chr17.10588_chr17_30824937_30827847_-_2.R fitVsDatCorrelation=0.918766710116562 cont.fitVsDatCorrelation=0.255235152107006 fstatistic=4670.58596306055,43,485 cont.fstatistic=769.325058800788,43,485 residuals=-0.764214163055622,-0.137115940777951,-0.0049034608163852,0.114743348069269,0.761811200724188 cont.residuals=-0.922335119250676,-0.412602751751814,-0.143589846782601,0.426350403508742,1.63143047620904 predictedValues: Include Exclude Both chr17.10588_chr17_30824937_30827847_-_2.R.tl.Lung 120.526837140812 49.3287956543681 70.7029390046961 chr17.10588_chr17_30824937_30827847_-_2.R.tl.cerebhem 82.5814631530526 52.9857865930332 60.0081552704851 chr17.10588_chr17_30824937_30827847_-_2.R.tl.cortex 124.896766622313 58.826767944966 72.4198334380454 chr17.10588_chr17_30824937_30827847_-_2.R.tl.heart 120.259448821291 53.035753464675 68.5643002639456 chr17.10588_chr17_30824937_30827847_-_2.R.tl.kidney 152.085022878074 77.8006454540688 112.595149173657 chr17.10588_chr17_30824937_30827847_-_2.R.tl.liver 226.493204364904 101.531981336264 134.772011827426 chr17.10588_chr17_30824937_30827847_-_2.R.tl.stomach 120.620837257628 51.2616318082155 70.2489579633394 chr17.10588_chr17_30824937_30827847_-_2.R.tl.testicle 101.237687870746 50.9825681206201 68.268213037299 diffExp=71.1980414864442,29.5956765600194,66.0699986773469,67.223695356616,74.2843774240054,124.961223028639,69.3592054494122,50.2551197501261 diffExpScore=0.998194774246785 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 93.6830777917535 82.3471459571245 69.9747899530008 cerebhem 85.5902055187603 85.9077427521812 103.427893775233 cortex 85.601231626288 86.7044663374941 120.428252716987 heart 94.078203594527 87.4046331142142 96.1774558448342 kidney 97.050186657466 86.7664144641452 79.396332849904 liver 84.7254060226222 94.0185178898042 88.774314907199 stomach 80.180670725138 73.3024021511752 103.362311879747 testicle 87.7950592839323 85.08302327882 93.1401996742471 cont.diffExp=11.3359318346290,-0.317537233420879,-1.10323471120601,6.67357048031282,10.2837721933209,-9.29311186718196,6.87826857396291,2.71203600511237 cont.diffExpScore=1.72516821441669 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=0.93454069668271 cont.tran.correlation=0.299408674191467 tran.covariance=0.0667171933058935 cont.tran.covariance=0.00157073128768038 tran.mean=96.5284499053145 cont.tran.mean=86.8898991978404 weightedLogRatios: wLogRatio Lung 3.88183843847892 cerebhem 1.86020597529193 cortex 3.35114761239364 heart 3.58609406369866 kidney 3.14318735296804 liver 4.02899147752349 stomach 3.73499849450882 testicle 2.93223733961396 cont.weightedLogRatios: wLogRatio Lung 0.577213188103071 cerebhem -0.0164840919191741 cortex -0.0570636285623194 heart 0.331641307690405 kidney 0.506192018441663 liver -0.467453660316539 stomach 0.389200249829417 testicle 0.139923102237254 varWeightedLogRatios=0.482105615020695 cont.varWeightedLogRatios=0.120911053246185 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.66694852710209 0.120582735621753 38.7032895135295 2.06072099673138e-150 *** df.mm.trans1 0.418898936114638 0.104873035061297 3.99434359718679 7.4940317852537e-05 *** df.mm.trans2 -0.73581387392393 0.0984553913540732 -7.47357624406505 3.67133619173459e-13 *** df.mm.exp2 -0.142564837518266 0.133774534221523 -1.06570984042588 0.287085065161455 df.mm.exp3 0.187711011386484 0.133774534221523 1.40318942225316 0.161200230243963 df.mm.exp4 0.100952465443254 0.133774534221523 0.754646360988874 0.450827465112026 df.mm.exp5 0.222897516599754 0.133774534221523 1.66621784853797 0.0963156955647106 . df.mm.exp6 0.707610926288063 0.133774534221523 5.28957869601924 1.85901336005091e-07 *** df.mm.exp7 0.0456558270561871 0.133774534221523 0.341289374109005 0.733033553620677 df.mm.exp8 -0.106382687860256 0.133774534221523 -0.795238708767187 0.426863660224579 df.mm.trans1:exp2 -0.235522367168283 0.122118883354791 -1.92863184380766 0.0543598546228213 . df.mm.trans2:exp2 0.214080536727872 0.109226449807072 1.95996974273177 0.0505715157378384 . df.mm.trans1:exp3 -0.152095925316678 0.122118883354791 -1.24547425540073 0.213558838574641 df.mm.trans2:exp3 -0.0116220237981798 0.109226449807072 -0.106403017022964 0.915306595107278 df.mm.trans1:exp4 -0.103173426062869 0.122118883354791 -0.844860542682164 0.398605275340853 df.mm.trans2:exp4 -0.0284941865406123 0.109226449807072 -0.260872587097189 0.7943014951027 df.mm.trans1:exp5 0.0096697659074625 0.122118883354791 0.079183215910753 0.936919554101707 df.mm.trans2:exp5 0.232744209988406 0.109226449807072 2.13084111402966 0.0336046048507083 * df.mm.trans1:exp6 -0.0767684276139738 0.122118883354791 -0.628636829170301 0.529882780094649 df.mm.trans2:exp6 0.0142549087345052 0.109226449807072 0.130507846402440 0.896218759209081 df.mm.trans1:exp7 -0.044876220768787 0.122118883354791 -0.367479783109451 0.713421469713241 df.mm.trans2:exp7 -0.0072212735647134 0.109226449807072 -0.0661128653130117 0.947315211336978 df.mm.trans1:exp8 -0.0680186578186597 0.122118883354791 -0.556987223843552 0.577793091161944 df.mm.trans2:exp8 0.139358459714085 0.109226449807072 1.27586733762963 0.202613113370858 df.mm.trans1:probe2 -0.453732253416372 0.0668872671107613 -6.78353703201864 3.42535833384997e-11 *** df.mm.trans1:probe3 0.0191365160920401 0.0668872671107613 0.286101031162646 0.77492290279695 df.mm.trans1:probe4 -0.08479183749003 0.0668872671107613 -1.26768279154865 0.205519520268942 df.mm.trans1:probe5 0.285386780689685 0.0668872671107613 4.26668322712455 2.38656918581411e-05 *** df.mm.trans1:probe6 0.30822947469637 0.0668872671107613 4.60819357720148 5.19949718563756e-06 *** df.mm.trans1:probe7 -0.758839537326756 0.0668872671107613 -11.3450522065756 1.27638647701134e-26 *** df.mm.trans1:probe8 -0.814849036808233 0.0668872671107613 -12.1824238305161 5.68363098748593e-30 *** df.mm.trans1:probe9 -0.724719359045997 0.0668872671107613 -10.8349375053088 1.20598070046430e-24 *** df.mm.trans1:probe10 -0.859470305851766 0.0668872671107613 -12.8495353895762 9.92752219168708e-33 *** df.mm.trans1:probe11 -0.85296114063773 0.0668872671107613 -12.7522199288734 2.53310806490992e-32 *** df.mm.trans1:probe12 -0.766989624449739 0.0668872671107613 -11.4669003172106 4.23130366363825e-27 *** df.mm.trans2:probe2 -0.0851240660712694 0.0668872671107613 -1.27264978445463 0.203752092142433 df.mm.trans2:probe3 -0.0447497781966283 0.0668872671107613 -0.669032838829031 0.503792858532746 df.mm.trans2:probe4 -0.0559629728615931 0.0668872671107613 -0.836676026379158 0.403186745469823 df.mm.trans2:probe5 -0.0319937012888694 0.0668872671107613 -0.478322746179623 0.632636045438316 df.mm.trans2:probe6 -0.108436004497641 0.0668872671107613 -1.62117558664906 0.105629889764602 df.mm.trans3:probe2 -0.0823827858617277 0.0668872671107613 -1.23166619627779 0.218670647154104 df.mm.trans3:probe3 -0.375060907052731 0.0668872671107613 -5.60735881810887 3.45410918475353e-08 *** df.mm.trans3:probe4 0.0912579042195834 0.0668872671107613 1.36435390712653 0.173088935811755 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.7558622752566 0.295310681136483 16.1046063655876 4.69197585681670e-47 *** df.mm.trans1 -0.17873425975873 0.256837160453465 -0.695904982920546 0.486821667096676 df.mm.trans2 -0.427987927631241 0.241120161459376 -1.77499851128521 0.0765252516337026 . df.mm.exp2 -0.438756212536825 0.327617785547626 -1.33923197058251 0.181122441221955 df.mm.exp3 -0.58157543191001 0.327617785547626 -1.77516440671218 0.0764978267307282 . df.mm.exp4 -0.254246640198724 0.327617785547626 -0.776046513389804 0.438099787034276 df.mm.exp5 -0.0387306697950611 0.327617785547626 -0.118219069609793 0.9059430252081 df.mm.exp6 -0.205916428155594 0.327617785547626 -0.628526402531526 0.529955032124163 df.mm.exp7 -0.662090892717792 0.327617785547626 -2.02092475416462 0.0438364813344498 * df.mm.exp8 -0.31819946840845 0.327617785547626 -0.971252118918292 0.331906868956824 df.mm.trans1:exp2 0.348409494568379 0.299072752307208 1.16496568771498 0.244605542458115 df.mm.trans2:exp2 0.481086375726928 0.267498801750749 1.79846179713057 0.07272552996004 . df.mm.trans1:exp3 0.491357530052075 0.299072752307208 1.64293646365802 0.101044195446242 df.mm.trans2:exp3 0.633137030669269 0.267498801750749 2.36687800665071 0.018330647412465 * df.mm.trans1:exp4 0.258455456661117 0.299072752307208 0.864189247155594 0.387911131885087 df.mm.trans2:exp4 0.313851133364944 0.267498801750749 1.17328052055121 0.241259210099342 df.mm.trans1:exp5 0.0740413296609141 0.299072752307208 0.247569626753757 0.804572185161639 df.mm.trans2:exp5 0.0910064878976275 0.267498801750749 0.340212693671898 0.733843657661681 df.mm.trans1:exp6 0.105414364818575 0.299072752307208 0.352470641358506 0.724638447192856 df.mm.trans2:exp6 0.338464391301199 0.267498801750749 1.26529311191672 0.206373821999007 df.mm.trans1:exp7 0.506455792079902 0.299072752307208 1.69342003968208 0.0910176459428814 . df.mm.trans2:exp7 0.545740474026113 0.267498801750749 2.04016044353957 0.041875514757669 * df.mm.trans1:exp8 0.253287122013907 0.299072752307208 0.8469080518366 0.397464071670573 df.mm.trans2:exp8 0.350883194100610 0.267498801750749 1.31171875090325 0.190235610774666 df.mm.trans1:probe2 -0.0450286298243342 0.163808892773813 -0.274885136343054 0.78352154410034 df.mm.trans1:probe3 -0.173634727119630 0.163808892773813 -1.05998352213628 0.289679680201785 df.mm.trans1:probe4 0.0720263597404882 0.163808892773813 0.439697494567295 0.660352075109228 df.mm.trans1:probe5 -0.149453064166109 0.163808892773813 -0.912362336594716 0.362031285747436 df.mm.trans1:probe6 -0.266735793607336 0.163808892773813 -1.62833524536207 0.104103189142715 df.mm.trans1:probe7 -0.0192426881386319 0.163808892773813 -0.117470351046217 0.906535968294579 df.mm.trans1:probe8 -0.0796161696915486 0.163808892773813 -0.486030815198064 0.62716483438526 df.mm.trans1:probe9 0.194051343520159 0.163808892773813 1.18462032331850 0.236747775431036 df.mm.trans1:probe10 0.0857760370338023 0.163808892773813 0.523634801391654 0.600771748529253 df.mm.trans1:probe11 -0.0669967090773863 0.163808892773813 -0.408993113517318 0.682725255775946 df.mm.trans1:probe12 -0.146513037680235 0.163808892773813 -0.89441443134922 0.37154382810063 df.mm.trans2:probe2 0.204861069268751 0.163808892773813 1.25061018238871 0.211679736257826 df.mm.trans2:probe3 0.193933476103231 0.163808892773813 1.18390078108283 0.237032248918772 df.mm.trans2:probe4 0.328146725845544 0.163808892773813 2.00322900844369 0.0457086089435121 * df.mm.trans2:probe5 0.0581933681629673 0.163808892773813 0.355251581141694 0.722555566283921 df.mm.trans2:probe6 0.0455598698697834 0.163808892773813 0.278128183997265 0.781032490564459 df.mm.trans3:probe2 0.155147162293866 0.163808892773813 0.947122953258055 0.344047775484792 df.mm.trans3:probe3 0.111722083005092 0.163808892773813 0.682026971266802 0.495547470701996 df.mm.trans3:probe4 0.129110537848717 0.163808892773813 0.78817783126703 0.430977748830289