chr3.15132_chr3_88778995_88779831_-_0.R fitVsDatCorrelation=0.818675851417936 cont.fitVsDatCorrelation=0.250538492447775 fstatistic=7373.265269544,36,324 cont.fstatistic=2588.49187299625,36,324 residuals=-0.490051175449967,-0.084961328922462,-0.000793475562322491,0.081733234884761,0.524778935629396 cont.residuals=-0.508216003426616,-0.176496239423241,-0.0436031412005059,0.150690470573108,0.962499571730696 predictedValues: Include Exclude Both chr3.15132_chr3_88778995_88779831_-_0.R.tl.Lung 46.3344412930734 68.1794861163776 50.5015672401826 chr3.15132_chr3_88778995_88779831_-_0.R.tl.cerebhem 60.4742549313724 60.1225990292646 63.4307616675118 chr3.15132_chr3_88778995_88779831_-_0.R.tl.cortex 77.1772955167399 61.3034529260026 90.4078304203358 chr3.15132_chr3_88778995_88779831_-_0.R.tl.heart 50.2051231578949 63.5020639959768 51.6498973106516 chr3.15132_chr3_88778995_88779831_-_0.R.tl.kidney 47.5760649106741 81.3404878194843 49.4565438477504 chr3.15132_chr3_88778995_88779831_-_0.R.tl.liver 49.0175015704549 74.8636993042735 52.1772293636573 chr3.15132_chr3_88778995_88779831_-_0.R.tl.stomach 49.2504831370706 58.1950036591776 50.8516891130765 chr3.15132_chr3_88778995_88779831_-_0.R.tl.testicle 50.2463575216664 71.8413891093592 49.9293443894571 diffExp=-21.8450448233041,0.351655902107836,15.8738425907373,-13.2969408380819,-33.7644229088101,-25.8461977338186,-8.94452052210698,-21.5950315876928 diffExpScore=1.28574499315481 diffExp1.5=0,0,0,0,-1,-1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,0,0,0,-1,-1,0,-1 diffExp1.4Score=0.8 diffExp1.3=-1,0,0,0,-1,-1,0,-1 diffExp1.3Score=0.8 diffExp1.2=-1,0,1,-1,-1,-1,0,-1 diffExp1.2Score=1.2 cont.predictedValues: Include Exclude Both Lung 52.0238639056343 55.6686077427032 56.6078587819214 cerebhem 59.2957268466641 52.1708590036451 63.7165565035138 cortex 56.6389336347909 57.811740260448 65.5941339899953 heart 58.0815819591774 56.8578946980068 54.0696167546513 kidney 54.8039212444465 58.7718456855741 61.5557865332568 liver 60.0892030771153 58.2390015567667 59.3196908383013 stomach 61.797328331272 58.401108674005 57.7476223620405 testicle 59.5286598233127 52.7164040907685 55.8760569855635 cont.diffExp=-3.64474383706884,7.12486784301895,-1.17280662565713,1.22368726117060,-3.96792444112762,1.8502015203486,3.39621965726690,6.81225573254417 cont.diffExpScore=2.31288771148413 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=-0.473762273469913 cont.tran.correlation=-0.084909272286312 tran.covariance=-0.0100165585410574 cont.tran.covariance=-0.000244826160296086 tran.mean=60.6018564999289 cont.tran.mean=57.0560425333957 weightedLogRatios: wLogRatio Lung -1.55623983325323 cerebhem 0.0239068880942847 cortex 0.974262013475089 heart -0.94771459524587 kidney -2.21523834013337 liver -1.73797747359081 stomach -0.664243663569345 testicle -1.46430552233068 cont.weightedLogRatios: wLogRatio Lung -0.269877608365261 cerebhem 0.514424813201797 cortex -0.0829431405991829 heart 0.0862643809489012 kidney -0.28231075157805 liver 0.127607684364141 stomach 0.231504708997313 testicle 0.489247189544724 varWeightedLogRatios=1.08381355976356 cont.varWeightedLogRatios=0.09398865394746 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37659286332068 0.0845880200882254 51.7401028982105 1.15155200876797e-158 *** df.mm.trans1 -0.515342176570014 0.07164516939642 -7.19297868804767 4.42196179347211e-12 *** df.mm.trans2 -0.0953258256520873 0.07164516939642 -1.33052690718951 0.184280399048651 df.mm.exp2 -0.0873703290247899 0.0997745851926216 -0.875677196313225 0.381854261479171 df.mm.exp3 -0.178414315652955 0.0997745851926216 -1.78817396542931 0.074682267050452 . df.mm.exp4 -0.0133236379524834 0.0997745851926216 -0.133537392581099 0.893851297328074 df.mm.exp5 0.223854396345093 0.0997745851926216 2.24360137316459 0.025533251181918 * df.mm.exp6 0.117175429933261 0.0997745851926216 1.17440157437935 0.241096661389124 df.mm.exp7 -0.104219575012862 0.0997745851926216 -1.04455032122318 0.297009541709126 df.mm.exp8 0.144765007071437 0.0997745851926216 1.45092066072696 0.147769415593751 df.mm.trans1:exp2 0.353702508193215 0.086407325428865 4.09343196815415 5.37171542597049e-05 *** df.mm.trans2:exp2 -0.0383876058523987 0.086407325428865 -0.444263326770847 0.657148796054163 df.mm.trans1:exp3 0.688634072927023 0.086407325428865 7.96962606479404 2.74189906119415e-14 *** df.mm.trans2:exp3 0.0721067558079566 0.086407325428865 0.834498179987281 0.404615170300413 df.mm.trans1:exp4 0.0935551574042404 0.086407325428865 1.08272252311825 0.279736721369233 df.mm.trans2:exp4 -0.0577476823240255 0.086407325428865 -0.668319289335791 0.504405628392181 df.mm.trans1:exp5 -0.197410156561016 0.086407325428865 -2.28464607116597 0.0229797368925628 * df.mm.trans2:exp5 -0.0473542281156006 0.086407325428865 -0.548034878762508 0.58404532172341 df.mm.trans1:exp6 -0.0608835776639861 0.086407325428865 -0.704611297269103 0.481558328977188 df.mm.trans2:exp6 -0.0236500418977484 0.086407325428865 -0.273704130759357 0.784486402378052 df.mm.trans1:exp7 0.165253195495781 0.086407325428865 1.91249057502452 0.0566948218619931 . df.mm.trans2:exp7 -0.0541246512358975 0.086407325428865 -0.626389614158995 0.531500478502538 df.mm.trans1:exp8 -0.0637125068674091 0.086407325428865 -0.737350757602845 0.461442706675544 df.mm.trans2:exp8 -0.0924479766302218 0.086407325428865 -1.06990901722019 0.285456663061671 df.mm.trans1:probe2 -0.0902135078095957 0.0432036627144325 -2.08809860418291 0.0375694959168760 * df.mm.trans1:probe3 -0.0382268139915068 0.0432036627144325 -0.884804935270844 0.376917989748401 df.mm.trans1:probe4 -0.111458888416839 0.0432036627144325 -2.57984812893203 0.0103244715550093 * df.mm.trans1:probe5 0.00210250763468962 0.0432036627144325 0.0486650321429174 0.961216227635917 df.mm.trans1:probe6 0.00951053461862883 0.0432036627144325 0.220132600365195 0.825906521335166 df.mm.trans2:probe2 -0.0426138092934636 0.0432036627144325 -0.986347143183954 0.324698788486423 df.mm.trans2:probe3 -0.141185415128152 0.0432036627144325 -3.26790383633349 0.00119969389918573 ** df.mm.trans2:probe4 0.0370101947935574 0.0432036627144325 0.856644841391975 0.392274328982639 df.mm.trans2:probe5 -0.239125185360842 0.0432036627144325 -5.53483594530888 6.43881677213602e-08 *** df.mm.trans2:probe6 -0.146195558343365 0.0432036627144325 -3.38386954156383 0.00080244170090911 *** df.mm.trans3:probe2 0.125447909790582 0.0432036627144325 2.90364061537484 0.00394167369817528 ** df.mm.trans3:probe3 0.342790617690126 0.0432036627144325 7.93429529241312 3.48096433973685e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92054067087856 0.142602317808189 27.4928257207711 1.06239526853102e-86 *** df.mm.trans1 0.0141332139614059 0.120782673539749 0.117013587687763 0.906921785437375 df.mm.trans2 0.0737353427360953 0.120782673539749 0.610479471725135 0.541972212348188 df.mm.exp2 -0.0523542206970244 0.168204517518894 -0.311253356742595 0.755808188838033 df.mm.exp3 -0.0245688792170529 0.168204517518894 -0.146065513456219 0.883960504446686 df.mm.exp4 0.177160167753662 0.168204517518894 1.05324262610106 0.29301463384122 df.mm.exp5 0.0225097065314096 0.168204517518894 0.133823436275314 0.893625278329736 df.mm.exp6 0.142473026716739 0.168204517518894 0.847022593794099 0.397607738239493 df.mm.exp7 0.200141725073464 0.168204517518894 1.18987128304079 0.234968147834973 df.mm.exp8 0.0932774807102095 0.168204517518894 0.554548011469025 0.57958644145755 df.mm.trans1:exp2 0.183188929611445 0.145669385202667 1.25756643619095 0.209454253070603 df.mm.trans2:exp2 -0.0125380897028985 0.145669385202667 -0.0860722360120795 0.931462159165704 df.mm.trans1:exp3 0.109562966784445 0.145669385202667 0.752134476520324 0.452516240073250 df.mm.trans2:exp3 0.0623443601923364 0.145669385202667 0.427985332028400 0.668946141315766 df.mm.trans1:exp4 -0.0670140945903856 0.145669385202667 -0.460042406969386 0.64579434824263 df.mm.trans2:exp4 -0.156021481050299 0.145669385202667 -1.07106569326993 0.284937085174437 df.mm.trans1:exp5 0.0295495058467062 0.145669385202667 0.2028532337498 0.839377075534432 df.mm.trans2:exp5 0.0317368260634251 0.145669385202667 0.217868881778215 0.827668421378825 df.mm.trans1:exp6 0.00165461481483737 0.145669385202667 0.0113586997881218 0.990944254433032 df.mm.trans2:exp6 -0.097334159344847 0.145669385202667 -0.668185420082798 0.504490954894774 df.mm.trans1:exp7 -0.0279841270013381 0.145669385202667 -0.192107126438437 0.847778638615699 df.mm.trans2:exp7 -0.152223244045669 0.145669385202667 -1.04499132630981 0.296805982676348 df.mm.trans1:exp8 0.0414778590031732 0.145669385202667 0.284739713464610 0.776025471964184 df.mm.trans2:exp8 -0.147767193298804 0.145669385202667 -1.01440115981281 0.311148280274332 df.mm.trans1:probe2 -0.00910444503083332 0.0728346926013335 -0.125001489066031 0.900599892729374 df.mm.trans1:probe3 0.00483147656963585 0.0728346926013335 0.0663348247528321 0.947152170932224 df.mm.trans1:probe4 0.0879094411632802 0.0728346926013335 1.20697209013374 0.228323336794678 df.mm.trans1:probe5 0.0151531722494940 0.0728346926013335 0.208048825474367 0.835321560622834 df.mm.trans1:probe6 0.054468202631955 0.0728346926013335 0.747833219123901 0.455103198395844 df.mm.trans2:probe2 0.105426512647303 0.0728346926013335 1.44747659229322 0.148730359612969 df.mm.trans2:probe3 0.0361671941444055 0.0728346926013335 0.496565480716306 0.619832229207302 df.mm.trans2:probe4 0.0305882088795210 0.0728346926013334 0.419967570220252 0.67478750183323 df.mm.trans2:probe5 0.0243299701975615 0.0728346926013335 0.334043699899079 0.738562689172937 df.mm.trans2:probe6 0.0297515265303131 0.0728346926013335 0.408480155098072 0.68319103197213 df.mm.trans3:probe2 0.0356085305557233 0.0728346926013335 0.488895185576323 0.625246670110015 df.mm.trans3:probe3 0.0476072566395196 0.0728346926013335 0.653634345655878 0.513811018550985