chr3.15319_chr3_40890351_40895084_+_2.R fitVsDatCorrelation=0.740372380980024 cont.fitVsDatCorrelation=0.294939921694406 fstatistic=10298.9744088638,46,554 cont.fstatistic=5090.87779964932,46,554 residuals=-0.390902310209085,-0.078088080035059,-0.010322007165274,0.0731820240791589,0.947859192689394 cont.residuals=-0.395815650993956,-0.134294895458782,-0.0346215617418746,0.0994999887184772,0.937090960044533 predictedValues: Include Exclude Both chr3.15319_chr3_40890351_40895084_+_2.R.tl.Lung 55.8628292585627 44.8234989623931 52.3941271818054 chr3.15319_chr3_40890351_40895084_+_2.R.tl.cerebhem 59.2486480844896 51.4049925246702 53.8453197423613 chr3.15319_chr3_40890351_40895084_+_2.R.tl.cortex 53.1507469228311 45.4926439616104 55.7802519083043 chr3.15319_chr3_40890351_40895084_+_2.R.tl.heart 53.2507550599323 57.1761677814348 70.7418494429554 chr3.15319_chr3_40890351_40895084_+_2.R.tl.kidney 53.6170980302892 47.7344695595646 56.6355316480333 chr3.15319_chr3_40890351_40895084_+_2.R.tl.liver 57.6557824163197 47.3146339257683 53.0027005992979 chr3.15319_chr3_40890351_40895084_+_2.R.tl.stomach 54.7962568018157 43.5955231585268 53.8029640639537 chr3.15319_chr3_40890351_40895084_+_2.R.tl.testicle 53.0377672401789 44.7370442944673 50.2711211975136 diffExp=11.0393302961695,7.84365555981933,7.65810296122066,-3.9254127215025,5.8826284707246,10.3411484905514,11.2007336432889,8.3007229457116 diffExpScore=1.11544860845369 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=1,0,0,0,0,1,1,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 50.2911767257584 54.8887472674225 50.5592487679818 cerebhem 50.9758210907173 54.315498278425 52.8108321371719 cortex 51.7378492187121 53.0789917924503 46.7897476651198 heart 50.5301876148695 51.4251532497507 48.5421192763376 kidney 52.0551610278207 54.5529345888851 48.7520180614731 liver 49.7076019135006 51.7809620891412 55.4761716607351 stomach 50.41262075949 57.8533220371014 56.8884730091471 testicle 49.9157675358603 55.0036028811193 47.8501927411192 cont.diffExp=-4.59757054166406,-3.33967718770777,-1.34114257373822,-0.894965634881203,-2.49777356106443,-2.07336017564059,-7.44070127761138,-5.08783534525909 cont.diffExpScore=0.964630599162776 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=0.0854380105776654 cont.tran.correlation=0.0216818412156818 tran.covariance=0.000376287633577280 cont.tran.covariance=1.92474198884430e-05 tran.mean=51.4311786239284 cont.tran.mean=52.407837379439 weightedLogRatios: wLogRatio Lung 0.86147166403928 cerebhem 0.569555695014113 cortex 0.60604312064883 heart -0.285252839970404 kidney 0.45599689411713 liver 0.781917944204558 stomach 0.889353492074497 testicle 0.661389870782511 cont.weightedLogRatios: wLogRatio Lung -0.346552949977813 cerebhem -0.251489883257928 cortex -0.101316803903785 heart -0.0690206663567678 kidney -0.186333133610702 liver -0.160458841129477 stomach -0.54917341563919 testicle -0.384254655695323 varWeightedLogRatios=0.140844212954060 cont.varWeightedLogRatios=0.0262128269814814 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05943626553961 0.0705982789048999 57.5004989995282 1.05060554876321e-235 *** df.mm.trans1 0.142865727799524 0.0612517784829826 2.33243395274173 0.0200351349395514 * df.mm.trans2 -0.275782274477355 0.0566045510910098 -4.87208659307178 1.44346127291674e-06 *** df.mm.exp2 0.168525541944849 0.0758862871154345 2.22076409784677 0.0267706073054594 * df.mm.exp3 -0.0975743310625444 0.0758862871154345 -1.28579661453352 0.199051224107695 df.mm.exp4 -0.104725411544773 0.0758862871154345 -1.38003077401151 0.168133818388812 df.mm.exp5 -0.0559520086517422 0.0758862871154345 -0.737313825442938 0.461243676663021 df.mm.exp6 0.0741300775206085 0.0758862871154345 0.976857352473254 0.329066137232625 df.mm.exp7 -0.0735894417402951 0.0758862871154346 -0.969733064266991 0.332602736398077 df.mm.exp8 -0.0124618741999883 0.0758862871154345 -0.164217735162505 0.869619635062299 df.mm.trans1:exp2 -0.109681787660824 0.0694248185838461 -1.57986423152635 0.114708714855954 df.mm.trans2:exp2 -0.0315227756839733 0.0597319673182764 -0.527737107937648 0.59789310898931 df.mm.trans1:exp3 0.0478072805309236 0.0694248185838461 0.688619452036241 0.491351038788471 df.mm.trans2:exp3 0.112392440462349 0.0597319673182764 1.88161290358103 0.0604126450039573 . df.mm.trans1:exp4 0.0568381872315399 0.0694248185838461 0.818701271259283 0.413308890935383 df.mm.trans2:exp4 0.34813004361775 0.0597319673182765 5.82820320922581 9.51596708584096e-09 *** df.mm.trans1:exp5 0.0149208106319419 0.0694248185838461 0.214920412271898 0.829908461061585 df.mm.trans2:exp5 0.118873245535529 0.0597319673182764 1.99011100542066 0.0470697382040841 * df.mm.trans1:exp6 -0.0425387420625264 0.0694248185838461 -0.612731051088758 0.540305619388918 df.mm.trans2:exp6 -0.0200429768632041 0.0597319673182764 -0.335548580819494 0.73733833415196 df.mm.trans1:exp7 0.0543121184935739 0.0694248185838461 0.782315598390506 0.434363364792508 df.mm.trans2:exp7 0.0458113746595346 0.0597319673182765 0.766949034432982 0.443438524067365 df.mm.trans1:exp8 -0.0394330849133414 0.0694248185838461 -0.567996945727947 0.570267167907915 df.mm.trans2:exp8 0.0105312317175732 0.0597319673182765 0.176308134327110 0.860116301642915 df.mm.trans1:probe2 -0.241737893585178 0.0405353609745971 -5.96362997079689 4.39822443839493e-09 *** df.mm.trans1:probe3 -0.0338842840990595 0.0405353609745971 -0.835919140335133 0.403560774752030 df.mm.trans1:probe4 -0.0707173346409655 0.0405353609745971 -1.74458381375419 0.0816118835047034 . df.mm.trans1:probe5 -0.361008916027542 0.0405353609745971 -8.90602445242268 7.56688379555839e-18 *** df.mm.trans1:probe6 -0.244532041498273 0.0405353609745971 -6.03256109280777 2.95273109175575e-09 *** df.mm.trans1:probe7 -0.149832120094774 0.0405353609745971 -3.69633121532263 0.000240483937556406 *** df.mm.trans1:probe8 -0.138514802422826 0.0405353609745971 -3.41713504191147 0.00067929866725697 *** df.mm.trans1:probe9 -0.425473022151774 0.0405353609745971 -10.4963422533331 1.24887895952465e-23 *** df.mm.trans1:probe10 -0.324975231914766 0.0405353609745971 -8.01708000376322 6.45993760598921e-15 *** df.mm.trans1:probe11 -0.381964359341691 0.0405353609745971 -9.4229914365648 1.17932768287987e-19 *** df.mm.trans1:probe12 -0.345164569984113 0.0405353609745971 -8.51514731052777 1.57060050162783e-16 *** df.mm.trans1:probe13 -0.319004304906326 0.0405353609745971 -7.86977831790473 1.87680466411656e-14 *** df.mm.trans1:probe14 -0.371844034477986 0.0405353609745971 -9.17332485853562 8.98550125596135e-19 *** df.mm.trans2:probe2 0.0193780103727992 0.0405353609745971 0.478051999708183 0.632801771547815 df.mm.trans2:probe3 -0.0340294085320644 0.0405353609745971 -0.839499333764171 0.401551277109051 df.mm.trans2:probe4 0.0875577822110332 0.0405353609745971 2.16003459956615 0.0311982000970868 * df.mm.trans2:probe5 0.0495773004219589 0.0405353609745971 1.22306300548374 0.221825902857581 df.mm.trans2:probe6 0.0873802692901284 0.0405353609745971 2.15565538801760 0.0315405321826350 * df.mm.trans3:probe2 -0.116376417644200 0.0405353609745971 -2.87098510648841 0.00424841364554641 ** df.mm.trans3:probe3 0.307226092265881 0.0405353609745971 7.57921194925129 1.47103916789753e-13 *** df.mm.trans3:probe4 0.0726837404085592 0.0405353609745971 1.79309468723145 0.0735031461155489 . df.mm.trans3:probe5 -0.102159467524955 0.0405353609745971 -2.52025552674803 0.0120068293673216 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91420083658867 0.100354176034053 39.0038660200892 5.22212014307205e-161 *** df.mm.trans1 -0.0298501765288843 0.0870682948030543 -0.342836351583599 0.731851620275278 df.mm.trans2 0.119746337113977 0.0804623451538775 1.48822827479079 0.13725967801465 df.mm.exp2 -0.040547410944144 0.107870984022310 -0.375888023194059 0.707144036026065 df.mm.exp3 0.0723145931874148 0.107870984022310 0.670380397869168 0.502894678460337 df.mm.exp4 -0.0197256288336086 0.107870984022310 -0.182863158358961 0.854972289781803 df.mm.exp5 0.0647367428519419 0.107870984022310 0.600131197825664 0.548664196621782 df.mm.exp6 -0.162765274139892 0.107870984022310 -1.50888837823366 0.131897381077214 df.mm.exp7 -0.0629324215731272 0.10787098402231 -0.583404537777383 0.559858426233631 df.mm.exp8 0.0496683627136023 0.107870984022310 0.460442288199854 0.645379461308256 df.mm.trans1:exp2 0.0540691862766168 0.0986861234206654 0.547890467296382 0.583987792063674 df.mm.trans2:exp2 0.0300486569516115 0.0849079107324045 0.353897024345738 0.72355070173187 df.mm.trans1:exp3 -0.0439546350295219 0.0986861234206654 -0.445398334699583 0.656205903692615 df.mm.trans2:exp3 -0.105841737717597 0.0849079107324045 -1.24654742773223 0.213090292253752 df.mm.trans1:exp4 0.0244669123585137 0.0986861234206654 0.247926572758559 0.80428308449526 df.mm.trans2:exp4 -0.0454553152688013 0.0849079107324045 -0.535348413083183 0.592623782573828 df.mm.trans1:exp5 -0.0302624462252769 0.0986861234206654 -0.306653510912353 0.7592223379311 df.mm.trans2:exp5 -0.0708735954068333 0.0849079107324044 -0.834711333673 0.404240054528234 df.mm.trans1:exp6 0.151093502839704 0.0986861234206654 1.53105115088617 0.126327546799816 df.mm.trans2:exp6 0.104479468913784 0.0849079107324045 1.23050335372238 0.219030876493496 df.mm.trans1:exp7 0.0653443284850247 0.0986861234206654 0.662143027003746 0.508154899789529 df.mm.trans2:exp7 0.115534938878574 0.0849079107324045 1.36070877120854 0.174159042812635 df.mm.trans1:exp8 -0.0571610758978156 0.0986861234206654 -0.579221008146782 0.562675494010149 df.mm.trans2:exp8 -0.0475780323973755 0.0849079107324045 -0.560348641098028 0.575468177587958 df.mm.trans1:probe2 0.0686125036355711 0.0576202821647863 1.19077000420353 0.234253841740435 df.mm.trans1:probe3 0.0929038769799545 0.0576202821647863 1.61234678987274 0.107455984542515 df.mm.trans1:probe4 0.0534638905694011 0.0576202821647863 0.927865823643514 0.353881239444026 df.mm.trans1:probe5 0.0563222025424404 0.0576202821647863 0.977471828085924 0.328762251657501 df.mm.trans1:probe6 0.0968523421027564 0.0576202821647863 1.68087240228661 0.09335135839307 . df.mm.trans1:probe7 0.0415417794118967 0.0576202821647863 0.720957583878066 0.471239728453353 df.mm.trans1:probe8 0.0324902908426171 0.0576202821647863 0.563868999282218 0.57307146856138 df.mm.trans1:probe9 -0.0189108263617589 0.0576202821647863 -0.328197392502807 0.742886423428607 df.mm.trans1:probe10 -0.0184590443944758 0.0576202821647863 -0.320356716436851 0.74881872607151 df.mm.trans1:probe11 0.0385846677174534 0.0576202821647863 0.669636910265494 0.503368267322274 df.mm.trans1:probe12 0.0877946341725127 0.0576202821647863 1.5236758806809 0.128160303496863 df.mm.trans1:probe13 0.0423863390376623 0.0576202821647863 0.735614916227641 0.462276397845132 df.mm.trans1:probe14 0.0625181281407077 0.0576202821647863 1.08500211716968 0.278392636550238 df.mm.trans2:probe2 0.0218451684342587 0.0576202821647863 0.379122899325352 0.704741864764614 df.mm.trans2:probe3 -0.129970075616128 0.0576202821647863 -2.25563066915277 0.0244830632898042 * df.mm.trans2:probe4 -0.0142648448187762 0.0576202821647863 -0.247566382580020 0.8045616481771 df.mm.trans2:probe5 -0.106916643508594 0.0576202821647863 -1.85553835371418 0.0640502914671135 . df.mm.trans2:probe6 -0.0857205585176152 0.0576202821647863 -1.48768029758108 0.137404166387604 df.mm.trans3:probe2 -0.0775243237211612 0.0576202821647863 -1.3454346422576 0.179035329747809 df.mm.trans3:probe3 -0.135447581773797 0.0576202821647863 -2.35069278880715 0.0190881181892910 * df.mm.trans3:probe4 -0.123506945111284 0.0576202821647863 -2.14346303890131 0.0325107003214319 * df.mm.trans3:probe5 -0.0682766777625084 0.0576202821647863 -1.18494174615886 0.236548537817229