chr10.2857_chr10_70993652_70999251_-_2.R fitVsDatCorrelation=0.87413836972076 cont.fitVsDatCorrelation=0.326216821747887 fstatistic=9336.37925793104,37,347 cont.fstatistic=2457.65384978998,37,347 residuals=-0.368051423328489,-0.0852077744633354,-0.0128900236255641,0.0647522644770826,0.812627453757434 cont.residuals=-0.602199849887844,-0.174544750197741,-0.0514164366532823,0.120947676830958,1.05048382469824 predictedValues: Include Exclude Both chr10.2857_chr10_70993652_70999251_-_2.R.tl.Lung 57.0433311940569 50.2459919727534 86.9095008458985 chr10.2857_chr10_70993652_70999251_-_2.R.tl.cerebhem 55.0087033243753 43.3811316559449 68.8985639360965 chr10.2857_chr10_70993652_70999251_-_2.R.tl.cortex 48.6219478342177 46.1567498868075 69.296092103518 chr10.2857_chr10_70993652_70999251_-_2.R.tl.heart 55.2519698062422 49.5902022322338 86.2350687073629 chr10.2857_chr10_70993652_70999251_-_2.R.tl.kidney 60.1503663153287 59.814122850245 104.819016784504 chr10.2857_chr10_70993652_70999251_-_2.R.tl.liver 51.5035804656425 44.3034580587333 68.7882123547922 chr10.2857_chr10_70993652_70999251_-_2.R.tl.stomach 50.0216734166251 45.6386095833622 78.326732317098 chr10.2857_chr10_70993652_70999251_-_2.R.tl.testicle 49.9719437356097 45.8694178983841 65.5889326860545 diffExp=6.7973392213035,11.6275716684304,2.46519794741027,5.66176757400835,0.336243465083669,7.20012240690912,4.38306383326287,4.10252583722554 diffExpScore=0.977050446215883 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 56.4126259006307 57.1479702445993 54.0750960315089 cerebhem 58.9025257411706 65.0706463044134 62.3953804974294 cortex 64.3919271671298 54.5176920819995 60.1977491298016 heart 54.1558577104484 49.7852967027109 59.6076874255086 kidney 57.1751385034668 55.63990199332 52.4486126141852 liver 57.3681938276999 50.7205610265948 54.7025444507556 stomach 56.0665068367159 52.740789246673 59.020541346292 testicle 55.6902677950759 50.1346749648493 59.5530998698024 cont.diffExp=-0.735344343968535,-6.16812056324284,9.87423508513024,4.37056100773755,1.53523651014686,6.64763280110511,3.32571759004286,5.5555928302266 cont.diffExpScore=1.50410046293395 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=0.765450810793484 cont.tran.correlation=0.350103587274561 tran.covariance=0.00573831288420108 cont.tran.covariance=0.00176316274250817 tran.mean=50.7858250144102 cont.tran.mean=55.9950360029686 weightedLogRatios: wLogRatio Lung 0.505031035645213 cerebhem 0.923451008021965 cortex 0.200741839632519 heart 0.427885841038852 kidney 0.0229501143115167 liver 0.58222927250117 stomach 0.354576669870992 testicle 0.331398870056798 cont.weightedLogRatios: wLogRatio Lung -0.0523107711357028 cerebhem -0.410874591341234 cortex 0.67946146275733 heart 0.332361051228832 kidney 0.109759099094326 liver 0.49114575408703 stomach 0.244351043169816 testicle 0.416929270991664 varWeightedLogRatios=0.0721299468585633 cont.varWeightedLogRatios=0.117230010203691 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.65020682739350 0.0731207977452319 49.9202270756342 1.91334986689082e-160 *** df.mm.trans1 0.42284185593765 0.0609196590225528 6.94097542110523 1.92166618437434e-11 *** df.mm.trans2 0.236342950901425 0.0609196590225527 3.87958427039011 0.000125196636037941 *** df.mm.exp2 0.0490060716491873 0.0839344925974103 0.583860938842434 0.559693207310394 df.mm.exp3 -0.0181448734735362 0.0839344925974103 -0.216178985683129 0.828975258404122 df.mm.exp4 -0.0372542538994018 0.0839344925974103 -0.443849158391782 0.657428286189011 df.mm.exp5 0.0399795199249099 0.0839344925974103 0.476318122475234 0.634147529851549 df.mm.exp6 0.00580706431864189 0.0839344925974102 0.069185672527924 0.94488167885346 df.mm.exp7 -0.123553114484450 0.0839344925974103 -1.47201836409579 0.141922276491881 df.mm.exp8 0.0579787679170935 0.0839344925974103 0.690762118443811 0.490176848069342 df.mm.trans1:exp2 -0.085325832092929 0.0709375935371805 -1.20282952717035 0.229862359656046 df.mm.trans2:exp2 -0.195912261569069 0.0709375935371805 -2.76175511178548 0.00605510615830407 ** df.mm.trans1:exp3 -0.141591271452776 0.0709375935371805 -1.99599767052380 0.046715353067392 * df.mm.trans2:exp3 -0.066742698558507 0.0709375935371805 -0.940864994574776 0.347428559696507 df.mm.trans1:exp4 0.00534707084479719 0.0709375935371805 0.0753771107557326 0.939958107301947 df.mm.trans2:exp4 0.0241167504553029 0.0709375935371805 0.339971364304353 0.734083787923945 df.mm.trans1:exp5 0.0130568370890404 0.0709375935371805 0.184060896881101 0.85407319588029 df.mm.trans2:exp5 0.134331499190887 0.0709375935371805 1.89365740353851 0.0591020914605248 . df.mm.trans1:exp6 -0.107966910879493 0.0709375935371805 -1.52199849890459 0.128920188915680 df.mm.trans2:exp6 -0.131675112319322 0.0709375935371805 -1.85621058952764 0.0642710460678172 . df.mm.trans1:exp7 -0.00780168109375524 0.0709375935371805 -0.109979500357115 0.91248920947319 df.mm.trans2:exp7 0.0273763921230985 0.0709375935371805 0.385922199471705 0.69979078735607 df.mm.trans1:exp8 -0.190328220685061 0.0709375935371805 -2.68303745862628 0.0076450344262245 ** df.mm.trans2:exp8 -0.149110931657027 0.0709375935371805 -2.10200155124904 0.036273474621103 * df.mm.trans1:probe2 -0.136184122300967 0.0388541201554464 -3.50501109679297 0.000516453100592907 *** df.mm.trans1:probe3 -0.0232912914590552 0.0388541201554464 -0.599454867743037 0.54926079197166 df.mm.trans1:probe4 -0.0221974191409711 0.0388541201554464 -0.571301551860248 0.568165081755981 df.mm.trans1:probe5 -0.0487523515661481 0.0388541201554464 -1.25475371392020 0.210412628856329 df.mm.trans1:probe6 -0.0619498946583587 0.0388541201554464 -1.59442278992579 0.111751754287607 df.mm.trans2:probe2 0.055677004625286 0.0388541201554464 1.43297556095814 0.15276513550076 df.mm.trans2:probe3 0.132068407406389 0.0388541201554464 3.39908372337384 0.000754777371328988 *** df.mm.trans2:probe4 0.135497660244865 0.0388541201554464 3.48734341950789 0.000550555240218788 *** df.mm.trans2:probe5 0.0181300271491119 0.0388541201554464 0.466617879302833 0.641066109175448 df.mm.trans2:probe6 -0.0375630621568691 0.0388541201554464 -0.966771657847042 0.334331504631263 df.mm.trans3:probe2 0.0556194363000188 0.0388541201554464 1.43149390791757 0.153188751082202 df.mm.trans3:probe3 0.294181565392260 0.0388541201554464 7.57143809241613 3.36907933041862e-13 *** df.mm.trans3:probe4 0.272101889729512 0.0388541201554464 7.00316693933345 1.30392156799343e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00845679186662 0.142318306263317 28.1654335068475 1.16040408703505e-91 *** df.mm.trans1 0.0403251334104016 0.118570679718738 0.340093634497643 0.733991791152278 df.mm.trans2 0.0216235116118193 0.118570679718738 0.182368117169544 0.855400339153273 df.mm.exp2 0.0299031528179556 0.163365488231607 0.183044492087345 0.854870010130743 df.mm.exp3 -0.0220845392271839 0.163365488231607 -0.135184851257410 0.892544106082253 df.mm.exp4 -0.276161835206485 0.163365488231607 -1.69045395203034 0.091839159990431 . df.mm.exp5 0.0172227625262965 0.163365488231607 0.105424730233594 0.916099708159896 df.mm.exp6 -0.114051940782743 0.163365488231607 -0.698139747980612 0.485557371769444 df.mm.exp7 -0.173920910432417 0.163365488231607 -1.06461231386794 0.287791648556143 df.mm.exp8 -0.240313181868643 0.163365488231607 -1.47101560109162 0.142193129231222 df.mm.trans1:exp2 0.0132878218369935 0.138069037454744 0.0962404177066056 0.923385188607986 df.mm.trans2:exp2 0.0999265197578261 0.138069037454744 0.72374314763062 0.469711054278754 df.mm.trans1:exp3 0.154379812995598 0.138069037454744 1.11813492613216 0.264282863128302 df.mm.trans2:exp3 -0.0250340597553631 0.138069037454744 -0.181315523138696 0.85622578277457 df.mm.trans1:exp4 0.235334981497790 0.138069037454744 1.70447325364261 0.0891878306204758 . df.mm.trans2:exp4 0.138237655379495 0.138069037454744 1.00122125805944 0.317417520766585 df.mm.trans1:exp5 -0.00379659710902552 0.138069037454744 -0.0274978168821519 0.978078488295557 df.mm.trans2:exp5 -0.0439660302455764 0.138069037454744 -0.3184351180835 0.750346473480212 df.mm.trans1:exp6 0.130848979093405 0.138069037454744 0.94770689725634 0.343938200903990 df.mm.trans2:exp6 -0.005260561229688 0.138069037454744 -0.0381009480957112 0.969629105469477 df.mm.trans1:exp7 0.167766521649850 0.138069037454744 1.21509155667751 0.225157297745874 df.mm.trans2:exp7 0.093666182774638 0.138069037454744 0.678401070227928 0.49796958671647 df.mm.trans1:exp8 0.227425591196349 0.138069037454744 1.64718748959842 0.100424744728375 df.mm.trans2:exp8 0.109382192295012 0.138069037454744 0.792228252701952 0.428768858567806 df.mm.trans1:probe2 -0.0874403820633111 0.0756235263069888 -1.15625898888069 0.248370673478719 df.mm.trans1:probe3 -0.0144260319671756 0.0756235263069888 -0.190761164833998 0.848824273833108 df.mm.trans1:probe4 -0.132111784040354 0.0756235263069888 -1.74696672440339 0.0815278267582575 . df.mm.trans1:probe5 0.0413186579806878 0.0756235263069888 0.546373066669192 0.585160646301144 df.mm.trans1:probe6 0.0317702566867707 0.0756235263069888 0.420110754394095 0.674664560983872 df.mm.trans2:probe2 -0.0345739457306856 0.0756235263069888 -0.457185050989753 0.647824131006532 df.mm.trans2:probe3 0.07175821695901 0.0756235263069888 0.9488874754096 0.343338217806655 df.mm.trans2:probe4 0.0198788208925030 0.0756235263069888 0.262865563975504 0.79281020179591 df.mm.trans2:probe5 0.0371281329586402 0.0756235263069888 0.490960085726774 0.623765026410722 df.mm.trans2:probe6 0.0614444734373785 0.0756235263069888 0.812504738114812 0.417059136269027 df.mm.trans3:probe2 -0.0703780012230967 0.0756235263069888 -0.930636333161743 0.352688702012866 df.mm.trans3:probe3 -0.121720802633771 0.0756235263069888 -1.60956263980145 0.108402691042574 df.mm.trans3:probe4 -0.128027952769291 0.0756235263069888 -1.69296459741338 0.091359726385394 .