chr11.4105_chr11_118679834_118681996_-_0.R fitVsDatCorrelation=0.845252083826622 cont.fitVsDatCorrelation=0.310563611776046 fstatistic=5945.71644093635,36,324 cont.fstatistic=1872.86858524897,36,324 residuals=-0.524813170397491,-0.104733838553794,-0.00711437093822782,0.0938648187600291,0.982346411889038 cont.residuals=-0.865847818729996,-0.200132092740288,-0.00666399139265105,0.208866412364131,1.07396590454741 predictedValues: Include Exclude Both chr11.4105_chr11_118679834_118681996_-_0.R.tl.Lung 126.099630251801 82.4220544220835 73.8207054588664 chr11.4105_chr11_118679834_118681996_-_0.R.tl.cerebhem 123.733020854194 97.2135154832145 62.6838937396371 chr11.4105_chr11_118679834_118681996_-_0.R.tl.cortex 106.136526857778 78.794284901121 65.2007546808532 chr11.4105_chr11_118679834_118681996_-_0.R.tl.heart 112.276934113062 83.8230613854637 64.589872048962 chr11.4105_chr11_118679834_118681996_-_0.R.tl.kidney 132.686519551712 83.4569715783175 75.3911658161148 chr11.4105_chr11_118679834_118681996_-_0.R.tl.liver 136.052100923816 82.9422374463931 65.5698122416513 chr11.4105_chr11_118679834_118681996_-_0.R.tl.stomach 116.060346486030 109.777832482358 80.191276527941 chr11.4105_chr11_118679834_118681996_-_0.R.tl.testicle 110.560054026732 91.6463436253316 72.1922636011836 diffExp=43.677575829717,26.5195053709790,27.3422419566568,28.4538727275985,49.229547973395,53.1098634774232,6.28251400367192,18.9137104014009 diffExpScore=0.996071172003735 diffExp1.5=1,0,0,0,1,1,0,0 diffExp1.5Score=0.75 diffExp1.4=1,0,0,0,1,1,0,0 diffExp1.4Score=0.75 diffExp1.3=1,0,1,1,1,1,0,0 diffExp1.3Score=0.833333333333333 diffExp1.2=1,1,1,1,1,1,0,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 109.190449727356 96.2120991662158 99.4013868878078 cerebhem 103.481350028308 98.4607311124277 96.4203061246468 cortex 94.6379900682226 105.841789884492 100.862541529943 heart 95.8644562730245 110.517437224427 100.039315108252 kidney 112.018064668623 90.335506004783 117.553028388699 liver 106.060701343267 92.8268149582962 115.571677347465 stomach 81.3508656851415 84.5903954026272 106.465091158012 testicle 98.380830473595 103.967727264493 92.3094562982486 cont.diffExp=12.9783505611397,5.02061891587994,-11.2037998162693,-14.6529809514026,21.6825586638398,13.2338863849706,-3.23952971748564,-5.58689679089842 cont.diffExpScore=4.55478774038871 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=0,0,0,0,1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.129326928453671 cont.tran.correlation=0.0142610520955156 tran.covariance=-0.000981076188250441 cont.tran.covariance=0.000866605463253969 tran.mean=104.605089649338 cont.tran.mean=98.9835755803312 weightedLogRatios: wLogRatio Lung 1.96641059428847 cerebhem 1.13311854630726 cortex 1.34518768040336 heart 1.33704352513183 kidney 2.15886671103219 liver 2.30897063257293 stomach 0.263025601017859 testicle 0.865264316512813 cont.weightedLogRatios: wLogRatio Lung 0.585850946918435 cerebhem 0.229496937923836 cortex -0.51534923143439 heart -0.659138760831575 kidney 0.991982946600594 liver 0.61271970702106 stomach -0.172531173616081 testicle -0.254988332314029 varWeightedLogRatios=0.482259772968646 cont.varWeightedLogRatios=0.352361610304273 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.6827902090019 0.106638163945624 43.9128923055137 1.82540786488241e-138 *** df.mm.trans1 0.0932121494339718 0.090321410904745 1.03200501963234 0.302839485891387 df.mm.trans2 -0.388857926659648 0.090321410904745 -4.30526851567616 2.21267405179809e-05 *** df.mm.exp2 0.309645291702265 0.125783515943275 2.46173188418350 0.0143472775161382 * df.mm.exp3 -0.0931904301487887 0.125783515943275 -0.74087951390081 0.459303094928206 df.mm.exp4 0.0343328828730293 0.125783515943275 0.272952164006232 0.785063876727038 df.mm.exp5 0.042344326363727 0.125783515943275 0.336644480369137 0.736602861029331 df.mm.exp6 0.200780819139650 0.125783515943275 1.59624111024370 0.111409836425298 df.mm.exp7 0.120868056799217 0.125783515943275 0.96092127726597 0.337308311016301 df.mm.exp8 -0.00312306923455964 0.125783515943275 -0.0248289230201520 0.980206705685386 df.mm.trans1:exp2 -0.328591415669784 0.108931720184201 -3.01648973424952 0.00275958450350596 ** df.mm.trans2:exp2 -0.144588593577748 0.108931720184201 -1.32733232646332 0.185333296828446 df.mm.trans1:exp3 -0.0791556260455955 0.108931720184201 -0.726653594671459 0.467962840460496 df.mm.trans2:exp3 0.0481778458901254 0.108931720184201 0.442275636597472 0.658584841172982 df.mm.trans1:exp4 -0.15043674826254 0.108931720184201 -1.38101875200497 0.168224971233282 df.mm.trans2:exp4 -0.0174777695705353 0.108931720184201 -0.160447017094569 0.872628942152537 df.mm.trans1:exp5 0.00857271313489479 0.108931720184201 0.0786980424104063 0.937321383603898 df.mm.trans2:exp5 -0.0298661896210769 0.108931720184201 -0.274173487488986 0.784126019344458 df.mm.trans1:exp6 -0.124815222521886 0.108931720184201 -1.14581154424832 0.252718716756533 df.mm.trans2:exp6 -0.194489439855394 0.108931720184201 -1.78542521431329 0.0751276454148174 . df.mm.trans1:exp7 -0.203830083421033 0.108931720184201 -1.87117290607695 0.0622222015423032 . df.mm.trans2:exp7 0.165737510076592 0.108931720184201 1.52148070182251 0.129114568037108 df.mm.trans1:exp8 -0.128390392866017 0.108931720184201 -1.17863183147123 0.239409669547809 df.mm.trans2:exp8 0.109207095890367 0.108931720184201 1.00252796619479 0.316836796381172 df.mm.trans1:probe2 0.0921225041413996 0.0544658600921007 1.69138069215509 0.0917256789789958 . df.mm.trans1:probe3 -0.0172433780303876 0.0544658600921007 -0.316590576211032 0.751758165165543 df.mm.trans1:probe4 0.191174433471932 0.0544658600921007 3.50998649702143 0.000511584397922648 *** df.mm.trans1:probe5 0.11499995086478 0.0544658600921007 2.11141347387735 0.0355022378207298 * df.mm.trans1:probe6 0.168576060687083 0.0544658600921007 3.09507754769730 0.00213918964405856 ** df.mm.trans2:probe2 0.355421202307171 0.0544658600921007 6.52557770511951 2.60937900239786e-10 *** df.mm.trans2:probe3 0.252663924004389 0.0544658600921007 4.63894122992163 5.08950371311415e-06 *** df.mm.trans2:probe4 0.373639812782935 0.0544658600921007 6.86007367094024 3.50119992079806e-11 *** df.mm.trans2:probe5 0.168410929116609 0.0544658600921007 3.0920457114205 0.00216051837547257 ** df.mm.trans2:probe6 -0.088848942580217 0.0544658600921007 -1.63127769266794 0.103803328403674 df.mm.trans3:probe2 -0.413544713308424 0.0544658600921007 -7.59273263304992 3.37419463137518e-13 *** df.mm.trans3:probe3 0.157029180068524 0.0544658600921007 2.88307537607946 0.00420138383892864 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.79022571189358 0.189691799075462 25.2526768961054 1.5520756187071e-78 *** df.mm.trans1 -0.0610716488537426 0.160666972269811 -0.380113274003719 0.704110387581265 df.mm.trans2 -0.21833255845657 0.160666972269811 -1.35891375415926 0.175119129633908 df.mm.exp2 -0.000150277935960437 0.223748239377822 -0.0006716385182664 0.999464523364259 df.mm.exp3 -0.0622368595812829 0.223748239377823 -0.278155751099294 0.781070223106854 df.mm.exp4 0.00206267425294051 0.223748239377822 0.00921872841849478 0.992650296676523 df.mm.exp5 -0.205181460943294 0.223748239377822 -0.917019331699962 0.359814581950585 df.mm.exp6 -0.215626441179798 0.223748239377822 -0.963701174942835 0.335914470233617 df.mm.exp7 -0.491707600458103 0.223748239377822 -2.19759316017590 0.0286854242882762 * df.mm.exp8 0.047297263669307 0.223748239377822 0.211386081967959 0.832718914730395 df.mm.trans1:exp2 -0.0535519213355754 0.193771659353236 -0.276366118318432 0.782443083575908 df.mm.trans2:exp2 0.0232529569933213 0.193771659353236 0.120001846869321 0.904556061404132 df.mm.trans1:exp3 -0.0807977614072964 0.193771659353236 -0.416974090416422 0.676973489669502 df.mm.trans2:exp3 0.157627169752386 0.193771659353236 0.813468647987574 0.416546803689536 df.mm.trans1:exp4 -0.132220997074344 0.193771659353236 -0.68235467206952 0.495502247503811 df.mm.trans2:exp4 0.136555516469544 0.193771659353236 0.704723884418054 0.481488341888991 df.mm.trans1:exp5 0.230748008139369 0.193771659353236 1.19082433886127 0.234594239988421 df.mm.trans2:exp5 0.142156923857370 0.193771659353236 0.7336311426132 0.463704085932744 df.mm.trans1:exp6 0.186544422792747 0.193771659353236 0.962702303398691 0.336414875165546 df.mm.trans2:exp6 0.179806872802946 0.193771659353236 0.92793173884715 0.354133722875294 df.mm.trans1:exp7 0.197385472778625 0.193771659353236 1.01864985538882 0.309129250524863 df.mm.trans2:exp7 0.362973210383803 0.193771659353236 1.87320071260845 0.0619408408372144 . df.mm.trans1:exp8 -0.151544893626731 0.193771659353236 -0.782079764050905 0.434738863066939 df.mm.trans2:exp8 0.0302281518282489 0.193771659353236 0.155998828358818 0.876131094830248 df.mm.trans1:probe2 -0.0533852031004152 0.096885829676618 -0.551011466574652 0.582005563941931 df.mm.trans1:probe3 -0.0297982949102561 0.096885829676618 -0.307560919999506 0.758614065710698 df.mm.trans1:probe4 -0.173674199732749 0.096885829676618 -1.79256554144639 0.0739752009732059 . df.mm.trans1:probe5 -0.068652016162522 0.096885829676618 -0.708586760227643 0.479090449977032 df.mm.trans1:probe6 0.000965571540782019 0.096885829676618 0.0099660759886649 0.992054487026464 df.mm.trans2:probe2 0.0257407106005599 0.096885829676618 0.265680860518781 0.79065397583443 df.mm.trans2:probe3 -0.0369240825631387 0.096885829676618 -0.381109215727239 0.703371987366032 df.mm.trans2:probe4 0.036758125232502 0.096885829676618 0.379396299285375 0.704642132711603 df.mm.trans2:probe5 -0.0176533015545893 0.096885829676618 -0.182207259962699 0.855534022905375 df.mm.trans2:probe6 -0.0559637461527831 0.096885829676618 -0.577625709967875 0.563918033652445 df.mm.trans3:probe2 0.121715581898712 0.096885829676618 1.25627847028786 0.209920081323614 df.mm.trans3:probe3 0.14331810282797 0.096885829676618 1.47924730898556 0.140045814110371