chr2.12834_chr2_25747206_25747833_-_1.R fitVsDatCorrelation=0.856671191789175 cont.fitVsDatCorrelation=0.271486701700281 fstatistic=2973.63693389934,36,324 cont.fstatistic=847.879146900453,36,324 residuals=-0.728820359679057,-0.116993727068291,-0.00176270493086465,0.102688301788737,1.02993282299694 cont.residuals=-1.01900454290955,-0.267024281553126,-0.0694357611056433,0.132572066348388,2.58200818523992 predictedValues: Include Exclude Both chr2.12834_chr2_25747206_25747833_-_1.R.tl.Lung 72.4397170364049 83.8996154144344 65.5215898350874 chr2.12834_chr2_25747206_25747833_-_1.R.tl.cerebhem 87.2884290347445 106.949362108328 195.650927329636 chr2.12834_chr2_25747206_25747833_-_1.R.tl.cortex 71.0841737871367 137.143476676256 198.665315083277 chr2.12834_chr2_25747206_25747833_-_1.R.tl.heart 66.3380898191341 110.815384270308 144.132712325653 chr2.12834_chr2_25747206_25747833_-_1.R.tl.kidney 72.1593008673264 85.7131556825037 64.522333182024 chr2.12834_chr2_25747206_25747833_-_1.R.tl.liver 74.8764294028827 77.6942639611311 69.7454412342558 chr2.12834_chr2_25747206_25747833_-_1.R.tl.stomach 69.0565825977142 68.082684298499 71.2207380131245 chr2.12834_chr2_25747206_25747833_-_1.R.tl.testicle 68.7395792109422 79.5244770217707 65.8555095351222 diffExp=-11.4598983780295,-19.6609330735832,-66.0593028891197,-44.4772944511736,-13.5538548151773,-2.81783455824841,0.97389829921525,-10.7848978108285 diffExpScore=1.00561357461409 diffExp1.5=0,0,-1,-1,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,-1,-1,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,-1,-1,-1,0,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 78.0557948765655 84.7212679828156 91.8962958972384 cerebhem 90.5214007603199 78.6313181651495 93.2257565692246 cortex 80.1575959928135 84.0657007389627 78.7447432664799 heart 83.9143662219423 81.2814354780543 67.8762496077263 kidney 81.8541354588263 79.4867104926278 91.1008135694823 liver 84.2756242685751 80.5052786896613 92.8893606529354 stomach 87.193671680219 85.619058591472 80.5067653143239 testicle 101.988161339098 94.5871298132986 73.271250768252 cont.diffExp=-6.66547310625006,11.8900825951703,-3.90810474614921,2.63293074388802,2.36742496619854,3.77034557891388,1.57461308874699,7.40103152579927 cont.diffExpScore=2.00420204785287 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=0.132292627514248 cont.tran.correlation=0.633694559027225 tran.covariance=0.00298986246454948 cont.tran.covariance=0.00292455692587309 tran.mean=83.2377950743448 cont.tran.mean=84.8036656594 weightedLogRatios: wLogRatio Lung -0.639777207877289 cerebhem -0.92849845399571 cortex -3.01798539988065 heart -2.28397579809759 kidney -0.751337928785997 liver -0.160119325891109 stomach 0.060049035696728 testicle -0.627146398197106 cont.weightedLogRatios: wLogRatio Lung -0.360416728015086 cerebhem 0.624545049886826 cortex -0.209828742706437 heart 0.140710169372145 kidney 0.128849794746023 liver 0.201900591239521 stomach 0.0812605100776496 testicle 0.345577370657241 varWeightedLogRatios=1.12435443676178 cont.varWeightedLogRatios=0.0933924179749994 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.3547580471105 0.144886748376153 36.9582319095778 3.06614284014364e-118 *** df.mm.trans1 -0.950808985992197 0.122717562367332 -7.74794550714859 1.21179416949272e-13 *** df.mm.trans2 -0.882802024884244 0.122717562367332 -7.19377086583371 4.39988616676145e-12 *** df.mm.exp2 -0.66475475806199 0.170899084812019 -3.88975025110983 0.000121788214940642 *** df.mm.exp3 -0.636725253442774 0.170899084812019 -3.72573822816631 0.000229692976912797 *** df.mm.exp4 -0.598100689644376 0.170899084812019 -3.49973020804799 0.000530922530117534 *** df.mm.exp5 0.032875042374598 0.170899084812019 0.192365233615845 0.847576636411393 df.mm.exp6 -0.106227533688443 0.170899084812019 -0.621580471336572 0.534654861876541 df.mm.exp7 -0.340130974262840 0.170899084812019 -1.99024456238001 0.0474040020665617 * df.mm.exp8 -0.111069133667249 0.170899084812019 -0.649910640477799 0.516210460514759 df.mm.trans1:exp2 0.851217942736924 0.148002948930719 5.75135798905859 2.04831373695574e-08 *** df.mm.trans2:exp2 0.907489199575012 0.148002948930719 6.13156160827451 2.52999506852516e-09 *** df.mm.trans1:exp3 0.617835247747977 0.148002948930719 4.17447930741696 3.8423091211582e-05 *** df.mm.trans2:exp3 1.12813187666408 0.148002948930719 7.6223608030416 2.77801964120125e-13 *** df.mm.trans1:exp4 0.510110202117367 0.148002948930719 3.44662188019071 0.000642482376082014 *** df.mm.trans2:exp4 0.87634527191803 0.148002948930719 5.92113385746287 8.15141205975185e-09 *** df.mm.trans1:exp5 -0.0367535821416077 0.148002948930719 -0.248330066442204 0.804036358507818 df.mm.trans2:exp5 -0.0114897496108863 0.148002948930719 -0.0776318964851485 0.938168788148696 df.mm.trans1:exp6 0.139311953635730 0.148002948930719 0.94127822886112 0.347263559810779 df.mm.trans2:exp6 0.0293879358368436 0.148002948930719 0.198563177620198 0.842728995548231 df.mm.trans1:exp7 0.292302453632216 0.148002948930719 1.97497722676488 0.0491193306404624 * df.mm.trans2:exp7 0.131232856735927 0.148002948930719 0.886690823960255 0.375903052858805 df.mm.trans1:exp8 0.0586395567829482 0.148002948930719 0.396205327033028 0.692214358534283 df.mm.trans2:exp8 0.0575129654002447 0.148002948930719 0.388593374765571 0.697832195891232 df.mm.trans1:probe2 -0.168557278442958 0.0740014744653597 -2.27775567528536 0.0233921797524828 * df.mm.trans1:probe3 0.0679968961396701 0.0740014744653597 0.918858666410757 0.358853039722851 df.mm.trans1:probe4 -0.341472558146009 0.0740014744653597 -4.61440208608077 5.68736775787314e-06 *** df.mm.trans1:probe5 -0.282765939897391 0.0740014744653597 -3.82108521404874 0.000159269270563959 *** df.mm.trans1:probe6 -0.36595012825932 0.0740014744653597 -4.9451734698965 1.22364395000100e-06 *** df.mm.trans2:probe2 -0.0524487449461133 0.0740014744653597 -0.708752701551436 0.478987588325545 df.mm.trans2:probe3 -0.155795883869818 0.0740014744653597 -2.105307833329 0.0360339560422231 * df.mm.trans2:probe4 -0.149417740464196 0.0740014744653597 -2.01911842356789 0.0442981153650263 * df.mm.trans2:probe5 -0.0865906218876038 0.0740014744653597 -1.17012022413333 0.242812573494025 df.mm.trans2:probe6 0.0632380575862722 0.0740014744653597 0.854551318648038 0.393430999432761 df.mm.trans3:probe2 1.34773127009722 0.0740014744653597 18.2122218487429 1.57283844968589e-51 *** df.mm.trans3:probe3 0.635966733598754 0.0740014744653597 8.59397381191981 3.62215103465899e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4605437834687 0.270314333317126 16.501321734337 7.919872121619e-45 *** df.mm.trans1 0.0210493055420234 0.228953416578214 0.091937066747517 0.926804857919354 df.mm.trans2 0.0165449434578798 0.228953416578214 0.0722633612773705 0.94243690933165 df.mm.exp2 0.059202488625771 0.318845392647872 0.185677729679956 0.852813680495047 df.mm.exp3 0.173251933170242 0.318845392647872 0.543372860844752 0.587246751370105 df.mm.exp4 0.333898450098769 0.318845392647872 1.04721114934699 0.295782780933198 df.mm.exp5 -0.00756789835248758 0.318845392647872 -0.0237353229088227 0.981078341471465 df.mm.exp6 0.0148764750573864 0.318845392647872 0.0466573311091114 0.962815082851978 df.mm.exp7 0.253568604126650 0.318845392647872 0.795271344587649 0.427037881983349 df.mm.exp8 0.604080005785275 0.318845392647872 1.89458596459135 0.0590375031193797 . df.mm.trans1:exp2 0.0889599167133467 0.276128209912681 0.322168882134420 0.747532528486724 df.mm.trans2:exp2 -0.133799086540682 0.276128209912681 -0.484554209738271 0.628320004147478 df.mm.trans1:exp3 -0.146681176294284 0.276128209912681 -0.531206776521199 0.595639462134183 df.mm.trans2:exp3 -0.181019956322078 0.276128209912681 -0.65556487828361 0.512569341049389 df.mm.trans1:exp4 -0.261525510887829 0.276128209912681 -0.947116236224216 0.344285385491323 df.mm.trans2:exp4 -0.375347473451556 0.276128209912681 -1.35932316937212 0.174989543507071 df.mm.trans1:exp5 0.0550828358402354 0.276128209912681 0.199482826682772 0.842010209199485 df.mm.trans2:exp5 -0.0562089255036306 0.276128209912681 -0.203560967281848 0.838824386393044 df.mm.trans1:exp6 0.0617923036519791 0.276128209912681 0.223781205373835 0.823068591545614 df.mm.trans2:exp6 -0.0659203869263886 0.276128209912681 -0.238731084184533 0.811465038099857 df.mm.trans1:exp7 -0.142860738221051 0.276128209912681 -0.517371036687007 0.60525020168511 df.mm.trans2:exp7 -0.243027366592770 0.276128209912681 -0.88012509359193 0.379443885460705 df.mm.trans1:exp8 -0.336647154441337 0.276128209912681 -1.21916972752546 0.223666614508510 df.mm.trans2:exp8 -0.493925255396357 0.276128209912681 -1.78875333147797 0.0745886697726573 . df.mm.trans1:probe2 -0.323205572774205 0.138064104956340 -2.34098191471571 0.0198390968594307 * df.mm.trans1:probe3 -0.265708629920342 0.138064104956340 -1.92453085473857 0.0551635544491793 . df.mm.trans1:probe4 -0.156865532690481 0.138064104956340 -1.13617897092141 0.256721351370483 df.mm.trans1:probe5 -0.180624205610447 0.138064104956340 -1.30826332932492 0.191711347218566 df.mm.trans1:probe6 -0.191118850989611 0.138064104956340 -1.38427617410078 0.167226585972662 df.mm.trans2:probe2 -0.0128688216549461 0.138064104956340 -0.0932090325650937 0.925795108182047 df.mm.trans2:probe3 -0.0839133687618528 0.138064104956340 -0.607785555763306 0.543755476371952 df.mm.trans2:probe4 0.137325489186126 0.138064104956340 0.99465019694693 0.320648670422327 df.mm.trans2:probe5 -0.237347304592266 0.138064104956340 -1.71910942867678 0.0865495628444728 . df.mm.trans2:probe6 -0.142694525161915 0.138064104956340 -1.03353819015478 0.302122931494308 df.mm.trans3:probe2 0.076504796751193 0.138064104956340 0.554125178122046 0.579875424918579 df.mm.trans3:probe3 -0.00893672590055246 0.138064104956340 -0.0647288149470747 0.948429838029745