chr17.10165_chr17_25038184_25038716_-_2.R fitVsDatCorrelation=0.929983186833901 cont.fitVsDatCorrelation=0.246268294258295 fstatistic=9406.32520224437,57,807 cont.fstatistic=1341.03373129066,57,807 residuals=-0.744561928211533,-0.102208351873735,1.26824809850503e-05,0.100590920500878,1.05033928612746 cont.residuals=-0.90232979337223,-0.309482339580070,-0.126812757068280,0.256904491970401,1.57624880644927 predictedValues: Include Exclude Both chr17.10165_chr17_25038184_25038716_-_2.R.tl.Lung 64.0443290566869 72.3500330180742 96.7224961642818 chr17.10165_chr17_25038184_25038716_-_2.R.tl.cerebhem 67.5712603294439 56.6838675232447 85.332854550133 chr17.10165_chr17_25038184_25038716_-_2.R.tl.cortex 78.6804821203706 64.2957902323277 110.853454815971 chr17.10165_chr17_25038184_25038716_-_2.R.tl.heart 108.226222137741 64.5263346617571 160.057997243241 chr17.10165_chr17_25038184_25038716_-_2.R.tl.kidney 67.8543324280524 75.7851957560566 103.791189695659 chr17.10165_chr17_25038184_25038716_-_2.R.tl.liver 60.3472102733695 67.0777174245348 89.3043286868331 chr17.10165_chr17_25038184_25038716_-_2.R.tl.stomach 68.9894995295605 69.1015124382903 100.690842128868 chr17.10165_chr17_25038184_25038716_-_2.R.tl.testicle 73.9450700789042 64.4451021827819 113.861049747966 diffExp=-8.3057039613873,10.8873928061992,14.3846918880429,43.6998874759835,-7.93086332800416,-6.73050715116534,-0.112012908729824,9.49996789612229 diffExpScore=1.80077833489191 diffExp1.5=0,0,0,1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 80.9737692900014 79.7882751853531 84.084108386355 cerebhem 77.3663651981897 95.0815226183982 73.449318577054 cortex 80.2643379197374 78.96862306291 91.1079679765983 heart 83.8595564639349 93.8826221961799 69.6066077374064 kidney 82.4968913672945 86.6085843074236 84.2559746301054 liver 79.0215432288603 80.6420607375825 85.2295535465556 stomach 76.249819429892 84.9695764720567 76.2347771115446 testicle 77.231312767015 75.4133135199582 75.6389132400372 cont.diffExp=1.1854941046483,-17.7151574202084,1.29571485682733,-10.023065732245,-4.11169294012913,-1.62051750872227,-8.7197570421647,1.81799924705680 cont.diffExpScore=1.19537733277314 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,-1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.245689246409575 cont.tran.correlation=0.256731792112655 tran.covariance=-0.00383053852752258 cont.tran.covariance=0.000717750585636472 tran.mean=70.2452474494497 cont.tran.mean=82.0511358602992 weightedLogRatios: wLogRatio Lung -0.514655133264904 cerebhem 0.724793116530836 cortex 0.86099547830651 heart 2.28872528474867 kidney -0.472295708013694 liver -0.439124777647759 stomach -0.00687008509571782 testicle 0.58229033463489 cont.weightedLogRatios: wLogRatio Lung 0.0646988738581952 cerebhem -0.917851090387826 cortex 0.0712379230888026 heart -0.5064319001038 kidney -0.215812125338182 liver -0.0889106882987303 stomach -0.475141526618751 testicle 0.103262166738137 varWeightedLogRatios=0.91168708749951 cont.varWeightedLogRatios=0.130922778316379 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.08034812534511 0.0837666335191578 36.7729726734288 1.20722509872846e-174 *** df.mm.trans1 0.942990625735438 0.0727402939591056 12.9638000399831 4.75244042843626e-35 *** df.mm.trans2 1.15486978957045 0.0646556719230402 17.8618480826415 2.19535342254291e-60 *** df.mm.exp2 -0.0651325290356142 0.0840326069179266 -0.775086379257854 0.438515668234376 df.mm.exp3 -0.0485652030997901 0.0840326069179266 -0.577932839180188 0.563470684822629 df.mm.exp4 -0.0934844971155233 0.0840326069179266 -1.11247884058658 0.266263603912879 df.mm.exp5 0.0336397343340799 0.0840326069179266 0.400317633450731 0.689028528859483 df.mm.exp6 -0.0553287112717608 0.0840326069179266 -0.658419550470444 0.510456368278221 df.mm.exp7 -0.0117693034032362 0.0840326069179267 -0.140056388048642 0.888650385838224 df.mm.exp8 -0.135087969771325 0.0840326069179267 -1.60756609518568 0.10832139046403 df.mm.trans1:exp2 0.118739793525056 0.0781624557822047 1.51914102924194 0.129118662618968 df.mm.trans2:exp2 -0.178893733558013 0.0597529140979023 -2.9938913651124 0.00283853071372705 ** df.mm.trans1:exp3 0.254384839107094 0.0781624557822046 3.25456559113142 0.00118295736079716 ** df.mm.trans2:exp3 -0.0694565479690961 0.0597529140979023 -1.16239599386391 0.245418285017607 df.mm.trans1:exp4 0.618132697838842 0.0781624557822047 7.90830702097225 8.57284905199299e-15 *** df.mm.trans2:exp4 -0.0209579825238971 0.0597529140979023 -0.350744107468273 0.725871869173933 df.mm.trans1:exp5 0.0241480178328115 0.0781624557822047 0.308946508795714 0.757441958483092 df.mm.trans2:exp5 0.0127473231477823 0.0597529140979023 0.213333915847123 0.831120403262806 df.mm.trans1:exp6 -0.0041320531515054 0.0781624557822047 -0.0528649350913326 0.957852606191403 df.mm.trans2:exp6 -0.0203352894559752 0.0597529140979023 -0.340322974418567 0.733701883759366 df.mm.trans1:exp7 0.0861481305838436 0.0781624557822047 1.10216765481231 0.270717382913853 df.mm.trans2:exp7 -0.034169987831734 0.0597529140979023 -0.571854751313853 0.567579627093624 df.mm.trans1:exp8 0.278835006001229 0.0781624557822047 3.56737775458575 0.000381830927067102 *** df.mm.trans2:exp8 0.0193857927647267 0.0597529140979023 0.324432591404061 0.745694623195065 df.mm.trans1:probe2 0.228046704960266 0.0511693385893702 4.45670613001904 9.49902156828465e-06 *** df.mm.trans1:probe3 -0.0911261440685782 0.0511693385893702 -1.78087398783592 0.0753091206514719 . df.mm.trans1:probe4 -0.0164225601478327 0.0511693385893702 -0.320945327818723 0.7483349178547 df.mm.trans1:probe5 -0.222375362534908 0.0511693385893702 -4.34587134923614 1.56435662717687e-05 *** df.mm.trans1:probe6 0.0621268206546792 0.0511693385893702 1.21414156147770 0.225048852644641 df.mm.trans1:probe7 -0.260848015884802 0.0511693385893702 -5.09774062115764 4.28501186661947e-07 *** df.mm.trans1:probe8 0.00472594868850797 0.0511693385893702 0.0923589950308587 0.926435739463057 df.mm.trans1:probe9 -0.0745749674677095 0.0511693385893702 -1.45741511466794 0.145390834235801 df.mm.trans1:probe10 -0.165561938952677 0.0511693385893702 -3.23556925918661 0.00126346752718884 ** df.mm.trans1:probe11 -0.0822848920347702 0.0511693385893702 -1.60808981126568 0.108206639993746 df.mm.trans1:probe12 0.0762124717265142 0.0511693385893702 1.48941678410411 0.136768345150277 df.mm.trans1:probe13 -0.124813524390482 0.0511693385893702 -2.43922489192405 0.0149332788044479 * df.mm.trans1:probe14 -0.0396953686503784 0.0511693385893702 -0.775764740070817 0.438115183634999 df.mm.trans1:probe15 -0.163419278695336 0.0511693385893702 -3.19369534960697 0.00145915017909301 ** df.mm.trans1:probe16 -0.0693398954179053 0.0511693385893702 -1.35510634550804 0.175762859214557 df.mm.trans1:probe17 0.94395232067045 0.0511693385893702 18.4476162227851 1.12872668707016e-63 *** df.mm.trans1:probe18 0.885599701319095 0.0511693385893702 17.3072337015329 2.59045417135262e-57 *** df.mm.trans1:probe19 0.815771751039774 0.0511693385893702 15.9425893226073 5.98255848277867e-50 *** df.mm.trans1:probe20 0.703668242109787 0.0511693385893702 13.7517556706501 8.1891860491532e-39 *** df.mm.trans1:probe21 0.850567515531208 0.0511693385893702 16.6226013268794 1.39343451983990e-53 *** df.mm.trans1:probe22 0.826892492278099 0.0511693385893702 16.1599214504968 4.20823138473105e-51 *** df.mm.trans2:probe2 0.102907650732042 0.0511693385893702 2.01111942364289 0.0446449418314037 * df.mm.trans2:probe3 0.353844329920587 0.0511693385893702 6.91516325352882 9.5064624904111e-12 *** df.mm.trans2:probe4 0.0129674137329528 0.0511693385893702 0.253421562412898 0.80000701249402 df.mm.trans2:probe5 0.0314260963089552 0.0511693385893702 0.614158735979511 0.539283532083427 df.mm.trans2:probe6 0.147026432693941 0.0511693385893702 2.87333072396766 0.00416831127509541 ** df.mm.trans3:probe2 -0.03866269367093 0.0511693385893702 -0.755583221061248 0.450119594417203 df.mm.trans3:probe3 -0.373817634810573 0.0511693385893702 -7.3055006204092 6.64036474314301e-13 *** df.mm.trans3:probe4 -0.731996785702327 0.0511693385893702 -14.3053790782121 1.54206984071679e-41 *** df.mm.trans3:probe5 -0.97401076724406 0.0511693385893702 -19.0350470437075 5.1522750289203e-67 *** df.mm.trans3:probe6 -0.83768537846157 0.0511693385893702 -16.3708463223245 3.1463397145183e-52 *** df.mm.trans3:probe7 -1.09517688117538 0.0511693385893702 -21.4029907629661 7.68716714052442e-81 *** df.mm.trans3:probe8 -0.799550071153897 0.0511693385893702 -15.6255697883887 2.77953252346131e-48 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37641325983291 0.220855029910685 19.8157735488422 1.63145280696857e-71 *** df.mm.trans1 -0.0278270088206297 0.191783519560639 -0.145095933604614 0.884671358972208 df.mm.trans2 0.0148057680882505 0.170467998492415 0.086853651237709 0.930809386493742 df.mm.exp2 0.265006914961005 0.221556282431799 1.19611555155328 0.232002782381618 df.mm.exp3 -0.0993534998977343 0.221556282431799 -0.448434586495276 0.653959960203667 df.mm.exp4 0.386645008827317 0.221556282431799 1.74513222817925 0.0813424777776005 . df.mm.exp5 0.0986158223737556 0.221556282431799 0.445105059948423 0.65636319497779 df.mm.exp6 -0.0272916173911637 0.221556282431799 -0.123181419599622 0.901994109598563 df.mm.exp7 0.10080632053804 0.221556282431799 0.454991929958343 0.649237418982934 df.mm.exp8 0.00213369147874290 0.221556282431799 0.00963047156832353 0.992318494359533 df.mm.trans1:exp2 -0.310580053276362 0.206079327584812 -1.50708980331151 0.132178978718057 df.mm.trans2:exp2 -0.0896488246797079 0.157541625775431 -0.569048492666305 0.569481567213953 df.mm.trans1:exp3 0.0905536453152053 0.206079327584812 0.439411591528694 0.660480965427668 df.mm.trans2:exp3 0.089027530891431 0.157541625775431 0.565104812478806 0.572159533409492 df.mm.trans1:exp4 -0.351626822405303 0.206079327584812 -1.70626926303703 0.0883427021440918 . df.mm.trans2:exp4 -0.223976273032489 0.157541625775431 -1.42169583391096 0.155500908186330 df.mm.trans1:exp5 -0.0799804764398293 0.206079327584812 -0.388105286334037 0.698040474351548 df.mm.trans2:exp5 -0.0165934519252649 0.157541625775431 -0.105327413269927 0.916142184854746 df.mm.trans1:exp6 0.002886865481687 0.206079327584812 0.0140085156309476 0.98882664958572 df.mm.trans2:exp6 0.0379354100102146 0.157541625775431 0.240796105940217 0.809774308689983 df.mm.trans1:exp7 -0.160916539486241 0.206079327584812 -0.780847556968158 0.435121147773159 df.mm.trans2:exp7 -0.0378896181349326 0.157541625775431 -0.240505440695037 0.809999529176285 df.mm.trans1:exp8 -0.0494539771938096 0.206079327584812 -0.239975439426144 0.810410238526323 df.mm.trans2:exp8 -0.0585264263280976 0.157541625775431 -0.371498174149381 0.710364029336792 df.mm.trans1:probe2 0.031750400120615 0.134910588260429 0.235344019546669 0.814001438586987 df.mm.trans1:probe3 0.0809071570059214 0.134910588260429 0.599709467204603 0.548868217158833 df.mm.trans1:probe4 0.286920574050844 0.134910588260429 2.12674614906414 0.0337439929163508 * df.mm.trans1:probe5 0.154842037715428 0.134910588260429 1.14773821470946 0.251416776046649 df.mm.trans1:probe6 0.0791429628642717 0.134910588260429 0.586632701589705 0.557614472986611 df.mm.trans1:probe7 0.0283908712677050 0.134910588260429 0.210442127884727 0.833375737199494 df.mm.trans1:probe8 -0.0292847399870501 0.134910588260429 -0.217067765878534 0.82821039926338 df.mm.trans1:probe9 0.0339976727559299 0.134910588260429 0.252001515924765 0.801104055654081 df.mm.trans1:probe10 0.154415303001316 0.134910588260429 1.14457512188173 0.252724557953772 df.mm.trans1:probe11 0.208690851600909 0.134910588260429 1.54688267460561 0.122283584471828 df.mm.trans1:probe12 0.188227461393922 0.134910588260429 1.39520154660190 0.163338898470280 df.mm.trans1:probe13 0.112295551218314 0.134910588260429 0.832370184329347 0.405446274672108 df.mm.trans1:probe14 0.0134230376085526 0.134910588260429 0.099495805196854 0.920769328298805 df.mm.trans1:probe15 0.00604148809166626 0.134910588260429 0.0447814227894692 0.964292610962504 df.mm.trans1:probe16 -0.0498351128014454 0.134910588260429 -0.369393636511648 0.711931244438185 df.mm.trans1:probe17 0.0742515472472354 0.134910588260429 0.550375980155847 0.582213826205767 df.mm.trans1:probe18 -0.0352761158488154 0.134910588260429 -0.261477740951801 0.793790831169089 df.mm.trans1:probe19 -0.0673722653891702 0.134910588260429 -0.499384564680098 0.617644604533542 df.mm.trans1:probe20 0.0578284182383704 0.134910588260429 0.428642547512576 0.668297764929827 df.mm.trans1:probe21 -0.0419874652424595 0.134910588260429 -0.311224387824977 0.755710420150906 df.mm.trans1:probe22 0.0788008237302672 0.134910588260429 0.584096657989153 0.559318513348116 df.mm.trans2:probe2 -0.112790828811937 0.134910588260429 -0.836041338684311 0.403379117799688 df.mm.trans2:probe3 0.0601573604667135 0.134910588260429 0.445905404775101 0.65578518374961 df.mm.trans2:probe4 0.115410165752205 0.134910588260429 0.855456693505917 0.392552276238259 df.mm.trans2:probe5 -0.117505709363676 0.134910588260429 -0.870989526313867 0.384018894678419 df.mm.trans2:probe6 -0.111065452713229 0.134910588260429 -0.823252304695542 0.410607738986808 df.mm.trans3:probe2 0.24647796047299 0.134910588260429 1.82697269095880 0.068073092627486 . df.mm.trans3:probe3 0.0557469031170296 0.134910588260429 0.413213698315634 0.679559824229422 df.mm.trans3:probe4 0.0472174976773692 0.134910588260429 0.349991044336872 0.726436740406456 df.mm.trans3:probe5 -0.159051800775037 0.134910588260429 -1.17894231154049 0.238768620856425 df.mm.trans3:probe6 0.0251305217659774 0.134910588260429 0.186275384979168 0.852275623169563 df.mm.trans3:probe7 0.0859621309091216 0.134910588260429 0.637178534446696 0.524189273537342 df.mm.trans3:probe8 0.245921339630656 0.134910588260429 1.82284684101988 0.0686964670122041 .