chr10.1885_chr10_127694791_127695163_+_1.R fitVsDatCorrelation=0.951894219319203 cont.fitVsDatCorrelation=0.295135317078507 fstatistic=6962.44613502582,38,370 cont.fstatistic=707.398991576921,38,370 residuals=-0.507101119628336,-0.115908993929050,0.0068612281427067,0.10338883389912,0.683722457192011 cont.residuals=-1.25938539041811,-0.516419658856084,0.101551813280839,0.432938971215824,1.29907330959495 predictedValues: Include Exclude Both chr10.1885_chr10_127694791_127695163_+_1.R.tl.Lung 165.811250814197 301.735763148770 55.3154143346744 chr10.1885_chr10_127694791_127695163_+_1.R.tl.cerebhem 108.923386258527 232.983982740670 51.7645789990289 chr10.1885_chr10_127694791_127695163_+_1.R.tl.cortex 130.806523450919 186.508284617983 58.8673402446897 chr10.1885_chr10_127694791_127695163_+_1.R.tl.heart 143.011137984774 192.561629667030 59.6920599232948 chr10.1885_chr10_127694791_127695163_+_1.R.tl.kidney 180.355675281333 267.403655671894 63.0936935676292 chr10.1885_chr10_127694791_127695163_+_1.R.tl.liver 189.882240033699 227.466711921134 55.5030654288547 chr10.1885_chr10_127694791_127695163_+_1.R.tl.stomach 154.102805324998 205.716080520012 59.7945009196039 chr10.1885_chr10_127694791_127695163_+_1.R.tl.testicle 162.938799711156 195.797822467882 63.9018154706883 diffExp=-135.924512334573,-124.060596482143,-55.7017611670641,-49.5504916822556,-87.0479803905609,-37.5844718874344,-51.6132751950147,-32.8590227567263 diffExpScore=0.99826190369291 diffExp1.5=-1,-1,0,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,-1,-1,0,-1,0,0,0 diffExp1.4Score=0.8 diffExp1.3=-1,-1,-1,-1,-1,0,-1,0 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 136.732899528416 131.780802638924 120.544945353728 cerebhem 106.218031436985 120.740794935946 108.664691819169 cortex 175.862258648407 140.361863752177 161.116049998276 heart 126.928053983224 121.25120506484 133.602047889254 kidney 109.585238788368 104.874916483763 113.085819799075 liver 132.952523219643 145.351081787094 108.729342358122 stomach 131.093840966389 143.089783278263 169.115766880864 testicle 137.133012973800 121.455654802753 143.024258509423 cont.diffExp=4.95209688949214,-14.5227634989613,35.5003948962301,5.67684891838395,4.71032230460536,-12.3985585674511,-11.9959423118740,15.6773581710469 cont.diffExpScore=3.68654482938184 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=0,0,1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.364523393356684 cont.tran.correlation=0.609394878192153 tran.covariance=0.0103970289689677 cont.tran.covariance=0.0110666746154289 tran.mean=190.375359350936 cont.tran.mean=130.338247643062 weightedLogRatios: wLogRatio Lung -3.23909581854539 cerebhem -3.85546125965937 cortex -1.79190889926952 heart -1.52068965027183 kidney -2.12346205840569 liver -0.963807758824294 stomach -1.49697761239158 testicle -0.952567673807984 cont.weightedLogRatios: wLogRatio Lung 0.180742876483293 cerebhem -0.606104679714107 cortex 1.14022992707958 heart 0.220577666761394 kidney 0.205381300363925 liver -0.439966315617696 stomach -0.43076278759866 testicle 0.590044772356784 varWeightedLogRatios=1.09854658317833 cont.varWeightedLogRatios=0.346541459694737 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 7.04705275657411 0.101570899327789 69.3806277507885 3.49078560455127e-214 *** df.mm.trans1 -1.87229966837823 0.0836476793267907 -22.3831633279821 8.91627913010712e-71 *** df.mm.trans2 -1.30321859940914 0.0836476793267908 -15.5798536181474 1.94599588902015e-42 *** df.mm.exp2 -0.612441814069035 0.114344056612927 -5.35613159276172 1.49701800995992e-07 *** df.mm.exp3 -0.780441637116523 0.114344056612927 -6.82537999992811 3.60111466257657e-11 *** df.mm.exp4 -0.673210406251229 0.114344056612927 -5.88758546961607 8.79768994069709e-09 *** df.mm.exp5 -0.168280776480207 0.114344056612927 -1.47170549537056 0.141950435455517 df.mm.exp6 -0.150380421148638 0.114344056612927 -1.31515730334546 0.189271499707692 df.mm.exp7 -0.53414695004381 0.114344056612927 -4.67140108429055 4.19369284479361e-06 *** df.mm.exp8 -0.594240661008382 0.114344056612927 -5.19695276353523 3.35619413558206e-07 *** df.mm.trans1:exp2 0.192236472656812 0.0948090831980591 2.02761661828565 0.0433156641377285 * df.mm.trans2:exp2 0.353859843753490 0.0948090831980591 3.73234116201995 0.000219523976072732 *** df.mm.trans1:exp3 0.543310850229531 0.0948090831980592 5.73057804065605 2.07736563841665e-08 *** df.mm.trans2:exp3 0.299365618841909 0.0948090831980592 3.15756263792283 0.00172157591895707 ** df.mm.trans1:exp4 0.525282823339014 0.0948090831980591 5.54042719980407 5.73851429961574e-08 *** df.mm.trans2:exp4 0.224074984939096 0.0948090831980591 2.36343372787391 0.0186227616627262 * df.mm.trans1:exp5 0.252361553342887 0.0948090831980591 2.66178666463524 0.0081116221592503 ** df.mm.trans2:exp5 0.0474884343124555 0.0948090831980592 0.500884859452239 0.616749991515963 df.mm.trans1:exp6 0.285934413719597 0.0948090831980591 3.01589683260908 0.00273919653290006 ** df.mm.trans2:exp6 -0.132167350308158 0.0948090831980592 -1.39403679320531 0.164143092826236 df.mm.trans1:exp7 0.460916798634692 0.0948090831980591 4.8615257429694 1.72482849020346e-06 *** df.mm.trans2:exp7 0.151092240354524 0.0948090831980592 1.59364730949763 0.111868907242826 df.mm.trans1:exp8 0.576765231272507 0.0948090831980591 6.08343854636403 2.93730246628045e-09 *** df.mm.trans2:exp8 0.161771591921017 0.0948090831980592 1.70628790474718 0.0887936029626412 . df.mm.trans1:probe2 -0.111387822905740 0.0553565783749583 -2.01218764193215 0.0449244671200629 * df.mm.trans1:probe3 -0.0986081039171273 0.0553565783749583 -1.78132584801764 0.0756794381931894 . df.mm.trans1:probe4 -0.209528553984942 0.0553565783749583 -3.78507053968724 0.000179208418223857 *** df.mm.trans1:probe5 -0.440510041643577 0.0553565783749583 -7.95768189752947 2.16482950738382e-14 *** df.mm.trans1:probe6 0.157101631885336 0.0553565783749583 2.83799390239054 0.00479002138975856 ** df.mm.trans2:probe2 -0.0475576595227124 0.0553565783749583 -0.85911486798516 0.390833389262799 df.mm.trans2:probe3 0.00103206349642452 0.0553565783749583 0.0186439177911941 0.985135217066892 df.mm.trans2:probe4 -0.109316568324644 0.0553565783749583 -1.97477104860397 0.0490372171821923 * df.mm.trans2:probe5 -0.0596780033935446 0.0553565783749583 -1.07806524798760 0.281706972667286 df.mm.trans2:probe6 -0.161587103994058 0.0553565783749583 -2.91902261190253 0.003726072247589 ** df.mm.trans3:probe2 0.275412952442822 0.0553565783749583 4.97525245468226 9.99999624302334e-07 *** df.mm.trans3:probe3 0.234946366864245 0.0553565783749583 4.24423571256218 2.77635257874901e-05 *** df.mm.trans3:probe4 0.0387191931827843 0.0553565783749583 0.699450622119733 0.484709941724678 df.mm.trans3:probe5 0.158507093159620 0.0553565783749583 2.8633831391451 0.00443016638619495 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.00463951948055 0.316703580981788 15.8022827022229 2.37342928329964e-43 *** df.mm.trans1 -0.0232882314216988 0.260818007509392 -0.0892892007115736 0.928900387451695 df.mm.trans2 -0.137395920698358 0.260818007509392 -0.526788476035001 0.59865615499209 df.mm.exp2 -0.236273646612403 0.356530979177723 -0.662701589514962 0.507934292307082 df.mm.exp3 0.0246533168698176 0.356530979177723 0.0691477552011785 0.944909363238058 df.mm.exp4 -0.260527365857015 0.356530979177723 -0.730728551156695 0.465407428830819 df.mm.exp5 -0.385822613081957 0.356530979177723 -1.08215733166243 0.279887383344689 df.mm.exp6 0.173135802563235 0.356530979177723 0.485612226355597 0.627529558625306 df.mm.exp7 -0.298344459872929 0.356530979177723 -0.836798139003261 0.403246172465344 df.mm.exp8 -0.249660354928640 0.356530979177723 -0.700248700700396 0.484212068360050 df.mm.trans1:exp2 -0.0162618557802406 0.29561987101763 -0.0550093460370624 0.956160708842063 df.mm.trans2:exp2 0.148779747608444 0.29561987101763 0.50328060524582 0.615066472570475 df.mm.trans1:exp3 0.227018366114086 0.29561987101763 0.767940143308388 0.443012561238041 df.mm.trans2:exp3 0.0384305559119276 0.29561987101763 0.129999907582788 0.896637144720473 df.mm.trans1:exp4 0.186118403392006 0.29561987101763 0.629586917656514 0.529353444981552 df.mm.trans2:exp4 0.177251878088032 0.29561987101763 0.599593922688172 0.549143885752989 df.mm.trans1:exp5 0.164495911607038 0.29561987101763 0.556444027394991 0.57824372115401 df.mm.trans2:exp5 0.157451025311972 0.29561987101763 0.53261313175589 0.594621121814675 df.mm.trans1:exp6 -0.201173090673621 0.29561987101763 -0.680512747607631 0.496605460495403 df.mm.trans2:exp6 -0.0751236891666583 0.295619871017630 -0.25412259638723 0.799542010157954 df.mm.trans1:exp7 0.256228485547155 0.29561987101763 0.866749872615547 0.38664079935452 df.mm.trans2:exp7 0.380676791990761 0.295619871017630 1.28772396348166 0.198646896656453 df.mm.trans1:exp8 0.252582323029059 0.29561987101763 0.85441591649296 0.39342744164062 df.mm.trans2:exp8 0.168069613823872 0.295619871017630 0.56853287042348 0.570018087150847 df.mm.trans1:probe2 -0.0944778360273596 0.172604818095293 -0.547364998671124 0.584458000189555 df.mm.trans1:probe3 0.0686536259738701 0.172604818095293 0.397750345161091 0.691043601275081 df.mm.trans1:probe4 -0.267654037930109 0.172604818095293 -1.55067535705950 0.121834401827625 df.mm.trans1:probe5 -0.192321358298909 0.172604818095293 -1.11422937332335 0.265904053067188 df.mm.trans1:probe6 -0.210741334730056 0.172604818095293 -1.22094699936886 0.222883549298676 df.mm.trans2:probe2 0.251229255334127 0.172604818095293 1.45551704817085 0.146374091555096 df.mm.trans2:probe3 -0.131310349794392 0.172604818095293 -0.760757151760948 0.447286667890328 df.mm.trans2:probe4 0.0557258325857691 0.172604818095293 0.322852126613311 0.746989601982176 df.mm.trans2:probe5 0.0126324538409675 0.172604818095293 0.073187144949762 0.941696743594533 df.mm.trans2:probe6 -0.0354172601915554 0.172604818095293 -0.205192766820692 0.837534358864161 df.mm.trans3:probe2 -0.178058163817060 0.172604818095293 -1.03159440032987 0.302935996607928 df.mm.trans3:probe3 -0.08101932555646 0.172604818095293 -0.46939202769954 0.639065929789419 df.mm.trans3:probe4 -0.0220149929461194 0.172604818095293 -0.127545645533285 0.898577838279652 df.mm.trans3:probe5 0.0214290148892642 0.172604818095293 0.124150734178426 0.901263346611694