chr12.5480_chr12_9030339_9036677_+_2.R fitVsDatCorrelation=0.660900167803571 cont.fitVsDatCorrelation=0.271659923478406 fstatistic=8344.70164210005,47,577 cont.fstatistic=5069.49548610907,47,577 residuals=-0.561022294380603,-0.0878040811997405,-0.00345392027077006,0.0776442421148299,0.757429735841232 cont.residuals=-0.474656141529123,-0.116690639287685,-0.0296056815110885,0.078312754299363,1.13907758725821 predictedValues: Include Exclude Both chr12.5480_chr12_9030339_9036677_+_2.R.tl.Lung 54.0372105869469 57.035781882002 61.0116315361022 chr12.5480_chr12_9030339_9036677_+_2.R.tl.cerebhem 65.4488677234465 87.7807740024773 63.6574516107476 chr12.5480_chr12_9030339_9036677_+_2.R.tl.cortex 60.498966489069 51.896792328724 71.0370643455027 chr12.5480_chr12_9030339_9036677_+_2.R.tl.heart 51.6262674235999 52.9342121015295 58.7936603077256 chr12.5480_chr12_9030339_9036677_+_2.R.tl.kidney 52.517077240734 54.7608410218819 60.2817446631032 chr12.5480_chr12_9030339_9036677_+_2.R.tl.liver 55.7234903347195 56.0243349425947 51.8315465725322 chr12.5480_chr12_9030339_9036677_+_2.R.tl.stomach 51.3740768127643 55.7884599336812 56.1492758784911 chr12.5480_chr12_9030339_9036677_+_2.R.tl.testicle 52.5842577314488 58.1394513030232 52.9685088950854 diffExp=-2.99857129505506,-22.3319062790308,8.60217416034494,-1.30794467792960,-2.24376378114795,-0.300844607875241,-4.41438312091687,-5.55519357157437 diffExpScore=1.51360145300516 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 55.7893848645921 55.6204949516784 61.7025350234583 cerebhem 56.3161129532051 52.9921750198459 51.1019681780845 cortex 54.1797211525014 55.2062242192116 59.1140428624004 heart 59.0230752350966 55.6563584243407 58.0294770573949 kidney 57.3214014914125 56.2177423617099 60.7697516221627 liver 57.0222088533143 55.1022034115836 55.7365100735368 stomach 53.7811458708264 57.7987046940756 66.2727486239338 testicle 55.9493039268415 58.9462648008648 59.9397443498879 cont.diffExp=0.168889912913649,3.32393793335926,-1.02650306671023,3.36671681075590,1.10365912970261,1.92000544173072,-4.01755882324919,-2.99696087402324 cont.diffExpScore=6.30649403776098 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=0.744181271129527 cont.tran.correlation=-0.2416197508206 tran.covariance=0.0102801943705078 cont.tran.covariance=-0.000233099717458823 tran.mean=57.3856788661652 cont.tran.mean=56.0576576394438 weightedLogRatios: wLogRatio Lung -0.216924311235153 cerebhem -1.27060167833771 cortex 0.617455844600586 heart -0.0989897082115051 kidney -0.16659715888935 liver -0.0216617915989485 stomach -0.328113550290096 testicle -0.402979125356926 cont.weightedLogRatios: wLogRatio Lung 0.0121883158276765 cerebhem 0.243379942226684 cortex -0.0751078003617743 heart 0.237780752058732 kidney 0.0785238969001017 liver 0.137905792223104 stomach -0.289682556943951 testicle -0.211358065271202 varWeightedLogRatios=0.27214967838319 cont.varWeightedLogRatios=0.0390147590223718 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88911564466913 0.0830539526429287 46.8263763603098 1.06817348183523e-198 *** df.mm.trans1 0.0405249844679209 0.0730577659544732 0.55469783312529 0.579316304215765 df.mm.trans2 0.160486684144880 0.0674230531420183 2.3802939301315 0.0176225979025198 * df.mm.exp2 0.580308113800138 0.0912292901314854 6.36098464609076 4.08300918469899e-10 *** df.mm.exp3 -0.133605698857149 0.0912292901314854 -1.46450442247866 0.143600790680442 df.mm.exp4 -0.0832407586948441 0.0912292901314854 -0.912434576383004 0.361921060310631 df.mm.exp5 -0.057202750361902 0.0912292901314854 -0.627021763289595 0.530892986550057 df.mm.exp6 0.175901774883523 0.0912292901314854 1.92812828675968 0.0543295382044521 . df.mm.exp7 0.0103997644664823 0.0912292901314854 0.113995893769353 0.90928070223185 df.mm.exp8 0.133276494201713 0.0912292901314854 1.46089588124194 0.144588330530888 df.mm.trans1:exp2 -0.388711815662539 0.0850717516999607 -4.56922313100459 5.99046379777303e-06 *** df.mm.trans2:exp2 -0.149144435248721 0.073674298116608 -2.02437538003637 0.0433917073251953 * df.mm.trans1:exp3 0.246559086725006 0.0850717516999608 2.89824861717429 0.00389511233436196 ** df.mm.trans2:exp3 0.0391838590811417 0.073674298116608 0.531852492427188 0.595032870417269 df.mm.trans1:exp4 0.0375984660551692 0.0850717516999608 0.441961818157631 0.65868243200456 df.mm.trans2:exp4 0.00861179691994172 0.073674298116608 0.116890111478380 0.906987808025461 df.mm.trans1:exp5 0.0286682536453256 0.0850717516999608 0.336989107106147 0.736247649865997 df.mm.trans2:exp5 0.0164992857929919 0.073674298116608 0.223949005484621 0.822876254719902 df.mm.trans1:exp6 -0.145172881522727 0.0850717516999608 -1.70647575278262 0.0884576093967366 . df.mm.trans2:exp6 -0.193794449188963 0.073674298116608 -2.63042138361786 0.00875562015759224 ** df.mm.trans1:exp7 -0.060938955590485 0.0850717516999608 -0.716324213064407 0.474080993242194 df.mm.trans2:exp7 -0.0325115508882685 0.073674298116608 -0.441287555082111 0.659170144961299 df.mm.trans1:exp8 -0.160532596121840 0.0850717516999608 -1.88702586832844 0.0596585804539429 . df.mm.trans2:exp8 -0.114110859824329 0.073674298116608 -1.54885574401700 0.121964686247769 df.mm.trans1:probe2 -0.064655228389443 0.0465957174125469 -1.38757877289455 0.165800984545653 df.mm.trans1:probe3 0.0585517583117052 0.0465957174125469 1.25659098224205 0.209410259419027 df.mm.trans1:probe4 -0.0276320527334644 0.046595717412547 -0.593017003876494 0.553402126696104 df.mm.trans1:probe5 0.0874025642261335 0.0465957174125469 1.87576389161031 0.061192377004251 . df.mm.trans1:probe6 -0.0351886235574227 0.0465957174125469 -0.755190079935272 0.450443083768549 df.mm.trans1:probe7 0.0820120322238355 0.046595717412547 1.76007660742126 0.0789246357744456 . df.mm.trans1:probe8 0.056420917337289 0.0465957174125469 1.21086057840364 0.226444571464659 df.mm.trans1:probe9 0.120077734606795 0.0465957174125469 2.57701225079672 0.0102130570561478 * df.mm.trans1:probe10 0.0570928888595751 0.046595717412547 1.22528189348581 0.220968947336685 df.mm.trans1:probe11 0.0945791087094558 0.046595717412547 2.02978114645335 0.0428371276892600 * df.mm.trans1:probe12 0.107460707486704 0.0465957174125469 2.30623571121941 0.0214509701120731 * df.mm.trans1:probe13 0.227768029358720 0.0465957174125469 4.88817518018057 1.32089187549416e-06 *** df.mm.trans1:probe14 0.311403762175961 0.0465957174125469 6.68309835040995 5.52329220643533e-11 *** df.mm.trans1:probe15 0.0555586369429147 0.046595717412547 1.19235500659883 0.233612201935447 df.mm.trans1:probe16 0.0697930656546628 0.046595717412547 1.49784292484934 0.134720906298866 df.mm.trans2:probe2 -0.123195856880967 0.0465957174125469 -2.64393089584223 0.00841783187919485 ** df.mm.trans2:probe3 0.00262545865122956 0.0465957174125469 0.0563454926122159 0.955086083969065 df.mm.trans2:probe4 0.0193597576840522 0.0465957174125469 0.415483627232213 0.677942361207877 df.mm.trans2:probe5 0.0924865528603513 0.0465957174125469 1.98487238733762 0.0476317882536757 * df.mm.trans2:probe6 -0.0505109684916121 0.0465957174125469 -1.08402598557289 0.278806024859177 df.mm.trans3:probe2 -0.101142553857716 0.0465957174125469 -2.17064055398535 0.0303654036391343 * df.mm.trans3:probe3 0.0914943645195312 0.0465957174125469 1.96357883514192 0.0500587422743077 . df.mm.trans3:probe4 0.0933970438733746 0.046595717412547 2.00441261686048 0.0454926786280220 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90869730610279 0.106506803320223 36.699038786761 5.2010275551021e-153 *** df.mm.trans1 0.079574179013993 0.0936878843440626 0.849353996742653 0.396036599215601 df.mm.trans2 0.0791458665109582 0.08646203620337 0.915382866126317 0.360373058860655 df.mm.exp2 0.149491594030476 0.116990700043521 1.27780750072326 0.201831151664794 df.mm.exp3 0.00610353929037483 0.116990700043521 0.0521711493999462 0.958410377396078 df.mm.exp4 0.118363348031970 0.116990700043521 1.01173296670537 0.312089814237003 df.mm.exp5 0.0530039543520699 0.116990700043521 0.453061263265817 0.650674945192939 df.mm.exp6 0.114184769234014 0.116990700043521 0.976015778959667 0.329465655891762 df.mm.exp7 -0.069699805958715 0.116990700043521 -0.595772193283624 0.551561038353895 df.mm.exp8 0.0899221126193998 0.116990700043521 0.768626160762765 0.442429748567634 df.mm.trans1:exp2 -0.140094517617225 0.109094390309984 -1.28415876580965 0.199601788219168 df.mm.trans2:exp2 -0.197899080087182 0.0944785134188207 -2.09464642198487 0.0366379520861248 * df.mm.trans1:exp3 -0.0353804652222527 0.109094390309984 -0.324310582072293 0.745820440918156 df.mm.trans2:exp3 -0.0135795823433913 0.0944785134188208 -0.143731964570541 0.885762312183403 df.mm.trans1:exp4 -0.0620184907971762 0.109094390309984 -0.568484691293065 0.569927074355904 df.mm.trans2:exp4 -0.117718767010563 0.0944785134188208 -1.24598453924353 0.213275734007125 df.mm.trans1:exp5 -0.0259135172758652 0.109094390309984 -0.237532995071825 0.81232764221396 df.mm.trans2:exp5 -0.0423232944628278 0.0944785134188207 -0.447967404770752 0.654344867610361 df.mm.trans1:exp6 -0.0923275641682597 0.109094390309984 -0.846308998161296 0.397731234859117 df.mm.trans2:exp6 -0.123546812127581 0.0944785134188208 -1.30767100007068 0.19150570206711 df.mm.trans1:exp7 0.033039147318739 0.109094390309984 0.302849186148442 0.762113947310164 df.mm.trans2:exp7 0.108114423675014 0.0944785134188208 1.14432816269818 0.25296204764356 df.mm.trans1:exp8 -0.087059734813348 0.109094390309984 -0.798022103299484 0.425185969718718 df.mm.trans2:exp8 -0.0318475973739678 0.0944785134188207 -0.337088256594261 0.736172947356136 df.mm.trans1:probe2 -0.0138442851184974 0.059753458470051 -0.231690105861173 0.816860858825382 df.mm.trans1:probe3 0.0546748630970918 0.059753458470051 0.91500750746495 0.360569909377267 df.mm.trans1:probe4 0.0296296257458475 0.059753458470051 0.49586461611587 0.620178746735943 df.mm.trans1:probe5 0.0209938143158405 0.059753458470051 0.351340572635855 0.725461114915898 df.mm.trans1:probe6 0.0860377211501502 0.059753458470051 1.43987851671001 0.150444093413814 df.mm.trans1:probe7 0.0642404506080854 0.059753458470051 1.07509175624175 0.282782997792180 df.mm.trans1:probe8 0.0763862405090255 0.059753458470051 1.27835680920981 0.201637622349856 df.mm.trans1:probe9 0.0464860955646068 0.059753458470051 0.777964937174407 0.436908560513291 df.mm.trans1:probe10 -0.00266508881840567 0.059753458470051 -0.0446014153263018 0.964440445357274 df.mm.trans1:probe11 0.0147858164043983 0.059753458470051 0.247447039602052 0.804650297511089 df.mm.trans1:probe12 0.0977194117639916 0.059753458470051 1.63537666715926 0.102515474660320 df.mm.trans1:probe13 0.0547016629903403 0.059753458470051 0.915456015282484 0.360334704895506 df.mm.trans1:probe14 -0.010298278392042 0.059753458470051 -0.172346147917172 0.86322583895395 df.mm.trans1:probe15 0.101449594418413 0.059753458470051 1.69780288900365 0.090084160256686 . df.mm.trans1:probe16 0.0459449711983002 0.059753458470051 0.768908986604151 0.442261953357758 df.mm.trans2:probe2 0.0428444430209979 0.059753458470051 0.717020305066892 0.473651709977838 df.mm.trans2:probe3 0.0223520267710059 0.059753458470051 0.374070846162134 0.708489026630761 df.mm.trans2:probe4 0.0738005386234679 0.059753458470051 1.23508396857828 0.217301934402632 df.mm.trans2:probe5 0.0436126174930800 0.059753458470051 0.72987603746717 0.465762164131055 df.mm.trans2:probe6 0.124476123692174 0.059753458470051 2.08316182660059 0.0376761448093632 * df.mm.trans3:probe2 0.0473372363639086 0.059753458470051 0.792209146984094 0.428564312236337 df.mm.trans3:probe3 0.0516774117191351 0.059753458470051 0.86484386079571 0.387483901264054 df.mm.trans3:probe4 0.120616010794142 0.059753458470051 2.01856116587119 0.0439949659411287 *