chr6.19488_chr6_84659580_84660747_-_1.R fitVsDatCorrelation=0.899741972212882 cont.fitVsDatCorrelation=0.246252654537001 fstatistic=9261.9182012286,43,485 cont.fstatistic=1868.95267967725,43,485 residuals=-0.7939158595412,-0.0949951336523785,-0.00682185681565325,0.0849402606711776,0.64687491581151 cont.residuals=-0.644612504529267,-0.264784626642690,-0.0569468217239405,0.206941874642843,1.27167345303438 predictedValues: Include Exclude Both chr6.19488_chr6_84659580_84660747_-_1.R.tl.Lung 61.5079442943322 116.082221197918 77.0034175069011 chr6.19488_chr6_84659580_84660747_-_1.R.tl.cerebhem 81.929533768683 143.259704790635 76.2070266011435 chr6.19488_chr6_84659580_84660747_-_1.R.tl.cortex 58.0882345714907 94.9589955108173 70.9074549076098 chr6.19488_chr6_84659580_84660747_-_1.R.tl.heart 60.0820788002146 133.713848342948 84.2052449265161 chr6.19488_chr6_84659580_84660747_-_1.R.tl.kidney 63.3919882463193 100.436856007200 74.1506716527932 chr6.19488_chr6_84659580_84660747_-_1.R.tl.liver 62.9688587575572 103.238555048870 68.7525332485296 chr6.19488_chr6_84659580_84660747_-_1.R.tl.stomach 62.1933247071892 101.294550619565 77.4880837161714 chr6.19488_chr6_84659580_84660747_-_1.R.tl.testicle 63.7257311606482 139.039818367604 85.9163053353033 diffExp=-54.5742769035854,-61.330171021952,-36.8707609393266,-73.6317695427335,-37.0448677608809,-40.2696962913132,-39.1012259123753,-75.3140872069562 diffExpScore=0.99761414443352 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 74.3311044499261 88.7719976174574 80.9077402345547 cerebhem 84.7756746113602 71.1786712982155 76.6040153318055 cortex 89.677211879803 84.8150345477672 84.7836922707382 heart 90.7646400516123 82.1809138421843 77.457360871374 kidney 89.8373296428128 85.0959940558742 83.0250522662955 liver 84.6712129728217 90.6995810053965 83.7398852528713 stomach 83.3563648172857 85.6950831146962 79.2505467150249 testicle 92.3357590710567 77.46766433534 81.3952093721108 cont.diffExp=-14.4408931675313,13.5970033131447,4.86217733203586,8.58372620942798,4.74133558693863,-6.02836803257475,-2.33871829741055,14.8680947357166 cont.diffExpScore=2.79581857458942 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=0.581688106305624 cont.tran.correlation=-0.369498668065235 tran.covariance=0.00982928486637428 cont.tran.covariance=-0.00191771980030519 tran.mean=90.3695152619995 cont.tran.mean=84.7283898321006 weightedLogRatios: wLogRatio Lung -2.8179125945231 cerebhem -2.61812087762895 cortex -2.11715919172013 heart -3.59652252742983 kidney -2.01537908585040 liver -2.1703466885893 stomach -2.13363969315657 testicle -3.54562739562004 cont.weightedLogRatios: wLogRatio Lung -0.780704451644976 cerebhem 0.760901814084788 cortex 0.249082817284535 heart 0.442946840559193 kidney 0.242414466802705 liver -0.307651101320305 stomach -0.122772827253211 testicle 0.779121394893947 varWeightedLogRatios=0.415190129100352 cont.varWeightedLogRatios=0.288961327139473 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.73986930144382 0.0805176227093778 58.8674769814307 4.95009370173185e-223 *** df.mm.trans1 -0.639753332117794 0.0644585676917272 -9.92503176889394 2.93214004436841e-21 *** df.mm.trans2 0.0366023741562576 0.0644585676917272 0.567843429771947 0.570404058815892 df.mm.exp2 0.507449681640371 0.0863144134967174 5.87908393375887 7.6843960257079e-09 *** df.mm.exp3 -0.175582543669752 0.0863144134967174 -2.03422043383785 0.0424729577981977 * df.mm.exp4 0.0285411719073576 0.0863144134967174 0.330665189637684 0.741040198486417 df.mm.exp5 -0.0768476912330912 0.0863144134967174 -0.890322810755284 0.373733998890355 df.mm.exp6 0.0195538129738846 0.0863144134967174 0.226541688482054 0.820875585902518 df.mm.exp7 -0.131459157905418 0.0863144134967174 -1.52302671801642 0.128403753048119 df.mm.exp8 0.106359869082790 0.0863144134967175 1.23223763881377 0.218457362640709 df.mm.trans1:exp2 -0.220756490096073 0.0677105967267813 -3.26029455901632 0.00119122906738058 ** df.mm.trans2:exp2 -0.297089324564874 0.0677105967267813 -4.38763412119461 1.40694836428423e-05 *** df.mm.trans1:exp3 0.118379341961022 0.0677105967267813 1.74831337609819 0.0810424882057099 . df.mm.trans2:exp3 -0.0252710275632082 0.0677105967267813 -0.373221161603097 0.709146915101203 df.mm.trans1:exp4 -0.0519959065014998 0.0677105967267813 -0.767913871905579 0.442912062586277 df.mm.trans2:exp4 0.112862141115905 0.0677105967267813 1.66683128744699 0.0961935482679865 . df.mm.trans1:exp5 0.107018834348901 0.0677105967267813 1.58053302617804 0.114636759020441 df.mm.trans2:exp5 -0.0679218206199505 0.0677105967267813 -1.00311951014140 0.316303406332824 df.mm.trans1:exp6 0.00392014378521166 0.0677105967267813 0.0578955728455593 0.95385565452789 df.mm.trans2:exp6 -0.136810177715206 0.0677105967267813 -2.02051354335641 0.0438792369645667 * df.mm.trans1:exp7 0.142540490083627 0.0677105967267813 2.10514302006216 0.0357925560175887 * df.mm.trans2:exp7 -0.00480697021725947 0.0677105967267813 -0.0709928792483701 0.943432686442196 df.mm.trans1:exp8 -0.0709377871879414 0.0677105967267813 -1.04766152739994 0.295316386941635 df.mm.trans2:exp8 0.0741017426473033 0.0677105967267813 1.09438915368463 0.274327546474500 df.mm.trans1:probe2 0.291530178717643 0.046358276511742 6.28863281066546 7.15368847050445e-10 *** df.mm.trans1:probe3 0.0142088253106743 0.046358276511742 0.306500292500637 0.75935529287976 df.mm.trans1:probe4 -0.095205707563266 0.046358276511742 -2.05369385419563 0.0405409304938695 * df.mm.trans1:probe5 0.0515078856817569 0.046358276511742 1.11108284339929 0.267083194864371 df.mm.trans1:probe6 0.0427647804388726 0.046358276511742 0.922484260777916 0.356734705688446 df.mm.trans2:probe2 -0.0405199576081097 0.046358276511742 -0.874060915483915 0.382517669491949 df.mm.trans2:probe3 0.00610103398783905 0.046358276511742 0.131606143431448 0.895350401518921 df.mm.trans2:probe4 -0.133206015169443 0.046358276511742 -2.87340309417463 0.00423890567966601 ** df.mm.trans2:probe5 0.0653309551383604 0.046358276511742 1.40926194962862 0.159398567550447 df.mm.trans2:probe6 -0.252472930683055 0.046358276511742 -5.44612418063269 8.19608814293456e-08 *** df.mm.trans3:probe2 0.117550409854705 0.046358276511742 2.53569413489586 0.0115353421901877 * df.mm.trans3:probe3 0.979466023830167 0.046358276511742 21.1281802847455 9.87239462503076e-71 *** df.mm.trans3:probe4 -0.0658895464853625 0.046358276511742 -1.42131139126092 0.155868909077828 df.mm.trans3:probe5 0.378414291361721 0.046358276511742 8.16282053250778 2.84454284940197e-15 *** df.mm.trans3:probe6 0.114691991699222 0.046358276511742 2.47403485050122 0.0137008521143195 * df.mm.trans3:probe7 -0.0563749249969724 0.046358276511742 -1.21607033822091 0.224549661825368 df.mm.trans3:probe8 0.433613504205683 0.046358276511742 9.35352944141169 3.13235182336802e-19 *** df.mm.trans3:probe9 -0.0307190679520131 0.046358276511742 -0.662644737110369 0.507872883702404 df.mm.trans3:probe10 0.200561924412881 0.046358276511742 4.32634557417338 1.84179926326132e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.52527546355747 0.178813375795692 25.3072536851379 1.06095132500668e-90 *** df.mm.trans1 -0.178597210936175 0.143149458467189 -1.24762756945470 0.212769532128676 df.mm.trans2 -0.0383622824723595 0.143149458467189 -0.267987618557094 0.788822785101781 df.mm.exp2 -0.0347388955040556 0.191686877205525 -0.181227301578965 0.856264875222061 df.mm.exp3 0.0952950343013814 0.191686877205525 0.497139061842022 0.61931627334873 df.mm.exp4 0.166174004341306 0.191686877205525 0.866903393512622 0.386423623280208 df.mm.exp5 0.121346891394034 0.191686877205525 0.633047463462653 0.527001018450062 df.mm.exp6 0.117321761244306 0.191686877205525 0.612048998630796 0.540792155320372 df.mm.exp7 0.100014848776891 0.191686877205525 0.521761584491025 0.602074528573138 df.mm.exp8 0.0746844258306008 0.191686877205525 0.389616790253852 0.696990961094825 df.mm.trans1:exp2 0.166218043788873 0.150371558057021 1.10538220084042 0.269542046434873 df.mm.trans2:exp2 -0.186139149495106 0.150371558057021 -1.23786141408818 0.216366346439955 df.mm.trans1:exp3 0.0923921569610597 0.150371558057021 0.614425747494249 0.539222145540238 df.mm.trans2:exp3 -0.140893471144499 0.150371558057021 -0.936968885373075 0.349240888194968 df.mm.trans1:exp4 0.033566281311418 0.150371558057021 0.223222275177130 0.823456501016013 df.mm.trans2:exp4 -0.243322179062346 0.150371558057021 -1.61813964160748 0.106282619928065 df.mm.trans1:exp5 0.0681241977533741 0.150371558057021 0.453039116130868 0.650723178949723 df.mm.trans2:exp5 -0.163638188418259 0.150371558057021 -1.08822566270283 0.277035964038902 df.mm.trans1:exp6 0.0129244148300270 0.150371558057021 0.0859498631059341 0.931541743261965 df.mm.trans2:exp6 -0.0958402818211094 0.150371558057021 -0.637356445989387 0.524193441411362 df.mm.trans1:exp7 0.0145806226754853 0.150371558057021 0.0969639662173103 0.922795065502932 df.mm.trans2:exp7 -0.135290656164730 0.150371558057021 -0.89970908004709 0.368721578759745 df.mm.trans1:exp8 0.142217565507349 0.150371558057021 0.945774369468327 0.344734622373381 df.mm.trans2:exp8 -0.210895069042078 0.150371558057021 -1.40249307626451 0.161407811796760 df.mm.trans1:probe2 -0.150809626419642 0.102952367943785 -1.46484854531941 0.143610154642049 df.mm.trans1:probe3 -0.0901004043504721 0.102952367943785 -0.87516592527206 0.381916821699135 df.mm.trans1:probe4 0.00692628254893438 0.102952367943785 0.0672765734996629 0.946389251040811 df.mm.trans1:probe5 -0.138503617180411 0.102952367943785 -1.34531745064901 0.179151394978547 df.mm.trans1:probe6 -0.237892718717058 0.102952367943785 -2.31070662548485 0.0212676944947727 * df.mm.trans2:probe2 -0.0448741296406144 0.102952367943785 -0.435872729659963 0.663123005588086 df.mm.trans2:probe3 -0.0457903296228878 0.102952367943785 -0.444771990556747 0.656682955022109 df.mm.trans2:probe4 0.0411736848204311 0.102952367943785 0.39992945905735 0.689384549459096 df.mm.trans2:probe5 0.113230974920025 0.102952367943785 1.09983847075623 0.271948117349593 df.mm.trans2:probe6 -0.0772109678933307 0.102952367943785 -0.749967868009507 0.453637698937806 df.mm.trans3:probe2 0.081547751571972 0.102952367943785 0.79209204412374 0.428694251653336 df.mm.trans3:probe3 0.150204612748845 0.102952367943785 1.45897190855154 0.145220160402738 df.mm.trans3:probe4 0.137471549073788 0.102952367943785 1.33529273604325 0.182406914841239 df.mm.trans3:probe5 0.0695250651966216 0.102952367943785 0.675312929514981 0.499798799158333 df.mm.trans3:probe6 0.0806159260236178 0.102952367943785 0.783041008514118 0.433985211473648 df.mm.trans3:probe7 0.0606169242784325 0.102952367943785 0.588786110403319 0.556278875974815 df.mm.trans3:probe8 0.166438065462355 0.102952367943785 1.61665116389780 0.106603813446128 df.mm.trans3:probe9 0.0398678165351315 0.102952367943785 0.387245260418881 0.698744516017442 df.mm.trans3:probe10 0.0636475482024074 0.102952367943785 0.618223256770168 0.536718386669242