chr12.5455_chr12_94306057_94308237_-_1.R fitVsDatCorrelation=0.74879577159554 cont.fitVsDatCorrelation=0.307174104126823 fstatistic=5600.42235123246,40,416 cont.fstatistic=2711.26714477748,40,416 residuals=-0.724997356000504,-0.083751177831035,-0.00276790087891718,0.0705436757408232,1.23464627549730 cont.residuals=-0.486032067248003,-0.158255260779322,-0.0568917013446028,0.101462723974173,1.64474023088758 predictedValues: Include Exclude Both chr12.5455_chr12_94306057_94308237_-_1.R.tl.Lung 56.4790505308153 58.306145158841 67.5260790694988 chr12.5455_chr12_94306057_94308237_-_1.R.tl.cerebhem 76.9492937943041 100.835076468693 68.3791195588078 chr12.5455_chr12_94306057_94308237_-_1.R.tl.cortex 55.5052401422192 55.2708499052923 65.103111094589 chr12.5455_chr12_94306057_94308237_-_1.R.tl.heart 54.3755102917019 57.4471798466837 63.9470777250068 chr12.5455_chr12_94306057_94308237_-_1.R.tl.kidney 52.0243880226543 58.9173966604252 69.4838692554871 chr12.5455_chr12_94306057_94308237_-_1.R.tl.liver 52.6797547882927 57.2920182176134 68.2557011705027 chr12.5455_chr12_94306057_94308237_-_1.R.tl.stomach 50.4899244921659 58.2631142203485 65.4771934804552 chr12.5455_chr12_94306057_94308237_-_1.R.tl.testicle 57.0405042501647 62.5978400428685 62.2791390793922 diffExp=-1.82709462802569,-23.8857826743893,0.234390236926885,-3.07166955498182,-6.8930086377709,-4.61226342932062,-7.7731897281827,-5.55733579270388 diffExpScore=0.990232413242026 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 66.1406548441026 70.8627005883033 66.4306008828869 cerebhem 64.8829327719294 64.4249783598746 57.1480006954381 cortex 62.9893018129302 67.666002898422 65.0869081692202 heart 60.4998845803659 65.1911676502063 60.47210893215 kidney 57.2881896660357 59.1510725072393 60.686817060196 liver 57.310674483459 53.7895751443152 60.6792999204276 stomach 61.8881027707012 62.7370166500974 60.8006978403955 testicle 59.9473528827583 58.6377486388859 62.2341259461166 cont.diffExp=-4.72204574420071,0.457954412054804,-4.67670108549189,-4.69128306984046,-1.86288284120359,3.52109933914379,-0.848913879396278,1.30960424387235 cont.diffExpScore=1.76537896012685 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=0.96364719351012 cont.tran.correlation=0.863833481134203 tran.covariance=0.0245805284491964 cont.tran.covariance=0.00402296908888941 tran.mean=60.2795804270678 cont.tran.mean=62.0879597656017 weightedLogRatios: wLogRatio Lung -0.128936001421039 cerebhem -1.21066625509682 cortex 0.0169879358590451 heart -0.221093483282069 kidney -0.49942772906941 liver -0.336240428525881 stomach -0.571832439923544 testicle -0.380266740848105 cont.weightedLogRatios: wLogRatio Lung -0.291445703114776 cerebhem 0.0295301286524768 cortex -0.299279565594954 heart -0.30918441308127 kidney -0.130051977576964 liver 0.25469306037121 stomach -0.056294992607337 testicle 0.090172802178559 varWeightedLogRatios=0.139749739987590 cont.varWeightedLogRatios=0.0429592883301723 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04675287744263 0.0956557096999463 42.3053980795974 8.8943258024314e-153 *** df.mm.trans1 -0.0190552557773446 0.0775870258339848 -0.245598482124030 0.806114183802462 df.mm.trans2 0.0302274353894207 0.0775870258339848 0.389593943890815 0.697036202247849 df.mm.exp2 0.844502054215689 0.104916807992518 8.04925416979819 8.82089601815268e-15 *** df.mm.exp3 -0.0343126573052060 0.104916807992518 -0.327046332820696 0.743797339382688 df.mm.exp4 0.00166056147014171 0.104916807992518 0.0158274112786593 0.987379668008844 df.mm.exp5 -0.100309009305339 0.104916807992518 -0.956081406065009 0.339586371080818 df.mm.exp6 -0.0979318222851804 0.104916807992518 -0.933423577775685 0.351142763058180 df.mm.exp7 -0.0820222816604032 0.104916807992518 -0.781783998482424 0.434786245850665 df.mm.exp8 0.161802470183247 0.104916807992518 1.54219779727559 0.123785852136933 df.mm.trans1:exp2 -0.535225153763507 0.0845866348164427 -6.32753809068032 6.44837665771704e-10 *** df.mm.trans2:exp2 -0.296723271883166 0.0845866348164427 -3.50792146450879 0.00050085188241163 *** df.mm.trans1:exp3 0.0169203082488568 0.0845866348164427 0.200035245350223 0.841550764559972 df.mm.trans2:exp3 -0.0191491932467586 0.0845866348164427 -0.226385566565135 0.821012745627097 df.mm.trans1:exp4 -0.0396164697590095 0.0845866348164427 -0.468353775333175 0.639776920528854 df.mm.trans2:exp4 -0.0165021408245124 0.0845866348164427 -0.195091586990343 0.845416495086746 df.mm.trans1:exp5 0.0181518360474403 0.0845866348164427 0.21459461162902 0.830188548739452 df.mm.trans2:exp5 0.110737921999443 0.0845866348164427 1.30916571205073 0.191200881634806 df.mm.trans1:exp6 0.0282932620806935 0.0845866348164427 0.334488564796215 0.738179491862653 df.mm.trans2:exp6 0.080385644567391 0.0845866348164427 0.95033505874577 0.342493812717178 df.mm.trans1:exp7 -0.0300736992993232 0.0845866348164427 -0.355537247280077 0.722367353681606 df.mm.trans2:exp7 0.0812839919830706 0.0845866348164427 0.960955500351337 0.337132745953736 df.mm.trans1:exp8 -0.151910636154536 0.0845866348164427 -1.79591771778355 0.0732331268520711 . df.mm.trans2:exp8 -0.0907791903936742 0.0845866348164427 -1.07320962218995 0.283799218594783 df.mm.trans1:probe2 -0.0407513264207982 0.0537538683010483 -0.75810965254762 0.448814594500752 df.mm.trans1:probe3 0.0453148727338178 0.0537538683010483 0.843006729860484 0.399709113551463 df.mm.trans1:probe4 0.112484314308907 0.0537538683010483 2.09258082932634 0.0369919875368279 * df.mm.trans1:probe5 -0.0381722212806149 0.0537538683010483 -0.710129754138467 0.478021554859581 df.mm.trans1:probe6 0.00136244928248006 0.0537538683010483 0.0253460695116799 0.979791081188369 df.mm.trans2:probe2 -0.0881665438349998 0.0537538683010483 -1.64018975046081 0.101721604191728 df.mm.trans2:probe3 -0.049616298592992 0.0537538683010483 -0.923027498507014 0.356527914885861 df.mm.trans2:probe4 0.112468526681100 0.0537538683010483 2.09228712715558 0.0370183842349409 * df.mm.trans2:probe5 -0.147202878719517 0.0537538683010483 -2.73846112609251 0.00643787866587605 ** df.mm.trans2:probe6 0.0259705446273862 0.0537538683010483 0.483138152624445 0.629251815308105 df.mm.trans3:probe2 0.0289102950722590 0.0537538683010483 0.537827248270711 0.590983919284326 df.mm.trans3:probe3 0.227638298209731 0.0537538683010483 4.23482635584185 2.8177978039015e-05 *** df.mm.trans3:probe4 0.421277411863461 0.0537538683010483 7.83715526302402 3.89535922576944e-14 *** df.mm.trans3:probe5 -0.177352694981946 0.0537538683010483 -3.29934757418169 0.00105251614831962 ** df.mm.trans3:probe6 0.109047039636736 0.0537538683010483 2.02863613509671 0.0431318555293067 * df.mm.trans3:probe7 0.472601409746935 0.0537538683010483 8.79195162476741 3.92437369390005e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.31901019651865 0.137343038764513 31.4468810022762 5.35430156472371e-112 *** df.mm.trans1 -0.122746796012053 0.111399914653984 -1.10185718178792 0.271160848298279 df.mm.trans2 -0.057276364064746 0.111399914653984 -0.514150879223298 0.60741951710366 df.mm.exp2 0.0360716145671221 0.150640178953932 0.239455468106907 0.810870357160828 df.mm.exp3 -0.0745446568861805 0.150640178953932 -0.494852418550147 0.620965548175274 df.mm.exp4 -0.0785868095205008 0.150640178953932 -0.52168558259967 0.602166898690188 df.mm.exp5 -0.233907293615449 0.150640178953932 -1.55275501688684 0.121242086368133 df.mm.exp6 -0.328406053514006 0.150640178953932 -2.18006945951941 0.02981222762449 * df.mm.exp7 -0.0996916672020758 0.150640178953932 -0.661786701890222 0.508474333300849 df.mm.exp8 -0.222428120653888 0.150640178953932 -1.47655241913852 0.140552216247069 df.mm.trans1:exp2 -0.0552706111448289 0.121449994997642 -0.455089447685049 0.649282300297772 df.mm.trans2:exp2 -0.131314404240863 0.121449994997642 -1.08122198146993 0.280224768354172 df.mm.trans1:exp3 0.0257259479816549 0.121449994997642 0.211823376214666 0.832348570226086 df.mm.trans2:exp3 0.0283843275131033 0.121449994997642 0.233712051726757 0.815323496350713 df.mm.trans1:exp4 -0.0105553415901838 0.121449994997642 -0.0869110088509162 0.930784066316494 df.mm.trans2:exp4 -0.00483340652651972 0.121449994997642 -0.0397975028867935 0.968273659216023 df.mm.trans1:exp5 0.0902181736778243 0.121449994997642 0.742842135807216 0.457996528864867 df.mm.trans2:exp5 0.0532578055502671 0.121449994997642 0.438516325598046 0.661239677216115 df.mm.trans1:exp6 0.185109342661387 0.121449994997642 1.52416097394636 0.128228331799320 df.mm.trans2:exp6 0.0527415210383278 0.121449994997642 0.434265320796034 0.664320932057618 df.mm.trans1:exp7 0.0332360192401461 0.121449994997642 0.2736601120551 0.784481653210619 df.mm.trans2:exp7 -0.0221008924842849 0.121449994997642 -0.181975244088844 0.855690792457424 df.mm.trans1:exp8 0.124111237352543 0.121449994997642 1.02191224754643 0.307416335071863 df.mm.trans2:exp8 0.0330625742546754 0.121449994997642 0.272231993548598 0.785578708392234 df.mm.trans1:probe2 0.0492971069173129 0.0771801248558144 0.638728001663748 0.523351389782 df.mm.trans1:probe3 0.0151301212506379 0.0771801248558145 0.196036496169234 0.844677324497374 df.mm.trans1:probe4 0.00263048429594031 0.0771801248558144 0.0340824052935195 0.972827785870075 df.mm.trans1:probe5 -0.103676307823308 0.0771801248558145 -1.34330318870296 0.17990615448139 df.mm.trans1:probe6 -0.0216187022458925 0.0771801248558145 -0.280107116777537 0.779534556650966 df.mm.trans2:probe2 -0.0505898597593126 0.0771801248558145 -0.655477817039336 0.512522480720855 df.mm.trans2:probe3 0.0104881090694175 0.0771801248558145 0.135891320323867 0.891972930082835 df.mm.trans2:probe4 -0.0171711046086960 0.0771801248558145 -0.222480912550666 0.824048719058366 df.mm.trans2:probe5 0.111560163374572 0.0771801248558145 1.44545196814576 0.149083858518320 df.mm.trans2:probe6 -0.0671523958724205 0.0771801248558145 -0.87007368798473 0.384761899973028 df.mm.trans3:probe2 0.0738877285390881 0.0771801248558144 0.957341396857325 0.338950991337044 df.mm.trans3:probe3 0.194196080340571 0.0771801248558145 2.51614104930981 0.0122402226969346 * df.mm.trans3:probe4 0.0449851634986367 0.0771801248558144 0.582859428935579 0.560303740521962 df.mm.trans3:probe5 0.0201071364843707 0.0771801248558144 0.260522207264296 0.794589894122726 df.mm.trans3:probe6 0.0348578853559844 0.0771801248558144 0.451643287972193 0.651761350844523 df.mm.trans3:probe7 0.0321613996222521 0.0771801248558145 0.416705721613369 0.677108597924486