chr3.15767_chr3_139397239_139399806_+_1.R fitVsDatCorrelation=0.899801590483727 cont.fitVsDatCorrelation=0.324768522105970 fstatistic=4487.95246864060,37,347 cont.fstatistic=947.664139332192,37,347 residuals=-0.662262896641261,-0.096215048903684,-0.00323478343829785,0.0859654128280316,1.07792571342641 cont.residuals=-0.701536666968405,-0.273919548130693,-0.073041674978421,0.149786404946314,1.70795044096656 predictedValues: Include Exclude Both chr3.15767_chr3_139397239_139399806_+_1.R.tl.Lung 70.3538972597745 42.8384623372631 62.5057800665823 chr3.15767_chr3_139397239_139399806_+_1.R.tl.cerebhem 146.169515156111 48.9521117550348 81.5470427186044 chr3.15767_chr3_139397239_139399806_+_1.R.tl.cortex 147.10733930265 45.7467640240203 130.939401684546 chr3.15767_chr3_139397239_139399806_+_1.R.tl.heart 55.9666543623805 46.3690813521183 57.5029039230296 chr3.15767_chr3_139397239_139399806_+_1.R.tl.kidney 61.3385921649634 46.5832443217645 59.4292462430608 chr3.15767_chr3_139397239_139399806_+_1.R.tl.liver 52.7776240849519 49.0876706234002 48.2161224823251 chr3.15767_chr3_139397239_139399806_+_1.R.tl.stomach 64.2795120110656 47.0960271820568 55.573471365177 chr3.15767_chr3_139397239_139399806_+_1.R.tl.testicle 63.6530986117373 48.1851903250615 58.385958142159 diffExp=27.5154349225114,97.2174034010758,101.360575278630,9.5975730102622,14.7553478431989,3.68995346155172,17.1834848290088,15.4679082866758 diffExpScore=0.996525216102333 diffExp1.5=1,1,1,0,0,0,0,0 diffExp1.5Score=0.75 diffExp1.4=1,1,1,0,0,0,0,0 diffExp1.4Score=0.75 diffExp1.3=1,1,1,0,1,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,0,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 61.2505891069967 57.1517375016815 70.704163630595 cerebhem 47.6171714136017 85.824251157875 58.684155697157 cortex 61.2823181561564 68.9811043847113 56.670424031398 heart 62.1834241995692 62.4409372454233 53.9265063494993 kidney 52.5240225627469 59.0511276763765 64.4379421018747 liver 63.1160508223868 65.1125939198824 68.6820285397477 stomach 52.4492540231518 59.7640713748278 51.2799912444418 testicle 68.1568432977026 65.9663740527438 72.8647765526204 cont.diffExp=4.0988516053152,-38.2070797442733,-7.69878622855484,-0.257513045854090,-6.52710511362968,-1.99654309749560,-7.31481735167596,2.19046924495873 cont.diffExpScore=1.20416375323774 cont.diffExp1.5=0,-1,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,-1,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,-1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.0592180095467702 cont.tran.correlation=-0.385982558662420 tran.covariance=0.000198908535005462 cont.tran.covariance=-0.00584042598449466 tran.mean=64.7815490546471 cont.tran.mean=62.0544919309896 weightedLogRatios: wLogRatio Lung 1.98712955626032 cerebhem 4.85462336118048 cortex 5.14772433861554 heart 0.73945469878164 kidney 1.09484650591689 liver 0.28483307046999 stomach 1.24660890734618 testicle 1.11755225552798 cont.weightedLogRatios: wLogRatio Lung 0.282619457331677 cerebhem -2.44936305330982 cortex -0.494034775987177 heart -0.0170766817620872 kidney -0.470855591057875 liver -0.129571389780392 stomach -0.525514555187926 testicle 0.137378006899473 varWeightedLogRatios=3.53223664321974 cont.varWeightedLogRatios=0.7395455630876 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68337159984016 0.107986350911811 34.109603378007 7.23155809864203e-113 *** df.mm.trans1 0.511182971820479 0.0899674494739253 5.68186577267184 2.82405162917439e-08 *** df.mm.trans2 0.128246059730657 0.0899674494739253 1.42547177318643 0.154919763568811 df.mm.exp2 0.598713956598465 0.123956245701925 4.83004267520475 2.05148393548961e-06 *** df.mm.exp3 0.0638334451148465 0.123956245701925 0.514967557732793 0.606903562568625 df.mm.exp4 -0.0661620054440058 0.123956245701925 -0.533752898608304 0.593854243622854 df.mm.exp5 -0.00285184496953372 0.123956245701925 -0.0230068678942687 0.981658018705476 df.mm.exp6 0.108286255535342 0.123956245701925 0.873584504936813 0.382948719037378 df.mm.exp7 0.122008146018841 0.123956245701925 0.984283973170911 0.325661696529659 df.mm.exp8 0.0857088084580106 0.123956245701925 0.69144404925035 0.489748866087663 df.mm.trans1:exp2 0.132514874136053 0.104762148455156 1.26491176527156 0.206751777488966 df.mm.trans2:exp2 -0.465307799270394 0.104762148455156 -4.44156411577959 1.20230683150376e-05 *** df.mm.trans1:exp3 0.673790894360949 0.104762148455156 6.43162539425554 4.18539839438004e-10 *** df.mm.trans2:exp3 0.00185126060875686 0.104762148455156 0.0176710828868626 0.985911405599842 df.mm.trans1:exp4 -0.162620119165761 0.104762148455156 -1.55227934481863 0.121506942142672 df.mm.trans2:exp4 0.145358540615485 0.104762148455156 1.38751011466424 0.166176668977990 df.mm.trans1:exp5 -0.134277128120023 0.104762148455156 -1.28173324144361 0.200791994054935 df.mm.trans2:exp5 0.0866564057509805 0.104762148455156 0.827172858029677 0.408707550897975 df.mm.trans1:exp6 -0.395737121090625 0.104762148455156 -3.77748191428151 0.000186373291724583 *** df.mm.trans2:exp6 0.0278852884850652 0.104762148455156 0.266177134549715 0.790260880224315 df.mm.trans1:exp7 -0.212305376967003 0.104762148455156 -2.02654661151668 0.0434732221380612 * df.mm.trans2:exp7 -0.0272558489112154 0.104762148455156 -0.260168861684643 0.794887829598793 df.mm.trans1:exp8 -0.185798982776103 0.104762148455156 -1.77353161915762 0.0770176669069413 . df.mm.trans2:exp8 0.0319065589384659 0.104762148455156 0.304561899588416 0.760882416061957 df.mm.trans1:probe2 0.232582201578662 0.0573805918815938 4.05332524381417 6.23686251753849e-05 *** df.mm.trans1:probe3 -0.0214620016486881 0.0573805918815938 -0.374028934608683 0.708611226873725 df.mm.trans1:probe4 0.241486381868415 0.0573805918815938 4.20850280468922 3.27778907876286e-05 *** df.mm.trans1:probe5 -0.0702250595467819 0.0573805918815938 -1.22384690091195 0.221840326902678 df.mm.trans1:probe6 0.207454562890677 0.0573805918815938 3.61541343663315 0.000344289472426098 *** df.mm.trans2:probe2 -0.0895974774132978 0.0573805918815938 -1.56145962380772 0.119326860856335 df.mm.trans2:probe3 -0.144680369793345 0.0573805918815938 -2.52141647635661 0.012135629171994 * df.mm.trans2:probe4 -0.147129428072506 0.0573805918815938 -2.56409742820554 0.0107648810521296 * df.mm.trans2:probe5 -0.0912251004273577 0.0573805918815938 -1.58982501636795 0.112784891593099 df.mm.trans2:probe6 -0.0691807021924927 0.0573805918815938 -1.20564636794351 0.228775344571199 df.mm.trans3:probe2 -0.343442654368694 0.0573805918815938 -5.98534527279531 5.38635859542767e-09 *** df.mm.trans3:probe3 -0.0963773749066432 0.0573805918815938 -1.67961625605955 0.0939320754893532 . df.mm.trans3:probe4 -0.310346362163598 0.0573805918815938 -5.40855979324865 1.18555559282426e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.87818811395152 0.234088817833822 16.5671651890037 7.8706972417284e-46 *** df.mm.trans1 0.172134328521985 0.195028109692072 0.882612915613885 0.378056465705348 df.mm.trans2 0.252465120945391 0.195028109692072 1.29450632190408 0.196351354793711 df.mm.exp2 0.341146486814195 0.268707765143204 1.26958179504930 0.20508441895408 df.mm.exp3 0.409892721592685 0.268707765143204 1.52542194444674 0.128064782066905 df.mm.exp4 0.374508655069718 0.268707765143204 1.39373960730212 0.164288190573864 df.mm.exp5 -0.0282071427109451 0.268707765143204 -0.104973306952676 0.91645764063161 df.mm.exp6 0.189426725171695 0.268707765143204 0.704954414215545 0.481311505683728 df.mm.exp7 0.210771340487434 0.268707765143204 0.784388721982435 0.433347188684778 df.mm.exp8 0.220172552067710 0.268707765143204 0.81937547264543 0.413134632293547 df.mm.trans1:exp2 -0.592926514098057 0.227099510989370 -2.61086653826309 0.0094230795202551 ** df.mm.trans2:exp2 0.0654453350123246 0.227099510989370 0.288179110237661 0.773381690443439 df.mm.trans1:exp3 -0.409374835408841 0.227099510989370 -1.80262314799966 0.0723149720981021 . df.mm.trans2:exp3 -0.221769896253774 0.227099510989370 -0.976531808842844 0.329481281124582 df.mm.trans1:exp4 -0.359393650260217 0.227099510989370 -1.58253819523654 0.114437738698287 df.mm.trans2:exp4 -0.285997341456234 0.227099510989370 -1.25934811664839 0.208751062508233 df.mm.trans1:exp5 -0.125495687174155 0.227099510989370 -0.552602190235579 0.580891622676007 df.mm.trans2:exp5 0.0609009898892292 0.227099510989370 0.268168740759992 0.788728779793946 df.mm.trans1:exp6 -0.159425084228674 0.227099510989370 -0.702005405181767 0.483146385356801 df.mm.trans2:exp6 -0.0590185319344519 0.227099510989370 -0.259879608182928 0.795110767047498 df.mm.trans1:exp7 -0.365898695826451 0.227099510989370 -1.61118222682381 0.108049198094339 df.mm.trans2:exp7 -0.166076465479142 0.227099510989370 -0.731293804885894 0.465093248402756 df.mm.trans1:exp8 -0.113334451191694 0.227099510989370 -0.499051938500207 0.618059024043244 df.mm.trans2:exp8 -0.0767372161795081 0.227099510989370 -0.337901283209281 0.735641909377547 df.mm.trans1:probe2 0.123247195944188 0.124387524967270 0.990832448644815 0.322457807213647 df.mm.trans1:probe3 0.169201672227497 0.124387524967270 1.36027847062652 0.174624822226111 df.mm.trans1:probe4 0.0281434299843610 0.124387524967270 0.226256049324611 0.821135494738458 df.mm.trans1:probe5 0.19727872814265 0.124387524967270 1.58600091282916 0.113649929255355 df.mm.trans1:probe6 0.128639223486250 0.124387524967270 1.03418106856052 0.301771723431375 df.mm.trans2:probe2 -0.21782950505317 0.124387524967270 -1.75121665223653 0.0807921425497794 . df.mm.trans2:probe3 -0.0354821593341577 0.124387524967270 -0.285254967035433 0.775618996466233 df.mm.trans2:probe4 -0.214924005947113 0.124387524967270 -1.72785820767529 0.084903342219981 . df.mm.trans2:probe5 -0.194835153016234 0.124387524967270 -1.56635605594292 0.118176736184431 df.mm.trans2:probe6 -0.186363603040521 0.124387524967270 -1.49824994982060 0.134977447757553 df.mm.trans3:probe2 -0.109011542459996 0.124387524967270 -0.876386458277708 0.381426263022680 df.mm.trans3:probe3 0.0261425198281633 0.124387524967270 0.210169949398400 0.833658382674385 df.mm.trans3:probe4 -0.0942633802823699 0.124387524967270 -0.757820209921961 0.44907295395268