chr5.18804_chr5_133942816_133949187_-_2.R fitVsDatCorrelation=0.83547934302317 cont.fitVsDatCorrelation=0.221898410053856 fstatistic=10658.7650041764,53,715 cont.fstatistic=3376.15860238742,53,715 residuals=-0.402843887197856,-0.0885831915730039,-0.00509854908720609,0.0786522402048232,0.711639474436664 cont.residuals=-0.511596102486015,-0.178811022253874,-0.0490185363498295,0.146458459185754,1.05460356430050 predictedValues: Include Exclude Both chr5.18804_chr5_133942816_133949187_-_2.R.tl.Lung 58.794735698943 61.8580992810528 53.8964543071304 chr5.18804_chr5_133942816_133949187_-_2.R.tl.cerebhem 58.5476430843323 63.5137739425667 60.8107463452786 chr5.18804_chr5_133942816_133949187_-_2.R.tl.cortex 54.8498506303986 59.5629109811127 51.7653629352896 chr5.18804_chr5_133942816_133949187_-_2.R.tl.heart 56.4370196432421 57.9032091795984 52.8973827318751 chr5.18804_chr5_133942816_133949187_-_2.R.tl.kidney 59.456358900492 54.7260612729194 65.5050921591176 chr5.18804_chr5_133942816_133949187_-_2.R.tl.liver 60.2538051019455 57.2112654653889 59.3031571400535 chr5.18804_chr5_133942816_133949187_-_2.R.tl.stomach 59.7126409816132 74.459049267471 61.0426180489133 chr5.18804_chr5_133942816_133949187_-_2.R.tl.testicle 57.8497152760344 62.0615998623376 60.2631540214387 diffExp=-3.06336358210984,-4.96613085823436,-4.7130603507141,-1.46618953635632,4.73029762757264,3.04253963655658,-14.7464082858578,-4.21188458630319 diffExpScore=1.55109359495016 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,-1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 56.3652771724249 57.9945621166789 59.7635611107876 cerebhem 56.7475282188936 60.1982836793734 55.5316429473494 cortex 55.9556808204986 56.0432198712749 60.5023339148343 heart 61.7725712422223 55.6299329252767 62.0876168726822 kidney 57.6919844291435 58.1722628032263 54.6622713644682 liver 57.2294184038226 59.798013597609 61.4014196077848 stomach 55.3590775409307 54.1851115136968 64.0619207490665 testicle 59.0937611443683 58.6106758989563 59.420549913627 cont.diffExp=-1.62928494425404,-3.45075546047981,-0.0875390507763143,6.14263831694563,-0.480278374082857,-2.56859519378639,1.17396602723382,0.483085245411992 cont.diffExpScore=11.3047402487999 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=0.207724337947068 cont.tran.correlation=0.00083909357199145 tran.covariance=0.000551917569497667 cont.tran.covariance=2.63718607814198e-05 tran.mean=59.8248586605905 cont.tran.mean=57.5529600861498 weightedLogRatios: wLogRatio Lung -0.208213893920371 cerebhem -0.334664279416175 cortex -0.333511272730730 heart -0.103768435956255 kidney 0.335240584889256 liver 0.211024372810485 stomach -0.926940514521206 testicle -0.287650718287201 cont.weightedLogRatios: wLogRatio Lung -0.115297433497117 cerebhem -0.240148679976498 cortex -0.00629246625933445 heart 0.426397936490899 kidney -0.0336529630722631 liver -0.178647767056782 stomach 0.0858048401386937 testicle 0.0334497776835291 varWeightedLogRatios=0.148077507101051 cont.varWeightedLogRatios=0.0420354086958137 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37324871414003 0.0763102667764911 57.3087855523956 1.66928427640785e-269 *** df.mm.trans1 -0.161673084391227 0.0677649937519402 -2.38579058950476 0.0173017933183835 * df.mm.trans2 -0.239196459282415 0.0616410740072463 -3.88047196021108 0.00011388748135035 *** df.mm.exp2 -0.098499562774408 0.0831013278039477 -1.18529469236385 0.236294739753494 df.mm.exp3 -0.066919381196713 0.0831013278039477 -0.805274512034138 0.420928991792189 df.mm.exp4 -0.0882864024088662 0.0831013278039477 -1.06239460598212 0.288415274538748 df.mm.exp5 -0.306375936533288 0.0831013278039477 -3.68677546592382 0.000244310676822571 *** df.mm.exp6 -0.149176661098233 0.0831013278039477 -1.7951176598546 0.0730571733224852 . df.mm.exp7 0.0763900932900623 0.0831013278039477 0.919240345596901 0.358279876448527 df.mm.exp8 -0.124575572373357 0.0831013278039477 -1.49908040780353 0.134294150832199 df.mm.trans1:exp2 0.0942880752898622 0.0789086094694834 1.19490225368027 0.232521421234145 df.mm.trans2:exp2 0.124913316986258 0.0664469955936022 1.87989413020633 0.0605288812807164 . df.mm.trans1:exp3 -0.00253347729078987 0.0789086094694834 -0.0321064749185531 0.974396099962458 df.mm.trans2:exp3 0.0291094219389732 0.0664469955936022 0.438084847613125 0.66145709483572 df.mm.trans1:exp4 0.0473594002780826 0.0789086094694834 0.600180393451212 0.548576188109499 df.mm.trans2:exp4 0.0222161710510122 0.0664469955936022 0.334344252174905 0.738217847892991 df.mm.trans1:exp5 0.317566194144295 0.0789086094694834 4.02448093154028 6.31903731259683e-05 *** df.mm.trans2:exp5 0.183872931943565 0.0664469955936022 2.76721212601024 0.00579995065987122 ** df.mm.trans1:exp6 0.173690064726716 0.0789086094694834 2.20115480293551 0.0280439002193259 * df.mm.trans2:exp6 0.0710844481416967 0.0664469955936022 1.06979175667261 0.285074061633562 df.mm.trans1:exp7 -0.0608986755371436 0.0789086094694834 -0.771762117550622 0.440510323645599 df.mm.trans2:exp7 0.109016166060988 0.0664469955936022 1.64064853628212 0.101310196607845 df.mm.trans1:exp8 0.108371782271259 0.0789086094694834 1.37338350022719 0.170063642791169 df.mm.trans2:exp8 0.127859969698820 0.0664469955936022 1.9242400436105 0.0547210863878889 . df.mm.trans1:probe2 -0.274607439903634 0.0432200253878018 -6.35370843583858 3.74056391567051e-10 *** df.mm.trans1:probe3 -0.386711939276794 0.0432200253878018 -8.94751763347962 3.08817264637204e-18 *** df.mm.trans1:probe4 -0.345194421912659 0.0432200253878018 -7.98690928141112 5.53234560480583e-15 *** df.mm.trans1:probe5 -0.108719119000358 0.0432200253878018 -2.51548022068128 0.0121051366985509 * df.mm.trans1:probe6 -0.258038117563769 0.0432200253878018 -5.97033701966765 3.72829687862679e-09 *** df.mm.trans1:probe7 -0.362402287401298 0.0432200253878018 -8.38505494037911 2.6919646884348e-16 *** df.mm.trans1:probe8 -0.0497247063553103 0.0432200253878018 -1.15050155360030 0.250321983913723 df.mm.trans1:probe9 -0.361627147895882 0.0432200253878018 -8.36712020992809 3.09245014469193e-16 *** df.mm.trans1:probe10 -0.080679710485895 0.0432200253878018 -1.866720571355 0.0623497112643996 . df.mm.trans1:probe11 0.200206166021755 0.0432200253878018 4.63225470659395 4.29748954914201e-06 *** df.mm.trans1:probe12 -0.0249608946212245 0.0432200253878018 -0.577530771841455 0.563762771227792 df.mm.trans1:probe13 0.356580481737781 0.0432200253878018 8.25035336139391 7.584876105672e-16 *** df.mm.trans1:probe14 0.0499499506966937 0.0432200253878018 1.15571312715590 0.248184559870142 df.mm.trans1:probe15 0.233027433093768 0.0432200253878018 5.39165423904485 9.49321284956982e-08 *** df.mm.trans1:probe16 -0.0225693464277866 0.0432200253878018 -0.522196510188919 0.601695256769956 df.mm.trans1:probe17 -0.41540428763292 0.0432200253878018 -9.61138462797297 1.18876609908544e-20 *** df.mm.trans1:probe18 -0.370863187597396 0.0432200253878018 -8.58081836532346 5.83401896747742e-17 *** df.mm.trans1:probe19 -0.382486539544337 0.0432200253878018 -8.84975277345135 6.82395629533435e-18 *** df.mm.trans1:probe20 -0.305402403435306 0.0432200253878018 -7.06622452659412 3.78724958783789e-12 *** df.mm.trans1:probe21 -0.370644668609113 0.0432200253878018 -8.5757623991059 6.07116096915657e-17 *** df.mm.trans1:probe22 -0.295333816210817 0.0432200253878018 -6.83326336717447 1.77845847033080e-11 *** df.mm.trans2:probe2 -0.246482215687722 0.0432200253878018 -5.70296323234664 1.72340628172866e-08 *** df.mm.trans2:probe3 0.359141551471914 0.0432200253878018 8.30960991460398 4.81678583705205e-16 *** df.mm.trans2:probe4 -0.205199612501207 0.0432200253878018 -4.74779018892297 2.48504477762126e-06 *** df.mm.trans2:probe5 0.188319233336691 0.0432200253878018 4.3572217194912 1.51019447619546e-05 *** df.mm.trans2:probe6 -0.187871098735286 0.0432200253878018 -4.34685303975572 1.58141082636727e-05 *** df.mm.trans3:probe2 0.0584197200495872 0.0432200253878018 1.35168176153074 0.176904596044810 df.mm.trans3:probe3 -0.091194276149524 0.0432200253878018 -2.11000052247221 0.0352051468850945 * df.mm.trans3:probe4 0.0915166460428194 0.0432200253878018 2.11745933098523 0.0345654932285129 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01152372758336 0.135404689272431 29.6261802241742 1.79481257496206e-126 *** df.mm.trans1 0.0619277313444984 0.120241984599593 0.515025858486269 0.606694129507856 df.mm.trans2 0.0212023785522696 0.109375721314362 0.193849039782154 0.846349145023263 df.mm.exp2 0.117496503693375 0.147454725880823 0.796831047574143 0.425813669082949 df.mm.exp3 -0.0538052376624594 0.147454725880823 -0.364893273789991 0.715298977279478 df.mm.exp4 0.0118278542219687 0.147454725880823 0.0802134631583683 0.93608992831211 df.mm.exp5 0.115546732542514 0.147454725880823 0.783608201448232 0.433529534274327 df.mm.exp6 0.0188011251474914 0.147454725880823 0.127504391840835 0.8985770479097 df.mm.exp7 -0.155409739907802 0.147454725880823 -1.05394885772199 0.292262354165452 df.mm.exp8 0.063595656105383 0.147454725880823 0.431289371876644 0.666387979497028 df.mm.trans1:exp2 -0.110737719835359 0.140015180099287 -0.790897956613224 0.429265799747779 df.mm.trans2:exp2 -0.080201911636447 0.117903332952442 -0.680234473683601 0.496576199863316 df.mm.trans1:exp3 0.0465118852039016 0.140015180099287 0.332191732145895 0.739841845810836 df.mm.trans2:exp3 0.0195791645995507 0.117903332952442 0.166061163066936 0.86815576224688 df.mm.trans1:exp4 0.0797782647468166 0.140015180099287 0.569782967034322 0.56900388249889 df.mm.trans2:exp4 -0.0534556854068452 0.117903332952442 -0.453385702238013 0.650408593666404 df.mm.trans1:exp5 -0.0922818026582864 0.140015180099287 -0.659084269240292 0.510053856640804 df.mm.trans2:exp5 -0.112487325086452 0.117903332952442 -0.954063996917074 0.340373679657815 df.mm.trans1:exp6 -0.00358636715422181 0.140015180099287 -0.0256141309226518 0.97957226185834 df.mm.trans2:exp6 0.0118220682840865 0.117903332952442 0.100269161083470 0.920158747726378 df.mm.trans1:exp7 0.13739707222096 0.140015180099287 0.981301256931781 0.326776123328444 df.mm.trans2:exp7 0.0874666657564761 0.117903332952442 0.741850663303614 0.458421429185442 df.mm.trans1:exp8 -0.0163236175994476 0.140015180099287 -0.116584627380204 0.907221958073347 df.mm.trans2:exp8 -0.0530280430816093 0.117903332952442 -0.449758643404922 0.653020796558586 df.mm.trans1:probe2 -0.0285253012508303 0.0766894725335279 -0.371958501062311 0.710033956487866 df.mm.trans1:probe3 -0.0569018413292933 0.0766894725335279 -0.741977216030744 0.458344796706281 df.mm.trans1:probe4 0.00936336191804857 0.0766894725335279 0.122094488444356 0.902858493787978 df.mm.trans1:probe5 -0.0755501267216492 0.0766894725335279 -0.985143387035546 0.324886885017861 df.mm.trans1:probe6 -0.0527501836104601 0.0766894725335279 -0.68784126253311 0.491775823374208 df.mm.trans1:probe7 -0.0740802185809887 0.0766894725335279 -0.965976373727196 0.334382611283601 df.mm.trans1:probe8 -0.0247142858776448 0.0766894725335279 -0.322264387290445 0.747346612795829 df.mm.trans1:probe9 -0.0653711094997318 0.0766894725335279 -0.852413080180623 0.3942702946543 df.mm.trans1:probe10 -0.0452341547478883 0.0766894725335279 -0.589835257089718 0.555487446632912 df.mm.trans1:probe11 -0.0953430224498664 0.0766894725335279 -1.24323481828856 0.214188818103224 df.mm.trans1:probe12 -0.140557558627463 0.076689472533528 -1.83281425708088 0.0672458629523422 . df.mm.trans1:probe13 0.0250057680232025 0.0766894725335279 0.326065197700639 0.744470436482536 df.mm.trans1:probe14 -0.0611494445959486 0.0766894725335279 -0.79736426103615 0.425504219417944 df.mm.trans1:probe15 -0.0945360803168054 0.0766894725335279 -1.23271261613483 0.218088164778179 df.mm.trans1:probe16 -0.032507882784396 0.0766894725335279 -0.423889768836053 0.671773699004066 df.mm.trans1:probe17 -0.0529336849986714 0.0766894725335279 -0.690234047124647 0.490270997645241 df.mm.trans1:probe18 -0.0236209500697915 0.0766894725335279 -0.308007726346855 0.758166157928492 df.mm.trans1:probe19 -0.073320806257429 0.0766894725335279 -0.956073941248896 0.339358017422776 df.mm.trans1:probe20 -0.0808788702468029 0.0766894725335279 -1.05462806790650 0.291951699342276 df.mm.trans1:probe21 -0.0385130269089110 0.0766894725335279 -0.502194442556291 0.615685370052695 df.mm.trans1:probe22 0.000567701267562749 0.0766894725335279 0.00740259710763502 0.99409570087927 df.mm.trans2:probe2 0.0912005074464063 0.0766894725335279 1.18921808213682 0.234748639989034 df.mm.trans2:probe3 0.0504287953289057 0.0766894725335279 0.657571289290831 0.511025278020813 df.mm.trans2:probe4 0.0522603424465148 0.0766894725335279 0.681453929985854 0.49580496266269 df.mm.trans2:probe5 0.129582154576555 0.0766894725335279 1.68969938500884 0.0915214078384145 . df.mm.trans2:probe6 -0.0472403657345755 0.0766894725335279 -0.615995444667095 0.538093571215869 df.mm.trans3:probe2 -0.0604423756974728 0.0766894725335279 -0.788144365852144 0.430873480965613 df.mm.trans3:probe3 0.0605754231127045 0.0766894725335279 0.789879250847911 0.429860162517932 df.mm.trans3:probe4 -0.0171638803100482 0.0766894725335279 -0.223810123384854 0.82296898539327