fitVsDatCorrelation=0.79819030171268 cont.fitVsDatCorrelation=0.258006291046074 fstatistic=14068.0121576601,59,853 cont.fstatistic=5460.40814756448,59,853 residuals=-0.507679363937365,-0.0807056442709763,0.00109986175283096,0.0696963975562237,1.01900991704637 cont.residuals=-0.448324236375632,-0.142680357205794,-0.0363276246446276,0.104515077344839,0.938678104356553 predictedValues: Include Exclude Both Lung 51.9609964574364 45.6259752834637 67.9792702486861 cerebhem 57.7145060389741 50.6025473246647 69.2184113996882 cortex 59.2337590538729 44.784791442899 81.0493246799656 heart 48.3928396684606 46.2004325863543 59.6305734574052 kidney 50.2848199603728 45.9031416981055 59.4281142507075 liver 50.2352856647395 48.4951597116382 54.2383526121996 stomach 50.0751790197745 45.0303986743043 63.4451480256975 testicle 51.3647993563285 48.4110480577919 58.2982920271336 diffExp=6.33502117397264,7.11195871430947,14.4489676109739,2.19240708210627,4.3816782622673,1.74012595310136,5.04478034547017,2.95375129853664 diffExpScore=0.977880359058158 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 55.4461735545115 58.5102728782614 58.090524247318 cerebhem 55.9043189750762 53.2177445269159 56.3942641500213 cortex 53.985920143683 52.2323001548402 53.2428146961147 heart 55.8225660807469 50.96352317784 57.119278240259 kidney 53.4225428768051 54.5710898650421 53.5026939160673 liver 51.5461300469978 55.7316664274809 56.9076988041386 stomach 54.6240888305508 54.862350827554 53.4290717254387 testicle 53.6210151241715 54.8470856207153 54.0246930772223 cont.diffExp=-3.06409932374984,2.68657444816034,1.75361998884275,4.85904290290694,-1.14854698823702,-4.1855363804831,-0.238261997003136,-1.22607049654371 cont.diffExpScore=12.2574196094748 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=0.178225145674199 cont.tran.correlation=-0.240519392188427 tran.covariance=0.000539505203840017 cont.tran.covariance=-0.000293395087502563 tran.mean=49.6447299999488 cont.tran.mean=54.3317993194495 weightedLogRatios: wLogRatio Lung 0.505175958249794 cerebhem 0.524679261061687 cortex 1.10218453266955 heart 0.178782479125183 kidney 0.35301979635163 liver 0.137457332888349 stomach 0.409930234435303 testicle 0.231530879782055 cont.weightedLogRatios: wLogRatio Lung -0.217434263630765 cerebhem 0.196950596681087 cortex 0.131171161325974 heart 0.362145240837752 kidney -0.0848489964980253 liver -0.310842149036938 stomach -0.0174209914177814 testicle -0.0902792333075957 varWeightedLogRatios=0.094408124915086 cont.varWeightedLogRatios=0.0495116487885643 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74310780222167 0.0616024594313463 60.7623110631358 0 *** df.mm.trans1 0.188906983805271 0.0531983771180385 3.55099147829486 0.000404796486593678 *** df.mm.trans2 0.082898345319129 0.0470005257314112 1.76377485206993 0.0781278311130154 . df.mm.exp2 0.190475828544217 0.0604577040456696 3.1505633823 0.00168639249482472 ** df.mm.exp3 -0.0634654246434425 0.0604577040456696 -1.04974917002308 0.294130769225468 df.mm.exp4 0.0724048560648856 0.0604577040456695 1.19761173878173 0.231400887198647 df.mm.exp5 0.107701637184647 0.0604577040456695 1.78143776520673 0.0751966894185682 . df.mm.exp6 0.253025656344786 0.0604577040456695 4.18516813264446 3.14505084312465e-05 *** df.mm.exp7 0.0189197750516439 0.0604577040456695 0.31294233465022 0.75440096897076 df.mm.exp8 0.201340609759035 0.0604577040456695 3.33027217849595 0.000904857991970521 *** df.mm.trans1:exp2 -0.0854606511520514 0.0558823130663674 -1.52929695394956 0.126561577032568 df.mm.trans2:exp2 -0.086951098883364 0.041271823408278 -2.10679082489784 0.0354271891314113 * df.mm.trans1:exp3 0.194463689275244 0.0558823130663673 3.47987902799037 0.000527070581655608 *** df.mm.trans2:exp3 0.0448568419580037 0.041271823408278 1.08686358521796 0.277404125668175 df.mm.trans1:exp4 -0.143546363034260 0.0558823130663673 -2.56872622405196 0.0103763516263295 * df.mm.trans2:exp4 -0.0598928824301343 0.041271823408278 -1.45118091434074 0.147097139916245 df.mm.trans1:exp5 -0.140491764692330 0.0558823130663674 -2.51406495156129 0.0121179268540110 * df.mm.trans2:exp5 -0.101645263628796 0.041271823408278 -2.46282464002811 0.0139813085552034 * df.mm.trans1:exp6 -0.286801343892119 0.0558823130663673 -5.13223823701618 3.54719434917931e-07 *** df.mm.trans2:exp6 -0.192038850891821 0.041271823408278 -4.65302560034948 3.79056773870037e-06 *** df.mm.trans1:exp7 -0.0558876874865765 0.0558823130663673 -1.00009617390395 0.317547581925847 df.mm.trans2:exp7 -0.0320591750008302 0.041271823408278 -0.776781163354173 0.437503239375119 df.mm.trans1:exp8 -0.212880878345063 0.0558823130663673 -3.80945001493119 0.000149267322530069 *** df.mm.trans2:exp8 -0.142089744178341 0.041271823408278 -3.44277844893671 0.000603795385263618 *** df.mm.trans1:probe2 0.163638981902575 0.0382600042900188 4.27702466163249 2.10819962934075e-05 *** df.mm.trans1:probe3 0.0328913442131381 0.0382600042900189 0.859679574623536 0.390207233861859 df.mm.trans1:probe4 0.0471445418618908 0.0382600042900189 1.23221475629028 0.218208412662926 df.mm.trans1:probe5 -0.0373999247338347 0.0382600042900189 -0.977520139578015 0.328588856081182 df.mm.trans1:probe6 0.104627492872128 0.0382600042900189 2.73464404444469 0.00637428630256924 ** df.mm.trans1:probe7 0.105447296938744 0.0382600042900188 2.7560712262192 0.00597482647745658 ** df.mm.trans1:probe8 0.0593617826192074 0.0382600042900188 1.55153622485854 0.121144154816147 df.mm.trans1:probe9 0.137483941374420 0.0382600042900189 3.59341155145366 0.000345076409278194 *** df.mm.trans1:probe10 0.208924344485863 0.0382600042900189 5.46064613328772 6.22465298920334e-08 *** df.mm.trans1:probe11 0.101026194139718 0.0382600042900189 2.64051706251568 0.00842927523896265 ** df.mm.trans1:probe12 0.00259101429135219 0.0382600042900188 0.0677212232312303 0.946023424628054 df.mm.trans1:probe13 0.065726379974147 0.0382600042900188 1.717887417783 0.0861800694414145 . df.mm.trans1:probe14 -0.035824688446488 0.0382600042900189 -0.936348260050608 0.349358922448689 df.mm.trans1:probe15 0.134692321325145 0.0382600042900188 3.52044710460953 0.000453644721643936 *** df.mm.trans1:probe16 -0.0257692847067808 0.0382600042900188 -0.67353062773972 0.500792220880379 df.mm.trans1:probe17 -0.135951656943236 0.0382600042900188 -3.55336230264622 0.000401218513916517 *** df.mm.trans1:probe18 -0.04393968550708 0.0382600042900188 -1.14844957083664 0.251104972611066 df.mm.trans1:probe19 -0.0136798962500774 0.0382600042900189 -0.357550828964392 0.720767929060293 df.mm.trans1:probe20 -0.101543898317204 0.0382600042900189 -2.65404827316485 0.00810131672139748 ** df.mm.trans1:probe21 -0.104333862635652 0.0382600042900189 -2.72696944424729 0.0065231159148391 ** df.mm.trans1:probe22 -0.0737980828924023 0.0382600042900189 -1.92885715153081 0.0540800995995764 . df.mm.trans2:probe2 -0.00522200707210518 0.0382600042900188 -0.136487362430001 0.891468219852004 df.mm.trans2:probe3 0.00460808548467399 0.0382600042900188 0.120441321693112 0.904161922302768 df.mm.trans2:probe4 -0.0760603331174019 0.0382600042900188 -1.98798548324377 0.0471325773311013 * df.mm.trans2:probe5 0.00493415386082216 0.0382600042900188 0.128963756078547 0.897416728636963 df.mm.trans2:probe6 -0.016723255807014 0.0382600042900188 -0.437094979923374 0.662153098729964 df.mm.trans3:probe2 0.432981395498358 0.0382600042900188 11.3168151319657 9.22147499674247e-28 *** df.mm.trans3:probe3 0.284927158728834 0.0382600042900189 7.44712824831451 2.33562852970573e-13 *** df.mm.trans3:probe4 0.0511691719138261 0.0382600042900189 1.33740632975242 0.181446537334158 df.mm.trans3:probe5 0.284126465258424 0.0382600042900188 7.42620055932785 2.71010279907581e-13 *** df.mm.trans3:probe6 0.0649114137902502 0.0382600042900188 1.69658668353009 0.0901396679965007 . df.mm.trans3:probe7 0.0160563439395475 0.0382600042900188 0.419663934636209 0.674836648839068 df.mm.trans3:probe8 0.246512921899728 0.0382600042900189 6.44309707942289 1.95483973709805e-10 *** df.mm.trans3:probe9 0.247363266898006 0.0382600042900188 6.46532250814559 1.69962001456506e-10 *** df.mm.trans3:probe10 0.414848642773253 0.0382600042900189 10.8428801949057 9.42481259351631e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05680123133421 0.0987985856130314 41.0613290277621 3.04588929062524e-204 *** df.mm.trans1 -0.0346929196977812 0.0853200418406735 -0.406620987863164 0.684388379362158 df.mm.trans2 0.0226977540719708 0.0753798713265243 0.301111605426475 0.763402774467903 df.mm.exp2 -0.0569464208450752 0.0969626164971586 -0.587302848276005 0.557155748166959 df.mm.exp3 -0.053050996430694 0.0969626164971586 -0.547128350566413 0.58443366316745 df.mm.exp4 -0.114465830553893 0.0969626164971586 -1.18051507569669 0.238124497130413 df.mm.exp5 -0.0246074363527013 0.0969626164971586 -0.253782717934622 0.799724582774117 df.mm.exp6 -0.101017488499276 0.0969626164971586 -1.04181892102960 0.297790822540941 df.mm.exp7 0.0043348055014004 0.0969626164971586 0.0447059460439316 0.964352157407536 df.mm.exp8 -0.0255635962720464 0.0969626164971586 -0.263643837135888 0.792118051527961 df.mm.trans1:exp2 0.0651753562623281 0.089624562764329 0.727204175419066 0.467300420948703 df.mm.trans2:exp2 -0.0378640382593578 0.0661921263541922 -0.572032360114077 0.567450766282823 df.mm.trans1:exp3 0.0263615666340103 0.0896245627643289 0.294133280218381 0.768727618815987 df.mm.trans2:exp3 -0.0604502667092663 0.0661921263541922 -0.913254642792387 0.361366680769456 df.mm.trans1:exp4 0.121231323955752 0.0896245627643289 1.35265735437432 0.176523560776601 df.mm.trans2:exp4 -0.0236263680068401 0.0661921263541922 -0.356936229551171 0.72122782706974 df.mm.trans1:exp5 -0.0125724597938186 0.0896245627643289 -0.140279175775488 0.888472533851832 df.mm.trans2:exp5 -0.0450906543545353 0.0661921263541922 -0.681208730374614 0.495924311005882 df.mm.trans1:exp6 0.0280819200280688 0.0896245627643289 0.313328390810801 0.754107780138941 df.mm.trans2:exp6 0.0523636478589613 0.0661921263541922 0.791085749062735 0.429113854840415 df.mm.trans1:exp7 -0.0192725369552798 0.0896245627643289 -0.215036328890749 0.829790395192506 df.mm.trans2:exp7 -0.0687098130923016 0.0661921263541922 -1.03803604562629 0.299547430074483 df.mm.trans1:exp8 -0.00790804356258981 0.0896245627643289 -0.0882352261330893 0.92971043038846 df.mm.trans2:exp8 -0.0390896956202122 0.0661921263541922 -0.590549024079456 0.554979052426232 df.mm.trans1:probe2 0.0345921734329612 0.0613617434157006 0.563741698123104 0.573078107511664 df.mm.trans1:probe3 -0.0198257603831220 0.0613617434157006 -0.323096432394539 0.746701398094765 df.mm.trans1:probe4 -0.0220579461814078 0.0613617434157006 -0.359473915725866 0.719329558440308 df.mm.trans1:probe5 0.044952716902104 0.0613617434157006 0.732585392784032 0.464012592187482 df.mm.trans1:probe6 -0.00106097132777761 0.0613617434157006 -0.0172904364954229 0.986208958355492 df.mm.trans1:probe7 -0.0868505267068158 0.0613617434157006 -1.41538557857522 0.157320458251786 df.mm.trans1:probe8 -0.0190275046862518 0.0613617434157006 -0.310087419735588 0.756570220995116 df.mm.trans1:probe9 -0.0382171902637699 0.0613617434157006 -0.622817868861126 0.533570682300242 df.mm.trans1:probe10 -0.0221092323522625 0.0613617434157006 -0.360309716144822 0.718704732365369 df.mm.trans1:probe11 -0.064640092007855 0.0613617434157006 -1.05342658812585 0.292443825736585 df.mm.trans1:probe12 0.00209875051738994 0.0613617434157006 0.0342029153763081 0.972723343502139 df.mm.trans1:probe13 -0.0173031132410295 0.0613617434157006 -0.281985358919939 0.778023136228206 df.mm.trans1:probe14 -0.0716984316306527 0.0613617434157006 -1.16845493037780 0.242949762674959 df.mm.trans1:probe15 0.100079031536041 0.0613617434157006 1.63096786312029 0.103266236250286 df.mm.trans1:probe16 0.089264618818622 0.0613617434157006 1.45472755253857 0.146112573168253 df.mm.trans1:probe17 -0.0549204039318321 0.0613617434157006 -0.895026785007866 0.371025251767748 df.mm.trans1:probe18 -0.00307690826510930 0.0613617434157006 -0.0501437556013445 0.960019570580671 df.mm.trans1:probe19 0.0217985873372603 0.0613617434157006 0.355247196768577 0.72249223154916 df.mm.trans1:probe20 -0.0497420642645295 0.0613617434157006 -0.810636424189375 0.417800598693429 df.mm.trans1:probe21 -0.036409705898122 0.0613617434157006 -0.593361659421265 0.553096436433721 df.mm.trans1:probe22 -0.000105462715661612 0.0613617434157006 -0.00171870468130518 0.998629074589642 df.mm.trans2:probe2 -0.0219938573614095 0.0613617434157006 -0.358429473106886 0.720110624987331 df.mm.trans2:probe3 0.00290495320637246 0.0613617434157006 0.0473414385685327 0.962252183278126 df.mm.trans2:probe4 -0.0674559716489866 0.0613617434157006 -1.09931641270360 0.271940242095234 df.mm.trans2:probe5 -0.0635977908409126 0.0613617434157006 -1.03644041548923 0.300290445438134 df.mm.trans2:probe6 -0.0146036026106207 0.0613617434157006 -0.237991976722162 0.811944472164604 df.mm.trans3:probe2 -0.00082504293827909 0.0613617434157006 -0.0134455589485090 0.989275463354927 df.mm.trans3:probe3 -0.0537306413357256 0.0613617434157006 -0.875637463096878 0.381473652603112 df.mm.trans3:probe4 0.0506927099642618 0.0613617434157006 0.826128906097721 0.408961976355658 df.mm.trans3:probe5 0.0741087431746972 0.0613617434157006 1.20773529318815 0.227483848759371 df.mm.trans3:probe6 0.0934682952991402 0.0613617434157006 1.52323402328925 0.128070795042580 df.mm.trans3:probe7 0.0272437400676511 0.0613617434157006 0.443985756452289 0.657165487251536 df.mm.trans3:probe8 0.0509121735366103 0.0613617434157006 0.82970545982863 0.406937466425573 df.mm.trans3:probe9 -0.0457805749701009 0.0613617434157006 -0.746076829335768 0.455826560301275 df.mm.trans3:probe10 -0.0241244928155266 0.0613617434157006 -0.393152010888821 0.69430544835854