chr17.10306_chr17_82514018_82515222_+_1.R fitVsDatCorrelation=0.862153725368069 cont.fitVsDatCorrelation=0.316475691549338 fstatistic=7264.27343558514,36,324 cont.fstatistic=2065.78772645723,36,324 residuals=-0.508618835604139,-0.084523667728249,-0.00547326197174363,0.0756396064366525,0.751612785913922 cont.residuals=-0.582130961004634,-0.170873920746869,-0.0435484544735238,0.121885853587620,1.13524487569216 predictedValues: Include Exclude Both chr17.10306_chr17_82514018_82515222_+_1.R.tl.Lung 63.7810527672852 53.4508062857387 64.6612433145184 chr17.10306_chr17_82514018_82515222_+_1.R.tl.cerebhem 55.6093760790573 77.062916628354 75.0822143216717 chr17.10306_chr17_82514018_82515222_+_1.R.tl.cortex 55.0759776324175 51.7012978086913 62.4786030817825 chr17.10306_chr17_82514018_82515222_+_1.R.tl.heart 72.1356928053526 54.8725983475623 90.4935070159833 chr17.10306_chr17_82514018_82515222_+_1.R.tl.kidney 65.8132046235485 51.1029362956798 61.7886597540815 chr17.10306_chr17_82514018_82515222_+_1.R.tl.liver 65.8944448554796 52.642965321436 69.191296598464 chr17.10306_chr17_82514018_82515222_+_1.R.tl.stomach 62.3771862230091 50.5276921360709 77.7713972498825 chr17.10306_chr17_82514018_82515222_+_1.R.tl.testicle 58.6688583894039 55.0814059180947 66.7063486096661 diffExp=10.3302464815465,-21.4535405492967,3.37467982372623,17.2630944577903,14.7102683278687,13.2514795340436,11.8494940869382,3.58745247130925 diffExpScore=1.77730687133795 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,-1,0,1,0,0,0,0 diffExp1.3Score=2 diffExp1.2=0,-1,0,1,1,1,1,0 diffExp1.2Score=1.25 cont.predictedValues: Include Exclude Both Lung 60.98341224528 64.5570833272582 68.0266941825102 cerebhem 64.7184307804011 55.592796205068 59.1409181786904 cortex 70.879772492822 67.085553821495 56.3497600459936 heart 61.7763291968105 72.5876519676086 65.729937990905 kidney 58.5564532818401 59.487169881451 65.2848187987472 liver 59.5428331940267 58.949283585342 63.0210587856183 stomach 67.644392389494 66.1332176129268 63.3825042036205 testicle 68.0336663104473 58.695512674398 65.1662129701633 cont.diffExp=-3.57367108197822,9.12563457533305,3.79421867132697,-10.8113227707981,-0.93071659961086,0.593549608684711,1.51117477656715,9.33815363604926 cont.diffExpScore=3.94927436189267 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=-0.426046712320569 cont.tran.correlation=0.158073864519656 tran.covariance=-0.00547280023844993 cont.tran.covariance=0.00100169703484844 tran.mean=59.1124007573238 cont.tran.mean=63.4514724354168 weightedLogRatios: wLogRatio Lung 0.718635559644164 cerebhem -1.36429529420447 cortex 0.251474944128175 heart 1.13292164537807 kidney 1.02717666184047 liver 0.915103340461106 stomach 0.84858184368057 testicle 0.254934093474639 cont.weightedLogRatios: wLogRatio Lung -0.235711879317285 cerebhem 0.622264450624579 cortex 0.232910528000004 heart -0.678023641166799 kidney -0.0643054730662599 liver 0.0408922361577676 stomach 0.0949589264327994 testicle 0.61213995300893 varWeightedLogRatios=0.658820330895447 cont.varWeightedLogRatios=0.185346960469068 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10775706240595 0.086846305232819 47.2991574183128 1.64313020325395e-147 *** df.mm.trans1 -0.104693896920197 0.0735579133235278 -1.42328530255778 0.155615658233579 df.mm.trans2 -0.105322166808625 0.0735579133235278 -1.43182646230582 0.153157443902627 df.mm.exp2 0.0793344154374028 0.102438313026817 0.774460385897161 0.439223365638745 df.mm.exp3 -0.145683509377171 0.102438313026817 -1.42215841976071 0.155942218062203 df.mm.exp4 -0.186770919533949 0.102438313026817 -1.82325258992751 0.0691864580391982 . df.mm.exp5 0.0318867158970735 0.102438313026817 0.311277245347901 0.755790046409233 df.mm.exp6 -0.0503442133972541 0.102438313026817 -0.49145882931589 0.623434720247773 df.mm.exp7 -0.263108460024524 0.102438313026817 -2.56845756485316 0.0106628541965326 * df.mm.exp8 -0.0846347560927514 0.102438313026817 -0.826202165888804 0.409297398171449 df.mm.trans1:exp2 -0.216438761557327 0.0887141814020458 -2.43973125983594 0.0152340868202217 * df.mm.trans2:exp2 0.286526049625353 0.0887141814020458 3.22976603173329 0.00136596059793631 ** df.mm.trans1:exp3 -0.00105901483759574 0.0887141814020458 -0.0119373793553521 0.990482922467673 df.mm.trans2:exp3 0.112404671042353 0.0887141814020459 1.26704287032695 0.206049948772533 df.mm.trans1:exp4 0.309863719499408 0.0887141814020458 3.49283186298174 0.000544311944959062 *** df.mm.trans2:exp4 0.213023301878923 0.0887141814020458 2.401231669078 0.0169030816612269 * df.mm.trans1:exp5 -0.000522387204219502 0.0887141814020458 -0.00588842951559326 0.995305364033648 df.mm.trans2:exp5 -0.0768064808000408 0.0887141814020459 -0.865774553585291 0.387254430016055 df.mm.trans1:exp6 0.0829421870994229 0.0887141814020458 0.934937185786964 0.350516949237481 df.mm.trans2:exp6 0.0351151087051221 0.0887141814020458 0.395822946795656 0.692496163706101 df.mm.trans1:exp7 0.240851895474783 0.0887141814020458 2.71491988843656 0.00698437666100339 ** df.mm.trans2:exp7 0.206868282925277 0.0887141814020458 2.33185134164476 0.0203210267346364 * df.mm.trans1:exp8 0.00108765300058243 0.0887141814020458 0.0122601931663357 0.99022557187072 df.mm.trans2:exp8 0.114685232430573 0.0887141814020459 1.29274971169297 0.197018735775189 df.mm.trans1:probe2 0.187280084879196 0.0443570907010229 4.22210027572608 3.14786420405473e-05 *** df.mm.trans1:probe3 0.254464183532100 0.0443570907010229 5.73671941758417 2.21556745713918e-08 *** df.mm.trans1:probe4 0.160944409488152 0.0443570907010229 3.62838064770646 0.000331248488774964 *** df.mm.trans1:probe5 0.420916595241131 0.0443570907010229 9.48927417440893 5.23985964674481e-19 *** df.mm.trans1:probe6 0.347931746559793 0.0443570907010229 7.84388112613006 6.3912421199512e-14 *** df.mm.trans2:probe2 -0.117865868074390 0.0443570907010229 -2.65720465908896 0.00826955056844623 ** df.mm.trans2:probe3 -0.173339500527654 0.0443570907010229 -3.9078194216118 0.000113412899321535 *** df.mm.trans2:probe4 -0.107727095120420 0.0443570907010229 -2.42863301938637 0.0156995741990095 * df.mm.trans2:probe5 0.123209035254621 0.0443570907010229 2.77766267596489 0.00579411387647479 ** df.mm.trans2:probe6 0.0626648680427094 0.0443570907010229 1.41273620637307 0.158693159860825 df.mm.trans3:probe2 0.770659131440915 0.0443570907010229 17.3739783033864 3.03938661222190e-48 *** df.mm.trans3:probe3 0.0436038743514498 0.0443570907010229 0.983019257176943 0.326331437240247 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09069484327205 0.162603342599773 25.157507698601 3.49393150321523e-78 *** df.mm.trans1 0.0774903901003748 0.137723332604714 0.562652592228389 0.574060568591713 df.mm.trans2 0.0847274238605769 0.137723332604714 0.61520021522974 0.538854355242389 df.mm.exp2 0.0499250621904497 0.191796439281884 0.260302341260229 0.794795913444959 df.mm.exp3 0.377124289498425 0.191796439281884 1.96627367489427 0.0501204129639103 . df.mm.exp4 0.164509041405321 0.191796439281884 0.857727296821924 0.391677086219491 df.mm.exp5 -0.0812591183539016 0.191796439281884 -0.423673758794212 0.672084875126762 df.mm.exp6 -0.0383470761853745 0.191796439281884 -0.199936330043206 0.841655805223024 df.mm.exp7 0.198496199252498 0.191796439281884 1.03493161810354 0.301472672205252 df.mm.exp8 0.0571732732912204 0.191796439281884 0.298093507394017 0.765822862977945 df.mm.trans1:exp2 0.00951906719203265 0.166100588773511 0.0573090514749019 0.954334336663889 df.mm.trans2:exp2 -0.199421279219679 0.166100588773511 -1.20060549268494 0.230781301020353 df.mm.trans1:exp3 -0.226741089746421 0.166100588773511 -1.36508299832457 0.173174083824965 df.mm.trans2:exp3 -0.338705407205514 0.166100588773511 -2.03915837810401 0.042245381649924 * df.mm.trans1:exp4 -0.151590669807227 0.166100588773511 -0.912643783664916 0.362108496936428 df.mm.trans2:exp4 -0.0472640623656223 0.166100588773511 -0.284550841840000 0.776170057773797 df.mm.trans1:exp5 0.0406485238421565 0.166100588773511 0.244722334473982 0.80682634660679 df.mm.trans2:exp5 -0.000530069825502286 0.166100588773511 -0.00319125795649689 0.99745571282062 df.mm.trans1:exp6 0.0144411185692060 0.166100588773511 0.0869420071044865 0.930771315309096 df.mm.trans2:exp6 -0.0525252950599928 0.166100588773511 -0.316225821039168 0.752034733958696 df.mm.trans1:exp7 -0.0948336363675297 0.166100588773511 -0.570941000677857 0.56843541740637 df.mm.trans2:exp7 -0.174374888110392 0.166100588773511 -1.04981499101225 0.294585597156686 df.mm.trans1:exp8 0.0522275053792901 0.166100588773511 0.314432993675331 0.75339457623144 df.mm.trans2:exp8 -0.152359839683616 0.166100588773511 -0.917274531105778 0.359681075256221 df.mm.trans1:probe2 -0.111491439947670 0.0830502943867555 -1.34245689038099 0.180387729552325 df.mm.trans1:probe3 -0.102571478960669 0.0830502943867555 -1.23505256324566 0.217705804766563 df.mm.trans1:probe4 -0.0996258878038264 0.0830502943867555 -1.19958500495953 0.231177032874187 df.mm.trans1:probe5 -0.132680769512606 0.0830502943867555 -1.59759541483052 0.111107820926850 df.mm.trans1:probe6 -0.071880453032487 0.0830502943867555 -0.865505096198071 0.387402023047146 df.mm.trans2:probe2 -0.0992689320268689 0.0830502943867555 -1.19528693738983 0.232849082674646 df.mm.trans2:probe3 -0.0250003297125458 0.0830502943867555 -0.301026382834023 0.763587464042487 df.mm.trans2:probe4 -0.0530059651224843 0.0830502943867555 -0.638239340557202 0.52376873690464 df.mm.trans2:probe5 -0.0371528636551054 0.0830502943867555 -0.44735378639465 0.654918559251001 df.mm.trans2:probe6 0.14357629343377 0.0830502943867555 1.72878729080901 0.084799773770827 . df.mm.trans3:probe2 -0.0505331063590837 0.0830502943867555 -0.608463904098364 0.54330616093105 df.mm.trans3:probe3 -0.0485042963854316 0.0830502943867555 -0.584035213163155 0.559603008865714