chr19.12432_chr19_24737059_24738946_+_1.R fitVsDatCorrelation=0.937963902096617 cont.fitVsDatCorrelation=0.259604525448037 fstatistic=8822.21076946567,36,324 cont.fstatistic=1129.44600672564,36,324 residuals=-0.434615401035558,-0.100115025953295,-0.00310847153392034,0.096238661053018,0.591105336603496 cont.residuals=-0.800197926810628,-0.307405640966965,-0.00738832605006156,0.255373042124221,1.12336844620463 predictedValues: Include Exclude Both chr19.12432_chr19_24737059_24738946_+_1.R.tl.Lung 159.937873768795 85.1442443490182 75.0628965638935 chr19.12432_chr19_24737059_24738946_+_1.R.tl.cerebhem 77.7503461558077 65.2203099955893 51.3977278557385 chr19.12432_chr19_24737059_24738946_+_1.R.tl.cortex 103.223120966057 66.5211445119846 54.3473528215745 chr19.12432_chr19_24737059_24738946_+_1.R.tl.heart 137.639552404608 80.2096153588429 61.3780489677154 chr19.12432_chr19_24737059_24738946_+_1.R.tl.kidney 128.17631854914 80.7959726935575 61.9713693852393 chr19.12432_chr19_24737059_24738946_+_1.R.tl.liver 134.072277253335 80.1285072389154 58.5312697505056 chr19.12432_chr19_24737059_24738946_+_1.R.tl.stomach 136.459400603994 87.5388923173288 58.4272808148396 chr19.12432_chr19_24737059_24738946_+_1.R.tl.testicle 174.081647398599 78.0874750520939 75.855020772944 diffExp=74.793629419777,12.5300361602184,36.7019764540724,57.4299370457654,47.3803458555824,53.9437700144195,48.9205082866649,95.994172346505 diffExpScore=0.997667335852867 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,0,1,1,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 98.5568988078824 90.4976497060894 79.7942099771458 cerebhem 96.7247918544812 94.3337043267802 91.6610061576349 cortex 97.320548016713 97.6057224138224 88.876224768491 heart 83.9739896919216 98.1469651165 88.1723054233429 kidney 89.365084473451 100.41763076076 138.062181274013 liver 96.7616916172206 85.1501751771595 94.302325231593 stomach 90.8304900506227 102.642326316954 89.1094107928591 testicle 100.175733759013 111.951075276410 99.8554869752756 cont.diffExp=8.05924910179303,2.39108752770103,-0.285174397109358,-14.1729754245785,-11.0525462873090,11.6115164400611,-11.8118362663315,-11.7753415173976 cont.diffExpScore=2.53815359216276 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=0.748506051618372 cont.tran.correlation=-0.0684631389011016 tran.covariance=0.0220046584042035 cont.tran.covariance=-0.000507099965392046 tran.mean=104.686668663604 cont.tran.mean=95.9034048353613 weightedLogRatios: wLogRatio Lung 3.00061439995859 cerebhem 0.749609255187764 cortex 1.94080124982778 heart 2.51348246338494 kidney 2.13326688626836 liver 2.38894521428100 stomach 2.08389743872100 testicle 3.81500642592552 cont.weightedLogRatios: wLogRatio Lung 0.387988785245218 cerebhem 0.114126136908122 cortex -0.0133994193750176 heart -0.703138580775152 kidney -0.530685803908229 liver 0.576321518099657 stomach -0.558722006080631 testicle -0.518170773998409 varWeightedLogRatios=0.777209033884502 cont.varWeightedLogRatios=0.236946249858073 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.83897147550649 0.0869424488168334 55.6571794486837 6.7367242247198e-168 *** df.mm.trans1 0.132200909179959 0.0736393459348587 1.79524828068006 0.0735459777972143 . df.mm.trans2 -0.523542913494571 0.0736393459348587 -7.10955409568272 7.47454966611076e-12 *** df.mm.exp2 -0.609125918213166 0.102551717811608 -5.93969492868135 7.36123464168897e-09 *** df.mm.exp3 -0.361789056724680 0.102551717811608 -3.52786929799951 0.00047943553028321 *** df.mm.exp4 -0.00857641213582384 0.102551717811608 -0.0836301167726817 0.933402167109223 df.mm.exp5 -0.0821444466900012 0.102551717811608 -0.801005077661444 0.423715700384136 df.mm.exp6 0.0116437078099015 0.102551717811608 0.113539861236567 0.909672882425335 df.mm.exp7 0.119521520637556 0.102551717811608 1.16547555894795 0.244683832745194 df.mm.exp8 -0.0122756751478151 0.102551717811608 -0.119702286902362 0.904793176911262 df.mm.trans1:exp2 -0.112156529457796 0.0888123928265853 -1.26284773879241 0.207551989578695 df.mm.trans2:exp2 0.342550031071501 0.0888123928265852 3.85700711544123 0.000138478025961106 *** df.mm.trans1:exp3 -0.0761035258375082 0.0888123928265853 -0.85690209908101 0.392132337292307 df.mm.trans2:exp3 0.114962105866210 0.0888123928265853 1.29443765906280 0.196436083352917 df.mm.trans1:exp4 -0.141570709753467 0.0888123928265853 -1.59404228675493 0.111901568929777 df.mm.trans2:exp4 -0.0511269984904339 0.0888123928265852 -0.575674147078373 0.565235061106762 df.mm.trans1:exp5 -0.139234199477512 0.0888123928265853 -1.56773390566540 0.117919340332411 df.mm.trans2:exp5 0.0297247575062135 0.0888123928265853 0.334691551034482 0.738074338249404 df.mm.trans1:exp6 -0.188050121670988 0.0888123928265852 -2.11738605036995 0.0349886288249723 * df.mm.trans2:exp6 -0.0723588320016125 0.0888123928265853 -0.814738007823977 0.415820738629715 df.mm.trans1:exp7 -0.278279832611753 0.0888123928265853 -3.13334461278531 0.00188615068771497 ** df.mm.trans2:exp7 -0.091785152968081 0.0888123928265852 -1.03347235725650 0.302153676357005 df.mm.trans1:exp8 0.0970146513425138 0.0888123928265853 1.09235488713770 0.275488709389529 df.mm.trans2:exp8 -0.0742414620862227 0.0888123928265853 -0.835935838720011 0.403807047473035 df.mm.trans1:probe2 0.19133111193416 0.0444061964132926 4.30865796641134 2.18085624451262e-05 *** df.mm.trans1:probe3 0.0863919234025312 0.0444061964132926 1.94549252988194 0.052580333022515 . df.mm.trans1:probe4 0.291904254561278 0.0444061964132926 6.57350275723905 1.96554202919959e-10 *** df.mm.trans1:probe5 -0.177691239426755 0.0444061964132926 -4.00149649776275 7.80525397262287e-05 *** df.mm.trans1:probe6 0.540581545316833 0.0444061964132926 12.173561101374 2.48685067444868e-28 *** df.mm.trans2:probe2 0.387946375187875 0.0444061964132926 8.7363117430104 1.31366656878551e-16 *** df.mm.trans2:probe3 0.00107162604234285 0.0444061964132926 0.0241323537906540 0.980761891929081 df.mm.trans2:probe4 0.361711165128629 0.0444061964132926 8.14551108503302 8.27355515155595e-15 *** df.mm.trans2:probe5 0.219172881102285 0.0444061964132926 4.93563733904212 1.28057321558107e-06 *** df.mm.trans2:probe6 0.190362189535687 0.0444061964132926 4.28683843497804 2.39360008695686e-05 *** df.mm.trans3:probe2 -0.192643976548592 0.0444061964132926 -4.33822286321568 1.92127382078438e-05 *** df.mm.trans3:probe3 -0.195265288200358 0.0444061964132926 -4.39725317572813 1.48865392206745e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.83857543888563 0.242150725342776 19.9816681615815 1.88877324027834e-58 *** df.mm.trans1 -0.248483958309463 0.205099134824934 -1.21153099217878 0.226574819716602 df.mm.trans2 -0.294744697749598 0.205099134824934 -1.43708406181812 0.151659086121672 df.mm.exp2 -0.115895735067629 0.285625413030929 -0.405761286566890 0.685185833242164 df.mm.exp3 -0.0448053662551273 0.285625413030930 -0.156867576241458 0.875446917596185 df.mm.exp4 -0.178826781892881 0.285625413030929 -0.626088484197717 0.531697715303079 df.mm.exp5 -0.542143293078682 0.285625413030929 -1.89809193560790 0.0585725220881025 . df.mm.exp6 -0.246345183961903 0.285625413030929 -0.862476420945174 0.389063323831489 df.mm.exp7 -0.0661265112478625 0.285625413030929 -0.231514803063766 0.817061028726668 df.mm.exp8 0.00475694930601738 0.285625413030929 0.0166545030273699 0.986722494267734 df.mm.trans1:exp2 0.097131449448216 0.247358863651208 0.392674222441358 0.694818321771257 df.mm.trans2:exp2 0.157410396557554 0.247358863651208 0.636364487748915 0.524988176050569 df.mm.trans1:exp3 0.0321814809619232 0.247358863651208 0.130100375167074 0.89656774746869 df.mm.trans2:exp3 0.120417608974401 0.247358863651208 0.486813398141243 0.626719726477208 df.mm.trans1:exp4 0.0186998519883188 0.247358863651208 0.0755980671656336 0.939785530600676 df.mm.trans2:exp4 0.259968900995984 0.247358863651208 1.05097871634209 0.294051600646403 df.mm.trans1:exp5 0.444239310711085 0.247358863651208 1.79593043141357 0.073437164415761 . df.mm.trans2:exp5 0.646157209836185 0.247358863651208 2.61222581757696 0.0094144440757756 ** df.mm.trans1:exp6 0.227962317886891 0.247358863651208 0.92158540236639 0.357430583345749 df.mm.trans2:exp6 0.185437768075562 0.247358863651208 0.749671005673125 0.453996853358333 df.mm.trans1:exp7 -0.0155125002746004 0.247358863651208 -0.062712530473434 0.950034088911629 df.mm.trans2:exp7 0.192053015846547 0.247358863651208 0.776414529933135 0.438070686194731 df.mm.trans1:exp8 0.0115349976883938 0.247358863651208 0.0466326434320093 0.962834744108333 df.mm.trans2:exp8 0.207981118392596 0.247358863651208 0.840807219610546 0.401076019911902 df.mm.trans1:probe2 0.0121528583268588 0.123679431825604 0.0982609488697774 0.921785847116401 df.mm.trans1:probe3 -0.0111355428005712 0.123679431825604 -0.0900355268147821 0.928314615306405 df.mm.trans1:probe4 0.0226386536245286 0.123679431825604 0.183042995026453 0.85487876946531 df.mm.trans1:probe5 0.0194038094528778 0.123679431825604 0.156887925231080 0.875430892989184 df.mm.trans1:probe6 -0.0381767953344619 0.123679431825604 -0.308675377715946 0.757766852591354 df.mm.trans2:probe2 -0.167692997629164 0.123679431825604 -1.35586811124441 0.176085380849945 df.mm.trans2:probe3 -0.135951000473845 0.123679431825604 -1.09922077153091 0.272487885141576 df.mm.trans2:probe4 0.0664390588778937 0.123679431825604 0.537187613956516 0.591506741799344 df.mm.trans2:probe5 -0.0252035607631517 0.123679431825604 -0.203781343357806 0.838652304770946 df.mm.trans2:probe6 -0.0841532478042837 0.123679431825604 -0.680414249662347 0.496728117074771 df.mm.trans3:probe2 0.149175022545179 0.123679431825604 1.20614252784995 0.228642541885772 df.mm.trans3:probe3 0.103137464413202 0.123679431825604 0.833909591035578 0.404946302408252