chr10.2444_chr10_86212543_86214221_+_0.R fitVsDatCorrelation=0.748437058572145 cont.fitVsDatCorrelation=0.361942679500215 fstatistic=9177.90918575039,37,347 cont.fstatistic=4640.75544862253,37,347 residuals=-0.374891327329906,-0.0827132852200884,-0.0103758456398385,0.0773271099576668,0.665886797625671 cont.residuals=-0.472059985060066,-0.129302197026817,-0.0272881091953629,0.110309755845654,0.821542098355344 predictedValues: Include Exclude Both chr10.2444_chr10_86212543_86214221_+_0.R.tl.Lung 58.3511053058308 55.0385838022029 62.7336781512604 chr10.2444_chr10_86212543_86214221_+_0.R.tl.cerebhem 67.6328417971628 57.9434783053661 59.535556010324 chr10.2444_chr10_86212543_86214221_+_0.R.tl.cortex 55.4487859460824 69.2220160504706 69.3808200558835 chr10.2444_chr10_86212543_86214221_+_0.R.tl.heart 58.8985215647386 59.6825425110077 61.9417404660418 chr10.2444_chr10_86212543_86214221_+_0.R.tl.kidney 57.6980782970207 73.0247839935271 75.7278663773021 chr10.2444_chr10_86212543_86214221_+_0.R.tl.liver 60.720363967567 58.933322520712 59.6657173700541 chr10.2444_chr10_86212543_86214221_+_0.R.tl.stomach 59.6401403036755 57.313710562638 64.8605411156459 chr10.2444_chr10_86212543_86214221_+_0.R.tl.testicle 59.8841857581337 55.0945862987507 57.2550818773991 diffExp=3.31252150362796,9.68936349179667,-13.7732301043882,-0.78402094626913,-15.3267056965064,1.78704144685507,2.32642974103749,4.78959945938303 diffExpScore=5.76778104683818 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,-1,0,-1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 56.9450738827826 54.379371011106 60.2098930860317 cerebhem 60.0138436834083 60.467696227337 56.175232653217 cortex 58.958858665734 54.5138778208542 61.7989840533507 heart 62.5683528306808 62.1010155194474 56.2196317088783 kidney 62.1809246749375 54.6723164555125 58.8721867668788 liver 67.505948350182 65.4104418664436 65.0532472285068 stomach 60.7079412782488 64.241786045195 70.9111815413695 testicle 62.429797616173 59.0165087448157 58.2535284600556 cont.diffExp=2.56570287167663,-0.453852543928654,4.44498084487977,0.467337311233379,7.50860821942505,2.09550648373843,-3.53384476694627,3.41328887135728 cont.diffExpScore=1.39841805310516 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=-0.464047779605179 cont.tran.correlation=0.667105171508229 tran.covariance=-0.00286940762701972 cont.tran.covariance=0.00251941287486028 tran.mean=60.2829404365554 cont.tran.mean=60.3821096670536 weightedLogRatios: wLogRatio Lung 0.235952759109409 cerebhem 0.639652499451048 cortex -0.915477218333062 heart -0.053984260492946 kidney -0.98305724913638 liver 0.122218433027159 stomach 0.161878814496479 testicle 0.337673068917704 cont.weightedLogRatios: wLogRatio Lung 0.185286863347254 cerebhem -0.0308769603141690 cortex 0.316489318314159 heart 0.0309825202231551 kidney 0.523218522979609 liver 0.132330021278663 stomach -0.233919655895671 testicle 0.230857854794613 varWeightedLogRatios=0.343073457649793 cont.varWeightedLogRatios=0.0527316858017714 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02044557714531 0.0751013700015626 53.5335850339569 9.07663611878822e-170 *** df.mm.trans1 0.0378602071683457 0.0625697475096283 0.605088060528289 0.545515983003649 df.mm.trans2 0.00343837106878144 0.0625697475096283 0.0549526121749546 0.956207842063389 df.mm.exp2 0.251373670660340 0.0862079678946415 2.91589834210632 0.00377727781688349 ** df.mm.exp3 0.0775539916212388 0.0862079678946414 0.899615122769405 0.368948972173856 df.mm.exp4 0.103046944419641 0.0862079678946414 1.19532970021495 0.232774507843043 df.mm.exp5 0.0832622203548896 0.0862079678946414 0.96582975319228 0.334802009871548 df.mm.exp6 0.158313869118821 0.0862079678946414 1.83641805954994 0.067151140722327 . df.mm.exp7 0.0290149415045185 0.0862079678946414 0.336569138713244 0.73664517454186 df.mm.exp8 0.118333184514332 0.0862079678946414 1.37264788167785 0.170748336022905 df.mm.trans1:exp2 -0.103758281425473 0.0728590308576556 -1.42409637081484 0.155317196394738 df.mm.trans2:exp2 -0.199940110192053 0.0728590308576556 -2.74420490965183 0.00638111622077235 ** df.mm.trans1:exp3 -0.128572473875668 0.0728590308576556 -1.76467450036303 0.0784981659584886 . df.mm.trans2:exp3 0.151730508465269 0.0728590308576555 2.08252164047728 0.0380268945906015 * df.mm.trans1:exp4 -0.0937092560864896 0.0728590308576556 -1.28617214617594 0.199240511227881 df.mm.trans2:exp4 -0.0220418499915231 0.0728590308576555 -0.302527356349087 0.762431344046018 df.mm.trans1:exp5 -0.0945166537140075 0.0728590308576555 -1.29725378723009 0.195405696572750 df.mm.trans2:exp5 0.199502206978595 0.0728590308576555 2.73819462913749 0.00649635725667882 ** df.mm.trans1:exp6 -0.118513043094558 0.0728590308576555 -1.62660745963114 0.104728216962791 df.mm.trans2:exp6 -0.0899416540116598 0.0728590308576555 -1.23446130085615 0.217866380027171 df.mm.trans1:exp7 -0.00716440034201359 0.0728590308576555 -0.0983323584966517 0.921725160798087 df.mm.trans2:exp7 0.0114904674727357 0.0728590308576555 0.157708211836973 0.874778459885866 df.mm.trans1:exp8 -0.0923990262064372 0.0728590308576555 -1.26818906481143 0.205580640112901 df.mm.trans2:exp8 -0.117316188429984 0.0728590308576555 -1.61018046835106 0.108267734784571 df.mm.trans1:probe2 -0.122849512663366 0.0399065347187029 -3.07843097701465 0.00224670074868107 ** df.mm.trans1:probe3 -0.096842011215744 0.0399065347187029 -2.42672063355973 0.0157447056208459 * df.mm.trans1:probe4 0.0826832428481174 0.0399065347187029 2.07192239143146 0.0390110074361118 * df.mm.trans1:probe5 0.00727543634664395 0.0399065347187029 0.182311904502051 0.85544441708623 df.mm.trans1:probe6 0.211458013956971 0.0399065347187029 5.2988317689701 2.07621217961514e-07 *** df.mm.trans2:probe2 0.00473600450156421 0.0399065347187029 0.118677417995519 0.905599599307504 df.mm.trans2:probe3 -0.00479458247243783 0.0399065347187029 -0.120145297160837 0.904437582901072 df.mm.trans2:probe4 -0.0418179206591542 0.0399065347187029 -1.04789656515968 0.295415594185801 df.mm.trans2:probe5 -0.0601203220268141 0.0399065347187029 -1.50652825284370 0.132841402351490 df.mm.trans2:probe6 -0.0564980319223132 0.0399065347187029 -1.41575890566701 0.157743026450022 df.mm.trans3:probe2 -0.177969200741351 0.0399065347187029 -4.45965058093461 1.11024525826340e-05 *** df.mm.trans3:probe3 0.118944330299081 0.0399065347187029 2.98057275926129 0.0030801142420672 ** df.mm.trans3:probe4 0.367641989050068 0.0399065347187029 9.21257612673058 3.11136591525162e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86513621717319 0.105562286532502 36.6147451342211 3.12795959590519e-121 *** df.mm.trans1 0.191963521237120 0.087947871187174 2.18269662068995 0.0297280402873637 * df.mm.trans2 0.0860330519018035 0.087947871187174 0.978227792673966 0.328643168494198 df.mm.exp2 0.227973195838565 0.12117369108033 1.88137535306599 0.06075784999123 . df.mm.exp3 0.0111728890623142 0.12117369108033 0.0922055684092953 0.92658792236289 df.mm.exp4 0.295520547683860 0.12117369108033 2.43881774211161 0.015236161205768 * df.mm.exp5 0.115801607062406 0.12117369108033 0.955666250899603 0.339906176031808 df.mm.exp6 0.277456118346659 0.12117369108033 2.28973893485447 0.0226362247034611 * df.mm.exp7 0.0670648760715861 0.12117369108033 0.553460701524118 0.580304406540897 df.mm.exp8 0.206819852298619 0.12117369108033 1.70680492155274 0.0887529312500486 . df.mm.trans1:exp2 -0.175485119339843 0.102410460577700 -1.71354682275548 0.0875050903987278 . df.mm.trans2:exp2 -0.121848792646814 0.102410460577700 -1.18980807194365 0.234935266618121 df.mm.trans1:exp3 0.0235798136923546 0.102410460577700 0.230248097307055 0.818034666280399 df.mm.trans2:exp3 -0.00870245348252358 0.102410460577700 -0.0849762166231149 0.9323293044278 df.mm.trans1:exp4 -0.201348130411518 0.102410460577700 -1.96608949198849 0.0500851462422875 . df.mm.trans2:exp4 -0.162743078497146 0.102410460577700 -1.58912554029255 0.112942727065501 df.mm.trans1:exp5 -0.0278405187707239 0.102410460577700 -0.271852295299474 0.785897265077081 df.mm.trans2:exp5 -0.110428996039757 0.102410460577700 -1.07829801191035 0.281649782462118 df.mm.trans1:exp6 -0.107327587810998 0.102410460577700 -1.04801391582033 0.295361602136313 df.mm.trans2:exp6 -0.0927590836508198 0.102410460577700 -0.905757899413435 0.365692487916045 df.mm.trans1:exp7 -0.00307754542273967 0.102410460577700 -0.0300510846780609 0.97604358811721 df.mm.trans2:exp7 0.0996041232873163 0.102410460577700 0.972597161710309 0.331431035564776 df.mm.trans1:exp8 -0.114864352168797 0.102410460577700 -1.12160761235565 0.262804866551620 df.mm.trans2:exp8 -0.124987510887217 0.102410460577700 -1.22045648639953 0.22312059354063 df.mm.trans1:probe2 -0.0477499743659132 0.0560925193828997 -0.851271700598104 0.395205507762752 df.mm.trans1:probe3 -0.0105648160190156 0.0560925193828997 -0.188346256064875 0.850715325888005 df.mm.trans1:probe4 -0.0862827873974337 0.0560925193828997 -1.53822271394959 0.124905508349181 df.mm.trans1:probe5 -0.0195996364077664 0.0560925193828997 -0.349416225610673 0.726988805638984 df.mm.trans1:probe6 0.0140717017526182 0.0560925193828997 0.25086592485821 0.802066250161604 df.mm.trans2:probe2 0.110757539725825 0.0560925193828997 1.97455099083302 0.0491116320357634 * df.mm.trans2:probe3 0.0816671256468049 0.0560925193828997 1.45593613097189 0.146314422988598 df.mm.trans2:probe4 0.0919527035416456 0.0560925193828997 1.63930421655616 0.10205617574376 df.mm.trans2:probe5 0.0828040214074817 0.0560925193828997 1.47620435520543 0.140795910098240 df.mm.trans2:probe6 0.0809746448200946 0.0560925193828997 1.44359079804107 0.14975629926489 df.mm.trans3:probe2 -0.0756591394917285 0.0560925193828997 -1.34882762129586 0.178271912942151 df.mm.trans3:probe3 -0.0605249611234491 0.0560925193828997 -1.07902019358932 0.28132818146731 df.mm.trans3:probe4 -0.0450003574724549 0.0560925193828997 -0.802252385300662 0.422956062217785