chr10.2433_chr10_115505196_115510284_-_1.R fitVsDatCorrelation=0.943748227356104 cont.fitVsDatCorrelation=0.321080480018929 fstatistic=6808.30867833272,39,393 cont.fstatistic=821.132026023529,39,393 residuals=-0.718715582223193,-0.0772543813262631,-0.00247901571237465,0.0753414156089917,0.872102948634844 cont.residuals=-0.688456925320096,-0.309665309738064,-0.148800257267555,0.171761610285085,1.42819727787576 predictedValues: Include Exclude Both chr10.2433_chr10_115505196_115510284_-_1.R.tl.Lung 43.5433710523602 45.780781666794 94.6355639416236 chr10.2433_chr10_115505196_115510284_-_1.R.tl.cerebhem 46.8874560495775 54.2674814743955 95.2821345057763 chr10.2433_chr10_115505196_115510284_-_1.R.tl.cortex 42.9911415674867 42.5763508608359 110.165069684159 chr10.2433_chr10_115505196_115510284_-_1.R.tl.heart 43.5835619594798 45.5771210562865 133.651999547617 chr10.2433_chr10_115505196_115510284_-_1.R.tl.kidney 44.0198553888611 42.9366330472768 86.580881279819 chr10.2433_chr10_115505196_115510284_-_1.R.tl.liver 46.01317210402 45.9902440601149 79.8197882988623 chr10.2433_chr10_115505196_115510284_-_1.R.tl.stomach 43.4962584763422 45.1723113503804 95.4497156778347 chr10.2433_chr10_115505196_115510284_-_1.R.tl.testicle 43.6137307965745 47.3136994659855 105.269952088432 diffExp=-2.23741061443383,-7.38002542481794,0.414790706650763,-1.99355909680664,1.08322234158423,0.0229280439051038,-1.67605287403819,-3.69996866941095 diffExpScore=1.12400539360130 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 57.6213908822836 53.5203066201697 66.9547055930126 cerebhem 54.7780444907959 62.0158151343493 65.3745341513183 cortex 61.857965843872 54.9059483920613 51.8317980602272 heart 43.4683620919634 52.7388725806137 61.5080794008517 kidney 58.8893602236264 67.965448745553 54.0864238050135 liver 57.6032320478615 58.8981700673262 60.1942910906084 stomach 42.3065156243514 58.7791171244521 53.9347460806988 testicle 55.5423441419158 57.9143020496578 48.9276651543338 cont.diffExp=4.10108426211395,-7.23777064355337,6.95201745181078,-9.27051048865029,-9.07608852192652,-1.29493801946467,-16.4726015001007,-2.37195790774201 cont.diffExpScore=1.59169471723957 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,-1,0,0,-1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.763743179480239 cont.tran.correlation=0.242226276846468 tran.covariance=0.00175337283639240 cont.tran.covariance=0.00293519136874397 tran.mean=45.2351981485482 cont.tran.mean=56.1753247538033 weightedLogRatios: wLogRatio Lung -0.190346684073294 cerebhem -0.573128454072996 cortex 0.0364163265471397 heart -0.169825781735645 kidney 0.0939854965635986 liver 0.00190827936948856 stomach -0.143357335456848 testicle -0.310735695964524 cont.weightedLogRatios: wLogRatio Lung 0.296584389007001 cerebhem -0.50450850236101 cortex 0.484653586447029 heart -0.747893574894461 kidney -0.594474278782736 liver -0.0903636168790244 stomach -1.28557684702091 testicle -0.168866129645623 varWeightedLogRatios=0.0462736442871921 cont.varWeightedLogRatios=0.33253014390559 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.94276125137983 0.0859711595229456 34.2296331433615 5.76891017411372e-120 *** df.mm.trans1 0.834638419372817 0.0701951578088772 11.8902563285820 4.63507669494330e-28 *** df.mm.trans2 0.880466427304348 0.0701951578088772 12.5431219871551 1.37836000821041e-30 *** df.mm.exp2 0.237244510200771 0.0953764381141767 2.48745408081566 0.0132799432764619 * df.mm.exp3 -0.237275355303521 0.0953764381141767 -2.48777748461811 0.0132680656817555 * df.mm.exp4 -0.348742004441001 0.0953764381141768 -3.65647964357314 0.000290483402942185 *** df.mm.exp5 0.0356986286851893 0.0953764381141768 0.374291904699291 0.7083890199429 df.mm.exp6 0.229997026276767 0.0953764381141767 2.41146588008911 0.0163463103091263 * df.mm.exp7 -0.0230288509349959 0.0953764381141767 -0.241452201301832 0.809330586652108 df.mm.exp8 -0.0719446332345088 0.0953764381141767 -0.754322919339708 0.451107083801882 df.mm.trans1:exp2 -0.163251809223384 0.0778745356212902 -2.0963439193691 0.0366905820550012 * df.mm.trans2:exp2 -0.0671837193240243 0.0778745356212902 -0.862717431160603 0.388818992483167 df.mm.trans1:exp3 0.224511962644518 0.0778745356212902 2.88299584521874 0.00415557581883478 ** df.mm.trans2:exp3 0.164709921522169 0.0778745356212902 2.11506778445738 0.0350529914447745 * df.mm.trans1:exp4 0.349664587357237 0.0778745356212902 4.49010173309646 9.36862933568743e-06 *** df.mm.trans2:exp4 0.344283475035028 0.0778745356212902 4.4210019653832 1.27296850005511e-05 *** df.mm.trans1:exp5 -0.0248153147869548 0.0778745356212902 -0.318657627798046 0.750155431988383 df.mm.trans2:exp5 -0.0998376387404377 0.0778745356212902 -1.282031898411 0.200587219654007 df.mm.trans1:exp6 -0.174826797761772 0.0778745356212902 -2.24498029255633 0.0253248798368911 * df.mm.trans2:exp6 -0.225432126725989 0.0778745356212902 -2.89481182683750 0.00400550266526858 ** df.mm.trans1:exp7 0.0219462962211473 0.0778745356212902 0.281816078209105 0.778232879238254 df.mm.trans2:exp7 0.00964878052984229 0.0778745356212902 0.123901612418789 0.901456520529839 df.mm.trans1:exp8 0.0735591834663573 0.0778745356212902 0.94458583771313 0.345450713073173 df.mm.trans2:exp8 0.104880127304646 0.0778745356212902 1.34678334153653 0.178826149761140 df.mm.trans1:probe2 0.00605568060552462 0.0476882190570884 0.126984834520141 0.899017346551969 df.mm.trans1:probe3 0.00757277639313817 0.0476882190570884 0.158797634780042 0.873909882332715 df.mm.trans1:probe4 0.051291948561411 0.0476882190570884 1.07556854870190 0.282780117375446 df.mm.trans1:probe5 -0.0664200363603924 0.0476882190570884 -1.39279758551855 0.164468410541064 df.mm.trans1:probe6 -0.0422066933779403 0.0476882190570884 -0.885054929969475 0.376668182338348 df.mm.trans2:probe2 -0.0378512278657754 0.0476882190570884 -0.793722823250393 0.427835781972769 df.mm.trans2:probe3 -0.0247712645612024 0.0476882190570884 -0.519442014212113 0.603744955594627 df.mm.trans2:probe4 0.0281668598718237 0.0476882190570884 0.590646084688226 0.555097080793965 df.mm.trans2:probe5 0.0378174950822421 0.0476882190570884 0.793015462308838 0.428247297957042 df.mm.trans2:probe6 0.00427865880630617 0.0476882190570884 0.0897215054557628 0.928554232563582 df.mm.trans3:probe2 0.142091486831066 0.0476882190570884 2.97959306597224 0.00306554617763611 ** df.mm.trans3:probe3 -0.895206896124729 0.0476882190570884 -18.7720764965675 1.38228546383022e-56 *** df.mm.trans3:probe4 -0.329170166331137 0.0476882190570884 -6.90254685202401 2.05830813074799e-11 *** df.mm.trans3:probe5 0.335279483374021 0.0476882190570884 7.03065641794365 9.15599400538347e-12 *** df.mm.trans3:probe6 0.100009577486717 0.0476882190570884 2.09715479974191 0.0366183303345165 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79302217726746 0.246228578710565 15.4044757807178 3.178113084323e-42 *** df.mm.trans1 0.19714660160752 0.201044792643870 0.980610335711331 0.32738820699116 df.mm.trans2 0.134540251331451 0.201044792643870 0.669205352509552 0.503757423891348 df.mm.exp2 0.120607446181913 0.273166081854019 0.441516916607407 0.659081638848376 df.mm.exp3 0.352520094351724 0.273166081854019 1.29049731196171 0.197636760127524 df.mm.exp4 -0.211721027818569 0.273166081854019 -0.775063384083365 0.438768295461314 df.mm.exp5 0.474138036006338 0.273166081854019 1.73571342674864 0.0833983192638342 . df.mm.exp6 0.201872526067269 0.273166081854019 0.7390102193403 0.460341930599173 df.mm.exp7 0.00101418052671023 0.273166081854019 0.0037126883390018 0.997039593924757 df.mm.exp8 0.355828410026866 0.273166081854019 1.30260831656627 0.193471307304977 df.mm.trans1:exp2 -0.171211848732322 0.22303917185923 -0.767631296803693 0.443167213203874 df.mm.trans2:exp2 0.0267208442671997 0.22303917185923 0.119803369266742 0.904700132265668 df.mm.trans1:exp3 -0.281573079103066 0.22303917185923 -1.2624377895412 0.20753990271105 df.mm.trans2:exp3 -0.326959547003435 0.22303917185923 -1.46592880648693 0.143467313085204 df.mm.trans1:exp4 -0.0701394751798194 0.22303917185923 -0.314471554907349 0.753329795775955 df.mm.trans2:exp4 0.197012686703385 0.22303917185923 0.883309801866231 0.377608917012162 df.mm.trans1:exp5 -0.452371471276346 0.22303917185923 -2.02821534668295 0.0432122740868911 * df.mm.trans2:exp5 -0.235199711523195 0.22303917185923 -1.05452199074538 0.292291861519941 df.mm.trans1:exp6 -0.202187716242956 0.22303917185923 -0.906512136668825 0.365220303153923 df.mm.trans2:exp6 -0.106123649238786 0.22303917185923 -0.475807224148795 0.63447609738349 df.mm.trans1:exp7 -0.309966940909194 0.22303917185923 -1.38974216199488 0.165393924704084 df.mm.trans2:exp7 0.0927113154721703 0.22303917185923 0.415672792807376 0.677876323061465 df.mm.trans1:exp8 -0.392576590911965 0.22303917185923 -1.76012396225959 0.0791646735771325 . df.mm.trans2:exp8 -0.276925187883109 0.22303917185923 -1.24159888854810 0.215125095191284 df.mm.trans1:probe2 0.169886133089743 0.136583040927009 1.24383036090499 0.214303397996104 df.mm.trans1:probe3 0.212652948463719 0.136583040927009 1.55694987474588 0.120287283216161 df.mm.trans1:probe4 0.197801264904546 0.136583040927009 1.44821248349751 0.148354952175716 df.mm.trans1:probe5 0.157301256983936 0.136583040927009 1.15168952101453 0.250148824192154 df.mm.trans1:probe6 0.0270594690025604 0.136583040927009 0.198117341793708 0.843055695786062 df.mm.trans2:probe2 0.0455724588700601 0.136583040927009 0.333661181950211 0.738813199666642 df.mm.trans2:probe3 0.0935133569234689 0.136583040927009 0.684663017375948 0.493960161508969 df.mm.trans2:probe4 -0.0175502703378464 0.136583040927009 -0.128495237906040 0.897822796210534 df.mm.trans2:probe5 0.309550583904811 0.136583040927009 2.26639106732318 0.0239704497484942 * df.mm.trans2:probe6 0.198898466012261 0.136583040927009 1.45624569977581 0.146123128375630 df.mm.trans3:probe2 -0.00382301844653893 0.136583040927009 -0.0279904329307030 0.977683989427598 df.mm.trans3:probe3 -0.00680930139677239 0.136583040927009 -0.0498546624131125 0.960263534028511 df.mm.trans3:probe4 0.0620704128203666 0.136583040927009 0.454451829444457 0.649754555398716 df.mm.trans3:probe5 0.127091410344136 0.136583040927009 0.930506521758098 0.352680109867904 df.mm.trans3:probe6 0.297314444542941 0.136583040927009 2.17680352205533 0.0300898414726081 *