chr3.14877_chr3_58377745_58380321_-_0.R fitVsDatCorrelation=0.931255645002743 cont.fitVsDatCorrelation=0.368659115047173 fstatistic=5040.91928797688,36,324 cont.fstatistic=766.893361074691,36,324 residuals=-0.856293259442454,-0.0781397227041983,-0.00164237307081614,0.0790056860263205,0.810037832475669 cont.residuals=-0.752628127967996,-0.291886278326708,-0.0912122945303048,0.123252569067722,1.78193073787309 predictedValues: Include Exclude Both chr3.14877_chr3_58377745_58380321_-_0.R.tl.Lung 56.6681930321655 45.406662802888 64.6432590186171 chr3.14877_chr3_58377745_58380321_-_0.R.tl.cerebhem 80.8836285728375 45.8102294224428 114.852458936114 chr3.14877_chr3_58377745_58380321_-_0.R.tl.cortex 238.420872541553 44.3926371298137 174.967475068723 chr3.14877_chr3_58377745_58380321_-_0.R.tl.heart 70.6034265590367 44.2645044579804 74.7970490173125 chr3.14877_chr3_58377745_58380321_-_0.R.tl.kidney 68.7472728830157 42.2018371728252 70.7209879632966 chr3.14877_chr3_58377745_58380321_-_0.R.tl.liver 57.5055537207227 45.8049774838741 58.754173916016 chr3.14877_chr3_58377745_58380321_-_0.R.tl.stomach 53.8665586474399 45.3151056449178 65.646241270713 chr3.14877_chr3_58377745_58380321_-_0.R.tl.testicle 58.5139349052746 45.0339917000405 68.1850265471125 diffExp=11.2615302292775,35.0733991503947,194.028235411739,26.3389221010564,26.5454357101905,11.7005762368486,8.5514530025221,13.4799432052341 diffExpScore=0.996951028905463 diffExp1.5=0,1,1,1,1,0,0,0 diffExp1.5Score=0.8 diffExp1.4=0,1,1,1,1,0,0,0 diffExp1.4Score=0.8 diffExp1.3=0,1,1,1,1,0,0,0 diffExp1.3Score=0.8 diffExp1.2=1,1,1,1,1,1,0,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 59.4799580709444 57.833481308151 72.312899517348 cerebhem 54.0171834104418 70.193187237025 55.1900777209683 cortex 66.844471428688 52.0433040654077 71.3636830735187 heart 48.3375169200011 75.2720392460272 53.6764882621202 kidney 46.2132360566589 65.1465958513275 64.16968618303 liver 61.1380432600251 54.2282198746116 49.1353231569884 stomach 58.7819536898692 71.3962471195082 53.7287628886999 testicle 69.1206897053185 54.0667585195429 56.9674569075194 cont.diffExp=1.64647676279340,-16.1760038265832,14.8011673632804,-26.9345223260261,-18.9333597946686,6.90982338541349,-12.6142934296390,15.0539311857757 cont.diffExpScore=3.03568727312704 cont.diffExp1.5=0,0,0,-1,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,-1,-1,0,0,0 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=0,0,0,-1,-1,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,-1,1,-1,-1,0,-1,1 cont.diffExp1.2Score=2 tran.correlation=-0.160921501442017 cont.tran.correlation=-0.767744591411595 tran.covariance=-0.00253205714199544 cont.tran.covariance=-0.0158954096492763 tran.mean=65.2149616673018 cont.tran.mean=60.2570553602218 weightedLogRatios: wLogRatio Lung 0.869918504707972 cerebhem 2.33584625683285 cortex 7.78883942454498 heart 1.87861546224495 kidney 1.94528141698127 liver 0.895881958608131 stomach 0.674203717323494 testicle 1.03124423474349 cont.weightedLogRatios: wLogRatio Lung 0.114296214279471 cerebhem -1.07930258070878 cortex 1.02049800277907 heart -1.81574170350705 kidney -1.37519598037144 liver 0.486108083425575 stomach -0.810893611599717 testicle 1.01030395403835 varWeightedLogRatios=5.51165389598386 cont.varWeightedLogRatios=1.22453588657284 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.58712359324386 0.104272247483148 34.4015179477512 3.39453164834711e-110 *** df.mm.trans1 0.46455502314554 0.0883175043757224 5.26005604924273 2.62261508559507e-07 *** df.mm.trans2 0.243446615256850 0.0883175043757225 2.75649336988932 0.00617342658387716 ** df.mm.exp2 -0.210117590508289 0.122992833132663 -1.70837263567749 0.0885249653989089 . df.mm.exp3 0.418522904337714 0.122992833132663 3.40282351156417 0.000750587353013768 *** df.mm.exp4 0.0484952426899975 0.122992833132663 0.394293240140988 0.693623948666974 df.mm.exp5 0.0301703302926562 0.122992833132663 0.245301531188519 0.80637826564955 df.mm.exp6 0.118923992603568 0.122992833132663 0.966918068106403 0.334306175436473 df.mm.exp7 -0.0681181630925566 0.122992833132663 -0.553838474629516 0.580071409680549 df.mm.exp8 -0.0295305885407199 0.122992833132663 -0.240100075659429 0.81040451161872 df.mm.trans1:exp2 0.565915943993234 0.106514917976306 5.31302051154111 2.00931983507746e-07 *** df.mm.trans2:exp2 0.218966154472264 0.106514917976306 2.0557322733044 0.040609303938903 * df.mm.trans1:exp3 1.01830149574703 0.106514917976306 9.56017725116723 3.07416122707940e-19 *** df.mm.trans2:exp3 -0.441108131112818 0.106514917976306 -4.14128029663356 4.41041813515146e-05 *** df.mm.trans1:exp4 0.171370351580838 0.106514917976306 1.60888591792333 0.108615194165542 df.mm.trans2:exp4 -0.0739709926367762 0.106514917976306 -0.694466033886733 0.487887660403849 df.mm.trans1:exp5 0.163053654429994 0.106514917976306 1.53080580192781 0.126793147456392 df.mm.trans2:exp5 -0.103365427349533 0.106514917976306 -0.970431459868613 0.332555353047332 df.mm.trans1:exp6 -0.104255546898215 0.106514917976306 -0.978788219330987 0.328414894281604 df.mm.trans2:exp6 -0.11019008075362 0.106514917976306 -1.03450373757159 0.301672247934426 df.mm.trans1:exp7 0.017414931320871 0.106514917976306 0.163497579979781 0.870228614319958 df.mm.trans2:exp7 0.0660997458897077 0.106514917976306 0.6205679649907 0.535320185979415 df.mm.trans1:exp8 0.0615824340404137 0.106514917976306 0.578157831883345 0.563559184860472 df.mm.trans2:exp8 0.0212893122215800 0.106514917976306 0.199871648272927 0.84170635078831 df.mm.trans1:probe2 -0.0463393847987433 0.0532574589881532 -0.870101309359336 0.384889205932694 df.mm.trans1:probe3 0.10946931049406 0.0532574589881532 2.05547377914539 0.0406344006764123 * df.mm.trans1:probe4 -0.063142147449114 0.0532574589881532 -1.18560195414429 0.236648315703599 df.mm.trans1:probe5 -0.00180489361114357 0.0532574589881532 -0.0338899685684414 0.972985760812956 df.mm.trans1:probe6 -0.128372677209508 0.0532574589881532 -2.4104168626983 0.0164908857609831 * df.mm.trans2:probe2 -0.0770858339264348 0.0532574589881532 -1.44741854739224 0.148746595942569 df.mm.trans2:probe3 -0.0494466479128667 0.0532574589881532 -0.928445495754235 0.353867678229013 df.mm.trans2:probe4 -0.0261378249906995 0.0532574589881532 -0.49078242723735 0.623912570674136 df.mm.trans2:probe5 0.0202248935775683 0.0532574589881532 0.379757013605684 0.704374590373462 df.mm.trans2:probe6 -0.00175679478330882 0.0532574589881532 -0.0329868307028994 0.973705401066008 df.mm.trans3:probe2 0.165842164469483 0.0532574589881532 3.11397065538508 0.00201058974199344 ** df.mm.trans3:probe3 -0.54456636662446 0.0532574589881532 -10.2251661451891 1.87075995751425e-21 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.61063376271403 0.266017332939255 13.5729267067666 1.5548246762407e-33 *** df.mm.trans1 0.429969043494337 0.225313902145225 1.90831120228525 0.057234590208953 . df.mm.trans2 0.335955125150581 0.225313902145225 1.49105368977207 0.136920318716719 df.mm.exp2 0.367565523761420 0.313776927517378 1.17142304461205 0.242289508582542 df.mm.exp3 0.0244508857669132 0.313776927517378 0.0779244221695011 0.937936272975429 df.mm.exp4 0.354136921187231 0.313776927517378 1.12862639069476 0.259890457409333 df.mm.exp5 -0.0138293745063166 0.313776927517378 -0.0440739050373636 0.964872643285805 df.mm.exp6 0.349552801615143 0.313776927517378 1.11401690487833 0.266097654337601 df.mm.exp7 0.495926968278905 0.313776927517378 1.58050807687713 0.114966262254945 df.mm.exp8 0.321388725159587 0.313776927517378 1.02425862762579 0.306477247528141 df.mm.trans1:exp2 -0.463902733175892 0.271738790351478 -1.70716419461447 0.0887495675283407 . df.mm.trans2:exp2 -0.173882134702644 0.271738790351478 -0.63988705652858 0.522698237902397 df.mm.trans1:exp3 0.0922782965548266 0.271738790351478 0.339584556314062 0.734389427121216 df.mm.trans2:exp3 -0.129942612615740 0.271738790351478 -0.478189412883109 0.632837882984594 df.mm.trans1:exp4 -0.561568330879809 0.271738790351478 -2.06657404396867 0.0395685190360134 * df.mm.trans2:exp4 -0.0905960494513556 0.271738790351478 -0.333393879225616 0.739052631205634 df.mm.trans1:exp5 -0.238543790369638 0.271738790351478 -0.8778422471856 0.380679814463872 df.mm.trans2:exp5 0.132901556415184 0.271738790351478 0.489078339692626 0.625117143516586 df.mm.trans1:exp6 -0.32205790658147 0.271738790351478 -1.18517457947357 0.236816975019699 df.mm.trans2:exp6 -0.413919236131623 0.271738790351478 -1.52322469529007 0.128677909223994 df.mm.trans1:exp7 -0.50773148718183 0.271738790351478 -1.86845421121184 0.0626010898776963 . df.mm.trans2:exp7 -0.285249531024099 0.271738790351478 -1.04971958789963 0.294629403549375 df.mm.trans1:exp8 -0.171174039002758 0.271738790351478 -0.62992125188073 0.529190079354395 df.mm.trans2:exp8 -0.388737042623934 0.271738790351478 -1.43055410720393 0.153521745426992 df.mm.trans1:probe2 0.226906903046420 0.135869395175739 1.67003689648378 0.0958778138687266 . df.mm.trans1:probe3 0.176032943596111 0.135869395175739 1.29560408632439 0.196034192867509 df.mm.trans1:probe4 -0.0150831510082037 0.135869395175739 -0.111012130352788 0.911675462759805 df.mm.trans1:probe5 0.0623464968564065 0.135869395175739 0.458870791142958 0.646634627799991 df.mm.trans1:probe6 -0.0448736983833713 0.135869395175739 -0.330270833437727 0.741408780237383 df.mm.trans2:probe2 0.161682270101334 0.135869395175739 1.18998299721735 0.234924297625050 df.mm.trans2:probe3 0.0300829773829806 0.135869395175739 0.221410990636044 0.824911911561858 df.mm.trans2:probe4 0.290231047587964 0.135869395175739 2.13610318359457 0.0334201379859811 * df.mm.trans2:probe5 0.238305258947887 0.135869395175739 1.75392890090997 0.0803881462820707 . df.mm.trans2:probe6 0.278509279419491 0.135869395175739 2.04983086190423 0.0411855617402335 * df.mm.trans3:probe2 -0.186675576677141 0.135869395175739 -1.37393396383113 0.170411938848529 df.mm.trans3:probe3 -0.0999906279660992 0.135869395175739 -0.735931942854145 0.462304559163985