chr10.2127_chr10_81136464_81139261_-_2.R fitVsDatCorrelation=0.831870900743164 cont.fitVsDatCorrelation=0.289963613675658 fstatistic=6295.2515067679,49,623 cont.fstatistic=2108.42415442416,49,623 residuals=-0.677275591084941,-0.142012229285618,0.00655726548627001,0.131548436904676,0.732596794179628 cont.residuals=-0.927344194210618,-0.282729725402085,0.0142193605747750,0.246391227682884,1.29753693400689 predictedValues: Include Exclude Both chr10.2127_chr10_81136464_81139261_-_2.R.tl.Lung 101.749058321863 141.154754626202 109.571158616967 chr10.2127_chr10_81136464_81139261_-_2.R.tl.cerebhem 78.6385514168855 92.2946145201656 67.2154074034128 chr10.2127_chr10_81136464_81139261_-_2.R.tl.cortex 80.8979604983913 112.441268384341 67.5740996226549 chr10.2127_chr10_81136464_81139261_-_2.R.tl.heart 112.458852015674 115.075489288731 106.230244851343 chr10.2127_chr10_81136464_81139261_-_2.R.tl.kidney 90.1514303907983 147.699241485854 81.7707633995228 chr10.2127_chr10_81136464_81139261_-_2.R.tl.liver 86.4941744391916 129.774053699420 77.6877226152666 chr10.2127_chr10_81136464_81139261_-_2.R.tl.stomach 107.798706203276 109.311875364317 86.8877955046 chr10.2127_chr10_81136464_81139261_-_2.R.tl.testicle 142.251930047145 104.093743718938 144.318611126723 diffExp=-39.4056963043392,-13.6560631032801,-31.5433078859498,-2.61663727305654,-57.5478110950556,-43.2798792602286,-1.51316916104093,38.1581863282071 diffExpScore=1.49418772456535 diffExp1.5=0,0,0,0,-1,-1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,-1,-1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=-1,0,-1,0,-1,-1,0,1 diffExp1.3Score=1.25 diffExp1.2=-1,0,-1,0,-1,-1,0,1 diffExp1.2Score=1.25 cont.predictedValues: Include Exclude Both Lung 104.420417464514 102.092784543382 107.350357036560 cerebhem 105.754228470429 120.137742805987 102.295982621782 cortex 101.70520886454 122.384599389964 86.6269964702698 heart 99.2795123278853 105.679456434824 99.1326788280693 kidney 104.022793590977 118.994374149444 109.622182474220 liver 97.8093815440206 110.067374983086 105.118512600730 stomach 99.8511025264856 104.819233783378 104.535248624670 testicle 101.141447313677 121.487384775512 115.943549232562 cont.diffExp=2.32763292113238,-14.3835143355580,-20.6793905254236,-6.39994410693825,-14.9715805584669,-12.2579934390651,-4.96813125689289,-20.3459374618342 cont.diffExpScore=1.03944012573148 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,-1,0,0,0,0,-1 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.160187244769584 cont.tran.correlation=0.322436398464999 tran.covariance=-0.00256605931007805 cont.tran.covariance=0.000643691700938133 tran.mean=109.517856526325 cont.tran.mean=107.477940185507 weightedLogRatios: wLogRatio Lung -1.56674400368192 cerebhem -0.71173782940816 cortex -1.50062430282019 heart -0.108888502865685 kidney -2.34419211101413 liver -1.89183624589153 stomach -0.0653371638668337 testicle 1.49952879754837 cont.weightedLogRatios: wLogRatio Lung 0.104536160067192 cerebhem -0.602521869508516 cortex -0.872629765811016 heart -0.289190516232589 kidney -0.633583507203298 liver -0.548097740536054 stomach -0.224720631998617 testicle -0.862961593369837 varWeightedLogRatios=1.56541337898910 cont.varWeightedLogRatios=0.112331279646846 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.10115211636826 0.103864363904594 49.1135931959685 2.17950901345022e-216 *** df.mm.trans1 -0.494411385361418 0.088700611193132 -5.57393436990995 3.71146942698225e-08 *** df.mm.trans2 -0.205404514448937 0.0810556428779592 -2.53411739338373 0.0115169284913133 * df.mm.exp2 -0.193846870536535 0.106344891880973 -1.82281317990807 0.0688108365274714 . df.mm.exp3 0.0266026522248235 0.106344891880973 0.250154490303105 0.802550345126792 df.mm.exp4 -0.0732253431528794 0.106344891880972 -0.68856474305167 0.491353536175409 df.mm.exp5 0.216956886332139 0.106344891880973 2.04012512961103 0.0417590599127219 * df.mm.exp6 0.0973824992581191 0.106344891880973 0.915723336924498 0.360166328404133 df.mm.exp7 0.0340608957673484 0.106344891880972 0.320287088217376 0.748858102348541 df.mm.exp8 -0.244924136668266 0.106344891880972 -2.30311143616000 0.0216010778399863 * df.mm.trans1:exp2 -0.0638006436884545 0.0953124770726862 -0.669383963652519 0.50349856736515 df.mm.trans2:exp2 -0.231024176267809 0.0782183565058236 -2.95358003655586 0.00325953835543506 ** df.mm.trans1:exp3 -0.255923608091906 0.0953124770726861 -2.68510079637041 0.00744383060597323 ** df.mm.trans2:exp3 -0.254028464756712 0.0782183565058236 -3.24768348639234 0.00122597713330794 ** df.mm.trans1:exp4 0.173303168572336 0.0953124770726861 1.81826318961550 0.0695039887722301 . df.mm.trans2:exp4 -0.131043154291586 0.0782183565058236 -1.67535039274098 0.0943672957443669 . df.mm.trans1:exp5 -0.337975639480134 0.0953124770726862 -3.54597477539474 0.000420514985330843 *** df.mm.trans2:exp5 -0.171635671363744 0.0782183565058236 -2.19431446825357 0.0285812760138795 * df.mm.trans1:exp6 -0.259815004569921 0.0953124770726862 -2.7259285725182 0.00659237723011225 ** df.mm.trans2:exp6 -0.181444448477523 0.0782183565058236 -2.31971696393306 0.0206785206765645 * df.mm.trans1:exp7 0.0236951913527332 0.0953124770726862 0.248605346125492 0.803748001200925 df.mm.trans2:exp7 -0.289712696275302 0.0782183565058236 -3.70389649204318 0.000231166045170178 *** df.mm.trans1:exp8 0.580014208118757 0.0953124770726861 6.08539643426152 2.03021981613136e-09 *** df.mm.trans2:exp8 -0.0596408273285807 0.0782183565058236 -0.762491440537239 0.446055337423818 df.mm.trans1:probe2 -0.152930041922073 0.0623966629799039 -2.45093302459663 0.0145223901361216 * df.mm.trans1:probe3 -0.157639730304272 0.0623966629799039 -2.52641283645318 0.0117699542423418 * df.mm.trans1:probe4 -0.131547301961841 0.0623966629799039 -2.10824258348893 0.035408727936449 * df.mm.trans1:probe5 -0.0272173228446432 0.0623966629799039 -0.43619837255414 0.66284392185948 df.mm.trans1:probe6 -0.200800407434515 0.0623966629799039 -3.21812734599584 0.00135726369331824 ** df.mm.trans1:probe7 -0.0569176642866437 0.0623966629799039 -0.912190837913483 0.362021104255479 df.mm.trans1:probe8 -0.343147962739329 0.0623966629799039 -5.49946016904537 5.5649686537666e-08 *** df.mm.trans1:probe9 0.122803621426699 0.0623966629799039 1.96811200410269 0.0494981945746486 * df.mm.trans1:probe10 0.299792980095443 0.0623966629799039 4.80463162255964 1.94466265241970e-06 *** df.mm.trans1:probe11 0.217372038453853 0.0623966629799039 3.4837125588569 0.000529178459719352 *** df.mm.trans1:probe12 0.177717934653128 0.0623966629799039 2.84819614007829 0.00454183170207592 ** df.mm.trans1:probe13 0.358574916054563 0.0623966629799039 5.74670020686922 1.42473524922467e-08 *** df.mm.trans1:probe14 0.240853386732723 0.0623966629799039 3.86003634217258 0.000125199714279178 *** df.mm.trans2:probe2 -0.00879996223559217 0.0623966629799039 -0.141032577950946 0.887889794770342 df.mm.trans2:probe3 0.0471733396559933 0.0623966629799039 0.756023437842925 0.449920906407971 df.mm.trans2:probe4 0.154574341724707 0.0623966629799039 2.47728539224109 0.0135027574824968 * df.mm.trans2:probe5 0.186176490568494 0.0623966629799039 2.98375717029060 0.00295878420200038 ** df.mm.trans2:probe6 0.378405110120907 0.0623966629799039 6.06450877417563 2.29566031618532e-09 *** df.mm.trans3:probe2 0.319644434697222 0.0623966629799039 5.12278092179656 4.02009751014579e-07 *** df.mm.trans3:probe3 0.187753972926524 0.0623966629799039 3.0090386882868 0.00272660026443682 ** df.mm.trans3:probe4 0.578570178492092 0.0623966629799039 9.27245385988722 2.94165646372633e-19 *** df.mm.trans3:probe5 0.709728976039604 0.0623966629799039 11.3744700781224 2.27304155070365e-27 *** df.mm.trans3:probe6 0.321810113000819 0.0623966629799039 5.1574891609903 3.36731370299055e-07 *** df.mm.trans3:probe7 0.337071088025214 0.0623966629799039 5.40206914805324 9.38581039558817e-08 *** df.mm.trans3:probe8 -0.0926749870168493 0.0623966629799039 -1.48525550231263 0.137981964030559 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.65754550160597 0.179112841293655 26.0034147633778 1.66330237382634e-101 *** df.mm.trans1 -0.00468012264590087 0.152963132859305 -0.0305964094642702 0.975601204563063 df.mm.trans2 0.0658162788371975 0.139779477319968 0.470857954966723 0.637906991239107 df.mm.exp2 0.22367691438283 0.183390481834213 1.21967570042723 0.223049367691452 df.mm.exp3 0.369426127479813 0.183390481834213 2.01442367011053 0.0443943153382046 * df.mm.exp4 0.0636811745644692 0.183390481834213 0.347243618793901 0.72852557466406 df.mm.exp5 0.128437083469860 0.183390481834213 0.700347598115633 0.483971706106382 df.mm.exp6 0.0308153434178396 0.183390481834213 0.168031312801157 0.8666131776029 df.mm.exp7 0.00818363533310036 0.183390481834213 0.0446241007234959 0.964421231190746 df.mm.exp8 0.0650175779567092 0.183390481834213 0.354530820282625 0.72306108153689 df.mm.trans1:exp2 -0.210984337630642 0.164365215724101 -1.28363131275168 0.199748212349895 df.mm.trans2:exp2 -0.0609200253554192 0.134886611234120 -0.451638786074043 0.651686406343121 df.mm.trans1:exp3 -0.395772833707251 0.164365215724101 -2.40788680234864 0.0163347234220691 * df.mm.trans2:exp3 -0.188139639482626 0.134886611234120 -1.39479847377941 0.163573710494643 df.mm.trans1:exp4 -0.114167171679134 0.164365215724101 -0.69459448081017 0.48756841857211 df.mm.trans2:exp4 -0.0291527100519073 0.134886611234120 -0.216127529524095 0.828959086285003 df.mm.trans1:exp5 -0.132252265109293 0.164365215724101 -0.804624412328749 0.421343392534888 df.mm.trans2:exp5 0.0247570802816636 0.134886611234120 0.183539938138807 0.854434103137158 df.mm.trans1:exp6 -0.0962200710804429 0.164365215724101 -0.585404099380462 0.558488012035045 df.mm.trans2:exp6 0.0443952825319712 0.134886611234120 0.329130386817379 0.742167751183163 df.mm.trans1:exp7 -0.0529287596154099 0.164365215724101 -0.322019226405267 0.747546150922766 df.mm.trans2:exp7 0.0181715959945363 0.134886611234120 0.134717566319434 0.892878694795793 df.mm.trans1:exp8 -0.0969227983152877 0.164365215724101 -0.589679500545781 0.555619329298177 df.mm.trans2:exp8 0.108910798237786 0.134886611234120 0.807424823274357 0.419729928086413 df.mm.trans1:probe2 0.0703834490673512 0.107602291810492 0.654107341796307 0.513284026486406 df.mm.trans1:probe3 0.0693586784415239 0.107602291810492 0.644583654070101 0.519434293869695 df.mm.trans1:probe4 0.0980446345847747 0.107602291810492 0.911176081244164 0.362555020228011 df.mm.trans1:probe5 -0.0580391676182589 0.107602291810492 -0.539385979998243 0.589813264815912 df.mm.trans1:probe6 -0.164950298459843 0.107602291810492 -1.53296268773115 0.125792680422093 df.mm.trans1:probe7 0.0218598639717923 0.107602291810491 0.203154260043938 0.839080795541316 df.mm.trans1:probe8 -0.060806796631453 0.107602291810491 -0.565106891389874 0.572204494378246 df.mm.trans1:probe9 0.080696761781721 0.107602291810491 0.749953931500304 0.453565562754417 df.mm.trans1:probe10 0.0662667485991029 0.107602291810491 0.615848858645237 0.538219201960201 df.mm.trans1:probe11 -0.00821904966324652 0.107602291810491 -0.0763835929974601 0.939138444818531 df.mm.trans1:probe12 -0.177765465805599 0.107602291810491 -1.65206021929978 0.0990261514960944 . df.mm.trans1:probe13 -0.0147466336847897 0.107602291810491 -0.137047579904351 0.891037463517828 df.mm.trans1:probe14 -0.0197660916675709 0.107602291810491 -0.183695824085074 0.854311855643928 df.mm.trans2:probe2 -0.322610077356128 0.107602291810491 -2.99817106055981 0.00282428336401744 ** df.mm.trans2:probe3 -0.214874136718604 0.107602291810491 -1.99692899754439 0.0462674645004524 * df.mm.trans2:probe4 -0.256068318586892 0.107602291810491 -2.37976639975176 0.0176236021383587 * df.mm.trans2:probe5 -0.267999229473339 0.107602291810491 -2.49064610952096 0.0130104390769005 * df.mm.trans2:probe6 -0.303164433446186 0.107602291810491 -2.81745331205507 0.00499409545685442 ** df.mm.trans3:probe2 0.0461764236149775 0.107602291810492 0.429139777954759 0.667969863370347 df.mm.trans3:probe3 -0.0624294376398589 0.107602291810492 -0.580186876965494 0.56199837779982 df.mm.trans3:probe4 -0.0503599180593941 0.107602291810491 -0.468019009744585 0.639934678960757 df.mm.trans3:probe5 0.127121336768268 0.107602291810491 1.18139989984742 0.237894674328709 df.mm.trans3:probe6 -0.104315055076317 0.107602291810491 -0.969450123423358 0.33269686299627 df.mm.trans3:probe7 -0.104602168055452 0.107602291810491 -0.972118402828042 0.331368920950091 df.mm.trans3:probe8 -0.192261642087233 0.107602291810491 -1.78678017774791 0.0744591336592873 .