chr10.2119_chr10_77715976_77717234_+_0.R fitVsDatCorrelation=0.828655275927374 cont.fitVsDatCorrelation=0.295820159498149 fstatistic=9704.5888580689,36,324 cont.fstatistic=3326.44648646607,36,324 residuals=-0.385039380893443,-0.0807508153830423,-1.20569507107494e-06,0.0716030052885508,0.546439481565353 cont.residuals=-0.54564547307298,-0.163624306446088,0.00798905630055996,0.143778175678339,0.786683857417187 predictedValues: Include Exclude Both chr10.2119_chr10_77715976_77717234_+_0.R.tl.Lung 58.3804061111823 82.5535253524017 89.1016143549069 chr10.2119_chr10_77715976_77717234_+_0.R.tl.cerebhem 56.5007672981713 67.8019318946318 60.6274312511619 chr10.2119_chr10_77715976_77717234_+_0.R.tl.cortex 51.3821816876688 75.2752862010925 73.5489759615498 chr10.2119_chr10_77715976_77717234_+_0.R.tl.heart 52.0910489005909 86.196821128904 95.5004121326532 chr10.2119_chr10_77715976_77717234_+_0.R.tl.kidney 59.00384664667 65.2446864412725 74.6647776644643 chr10.2119_chr10_77715976_77717234_+_0.R.tl.liver 57.5400024022827 60.6988800325584 68.3068927072408 chr10.2119_chr10_77715976_77717234_+_0.R.tl.stomach 53.3060380032519 68.3392280855629 71.8581693013728 chr10.2119_chr10_77715976_77717234_+_0.R.tl.testicle 54.7778118303755 66.7677144081777 65.7474036608645 diffExp=-24.1731192412194,-11.3011645964606,-23.8931045134237,-34.1057722283131,-6.24083979460257,-3.15887763027566,-15.0331900823110,-11.9899025778022 diffExpScore=0.992360345433674 diffExp1.5=0,0,0,-1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=-1,0,-1,-1,0,0,0,0 diffExp1.4Score=0.75 diffExp1.3=-1,0,-1,-1,0,0,0,0 diffExp1.3Score=0.75 diffExp1.2=-1,-1,-1,-1,0,0,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 62.0305937236557 64.7335793094667 73.1926838270808 cerebhem 68.0460689392904 65.8409566531253 67.3372391880963 cortex 71.1652616004756 74.2898991808364 67.7523681481988 heart 67.3082213163679 60.3404028044014 66.4714306625617 kidney 66.2246791389299 64.7996968519735 65.4433978400775 liver 64.607501281038 62.8936627055404 60.918467092576 stomach 63.4017089814311 59.667433886036 67.8560963358016 testicle 66.2397723747835 60.7228255832424 66.5894833081808 cont.diffExp=-2.70298558581101,2.20511228616502,-3.12463758036077,6.96781851196653,1.42498228695635,1.71383857549758,3.73427509539513,5.51694679154106 cont.diffExpScore=1.63669096191843 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=-0.390914001924695 cont.tran.correlation=0.652894804300714 tran.covariance=-0.00263270368553962 cont.tran.covariance=0.00189621230309185 tran.mean=63.4912610265497 cont.tran.mean=65.1445165206621 weightedLogRatios: wLogRatio Lung -1.46909219101590 cerebhem -0.752214967452176 cortex -1.57716819538995 heart -2.11771284699126 kidney -0.415024545480475 liver -0.218012371426287 stomach -1.01864708477691 testicle -0.811976989164554 cont.weightedLogRatios: wLogRatio Lung -0.176962660462271 cerebhem 0.138482404745324 cortex -0.184191309251423 heart 0.454021284663867 kidney 0.0909718236141938 liver 0.111704645805054 stomach 0.250050006319953 testicle 0.360872479195887 varWeightedLogRatios=0.405030809154091 cont.varWeightedLogRatios=0.0525104229806724 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81610727177975 0.0759021468267664 50.2766710998222 4.4951459746784e-155 *** df.mm.trans1 0.246771357256787 0.064288325477813 3.83850964265591 0.000148840542255965 *** df.mm.trans2 0.566234833702657 0.064288325477813 8.80773965559382 7.86723969102041e-17 *** df.mm.exp2 0.155447711469803 0.0895292880359573 1.73627775759114 0.083465300062158 . df.mm.exp3 -0.0281579389891132 0.0895292880359574 -0.314510922702794 0.75333545180178 df.mm.exp4 -0.140153880084589 0.0895292880359573 -1.56545286083700 0.118452899460035 df.mm.exp5 -0.0479109595969779 0.0895292880359573 -0.53514286383843 0.592918161703619 df.mm.exp6 -0.0562547767801984 0.0895292880359573 -0.628339373787995 0.530224309840159 df.mm.exp7 -0.064810490004278 0.0895292880359573 -0.723902662760464 0.469647838848477 df.mm.exp8 0.0280349385049302 0.0895292880359573 0.313137065198939 0.754378004501114 df.mm.trans1:exp2 -0.188173814653698 0.0775346378218733 -2.42696451469864 0.0157706316794772 * df.mm.trans2:exp2 -0.352303898024600 0.0775346378218732 -4.54382593279119 7.80725805682664e-06 *** df.mm.trans1:exp3 -0.0995309301658182 0.0775346378218733 -1.28369633188303 0.200165521444406 df.mm.trans2:exp3 -0.0641370596930064 0.0775346378218733 -0.82720525296518 0.408729545818969 df.mm.trans1:exp4 0.0261666862101725 0.0775346378218733 0.337483825877763 0.73597073550541 df.mm.trans2:exp4 0.183340304102970 0.0775346378218732 2.3646244988488 0.0186374936258434 * df.mm.trans1:exp5 0.058533277035942 0.0775346378218733 0.754930682341167 0.450838969754536 df.mm.trans2:exp5 -0.187391306482684 0.0775346378218733 -2.41687214575238 0.0162065298931537 * df.mm.trans1:exp6 0.0417548549107038 0.0775346378218732 0.538531630297038 0.590579859600425 df.mm.trans2:exp6 -0.251266851304835 0.0775346378218733 -3.24070452076001 0.00131621071713433 ** df.mm.trans1:exp7 -0.0261202236196042 0.0775346378218733 -0.336884576408449 0.736422022268245 df.mm.trans2:exp7 -0.124152433695552 0.0775346378218732 -1.60125122375341 0.110295801785007 df.mm.trans1:exp8 -0.0917300418914774 0.0775346378218733 -1.18308467632513 0.237642964576464 df.mm.trans2:exp8 -0.240262167156925 0.0775346378218733 -3.09877203152607 0.00211346146290850 ** df.mm.trans1:probe2 -0.0796272986210642 0.0387673189109366 -2.05398002384427 0.0407796855567629 * df.mm.trans1:probe3 0.186501614303791 0.0387673189109366 4.81079475039935 2.30760656795400e-06 *** df.mm.trans1:probe4 0.0844205226593497 0.0387673189109366 2.17762086806405 0.0301553334993169 * df.mm.trans1:probe5 -0.136923608745351 0.0387673189109366 -3.5319339224855 0.000472397265842657 *** df.mm.trans1:probe6 -0.0174559952414413 0.0387673189109366 -0.450276050338803 0.652812542180793 df.mm.trans2:probe2 -0.00688318867715909 0.0387673189109366 -0.177551320816701 0.859186305736762 df.mm.trans2:probe3 0.110527900363054 0.0387673189109366 2.85105866147150 0.00463686634457401 ** df.mm.trans2:probe4 0.00985579652783632 0.0387673189109366 0.254229510956867 0.79947952144165 df.mm.trans2:probe5 0.0813566744617786 0.0387673189109366 2.09858914021591 0.0366269314608425 * df.mm.trans2:probe6 0.0850857440296921 0.0387673189109366 2.19478020198319 0.0288886257651529 * df.mm.trans3:probe2 -0.191137892784076 0.0387673189109366 -4.9303871960605 1.31299959404887e-06 *** df.mm.trans3:probe3 -0.226870137111564 0.0387673189109366 -5.85209768136847 1.18854716301333e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01764629307216 0.129528975275131 31.0173556498716 5.19985597963388e-99 *** df.mm.trans1 0.121583779682027 0.109709688980216 1.10823192383631 0.268583654936500 df.mm.trans2 0.147213101337246 0.109709688980216 1.34184229948727 0.180586755158092 df.mm.exp2 0.192901322365197 0.152784043946437 1.26257505288199 0.207649898561846 df.mm.exp3 0.352308073920671 0.152784043946437 2.30592190663695 0.0217460027172709 * df.mm.exp4 0.107699747194976 0.152784043946437 0.70491488779243 0.481369621964518 df.mm.exp5 0.178356192078208 0.152784043946437 1.16737446837535 0.243917568125385 df.mm.exp6 0.195427255339563 0.152784043946437 1.27910775426311 0.201774433026439 df.mm.exp7 0.0160756504371774 0.152784043946437 0.10521812371201 0.916267837806592 df.mm.exp8 0.0962417742908757 0.152784043946437 0.629920322861831 0.529190686443424 df.mm.trans1:exp2 -0.100344072672536 0.132314863350532 -0.758373399114665 0.44877876272703 df.mm.trans2:exp2 -0.175939303152630 0.132314863350532 -1.32970173340637 0.184551939909619 df.mm.trans1:exp3 -0.214930983929663 0.132314863350532 -1.62439032537306 0.105264916208040 df.mm.trans2:exp3 -0.214613144879809 0.132314863350532 -1.62198818368009 0.105778523910632 df.mm.trans1:exp4 -0.0260450692681353 0.132314863350532 -0.196841598960322 0.844074913627477 df.mm.trans2:exp4 -0.177977904927109 0.132314863350532 -1.34510893500759 0.179530783490278 df.mm.trans1:exp5 -0.112930712441899 0.132314863350532 -0.853499822939163 0.394012732485357 df.mm.trans2:exp5 -0.177335333960384 0.132314863350532 -1.34025255719444 0.181102328189457 df.mm.trans1:exp6 -0.154724442886584 0.132314863350532 -1.1693655494824 0.243115929794134 df.mm.trans2:exp6 -0.224261915627358 0.132314863350532 -1.69491098693302 0.0910531112814475 . df.mm.trans1:exp7 0.00578745568996906 0.132314863350532 0.0437400269585494 0.965138576725292 df.mm.trans2:exp7 -0.0975693419411957 0.132314863350532 -0.737402733680132 0.461411151177027 df.mm.trans1:exp8 -0.0305884110866142 0.132314863350532 -0.231178949303515 0.817321703043661 df.mm.trans2:exp8 -0.160202174667001 0.132314863350532 -1.21076476678655 0.226868024280489 df.mm.trans1:probe2 -0.0602231886158074 0.0661574316752662 -0.91030118749792 0.363340396045365 df.mm.trans1:probe3 -0.0791786299937634 0.0661574316752662 -1.19682139993602 0.232251154032677 df.mm.trans1:probe4 0.0402947999972488 0.0661574316752662 0.609074430141663 0.542901927567284 df.mm.trans1:probe5 0.0048232242930244 0.0661574316752662 0.0729052529835076 0.941926499352241 df.mm.trans1:probe6 -0.0101374660060192 0.0661574316752662 -0.153232460047406 0.87831034893452 df.mm.trans2:probe2 -0.0332384864516823 0.0661574316752662 -0.502415006296396 0.615716918162598 df.mm.trans2:probe3 0.0472072787491374 0.0661574316752662 0.713559724942987 0.47601313478084 df.mm.trans2:probe4 0.0593401743696141 0.0661574316752662 0.896954021142862 0.370409427589737 df.mm.trans2:probe5 -0.0630301442584134 0.0661574316752662 -0.952729612718294 0.341437294123189 df.mm.trans2:probe6 0.0385072310793275 0.0661574316752662 0.58205450399496 0.560934748398988 df.mm.trans3:probe2 0.0163578478907845 0.0661574316752662 0.247256392465129 0.804866408630681 df.mm.trans3:probe3 0.00359903459897604 0.0661574316752662 0.0544010628562775 0.956649160612513