chr8.23681_chr8_104620715_104621188_+_1.R fitVsDatCorrelation=0.920160159095133 cont.fitVsDatCorrelation=0.29012680885648 fstatistic=6722.43546162499,37,347 cont.fstatistic=1117.49738903674,37,347 residuals=-0.643299894366343,-0.0871973893247603,-0.0161408179633538,0.0874103024337965,0.937923432011223 cont.residuals=-0.702471283068434,-0.222664936166981,-0.0930634134020387,0.0822051999166976,1.49036994770645 predictedValues: Include Exclude Both chr8.23681_chr8_104620715_104621188_+_1.R.tl.Lung 63.2444967903195 53.5250958130809 52.0483150513636 chr8.23681_chr8_104620715_104621188_+_1.R.tl.cerebhem 68.011109447852 53.89085836232 64.5768794929538 chr8.23681_chr8_104620715_104621188_+_1.R.tl.cortex 57.3902885834196 48.5413516189871 50.5208027055292 chr8.23681_chr8_104620715_104621188_+_1.R.tl.heart 57.2970713839638 51.7084923796193 53.5796522072985 chr8.23681_chr8_104620715_104621188_+_1.R.tl.kidney 74.5834610057494 49.480194267066 51.623608165717 chr8.23681_chr8_104620715_104621188_+_1.R.tl.liver 234.365859025265 57.7382638630742 151.308701883636 chr8.23681_chr8_104620715_104621188_+_1.R.tl.stomach 89.1908209483083 52.9744815297853 68.090076674975 chr8.23681_chr8_104620715_104621188_+_1.R.tl.testicle 60.6015273805283 52.134561854256 55.2378747142139 diffExp=9.71940097723855,14.1202510855320,8.84893696443248,5.58857900434443,25.1032667386834,176.627595162191,36.216339418523,8.4669655262723 diffExpScore=0.99649971882966 diffExp1.5=0,0,0,0,1,1,1,0 diffExp1.5Score=0.75 diffExp1.4=0,0,0,0,1,1,1,0 diffExp1.4Score=0.75 diffExp1.3=0,0,0,0,1,1,1,0 diffExp1.3Score=0.75 diffExp1.2=0,1,0,0,1,1,1,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 63.9780685825424 62.3631838444567 57.4246806618534 cerebhem 65.8107063795348 68.6327033050669 63.3104240929495 cortex 71.6614480280062 67.0949025945106 53.4678438838856 heart 67.0401202203586 64.6571758660312 70.926710928831 kidney 57.9963273157393 67.7991252374794 76.2851153694655 liver 69.2453231879847 63.3267596693788 60.5467721745391 stomach 68.309375414481 57.0669739224439 56.073647137136 testicle 62.5951108767557 74.3092291036624 60.5045031192077 cont.diffExp=1.61488473808571,-2.82199692553210,4.56654543349559,2.38294435432739,-9.80279792174004,5.91856351860591,11.2424014920371,-11.7141182269067 cont.diffExpScore=20.9787535464014 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=0.764033456722643 cont.tran.correlation=-0.398175133519201 tran.covariance=0.0187610486189568 cont.tran.covariance=-0.00204896371194469 tran.mean=70.2923708908497 cont.tran.mean=65.742908346777 weightedLogRatios: wLogRatio Lung 0.678038662386222 cerebhem 0.954883503853402 cortex 0.664166990400975 heart 0.410195451941343 kidney 1.68518790019927 liver 6.66354423893026 stomach 2.20384713038909 testicle 0.606343390989562 cont.weightedLogRatios: wLogRatio Lung 0.105987322132551 cerebhem -0.176670241800738 cortex 0.279118305112857 heart 0.151543615716614 kidney -0.646302370280751 liver 0.374632961674866 stomach 0.743406449227063 testicle -0.724354700599404 varWeightedLogRatios=4.34488979928031 cont.varWeightedLogRatios=0.253896695163337 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42957288665928 0.0885542202706951 50.0210252331152 1.03464716535118e-160 *** df.mm.trans1 -0.157941179401885 0.0737778179430565 -2.14076783246406 0.0329892138324338 * df.mm.trans2 -0.482022849154193 0.0737778179430565 -6.5334386756495 2.29229126206393e-10 *** df.mm.exp2 -0.136210836497786 0.101650334446259 -1.33999398270351 0.181124098305053 df.mm.exp3 -0.165080358560598 0.101650334446259 -1.62400211922444 0.105283351002015 df.mm.exp4 -0.162284148415599 0.101650334446259 -1.59649399384313 0.111288803337943 df.mm.exp5 0.0945258452513376 0.101650334446259 0.929911797794544 0.353063207737605 df.mm.exp6 0.318495048542788 0.101650334446259 3.13324152131709 0.00187608636360288 ** df.mm.exp7 0.0647706432943613 0.101650334446259 0.637190656058338 0.524420830622882 df.mm.exp8 -0.128486971210460 0.101650334446259 -1.26400933071587 0.207075113387443 df.mm.trans1:exp2 0.208873786008294 0.0859102126518327 2.43130332891613 0.0155503179162795 * df.mm.trans2:exp2 0.143021071824502 0.0859102126518326 1.66477380755792 0.09686050963124 . df.mm.trans1:exp3 0.067947342575371 0.0859102126518326 0.790911120785376 0.429536090551659 df.mm.trans2:exp3 0.0673457794023898 0.0859102126518326 0.783908889567313 0.43362833276248 df.mm.trans1:exp4 0.0635255440480106 0.0859102126518326 0.739441122156919 0.460139102182534 df.mm.trans2:exp4 0.127755554565856 0.0859102126518326 1.48708227604568 0.137901156554038 df.mm.trans1:exp5 0.0703848184883813 0.0859102126518326 0.819283485813603 0.413187028862959 df.mm.trans2:exp5 -0.173103996097609 0.0859102126518326 -2.01494084060932 0.0446817165592249 * df.mm.trans1:exp6 0.991380229374507 0.0859102126518326 11.539725007925 2.70800864641947e-26 *** df.mm.trans2:exp6 -0.242725567480112 0.0859102126518326 -2.82534008458114 0.00499631574688138 ** df.mm.trans1:exp7 0.278999370275057 0.0859102126518326 3.24756931292621 0.00127755202166999 ** df.mm.trans2:exp7 -0.0751109507976931 0.0859102126518326 -0.874295947818153 0.382561799059944 df.mm.trans1:exp8 0.0857989517126525 0.0859102126518326 0.998704916030984 0.318633575267039 df.mm.trans2:exp8 0.102164450818872 0.0859102126518326 1.18920030186531 0.235173971634199 df.mm.trans1:probe2 -0.0672937398872268 0.0470549613894745 -1.43010934235469 0.153585419127374 df.mm.trans1:probe3 -0.338900648445359 0.0470549613894745 -7.20222986988076 3.70657147913824e-12 *** df.mm.trans1:probe4 -0.365107936151999 0.0470549613894745 -7.7591804428442 9.64420214755723e-14 *** df.mm.trans1:probe5 -0.166830164627462 0.0470549613894745 -3.545431973615 0.000445727201364835 *** df.mm.trans1:probe6 -0.308103417915887 0.0470549613894745 -6.54773500642603 2.10529096249979e-10 *** df.mm.trans2:probe2 -0.0758930304341702 0.0470549613894745 -1.61285926484994 0.107684132194916 df.mm.trans2:probe3 -0.000835497143427332 0.0470549613894745 -0.01775577152241 0.985843893133273 df.mm.trans2:probe4 0.199650676570687 0.0470549613894745 4.24292509599946 2.83433435418350e-05 *** df.mm.trans2:probe5 0.198965852370803 0.0470549613894745 4.22837138732217 3.01434784037988e-05 *** df.mm.trans2:probe6 0.00411786894898342 0.0470549613894745 0.0875118973087613 0.930315107960663 df.mm.trans3:probe2 0.264876717699621 0.0470549613894745 5.62909223338286 3.74147783815864e-08 *** df.mm.trans3:probe3 0.201359875157092 0.0470549613894745 4.2792485470434 2.42874259323961e-05 *** df.mm.trans3:probe4 0.184017665951578 0.0470549613894745 3.91069635417318 0.000110712168473528 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36366891600593 0.216439883889993 20.1611128114622 2.1907712165733e-60 *** df.mm.trans1 -0.130250559549427 0.180324125721387 -0.7223135508261 0.470588219063561 df.mm.trans2 -0.159411792068709 0.180324125721387 -0.884029197041616 0.377292544185072 df.mm.exp2 0.0264604311253075 0.248448764132002 0.106502566908519 0.915245163183217 df.mm.exp3 0.257939310693436 0.248448764132002 1.03819921018561 0.299900211016029 df.mm.exp4 -0.128298016771613 0.248448764132002 -0.516396276793102 0.605906587609464 df.mm.exp5 -0.298590117149496 0.248448764132002 -1.20181767936207 0.230253729644497 df.mm.exp6 0.0415061734612494 0.248448764132002 0.167061299766406 0.867419127057247 df.mm.exp7 0.000565372924992499 0.248448764132002 0.00227561174219451 0.998185633744582 df.mm.exp8 0.101163280715172 0.248448764132002 0.407179649569210 0.684127106871638 df.mm.trans1:exp2 0.00178175867091665 0.209977530088203 0.00848547304165454 0.993234529558218 df.mm.trans2:exp2 0.0693336158103969 0.209977530088203 0.330195406057365 0.741451576915115 df.mm.trans1:exp3 -0.144526738333362 0.209977530088203 -0.688296210897672 0.49172614437091 df.mm.trans2:exp3 -0.184806335659124 0.209977530088203 -0.880124342740359 0.379401081388137 df.mm.trans1:exp4 0.1750489199727 0.209977530088203 0.833655486371179 0.405048626107147 df.mm.trans2:exp4 0.164422012674781 0.209977530088203 0.783045750684436 0.434134330837785 df.mm.trans1:exp5 0.200429457314320 0.209977530088203 0.954528121319114 0.340480854160554 df.mm.trans2:exp5 0.382164311147800 0.209977530088203 1.82002479497338 0.0696166349060665 . df.mm.trans1:exp6 0.0376090878997997 0.209977530088203 0.179110059462084 0.857955817364147 df.mm.trans2:exp6 -0.0261732888126733 0.209977530088203 -0.124648045920337 0.900874366922793 df.mm.trans1:exp7 0.0649413061447035 0.209977530088203 0.309277407527483 0.75729613831884 df.mm.trans2:exp7 -0.0893149125077187 0.209977530088203 -0.425354619945292 0.67084184836836 df.mm.trans1:exp8 -0.123016452903692 0.209977530088203 -0.585855319147807 0.558353590587693 df.mm.trans2:exp8 0.0740967785784773 0.209977530088203 0.352879560719436 0.724392990292253 df.mm.trans1:probe2 0.0133120579516147 0.115009429798529 0.115747534571161 0.907919585946073 df.mm.trans1:probe3 -0.180067715464407 0.115009429798529 -1.56567783858981 0.118335519893879 df.mm.trans1:probe4 -0.164768666245006 0.115009429798529 -1.43265353574611 0.152857128675665 df.mm.trans1:probe5 -0.203500314948646 0.115009429798529 -1.76942286650003 0.0777015928776676 . df.mm.trans1:probe6 -0.213755463516245 0.115009429798529 -1.85859075982463 0.0639317003298687 . df.mm.trans2:probe2 -0.0360445262929677 0.115009429798529 -0.313404964759062 0.754161312052845 df.mm.trans2:probe3 -0.0765527113976186 0.115009429798529 -0.665621171513694 0.506095474850145 df.mm.trans2:probe4 -0.254949405665835 0.115009429798529 -2.21676958239381 0.0272866893014172 * df.mm.trans2:probe5 -0.217947109583294 0.115009429798529 -1.89503686754286 0.0589184962677788 . df.mm.trans2:probe6 -0.127326498805138 0.115009429798529 -1.10709616618556 0.269019210938789 df.mm.trans3:probe2 -0.0302344325775614 0.115009429798529 -0.262886553133299 0.792794036832237 df.mm.trans3:probe3 -0.0308255043552268 0.115009429798529 -0.268025886305378 0.78883864745696 df.mm.trans3:probe4 -0.0330695702208992 0.115009429798529 -0.287537902577466 0.773872126958148