chr4.16817_chr4_120147615_120155563_+_2.R fitVsDatCorrelation=0.814671288734805 cont.fitVsDatCorrelation=0.27986471025648 fstatistic=6648.2982023775,43,485 cont.fstatistic=2418.73718153287,43,485 residuals=-0.516605719205965,-0.130179549995359,-0.00597525907461852,0.106225682142202,0.869696756711431 cont.residuals=-0.7388939192884,-0.229957029528129,-0.0200756968637306,0.190685392060723,0.8572905863687 predictedValues: Include Exclude Both chr4.16817_chr4_120147615_120155563_+_2.R.tl.Lung 78.9940482245914 156.050116690041 76.2804055902404 chr4.16817_chr4_120147615_120155563_+_2.R.tl.cerebhem 84.5547421903361 112.542844631790 93.5743173574956 chr4.16817_chr4_120147615_120155563_+_2.R.tl.cortex 75.9791204808565 123.044009573273 92.8252931084858 chr4.16817_chr4_120147615_120155563_+_2.R.tl.heart 78.7040034861704 118.040804998655 77.4237006130028 chr4.16817_chr4_120147615_120155563_+_2.R.tl.kidney 84.8163713048488 159.732910044365 82.4455493785207 chr4.16817_chr4_120147615_120155563_+_2.R.tl.liver 79.2336771093569 137.578588094076 66.1538976395362 chr4.16817_chr4_120147615_120155563_+_2.R.tl.stomach 82.7820828702774 120.175355545535 68.3314207060263 chr4.16817_chr4_120147615_120155563_+_2.R.tl.testicle 73.5451974569897 110.566141741835 68.9004250873444 diffExp=-77.0560684654493,-27.988102441454,-47.0648890924165,-39.3368015124843,-74.9165387395162,-58.344910984719,-37.3932726752579,-37.0209442848451 diffExpScore=0.997500759320529 diffExp1.5=-1,0,-1,0,-1,-1,0,-1 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 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 92.9903191631017 75.7326118745609 84.5978196435591 cerebhem 88.0340098647917 79.1861532363943 95.7026421014973 cortex 91.0065046590232 85.2715987339087 88.6339062776694 heart 91.670739910865 94.384913201876 92.7328965366321 kidney 94.6469223068726 86.8522801921447 96.8312813090525 liver 90.3525812537468 92.4688589609775 85.0820854802905 stomach 90.4168822202846 95.1442751310993 99.2964947047322 testicle 95.6109304158655 91.5535574381859 86.9166566784408 cont.diffExp=17.2577072885408,8.84785662839738,5.73490592511449,-2.71417329101099,7.7946421147279,-2.11627770723064,-4.72739291081463,4.05737297767958 cont.diffExpScore=1.51560759664555 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=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.320175658863747 cont.tran.correlation=0.112677690615611 tran.covariance=0.00232124674829426 cont.tran.covariance=0.000272376211950376 tran.mean=104.771250902687 cont.tran.mean=89.7076961602312 weightedLogRatios: wLogRatio Lung -3.20643689034793 cerebhem -1.30968633421061 cortex -2.20384497539954 heart -1.85172323748414 kidney -3.01124747078164 liver -2.56490258569541 stomach -1.71556783884722 testicle -1.83542828979343 cont.weightedLogRatios: wLogRatio Lung 0.909388932270364 cerebhem 0.468678635543416 cortex 0.291496107820412 heart -0.132257808440104 kidney 0.387367935185501 liver -0.104539670833482 stomach -0.230859866641387 testicle 0.196807647874701 varWeightedLogRatios=0.440646774567662 cont.varWeightedLogRatios=0.143337273671325 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.03842969618149 0.101015878574160 49.8776010989456 4.44550163847631e-193 *** df.mm.trans1 -0.726410453988821 0.087855377644497 -8.26825259266667 1.31538411123334e-15 *** df.mm.trans2 0.0417176888444518 0.0824791194752122 0.50579697152307 0.613228986441342 df.mm.exp2 -0.463156436479497 0.112067055333901 -4.13285095338263 4.22101606394536e-05 *** df.mm.exp3 -0.472852122239919 0.112067055333901 -4.21936777789929 2.92471558136527e-05 *** df.mm.exp4 -0.297702178430779 0.112067055333901 -2.65646471698379 0.00815667196088582 ** df.mm.exp5 0.0167199929078056 0.112067055333901 0.149196325878187 0.881460735971392 df.mm.exp6 0.0194792886732544 0.112067055333901 0.173818153918797 0.862080901370728 df.mm.exp7 -0.104339709897533 0.112067055333901 -0.931047126978172 0.352292391105097 df.mm.exp8 -0.314281918674910 0.112067055333901 -2.80440953622377 0.00524316432840307 ** df.mm.trans1:exp2 0.531183086959447 0.102302756932596 5.19226561322711 3.06221750936686e-07 *** df.mm.trans2:exp2 0.136313210303929 0.0915023675147686 1.48972331543146 0.136946995401950 df.mm.trans1:exp3 0.433938183566184 0.102302756932596 4.24170566441424 2.65763449812379e-05 *** df.mm.trans2:exp3 0.235216998535481 0.0915023675147686 2.57061106640237 0.0104491489851643 * df.mm.trans1:exp4 0.294023692078787 0.102302756932596 2.87405443308347 0.00423032182260218 ** df.mm.trans2:exp4 0.0185553317030745 0.0915023675147686 0.202785263453206 0.839387918250385 df.mm.trans1:exp5 0.054396078397304 0.102302756932596 0.531716642134523 0.595165717281815 df.mm.trans2:exp5 0.0066058987592201 0.0915023675147686 0.0721937468793241 0.942477482707696 df.mm.trans1:exp6 -0.016450374904035 0.102302756932596 -0.160800895276690 0.872317175321324 df.mm.trans2:exp6 -0.145461201553814 0.0915023675147686 -1.58969877506542 0.112554329550105 df.mm.trans1:exp7 0.151178846717689 0.102302756932596 1.47775926329431 0.140121301296637 df.mm.trans2:exp7 -0.156885534251852 0.0915023675147686 -1.71455163962321 0.0870662925035226 . df.mm.trans1:exp8 0.242809556588806 0.102302756932596 2.37344098897341 0.0180118811502014 * df.mm.trans2:exp8 -0.0302813881280430 0.0915023675147686 -0.330935569761684 0.740836079938559 df.mm.trans1:probe2 -0.0314044586717115 0.0560335276669506 -0.560458353762266 0.575425635268507 df.mm.trans1:probe3 -0.113771996780137 0.0560335276669506 -2.03042716597052 0.0428582539221217 * df.mm.trans1:probe4 0.101262039442996 0.0560335276669506 1.80716873734726 0.0713555027247112 . df.mm.trans1:probe5 -0.220101921687504 0.0560335276669506 -3.92803970857833 9.80681302639197e-05 *** df.mm.trans1:probe6 0.0287044511184960 0.0560335276669506 0.512272782272573 0.60869330658091 df.mm.trans1:probe7 0.202446688127753 0.0560335276669506 3.61295632377539 0.000334313392929302 *** df.mm.trans1:probe8 0.206154544128617 0.0560335276669506 3.67912842029058 0.000260246112922636 *** df.mm.trans1:probe9 0.194677869047899 0.0560335276669506 3.47431042009378 0.000558091881196684 *** df.mm.trans1:probe10 0.0306583322121557 0.0560335276669506 0.547142639213814 0.584532402959482 df.mm.trans1:probe11 0.268145229689800 0.0560335276669506 4.78544258865137 2.26825269609881e-06 *** df.mm.trans1:probe12 0.250881519483815 0.0560335276669506 4.47734650895072 9.43151490291913e-06 *** df.mm.trans2:probe2 -0.0131719660224118 0.0560335276669506 -0.235072938843020 0.814251338399496 df.mm.trans2:probe3 -0.142029025745051 0.0560335276669506 -2.53471504755574 0.0115671976233190 * df.mm.trans2:probe4 -0.0269250048401264 0.0560335276669506 -0.480515968942059 0.63107721793341 df.mm.trans2:probe5 -0.0204476535797379 0.0560335276669506 -0.364918191502659 0.715331543594036 df.mm.trans2:probe6 -0.0971280351728664 0.0560335276669506 -1.73339140362840 0.0836616732023141 . df.mm.trans3:probe2 0.0412418942934202 0.0560335276669506 0.73602173574635 0.462073286109515 df.mm.trans3:probe3 0.197389352164494 0.0560335276669506 3.52270078974373 0.000467577243326852 *** df.mm.trans3:probe4 -0.31591457923692 0.0560335276669506 -5.63795628065108 2.92485097040966e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30694738194176 0.167227765836584 25.754977711958 8.02749668642293e-93 *** df.mm.trans1 0.220710515346443 0.14544108042809 1.51752527344272 0.129785645267725 df.mm.trans2 0.000488462433015762 0.136540899041753 0.00357740747602954 0.997147118844866 df.mm.exp2 -0.133517010829020 0.18552254904772 -0.719680769342364 0.472068218390761 df.mm.exp3 0.0504621808213672 0.185522549047720 0.272000255927852 0.785737572669618 df.mm.exp4 0.11406543464107 0.185522549047720 0.614833265425489 0.538953181518818 df.mm.exp5 0.0195962403755206 0.18552254904772 0.105627269979349 0.915921758876707 df.mm.exp6 0.165179241255799 0.18552254904772 0.890345901906034 0.373721616169604 df.mm.exp7 0.0399193841676069 0.185522549047720 0.215172680477449 0.829723140499488 df.mm.exp8 0.190465815559150 0.18552254904772 1.02664509805845 0.305099360429751 df.mm.trans1:exp2 0.0787448336664027 0.169358141732158 0.464960425646017 0.642168474563579 df.mm.trans2:exp2 0.178109589661245 0.151478526982460 1.17580751020816 0.240248650580868 df.mm.trans1:exp3 -0.0720265898041704 0.169358141732158 -0.425291568905387 0.670812773761237 df.mm.trans2:exp3 0.0681703890058566 0.151478526982460 0.450033350362262 0.652887472581065 df.mm.trans1:exp4 -0.128357583953158 0.169358141732158 -0.75790619004404 0.448875239529733 df.mm.trans2:exp4 0.106106936132129 0.151478526982460 0.700475098654846 0.483966538985058 df.mm.trans1:exp5 -0.00193827253108849 0.169358141732158 -0.0114448145879747 0.99087326460828 df.mm.trans2:exp5 0.117403634508925 0.151478526982460 0.775051334652332 0.438687033197606 df.mm.trans1:exp6 -0.193955047846642 0.169358141732158 -1.14523604157977 0.252676195434927 df.mm.trans2:exp6 0.0344838148692994 0.151478526982460 0.227648205697778 0.820015675670394 df.mm.trans1:exp7 -0.0679837766953234 0.169358141732158 -0.401420185649183 0.688287598582945 df.mm.trans2:exp7 0.188266169157433 0.151478526982460 1.24285714224851 0.214521003195368 df.mm.trans1:exp8 -0.162674059858122 0.169358141732158 -0.960532857731714 0.337265712362414 df.mm.trans2:exp8 -0.000750559150828614 0.151478526982460 -0.00495488810051292 0.996048624810307 df.mm.trans1:probe2 0.0131855509767693 0.09276127452386 0.142144995791080 0.887024505224012 df.mm.trans1:probe3 0.0364676402691747 0.0927612745238599 0.393134316624709 0.694393024412316 df.mm.trans1:probe4 0.0170945692545157 0.09276127452386 0.184285622877234 0.853866419316624 df.mm.trans1:probe5 -0.0108681706991300 0.0927612745238599 -0.117162800478065 0.90677954624648 df.mm.trans1:probe6 -0.0425155201347775 0.09276127452386 -0.458332643153169 0.646918770930997 df.mm.trans1:probe7 -0.100158195633684 0.09276127452386 -1.07974147776421 0.280793968206356 df.mm.trans1:probe8 -0.00284956112983212 0.09276127452386 -0.0307192968667023 0.975506038693293 df.mm.trans1:probe9 0.00663165340119175 0.09276127452386 0.0714916158195516 0.943035967423566 df.mm.trans1:probe10 0.102290892095443 0.09276127452386 1.10273271492332 0.270690133324215 df.mm.trans1:probe11 0.000870981445348304 0.09276127452386 0.00938949415927086 0.992512238547682 df.mm.trans1:probe12 0.057250086977092 0.09276127452386 0.617176588732254 0.537407885818155 df.mm.trans2:probe2 -0.0166465125201082 0.09276127452386 -0.179455409658331 0.857655073564832 df.mm.trans2:probe3 0.0415053488352255 0.09276127452386 0.44744263215734 0.654755267503695 df.mm.trans2:probe4 0.159885071565408 0.09276127452386 1.72361874484899 0.085413986046248 . df.mm.trans2:probe5 0.0397574684221002 0.09276127452386 0.428599850812462 0.66840474198716 df.mm.trans2:probe6 -0.0267711017573863 0.09276127452386 -0.288602133754645 0.773009166425848 df.mm.trans3:probe2 -0.00840987254442457 0.09276127452386 -0.0906614596187054 0.92779902511539 df.mm.trans3:probe3 -0.304977241675914 0.09276127452386 -3.28776467595287 0.00108317345985363 ** df.mm.trans3:probe4 -0.0475643496833343 0.09276127452386 -0.512760846888751 0.608352072372419