chr2.14348_chr2_9383117_9441096_+_2.R fitVsDatCorrelation=0.735779929293455 cont.fitVsDatCorrelation=0.244711558774965 fstatistic=11832.6545746715,50,646 cont.fstatistic=5765.84504377674,50,646 residuals=-0.500763045940789,-0.0742467578546414,-0.00353131169647314,0.0630205674325349,0.948835248301179 cont.residuals=-0.402437668292742,-0.108968588098029,-0.0237849688489876,0.0672387248388628,1.26163477111756 predictedValues: Include Exclude Both chr2.14348_chr2_9383117_9441096_+_2.R.tl.Lung 44.7458099847623 50.1618262496393 48.1168754041917 chr2.14348_chr2_9383117_9441096_+_2.R.tl.cerebhem 47.5827250285836 53.8190838354287 56.6333661607602 chr2.14348_chr2_9383117_9441096_+_2.R.tl.cortex 43.8846728008425 48.3540144681008 50.2253297236655 chr2.14348_chr2_9383117_9441096_+_2.R.tl.heart 46.1093744046752 50.7074703868989 50.1090548321932 chr2.14348_chr2_9383117_9441096_+_2.R.tl.kidney 44.4017920399441 49.8482239719725 48.5524692382224 chr2.14348_chr2_9383117_9441096_+_2.R.tl.liver 48.1808456605628 50.9893545034761 52.3696781060714 chr2.14348_chr2_9383117_9441096_+_2.R.tl.stomach 45.3895717143918 50.7575019082615 51.9559421107274 chr2.14348_chr2_9383117_9441096_+_2.R.tl.testicle 46.9922378500472 50.9226794238131 53.6101087565395 diffExp=-5.41601626487703,-6.2363588068451,-4.46934166725828,-4.59809598222373,-5.44643193202842,-2.80850884291329,-5.3679301938697,-3.93044157376586 diffExpScore=0.97453729507689 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 52.6742658349208 49.8417187225387 51.8207660602052 cerebhem 51.329143859617 52.8735353663223 49.203720178075 cortex 50.3859048482085 52.9192310579332 52.2991272629737 heart 47.8065524462982 50.7405847730446 49.1827799116403 kidney 48.7431811605475 50.6095928318285 49.3964122811103 liver 49.8626923546001 47.9495927129141 50.9527816665079 stomach 54.1125805956834 50.0189184814941 49.6487364268371 testicle 47.3128775885314 49.0065508319684 50.1087344445228 cont.diffExp=2.83254711238202,-1.54439150670535,-2.53332620972463,-2.9340323267464,-1.86641167128102,1.91309964168601,4.09366211418928,-1.69367324343694 cont.diffExpScore=7.10373595325123 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=0.760187781782085 cont.tran.correlation=0.141981512623287 tran.covariance=0.000782398829577953 cont.tran.covariance=0.00023991103520439 tran.mean=48.3029490144625 cont.tran.mean=50.3866827166532 weightedLogRatios: wLogRatio Lung -0.440815962157186 cerebhem -0.483279380557303 cortex -0.371454754637183 heart -0.368682697461492 kidney -0.445587598548668 liver -0.221142125025019 stomach -0.432708052533005 testicle -0.312479651806342 cont.weightedLogRatios: wLogRatio Lung 0.217588142092084 cerebhem -0.117186075552478 cortex -0.193485986567278 heart -0.232115134729812 kidney -0.146746873134953 liver 0.152176286627081 stomach 0.310864730819919 testicle -0.136267217699834 varWeightedLogRatios=0.00732537902543313 cont.varWeightedLogRatios=0.0442364246823054 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90179808501106 0.0656764787959434 59.409367806303 5.62600846993752e-264 *** df.mm.trans1 -0.10332588251064 0.0518331359133646 -1.99343297853600 0.0466341512046306 * df.mm.trans2 0.0190894181113194 0.0518331359133646 0.368285996495099 0.71278064236783 df.mm.exp2 -0.0311195787322851 0.0686358536106039 -0.453401204985921 0.65041212831279 df.mm.exp3 -0.0990242649166551 0.0686358536106039 -1.44274835537787 0.149576051833509 df.mm.exp4 0.000268639117099913 0.0686358536106039 0.00391397648558434 0.996878314891613 df.mm.exp5 -0.0230015121991748 0.0686358536106039 -0.335123860040711 0.737640447220652 df.mm.exp6 0.00563150583770592 0.0686358536106039 0.0820490391167201 0.934633132213782 df.mm.exp7 -0.0506734410281844 0.0686358536106039 -0.738294030925488 0.460603914890040 df.mm.exp8 -0.0440659752598254 0.0686358536106039 -0.642025602388916 0.521084314652955 df.mm.trans1:exp2 0.092591545819117 0.0520457128925365 1.77904270444521 0.0757028925055311 . df.mm.trans2:exp2 0.101493396985541 0.0520457128925365 1.95008179050412 0.0515985533776641 . df.mm.trans1:exp3 0.079591576389894 0.0520457128925365 1.5292628723183 0.126688783202673 df.mm.trans2:exp3 0.0623192085515916 0.0520457128925365 1.19739369658107 0.231592321464276 df.mm.trans1:exp4 0.0297498308432968 0.0520457128925366 0.571609632953322 0.56778529263611 df.mm.trans2:exp4 0.0105503013875666 0.0520457128925365 0.202712208195721 0.839423807885097 df.mm.trans1:exp5 0.0152835333711348 0.0520457128925366 0.293655952079897 0.769115134042154 df.mm.trans2:exp5 0.0167300763819461 0.0520457128925365 0.321449653624501 0.747973636730748 df.mm.trans1:exp6 0.068332234591243 0.0520457128925366 1.31292724786640 0.189673662730983 df.mm.trans2:exp6 0.0107310657249066 0.0520457128925365 0.206185392196741 0.836711061385995 df.mm.trans1:exp7 0.0649580130582656 0.0520457128925365 1.24809536555663 0.212448316598963 df.mm.trans2:exp7 0.0624785647305724 0.0520457128925365 1.20045554683010 0.230402482243116 df.mm.trans1:exp8 0.0930506024923879 0.0520457128925366 1.78786296355509 0.0742667301484486 . df.mm.trans2:exp8 0.0591200636805233 0.0520457128925365 1.13592571596807 0.256408889884423 df.mm.trans1:probe2 -0.0149058879932098 0.0387503951950549 -0.384664154215181 0.70061285602302 df.mm.trans1:probe3 -0.0759064822365924 0.0387503951950549 -1.95885698338063 0.0505593430814751 . df.mm.trans1:probe4 0.0147400720481496 0.0387503951950549 0.38038507669286 0.703784629713668 df.mm.trans1:probe5 -0.0121034186479824 0.0387503951950549 -0.31234310223311 0.754880579825696 df.mm.trans1:probe6 0.146264659689776 0.0387503951950549 3.77453336807367 0.000175067752279675 *** df.mm.trans2:probe2 -0.013774512646129 0.0387503951950549 -0.355467668827461 0.722355297908628 df.mm.trans2:probe3 -0.0492237262183994 0.0387503951950549 -1.27027675384021 0.204443382403299 df.mm.trans2:probe4 -0.00963217425629877 0.0387503951950549 -0.248569703813704 0.803772646087557 df.mm.trans2:probe5 0.00712944150644888 0.0387503951950549 0.183983710890224 0.854083969755784 df.mm.trans2:probe6 -0.0640626048071926 0.0387503951950549 -1.65321165073867 0.0987736451924895 . df.mm.trans3:probe2 0.153377797963842 0.0387503951950549 3.95809635467707 8.39307425315364e-05 *** df.mm.trans3:probe3 -0.0246972278159857 0.0387503951950549 -0.637341314628385 0.524128354968158 df.mm.trans3:probe4 -0.00206801412374408 0.0387503951950549 -0.0533675621457401 0.957455549010437 df.mm.trans3:probe5 0.282351048291031 0.0387503951950549 7.28640435458227 9.31497068395825e-13 *** df.mm.trans3:probe6 -0.0114951260619776 0.0387503951950549 -0.296645389140303 0.766832602934371 df.mm.trans3:probe7 -0.0249228729965963 0.0387503951950549 -0.643164356676724 0.520345686856375 df.mm.trans3:probe8 0.351839474570929 0.0387503951950549 9.07963577661343 1.30807811852984e-18 *** df.mm.trans3:probe9 0.0607337314219338 0.0387503951950549 1.56730611691115 0.117532838556960 df.mm.trans3:probe10 -0.11180876045059 0.0387503951950549 -2.88535793990712 0.0040400906591514 ** df.mm.trans3:probe11 -0.0371229960352115 0.0387503951950549 -0.95800303063616 0.338419620903145 df.mm.trans3:probe12 -0.0509683854854811 0.0387503951950549 -1.31529975962633 0.188875832068931 df.mm.trans3:probe13 -0.0960924533907683 0.0387503951950549 -2.47977995855462 0.0134001970971380 * df.mm.trans3:probe14 0.0214505586310699 0.0387503951950549 0.553557157884349 0.580073486857575 df.mm.trans3:probe15 0.478760900336246 0.0387503951950549 12.3549940052571 1.25278926096174e-31 *** df.mm.trans3:probe16 -0.0544759974665948 0.0387503951950549 -1.40581785533756 0.160258994596332 df.mm.trans3:probe17 0.0183510171699346 0.0387503951950549 0.473569806902937 0.635966705791859 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93749898980501 0.0940308598339953 41.874539877189 3.10424835857350e-186 *** df.mm.trans1 0.0319828357905384 0.0742109569084734 0.430971882359417 0.666632580729309 df.mm.trans2 -0.0213954919219803 0.0742109569084733 -0.288306374331865 0.773204706438167 df.mm.exp2 0.0850039778125639 0.0982678798980306 0.865023015666663 0.387347262138952 df.mm.exp3 0.00631017280409744 0.0982678798980306 0.0642139914959527 0.948819701598788 df.mm.exp4 -0.0268431933889051 0.0982678798980306 -0.273163452969164 0.784814875848277 df.mm.exp5 -0.0143597641733592 0.0982678798980306 -0.146128767490048 0.88386532358156 df.mm.exp6 -0.0766643944798037 0.0982678798980306 -0.780157204565274 0.435584098237613 df.mm.exp7 0.0733066425962611 0.0982678798980306 0.745987831144104 0.455946129857607 df.mm.exp8 -0.09064726409915 0.0982678798980306 -0.92245059314612 0.356637975552024 df.mm.trans1:exp2 -0.110872302477091 0.0745153093417784 -1.48791306721353 0.137261774431656 df.mm.trans2:exp2 -0.0259533994442343 0.0745153093417784 -0.348296204813351 0.727731215358956 df.mm.trans1:exp3 -0.0507257245058387 0.0745153093417784 -0.680742319315561 0.496278471114827 df.mm.trans2:exp3 0.0536042773769683 0.0745153093417783 0.719372674561441 0.472171479988678 df.mm.trans1:exp4 -0.0701211179970326 0.0745153093417784 -0.941029683919167 0.347041396653483 df.mm.trans2:exp4 0.0447169136993609 0.0745153093417784 0.600103711497169 0.548647563749599 df.mm.trans1:exp5 -0.0632019437509775 0.0745153093417784 -0.848173943170389 0.396655262300770 df.mm.trans2:exp5 0.0296485454683845 0.0745153093417784 0.397885290020013 0.6908461929499 df.mm.trans1:exp6 0.0218104474493985 0.0745153093417784 0.292697536144698 0.769847341220611 df.mm.trans2:exp6 0.0379623430908896 0.0745153093417783 0.509456961612656 0.610605940367467 df.mm.trans1:exp7 -0.0463669623747216 0.0745153093417784 -0.622247465444327 0.533998718884143 df.mm.trans2:exp7 -0.0697576977887227 0.0745153093417783 -0.936152562539411 0.349544468537451 df.mm.trans1:exp8 -0.0166972459550546 0.0745153093417784 -0.224078059965766 0.822767412907468 df.mm.trans2:exp8 0.0737488850355867 0.0745153093417783 0.989714539026117 0.322684219597350 df.mm.trans1:probe2 0.00574993415048485 0.0554800294702034 0.103639709736869 0.9174874394744 df.mm.trans1:probe3 0.0420221269825263 0.0554800294702034 0.757427985958353 0.449069678365143 df.mm.trans1:probe4 -0.0842092192891523 0.0554800294702034 -1.51782939002183 0.129546620923308 df.mm.trans1:probe5 -0.0746264353390776 0.0554800294702034 -1.34510446464628 0.179063505199612 df.mm.trans1:probe6 -0.0120968902333967 0.0554800294702034 -0.218040443541826 0.827466456573459 df.mm.trans2:probe2 -0.00406540118694713 0.0554800294702034 -0.0732768389953816 0.941608504372854 df.mm.trans2:probe3 -0.0819748214925531 0.0554800294702034 -1.47755547852726 0.140014147706723 df.mm.trans2:probe4 -0.0543052724876449 0.0554800294702034 -0.978825588346353 0.328032579145351 df.mm.trans2:probe5 -0.0108651552454770 0.0554800294702034 -0.195839031616094 0.84479774404403 df.mm.trans2:probe6 -0.0155655511016533 0.0554800294702034 -0.280561334416974 0.779136701479536 df.mm.trans3:probe2 0.0177254676750485 0.0554800294702034 0.319492758823574 0.74945617273725 df.mm.trans3:probe3 0.00999245056804292 0.0554800294702034 0.18010896287302 0.857123509239472 df.mm.trans3:probe4 -0.0284682783487765 0.0554800294702034 -0.513126590245702 0.608038200180008 df.mm.trans3:probe5 0.0488644148635061 0.0554800294702034 0.880756829621902 0.378777002134564 df.mm.trans3:probe6 0.00356303781343618 0.0554800294702034 0.0642219884787512 0.948813336556804 df.mm.trans3:probe7 -0.062391971324727 0.0554800294702034 -1.12458432197185 0.261182902228559 df.mm.trans3:probe8 0.0495040342748924 0.0554800294702034 0.892285652109818 0.37257208761384 df.mm.trans3:probe9 0.00837343811255244 0.0554800294702034 0.150927066775434 0.880080364434055 df.mm.trans3:probe10 -0.0244566573245577 0.0554800294702034 -0.440819111995833 0.659491436476601 df.mm.trans3:probe11 -0.0148666838897558 0.0554800294702034 -0.267964599725749 0.788812105953436 df.mm.trans3:probe12 -0.039143355974871 0.0554800294702034 -0.705539567095826 0.480728847625228 df.mm.trans3:probe13 -0.020449404380698 0.0554800294702034 -0.368590366226837 0.71255383670044 df.mm.trans3:probe14 -0.0167457984710964 0.0554800294702034 -0.301834707569686 0.762875213832511 df.mm.trans3:probe15 -0.0164248256894424 0.0554800294702034 -0.296049332458694 0.767287550212258 df.mm.trans3:probe16 0.0177269890466912 0.0554800294702034 0.319520180792474 0.749435391532477 df.mm.trans3:probe17 0.062175758706901 0.0554800294702034 1.12068719682807 0.262837478274902