chr3.15622_chr3_104573546_104575108_-_0.R fitVsDatCorrelation=0.7734814779371 cont.fitVsDatCorrelation=0.233958751792058 fstatistic=9297.56019847024,43,485 cont.fstatistic=3944.87421396387,43,485 residuals=-0.662958239381261,-0.08209996685715,-0.00517919902190549,0.079633055840769,0.486083145238156 cont.residuals=-0.457412518784687,-0.153687001821012,-0.0388278561628874,0.12281064928302,0.807868808478183 predictedValues: Include Exclude Both chr3.15622_chr3_104573546_104575108_-_0.R.tl.Lung 52.6278156123298 52.1687368535802 62.7059339432109 chr3.15622_chr3_104573546_104575108_-_0.R.tl.cerebhem 53.7003893585007 48.2318859227104 70.6738736181252 chr3.15622_chr3_104573546_104575108_-_0.R.tl.cortex 58.4977128932427 63.5414455802153 69.2223752107157 chr3.15622_chr3_104573546_104575108_-_0.R.tl.heart 57.1543464202081 58.4823882777496 62.8584390275661 chr3.15622_chr3_104573546_104575108_-_0.R.tl.kidney 53.8455206208125 52.126112200294 58.2236693077445 chr3.15622_chr3_104573546_104575108_-_0.R.tl.liver 54.5091800422518 51.4487443198598 54.89474491189 chr3.15622_chr3_104573546_104575108_-_0.R.tl.stomach 57.9183884092189 60.6027160736806 64.9682547734786 chr3.15622_chr3_104573546_104575108_-_0.R.tl.testicle 57.2226193117664 51.3851331294363 61.2570679758477 diffExp=0.459078758749605,5.46850343579022,-5.04373268697254,-1.32804185754152,1.71940842051844,3.06043572239193,-2.68432766446173,5.83748618233006 diffExpScore=3.01585425889083 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 59.530898303328 58.278674390185 55.5605801018007 cerebhem 57.0082421068526 60.8955209343517 60.1253372802178 cortex 55.3502681808696 60.4122337968527 56.9258792166167 heart 56.4065515309569 58.8242847195166 61.1848264115297 kidney 55.5966388831417 55.001120533843 64.085609469539 liver 61.2362026737985 55.7861104146927 59.0789082517132 stomach 59.152894506463 56.8412642916158 60.832562578435 testicle 54.9994501264547 60.7126980021552 56.9142641063711 cont.diffExp=1.25222391314304,-3.88727882749905,-5.06196561598308,-2.41773318855978,0.595518349298757,5.45009225910582,2.31163021484715,-5.71324787570054 cont.diffExpScore=3.15080203119595 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=0.795711246389635 cont.tran.correlation=-0.490067431416618 tran.covariance=0.00308595081531586 cont.tran.covariance=-0.000749790860043309 tran.mean=55.2164459391161 cont.tran.mean=57.8770658371924 weightedLogRatios: wLogRatio Lung 0.0346851494587467 cerebhem 0.422051629904261 cortex -0.339944527165765 heart -0.093195682131226 kidney 0.128835721257771 liver 0.229368611421581 stomach -0.184919980662435 testicle 0.429664321521491 cont.weightedLogRatios: wLogRatio Lung 0.086649816336743 cerebhem -0.268880044345897 cortex -0.355066786928478 heart -0.170126151594804 kidney 0.043213936860193 liver 0.379205369554747 stomach 0.161851426613739 testicle -0.400926127229365 varWeightedLogRatios=0.077503416892065 cont.varWeightedLogRatios=0.07615564396535 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.64130183696642 0.0742242463639697 49.0581180051429 3.64151503961746e-190 *** df.mm.trans1 0.347700400884774 0.0594203908116894 5.85153339005595 8.97307157961148e-09 *** df.mm.trans2 0.285917212545516 0.0594203908116894 4.81176930410342 2.00083087206706e-06 *** df.mm.exp2 -0.177907492956734 0.0795679514193573 -2.23591898224305 0.0258111955462471 * df.mm.exp3 0.204083770261281 0.0795679514193573 2.56489914118402 0.0106203408371065 * df.mm.exp4 0.194323780379929 0.0795679514193572 2.44223681662682 0.0149526299739771 * df.mm.exp5 0.0962211583734739 0.0795679514193573 1.20929540923263 0.227138532737270 df.mm.exp6 0.154265446827388 0.0795679514193573 1.93878872178502 0.0531068197896498 . df.mm.exp7 0.210203757428484 0.0795679514193573 2.64181436971551 0.00851268129113595 ** df.mm.exp8 0.0919468012286449 0.0795679514193573 1.15557582655416 0.248423659531049 df.mm.trans1:exp2 0.198082951175641 0.0624182364529085 3.17347881696537 0.00160188597225952 ** df.mm.trans2:exp2 0.0994444243436432 0.0624182364529085 1.59319503393322 0.111767916707145 df.mm.trans1:exp3 -0.0983409065232142 0.0624182364529085 -1.57551562030125 0.115789511148978 df.mm.trans2:exp3 -0.00687479558599576 0.0624182364529086 -0.110140817438545 0.91234325661081 df.mm.trans1:exp4 -0.111813134077420 0.0624182364529085 -1.7913536240611 0.0738599397238846 . df.mm.trans2:exp4 -0.0800815311683933 0.0624182364529085 -1.28298291844261 0.200110813001208 df.mm.trans1:exp5 -0.0733467346534267 0.0624182364529085 -1.17508502036521 0.240537273015402 df.mm.trans2:exp5 -0.0970385459251333 0.0624182364529085 -1.55465055470358 0.120681586796357 df.mm.trans1:exp6 -0.119141112094048 0.0624182364529085 -1.90875485858903 0.0568836837943092 . df.mm.trans2:exp6 -0.168162795297680 0.0624182364529085 -2.69412922975724 0.00730198174054768 ** df.mm.trans1:exp7 -0.114413628058074 0.0624182364529085 -1.83301603121059 0.0674129730452824 . df.mm.trans2:exp7 -0.0603474502787815 0.0624182364529085 -0.966824019840911 0.334113866334626 df.mm.trans1:exp8 -0.00824233238975541 0.0624182364529085 -0.132050068347795 0.894999452302141 df.mm.trans2:exp8 -0.107081313950929 0.0624182364529085 -1.71554532835474 0.0868839585070365 . df.mm.trans1:probe2 -0.124145517345533 0.0427348451311866 -2.90501853848853 0.00384004937497024 ** df.mm.trans1:probe3 -0.149783561899145 0.0427348451311866 -3.50495155509147 0.000499050851014772 *** df.mm.trans1:probe4 -0.0719540770382751 0.0427348451311866 -1.68373318816043 0.0928767000036442 . df.mm.trans1:probe5 0.0270345133148696 0.0427348451311866 0.632610536714937 0.527286133390529 df.mm.trans1:probe6 -0.0932704603554842 0.0427348451311866 -2.18253886422577 0.0295487473011158 * df.mm.trans2:probe2 0.153826092746373 0.0427348451311866 3.59954721432033 0.0003515556875665 *** df.mm.trans2:probe3 0.0401876433862243 0.0427348451311866 0.940395203559464 0.347483006384798 df.mm.trans2:probe4 0.136120246690179 0.0427348451311866 3.18522850082456 0.00153953277051291 ** df.mm.trans2:probe5 0.0933015813186856 0.0427348451311866 2.18326709813199 0.0294947722279572 * df.mm.trans2:probe6 0.0127941175698171 0.0427348451311866 0.299383735463226 0.764775522158204 df.mm.trans3:probe2 -0.143948767023679 0.0427348451311866 -3.36841672367802 0.000816206373087934 *** df.mm.trans3:probe3 -0.475252551542547 0.0427348451311866 -11.120961128644 9.55495117916544e-26 *** df.mm.trans3:probe4 0.00274670502774488 0.0427348451311866 0.064273194843999 0.948779178620656 df.mm.trans3:probe5 -0.471544469721851 0.0427348451311866 -11.0341916128235 2.07032910241630e-25 *** df.mm.trans3:probe6 0.0172168507471754 0.0427348451311866 0.40287617035521 0.687216847061575 df.mm.trans3:probe7 0.0461613829617643 0.0427348451311866 1.08018135598852 0.280598278182648 df.mm.trans3:probe8 -0.266389190402393 0.0427348451311866 -6.23353587885102 9.91986580192973e-10 *** df.mm.trans3:probe9 -0.155804329943434 0.0427348451311866 -3.64583817877775 0.000295331966427413 *** df.mm.trans3:probe10 0.0821806999637509 0.0427348451311866 1.92303727114195 0.0550605512860846 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07698815003443 0.11385628451854 35.8081959838637 2.83242210609923e-138 *** df.mm.trans1 0.00974644245501103 0.0911479099334127 0.106929961006579 0.91488875999358 df.mm.trans2 -0.0143145231610695 0.0911479099334127 -0.157047190347281 0.87527301217415 df.mm.exp2 -0.0783336118602123 0.122053261018455 -0.641798598468974 0.521307166483958 df.mm.exp3 -0.0611346756257228 0.122053261018455 -0.500885229248229 0.61667913392125 df.mm.exp4 -0.141016889277867 0.122053261018455 -1.15537174591791 0.248507104818262 df.mm.exp5 -0.269001285094419 0.122053261018455 -2.20396639016261 0.0279957643079318 * df.mm.exp6 -0.076868249338978 0.122053261018455 -0.629792671638287 0.529126815094371 df.mm.exp7 -0.121994897792307 0.122053261018455 -0.999521821656703 0.318040386544083 df.mm.exp8 -0.0623276942237726 0.122053261018455 -0.510659802971985 0.609821641284694 df.mm.trans1:exp2 0.0350339906292568 0.0957464553278055 0.365903787344518 0.714596412766845 df.mm.trans2:exp2 0.122257000462572 0.0957464553278056 1.27688278426603 0.202254624106642 df.mm.trans1:exp3 -0.0116792970265449 0.0957464553278056 -0.121981508208932 0.902964187749752 df.mm.trans2:exp3 0.0970900708295506 0.0957464553278056 1.01403305738207 0.311072575335043 df.mm.trans1:exp4 0.087106725954003 0.0957464553278055 0.909764498913063 0.363398607707199 df.mm.trans2:exp4 0.15033542882042 0.0957464553278055 1.57014093425933 0.117034467748952 df.mm.trans1:exp5 0.200628555803615 0.0957464553278056 2.09541496984642 0.0366520398392874 * df.mm.trans2:exp5 0.211118607929239 0.0957464553278056 2.20497570595628 0.0279243868051254 * df.mm.trans1:exp6 0.105111333941813 0.0957464553278056 1.09780914167470 0.272832556637169 df.mm.trans2:exp6 0.0331569349090604 0.0957464553278055 0.34629934649321 0.729267940305408 df.mm.trans1:exp7 0.115624945061926 0.0957464553278056 1.20761593383340 0.22778359141535 df.mm.trans2:exp7 0.09702120823225 0.0957464553278056 1.01331383913984 0.311415522494773 df.mm.trans1:exp8 -0.0168445952725628 0.0957464553278056 -0.175929178943411 0.860423012242803 df.mm.trans2:exp8 0.103244327665491 0.0957464553278056 1.07830965973639 0.281431589258912 df.mm.trans1:probe2 -0.0184012964395840 0.0655531167302497 -0.280708185322524 0.779053932679489 df.mm.trans1:probe3 -0.0106874588612644 0.0655531167302497 -0.163035098777121 0.870558708372468 df.mm.trans1:probe4 0.00265477802168364 0.0655531167302497 0.0404981205181749 0.967712670848619 df.mm.trans1:probe5 0.00671451796637438 0.0655531167302497 0.102428660928580 0.918458770342788 df.mm.trans1:probe6 0.0158936111298250 0.0655531167302497 0.24245393541282 0.808530939023772 df.mm.trans2:probe2 -0.00916366604878 0.0655531167302497 -0.139789936861254 0.888883995698292 df.mm.trans2:probe3 -0.0525267861617901 0.0655531167302497 -0.80128586986851 0.423358546944513 df.mm.trans2:probe4 0.00244369904915482 0.0655531167302497 0.0372781519940633 0.970278564023728 df.mm.trans2:probe5 0.0515327903644593 0.0655531167302497 0.786122658004441 0.432179536152195 df.mm.trans2:probe6 0.0487157013466553 0.0655531167302497 0.743148514922941 0.457751585097686 df.mm.trans3:probe2 -0.0247602329442457 0.0655531167302497 -0.377712520460831 0.705809405128443 df.mm.trans3:probe3 -0.0939608612176776 0.0655531167302497 -1.43335459707164 0.152400840031788 df.mm.trans3:probe4 -0.0848529491578375 0.0655531167302497 -1.29441517642870 0.196138007744590 df.mm.trans3:probe5 -0.0667637045552797 0.0655531167302497 -1.01846728096868 0.308963712232143 df.mm.trans3:probe6 -0.118984762778998 0.0655531167302497 -1.81508933081883 0.0701277049611176 . df.mm.trans3:probe7 -0.0322590093124716 0.0655531167302497 -0.492104890225389 0.622867860586764 df.mm.trans3:probe8 -0.0276684625964372 0.0655531167302497 -0.422076996129606 0.67315584997862 df.mm.trans3:probe9 -0.062428217421303 0.0655531167302497 -0.95233027101052 0.341403869170902 df.mm.trans3:probe10 -0.0377829050117526 0.0655531167302497 -0.576370840874413 0.564631926568425