chr3.15263_chr3_152316790_152325403_-_2.R fitVsDatCorrelation=0.727126819123128 cont.fitVsDatCorrelation=0.288359760796272 fstatistic=9885.95652201018,52,692 cont.fstatistic=5075.19897962936,52,692 residuals=-0.344274966383646,-0.0823672249550966,-0.00523170196268894,0.0703969830653933,2.13876527711589 cont.residuals=-0.483392543240372,-0.127020269662753,-0.0303302570586617,0.102929655798863,2.31896690830508 predictedValues: Include Exclude Both chr3.15263_chr3_152316790_152325403_-_2.R.tl.Lung 50.1199005702706 53.3485010787069 72.6588603238033 chr3.15263_chr3_152316790_152325403_-_2.R.tl.cerebhem 52.9419046900572 57.9193881176889 84.5566442870113 chr3.15263_chr3_152316790_152325403_-_2.R.tl.cortex 48.2219426490602 50.7537950855547 60.8393319469545 chr3.15263_chr3_152316790_152325403_-_2.R.tl.heart 49.2739727197641 48.9397026575825 71.1147234206591 chr3.15263_chr3_152316790_152325403_-_2.R.tl.kidney 51.3232872033842 53.5026909189521 68.6650197983596 chr3.15263_chr3_152316790_152325403_-_2.R.tl.liver 49.3557148571731 55.7244806629186 66.735727938731 chr3.15263_chr3_152316790_152325403_-_2.R.tl.stomach 52.3023931115889 51.2480517724928 65.1533719463279 chr3.15263_chr3_152316790_152325403_-_2.R.tl.testicle 48.8675234071898 51.8611294961818 70.9602469639303 diffExp=-3.22860050843632,-4.97748342763175,-2.53185243649444,0.334270062181623,-2.17940371556786,-6.36876580574547,1.05434133909603,-2.99360608899204 diffExpScore=1.08118471686389 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 54.0368264535359 55.1886667867397 56.2367703531327 cerebhem 55.0944290624678 51.3104350980052 51.7173231924103 cortex 52.1563061299663 51.1113917798402 44.5488203128578 heart 52.637155408998 51.645293396144 50.0752850751369 kidney 52.3392028549542 51.259062305309 50.1274932318886 liver 52.2721928517825 48.9306534211722 49.3427284292208 stomach 53.258891224787 52.9406869316289 64.8659801349132 testicle 53.371897732725 52.2415417991681 53.7827769747825 cont.diffExp=-1.15184033320373,3.78399396446255,1.04491435012608,0.991862012853986,1.08014054964526,3.34153943061027,0.318204293158054,1.13035593355684 cont.diffExpScore=1.11297871889183 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=0.519395525200179 cont.tran.correlation=0.470507910585386 tran.covariance=0.000933433094605147 cont.tran.covariance=0.000311338488516012 tran.mean=51.6065236874104 cont.tran.mean=52.4871645773265 weightedLogRatios: wLogRatio Lung -0.246316870419676 cerebhem -0.360697148864379 cortex -0.199643375672175 heart 0.0265064948239455 kidney -0.164641788794608 liver -0.480577378845733 stomach 0.0803759236943363 testicle -0.233000692371321 cont.weightedLogRatios: wLogRatio Lung -0.08437180472327 cerebhem 0.282730211578183 cortex 0.0798200184687157 heart 0.0752159561597174 kidney 0.082314387499994 liver 0.259183972163412 stomach 0.0238035755672535 testicle 0.084910022738789 varWeightedLogRatios=0.0341267414311601 cont.varWeightedLogRatios=0.0142984382523962 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68619733829024 0.0806004258661536 45.7342166455994 2.42845816517427e-211 *** df.mm.trans1 0.104740735930974 0.0723909693551364 1.44687572032826 0.148384549417266 df.mm.trans2 0.279309697056543 0.0665733279172545 4.19551952403075 3.07691422868102e-05 *** df.mm.exp2 -0.0146629841253611 0.0911889723143693 -0.160797777990207 0.872299604858735 df.mm.exp3 0.0890753815062726 0.0911889723143693 0.976821859546676 0.328998703623503 df.mm.exp4 -0.0817980945019049 0.0911889723143693 -0.897017396137662 0.370021591730103 df.mm.exp5 0.0831479586979434 0.0911889723143693 0.91182032857323 0.362180836550983 df.mm.exp6 0.113243974633144 0.0911889723143693 1.24186041095782 0.214708798395719 df.mm.exp7 0.111486985688396 0.0911889723143693 1.22259285151334 0.221899745020102 df.mm.exp8 -0.0299259118295148 0.0911889723143693 -0.328174680227197 0.742878863355953 df.mm.trans1:exp2 0.0694400123629763 0.0873067940222194 0.795356342431998 0.426678967371721 df.mm.trans2:exp2 0.0968692865217833 0.0759908102619744 1.27475001500617 0.202825594169971 df.mm.trans1:exp3 -0.127679368551539 0.0873067940222194 -1.46242191093450 0.144079621525944 df.mm.trans2:exp3 -0.138934867728148 0.0759908102619745 -1.82831143988565 0.0679331596335905 . df.mm.trans1:exp4 0.0647759532315986 0.0873067940222194 0.741934851199705 0.458378544973759 df.mm.trans2:exp4 -0.00445880451301901 0.0759908102619744 -0.0586755753445385 0.953227453245915 df.mm.trans1:exp5 -0.0594215141276072 0.0873067940222194 -0.68060584279941 0.496348583271498 df.mm.trans2:exp5 -0.080261889869171 0.0759908102619744 -1.05620521208383 0.291243060823546 df.mm.trans1:exp6 -0.128608559073804 0.0873067940222194 -1.47306473126333 0.141188275556406 df.mm.trans2:exp6 -0.0696702964060114 0.0759908102619745 -0.91682528671331 0.359553602066182 df.mm.trans1:exp7 -0.068863004465112 0.0873067940222194 -0.788747373401279 0.430529803561244 df.mm.trans2:exp7 -0.151655263946514 0.0759908102619744 -1.99570531520443 0.0463575469203036 * df.mm.trans1:exp8 0.00462079847491522 0.0873067940222194 0.0529259896284731 0.957806151316269 df.mm.trans2:exp8 0.00164959006216591 0.0759908102619744 0.0217077572469491 0.982687333512225 df.mm.trans1:probe2 0.166968072064236 0.0436533970111097 3.82485862490251 0.000142666204736184 *** df.mm.trans1:probe3 -0.0295576803666278 0.0436533970111097 -0.677099203965855 0.498569296751229 df.mm.trans1:probe4 0.0624006659350616 0.0436533970111097 1.42945727497864 0.153324124880722 df.mm.trans1:probe5 0.132705286393279 0.0436533970111097 3.03997616404298 0.00245522106868385 ** df.mm.trans1:probe6 0.0686200896266207 0.0436533970111097 1.57193012056214 0.116423843589532 df.mm.trans1:probe7 0.0888815767108015 0.0436533970111097 2.03607468825808 0.0421231771174583 * df.mm.trans1:probe8 0.0410918326866621 0.0436533970111097 0.941320389709977 0.34686917877832 df.mm.trans1:probe9 0.203318017417146 0.0436533970111097 4.6575531651157 3.83751350587808e-06 *** df.mm.trans1:probe10 0.0379190698437185 0.0436533970111097 0.868639612034504 0.385345435376267 df.mm.trans1:probe11 0.221816982038342 0.0436533970111097 5.08132235349038 4.82748794410834e-07 *** df.mm.trans1:probe12 0.41646933942557 0.0436533970111097 9.54036496448556 2.37680351095118e-20 *** df.mm.trans1:probe13 0.367617641739584 0.0436533970111097 8.42128372383084 2.14313576384794e-16 *** df.mm.trans1:probe14 0.282843216816433 0.0436533970111097 6.47929453793596 1.74979795747738e-10 *** df.mm.trans1:probe15 0.294153910947866 0.0436533970111097 6.7383968050185 3.37416497376603e-11 *** df.mm.trans1:probe16 0.283574541629269 0.0436533970111097 6.49604752539876 1.57573205453993e-10 *** df.mm.trans1:probe17 0.0289558001125819 0.0436533970111097 0.663311496816907 0.507351973589394 df.mm.trans1:probe18 0.0933437189194388 0.0436533970111097 2.13829221344866 0.032842616120637 * df.mm.trans1:probe19 0.0337711403363859 0.0436533970111097 0.77361998489582 0.439419743909992 df.mm.trans1:probe20 0.0640359238028557 0.0436533970111097 1.46691731199198 0.142852849636764 df.mm.trans1:probe21 0.188340678639206 0.0436533970111097 4.31445641197806 1.83304947908897e-05 *** df.mm.trans1:probe22 0.0397319745639466 0.0436533970111097 0.910169134233355 0.363050229444355 df.mm.trans2:probe2 -0.0336598797404698 0.0436533970111097 -0.771071257797037 0.440927888428757 df.mm.trans2:probe3 0.122329576163428 0.0436533970111097 2.80229225075645 0.00521600201934052 ** df.mm.trans2:probe4 0.0198361460966467 0.0436533970111097 0.454400973459144 0.649682748085122 df.mm.trans2:probe5 -0.0109057007227644 0.0436533970111097 -0.249824789580268 0.802796990738527 df.mm.trans2:probe6 0.004449472311419 0.0436533970111097 0.101927286673397 0.918843918028845 df.mm.trans3:probe2 0.383387681717648 0.0436533970111097 8.78253945781302 1.24702600802351e-17 *** df.mm.trans3:probe3 0.263195029290057 0.0436533970111097 6.02919926765549 2.68051931691564e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95402673027274 0.112420010174668 35.1719122256736 3.34716040457487e-156 *** df.mm.trans1 0.0196037359590576 0.100969609329024 0.194154816378224 0.846111641360185 df.mm.trans2 0.0154820596483627 0.0928552687084753 0.166733238336422 0.867628657803992 df.mm.exp2 0.0302973668416567 0.127188722456909 0.238207965740998 0.81179031653051 df.mm.exp3 0.120814240872625 0.127188722456909 0.949881707582652 0.342503981130952 df.mm.exp4 0.0234411527765050 0.127188722456909 0.184302132482278 0.853830436250327 df.mm.exp5 0.00921588387617164 0.127188722456909 0.0724583414169754 0.942258094171205 df.mm.exp6 -0.0227743905987534 0.127188722456909 -0.179059826679753 0.857943138370405 df.mm.exp7 -0.198838949061831 0.127188722456909 -1.56333789050516 0.118430311925648 df.mm.exp8 -0.0226435254378175 0.127188722456909 -0.178030921298773 0.858750794366992 df.mm.trans1:exp2 -0.0109145472279162 0.121773930681142 -0.0896295879328663 0.928607505696636 df.mm.trans2:exp2 -0.103160842503383 0.105990602047424 -0.973301788183303 0.330743426289187 df.mm.trans1:exp3 -0.156234929311660 0.121773930681142 -1.28299159300977 0.199924551375066 df.mm.trans2:exp3 -0.197564457765345 0.105990602047424 -1.86398089971172 0.0627476516479306 . df.mm.trans1:exp4 -0.0496846912916976 0.121773930681142 -0.408007617178706 0.683394287860762 df.mm.trans2:exp4 -0.089799706685901 0.105990602047424 -0.847242160637234 0.397153077544176 df.mm.trans1:exp5 -0.0411360025101985 0.121773930681142 -0.337806312731341 0.735611600911814 df.mm.trans2:exp5 -0.083081076389255 0.105990602047424 -0.783853235894267 0.43339444291911 df.mm.trans1:exp6 -0.0104268506488123 0.121773930681142 -0.0856246537373786 0.931789566628275 df.mm.trans2:exp6 -0.0975791703552475 0.105990602047424 -0.92063983476183 0.357559323449215 df.mm.trans1:exp7 0.184337925581448 0.121773930681142 1.51377166319881 0.130540339570138 df.mm.trans2:exp7 0.157253501070900 0.105990602047424 1.48365513576892 0.138355739067870 df.mm.trans1:exp8 0.0102620877046847 0.121773930681142 0.0842716306132496 0.932864840562325 df.mm.trans2:exp8 -0.0322360966321747 0.105990602047424 -0.304141084298691 0.761111840161022 df.mm.trans1:probe2 0.00131429818215112 0.060886965340571 0.0215858710448058 0.982784526696423 df.mm.trans1:probe3 -0.0163481936254422 0.060886965340571 -0.268500713313574 0.788393948733218 df.mm.trans1:probe4 0.0189462970469568 0.060886965340571 0.311171643076327 0.755763852625961 df.mm.trans1:probe5 0.110700320929466 0.060886965340571 1.81812840088621 0.0694767307072784 . df.mm.trans1:probe6 0.0344798891756782 0.060886965340571 0.566293442000519 0.571377919022626 df.mm.trans1:probe7 -0.000562061090689414 0.060886965340571 -0.00923122194620026 0.99263731570031 df.mm.trans1:probe8 0.0282617281328904 0.060886965340571 0.464167132896319 0.642673991619425 df.mm.trans1:probe9 -0.0723956948284231 0.060886965340571 -1.18901795192909 0.234840446108513 df.mm.trans1:probe10 0.0585289810289394 0.060886965340571 0.961272756846359 0.336750802443843 df.mm.trans1:probe11 0.0781035894278936 0.060886965340571 1.28276370797956 0.200004356708988 df.mm.trans1:probe12 0.0388742790710291 0.060886965340571 0.638466358991386 0.523381385105715 df.mm.trans1:probe13 -0.0225785712713035 0.060886965340571 -0.37082766639806 0.710879382537132 df.mm.trans1:probe14 0.0318115206355626 0.060886965340571 0.522468486606697 0.601511398738144 df.mm.trans1:probe15 0.00215341699961765 0.060886965340571 0.0353674548825438 0.971796935010499 df.mm.trans1:probe16 -0.0158199491442924 0.060886965340571 -0.259824891186539 0.795076132828958 df.mm.trans1:probe17 -0.0318453620577993 0.060886965340571 -0.523024293946535 0.601124769280887 df.mm.trans1:probe18 0.0811428731234834 0.060886965340571 1.33268052808366 0.183075299690192 df.mm.trans1:probe19 0.0695490556436256 0.060886965340571 1.14226510148113 0.253738912263993 df.mm.trans1:probe20 -0.030902848533365 0.060886965340571 -0.507544568209469 0.611934490316329 df.mm.trans1:probe21 0.0624277116795457 0.060886965340571 1.02530502760906 0.305577554014477 df.mm.trans1:probe22 -0.0249582990891577 0.060886965340571 -0.409912022212859 0.681997350698121 df.mm.trans2:probe2 0.107994114497726 0.060886965340571 1.77368200063284 0.0765554821359213 . df.mm.trans2:probe3 0.128855476184441 0.060886965340571 2.11630642886680 0.0346751873669801 * df.mm.trans2:probe4 -0.0325126070109264 0.060886965340571 -0.533983042660565 0.593524754327718 df.mm.trans2:probe5 0.0478251351147258 0.060886965340571 0.785474113337988 0.432444488591692 df.mm.trans2:probe6 0.119077355247484 0.060886965340571 1.95571177806983 0.0509011346966861 . df.mm.trans3:probe2 0.0869974332474616 0.060886965340571 1.42883510059734 0.153502853566333 df.mm.trans3:probe3 0.0343774482122465 0.060886965340571 0.564610964267251 0.572521372099229