chr2.13849_chr2_72705892_72709528_+_2.R fitVsDatCorrelation=0.944974296718932 cont.fitVsDatCorrelation=0.332807096161447 fstatistic=10800.6444653638,45,531 cont.fstatistic=1289.52161444688,45,531 residuals=-0.668742803741852,-0.0836179640141731,0.00432226256586412,0.0771447216773943,0.526618102645009 cont.residuals=-0.661239945510497,-0.243023455015746,-0.0927551938159854,0.0907985329874144,1.60003535553579 predictedValues: Include Exclude Both chr2.13849_chr2_72705892_72709528_+_2.R.tl.Lung 43.3886781440909 43.4925068926644 83.1568002965431 chr2.13849_chr2_72705892_72709528_+_2.R.tl.cerebhem 47.8579972513202 45.5005719063447 66.3824726947347 chr2.13849_chr2_72705892_72709528_+_2.R.tl.cortex 44.0970886737479 45.6183447229601 98.1562908909018 chr2.13849_chr2_72705892_72709528_+_2.R.tl.heart 44.4199117394885 50.8750036069007 150.795328125649 chr2.13849_chr2_72705892_72709528_+_2.R.tl.kidney 42.6521823317531 44.5802359562151 81.8294797265543 chr2.13849_chr2_72705892_72709528_+_2.R.tl.liver 49.2302117462313 50.3420467122714 89.135190091651 chr2.13849_chr2_72705892_72709528_+_2.R.tl.stomach 46.9807094919002 45.4248501586062 93.9902269451792 chr2.13849_chr2_72705892_72709528_+_2.R.tl.testicle 46.5191783665085 47.5193227565773 119.981495958475 diffExp=-0.103828748573498,2.35742534497555,-1.52125604921213,-6.45509186741221,-1.92805362446202,-1.11183496604011,1.55585933329409,-1.00014439006876 diffExpScore=1.74146030087542 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 48.1883765136251 52.5895385132533 60.9536571681986 cerebhem 51.2647762915698 57.3626510589651 53.5929195982792 cortex 50.7807530202943 47.2626887039746 46.5000551147712 heart 50.485474851294 58.4766210452068 49.046514081008 kidney 68.3509661205583 49.8915574948151 54.4461981436231 liver 60.9334933469512 61.9324666665644 61.0512540073965 stomach 60.3114166873714 62.2994145321322 54.7189675045483 testicle 52.6528789240871 59.459058255294 55.3056156268137 cont.diffExp=-4.40116199962829,-6.09787476739532,3.51806431631974,-7.99114619391275,18.4594086257432,-0.9989733196132,-1.98799784476083,-6.80617933120697 cont.diffExpScore=6.87951902436964 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=0,0,0,0,1,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.445555433761897 cont.tran.correlation=0.0811476172568945 tran.covariance=0.00131352248471999 cont.tran.covariance=0.00126839422326519 tran.mean=46.1561775285988 cont.tran.mean=55.7651332516223 weightedLogRatios: wLogRatio Lung -0.00901413267459445 cerebhem 0.194121835869421 cortex -0.128995121652912 heart -0.523947354511877 kidney -0.166909137253571 liver -0.0872706779337222 stomach 0.129083446684954 testicle -0.0819069143870102 cont.weightedLogRatios: wLogRatio Lung -0.342502189148602 cerebhem -0.44879367508652 cortex 0.279403645112984 heart -0.58705373124556 kidney 1.28038733436803 liver -0.0669636511088247 stomach -0.133475856277204 testicle -0.489247180381783 varWeightedLogRatios=0.0472636319161641 cont.varWeightedLogRatios=0.372883899405876 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.55867043650302 0.0691227535754473 37.0163268121291 3.46892664402079e-149 *** df.mm.trans1 1.17757967647907 0.0591552818372437 19.9065855136823 2.76560345459428e-66 *** df.mm.trans2 1.18246972590378 0.0550298226167425 21.4877982460372 3.62185174833401e-74 *** df.mm.exp2 0.368470857718328 0.0733730968223234 5.02187959451403 6.99827128078019e-07 *** df.mm.exp3 -0.101916534302701 0.0733730968223234 -1.38901775605161 0.165409601998015 df.mm.exp4 -0.414923145280971 0.0733730968223234 -5.65497659565505 2.54892578596167e-08 *** df.mm.exp5 0.0236722783360497 0.0733730968223234 0.322628856641737 0.747103329810961 df.mm.exp6 0.203134717592503 0.0733730968223234 2.76851770458052 0.00582794916255987 ** df.mm.exp7 0.000546371584675543 0.0733730968223234 0.00744648390674594 0.994061417070036 df.mm.exp8 -0.208395658751245 0.0733730968223234 -2.84021893277702 0.00468120702731903 ** df.mm.trans1:exp2 -0.270431156912580 0.0656268928857556 -4.12073686595755 4.38056583899124e-05 *** df.mm.trans2:exp2 -0.32333463038614 0.0568345564105046 -5.68904995141968 2.11300006203174e-08 *** df.mm.trans1:exp3 0.118111763290715 0.0656268928857556 1.79974638592636 0.0724682167510644 . df.mm.trans2:exp3 0.149637798563686 0.0568345564105046 2.63286648149203 0.00871336851710053 ** df.mm.trans1:exp4 0.438412441973642 0.0656268928857556 6.68037785571896 6.0294692730292e-11 *** df.mm.trans2:exp4 0.571706192025104 0.0568345564105046 10.0591300105483 6.62427248797002e-22 *** df.mm.trans1:exp5 -0.0407923721366075 0.0656268928857556 -0.62158012276491 0.534484753594149 df.mm.trans2:exp5 0.00102967456331279 0.0568345564105046 0.0181170511101672 0.985552280578688 df.mm.trans1:exp6 -0.0768257574864181 0.0656268928857556 -1.17064444327964 0.242266815000749 df.mm.trans2:exp6 -0.0568827388551984 0.0568345564105046 -1.00084776670633 0.317356272308637 df.mm.trans1:exp7 0.0789921747134823 0.0656268928857556 1.20365556313923 0.229259047454007 df.mm.trans2:exp7 0.0429242761213941 0.0568345564105046 0.755249602220182 0.450434090120019 df.mm.trans1:exp8 0.278061789487514 0.0656268928857556 4.23700981808736 2.67071642552908e-05 *** df.mm.trans2:exp8 0.296943414032613 0.0568345564105046 5.22469836639264 2.51018078837389e-07 *** df.mm.trans1:probe2 0.129599389398558 0.0401881002435971 3.22481999927841 0.00133806603747960 ** df.mm.trans1:probe3 0.0829444742081641 0.0401881002435971 2.06390632315045 0.0395118559000436 * df.mm.trans1:probe4 0.0903589323830568 0.0401881002435971 2.24840019397167 0.0249599989940534 * df.mm.trans1:probe5 0.0622714530899388 0.0401881002435971 1.54949979502602 0.121857390784051 df.mm.trans1:probe6 0.0731199053000147 0.0401881002435971 1.81944169684070 0.0694070576212677 . df.mm.trans1:probe7 0.105708738513949 0.0401881002435972 2.63034922957801 0.00877730495890749 ** df.mm.trans1:probe8 -0.0218082988814703 0.0401881002435971 -0.542655630628991 0.587594609842262 df.mm.trans1:probe9 -0.0548720750025333 0.0401881002435971 -1.36538116183473 0.172711414470174 df.mm.trans1:probe10 0.00426215723476673 0.0401881002435971 0.106055205618877 0.915578603850186 df.mm.trans1:probe11 0.0403668324891577 0.0401881002435971 1.00444739224988 0.315620511052558 df.mm.trans1:probe12 0.0991200843001458 0.0401881002435971 2.46640382847999 0.0139624886706094 * df.mm.trans2:probe2 0.053447248735677 0.0401881002435971 1.32992722750542 0.184113214495199 df.mm.trans2:probe3 0.0710355587287065 0.0401881002435971 1.76757692695424 0.0777059256285057 . df.mm.trans2:probe4 0.0195998690236132 0.0401881002435972 0.487703297862054 0.625961311914538 df.mm.trans2:probe5 0.15451013806786 0.0401881002435971 3.84467384950541 0.00013534680625392 *** df.mm.trans2:probe6 0.0787892515537488 0.0401881002435972 1.96051196936839 0.0504583855124501 . df.mm.trans3:probe2 -0.333299541617366 0.0401881002435972 -8.29348836091021 9.093258026115e-16 *** df.mm.trans3:probe3 -0.603866896344487 0.0401881002435971 -15.0260124933548 8.82933738721053e-43 *** df.mm.trans3:probe4 -1.07815401814124 0.0401881002435972 -26.8276930635212 7.49385874315429e-101 *** df.mm.trans3:probe5 -0.857769138859279 0.0401881002435971 -21.3438588452795 1.90020996944354e-73 *** df.mm.trans3:probe6 -0.114861523690681 0.0401881002435972 -2.85809786962949 0.00442919328996744 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.7218676209204 0.199246478170075 18.6797159734158 3.16369885744037e-60 *** df.mm.trans1 0.109295008594344 0.170515220554171 0.640969223973889 0.521819208480986 df.mm.trans2 0.214516868702545 0.158623575936427 1.35236434707864 0.176834427042190 df.mm.exp2 0.277459148869555 0.211498101248570 1.31187536545996 0.190129058297906 df.mm.exp3 0.216263872969463 0.211498101248570 1.02253340192067 0.306994127236021 df.mm.exp4 0.370022391207536 0.211498101248570 1.74953055853989 0.0807770282865253 . df.mm.exp5 0.409773277583351 0.211498101248570 1.93747969917589 0.053216646115429 . df.mm.exp6 0.396592603155021 0.211498101248570 1.8751591660339 0.0613194037526393 . df.mm.exp7 0.50174184473536 0.211498101248570 2.37232316400643 0.0180320001949496 * df.mm.exp8 0.30861324858078 0.211498101248570 1.45917739572552 0.145107575228561 df.mm.trans1:exp2 -0.215573095327694 0.189169652601574 -1.13957546764507 0.254977000285459 df.mm.trans2:exp2 -0.190582948124521 0.16382572477804 -1.16332736133311 0.245219199659051 df.mm.trans1:exp3 -0.163864308535853 0.189169652601574 -0.86622936756659 0.386755613591236 df.mm.trans2:exp3 -0.323059923463906 0.16382572477804 -1.97197310679752 0.0491312320170422 * df.mm.trans1:exp4 -0.323454563811097 0.189169652601574 -1.70986497761537 0.087875033783655 . df.mm.trans2:exp4 -0.263912569482221 0.16382572477804 -1.61093485067613 0.107788150577360 df.mm.trans1:exp5 -0.0602354204165236 0.189169652601574 -0.318420103796408 0.75029148937462 df.mm.trans2:exp5 -0.462438690002694 0.16382572477804 -2.8227477133352 0.00493998088215977 ** df.mm.trans1:exp6 -0.161927447567803 0.189169652601574 -0.855990616575545 0.392389063607203 df.mm.trans2:exp6 -0.233065271466568 0.16382572477804 -1.42264147942784 0.155427294870157 df.mm.trans1:exp7 -0.277338268262963 0.189169652601574 -1.46608224125192 0.143217797740369 df.mm.trans2:exp7 -0.332307028926352 0.16382572477804 -2.02841787745227 0.0430162019744808 * df.mm.trans1:exp8 -0.220010171881539 0.189169652601574 -1.16303100870477 0.245339306657573 df.mm.trans2:exp8 -0.185842481808134 0.16382572477804 -1.13439132993261 0.257142271895248 df.mm.trans1:probe2 0.0783829638397837 0.115842280923353 0.676635190666228 0.498932077436966 df.mm.trans1:probe3 0.153907441801629 0.115842280923353 1.32859471148934 0.184552390534110 df.mm.trans1:probe4 0.0816406469485068 0.115842280923353 0.704756901346964 0.481270524475207 df.mm.trans1:probe5 -0.0179510106506356 0.115842280923353 -0.154960783813579 0.876911117348516 df.mm.trans1:probe6 -0.0568666682084319 0.115842280923353 -0.490897345556047 0.623701863575247 df.mm.trans1:probe7 0.144936064340616 0.115842280923353 1.25114995306862 0.211430720581458 df.mm.trans1:probe8 0.153988177202802 0.115842280923353 1.32929165392287 0.184322592526669 df.mm.trans1:probe9 0.208652950358139 0.115842280923353 1.80118130180978 0.0722415119362052 . df.mm.trans1:probe10 -0.076168215637425 0.115842280923353 -0.657516539128072 0.511133749818757 df.mm.trans1:probe11 0.0778716957867143 0.115842280923353 0.672221706668898 0.501734948202771 df.mm.trans1:probe12 0.0427997574488368 0.115842280923353 0.369465769386527 0.71192787377475 df.mm.trans2:probe2 0.0522079463670622 0.115842280923353 0.450681270697748 0.652403247784596 df.mm.trans2:probe3 0.0819613593848904 0.115842280923353 0.707525427949058 0.479550423569319 df.mm.trans2:probe4 0.227450591190418 0.115842280923353 1.96345055861694 0.0501152691261543 . df.mm.trans2:probe5 -0.0746004662539394 0.115842280923353 -0.643983057475351 0.519864474080759 df.mm.trans2:probe6 0.0265732424222720 0.115842280923353 0.229391567659602 0.81865291819959 df.mm.trans3:probe2 0.0883630014654076 0.115842280923353 0.762787134033323 0.445929075442571 df.mm.trans3:probe3 0.213142490804299 0.115842280923353 1.83993693067322 0.066335448121523 . df.mm.trans3:probe4 0.0389845386852638 0.115842280923353 0.336531173027039 0.736603301903052 df.mm.trans3:probe5 -0.0214787040732214 0.115842280923353 -0.185413338739702 0.85297564740983 df.mm.trans3:probe6 0.0675948251353276 0.115842280923353 0.583507373961772 0.559799548462937