chr15.8216_chr15_75697181_75698610_+_0.R fitVsDatCorrelation=0.891877492376829 cont.fitVsDatCorrelation=0.283376074918987 fstatistic=6411.8189717105,41,439 cont.fstatistic=1417.75822418181,41,439 residuals=-0.583542504285538,-0.106925244039395,-0.00412684270907167,0.087769150655733,0.812356822280503 cont.residuals=-0.702329580945001,-0.297165217775231,-0.079713167788661,0.260280945057902,1.22374380903369 predictedValues: Include Exclude Both chr15.8216_chr15_75697181_75698610_+_0.R.tl.Lung 51.6637881684563 105.112851235451 51.6290764712844 chr15.8216_chr15_75697181_75698610_+_0.R.tl.cerebhem 66.7618716383887 102.989690598620 58.2592080370753 chr15.8216_chr15_75697181_75698610_+_0.R.tl.cortex 50.0385476592501 91.1202974294285 51.1004617475273 chr15.8216_chr15_75697181_75698610_+_0.R.tl.heart 50.9151576343478 95.2123448037411 54.615172618557 chr15.8216_chr15_75697181_75698610_+_0.R.tl.kidney 56.4679973862285 126.772923798458 79.4951891642509 chr15.8216_chr15_75697181_75698610_+_0.R.tl.liver 62.588859668056 122.435187620823 99.8030891933482 chr15.8216_chr15_75697181_75698610_+_0.R.tl.stomach 58.3006657774959 95.1288480722278 58.7096027625049 chr15.8216_chr15_75697181_75698610_+_0.R.tl.testicle 54.169659819954 91.5116053860531 52.275157216816 diffExp=-53.4490630669945,-36.2278189602316,-41.0817497701784,-44.2971871693934,-70.3049264122297,-59.8463279527669,-36.8281822947319,-37.3419455660991 diffExpScore=0.99737103065887 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 81.857988600835 63.2056921161355 67.1432372138803 cerebhem 71.9787122361223 63.9363642099918 67.6507505262766 cortex 79.5277563134166 66.1751715813955 74.2971261136054 heart 77.90856403974 70.4184103778697 73.69688682337 kidney 76.0650753272755 68.5079268402299 62.9593866060559 liver 71.1589544504738 80.6487853353094 72.5661110386305 stomach 68.9613545662131 76.451903075061 85.0817114907567 testicle 67.5594098929736 81.3292835095496 60.4969782527498 cont.diffExp=18.6522964846995,8.04234802613052,13.3525847320211,7.49015366187021,7.55714848704562,-9.48983088483551,-7.49054850884788,-13.769873616576 cont.diffExpScore=3.3871465231641 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=1,0,1,0,0,0,0,-1 cont.diffExp1.2Score=1.5 tran.correlation=0.418937880472624 cont.tran.correlation=-0.768909844419121 tran.covariance=0.00590076717535558 cont.tran.covariance=-0.00541273452721739 tran.mean=80.0743935435612 cont.tran.mean=72.855709529537 weightedLogRatios: wLogRatio Lung -3.05411897180017 cerebhem -1.9151368805071 cortex -2.52490968902897 heart -2.65598591708433 kidney -3.58914348880539 liver -3.00074417440839 stomach -2.11046388930676 testicle -2.23071673831756 cont.weightedLogRatios: wLogRatio Lung 1.10565728408281 cerebhem 0.499654477587865 cortex 0.787440249090018 heart 0.435153847283156 kidney 0.447781757084367 liver -0.541750471929814 stomach -0.441860887139729 testicle -0.798712646176127 varWeightedLogRatios=0.3127379154403 cont.varWeightedLogRatios=0.475710256450773 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.71837388685505 0.0926401003841171 50.9323054194791 2.29655832086629e-186 *** df.mm.trans1 -0.774950185388914 0.0747466528765285 -10.3676908004032 1.12583071646443e-22 *** df.mm.trans2 -0.0685252213378187 0.0747466528765285 -0.916766419641736 0.359768396531944 df.mm.exp2 0.115152218613772 0.100684407030904 1.14369465947619 0.253373546106138 df.mm.exp3 -0.164525937673456 0.100684407030904 -1.63407564810861 0.102959848224245 df.mm.exp4 -0.169748102802356 0.100684407030904 -1.68594232024680 0.0925174486226765 . df.mm.exp5 -0.155331611164248 0.100684407030904 -1.54275737172063 0.123610162563118 df.mm.exp6 -0.314736674874000 0.100684407030904 -3.12597237402803 0.00188985443887897 ** df.mm.exp7 -0.107464171867473 0.100684407030904 -1.06733679063619 0.286406373389240 df.mm.exp8 -0.103641133172415 0.100684407030904 -1.02936627655366 0.303874386309863 df.mm.trans1:exp2 0.141222801282723 0.0803183714601708 1.75828765841890 0.0793953642178225 . df.mm.trans2:exp2 -0.1355578733419 0.0803183714601708 -1.68775674702421 0.0921681964372335 . df.mm.trans1:exp3 0.132562485424323 0.0803183714601708 1.65046281460101 0.0995635176718803 . df.mm.trans2:exp3 0.0216719742851731 0.0803183714601708 0.269825867870342 0.787421009466228 df.mm.trans1:exp4 0.155151660620277 0.0803183714601708 1.93170824805898 0.0540387888228924 . df.mm.trans2:exp4 0.0708231618283887 0.0803183714601708 0.881780351628635 0.378378134420423 df.mm.trans1:exp5 0.244248557068011 0.0803183714601708 3.04100484892341 0.00249909690621358 ** df.mm.trans2:exp5 0.34269454898035 0.0803183714601708 4.26670191078625 2.43179208932927e-05 *** df.mm.trans1:exp6 0.506566862738476 0.0803183714601708 6.30698622904323 6.94922259221633e-10 *** df.mm.trans2:exp6 0.46728393752712 0.0803183714601708 5.81789606825933 1.15001222643221e-08 *** df.mm.trans1:exp7 0.228320571182316 0.0803183714601708 2.8426942308652 0.00468203855027113 ** df.mm.trans2:exp7 0.00766189335445355 0.0803183714601708 0.0953940327120927 0.924045383813301 df.mm.trans1:exp8 0.151004989131425 0.0803183714601708 1.88008031520294 0.0607590576886616 . df.mm.trans2:exp8 -0.0349276144414775 0.0803183714601707 -0.434864574648378 0.663874418243706 df.mm.trans1:probe2 0.0183598341194874 0.0525807167016876 0.349174284246645 0.727125891493246 df.mm.trans1:probe3 -0.0430794062839958 0.0525807167016876 -0.819300477176895 0.413059557789023 df.mm.trans1:probe4 0.0409394180565543 0.0525807167016877 0.778601369943674 0.436634034079092 df.mm.trans1:probe5 0.0368697034739267 0.0525807167016876 0.701201995459741 0.483548482593398 df.mm.trans1:probe6 -0.0344217762645255 0.0525807167016876 -0.654646387948924 0.513038282315766 df.mm.trans2:probe2 0.0385930143141928 0.0525807167016876 0.733976574209649 0.463354830650618 df.mm.trans2:probe3 0.0550532155225497 0.0525807167016877 1.04702291973100 0.295665126786357 df.mm.trans2:probe4 -0.18095622982552 0.0525807167016877 -3.44149416699967 0.000633913286285956 *** df.mm.trans2:probe5 0.238270381626836 0.0525807167016876 4.53151642984715 7.55979338976187e-06 *** df.mm.trans2:probe6 -0.0783580454606492 0.0525807167016877 -1.49024300876702 0.136878462536213 df.mm.trans3:probe2 -0.287896828827752 0.0525807167016877 -5.47533101271918 7.3617272458382e-08 *** df.mm.trans3:probe3 0.00381840316319105 0.0525807167016876 0.0726198386540534 0.942141713344644 df.mm.trans3:probe4 0.491717731235058 0.0525807167016876 9.35167418931883 4.44449800176444e-19 *** df.mm.trans3:probe5 0.189190744178304 0.0525807167016877 3.59810128210428 0.000357034799454847 *** df.mm.trans3:probe6 0.0205598204455658 0.0525807167016876 0.391014458060933 0.695976244271661 df.mm.trans3:probe7 -0.0253334566608519 0.0525807167016876 -0.481801280963498 0.630187214320355 df.mm.trans3:probe8 0.161435835743323 0.0525807167016876 3.070247913493 0.00227155668937214 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32583006048932 0.196434188963399 22.0217777939630 6.02244956765765e-73 *** df.mm.trans1 0.113546587044589 0.158492899669256 0.716414345888925 0.474116472317677 df.mm.trans2 -0.158040945427588 0.158492899669255 -0.997148425938253 0.319241769988985 df.mm.exp2 -0.124651812924183 0.21349134720678 -0.583872904242108 0.559605698538825 df.mm.exp3 -0.0842128892988239 0.21349134720678 -0.394455749146865 0.693436145561148 df.mm.exp4 -0.034521989430179 0.21349134720678 -0.161702054354185 0.871614880489238 df.mm.exp5 0.0714967701060082 0.21349134720678 0.334893057922198 0.737865713769501 df.mm.exp6 0.0259698350070265 0.21349134720678 0.121643501466470 0.903237020363027 df.mm.exp7 -0.217956067819013 0.21349134720678 -1.02091288790224 0.307857976891923 df.mm.exp8 0.164368026740985 0.21349134720678 0.76990486448982 0.441770408214008 df.mm.trans1:exp2 -0.00396367471741433 0.170307178977805 -0.0232736796017910 0.981442541507994 df.mm.trans2:exp2 0.136145730185728 0.170307178977805 0.799412749379582 0.424483438009112 df.mm.trans1:exp3 0.0553330864897057 0.170307178977806 0.324901667808829 0.745410266902221 df.mm.trans2:exp3 0.13012386802586 0.170307178977805 0.764053921900836 0.445245566435211 df.mm.trans1:exp4 -0.0149280269638109 0.170307178977806 -0.087653539054606 0.930192016210453 df.mm.trans2:exp4 0.142582367229489 0.170307178977805 0.837207028413468 0.402931733455407 df.mm.trans1:exp5 -0.144893441443439 0.170307178977806 -0.850777062441516 0.395357003626210 df.mm.trans2:exp5 0.00905832653650352 0.170307178977805 0.0531881661763889 0.957606178916662 df.mm.trans1:exp6 -0.166039564809576 0.170307178977805 -0.9749416660305 0.330126102957125 df.mm.trans2:exp6 0.217739546286348 0.170307178977805 1.27851067461298 0.20174492073988 df.mm.trans1:exp7 0.0465164372096202 0.170307178977806 0.273132568390920 0.784879753852673 df.mm.trans2:exp7 0.4082235307265 0.170307178977806 2.396983692506 0.0169478433656059 * df.mm.trans1:exp8 -0.356346568955220 0.170307178977806 -2.09237550110357 0.0369786655260459 * df.mm.trans2:exp8 0.087743753503107 0.170307178977805 0.515208777631986 0.606666461508471 df.mm.trans1:probe2 -0.159579932825352 0.111492219865741 -1.43131003237283 0.153052872579731 df.mm.trans1:probe3 0.0100541937259256 0.111492219865741 0.0901784334192363 0.92818655084067 df.mm.trans1:probe4 -0.164538220388173 0.111492219865741 -1.47578208225031 0.140719273562327 df.mm.trans1:probe5 0.0204850654009367 0.111492219865741 0.183735380151233 0.854305886022665 df.mm.trans1:probe6 -0.187891577640018 0.111492219865741 -1.68524384810239 0.0926521788254444 . df.mm.trans2:probe2 -0.0533705930273877 0.111492219865741 -0.478693428937523 0.632395065195433 df.mm.trans2:probe3 -0.0546249714120696 0.111492219865741 -0.489944244341435 0.62441810341903 df.mm.trans2:probe4 -0.0529801757217545 0.111492219865741 -0.475191684097359 0.63488668615882 df.mm.trans2:probe5 -0.113155942419079 0.111492219865741 -1.01492231973981 0.310701884619059 df.mm.trans2:probe6 -0.0253948571210291 0.111492219865741 -0.227772459384248 0.81992918278783 df.mm.trans3:probe2 -0.0968951458381565 0.111492219865741 -0.869075402345005 0.38528077648106 df.mm.trans3:probe3 -0.177420101617994 0.111492219865741 -1.59132271141111 0.112256764883597 df.mm.trans3:probe4 -0.085350971952691 0.111492219865741 -0.765532985669052 0.444365604972870 df.mm.trans3:probe5 -0.0617931965606664 0.111492219865741 -0.55423774533396 0.579698365919584 df.mm.trans3:probe6 -0.0896754001179516 0.111492219865741 -0.804319801201725 0.421647647361306 df.mm.trans3:probe7 -0.0492821361472486 0.111492219865741 -0.442023095482306 0.65869016161315 df.mm.trans3:probe8 -0.0356430144955901 0.111492219865741 -0.319690598487603 0.749354969978969