chr14.7187_chr14_15821823_15822309_+_1.R fitVsDatCorrelation=0.862948364457631 cont.fitVsDatCorrelation=0.373057451830420 fstatistic=8909.37505294584,36,324 cont.fstatistic=2636.17444501572,36,324 residuals=-0.416472454736305,-0.0761240544892192,-0.0083859948541966,0.0607308272140688,0.653688111098192 cont.residuals=-0.487214955332607,-0.167693022438852,-0.0625895944342929,0.134930556502326,0.888531659531507 predictedValues: Include Exclude Both chr14.7187_chr14_15821823_15822309_+_1.R.tl.Lung 48.7329035442858 52.1939684892205 96.203222346595 chr14.7187_chr14_15821823_15822309_+_1.R.tl.cerebhem 60.5299101458506 60.2897271100097 77.3486059230673 chr14.7187_chr14_15821823_15822309_+_1.R.tl.cortex 50.7062672581472 51.247421123485 109.805415715096 chr14.7187_chr14_15821823_15822309_+_1.R.tl.heart 47.2566617925254 49.4699625678338 82.2481514552965 chr14.7187_chr14_15821823_15822309_+_1.R.tl.kidney 47.4361523071265 54.3845577416281 76.2583155477742 chr14.7187_chr14_15821823_15822309_+_1.R.tl.liver 48.8321531641276 53.3471443243249 66.7887865612036 chr14.7187_chr14_15821823_15822309_+_1.R.tl.stomach 48.0507133602731 51.2323842026404 72.9802538395663 chr14.7187_chr14_15821823_15822309_+_1.R.tl.testicle 50.442629259619 52.4055856738344 72.7160773371468 diffExp=-3.46106494493471,0.240183035840943,-0.541153865337826,-2.21330077530843,-6.94840543450155,-4.5149911601973,-3.18167084236723,-1.96295641421546 diffExpScore=0.977966077798836 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 56.6166060784236 55.057093064326 60.5605479239433 cerebhem 53.4035511898074 61.7948026344191 50.4718200642886 cortex 60.0928836306361 62.3059620985695 55.1326450860077 heart 55.9403255600409 55.0241928053166 57.9384381814098 kidney 62.1272901640882 58.8286321578056 48.6074850499112 liver 51.9626184691038 55.1297503844269 49.1065307322092 stomach 54.0499248575214 52.5153798728487 47.4366758977375 testicle 58.972543181742 60.7199655234343 61.2894782006387 cont.diffExp=1.55951301409760,-8.39125144461169,-2.21307846793334,0.916132754724337,3.29865800628263,-3.16713191532317,1.53454498467278,-1.74742234169236 cont.diffExpScore=2.47857167900674 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=0.858760933093117 cont.tran.correlation=0.473448314190088 tran.covariance=0.00402593165617388 cont.tran.covariance=0.00189653340928605 tran.mean=51.6598838790582 cont.tran.mean=57.1588451045319 weightedLogRatios: wLogRatio Lung -0.269006341918471 cerebhem 0.0163057578973029 cortex -0.0417344241352916 heart -0.177525903466557 kidney -0.536903757705988 liver -0.347766042683116 stomach -0.250324550410600 testicle -0.150412725552013 cont.weightedLogRatios: wLogRatio Lung 0.112350428457980 cerebhem -0.591188959476753 cortex -0.148784740079024 heart 0.0663148829374528 kidney 0.223786386028734 liver -0.235482716305307 stomach 0.114502862798799 testicle -0.119479283130625 varWeightedLogRatios=0.0306197073823184 cont.varWeightedLogRatios=0.0685460647859417 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.41044191254598 0.076387686245946 44.646487937406 1.81851274904389e-140 *** df.mm.trans1 0.500771837788972 0.0646995722938453 7.7399559229638 1.27781144348945e-13 *** df.mm.trans2 0.538847356909034 0.0646995722938453 8.32845315362761 2.33813316221654e-15 *** df.mm.exp2 0.579118269763576 0.0901019990899904 6.42736316188917 4.64162877030918e-10 *** df.mm.exp3 -0.110853564584436 0.0901019990899905 -1.23031193207733 0.219472819674156 df.mm.exp4 0.0723598602095798 0.0901019990899904 0.80308828816672 0.422512439952233 df.mm.exp5 0.246479899504704 0.0901019990899904 2.7355652704057 0.00657035817981744 ** df.mm.exp6 0.388815692731595 0.0901019990899904 4.31528375239778 2.11991711748220e-05 *** df.mm.exp7 0.243581380361038 0.0901019990899904 2.70339596036885 0.00722557789464761 ** df.mm.exp8 0.318428784286473 0.0901019990899904 3.5340923342715 0.00046869925879214 *** df.mm.trans1:exp2 -0.362335083725516 0.0780306201436941 -4.64349870676759 4.98533590605849e-06 *** df.mm.trans2:exp2 -0.43492348559105 0.078030620143694 -5.57375405693476 5.25389625742584e-08 *** df.mm.trans1:exp3 0.150548642693062 0.0780306201436941 1.92935340531480 0.0545600188509517 . df.mm.trans2:exp3 0.092551919766023 0.0780306201436941 1.18609745245633 0.236452878862424 df.mm.trans1:exp4 -0.103120665298008 0.0780306201436941 -1.32154101951402 0.187253432472037 df.mm.trans2:exp4 -0.125961133663314 0.078030620143694 -1.61425262840863 0.107446062438469 df.mm.trans1:exp5 -0.273449694023346 0.0780306201436941 -3.50438960397579 0.000522053970704868 *** df.mm.trans2:exp5 -0.20536659301282 0.0780306201436941 -2.63187185536442 0.00889795470831175 ** df.mm.trans1:exp6 -0.386781159930084 0.078030620143694 -4.95678695386276 1.15761140645585e-06 *** df.mm.trans2:exp6 -0.366962185637229 0.0780306201436941 -4.70279724756082 3.80363377130131e-06 *** df.mm.trans1:exp7 -0.257678838255360 0.0780306201436941 -3.30227848735333 0.00106612868520448 ** df.mm.trans2:exp7 -0.262176486333181 0.078030620143694 -3.3599180148816 0.000872747224176739 *** df.mm.trans1:exp8 -0.283946587476909 0.0780306201436941 -3.63891235202307 0.000318506057485232 *** df.mm.trans2:exp8 -0.314382543840717 0.0780306201436941 -4.02896379987470 6.98581540478159e-05 *** df.mm.trans1:probe2 -0.0914906178591587 0.0390153100718470 -2.34499271415959 0.0196305875138167 * df.mm.trans1:probe3 -0.0117035508142493 0.0390153100718470 -0.2999732872223 0.76438989289309 df.mm.trans1:probe4 -0.0892627681336165 0.0390153100718470 -2.28789077849794 0.0227877238680201 * df.mm.trans1:probe5 -0.0323222397448489 0.0390153100718470 -0.828450156754546 0.408025455517846 df.mm.trans1:probe6 0.00104537820913633 0.0390153100718470 0.0267940510330754 0.978640493430595 df.mm.trans2:probe2 -0.0663096268957658 0.0390153100718470 -1.69957964639153 0.0901697939375623 . df.mm.trans2:probe3 -0.0797464324156135 0.0390153100718470 -2.04397792222489 0.0417639453203424 * df.mm.trans2:probe4 0.194397723651619 0.039015310071847 4.98260101723231 1.02294275147710e-06 *** df.mm.trans2:probe5 0.0587393844547944 0.0390153100718470 1.50554703644865 0.133157742287340 df.mm.trans2:probe6 -0.0559819956281875 0.0390153100718470 -1.43487250325824 0.152287983537605 df.mm.trans3:probe2 0.188818843589358 0.0390153100718470 4.83960894432585 2.01647869617991e-06 *** df.mm.trans3:probe3 0.160386394989761 0.0390153100718470 4.11085788359514 5.00056701475338e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91077952538786 0.140261688440910 27.8820223031566 4.3068601552612e-88 *** df.mm.trans1 0.151934999246698 0.118800184916206 1.27891214440334 0.201843230414932 df.mm.trans2 0.0873527899902459 0.118800184916206 0.735291700529416 0.462693766699375 df.mm.exp2 0.239251962975009 0.165443661738532 1.44612347466731 0.149109207586591 df.mm.exp3 0.277177154614725 0.165443661738532 1.67535674502162 0.0948290279251321 . df.mm.exp4 0.0316480474645638 0.165443661738532 0.191291991074161 0.848416653224663 df.mm.exp5 0.3790071113516 0.165443661738532 2.29085301527346 0.022613651080736 * df.mm.exp6 0.125192672922374 0.165443661738532 0.756708789002924 0.449774235667372 df.mm.exp7 0.150589166253956 0.165443661738532 0.910214176061624 0.363386203364451 df.mm.exp8 0.126706965609800 0.165443661738532 0.765861709528942 0.444316160084509 df.mm.trans1:exp2 -0.297677053327586 0.143278413960688 -2.07761270591158 0.0385322887308696 * df.mm.trans2:exp2 -0.123803404354850 0.143278413960688 -0.86407575944286 0.388185506313668 df.mm.trans1:exp3 -0.217588064681865 0.143278413960689 -1.51863814420478 0.129828761262337 df.mm.trans2:exp3 -0.153490736123951 0.143278413960689 -1.07127606930423 0.284842653553786 df.mm.trans1:exp4 -0.0436648756893906 0.143278413960689 -0.304755437210318 0.76074808698407 df.mm.trans2:exp4 -0.0322457922988315 0.143278413960688 -0.225056876381105 0.822076905206967 df.mm.trans1:exp5 -0.286124099595373 0.143278413960688 -1.99697980795542 0.0466635201944973 * df.mm.trans2:exp5 -0.312749136045482 0.143278413960689 -2.18280707749383 0.0297675232950154 * df.mm.trans1:exp6 -0.210970424239440 0.143278413960688 -1.47245086267722 0.141869880384435 df.mm.trans2:exp6 -0.123873870634234 0.143278413960689 -0.864567573090397 0.387915811936889 df.mm.trans1:exp7 -0.1969833481992 0.143278413960689 -1.37482920667482 0.170134415342605 df.mm.trans2:exp7 -0.197853792139040 0.143278413960688 -1.38090439913248 0.168260101376527 df.mm.trans1:exp8 -0.0859373356125702 0.143278413960688 -0.599792622189054 0.549063676229049 df.mm.trans2:exp8 -0.0288051028586859 0.143278413960689 -0.201042865163130 0.840791205135672 df.mm.trans1:probe2 -0.0220357040536031 0.0716392069803442 -0.307592797051048 0.758589828657709 df.mm.trans1:probe3 -0.062436787644896 0.0716392069803443 -0.871544930166897 0.384102027937727 df.mm.trans1:probe4 -0.131436616571560 0.0716392069803443 -1.83470228261492 0.0674665463637506 . df.mm.trans1:probe5 -0.0203429119852364 0.0716392069803442 -0.283963388802139 0.776619817114716 df.mm.trans1:probe6 -0.00145767935135001 0.0716392069803443 -0.0203475081982686 0.983778682273301 df.mm.trans2:probe2 0.0577090927542779 0.0716392069803443 0.805551808664097 0.421092111489604 df.mm.trans2:probe3 0.098921986754729 0.0716392069803442 1.38083587080843 0.168281156492578 df.mm.trans2:probe4 0.0984527909760308 0.0716392069803442 1.37428644349795 0.170302630206272 df.mm.trans2:probe5 -0.0620775457904489 0.0716392069803443 -0.866530331742522 0.386840642702789 df.mm.trans2:probe6 -0.100860840235531 0.0716392069803443 -1.40790001016069 0.160119408964414 df.mm.trans3:probe2 -0.0521483650611012 0.0716392069803443 -0.727930518206451 0.467181845143995 df.mm.trans3:probe3 -0.0871226086847455 0.0716392069803443 -1.21613027777722 0.224820559133924