chr14.7746_chr14_47575663_47578890_+_2.R fitVsDatCorrelation=0.773833695862448 cont.fitVsDatCorrelation=0.264878780974023 fstatistic=12316.9244314022,65,991 cont.fstatistic=5305.49901466194,65,991 residuals=-0.612426862726377,-0.0789373106140506,-0.0043182883004536,0.0682661366909001,0.844721142745013 cont.residuals=-0.481237733824562,-0.131690127475811,-0.0415112473460417,0.0767448738974392,1.21574216780540 predictedValues: Include Exclude Both chr14.7746_chr14_47575663_47578890_+_2.R.tl.Lung 45.3763940804184 66.5370317369522 47.324048161345 chr14.7746_chr14_47575663_47578890_+_2.R.tl.cerebhem 56.5605874736318 107.249226360109 52.1381682776785 chr14.7746_chr14_47575663_47578890_+_2.R.tl.cortex 45.2641655422636 63.0479777715481 46.2259827241046 chr14.7746_chr14_47575663_47578890_+_2.R.tl.heart 45.4780914314840 61.442332605838 46.9300796090981 chr14.7746_chr14_47575663_47578890_+_2.R.tl.kidney 44.8246916138611 63.5883213473989 45.8724940174894 chr14.7746_chr14_47575663_47578890_+_2.R.tl.liver 47.4228083282675 67.5502894995089 51.80033716316 chr14.7746_chr14_47575663_47578890_+_2.R.tl.stomach 46.6709493774354 68.0544646261108 47.9446741171791 chr14.7746_chr14_47575663_47578890_+_2.R.tl.testicle 47.7917496097667 73.3282707591063 48.5842474205342 diffExp=-21.1606376565338,-50.6886388864768,-17.7838122292845,-15.9642411743541,-18.7636297335378,-20.1274811712414,-21.3835152486754,-25.5365211493397 diffExpScore=0.994802723797333 diffExp1.5=0,-1,0,0,0,0,0,-1 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,-1,0,0,-1,-1,-1,-1 diffExp1.4Score=0.857142857142857 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 55.1774596724915 49.0615965401429 51.8142512565693 cerebhem 50.2207023079721 51.6267680652618 48.8148560349818 cortex 54.2434431840818 48.2652886536639 51.1546708107234 heart 53.5917669057848 46.9448186071487 48.2745785718091 kidney 52.9783342568185 51.6346642272567 51.156353829835 liver 53.5696585549454 52.7297810150117 49.7656650114947 stomach 52.7278926978152 54.1665260176477 51.1792064779984 testicle 51.7170992707873 49.1216794160191 47.1256857241615 cont.diffExp=6.11586313234855,-1.40606575728969,5.97815453041789,6.64694829863613,1.34367002956171,0.839877539933681,-1.43863331983249,2.59541985476822 cont.diffExpScore=1.21634821047336 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=0.988552236419829 cont.tran.correlation=-0.345138759024868 tran.covariance=0.0134353812581658 cont.tran.covariance=-0.000491322734389194 tran.mean=59.3867095102313 cont.tran.mean=51.7360924620531 weightedLogRatios: wLogRatio Lung -1.53350684782073 cerebhem -2.78666563077353 cortex -1.31829910778872 heart -1.19374478813444 kidney -1.39084819630722 liver -1.42780757397867 stomach -1.52070824389508 testicle -1.7470053691858 cont.weightedLogRatios: wLogRatio Lung 0.464251189157844 cerebhem -0.108525391870608 cortex 0.459499279974874 heart 0.518458919067414 kidney 0.101655550335553 liver 0.0627843659167278 stomach -0.107098158276837 testicle 0.201835225217682 varWeightedLogRatios=0.250932856480361 cont.varWeightedLogRatios=0.065225515003563 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2422719012114 0.064399474607584 65.8743247062424 0 *** df.mm.trans1 -0.481946128784795 0.0545260236342661 -8.83882771312014 4.33948860929289e-18 *** df.mm.trans2 0.0133703780820678 0.0481199793287393 0.277855025471352 0.781181605537178 df.mm.exp2 0.600838383125363 0.0606820164421965 9.90142415088827 4.18732389753393e-22 *** df.mm.exp3 -0.0328624687834777 0.0606820164421965 -0.541552023321132 0.588248876135136 df.mm.exp4 -0.0690611618953158 0.0606820164421965 -1.13808284471069 0.255360954903864 df.mm.exp5 -0.0264088044587186 0.0606820164421965 -0.435199850088611 0.66351213269645 df.mm.exp6 -0.0311530992111725 0.0606820164421965 -0.513382729145921 0.607798055308072 df.mm.exp7 0.0376504210488668 0.0606820164421965 0.620454349679221 0.53510135397465 df.mm.exp8 0.122767798818762 0.0606820164421965 2.02313314580945 0.0433274894644643 * df.mm.trans1:exp2 -0.380517990506202 0.054137010174328 -7.02879581419229 3.87300946759813e-12 *** df.mm.trans2:exp2 -0.123441699661896 0.0377866824802583 -3.26680437549363 0.00112501571390086 ** df.mm.trans1:exp3 0.0303861249485896 0.054137010174328 0.561281918797187 0.574732260588893 df.mm.trans2:exp3 -0.0210002036222673 0.0377866824802583 -0.555756744012628 0.578502602319021 df.mm.trans1:exp4 0.0712998492416972 0.054137010174328 1.31702598669751 0.188134366422496 df.mm.trans2:exp4 -0.0105984452892124 0.0377866824802583 -0.280480968255142 0.779167065446834 df.mm.trans1:exp5 0.0141759284977914 0.054137010174328 0.261852814777601 0.793489333899107 df.mm.trans2:exp5 -0.0189200295629431 0.0377866824802583 -0.500706289122573 0.616689028064381 df.mm.trans1:exp6 0.0752643848898331 0.054137010174328 1.39025750863360 0.164762853512625 df.mm.trans2:exp6 0.0462667884221306 0.0377866824802583 1.22442049381559 0.22108462865033 df.mm.trans1:exp7 -0.0095205345933325 0.054137010174328 -0.175859999705842 0.860439858843348 df.mm.trans2:exp7 -0.0151007469714458 0.0377866824802583 -0.399631456911709 0.689514097196052 df.mm.trans1:exp8 -0.0709067921734296 0.054137010174328 -1.30976557340535 0.19057869557247 df.mm.trans2:exp8 -0.0255802395137201 0.0377866824802583 -0.676964418008502 0.498586516302925 df.mm.trans1:probe2 -0.0289340063775394 0.0411200110648132 -0.703647825676256 0.481817407387664 df.mm.trans1:probe3 -0.0189990645724504 0.0411200110648132 -0.46203938375659 0.644154491384277 df.mm.trans1:probe4 0.0170781970334115 0.0411200110648132 0.415325691583422 0.677993401590812 df.mm.trans1:probe5 0.328502154391382 0.0411200110648132 7.98886347266782 3.7633839360212e-15 *** df.mm.trans1:probe6 0.0157149143793572 0.0411200110648132 0.382171939462453 0.702415773593032 df.mm.trans1:probe7 0.0682260242928063 0.0411200110648132 1.65919275131684 0.0973933663215804 . df.mm.trans1:probe8 0.232075637884069 0.0411200110648132 5.64386127032583 2.16996776240582e-08 *** df.mm.trans1:probe9 0.250065237487413 0.0411200110648132 6.08135141533064 1.69929233640488e-09 *** df.mm.trans1:probe10 0.169299871948047 0.0411200110648132 4.11721367684453 4.15413150258177e-05 *** df.mm.trans1:probe11 0.0863498952167293 0.0411200110648132 2.0999482485699 0.0359857104325259 * df.mm.trans1:probe12 0.078702661462643 0.0411200110648132 1.91397471509899 0.0559111835362377 . df.mm.trans1:probe13 0.13043612448149 0.0411200110648132 3.17208388577272 0.00156003012903439 ** df.mm.trans1:probe14 0.126009326135454 0.0411200110648132 3.06442831294084 0.00223985914954814 ** df.mm.trans1:probe15 0.26597690860692 0.0411200110648132 6.46830829368426 1.55431862501598e-10 *** df.mm.trans1:probe16 0.159980119369340 0.0411200110648132 3.89056605838895 0.000106688109661867 *** df.mm.trans1:probe17 0.0883560279407376 0.0411200110648132 2.14873550985848 0.0318970876918549 * df.mm.trans1:probe18 0.108477212750248 0.0411200110648132 2.63806380254293 0.0084687698094751 ** df.mm.trans2:probe2 -0.345096339257076 0.0411200110648132 -8.39241844349547 1.62811745401665e-16 *** df.mm.trans2:probe3 -0.270102984159623 0.0411200110648132 -6.56865057098082 8.18518575186423e-11 *** df.mm.trans2:probe4 -0.352582347005399 0.0411200110648132 -8.57447111212253 3.78370165329234e-17 *** df.mm.trans2:probe5 -0.300268173434893 0.0411200110648132 -7.30223960693036 5.80587234291292e-13 *** df.mm.trans2:probe6 -0.236924234553045 0.0411200110648132 -5.76177458171414 1.11041301221908e-08 *** df.mm.trans3:probe2 0.0461727291098598 0.0411200110648132 1.12287735129941 0.26176154703248 df.mm.trans3:probe3 0.137491639849431 0.0411200110648132 3.34366738454221 0.000857749123185924 *** df.mm.trans3:probe4 -0.00261060552769425 0.0411200110648132 -0.0634874714303805 0.949391138451353 df.mm.trans3:probe5 0.0934872207433724 0.0411200110648132 2.27352129346508 0.0232080331035229 * df.mm.trans3:probe6 -0.0327815659694544 0.0411200110648132 -0.79721685672176 0.425516059777022 df.mm.trans3:probe7 -0.00132471082573835 0.0411200110648132 -0.0322157215291199 0.974306505728946 df.mm.trans3:probe8 0.0556046253685262 0.0411200110648132 1.35225219859213 0.176603146739454 df.mm.trans3:probe9 0.0477497275753437 0.0411200110648132 1.16122847097684 0.245828624100000 df.mm.trans3:probe10 -0.0228870760671823 0.0411200110648132 -0.556592167037792 0.577931768777542 df.mm.trans3:probe11 0.137494910845602 0.0411200110648132 3.34374693209308 0.000857506000231505 *** df.mm.trans3:probe12 0.684549369040668 0.0411200110648132 16.6475969075418 4.64731660941815e-55 *** df.mm.trans3:probe13 0.00403419573903743 0.0411200110648132 0.0981078466316253 0.921866496117011 df.mm.trans3:probe14 0.359310181863778 0.0411200110648132 8.73808572904893 9.96881768449324e-18 *** df.mm.trans3:probe15 0.0017187371737679 0.0411200110648132 0.0417980717723746 0.966668091676593 df.mm.trans3:probe16 0.00521861538178886 0.0411200110648132 0.126911818519779 0.899035937568907 df.mm.trans3:probe17 0.0797625202691981 0.0411200110648132 1.93974948458737 0.0526936097510426 . df.mm.trans3:probe18 0.0156193923815003 0.0411200110648132 0.379848934303085 0.704138943321972 df.mm.trans3:probe19 0.0857707256851692 0.0411200110648132 2.08586339021110 0.037246298068567 * df.mm.trans3:probe20 -0.0279319611149423 0.0411200110648132 -0.679279027209308 0.497119700467769 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95381969673906 0.0980428339923752 40.3274725519108 3.26981471271839e-211 *** df.mm.trans1 0.0914110990617576 0.0830113275925566 1.10118825602247 0.271082120079828 df.mm.trans2 -0.0570005628506235 0.0732586589221727 -0.77807270415882 0.436711873249851 df.mm.exp2 0.0164670643958056 0.0923833136934354 0.178247171891343 0.858565294894334 df.mm.exp3 -0.0206249204618642 0.0923833136934354 -0.223253741799151 0.823384037024733 df.mm.exp4 -0.00250268432113835 0.0923833136934354 -0.0270902203123310 0.978393229063251 df.mm.exp5 0.023223693109472 0.0923833136934354 0.25138406689478 0.8015693017391 df.mm.exp6 0.0828720686834352 0.0923833136934354 0.897045855688157 0.369912313051516 df.mm.exp7 0.0659084998394079 0.0923833136934354 0.713424288482642 0.47575121697713 df.mm.exp8 0.0313048642428114 0.0923833136934353 0.338858425740100 0.734788170480511 df.mm.trans1:exp2 -0.110594256760477 0.0824190870144164 -1.34185248547017 0.179951197128975 df.mm.trans2:exp2 0.0344966536317953 0.0575270754941574 0.599659435760798 0.548870303143525 df.mm.trans1:exp3 0.0035525119882729 0.0824190870144164 0.0431030252452506 0.965628090587926 df.mm.trans2:exp3 0.0042609804400064 0.0575270754941574 0.074069130116638 0.940970326032682 df.mm.trans1:exp4 -0.0266563925276011 0.0824190870144165 -0.323424991627710 0.746441640990287 df.mm.trans2:exp4 -0.0416010568244754 0.0575270754941574 -0.723156122002074 0.469754613246573 df.mm.trans1:exp5 -0.0638951813643888 0.0824190870144164 -0.775247381146221 0.438378440610039 df.mm.trans2:exp5 0.027892960275214 0.0575270754941574 0.484866648193282 0.627878096795324 df.mm.trans1:exp6 -0.112443763245276 0.0824190870144164 -1.36429275448790 0.172785181366544 df.mm.trans2:exp6 -0.0107682490030982 0.0575270754941574 -0.187185754022762 0.85155330925645 df.mm.trans1:exp7 -0.111318441727835 0.0824190870144164 -1.35063910266760 0.177119393139836 df.mm.trans2:exp7 0.0330780357569754 0.0575270754941574 0.574999432403527 0.565422196400602 df.mm.trans1:exp8 -0.0960709277869982 0.0824190870144164 -1.16563931083335 0.244040777490132 df.mm.trans2:exp8 -0.0300809718275109 0.0575270754941574 -0.522901113416865 0.601159954171752 df.mm.trans1:probe2 -0.0478200728167867 0.0626017905139453 -0.76387707802278 0.445122323617778 df.mm.trans1:probe3 -0.0477053066539349 0.0626017905139453 -0.762043805173719 0.446215182018338 df.mm.trans1:probe4 -0.0987257994185795 0.0626017905139453 -1.57704434023476 0.115104465424336 df.mm.trans1:probe5 -0.144104299333606 0.0626017905139453 -2.30191977179159 0.0215463139517417 * df.mm.trans1:probe6 -0.146512601168086 0.0626017905139453 -2.34038994676117 0.0194612203851505 * df.mm.trans1:probe7 -0.0544128053279692 0.0626017905139453 -0.86918928166836 0.384954068187399 df.mm.trans1:probe8 -0.108498463379984 0.0626017905139453 -1.73315271798519 0.0833795904289255 . df.mm.trans1:probe9 -0.0227845786479445 0.0626017905139453 -0.363960494754043 0.71596514521997 df.mm.trans1:probe10 -0.127383471653266 0.0626017905139453 -2.03482153797006 0.0421345903786468 * df.mm.trans1:probe11 -0.130983330711047 0.0626017905139453 -2.09232562895895 0.0366633318652207 * df.mm.trans1:probe12 -0.082938636331183 0.0626017905139453 -1.32486044968167 0.185522845697513 df.mm.trans1:probe13 -0.0522404990404638 0.0626017905139453 -0.834488895790075 0.404206653824465 df.mm.trans1:probe14 -0.106734783400954 0.0626017905139453 -1.70497972222021 0.0885116160909811 . df.mm.trans1:probe15 0.0733407352501046 0.0626017905139453 1.17154373138524 0.241661881171372 df.mm.trans1:probe16 -0.0210291392745063 0.0626017905139453 -0.335919134290924 0.737002954413094 df.mm.trans1:probe17 -0.0743932863817036 0.0626017905139453 -1.18835716631989 0.234977428436786 df.mm.trans1:probe18 -0.124771738451577 0.0626017905139453 -1.99310175359572 0.046524147593201 * df.mm.trans2:probe2 0.00461062744503216 0.0626017905139453 0.0736500890338765 0.941303676692445 df.mm.trans2:probe3 0.0422449062602288 0.0626017905139453 0.67481945665465 0.499947877456813 df.mm.trans2:probe4 -0.0620308713721831 0.0626017905139453 -0.990880146764572 0.321985978458368 df.mm.trans2:probe5 -0.0300564516508215 0.0626017905139453 -0.480121277747256 0.631247114337097 df.mm.trans2:probe6 -0.0520745882605508 0.0626017905139453 -0.831838639646425 0.405700423698427 df.mm.trans3:probe2 -0.108138519347123 0.0626017905139453 -1.72740297776361 0.0844068877950283 . df.mm.trans3:probe3 -0.0254620285212379 0.0626017905139453 -0.406730036189076 0.684294101487739 df.mm.trans3:probe4 -0.0535616396103565 0.0626017905139453 -0.855592774114425 0.392429879161429 df.mm.trans3:probe5 -0.053896649430455 0.0626017905139453 -0.860944215620301 0.389477017000927 df.mm.trans3:probe6 -0.109154955965434 0.0626017905139453 -1.74363952004086 0.0815320608001517 . df.mm.trans3:probe7 -0.031383682920767 0.0626017905139453 -0.501322448816794 0.6162555487398 df.mm.trans3:probe8 -0.0261330420557931 0.0626017905139453 -0.417448795653403 0.676440591317827 df.mm.trans3:probe9 -0.0530481999438345 0.0626017905139453 -0.847391097096774 0.396981766158103 df.mm.trans3:probe10 0.0559097548702396 0.0626017905139453 0.893101529704411 0.372019626013051 df.mm.trans3:probe11 -0.0279102986789754 0.0626017905139453 -0.445838664514845 0.655811175612039 df.mm.trans3:probe12 -0.054755824357425 0.0626017905139453 -0.874668662156356 0.381966139623184 df.mm.trans3:probe13 0.0204199851800154 0.0626017905139453 0.326188516532392 0.744350598032633 df.mm.trans3:probe14 -0.0199889836091486 0.0626017905139453 -0.319303704335675 0.74956351782544 df.mm.trans3:probe15 -0.0409989604362555 0.0626017905139453 -0.654916737998452 0.512673341453496 df.mm.trans3:probe16 -0.0793361223247466 0.0626017905139453 -1.26731394858544 0.205340773249102 df.mm.trans3:probe17 -0.0388289710079761 0.0626017905139453 -0.620253361592373 0.535233594943791 df.mm.trans3:probe18 -0.0956892461223875 0.0626017905139453 -1.52853848646824 0.126698001806436 df.mm.trans3:probe19 -0.0291208516885376 0.0626017905139453 -0.465176018919948 0.64190760855165 df.mm.trans3:probe20 -0.040221778836758 0.0626017905139453 -0.642502051563494 0.520695866929308