chr15.8191_chr15_76360883_76361915_+_1.R fitVsDatCorrelation=0.880348471954019 cont.fitVsDatCorrelation=0.324583128138065 fstatistic=10408.5622140457,36,324 cont.fstatistic=2610.82070138147,36,324 residuals=-0.390392771946044,-0.0674585881008972,0.00742375473429838,0.0795547536951692,0.677032270004339 cont.residuals=-0.5057316683256,-0.179437722761635,-0.0203798652282383,0.174215280656442,0.719992099711916 predictedValues: Include Exclude Both chr15.8191_chr15_76360883_76361915_+_1.R.tl.Lung 67.1714924173215 47.0020821964809 80.7044764943995 chr15.8191_chr15_76360883_76361915_+_1.R.tl.cerebhem 64.5292441555555 47.8458258097431 68.7024318210119 chr15.8191_chr15_76360883_76361915_+_1.R.tl.cortex 66.2896488703572 46.0633576844399 56.1848595391068 chr15.8191_chr15_76360883_76361915_+_1.R.tl.heart 71.6547377160086 48.3263131297609 68.9735688289745 chr15.8191_chr15_76360883_76361915_+_1.R.tl.kidney 67.0760460286072 46.8781582634847 63.6150999343946 chr15.8191_chr15_76360883_76361915_+_1.R.tl.liver 74.9511571350114 50.1569725006781 77.7745919940991 chr15.8191_chr15_76360883_76361915_+_1.R.tl.stomach 99.6531350961005 49.7824816493251 84.433282633996 chr15.8191_chr15_76360883_76361915_+_1.R.tl.testicle 70.2095376448015 48.0797113289264 61.446838992437 diffExp=20.1694102208406,16.6834183458124,20.2262911859173,23.3284245862478,20.1978877651224,24.7941846343333,49.8706534467753,22.1298263158751 diffExpScore=0.994959679870945 diffExp1.5=0,0,0,0,0,0,1,0 diffExp1.5Score=0.5 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 61.8416951134553 67.5498806434944 61.2354378131568 cerebhem 52.9715353548927 60.2467053776198 54.6250864363347 cortex 62.1302430411473 65.9191087191826 66.170458178714 heart 56.7571844057881 62.7881254885799 57.4011788417224 kidney 61.612845089511 61.0708601844373 57.5543450707095 liver 61.7761267012808 64.611296096857 72.0280704724792 stomach 56.7986627906693 60.5204116261554 62.8035550435686 testicle 59.943682692582 59.0163267110735 55.4805498754177 cont.diffExp=-5.70818553003913,-7.27517002272709,-3.78886567803537,-6.03094108279187,0.541984905073619,-2.83516939557624,-3.72174883548608,0.927355981508548 cont.diffExpScore=1.06710391620668 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=0.69100891051443 cont.tran.correlation=0.570965649388978 tran.covariance=0.00291884545072322 cont.tran.covariance=0.00156855261100949 tran.mean=60.3543688516627 cont.tran.mean=60.9721681272954 weightedLogRatios: wLogRatio Lung 1.43848296710196 cerebhem 1.20178873007079 cortex 1.46044145229785 heart 1.60504113863749 kidney 1.44266268834734 liver 1.65331011598291 stomach 2.95288513142022 testicle 1.5380354072053 cont.weightedLogRatios: wLogRatio Lung -0.368050143429697 cerebhem -0.5191613556614 cortex -0.24618401216678 heart -0.412950132876885 kidney 0.0363710842017858 liver -0.186038316314814 stomach -0.258393510702866 testicle 0.063700254741597 varWeightedLogRatios=0.290794149337649 cont.varWeightedLogRatios=0.0422908044735024 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.52002264499879 0.0720782641471256 48.836118442222 1.85317372343342e-151 *** df.mm.trans1 0.701385429796588 0.0610495367929714 11.4887919981293 7.40447865630816e-26 *** df.mm.trans2 0.314227943491201 0.0610495367929714 5.14709791422001 4.59648772970143e-07 *** df.mm.exp2 0.138671005932969 0.0850188820968127 1.63106127148392 0.103849007923167 df.mm.exp3 0.328757435997450 0.0850188820968128 3.86687554445950 0.000133232712022051 *** df.mm.exp4 0.249465306206069 0.0850188820968127 2.93423413780010 0.00358234546805976 ** df.mm.exp5 0.233881191114802 0.0850188820968127 2.75093232640341 0.00627672679983071 ** df.mm.exp6 0.211532608279210 0.0850188820968127 2.48806621614165 0.0133461603178776 * df.mm.exp7 0.406750191623187 0.0850188820968127 4.7842335913099 2.61158367936383e-06 *** df.mm.exp8 0.339525261777730 0.0850188820968127 3.99352771295093 8.05957546337518e-05 *** df.mm.trans1:exp2 -0.178801424666415 0.0736285116971938 -2.42842644167190 0.0157083564574981 * df.mm.trans2:exp2 -0.120879029526828 0.0736285116971938 -1.64174212870087 0.101613697728949 df.mm.trans1:exp3 -0.341972614172164 0.0736285116971938 -4.64456779431544 4.96119903806919e-06 *** df.mm.trans2:exp3 -0.348931548987804 0.0736285116971939 -4.73908192552944 3.21897185329056e-06 *** df.mm.trans1:exp4 -0.184854968617250 0.0736285116971938 -2.51064383017122 0.0125377645798626 * df.mm.trans2:exp4 -0.221681011375459 0.0736285116971938 -3.0108039163843 0.00281032891707433 ** df.mm.trans1:exp5 -0.235303137556449 0.0736285116971938 -3.19581548142874 0.00153168627284137 ** df.mm.trans2:exp5 -0.236521235587770 0.0736285116971939 -3.21235931754933 0.0014487339610797 ** df.mm.trans1:exp6 -0.101944882632473 0.0736285116971938 -1.38458431771286 0.167132373389061 df.mm.trans2:exp6 -0.146566973402165 0.0736285116971938 -1.99062795136943 0.0473615869232188 * df.mm.trans1:exp7 -0.0123036219265238 0.0736285116971938 -0.167104042210223 0.867392428731133 df.mm.trans2:exp7 -0.349278946371081 0.0736285116971938 -4.74380017088397 3.14962971378156e-06 *** df.mm.trans1:exp8 -0.295290033630583 0.0736285116971938 -4.01053921672352 7.52589356627026e-05 *** df.mm.trans2:exp8 -0.316856878324416 0.0736285116971939 -4.30345352663827 2.22989390263477e-05 *** df.mm.trans1:probe2 -0.0722887542813616 0.0368142558485969 -1.96360764641442 0.0504304761203067 . df.mm.trans1:probe3 0.092909351370988 0.0368142558485969 2.52373297325604 0.0120893176919132 * df.mm.trans1:probe4 -0.0284176902240818 0.0368142558485969 -0.771920810811795 0.440723981463924 df.mm.trans1:probe5 -0.0258829877168905 0.0368142558485969 -0.703069697329681 0.482517186608668 df.mm.trans1:probe6 -0.0937521544288818 0.0368142558485969 -2.54662636165862 0.0113392042251838 * df.mm.trans2:probe2 -0.00580298408204061 0.0368142558485969 -0.157628721490558 0.874847559016326 df.mm.trans2:probe3 0.0700182997122484 0.0368142558485969 1.90193440280871 0.0580664267192163 . df.mm.trans2:probe4 0.0400876096865721 0.0368142558485969 1.08891538787141 0.27700047361971 df.mm.trans2:probe5 0.0114582443451560 0.0368142558485969 0.311244763231924 0.755814715294678 df.mm.trans2:probe6 0.0277106589620125 0.0368142558485969 0.75271544469012 0.452167462128841 df.mm.trans3:probe2 -0.135847177455858 0.0368142558485969 -3.69006990157688 0.000262903930320512 *** df.mm.trans3:probe3 -0.298678743760293 0.0368142558485969 -8.11312728929374 1.03286321938533e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21598581272039 0.143731376781747 29.3323970528876 3.29158989277701e-93 *** df.mm.trans1 -0.111012423101716 0.121738974696044 -0.911888927756212 0.362505164998822 df.mm.trans2 0.0158711752071965 0.121738974696044 0.130370534554142 0.896354182186149 df.mm.exp2 -0.155008688405403 0.169536282828299 -0.91430982099797 0.361233980926537 df.mm.exp3 -0.0972909129981655 0.169536282828299 -0.573864846952547 0.566457406740954 df.mm.exp4 -0.0942346650829388 0.169536282828299 -0.555837744645946 0.578705398945182 df.mm.exp5 -0.0425424692559866 0.169536282828299 -0.250934304717960 0.802023963163479 df.mm.exp6 -0.207867704011793 0.169536282828299 -1.22609568019322 0.221053048328481 df.mm.exp7 -0.220236248909336 0.169536282828299 -1.29905082991813 0.194850160821672 df.mm.exp8 -0.0675309189248479 0.169536282828299 -0.398327235906435 0.69065134071297 df.mm.trans1:exp2 0.000185573726954438 0.146822727792490 0.00126393052182436 0.998992307461965 df.mm.trans2:exp2 0.0405902792829309 0.146822727792490 0.27645773847969 0.782372783475583 df.mm.trans1:exp3 0.101945973700557 0.146822727792490 0.694347361837883 0.487961962276785 df.mm.trans2:exp3 0.072852980395549 0.146822727792490 0.496196886482825 0.62009194892152 df.mm.trans1:exp4 0.00843909570862165 0.146822727792490 0.0574781291391679 0.954199758776905 df.mm.trans2:exp4 0.0211343386326367 0.146822727792490 0.143944598703456 0.885633706191167 df.mm.trans1:exp5 0.03883502705582 0.146822727792490 0.264502830315937 0.79156066032044 df.mm.trans2:exp5 -0.0582889958562755 0.146822727792490 -0.397002539951835 0.691626969189186 df.mm.trans1:exp6 0.206806879328737 0.14682272779249 1.40854813446202 0.159927706754535 df.mm.trans2:exp6 0.163390664225229 0.146822727792490 1.11284313186277 0.266600772906842 df.mm.trans1:exp7 0.135171216759653 0.146822727792490 0.92064232010929 0.357922157122567 df.mm.trans2:exp7 0.11035064182708 0.146822727792490 0.751591006966187 0.452842644312215 df.mm.trans1:exp8 0.0363586033935084 0.146822727792490 0.247636070655869 0.804572856671105 df.mm.trans2:exp8 -0.0675212490028949 0.146822727792490 -0.459882812546059 0.645908782229503 df.mm.trans1:probe2 0.036947225983944 0.0734113638962451 0.503290281272567 0.615102175362446 df.mm.trans1:probe3 -0.0334389232454207 0.0734113638962451 -0.455500640100913 0.649054212909501 df.mm.trans1:probe4 0.0690567224175453 0.0734113638962451 0.940681643173741 0.347568823997716 df.mm.trans1:probe5 0.0188145764793196 0.0734113638962451 0.256289700677826 0.79788979976504 df.mm.trans1:probe6 0.0850602284333411 0.0734113638962451 1.15867930956248 0.247440218264365 df.mm.trans2:probe2 -0.0568944691392037 0.0734113638962451 -0.775009019306802 0.438899569919947 df.mm.trans2:probe3 0.0358497035581762 0.0734113638962451 0.488339974296675 0.625639386559281 df.mm.trans2:probe4 0.0738481820175331 0.0734113638962451 1.00595027933149 0.315190159647616 df.mm.trans2:probe5 -0.156964584206846 0.0734113638962451 -2.13815104196525 0.033252253108606 * df.mm.trans2:probe6 -0.0667550463573081 0.0734113638962451 -0.90932851283972 0.36385266878518 df.mm.trans3:probe2 -0.0308299321623943 0.0734113638962451 -0.419961304709817 0.674792074350745 df.mm.trans3:probe3 0.0124744511904276 0.0734113638962451 0.169925343003546 0.865174900926602