chr14.7353_chr14_3370011_3370305_+_0.R fitVsDatCorrelation=0.884221817763736 cont.fitVsDatCorrelation=0.257966127946016 fstatistic=12161.4217765133,44,508 cont.fstatistic=2833.32523115251,44,508 residuals=-0.359827652233391,-0.0804111378540495,-0.00628437159174017,0.0769393560148753,0.607136900715264 cont.residuals=-0.564894456569813,-0.181811896494384,-0.0634818579111819,0.134958360963702,0.862059924659105 predictedValues: Include Exclude Both chr14.7353_chr14_3370011_3370305_+_0.R.tl.Lung 44.8001031626738 45.2912739870146 57.6358225739232 chr14.7353_chr14_3370011_3370305_+_0.R.tl.cerebhem 56.8844640302907 53.7522699495855 56.5183346122596 chr14.7353_chr14_3370011_3370305_+_0.R.tl.cortex 48.7083355917736 47.249283597296 69.5977771465293 chr14.7353_chr14_3370011_3370305_+_0.R.tl.heart 51.4854235443035 45.9689657732171 66.1955680865631 chr14.7353_chr14_3370011_3370305_+_0.R.tl.kidney 47.6631722330346 43.7361063603601 63.9529982006437 chr14.7353_chr14_3370011_3370305_+_0.R.tl.liver 54.2195579552722 48.8917540802432 62.8301341968128 chr14.7353_chr14_3370011_3370305_+_0.R.tl.stomach 49.0118659001055 47.1533604927606 58.1192143006984 chr14.7353_chr14_3370011_3370305_+_0.R.tl.testicle 49.5458896635291 48.3654446082067 62.3754221371197 diffExp=-0.491170824340884,3.13219408070519,1.45905199447765,5.51645777108637,3.92706587267451,5.32780387502897,1.85850540734485,1.18044505532241 diffExpScore=0.99922924142028 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 56.7151214831616 48.9360901199829 53.1279084834269 cerebhem 52.3721347610063 51.8472337823797 55.2609746129801 cortex 52.5230549480053 54.5745982417284 51.8825415428782 heart 51.7284544345693 53.3952601454358 53.7718337851255 kidney 52.0550725607568 50.9046151381011 55.9993803047273 liver 50.6097566488863 49.067416305424 52.41350634986 stomach 56.3222870497335 61.3201552646287 54.2484549403986 testicle 46.7076416715962 52.7803406576746 55.70035426098 cont.diffExp=7.77903136317873,0.524900978626576,-2.0515432937231,-1.66680571086646,1.15045742265573,1.54234034346231,-4.99786821489521,-6.07269898607846 cont.diffExpScore=5.38076898269578 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=0.834248889390886 cont.tran.correlation=0.274366141441367 tran.covariance=0.00380695084927384 cont.tran.covariance=0.00107696774588640 tran.mean=48.9204544331042 cont.tran.mean=52.6162020758169 weightedLogRatios: wLogRatio Lung -0.0415185398672429 cerebhem 0.227264977873187 cortex 0.117716674121647 heart 0.440254030745699 kidney 0.328563138682678 liver 0.407662844211952 stomach 0.149708857783663 testicle 0.093822606388471 cont.weightedLogRatios: wLogRatio Lung 0.584832907010382 cerebhem 0.0398223214514614 cortex -0.152514700693392 heart -0.125646486091712 kidney 0.0880789109673077 liver 0.120970130168017 stomach -0.346330365450295 testicle -0.477314904404634 varWeightedLogRatios=0.0278737939605401 cont.varWeightedLogRatios=0.106263454130776 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.84618575052998 0.0640116323998668 60.0857313949391 5.35067950016841e-233 *** df.mm.trans1 -0.027017033338749 0.0510913822415913 -0.528798246463483 0.597176372591936 df.mm.trans2 -0.0228734772955803 0.0510913822415913 -0.447697366797017 0.654562470797302 df.mm.exp2 0.429662534977456 0.0682574089795696 6.29473842328325 6.6625483000321e-10 *** df.mm.exp3 -0.062625494723138 0.0682574089795696 -0.917490066783558 0.359321271757929 df.mm.exp4 0.0154711931685966 0.0682574089795696 0.226659543628844 0.8207796639196 df.mm.exp5 -0.0769959226649413 0.0682574089795696 -1.12802293283631 0.259842700763609 df.mm.exp6 0.181035108204147 0.0682574089795696 2.65224113998134 0.00824597019572033 ** df.mm.exp7 0.121790854104935 0.0682574089795696 1.78428768284170 0.0749736496191929 . df.mm.exp8 0.0873330518115237 0.0682574089795696 1.27946626039766 0.201316972459052 df.mm.trans1:exp2 -0.190850713173333 0.0531857445892197 -3.58838095898382 0.000364970241235621 *** df.mm.trans2:exp2 -0.258391023956982 0.0531857445892197 -4.85827595256334 1.57979911147656e-06 *** df.mm.trans1:exp3 0.146265230057328 0.0531857445892197 2.75008333881585 0.00617045729565903 ** df.mm.trans2:exp3 0.104948599870193 0.0531857445892197 1.97324679161238 0.0490090519305731 * df.mm.trans1:exp4 0.123617094307055 0.0531857445892197 2.32425239623535 0.0205054145809875 * df.mm.trans2:exp4 -0.000619068182755864 0.0531857445892197 -0.0116397389476680 0.990717611346463 df.mm.trans1:exp5 0.138944509616425 0.0531857445892197 2.61243892869347 0.00925607851961521 ** df.mm.trans2:exp5 0.0420555294110627 0.0531857445892197 0.790729353060276 0.429470933590861 df.mm.trans1:exp6 0.0097961408782247 0.0531857445892197 0.184187341060753 0.853939990189315 df.mm.trans2:exp6 -0.104540740874304 0.0531857445892197 -1.9655782142701 0.0498916588145006 * df.mm.trans1:exp7 -0.0319388662405040 0.0531857445892197 -0.600515542034504 0.548430514103713 df.mm.trans2:exp7 -0.081499961900061 0.0531857445892197 -1.53236478175734 0.126054937227975 df.mm.trans1:exp8 0.0133558100579155 0.0531857445892197 0.251116350087211 0.801825696770865 df.mm.trans2:exp8 -0.0216618341673934 0.0531857445892197 -0.407286469987376 0.683969163404465 df.mm.trans1:probe2 -0.0413927107298603 0.0370508142936277 -1.11718761163582 0.264442208322717 df.mm.trans1:probe3 -0.0988736472043073 0.0370508142936277 -2.66859579443338 0.00786042983126474 ** df.mm.trans1:probe4 0.0226722275677577 0.0370508142936277 0.611922517763854 0.540862790822879 df.mm.trans1:probe5 -0.0803602042617463 0.0370508142936277 -2.16891870782897 0.0305520481494232 * df.mm.trans1:probe6 -0.0903363409615808 0.0370508142936277 -2.43817423945572 0.0151033566224448 * df.mm.trans2:probe2 -0.0834839886862227 0.0370508142936277 -2.25322952485233 0.0246703952078919 * df.mm.trans2:probe3 -0.0453954217625061 0.0370508142936277 -1.22522062275737 0.221059872199026 df.mm.trans2:probe4 -0.0258490142109035 0.0370508142936277 -0.697663862555087 0.485706616904467 df.mm.trans2:probe5 -0.0334501673599124 0.0370508142936277 -0.902818682872118 0.36705001378678 df.mm.trans2:probe6 0.0148145212027061 0.0370508142936277 0.399843336378547 0.689439975087165 df.mm.trans3:probe2 0.312890094972409 0.0370508142936277 8.44489118357169 3.21066110280653e-16 *** df.mm.trans3:probe3 0.739315776568516 0.0370508142936277 19.9541033217094 7.67128952108191e-66 *** df.mm.trans3:probe4 0.210559671209565 0.0370508142936277 5.68299712769819 2.23286714363501e-08 *** df.mm.trans3:probe5 0.127567445549029 0.0370508142936277 3.44304026729499 0.000622714970782313 *** df.mm.trans3:probe6 -0.113216463779852 0.0370508142936277 -3.05570784173896 0.00236334612484693 ** df.mm.trans3:probe7 -0.041413202395531 0.0370508142936277 -1.11774068087496 0.264206080037676 df.mm.trans3:probe8 0.548784071230681 0.0370508142936277 14.8116601940666 1.61555288371728e-41 *** df.mm.trans3:probe9 0.553660814505784 0.0370508142936277 14.9432833005510 4.10514967972771e-42 *** df.mm.trans3:probe10 0.290924021388448 0.0370508142936277 7.8520277336653 2.4487740374259e-14 *** df.mm.trans3:probe11 0.207267366735988 0.0370508142936277 5.59413796127107 3.62836671156212e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88498202298474 0.132411548319201 29.340205384649 2.01461204488086e-111 *** df.mm.trans1 0.142893331581947 0.105685307103515 1.35206430769026 0.176956369420017 df.mm.trans2 -0.0148715135913426 0.105685307103515 -0.140715053008991 0.88815083097146 df.mm.exp2 -0.0612443606852399 0.141194168440869 -0.433759845477533 0.66464698643135 df.mm.exp3 0.0559847509135838 0.141194168440869 0.396508946026549 0.691896113132493 df.mm.exp4 -0.0168734656140721 0.141194168440869 -0.119505400261191 0.904922206600946 df.mm.exp5 -0.098938446560739 0.141194168440869 -0.7007261535888 0.483794751473823 df.mm.exp6 -0.0976783984145053 0.141194168440869 -0.691801931291606 0.489377777108968 df.mm.exp7 0.197770735678202 0.141194168440869 1.40070045287336 0.161914188304865 df.mm.exp8 -0.165793595072173 0.141194168440869 -1.17422409794217 0.240855440942791 df.mm.trans1:exp2 -0.018421836804885 0.110017609699059 -0.167444437806602 0.867086996799333 df.mm.trans2:exp2 0.119030780022314 0.110017609699059 1.08192479683852 0.27979925284438 df.mm.trans1:exp3 -0.132773403846025 0.110017609699058 -1.20683774360498 0.228056303962607 df.mm.trans2:exp3 0.0530686263873047 0.110017609699059 0.48236483716078 0.629754566852017 df.mm.trans1:exp4 -0.0751593963646016 0.110017609699058 -0.683157874182071 0.494818530351962 df.mm.trans2:exp4 0.104080282792042 0.110017609699059 0.946032940333303 0.344581543605061 df.mm.trans1:exp5 0.0131998244090936 0.110017609699059 0.119979196468641 0.904547062328137 df.mm.trans2:exp5 0.138376873169254 0.110017609699059 1.25777022012903 0.209052724001393 df.mm.trans1:exp6 -0.0162180927335639 0.110017609699059 -0.147413607493626 0.88286405310556 df.mm.trans2:exp6 0.100358430361036 0.110017609699059 0.912203333953133 0.362094371663261 df.mm.trans1:exp7 -0.204721284527749 0.110017609699059 -1.86080469379168 0.063349289093668 . df.mm.trans2:exp7 0.027822686833928 0.110017609699059 0.252893031488632 0.800453174746097 df.mm.trans1:exp8 -0.0283394884168893 0.110017609699059 -0.257590475692109 0.79682729107792 df.mm.trans2:exp8 0.241417216893997 0.110017609699059 2.19435068217141 0.0286622402681742 * df.mm.trans1:probe2 0.0958255444818946 0.0766416275163863 1.25030675348598 0.211763136182456 df.mm.trans1:probe3 0.0964905054477372 0.0766416275163864 1.25898299102674 0.208614688904412 df.mm.trans1:probe4 0.0785451507866766 0.0766416275163863 1.02483667599417 0.305928012528521 df.mm.trans1:probe5 -0.0581626110882622 0.0766416275163863 -0.75889060518485 0.448269962653571 df.mm.trans1:probe6 -0.039884861224013 0.0766416275163863 -0.520407284089648 0.60300646029681 df.mm.trans2:probe2 0.0444703389729419 0.0766416275163863 0.580237403797745 0.562011807746236 df.mm.trans2:probe3 0.070911068905431 0.0766416275163863 0.925229163358644 0.355286019752654 df.mm.trans2:probe4 0.0637637572541518 0.0766416275163863 0.831972901939208 0.405815154207178 df.mm.trans2:probe5 0.094331137811216 0.0766416275163863 1.23080812435837 0.218964199381605 df.mm.trans2:probe6 0.0734028173314781 0.0766416275163864 0.957740848023931 0.338649025921067 df.mm.trans3:probe2 -0.0247245074433195 0.0766416275163863 -0.322598935389692 0.747131727688317 df.mm.trans3:probe3 -0.0153763969855236 0.0766416275163863 -0.200627224183569 0.841070365023276 df.mm.trans3:probe4 -0.0821063730743188 0.0766416275163863 -1.07130257713752 0.284542098975630 df.mm.trans3:probe5 -0.111614821590987 0.0766416275163863 -1.45632113001676 0.145921626945583 df.mm.trans3:probe6 -0.0497347135732436 0.0766416275163863 -0.648925592852397 0.516679724118482 df.mm.trans3:probe7 -0.0569974503064599 0.0766416275163864 -0.743687890686736 0.45740913666496 df.mm.trans3:probe8 -0.0637247958339828 0.0766416275163863 -0.83146454347356 0.406101895452237 df.mm.trans3:probe9 -0.0621524685141635 0.0766416275163863 -0.810949226004824 0.417774593143046 df.mm.trans3:probe10 -0.030456127894941 0.0766416275163863 -0.397383626651578 0.691251500676952 df.mm.trans3:probe11 0.0535715148504367 0.0766416275163863 0.698987176896562 0.484879935679252