chr14.7768_chr14_53441438_53443499_+_1.R fitVsDatCorrelation=0.929532935707488 cont.fitVsDatCorrelation=0.254687596755812 fstatistic=9574.71849054682,44,508 cont.fstatistic=1382.29737099823,44,508 residuals=-0.805817528760445,-0.0781389387465812,-0.00846726266513193,0.0804203807535864,0.783377839906117 cont.residuals=-0.755291702699364,-0.290459770491541,-0.125296655827235,0.345559379617443,1.04488316167699 predictedValues: Include Exclude Both chr14.7768_chr14_53441438_53443499_+_1.R.tl.Lung 129.468779189656 47.5436969123836 57.0626399483408 chr14.7768_chr14_53441438_53443499_+_1.R.tl.cerebhem 94.4365817269439 51.1310672843008 56.7622862428105 chr14.7768_chr14_53441438_53443499_+_1.R.tl.cortex 113.726156143569 48.526620633322 60.8118401472985 chr14.7768_chr14_53441438_53443499_+_1.R.tl.heart 120.636969972517 49.1581186797825 61.9720456820176 chr14.7768_chr14_53441438_53443499_+_1.R.tl.kidney 152.488330139567 48.6186667654513 68.2974929831301 chr14.7768_chr14_53441438_53443499_+_1.R.tl.liver 131.753652104496 52.1796789805096 59.7312931358568 chr14.7768_chr14_53441438_53443499_+_1.R.tl.stomach 121.583794210582 47.3399788802346 58.1111778457254 chr14.7768_chr14_53441438_53443499_+_1.R.tl.testicle 116.288845668194 47.5575190684404 64.4106012385306 diffExp=81.9250822772727,43.3055144426431,65.1995355102469,71.4788512927344,103.869663374116,79.5739731239867,74.2438153303477,68.731326599754 diffExpScore=0.998303151379312 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 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 72.2203037636857 70.565831361875 64.6911541584494 cerebhem 76.4949693788033 77.4197293969103 64.611308512632 cortex 83.639885132937 77.994927199699 66.7508369809873 heart 80.4381319469375 70.6274091967415 72.0715508896153 kidney 64.111587348718 63.8919549190762 73.7892813186379 liver 79.182834095792 70.6287431071475 66.8284341202335 stomach 80.3246687487898 67.5143589279912 74.8964787401323 testicle 68.967769639539 73.5145991815118 65.980302464609 cont.diffExp=1.65447240181076,-0.924760018106966,5.64495793323799,9.81072275019596,0.21963242964177,8.55409098864456,12.8103098207986,-4.54682954197288 cont.diffExpScore=1.29054426198735 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=-0.182087155979404 cont.tran.correlation=0.490187362768116 tran.covariance=-0.00105980704832336 cont.tran.covariance=0.00306815399190316 tran.mean=85.777403522497 cont.tran.mean=73.5961064591347 weightedLogRatios: wLogRatio Lung 4.37035581124629 cerebhem 2.60210557753503 cortex 3.66900123560798 heart 3.89972165534775 kidney 5.09304991644943 liver 4.09195903574750 stomach 4.08330348382351 testicle 3.8528449846775 cont.weightedLogRatios: wLogRatio Lung 0.0989147210325502 cerebhem -0.0521912056702869 cortex 0.306869323117172 heart 0.562221677697271 kidney 0.0142719839017802 liver 0.493255007986565 stomach 0.746929362895142 testicle -0.272333614977005 varWeightedLogRatios=0.489777883409585 cont.varWeightedLogRatios=0.121153225043282 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.71699008538958 0.0770310127586523 61.2349483209899 1.13421938588966e-236 *** df.mm.trans1 0.164919288421466 0.06148290193139 2.68236018861801 0.00754858712636249 ** df.mm.trans2 -0.913012034459192 0.06148290193139 -14.8498526546135 1.08613482956893e-41 *** df.mm.exp2 -0.237490708406325 0.0821403414481383 -2.89127978066992 0.00400109576392437 ** df.mm.exp3 -0.172817940332498 0.0821403414481383 -2.10393501275633 0.0358750227024168 * df.mm.exp4 -0.119794981963318 0.0821403414481383 -1.45841835876655 0.145343234116108 df.mm.exp5 0.00628317949033705 0.0821403414481383 0.0764932234218203 0.939056820943381 df.mm.exp6 0.0648316013365665 0.0821403414481383 0.789278449463226 0.430317495088600 df.mm.exp7 -0.085338564807441 0.0821403414481383 -1.03893608552044 0.299328699761103 df.mm.exp8 -0.228200560912047 0.0821403414481383 -2.77817886910207 0.00566872601931691 ** df.mm.trans1:exp2 -0.0780205399489513 0.0640032384182586 -1.21900925448631 0.223406416730299 df.mm.trans2:exp2 0.310233768058185 0.0640032384182586 4.84715735836396 1.66677431144194e-06 *** df.mm.trans1:exp3 0.0431715951359058 0.0640032384182586 0.674522042990719 0.500286317849907 df.mm.trans2:exp3 0.193281243577228 0.0640032384182586 3.01986662478143 0.00265609905332642 ** df.mm.trans1:exp4 0.0491410048971825 0.0640032384182586 0.767789351158265 0.442969069451533 df.mm.trans2:exp4 0.153187773442512 0.0640032384182586 2.39343785140114 0.0170532296755812 * df.mm.trans1:exp5 0.157365125219268 0.0640032384182586 2.45870567034269 0.0142763867688694 * df.mm.trans2:exp5 0.0160751443393253 0.0640032384182586 0.251161421462378 0.80179087062632 df.mm.trans1:exp6 -0.0473374590758658 0.0640032384182586 -0.739610373564496 0.459878080456541 df.mm.trans2:exp6 0.0282123030400040 0.0640032384182586 0.440794930650817 0.659548984333374 df.mm.trans1:exp7 0.0225024894287399 0.0640032384182586 0.351583607093239 0.725296341923792 df.mm.trans2:exp7 0.0810444996065142 0.0640032384182586 1.26625623342512 0.206001693133450 df.mm.trans1:exp8 0.120837941112472 0.0640032384182586 1.88799729668053 0.0595959359464605 . df.mm.trans2:exp8 0.228491243970336 0.0640032384182586 3.56999504426878 0.000390887284830262 *** df.mm.trans1:probe2 0.00253197461104132 0.0445866109263108 0.0567877790762336 0.954736577733102 df.mm.trans1:probe3 0.0653490737273424 0.0445866109263108 1.46566586626972 0.143358001882599 df.mm.trans1:probe4 -0.182503607080685 0.0445866109263108 -4.09323793150174 4.94766818757916e-05 *** df.mm.trans1:probe5 -0.270809126365099 0.0445866109263108 -6.07377687469161 2.45045721871654e-09 *** df.mm.trans1:probe6 0.0714483308112592 0.0445866109263108 1.60246157595031 0.109675152721294 df.mm.trans2:probe2 0.208067331825906 0.0445866109263108 4.66658773795979 3.92262343077012e-06 *** df.mm.trans2:probe3 0.151486689278332 0.0445866109263108 3.39758250584905 0.000733194012381554 *** df.mm.trans2:probe4 0.268186358280654 0.0445866109263108 6.01495275619605 3.44382362532877e-09 *** df.mm.trans2:probe5 0.190619126634515 0.0445866109263108 4.27525489545627 2.28092315061456e-05 *** df.mm.trans2:probe6 0.162050421837555 0.0445866109263108 3.63450862200268 0.000306864048384634 *** df.mm.trans3:probe2 0.4083267595149 0.0445866109263108 9.15805778980936 1.29414271786864e-18 *** df.mm.trans3:probe3 -0.228877135909837 0.0445866109263108 -5.13331538672242 4.06110564900081e-07 *** df.mm.trans3:probe4 -0.0754111772285264 0.0445866109263108 -1.69134131663786 0.0913849519822097 . df.mm.trans3:probe5 0.0126457603240447 0.0445866109263108 0.283622371409763 0.776815342710648 df.mm.trans3:probe6 0.187854434490523 0.0445866109263108 4.21324766757881 2.97891347242887e-05 *** df.mm.trans3:probe7 -0.0622466096720673 0.0445866109263108 -1.39608300292084 0.163299279952193 df.mm.trans3:probe8 -0.103191916851716 0.0445866109263108 -2.31441490411243 0.0210429044244270 * df.mm.trans3:probe9 0.709223309203086 0.0445866109263108 15.9066431484383 1.58786735286247e-46 *** df.mm.trans3:probe10 -0.0224847352259247 0.0445866109263108 -0.504293436051591 0.614273839187624 df.mm.trans3:probe11 0.00193629835245546 0.0445866109263108 0.0434277984405592 0.965377582837832 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42746610613191 0.202014897284417 21.9165327193592 1.94252766975922e-75 *** df.mm.trans1 -0.122139663686099 0.161239761410544 -0.757503376447642 0.449099564174458 df.mm.trans2 -0.169890422494693 0.161239761410544 -1.05365091717125 0.292543654851689 df.mm.exp2 0.151434375995578 0.215414182500006 0.702991670455932 0.482382971774316 df.mm.exp3 0.215554650679666 0.215414182500006 1.00065208417584 0.317471436273662 df.mm.exp4 0.000604452226167089 0.215414182500006 0.00280600013960117 0.997762240286386 df.mm.exp5 -0.350037714625026 0.215414182500006 -1.62495203687444 0.104793001321391 df.mm.exp6 0.0604252685062194 0.215414182500006 0.280507382591756 0.779202459390157 df.mm.exp7 -0.084332598340859 0.215414182500006 -0.391490464379506 0.695598894533127 df.mm.exp8 -0.0248757493255064 0.215414182500006 -0.115478697998474 0.908111335841556 df.mm.trans1:exp2 -0.0939306190081701 0.167849378735869 -0.559612550940574 0.575990391538413 df.mm.trans2:exp2 -0.0587387784141756 0.167849378735869 -0.34994933467468 0.726521759826204 df.mm.trans1:exp3 -0.0687553713996106 0.167849378735869 -0.409625414865583 0.682253417977982 df.mm.trans2:exp3 -0.115456914307505 0.167849378735869 -0.687860242182903 0.491854745221512 df.mm.trans1:exp4 0.107162667542891 0.167849378735869 0.638445422616218 0.523471483704684 df.mm.trans2:exp4 0.000267796877644289 0.167849378735869 0.00159545945097420 0.998727634393893 df.mm.trans1:exp5 0.230941610156932 0.167849378735869 1.37588599907984 0.169463207965167 df.mm.trans2:exp5 0.250685114671177 0.167849378735869 1.49351231776473 0.135923955881119 df.mm.trans1:exp6 0.0316130436790323 0.167849378735869 0.188341737795641 0.850683981033734 df.mm.trans2:exp6 -0.05953413299804 0.167849378735869 -0.354687836478228 0.722970657953943 df.mm.trans1:exp7 0.190688157518832 0.167849378735869 1.13606710346484 0.25646414231515 df.mm.trans2:exp7 0.0401268461545385 0.167849378735869 0.239064609334556 0.81115189934302 df.mm.trans1:exp8 -0.0212061838074994 0.167849378735869 -0.126340555843640 0.899512342110036 df.mm.trans2:exp8 0.0658137118389835 0.167849378735869 0.392099823869764 0.695148900775288 df.mm.trans1:probe2 -0.165581328186597 0.116929004357758 -1.41608430770505 0.157363661530339 df.mm.trans1:probe3 0.0943702242656553 0.116929004357758 0.807072845475693 0.42000221725625 df.mm.trans1:probe4 0.0302743749945263 0.116929004357758 0.258912450001698 0.795807668637366 df.mm.trans1:probe5 -0.243813755019846 0.116929004357758 -2.08514351386992 0.0375549343027951 * df.mm.trans1:probe6 -0.150538264846238 0.116929004357758 -1.28743305113288 0.198529619856531 df.mm.trans2:probe2 -0.0342295515761086 0.116929004357758 -0.292737903346712 0.769842026657321 df.mm.trans2:probe3 0.0348373722404185 0.116929004357758 0.297936106030883 0.765873740320002 df.mm.trans2:probe4 0.009686261868733 0.116929004357758 0.0828388296123412 0.9340123240679 df.mm.trans2:probe5 0.0394605529299433 0.116929004357758 0.337474462787772 0.735898656269044 df.mm.trans2:probe6 -0.0672583681751908 0.116929004357758 -0.575206883395723 0.565406062485338 df.mm.trans3:probe2 -0.000506507693985276 0.116929004357758 -0.00433175409956932 0.996545471591959 df.mm.trans3:probe3 0.155270801468611 0.116929004357758 1.32790664148257 0.184805289162375 df.mm.trans3:probe4 0.0413160446126471 0.116929004357758 0.353342995089874 0.723977897501749 df.mm.trans3:probe5 -0.0424923814793828 0.116929004357758 -0.363403260916961 0.716454840998103 df.mm.trans3:probe6 0.076081529331378 0.116929004357758 0.65066430479984 0.515557383248097 df.mm.trans3:probe7 0.0108081174140261 0.116929004357758 0.0924331603898498 0.926390333807541 df.mm.trans3:probe8 0.106281815236843 0.116929004357758 0.908943130240482 0.363811162682413 df.mm.trans3:probe9 0.00809179980844398 0.116929004357758 0.0692026743312223 0.944855525600197 df.mm.trans3:probe10 0.0090448335693084 0.116929004357758 0.0773532077775561 0.93837302038649 df.mm.trans3:probe11 0.0121769122419941 0.116929004357758 0.104139364812664 0.917099853125215