chr6.19873_chr6_125328994_125329468_-_0.R fitVsDatCorrelation=0.821522316643115 cont.fitVsDatCorrelation=0.249371250386918 fstatistic=4911.20892266313,38,370 cont.fstatistic=1696.15028879786,38,370 residuals=-0.572533729835369,-0.0935517551015607,-0.00589479168037992,0.0832709914440262,1.12551318310188 cont.residuals=-0.614803375505415,-0.217107283007880,-0.0450062426874006,0.141396359135951,1.56949441030291 predictedValues: Include Exclude Both chr6.19873_chr6_125328994_125329468_-_0.R.tl.Lung 44.5159907406628 69.2748005487031 61.0185691973115 chr6.19873_chr6_125328994_125329468_-_0.R.tl.cerebhem 55.9125880868466 109.971733921011 62.5667076235747 chr6.19873_chr6_125328994_125329468_-_0.R.tl.cortex 49.4984726431143 76.658894511643 67.6073136860637 chr6.19873_chr6_125328994_125329468_-_0.R.tl.heart 49.1889298455435 71.5230439991597 63.2798238246475 chr6.19873_chr6_125328994_125329468_-_0.R.tl.kidney 45.8048559463998 71.7208293714581 67.214200530869 chr6.19873_chr6_125328994_125329468_-_0.R.tl.liver 52.3539808240077 78.8068892177831 72.2733174633962 chr6.19873_chr6_125328994_125329468_-_0.R.tl.stomach 50.4472645543163 66.9935687780315 60.0372805467005 chr6.19873_chr6_125328994_125329468_-_0.R.tl.testicle 50.431693627608 128.885276474000 110.188498373632 diffExp=-24.7588098080403,-54.0591458341641,-27.1604218685288,-22.3341141536162,-25.9159734250583,-26.4529083937754,-16.5463042237151,-78.4535828463917 diffExpScore=0.99638573281761 diffExp1.5=-1,-1,-1,0,-1,-1,0,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,-1,-1,-1,-1,-1,0,-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 62.6665696509233 69.249121592195 60.1015016216261 cerebhem 65.5344520963806 57.8506228194227 71.212095030623 cortex 66.831745889073 63.3946476785546 67.9411049308622 heart 59.8706544332632 71.7970728191248 60.1700440090559 kidney 62.8953063470835 64.8836674691409 69.9982568628732 liver 70.7178704958817 70.0611005321901 61.6247944359305 stomach 65.2915340756475 59.7031458428161 67.3738250203872 testicle 68.3127651545036 57.1409406393514 66.1592319116657 cont.diffExp=-6.58255194127161,7.68382927695787,3.43709821051841,-11.9264183858616,-1.98836112205746,0.656769963691588,5.58838823283139,11.1718245151522 cont.diffExpScore=5.42390515082402 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=0.530474391072869 cont.tran.correlation=-0.350783645180439 tran.covariance=0.00961266587729884 cont.tran.covariance=-0.00174355086869222 tran.mean=66.999300818143 cont.tran.mean=64.762576095972 weightedLogRatios: wLogRatio Lung -1.77643338551387 cerebhem -2.95060870850488 cortex -1.80247211765783 heart -1.52841718156885 kidney -1.81534978699372 liver -1.70235359967585 stomach -1.15247614860763 testicle -4.11893491674629 cont.weightedLogRatios: wLogRatio Lung -0.418284172770521 cerebhem 0.513839945693677 cortex 0.220475981812420 heart -0.759874938307754 kidney -0.129385160577177 liver 0.0396925955477679 stomach 0.369911734484823 testicle 0.738376734379692 varWeightedLogRatios=0.922318810659496 cont.varWeightedLogRatios=0.246295864046521 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9406270784496 0.103612453242656 38.0323692290232 6.7173352744511e-130 *** df.mm.trans1 -0.159638171277780 0.085328980253822 -1.87085525694688 0.0621542667753151 . df.mm.trans2 0.327709638221652 0.085328980253822 3.84054323920007 0.000144397021605673 *** df.mm.exp2 0.665028155266935 0.116642348328024 5.7014297534265 2.43174071819121e-08 *** df.mm.exp3 0.104839836953688 0.116642348328024 0.89881452539737 0.369336005980874 df.mm.exp4 0.0953702993036104 0.116642348328024 0.817630137515818 0.414094609559873 df.mm.exp5 -0.0334646672210167 0.116642348328024 -0.286899806980108 0.77434963270263 df.mm.exp6 0.121821940777664 0.116642348328024 1.04440576277729 0.296979557144889 df.mm.exp7 0.107807991274270 0.116642348328024 0.924261152313992 0.355952737228905 df.mm.exp8 0.154598529662287 0.116642348328024 1.32540652583161 0.185854159585807 df.mm.trans1:exp2 -0.43708707817913 0.0967147260174987 -4.51934360130481 8.35905211105214e-06 *** df.mm.trans2:exp2 -0.202885998471123 0.0967147260174987 -2.09777773070892 0.0366026524795983 * df.mm.trans1:exp3 0.00125350933929907 0.0967147260174987 0.0129608942806938 0.989665977662724 df.mm.trans2:exp3 -0.00355540939374069 0.0967147260174987 -0.0367618204604892 0.97069474167475 df.mm.trans1:exp4 0.00444982862733804 0.0967147260174987 0.0460098354260232 0.96332723287735 df.mm.trans2:exp4 -0.0634318194557243 0.0967147260174987 -0.655865162087597 0.512318506497546 df.mm.trans1:exp5 0.0620063106405122 0.0967147260174987 0.64112584705346 0.521837835634766 df.mm.trans2:exp5 0.0681646682891396 0.0967147260174987 0.704801337872853 0.481377282406285 df.mm.trans1:exp6 0.0403575690202820 0.0967147260174987 0.417284633706971 0.676712243038511 df.mm.trans2:exp6 0.00709726726513698 0.0967147260174988 0.0733835224209068 0.941540583919348 df.mm.trans1:exp7 0.0172720660190457 0.0967147260174987 0.178587757317543 0.858359151883533 df.mm.trans2:exp7 -0.141292576499235 0.0967147260174987 -1.46092102327489 0.144885774540959 df.mm.trans1:exp8 -0.0298271775164592 0.0967147260174987 -0.308403681059517 0.757948739403492 df.mm.trans2:exp8 0.466242937731071 0.0967147260174987 4.82080606470118 2.09138794685983e-06 *** df.mm.trans1:probe2 0.0580616715607981 0.0564692340671203 1.02820009019044 0.304527400840444 df.mm.trans1:probe3 -0.033675311174626 0.0564692340671203 -0.596347935844127 0.551307480030579 df.mm.trans1:probe4 0.0218677046336641 0.0564692340671203 0.387249889163925 0.698793869767192 df.mm.trans1:probe5 0.0785965647115906 0.0564692340671203 1.39184754335732 0.164804667695239 df.mm.trans1:probe6 0.0386045295989802 0.0564692340671203 0.68363827200302 0.494631505466849 df.mm.trans2:probe2 -0.132411002082684 0.0564692340671203 -2.34483439115355 0.0195634561028411 * df.mm.trans2:probe3 -0.111466856716557 0.0564692340671203 -1.97393958954827 0.049132098885612 * df.mm.trans2:probe4 -0.0860015471583085 0.0564692340671203 -1.52298058542968 0.128617755484826 df.mm.trans2:probe5 0.0252948355175654 0.0564692340671203 0.447940120588489 0.654458574523154 df.mm.trans2:probe6 -0.0282259851110466 0.0564692340671203 -0.499847139373215 0.617479838821945 df.mm.trans3:probe2 0.123711425337708 0.0564692340671203 2.19077569195754 0.0290912859904229 * df.mm.trans3:probe3 -0.0403144215122586 0.0564692340671203 -0.713918334085074 0.475727780625727 df.mm.trans3:probe4 -0.0844646757806275 0.0564692340671203 -1.49576450213990 0.135567473863001 df.mm.trans3:probe5 0.0134660995864683 0.0564692340671203 0.238467898651897 0.811650226413783 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22661384269099 0.175979102917776 24.0177030829952 1.69708674751809e-77 *** df.mm.trans1 -0.0939611788797999 0.144925797315010 -0.648339913394204 0.517167273235245 df.mm.trans2 0.0062690029510022 0.144925797315010 0.043256639377846 0.965520289612994 df.mm.exp2 -0.304726227583321 0.198109543578858 -1.53817035806770 0.124861681053807 df.mm.exp3 -0.146587343835654 0.198109543578858 -0.73993075339808 0.459811235854167 df.mm.exp4 -0.0106481979954804 0.198109543578858 -0.0537490410765693 0.957164118734135 df.mm.exp5 -0.213906624383111 0.198109543578858 -1.07973912068484 0.280961695422941 df.mm.exp6 0.107497975726640 0.198109543578858 0.542618865223172 0.587718931554058 df.mm.exp7 -0.221513255046230 0.198109543578858 -1.11813520461752 0.26423469699067 df.mm.exp8 -0.201950694810105 0.198109543578858 -1.01938902670642 0.30868438619747 df.mm.trans1:exp2 0.349474092109478 0.164263755859909 2.12751796816046 0.0340378623831483 * df.mm.trans2:exp2 0.124879986876593 0.164263755859909 0.760240664307567 0.447594898887244 df.mm.trans1:exp3 0.210937423311804 0.164263755859909 1.28413856244527 0.199896975380906 df.mm.trans2:exp3 0.0582563198434833 0.164263755859909 0.354651088662352 0.723053046797468 df.mm.trans1:exp4 -0.0349934522593988 0.164263755859909 -0.213032096314922 0.831419253347447 df.mm.trans2:exp4 0.0467814442849781 0.164263755859909 0.284794683039364 0.775960780639157 df.mm.trans1:exp5 0.217550038395075 0.164263755859909 1.32439464357927 0.186189494108866 df.mm.trans2:exp5 0.148792099154556 0.164263755859909 0.90581210916334 0.365624784490539 df.mm.trans1:exp6 0.0133722043459224 0.164263755859909 0.0814069072992999 0.935162373198085 df.mm.trans2:exp6 -0.0958407100543645 0.164263755859909 -0.583456219862047 0.559941695988685 df.mm.trans1:exp7 0.262547510265671 0.164263755859909 1.59832891249353 0.110823381945157 df.mm.trans2:exp7 0.0731875078432662 0.164263755859909 0.445548730212182 0.656183829251975 df.mm.trans1:exp8 0.288219216195442 0.164263755859909 1.75461235916978 0.0801533596975171 . df.mm.trans2:exp8 0.00976109309122935 0.164263755859909 0.0594232917671385 0.952647042958141 df.mm.trans1:probe2 0.0179648647537614 0.0959093703757087 0.187310840258747 0.851519595110022 df.mm.trans1:probe3 0.00271575596053176 0.0959093703757088 0.0283158564162526 0.977425500411305 df.mm.trans1:probe4 0.0444132582797011 0.0959093703757088 0.463075277271863 0.643582658146726 df.mm.trans1:probe5 0.0448390659882658 0.0959093703757088 0.467514965561929 0.640406711006937 df.mm.trans1:probe6 -0.0530028604975036 0.0959093703757088 -0.552634849857463 0.580847170739422 df.mm.trans2:probe2 -0.0145095872236414 0.0959093703757088 -0.151284354873799 0.879833858321609 df.mm.trans2:probe3 -0.0457740754786854 0.0959093703757088 -0.477263851273063 0.633456033209926 df.mm.trans2:probe4 0.0672523704693357 0.0959093703757088 0.701207506689763 0.483614295155127 df.mm.trans2:probe5 0.0681681755717706 0.0959093703757088 0.710756157659396 0.477683131593004 df.mm.trans2:probe6 -0.0220331210253899 0.0959093703757088 -0.229728554562280 0.818429650030816 df.mm.trans3:probe2 -0.0691610307033405 0.0959093703757087 -0.72110817152082 0.471298347764425 df.mm.trans3:probe3 -0.135312770075428 0.0959093703757087 -1.41083993717572 0.159132032956101 df.mm.trans3:probe4 0.0344581547421863 0.0959093703757087 0.359278291653906 0.719591859492964 df.mm.trans3:probe5 -0.0444185547052558 0.0959093703757087 -0.463130500505359 0.64354311367425