chr15.8504_chr15_82425854_82431145_+_2.R fitVsDatCorrelation=0.900966314824098 cont.fitVsDatCorrelation=0.215025691298107 fstatistic=12860.9946861254,54,738 cont.fstatistic=2527.61188849751,54,738 residuals=-0.656806148342754,-0.0922397083073836,0.00072445599226804,0.0887109669492619,0.573507446313475 cont.residuals=-0.649381103853562,-0.235445907674858,-0.059187389732741,0.217570773566615,0.912607518513164 predictedValues: Include Exclude Both chr15.8504_chr15_82425854_82431145_+_2.R.tl.Lung 75.7905187850281 84.6364818429655 54.2572231835643 chr15.8504_chr15_82425854_82431145_+_2.R.tl.cerebhem 73.337734395103 69.0830356470675 59.6031446666966 chr15.8504_chr15_82425854_82431145_+_2.R.tl.cortex 75.8647824831909 70.1485573606299 62.5816265832223 chr15.8504_chr15_82425854_82431145_+_2.R.tl.heart 71.238804217175 65.4704102961806 56.1841157140842 chr15.8504_chr15_82425854_82431145_+_2.R.tl.kidney 79.3951333254461 90.847988293657 54.7478945324887 chr15.8504_chr15_82425854_82431145_+_2.R.tl.liver 75.6723725349994 79.8937371964069 54.6462885793351 chr15.8504_chr15_82425854_82431145_+_2.R.tl.stomach 73.6696367366619 71.6529735144622 52.2616380811689 chr15.8504_chr15_82425854_82431145_+_2.R.tl.testicle 73.5264470889879 67.9772321808857 54.9344925567368 diffExp=-8.84596305793731,4.25469874803545,5.71622512256099,5.76839392099448,-11.4528549682108,-4.22136466140749,2.01666322219968,5.54921490810219 diffExpScore=21.5917220594033 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 68.4195843396943 65.2666810992092 77.2238373211535 cerebhem 68.1425720894891 66.1296645962322 76.2128348107545 cortex 66.1172277049236 72.322101441983 76.016462934046 heart 65.3640011204826 72.7352419209846 72.2189277803226 kidney 67.7290342110629 66.3540410370654 66.9535665984113 liver 69.2684870531702 62.3711142571233 62.2873714253205 stomach 69.4397622087056 68.298089926736 62.5790805575033 testicle 68.9130848764732 77.6868516296694 61.9064150961967 cont.diffExp=3.15290324048513,2.01290749325692,-6.20487373705943,-7.37124080050195,1.37499317399747,6.89737279604685,1.14167228196956,-8.7737667531962 cont.diffExpScore=4.21090014177622 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=0.881121173369965 cont.tran.correlation=-0.367337392661553 tran.covariance=0.00334649520170289 cont.tran.covariance=-0.000602704663594437 tran.mean=74.887865368678 cont.tran.mean=68.4098462195628 weightedLogRatios: wLogRatio Lung -0.483867694701985 cerebhem 0.254913795124539 cortex 0.336050245830258 heart 0.356656586940636 kidney -0.598536784036692 liver -0.236329844587144 stomach 0.118954566183324 testicle 0.334167308436485 cont.weightedLogRatios: wLogRatio Lung 0.198242931133184 cerebhem 0.126134054110636 cortex -0.379996095352907 heart -0.452357124021043 kidney 0.0862510979961278 liver 0.439012790973977 stomach 0.0701602731402163 testicle -0.514444878102955 varWeightedLogRatios=0.154045438014443 cont.varWeightedLogRatios=0.121598677019673 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.14462090821432 0.0708329126208577 72.6303736195569 0 *** df.mm.trans1 -0.629150145812983 0.0623974890570931 -10.0829401201916 1.73621981712053e-22 *** df.mm.trans2 -0.603046104556465 0.0563031598131547 -10.7106973490957 5.55970322356023e-25 *** df.mm.exp2 -0.329926343086337 0.074979049894175 -4.40024704970246 1.24069498711543e-05 *** df.mm.exp3 -0.329506385030233 0.074979049894175 -4.39464604439902 1.27227634458840e-05 *** df.mm.exp4 -0.353600590188514 0.074979049894175 -4.71599187623188 2.87647985055956e-06 *** df.mm.exp5 0.108283338595266 0.074979049894175 1.44418125793934 0.149112386012476 df.mm.exp6 -0.0663731733252495 0.074979049894175 -0.885222917854097 0.376324739762326 df.mm.exp7 -0.157439694898568 0.074979049894175 -2.09978247418149 0.036086929475552 * df.mm.exp8 -0.261925911026681 0.074979049894175 -3.49332128636414 0.000505487707207467 *** df.mm.trans1:exp2 0.29702841061526 0.0707234053469932 4.19986013340191 2.99727168145708e-05 *** df.mm.trans2:exp2 0.126870138216295 0.0578646043748126 2.19253444462361 0.0286524494322166 * df.mm.trans1:exp3 0.330485760013867 0.0707234053469932 4.67293335766838 3.52916596612661e-06 *** df.mm.trans2:exp3 0.141756225182820 0.0578646043748126 2.44979165958877 0.0145252049412231 * df.mm.trans1:exp4 0.291665060363224 0.0707234053469932 4.1240245563999 4.14633734075095e-05 *** df.mm.trans2:exp4 0.0968334784374954 0.0578646043748126 1.67344924386358 0.0946626544068769 . df.mm.trans1:exp5 -0.0618194681842857 0.0707234053469932 -0.874101973469436 0.382347072332093 df.mm.trans2:exp5 -0.0374610882796377 0.0578646043748126 -0.647392109293394 0.517579524601842 df.mm.trans1:exp6 0.0648131044138319 0.0707234053469932 0.916430764268726 0.359740367621987 df.mm.trans2:exp6 0.00870523888311636 0.0578646043748126 0.150441517351937 0.880457395416124 df.mm.trans1:exp7 0.129057221794510 0.0707234053469932 1.82481628481138 0.0684328786162784 . df.mm.trans2:exp7 -0.00909105225617063 0.0578646043748126 -0.157109036765968 0.875201887134917 df.mm.trans1:exp8 0.231597874030981 0.0707234053469932 3.27469913099748 0.00110710290476048 ** df.mm.trans2:exp8 0.0427333381498777 0.0578646043748126 0.738505665278146 0.460442010321365 df.mm.trans1:probe2 0.0676171418902934 0.0412935722926062 1.63747377948215 0.101957798952866 df.mm.trans1:probe3 -0.475468410932413 0.0412935722926062 -11.5143443527541 2.49224901253591e-28 *** df.mm.trans1:probe4 -0.187546325455069 0.0412935722926062 -4.54178011352749 6.51367150782339e-06 *** df.mm.trans1:probe5 -0.180743882122443 0.0412935722926062 -4.37704640426098 1.37659135829194e-05 *** df.mm.trans1:probe6 -0.463605931359324 0.0412935722926062 -11.2270725350234 4.10087000212327e-27 *** df.mm.trans1:probe7 -0.354689148110312 0.0412935722926062 -8.58945178191379 5.16583959722251e-17 *** df.mm.trans1:probe8 -0.419450123982726 0.0412935722926062 -10.1577582343931 8.87318586019244e-23 *** df.mm.trans1:probe9 0.190976634611044 0.0412935722926062 4.62485137536137 4.42597363576144e-06 *** df.mm.trans1:probe10 -0.46085776494899 0.0412935722926062 -11.1605206176727 7.7914925586578e-27 *** df.mm.trans1:probe11 -0.631942639727638 0.0412935722926062 -15.3036563475229 4.03347908017957e-46 *** df.mm.trans1:probe12 -0.56289147954191 0.0412935722926062 -13.6314551706319 6.68915869676946e-38 *** df.mm.trans1:probe13 -0.679368986274972 0.0412935722926062 -16.4521727851726 4.79264701330112e-52 *** df.mm.trans1:probe14 -0.605494970869983 0.0412935722926062 -14.6631772756168 6.53335516168323e-43 *** df.mm.trans1:probe15 -0.556545655351203 0.0412935722926062 -13.4777793359102 3.57749591009923e-37 *** df.mm.trans1:probe16 -0.378414260413669 0.0412935722926062 -9.16399912635859 4.86756049504287e-19 *** df.mm.trans1:probe17 0.286404638215567 0.0412935722926062 6.93581645555158 8.8346033719263e-12 *** df.mm.trans1:probe18 0.352543163205192 0.0412935722926062 8.53748280015764 7.78534033534595e-17 *** df.mm.trans1:probe19 0.137703714648188 0.0412935722926062 3.33474938114872 0.000896426442525955 *** df.mm.trans1:probe20 0.00151562828305357 0.0412935722926062 0.0367037337509535 0.970731157047131 df.mm.trans1:probe21 0.0694728337947815 0.0412935722926062 1.68241278091653 0.0929118257751004 . df.mm.trans1:probe22 -0.211648283896839 0.0412935722926062 -5.12545348213276 3.79457700549933e-07 *** df.mm.trans2:probe2 -0.159232312455235 0.0412935722926062 -3.85610407660822 0.000125262080835393 *** df.mm.trans2:probe3 -0.0244465625299312 0.0412935722926062 -0.592018592063261 0.554019457202353 df.mm.trans2:probe4 -0.341341838742907 0.0412935722926062 -8.26622207263056 6.41318504724293e-16 *** df.mm.trans2:probe5 -0.200238950019381 0.0412935722926062 -4.84915542303988 1.51265436678934e-06 *** df.mm.trans2:probe6 -0.410043764184776 0.0412935722926062 -9.92996588619666 6.7715538477795e-22 *** df.mm.trans3:probe2 0.093091843793779 0.0412935722926062 2.25439066240456 0.0244640561347683 * df.mm.trans3:probe3 0.168935497102149 0.0412935722926062 4.09108458587869 4.7664197912518e-05 *** df.mm.trans3:probe4 0.211489524367896 0.0412935722926062 5.12160882738072 3.87006267223411e-07 *** df.mm.trans3:probe5 -0.066959505497108 0.0412935722926062 -1.62154790151438 0.105327307361913 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0050868329012 0.159432505663696 25.1208924819224 4.0574012113572e-101 *** df.mm.trans1 0.190363150087408 0.140445841620892 1.35542033776450 0.17569833934456 df.mm.trans2 0.183360820389240 0.126728571700037 1.446878300051 0.148355611304677 df.mm.exp2 0.0222571200916876 0.168764735976586 0.131882528437549 0.895113154008772 df.mm.exp3 0.0841765986636035 0.168764735976586 0.498780732695733 0.618082492534882 df.mm.exp4 0.129662937445661 0.168764735976586 0.76830587086422 0.442551260837977 df.mm.exp5 0.149087690217651 0.168764735976586 0.883405465928234 0.377304915917665 df.mm.exp6 0.181901042753852 0.168764735976586 1.07783798375443 0.281458240120644 df.mm.exp7 0.270477816323128 0.168764735976586 1.60269155021019 0.109430655968691 df.mm.exp8 0.402475699700005 0.168764735976586 2.38483292952767 0.0173384179322874 * df.mm.trans1:exp2 -0.0263140658428295 0.159186023930635 -0.165303870233582 0.868750082253941 df.mm.trans2:exp2 -0.00912135286335492 0.130243110488701 -0.0700332849018239 0.944186139189828 df.mm.trans1:exp3 -0.118406359229793 0.159186023930635 -0.743823837709448 0.457219803011845 df.mm.trans2:exp3 0.0184715119875151 0.130243110488701 0.141823332675378 0.887258259489566 df.mm.trans1:exp4 -0.175350376516230 0.159186023930635 -1.10154379251685 0.271019179199298 df.mm.trans2:exp4 -0.0213185747258066 0.130243110488701 -0.163682936055616 0.870025565725363 df.mm.trans1:exp5 -0.159231840891489 0.159186023930635 -1.00028782024780 0.317499096579009 df.mm.trans2:exp5 -0.132564689000206 0.130243110488701 -1.01782496212501 0.309094697252152 df.mm.trans1:exp6 -0.169570076659861 0.159186023930635 -1.06523218856042 0.287119251952852 df.mm.trans2:exp6 -0.227280449661843 0.130243110488701 -1.74504777111846 0.0813927750389716 . df.mm.trans1:exp7 -0.255677274793002 0.159186023930634 -1.60615403588706 0.108667833784185 df.mm.trans2:exp7 -0.225077678631207 0.130243110488701 -1.72813500680893 0.0843823198188223 . df.mm.trans1:exp8 -0.395288732989101 0.159186023930635 -2.48318742580912 0.0132423204006812 * df.mm.trans2:exp8 -0.228271338901228 0.130243110488701 -1.75265576846793 0.0800763420705228 . df.mm.trans1:probe2 0.0602743653530647 0.0929446136664524 0.648497669476249 0.516864843565332 df.mm.trans1:probe3 0.0134264224098728 0.0929446136664524 0.144456164593419 0.88517970765104 df.mm.trans1:probe4 -0.0182855336923653 0.0929446136664524 -0.196735808252278 0.844088451763637 df.mm.trans1:probe5 -0.0447272204957387 0.0929446136664524 -0.481224448963229 0.630499631343234 df.mm.trans1:probe6 0.0779628101929893 0.0929446136664524 0.838809341580268 0.401847914854534 df.mm.trans1:probe7 0.0517723925096717 0.0929446136664524 0.557024129396737 0.577679827318812 df.mm.trans1:probe8 -0.0258258331998134 0.0929446136664524 -0.277862612808245 0.781195648092589 df.mm.trans1:probe9 0.0261170630707132 0.0929446136664524 0.280995982881146 0.778792240780415 df.mm.trans1:probe10 0.0216538796361916 0.0929446136664524 0.232976164857711 0.81584451345716 df.mm.trans1:probe11 0.0992238210441406 0.0929446136664524 1.0675585935536 0.286068756112156 df.mm.trans1:probe12 0.0823582565512165 0.0929446136664524 0.886100369912485 0.375852082245093 df.mm.trans1:probe13 0.0205858932446801 0.0929446136664524 0.221485596987428 0.824775618839312 df.mm.trans1:probe14 0.124880828952553 0.0929446136664524 1.34360479888279 0.179489178749931 df.mm.trans1:probe15 0.0850091416332925 0.0929446136664524 0.914621496393135 0.360689097042106 df.mm.trans1:probe16 -0.0438901986279526 0.0929446136664524 -0.472218850523819 0.636910184055629 df.mm.trans1:probe17 0.0656427519764125 0.0929446136664524 0.706256655302078 0.480251440408681 df.mm.trans1:probe18 0.0294470374132831 0.0929446136664524 0.316823495753706 0.751467134564207 df.mm.trans1:probe19 0.0214666001743694 0.0929446136664524 0.230961207191693 0.817408953108851 df.mm.trans1:probe20 0.0299330694954574 0.0929446136664524 0.322052761474455 0.747503926636028 df.mm.trans1:probe21 0.0939094844842383 0.0929446136664524 1.01038113753690 0.31264386775461 df.mm.trans1:probe22 0.0447112460435854 0.0929446136664524 0.481052578302594 0.630621717416536 df.mm.trans2:probe2 0.0694319151254865 0.0929446136664524 0.747024624521597 0.45528662373621 df.mm.trans2:probe3 -0.0271783509791004 0.0929446136664524 -0.292414481129962 0.770051925579784 df.mm.trans2:probe4 -0.0754567779406008 0.0929446136664524 -0.811846700567182 0.417141373017671 df.mm.trans2:probe5 -0.0828636282931615 0.0929446136664524 -0.89153771288492 0.372931342759637 df.mm.trans2:probe6 0.00644094405455325 0.0929446136664524 0.0692987339499594 0.944770605580032 df.mm.trans3:probe2 -0.0503343513271441 0.0929446136664524 -0.541552106588743 0.588290539604818 df.mm.trans3:probe3 -0.0898648827511632 0.0929446136664524 -0.966864880128058 0.333928317858717 df.mm.trans3:probe4 -0.0764813591160029 0.0929446136664524 -0.822870267560305 0.41084757381823 df.mm.trans3:probe5 0.0561674911053175 0.0929446136664524 0.604311416118034 0.545822345582931