chr13.6338_chr13_65313075_65321620_+_1.R fitVsDatCorrelation=0.731756325505477 cont.fitVsDatCorrelation=0.345383176725540 fstatistic=11792.8143003057,39,393 cont.fstatistic=6215.3841496058,39,393 residuals=-0.401673936677922,-0.0623858889792222,-0.00736510986268232,0.0651281845202666,0.504385819739092 cont.residuals=-0.371287681021936,-0.113223830989461,-0.0172372249298984,0.09271949315933,0.587856803333939 predictedValues: Include Exclude Both chr13.6338_chr13_65313075_65321620_+_1.R.tl.Lung 51.4810717565106 53.3734343574205 60.2673027120479 chr13.6338_chr13_65313075_65321620_+_1.R.tl.cerebhem 60.9214110276912 66.3512030398642 67.8385239916284 chr13.6338_chr13_65313075_65321620_+_1.R.tl.cortex 54.2008165734787 51.5403764532251 70.4848233803968 chr13.6338_chr13_65313075_65321620_+_1.R.tl.heart 49.8963036556085 49.2174332871637 60.8559268053827 chr13.6338_chr13_65313075_65321620_+_1.R.tl.kidney 52.2092089311226 49.1093833283709 62.5042648554809 chr13.6338_chr13_65313075_65321620_+_1.R.tl.liver 50.9553551674121 48.2088328323471 63.7778147003901 chr13.6338_chr13_65313075_65321620_+_1.R.tl.stomach 51.0182153493654 51.1562581842069 58.3795958713376 chr13.6338_chr13_65313075_65321620_+_1.R.tl.testicle 49.3668775900271 54.4021966586774 58.8685806498851 diffExp=-1.8923626009099,-5.42979201217297,2.66044012025363,0.678870368444755,3.09982560275165,2.74652233506498,-0.138042834841464,-5.03531906865032 diffExpScore=5.03060065784897 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 57.1618303506931 55.767550718196 53.3667906554772 cerebhem 58.429494643621 52.5012150635721 54.5114621310034 cortex 59.6478424897012 50.5398597991499 58.7453059703626 heart 58.595492710642 53.1311460459243 56.442328273143 kidney 56.4492585978893 56.0230311275529 55.934925076543 liver 55.9170115649346 56.9271218821062 54.0524457065045 stomach 55.4460280346064 51.0156216568477 53.2625118383503 testicle 55.696768329962 55.3504713937699 57.4160877078649 cont.diffExp=1.39427963249715,5.92827958004887,9.10798269055129,5.4643466647177,0.426227470336386,-1.01011031717162,4.4304063777587,0.346296936192033 cont.diffExpScore=1.03766359986471 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=0.834629978588404 cont.tran.correlation=-0.515918804631418 tran.covariance=0.0055322130207286 cont.tran.covariance=-0.000625033353861471 tran.mean=52.7130236370308 cont.tran.mean=55.537484025573 weightedLogRatios: wLogRatio Lung -0.142925209127017 cerebhem -0.354509478023185 cortex 0.199687667007148 heart 0.0534686451400471 kidney 0.240223285426575 liver 0.216269505809543 stomach -0.010628809056502 testicle -0.383433485272594 cont.weightedLogRatios: wLogRatio Lung 0.0996050343617676 cerebhem 0.429470910643962 cortex 0.663712179963508 heart 0.393703208516431 kidney 0.0305411084242131 liver -0.0722005131395508 stomach 0.330928443032437 testicle 0.0250526048224166 varWeightedLogRatios=0.0623312856640901 cont.varWeightedLogRatios=0.0649333134988465 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89009247527189 0.0642718710594603 60.525583138431 2.6673731623083e-201 *** df.mm.trans1 0.0224553563620748 0.052477762969877 0.427902316929257 0.66895662680266 df.mm.trans2 0.0947973421201962 0.052477762969877 1.80642879488996 0.0716162097457678 . df.mm.exp2 0.267678721471846 0.0713032389768906 3.75408922950332 0.000200240941228240 *** df.mm.exp3 -0.140073558438881 0.0713032389768906 -1.96447679584765 0.0501790614753233 . df.mm.exp4 -0.122052020680147 0.0713032389768906 -1.71173178710302 0.0877350466024308 . df.mm.exp5 -0.105663399995922 0.0713032389768906 -1.48188780077954 0.139171505583246 df.mm.exp6 -0.168650905939079 0.0713032389768905 -2.36526290192425 0.0185021498840633 * df.mm.exp7 -0.0196365023486433 0.0713032389768906 -0.275394254600517 0.783158093235641 df.mm.exp8 0.000639098586372993 0.0713032389768906 0.00896310736431098 0.992853118818342 df.mm.trans1:exp2 -0.0993082331375128 0.0582188508337037 -1.70577453377046 0.0888401752062753 . df.mm.trans2:exp2 -0.050029967370877 0.0582188508337037 -0.859343093421451 0.390675118975023 df.mm.trans1:exp3 0.19155533130008 0.0582188508337037 3.29026300857842 0.00109131198546311 ** df.mm.trans2:exp3 0.105125929498180 0.0582188508337037 1.80570258589374 0.0717298538925316 . df.mm.trans1:exp4 0.09078474425377 0.0582188508337037 1.55937025471505 0.119713658012101 df.mm.trans2:exp4 0.0409867782849861 0.0582188508337037 0.70401214895259 0.481842131623431 df.mm.trans1:exp5 0.119708094227767 0.0582188508337037 2.05617411737825 0.0404256393192335 * df.mm.trans2:exp5 0.022400384785493 0.0582188508337037 0.384761713168771 0.700622097487809 df.mm.trans1:exp6 0.158386565010285 0.0582188508337037 2.72053746754123 0.00680706946581107 ** df.mm.trans2:exp6 0.0668800257533241 0.0582188508337037 1.14876925249453 0.251349836706055 df.mm.trans1:exp7 0.0106050336159884 0.0582188508337037 0.182158071898063 0.855552597794457 df.mm.trans2:exp7 -0.0227918013319842 0.0582188508337037 -0.391484905758217 0.695651018050174 df.mm.trans1:exp8 -0.0425735948054702 0.0582188508337037 -0.73126820945122 0.465051122848673 df.mm.trans2:exp8 0.0184522959788635 0.0582188508337037 0.316947100717783 0.751452041512827 df.mm.trans1:probe2 0.0215203252625247 0.0356516194884453 0.603628266297958 0.546439054743714 df.mm.trans1:probe3 0.0481333329619312 0.0356516194884453 1.35010228574697 0.177760207345376 df.mm.trans1:probe4 0.0648188280562833 0.0356516194884453 1.81811735305015 0.0698073589857535 . df.mm.trans1:probe5 0.206707724502363 0.0356516194884453 5.79798975385557 1.38002842179104e-08 *** df.mm.trans1:probe6 0.00281622643638471 0.0356516194884453 0.0789929455321783 0.937078439471071 df.mm.trans2:probe2 0.00379209283802847 0.0356516194884453 0.106365233681951 0.915346848169052 df.mm.trans2:probe3 -0.0260337842471679 0.0356516194884453 -0.73022725533143 0.46568634302571 df.mm.trans2:probe4 -0.0253488740416495 0.0356516194884453 -0.711016060570967 0.477496048552519 df.mm.trans2:probe5 -0.0483436180056615 0.0356516194884453 -1.35600061650298 0.175877575532270 df.mm.trans2:probe6 0.00501403358967707 0.0356516194884453 0.140639714594231 0.88822662767504 df.mm.trans3:probe2 0.0912318131033815 0.0356516194884453 2.55898089378380 0.0108716383900911 * df.mm.trans3:probe3 0.0923031668086181 0.0356516194884453 2.58903152600217 0.0099819071755237 ** df.mm.trans3:probe4 -0.0102349484951099 0.0356516194884453 -0.287082288040999 0.774200615565163 df.mm.trans3:probe5 0.161950294895496 0.0356516194884453 4.54257891280321 7.40338619983506e-06 *** df.mm.trans3:probe6 0.213416909670426 0.0356516194884453 5.98617714237623 4.84601444286533e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08832989018920 0.0884971703349118 46.1972950628497 6.61843318757095e-161 *** df.mm.trans1 -0.049947591668822 0.0722576370002341 -0.6912430815951 0.489820901722209 df.mm.trans2 -0.095425917038466 0.0722576370002341 -1.32063434399546 0.187391840407615 df.mm.exp2 -0.0596437641755715 0.0981787955003063 -0.607501486157318 0.543869010703455 df.mm.exp3 -0.151880836456320 0.0981787955003063 -1.54698207166176 0.122672427810844 df.mm.exp4 -0.0796881813583111 0.0981787955003063 -0.811663872552424 0.417475851144125 df.mm.exp5 -0.0549738210733998 0.0981787955003063 -0.559935786472633 0.575842246290713 df.mm.exp6 -0.0142041417774654 0.0981787955003062 -0.144676268486316 0.885040641409343 df.mm.exp7 -0.117580659813531 0.0981787955003063 -1.19761766493829 0.231787715111631 df.mm.exp8 -0.106607127251422 0.0981787955003063 -1.08584676261474 0.278212381929799 df.mm.trans1:exp2 0.0815781977051403 0.0801626508456025 1.01765843375445 0.309466379740799 df.mm.trans2:exp2 -0.00071209431296155 0.0801626508456025 -0.00888311832817358 0.99291689770491 df.mm.trans1:exp3 0.194452440772761 0.0801626508456025 2.42572368455338 0.0157273518515579 * df.mm.trans2:exp3 0.0534509924690042 0.0801626508456025 0.666781748172895 0.505302819133562 df.mm.trans1:exp4 0.104459584702767 0.0801626508456025 1.30309543909621 0.193305130987297 df.mm.trans2:exp4 0.0312593203011773 0.0801626508456025 0.389948685222304 0.696785740930875 df.mm.trans1:exp5 0.0424296037190936 0.0801626508456025 0.529293920192525 0.59690041041932 df.mm.trans2:exp5 0.0595445255087095 0.0801626508456025 0.742796363151655 0.458048737886958 df.mm.trans1:exp6 -0.00781357706120719 0.0801626508456025 -0.0974715404092182 0.922401624105798 df.mm.trans2:exp6 0.0347838561942199 0.0801626508456025 0.433915992389217 0.664587549683114 df.mm.trans1:exp7 0.0871043654877003 0.0801626508456025 1.08659537289339 0.277881673248037 df.mm.trans2:exp7 0.0285203807461280 0.0801626508456025 0.355781407491872 0.722195168294767 df.mm.trans1:exp8 0.0806428792110855 0.0801626508456025 1.00599067471469 0.315039039090075 df.mm.trans2:exp8 0.0991001310827458 0.0801626508456025 1.23623819867956 0.217108375229989 df.mm.trans1:probe2 0.0944432262363801 0.0490893977501531 1.92390272777558 0.0550886905165473 . df.mm.trans1:probe3 -0.0619581434266808 0.0490893977501531 -1.26214918630750 0.207643604622107 df.mm.trans1:probe4 0.0297020124841397 0.0490893977501531 0.605059622758297 0.545488586400814 df.mm.trans1:probe5 0.0391146092896793 0.0490893977501531 0.796803608973942 0.426046193962333 df.mm.trans1:probe6 -0.0112527978720854 0.0490893977501531 -0.229230717585048 0.818808845926634 df.mm.trans2:probe2 0.104322918986306 0.0490893977501531 2.12516192431756 0.034196432612951 * df.mm.trans2:probe3 0.00699632326472109 0.0490893977501531 0.142522083899456 0.88674067544247 df.mm.trans2:probe4 0.113710790061061 0.0490893977501531 2.31640222273264 0.0210499619020024 * df.mm.trans2:probe5 0.106916151629272 0.0490893977501531 2.17798866006537 0.0300008225156129 * df.mm.trans2:probe6 0.00751220088587404 0.0490893977501531 0.153031025642408 0.878452349108694 df.mm.trans3:probe2 0.0546458647737284 0.0490893977501531 1.11319077597683 0.266306909738103 df.mm.trans3:probe3 0.000522192419664421 0.0490893977501531 0.0106375804877906 0.991517996629732 df.mm.trans3:probe4 0.0529938440431936 0.0490893977501531 1.07953746576629 0.281010281998212 df.mm.trans3:probe5 0.0893198755100302 0.0490893977501531 1.81953496281693 0.0695905606680979 . df.mm.trans3:probe6 0.0079118623817638 0.0490893977501531 0.161172528985429 0.872040336711078