chr19.12224_chr19_34559230_34561286_+_1.R fitVsDatCorrelation=0.599045173166271 cont.fitVsDatCorrelation=0.298698409832982 fstatistic=10260.7975913425,36,324 cont.fstatistic=7220.44454157836,36,324 residuals=-0.289421857117464,-0.083058302060642,-0.00230845859858059,0.072634032016946,0.439473454832602 cont.residuals=-0.347195126276489,-0.096175355027095,-0.0163376566699955,0.0785628706501473,0.484227393492779 predictedValues: Include Exclude Both chr19.12224_chr19_34559230_34561286_+_1.R.tl.Lung 47.7526331850699 43.2070075816672 45.7236211536502 chr19.12224_chr19_34559230_34561286_+_1.R.tl.cerebhem 51.4084878639066 50.9054027118923 48.7816604530226 chr19.12224_chr19_34559230_34561286_+_1.R.tl.cortex 48.1831919947137 43.4697747707958 48.5059540219419 chr19.12224_chr19_34559230_34561286_+_1.R.tl.heart 52.1476238776253 44.9348937110472 47.392760326098 chr19.12224_chr19_34559230_34561286_+_1.R.tl.kidney 47.1210837649017 42.870848782506 46.6541508054126 chr19.12224_chr19_34559230_34561286_+_1.R.tl.liver 52.657671258604 48.9778455038144 48.5066805232064 chr19.12224_chr19_34559230_34561286_+_1.R.tl.stomach 51.7887783806969 47.3957613643456 52.0088820189645 chr19.12224_chr19_34559230_34561286_+_1.R.tl.testicle 48.9219435250868 46.3443971490363 51.9679060396154 diffExp=4.54562560340271,0.503085152014322,4.71341722391785,7.21273016657803,4.25023498239571,3.67982575478960,4.39301701635124,2.5775463760505 diffExpScore=0.969582195277931 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 47.3088146822083 49.3907542200668 48.156378408345 cerebhem 49.5346746185961 49.0854338116991 46.5084174481376 cortex 47.8687913127173 47.0354846489566 43.5676485112258 heart 45.6565585801964 48.5665097574184 45.8793731476179 kidney 45.2731930575777 48.6184605077478 47.8868138338324 liver 47.296134900462 44.6958798563778 48.8117144241337 stomach 47.2511081377431 47.0313215453902 50.8937860963401 testicle 44.7424601003237 46.5177130542754 47.0734456597736 cont.diffExp=-2.08193953785855,0.449240806896945,0.833306663760744,-2.90995117722203,-3.34526745017006,2.60025504408416,0.219786592352918,-1.77525295395172 cont.diffExpScore=2.02786892473796 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=0.756703135524496 cont.tran.correlation=0.128184014868573 tran.covariance=0.00215830826911032 cont.tran.covariance=0.000133069312959964 tran.mean=48.0054590891069 cont.tran.mean=47.2420807994848 weightedLogRatios: wLogRatio Lung 0.381722110938559 cerebhem 0.0386965788021107 cortex 0.393611764841264 heart 0.577539643493866 kidney 0.35972392369079 liver 0.284529809701354 stomach 0.345951596749525 testicle 0.209096163695438 cont.weightedLogRatios: wLogRatio Lung -0.167022216656106 cerebhem 0.035514169425736 cortex 0.0677816087259523 heart -0.238005755678354 kidney -0.274342646510114 liver 0.216471648233633 stomach 0.0179645589428180 testicle -0.148651768927353 varWeightedLogRatios=0.0243395332208672 cont.varWeightedLogRatios=0.0292706592042229 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88898830139116 0.0681158689100647 57.0937193288381 3.74527480613268e-171 *** df.mm.trans1 0.00503793047002941 0.0576934294189165 0.0873224303143534 0.930469168295893 df.mm.trans2 -0.0874790901885999 0.0576934294189166 -1.51627474860971 0.130424907529958 df.mm.exp2 0.172996055456047 0.0803450956583239 2.15316260486802 0.0320435887273027 * df.mm.exp3 -0.044032310228377 0.080345095658324 -0.548039800906195 0.58404194598189 df.mm.exp4 0.091401904943789 0.080345095658324 1.13761648044437 0.256121230416743 df.mm.exp5 -0.0412711490118898 0.0803450956583239 -0.513673531330398 0.607830378387275 df.mm.exp6 0.164056596321142 0.0803450956583239 2.04189932163140 0.0419710066969619 * df.mm.exp7 0.04486991552575 0.0803450956583239 0.558464896433307 0.576912697210446 df.mm.exp8 -0.0337217611286149 0.080345095658324 -0.419711506375202 0.674974384927105 df.mm.trans1:exp2 -0.099226973732294 0.0695808939095993 -1.42606638341283 0.154811959292141 df.mm.trans2:exp2 -0.00902968851392777 0.0695808939095993 -0.129772528154917 0.896826925845318 df.mm.trans1:exp3 0.0530083460241514 0.0695808939095993 0.761823297254858 0.446719653964503 df.mm.trans2:exp3 0.0500954793258937 0.0695808939095993 0.719960272298005 0.4720684914825 df.mm.trans1:exp4 -0.00335749840706878 0.0695808939095993 -0.0482531657531003 0.961544209487266 df.mm.trans2:exp4 -0.0521899638632843 0.0695808939095993 -0.750061704166814 0.453761849929927 df.mm.trans1:exp5 0.0279574775513856 0.0695808939095993 0.401798194597908 0.688097457068929 df.mm.trans2:exp5 0.0334605337775962 0.0695808939095993 0.480886805235194 0.630921518373378 df.mm.trans1:exp6 -0.0662788760807053 0.0695808939095993 -0.952544187874562 0.341531131563305 df.mm.trans2:exp6 -0.0386912276621103 0.0695808939095993 -0.556061089303892 0.578552891988604 df.mm.trans1:exp7 0.0362693660506009 0.0695808939095993 0.5212546722628 0.602545462567938 df.mm.trans2:exp7 0.0476601918500768 0.0695808939095993 0.684960901939515 0.493858308440276 df.mm.trans1:exp8 0.0579135891554305 0.0695808939095993 0.832320280775249 0.405841239465729 df.mm.trans2:exp8 0.103819469841673 0.0695808939095993 1.49206864137958 0.136654175089349 df.mm.trans1:probe2 -0.104297260338988 0.0347904469547997 -2.99787066474004 0.00292895853433458 ** df.mm.trans1:probe3 -0.0294613046524016 0.0347904469547997 -0.846821677533441 0.397719567965229 df.mm.trans1:probe4 -0.120972070702704 0.0347904469547997 -3.47716345409051 0.000575906707686028 *** df.mm.trans1:probe5 0.113022694622914 0.0347904469547997 3.24867038269889 0.00128104366842100 ** df.mm.trans1:probe6 -0.110220249889293 0.0347904469547997 -3.16811824902661 0.00168046341519251 ** df.mm.trans2:probe2 -0.0683932277458545 0.0347904469547997 -1.96586229072343 0.0501681525571436 . df.mm.trans2:probe3 -0.0409914063372643 0.0347904469547997 -1.17823741645288 0.239566604591764 df.mm.trans2:probe4 -0.0408387914989784 0.0347904469547997 -1.17385072839210 0.241316951538857 df.mm.trans2:probe5 -0.0957741387265115 0.0347904469547997 -2.75288612563509 0.00624025684160999 ** df.mm.trans2:probe6 -0.073561084811836 0.0347904469547997 -2.11440470734417 0.0352442058766506 * df.mm.trans3:probe2 0.042582364162928 0.0347904469547997 1.22396714874781 0.221853915858501 df.mm.trans3:probe3 0.00562132669721463 0.0347904469547997 0.161576731236536 0.87173989079788 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82703834241560 0.0811851837183855 47.1396154708574 4.27527125747516e-147 *** df.mm.trans1 0.0181647412812687 0.068763002537671 0.264164457788436 0.791821145063915 df.mm.trans2 0.0622012435374514 0.068763002537671 0.904574280382465 0.366363076419628 df.mm.exp2 0.0745956509768756 0.0957608183858677 0.778978837422763 0.436560747958624 df.mm.exp3 0.0630449136033982 0.0957608183858678 0.658358132961636 0.510775564649575 df.mm.exp4 -0.00394044905539220 0.0957608183858678 -0.0411488657032377 0.967202563327873 df.mm.exp5 -0.0541280651797254 0.0957608183858677 -0.565242299429989 0.572300151525522 df.mm.exp6 -0.113666713192133 0.0957608183858677 -1.18698560755938 0.236102855429134 df.mm.exp7 -0.105457214736145 0.0957608183858677 -1.10125640646894 0.271602519309943 df.mm.exp8 -0.0929592296962901 0.0957608183858678 -0.97074389362162 0.332399947046489 df.mm.trans1:exp2 -0.028619364343921 0.0829313014093494 -0.345097253480392 0.730245180350743 df.mm.trans2:exp2 -0.0807965690424963 0.0829313014093494 -0.97425902728433 0.330654751735475 df.mm.trans1:exp3 -0.0512777949398309 0.0829313014093494 -0.61831653511288 0.536801115287863 df.mm.trans2:exp3 -0.111905849365701 0.0829313014093494 -1.3493801190136 0.178157047271761 df.mm.trans1:exp4 -0.0316089182975241 0.0829313014093494 -0.381145812984440 0.703344859175919 df.mm.trans2:exp4 -0.0128886023579081 0.0829313014093494 -0.155413000144420 0.876592512897197 df.mm.trans1:exp5 0.0101465227201573 0.0829313014093494 0.122348528815122 0.902698852069001 df.mm.trans2:exp5 0.0383681246615761 0.0829313014093494 0.462649494334965 0.643926182592021 df.mm.trans1:exp6 0.113398655719914 0.0829313014093494 1.36738063665705 0.172453843136558 df.mm.trans2:exp6 0.0137847921881841 0.0829313014093494 0.166219412380161 0.868087959997266 df.mm.trans1:exp7 0.104236685998218 0.0829313014093494 1.25690401846831 0.209693740618651 df.mm.trans2:exp7 0.0565077652092625 0.0829313014093494 0.68138042269878 0.496117530485126 df.mm.trans1:exp8 0.0371855357896665 0.0829313014093494 0.448389632837407 0.654171728757886 df.mm.trans2:exp8 0.0330291505142842 0.0829313014093494 0.398271219105222 0.690692586221745 df.mm.trans1:probe2 -0.00362950444709870 0.0414656507046747 -0.087530386848349 0.930304005324824 df.mm.trans1:probe3 0.0195147015912844 0.0414656507046747 0.470623305305669 0.638226426413701 df.mm.trans1:probe4 0.0108851624475892 0.0414656507046747 0.262510349231346 0.793094840638273 df.mm.trans1:probe5 0.0245402002783954 0.0414656507046747 0.591819972950017 0.554383867480255 df.mm.trans1:probe6 0.0521314023378254 0.0414656507046747 1.25721896200578 0.209579852510564 df.mm.trans2:probe2 0.0157094266009675 0.0414656507046747 0.378853975133604 0.70504444441037 df.mm.trans2:probe3 0.0143386153731416 0.0414656507046747 0.345795016585259 0.729721188031263 df.mm.trans2:probe4 0.0131419325081714 0.0414656507046747 0.316935397970008 0.75149673995677 df.mm.trans2:probe5 -0.0244915209190891 0.0414656507046747 -0.590646004653871 0.555169397633392 df.mm.trans2:probe6 0.0760144791180655 0.0414656507046747 1.83319151698482 0.0676914413315776 . df.mm.trans3:probe2 -0.0474865994484451 0.0414656507046747 -1.14520328612838 0.252970167437377 df.mm.trans3:probe3 -0.0513655852224456 0.0414656507046747 -1.23875025110012 0.21633468535827