chr6.20173_chr6_130815518_130817385_-_0.R fitVsDatCorrelation=0.914382951259962 cont.fitVsDatCorrelation=0.288130330298254 fstatistic=5814.46272866323,36,324 cont.fstatistic=1031.90255807566,36,324 residuals=-0.539825752796429,-0.103031374201768,-0.00389905736681666,0.09730307772205,0.586607728934141 cont.residuals=-1.09112009761993,-0.320333779732408,-0.00388300817211675,0.24941183540038,1.40391266487268 predictedValues: Include Exclude Both chr6.20173_chr6_130815518_130817385_-_0.R.tl.Lung 87.0570212157356 92.5572995064676 52.7515776129319 chr6.20173_chr6_130815518_130817385_-_0.R.tl.cerebhem 71.9323871547255 63.1415450193783 48.7299273841593 chr6.20173_chr6_130815518_130817385_-_0.R.tl.cortex 106.109618020758 98.6455805668277 50.8618021552141 chr6.20173_chr6_130815518_130817385_-_0.R.tl.heart 114.183436275666 105.543022226416 56.7784199473208 chr6.20173_chr6_130815518_130817385_-_0.R.tl.kidney 89.4259590078735 85.7503177263089 50.9311440502767 chr6.20173_chr6_130815518_130817385_-_0.R.tl.liver 105.875617623162 87.508337750089 53.8208195148542 chr6.20173_chr6_130815518_130817385_-_0.R.tl.stomach 215.779383933620 188.56160658838 50.7778499384278 chr6.20173_chr6_130815518_130817385_-_0.R.tl.testicle 109.747551384006 87.1925875515711 50.8620050608822 diffExp=-5.50027829073201,8.79084213534724,7.46403745393049,8.64041404925035,3.67564128156461,18.367279873073,27.2177773452404,22.5549638324348 diffExpScore=1.10845334654363 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=0,0,0,0,0,1,0,1 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 86.6437678926056 75.0251923225064 89.5104680179798 cerebhem 77.3317688737511 92.2605140510429 89.5683074059972 cortex 83.0534059026695 98.6873142467087 96.7041271921619 heart 85.4438948131254 82.2441689943009 76.0909091522847 kidney 96.7004222765338 75.9333125015109 90.1845209298478 liver 92.5323526705281 88.312729363694 81.7822384948828 stomach 82.674718059276 115.157574924521 96.4192102382634 testicle 97.9502036381259 87.3995050649554 87.4662799668211 cont.diffExp=11.6185755700992,-14.9287451772918,-15.6339083440392,3.19972581882455,20.7671097750229,4.21962330683415,-32.4828568652453,10.5506985731704 cont.diffExpScore=8.28364410847187 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,-1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,1,0,-1,0 cont.diffExp1.2Score=2 tran.correlation=0.978762394116628 cont.tran.correlation=-0.476658217113677 tran.covariance=0.0960286442288087 cont.tran.covariance=-0.00560506683407723 tran.mean=106.813204471937 cont.tran.mean=88.584427849741 weightedLogRatios: wLogRatio Lung -0.275518830230737 cerebhem 0.548835693750449 cortex 0.337563117207042 heart 0.369710649310226 kidney 0.187713141212347 liver 0.870154572548617 stomach 0.715530537940979 testicle 1.05441539470513 cont.weightedLogRatios: wLogRatio Lung 0.632050482011746 cerebhem -0.783068199243291 cortex -0.777113104392196 heart 0.169035166428893 kidney 1.07602024090183 liver 0.210230404847849 stomach -1.51795637860821 testicle 0.515994465209501 varWeightedLogRatios=0.175850593440265 cont.varWeightedLogRatios=0.769919902382098 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.96648743562353 0.105461608213713 47.0928475275969 5.6610984913152e-147 *** df.mm.trans1 -0.548267445993021 0.0893248804902821 -6.13790293346845 2.44119090219762e-09 *** df.mm.trans2 -0.367240479286487 0.0893248804902821 -4.11128990344902 4.99168333401066e-05 *** df.mm.exp2 -0.493985351428903 0.124395726514169 -3.97107975708985 8.81897902308452e-05 *** df.mm.exp3 0.298096383537741 0.124395726514169 2.39635550103714 0.0171255754713953 * df.mm.exp4 0.328971037876668 0.124395726514169 2.64455256699833 0.00857823117544188 ** df.mm.exp5 -0.0144213511187698 0.124395726514169 -0.115931242357647 0.907778854805824 df.mm.exp6 0.119541143891249 0.124395726514169 0.960974683303394 0.337281498091696 df.mm.exp7 1.65742351145028 0.124395726514169 13.323797833694 1.35064808387810e-32 *** df.mm.exp8 0.208388332824534 0.124395726514169 1.67520491791812 0.0948588314584966 . df.mm.trans1:exp2 0.30314864171359 0.107729859283492 2.81397046027742 0.00519240721633283 ** df.mm.trans2:exp2 0.111536397139440 0.107729859283492 1.03533410218175 0.301285022738568 df.mm.trans1:exp3 -0.100187011736754 0.107729859283492 -0.929983687002794 0.353071897899051 df.mm.trans2:exp3 -0.234390858052312 0.107729859283492 -2.17572787722214 0.0302979687111249 * df.mm.trans1:exp4 -0.0577281127791854 0.107729859283492 -0.535859910735364 0.592423032630804 df.mm.trans2:exp4 -0.197680281330291 0.107729859283492 -1.83496277304228 0.0674278318450771 . df.mm.trans1:exp5 0.0412690401288798 0.107729859283492 0.383078938405368 0.701912445035984 df.mm.trans2:exp5 -0.0619667644388121 0.107729859283492 -0.575205099597745 0.56555182271375 df.mm.trans1:exp6 0.0761605223407368 0.107729859283492 0.706958338637756 0.48010049828732 df.mm.trans2:exp6 -0.175634973354745 0.107729859283492 -1.63032769673040 0.10400396124214 df.mm.trans1:exp7 -0.74973031663423 0.107729859283492 -6.95935483087663 1.90261865832913e-11 *** df.mm.trans2:exp7 -0.9458266394153 0.107729859283492 -8.77961454424952 9.62997210025722e-17 *** df.mm.trans1:exp8 0.023231087794009 0.107729859283492 0.215642050853108 0.829402461556943 df.mm.trans2:exp8 -0.268096917525119 0.107729859283492 -2.48860361749494 0.0133263931473704 * df.mm.trans1:probe2 0.0822137766438957 0.053864929641746 1.52629507159291 0.127911954997591 df.mm.trans1:probe3 0.089241038005906 0.053864929641746 1.65675586322019 0.0985368538177502 . df.mm.trans1:probe4 -0.126414426480532 0.053864929641746 -2.34687815098452 0.0195332355314995 * df.mm.trans1:probe5 0.141846790203086 0.053864929641746 2.63337929050505 0.00885939512259478 ** df.mm.trans1:probe6 0.248202797076768 0.053864929641746 4.60787378220963 5.85746022636892e-06 *** df.mm.trans2:probe2 -0.302878195758278 0.053864929641746 -5.62292010353883 4.05713372110383e-08 *** df.mm.trans2:probe3 0.0489369841199408 0.053864929641746 0.908512912676563 0.364282565634772 df.mm.trans2:probe4 0.0105329164138048 0.053864929641746 0.195543120242779 0.845090358041699 df.mm.trans2:probe5 -0.180361357218041 0.053864929641746 -3.3484004976451 0.000908560371095156 *** df.mm.trans2:probe6 -0.219001792852212 0.053864929641746 -4.06575845004878 6.01551053869508e-05 *** df.mm.trans3:probe2 -0.110736656607649 0.053864929641746 -2.05582105730305 0.0406006871047404 * df.mm.trans3:probe3 -0.145420836635058 0.053864929641746 -2.69973130202243 0.0073038400449392 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16639884074471 0.249447986032012 16.7024753617775 1.29491434937977e-45 *** df.mm.trans1 0.315239848308432 0.211279838400510 1.49204888973292 0.136659350595684 df.mm.trans2 0.115451644880964 0.21127983840051 0.546439479294328 0.58513997107616 df.mm.exp2 0.0924460911236705 0.294232792155675 0.314193705080834 0.753576132572968 df.mm.exp3 0.154510707294849 0.294232792155675 0.525130819589611 0.599851404842513 df.mm.exp4 0.240350188436014 0.294232792155675 0.816870841197223 0.414602464552025 df.mm.exp5 0.114341987252661 0.294232792155675 0.388610618194331 0.697819450789527 df.mm.exp6 0.319109046829097 0.294232792155675 1.08454616662938 0.278929054551503 df.mm.exp7 0.307236331701427 0.294232792155675 1.04419473251259 0.297173742551778 df.mm.exp8 0.298422029836359 0.294232792155675 1.01423783409725 0.311226068595959 df.mm.trans1:exp2 -0.206146329223180 0.254813072633241 -0.809010020925778 0.41910305584949 df.mm.trans2:exp2 0.114346203986776 0.254813072633241 0.448745438391842 0.653915278218259 df.mm.trans1:exp3 -0.196831952621776 0.254813072633241 -0.772456258180605 0.440407344230431 df.mm.trans2:exp3 0.119621747713213 0.254813072633241 0.469449021892953 0.639064478450841 df.mm.trans1:exp4 -0.254295319715771 0.254813072633241 -0.997968106925912 0.319039553762913 df.mm.trans2:exp4 -0.148481649758670 0.254813072633241 -0.582708132766732 0.560495107541368 df.mm.trans1:exp5 -0.00452930874310359 0.254813072633241 -0.0177750250263759 0.98582926946692 df.mm.trans2:exp5 -0.102310454020055 0.254813072633241 -0.401511794362735 0.68830805170004 df.mm.trans1:exp6 -0.253355795673969 0.254813072633241 -0.994280996087789 0.320828053970981 df.mm.trans2:exp6 -0.156048743973809 0.254813072633241 -0.612404781125237 0.540699533453913 df.mm.trans1:exp7 -0.354127573881162 0.254813072633241 -1.38975434117882 0.165557647899126 df.mm.trans2:exp7 0.121241120737735 0.254813072633241 0.475804162968674 0.634534551660665 df.mm.trans1:exp8 -0.175767897259472 0.254813072633241 -0.689791522244461 0.490819055122402 df.mm.trans2:exp8 -0.145756364832016 0.254813072633241 -0.572012900773764 0.567709881394704 df.mm.trans1:probe2 0.0131505262938549 0.127406536316621 0.103217045797198 0.917854568209186 df.mm.trans1:probe3 -0.0986947455166413 0.127406536316621 -0.774644287255192 0.439114814291642 df.mm.trans1:probe4 0.0383228321546132 0.127406536316621 0.300791727508990 0.763766242658769 df.mm.trans1:probe5 -0.112643431190644 0.127406536316621 -0.88412599892608 0.377283791125388 df.mm.trans1:probe6 -0.0186375659090162 0.127406536316621 -0.146284220950012 0.883787994297977 df.mm.trans2:probe2 0.125002193791018 0.127406536316621 0.981128577896287 0.327261380952245 df.mm.trans2:probe3 0.0351816397181105 0.127406536316621 0.27613685086517 0.782619008190574 df.mm.trans2:probe4 -0.0292232842428037 0.127406536316621 -0.229370368959567 0.818725788608413 df.mm.trans2:probe5 0.0403976617548356 0.127406536316621 0.317076838620293 0.751389515589138 df.mm.trans2:probe6 0.152403011266486 0.127406536316621 1.19619460408017 0.232495262978078 df.mm.trans3:probe2 -0.0640625526924345 0.127406536316621 -0.50281998510054 0.615432450633913 df.mm.trans3:probe3 -0.244141710501475 0.127406536316621 -1.91624164316620 0.0562139996228616 .