885634881004 0.214340231251981 0.458563298704997 0.646674885333287 df.mm.exp3 0.265499372307112 0.214340231251981 1.23868193458739 0.215834045721396 df.mm.exp4 0.297508351277061 0.214340231251981 1.38801917651804 0.165525510312616 df.mm.exp5 0.138353751232259 0.214340231251981 0.645486619213395 0.518800747035475 df.mm.exp6 0.259693918440872 0.214340231251981 1.21159670736555 0.226031841428074 df.mm.exp7 0.215153050345024 0.214340231251981 1.00379219098671 0.315788494411153 df.mm.exp8 0.32207513632851 0.214340231251981 1.50263501372206 0.133335857956422 df.mm.trans1:exp2 0.00394312392993408 0.200147324806200 0.0197011073405660 0.984286820095925 df.mm.trans2:exp2 -0.229919830098018 0.156113836705398 -1.47277035111179 0.141214304339191 df.mm.trans1:exp3 -0.239754484165501 0.200147324806200 -1.19789002624768 0.231321848316232 df.mm.trans2:exp3 -0.307734747185523 0.156113836705399 -1.97122019213612 0.0490499974290759 * df.mm.trans1:exp4 -0.323291235207416 0.200147324806200 -1.61526633204044 0.106655361664789 df.mm.trans2:exp4 -0.161392149917383 0.156113836705398 -1.03381066869777 0.30154346621679 df.mm.trans1:exp5 -0.124322155456669 0.200147324806200 -0.621153220893903 0.534679363476745 df.mm.trans2:exp5 -0.238941098266182 0.156113836705398 -1.53055682512682 0.126282332957267 df.mm.trans1:exp6 -0.239983075016287 0.200147324806200 -1.19903213919377 0.230877720471699 df.mm.trans2:exp6 -0.217938557203321 0.156113836705398 -1.39602332376592 0.163102643227504 df.mm.trans1:exp7 -0.227369291721617 0.200147324806200 -1.13600964660295 0.256299662953754 df.mm.trans2:exp7 -0.122491516999528 0.156113836705399 -0.784629470292766 0.432907860094325 df.mm.trans1:exp8 -0.237977689984853 0.200147324806200 -1.18901259467387 0.234794710231585 df.mm.trans2:exp8 -0.345537564984516 0.156113836705398 -2.21336924565231 0.0271594121333411 * df.mm.trans1:probe2 -0.0701417752769765 0.127191405140853 -0.551466313303958 0.581471187435538 df.mm.trans1:probe3 -0.0585532323470849 0.127191405140853 -0.460355259714619 0.645388895911301 df.mm.trans1:probe4 0.0154199006213503 0.127191405140853 0.121233825542489 0.903536897186112 df.mm.trans1:probe5 -0.149754267553056 0.127191405140853 -1.17739298018774 0.239395989556633 df.mm.trans1:probe6 -0.10968374210454 0.127191405140853 -0.86235183881391 0.388757629784178 df.mm.trans1:probe7 -0.09744130361581 0.127191405140853 -0.766099749490952 0.443847543409885 df.mm.trans1:probe8 -0.100201755172050 0.127191405140853 -0.787802879141759 0.43105012929411 df.mm.trans1:probe9 -0.173094162954704 0.127191405140853 -1.36089512308648 0.173937891732985 df.mm.trans1:probe10 -0.162287659741113 0.127191405140853 -1.27593259592811 0.202357227393699 df.mm.trans1:probe11 -0.15673541383243 0.127191405140853 -1.23227991434531 0.218213966473065 df.mm.trans1:probe12 0.0970171575600415 0.127191405140853 0.762765042595476 0.4458329668638 df.mm.trans1:probe13 -0.0921927789371252 0.127191405140853 -0.724834974777027 0.46876954285624 df.mm.trans1:probe14 -0.195473251193339 0.127191405140853 -1.53684324012986 0.124735162137710 df.mm.trans1:probe15 -0.0420061497353509 0.127191405140853 -0.330259341728576 0.741292241995883 df.mm.trans1:probe16 -0.2465162258224 0.127191405140853 -1.93815160347828 0.0529634197294584 . df.mm.trans1:probe17 -0.108051793387201 0.127191405140853 -0.849521186337576 0.395850679248476 df.mm.trans1:probe18 -0.0733334256309292 0.127191405140853 -0.576559599681431 0.564402487982518 df.mm.trans1:probe19 -0.177043047004545 0.127191405140853 -1.39194190683313 0.164334724978749 df.mm.trans1:probe20 -0.0722096373873119 0.127191405140853 -0.567724189439892 0.570384801596558 df.mm.trans1:probe21 -0.0791352630025019 0.127191405140853 -0.622174610893452 0.534007955036869 df.mm.trans1:probe22 -0.101963128265814 0.127191405140853 -0.801651087610037 0.422997716932777 df.mm.trans2:probe2 -0.0906565708927335 0.127191405140853 -0.712757051408774 0.476208182166897 df.mm.trans2:probe3 -0.150500397022580 0.127191405140853 -1.18325917428079 0.237065118498272 df.mm.trans2:probe4 -0.149461053390200 0.127191405140853 -1.17508768162979 0.240316393609692 df.mm.trans2:probe5 -0.0268900519678183 0.127191405140853 -0.211414064795022 0.832619125422422 df.mm.trans2:probe6 0.123082656381263 0.127191405140853 0.96769633329359 0.333494468805947 df.mm.trans3:probe2 -0.0602370710258062 0.127191405140853 -0.473593879705149 0.635921386661117 df.mm.trans3:probe3 -0.188519046506859 0.127191405140853 -1.48216812526043 0.138697384765392 df.mm.trans3:probe4 0.0374451080362768 0.127191405140853 0.294399672641477 0.768530447075879 df.mm.trans3:probe5 -0.0917608559406551 0.127191405140853 -0.721439124279176 0.47085447010193 df.mm.trans3:probe6 0.0092460542058582 0.127191405140853 0.0726940173010826 0.94206815051575 df.mm.trans3:probe7 0.0184400273761289 0.127191405140853 0.144978564830762 0.884765057826674