chr11.3474_chr11_69502935_69503530_-_1.R fitVsDatCorrelation=0.608436923254568 cont.fitVsDatCorrelation=0.360123551145317 fstatistic=12048.2980908612,36,324 cont.fstatistic=8716.31827999553,36,324 residuals=-0.290795110549682,-0.0693445235240714,-0.000443311574307945,0.0674168884806876,0.49073693762106 cont.residuals=-0.306887410020812,-0.0835353376440763,-0.0121407837432405,0.066450535488802,0.656273398355202 predictedValues: Include Exclude Both chr11.3474_chr11_69502935_69503530_-_1.R.tl.Lung 44.7304312422586 46.6146576596065 50.8580060184724 chr11.3474_chr11_69502935_69503530_-_1.R.tl.cerebhem 44.3932303982078 50.7122555240337 56.6739548634478 chr11.3474_chr11_69502935_69503530_-_1.R.tl.cortex 47.1086537355766 46.7316536701069 47.1574946200642 chr11.3474_chr11_69502935_69503530_-_1.R.tl.heart 47.8267026518647 46.8881015128866 47.7420131879316 chr11.3474_chr11_69502935_69503530_-_1.R.tl.kidney 46.4145875399573 45.9447516206562 53.9566019224714 chr11.3474_chr11_69502935_69503530_-_1.R.tl.liver 48.4043612658673 51.3124010128836 50.4402338493389 chr11.3474_chr11_69502935_69503530_-_1.R.tl.stomach 46.5421008243694 49.9229771175587 53.5764271364022 chr11.3474_chr11_69502935_69503530_-_1.R.tl.testicle 47.0076376086367 48.0355111499651 50.3634321446464 diffExp=-1.88422641734791,-6.31902512582584,0.377000065469687,0.938601138978129,0.46983591930104,-2.90803974701630,-3.38087629318932,-1.02787354132838 diffExpScore=1.17447867939514 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 49.4807370850492 49.226214055844 44.6944867161178 cerebhem 46.9579580265692 46.7254698190068 47.1492215196721 cortex 50.2019179192372 48.2360785358728 53.0159473674232 heart 46.8800226353204 51.3205146419779 46.2535028748504 kidney 46.2951194491074 49.4612921554675 45.7826840189208 liver 46.4373171531871 45.6469302626373 47.2108689865806 stomach 46.9412282817171 47.2729365384989 47.7705599472624 testicle 46.9570025876373 48.9995800142771 46.6020466333038 cont.diffExp=0.25452302920516,0.232488207562390,1.96583938336445,-4.44049200665744,-3.16617270636012,0.790386890549762,-0.331708256781823,-2.04257742663980 cont.diffExpScore=1.70905642305006 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=0.0470995157309769 cont.tran.correlation=0.122354875015526 tran.covariance=5.05133476454075e-05 cont.tran.covariance=0.000149010840390811 tran.mean=47.4118759084022 cont.tran.mean=47.9400199475879 weightedLogRatios: wLogRatio Lung -0.157669927344501 cerebhem -0.513641587653022 cortex 0.0309220991987371 heart 0.0764597636673078 kidney 0.0389927918277733 liver -0.228047054592206 stomach -0.2717601491882 testicle -0.0835179252635835 cont.weightedLogRatios: wLogRatio Lung 0.0201077779699677 cerebhem 0.0190926135552053 cortex 0.155632716783299 heart -0.352297991749806 kidney -0.255890570691148 liver 0.0657414644460513 stomach -0.0271272130605792 testicle -0.164804438261945 varWeightedLogRatios=0.0394734140078817 cont.varWeightedLogRatios=0.0300591276693580 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.83783797442984 0.063032300323529 60.8868461841179 1.95973633359063e-179 *** df.mm.trans1 -0.0211925638480312 0.0533876999297904 -0.396955925726362 0.69166130959139 df.mm.trans2 0.00640902763012168 0.0533876999297904 0.120046895418797 0.904520404141883 df.mm.exp2 -0.0315916965509198 0.0743488452851526 -0.424911731039738 0.671183066693013 df.mm.exp3 0.129853971751927 0.0743488452851527 1.74654994645705 0.0816630809251276 . df.mm.exp4 0.136004732522341 0.0743488452851527 1.82927834320381 0.068276844663936 . df.mm.exp5 -0.0366581741687528 0.0743488452851527 -0.493056402263633 0.622306732568774 df.mm.exp6 0.183201691090209 0.0743488452851527 2.46408253399995 0.0142552859550444 * df.mm.exp7 0.0561979397849536 0.0743488452851527 0.75586836042195 0.450277307538415 df.mm.exp8 0.0894537465670253 0.0743488452851527 1.20316255382233 0.229791830967301 df.mm.trans1:exp2 0.0240246274501332 0.0643879887589811 0.373122812393827 0.709301032692153 df.mm.trans2:exp2 0.115844270523999 0.0643879887589811 1.79915963764035 0.0729238509855604 . df.mm.trans1:exp3 -0.078051314895401 0.0643879887589811 -1.21220302730009 0.226317881723379 df.mm.trans2:exp3 -0.127347261629228 0.0643879887589811 -1.97781083217116 0.0487970689754 * df.mm.trans1:exp4 -0.0690746745236217 0.0643879887589811 -1.07278819939824 0.284164528631501 df.mm.trans2:exp4 -0.130155822067998 0.0643879887589811 -2.02143015454639 0.0440570849318052 * df.mm.trans1:exp5 0.0736179122232211 0.0643879887589811 1.14334852885046 0.253737995087703 df.mm.trans2:exp5 0.0221827633670627 0.0643879887589811 0.344517103183606 0.730680946481053 df.mm.trans1:exp6 -0.104265831033894 0.0643879887589811 -1.61933666579002 0.106347767962775 df.mm.trans2:exp6 -0.0871842667080482 0.0643879887589811 -1.35404550426942 0.176665522388203 df.mm.trans1:exp7 -0.0164947011467965 0.0643879887589811 -0.256176679295574 0.797976989652929 df.mm.trans2:exp7 0.0123683865443056 0.0643879887589811 0.192091518662017 0.847790854038897 df.mm.trans1:exp8 -0.0397977141532464 0.0643879887589811 -0.61809220819458 0.536948784739998 df.mm.trans2:exp8 -0.0594282272888033 0.0643879887589811 -0.922970703608349 0.356709281361816 df.mm.trans1:probe2 -0.0485560608915796 0.0321939943794905 -1.50823350216252 0.132469233495775 df.mm.trans1:probe3 -0.078768447485634 0.0321939943794905 -2.44668140762968 0.0149488359131596 * df.mm.trans1:probe4 -0.0579265915560166 0.0321939943794905 -1.79929805768119 0.0729019138826185 . df.mm.trans1:probe5 0.0301146400202558 0.0321939943794905 0.935411731308512 0.350272804739629 df.mm.trans1:probe6 0.0112142903273552 0.0321939943794906 0.348334853860177 0.727814943433413 df.mm.trans2:probe2 -0.0596747904134291 0.0321939943794905 -1.85360007552978 0.0647052166682181 . df.mm.trans2:probe3 -0.0295565768096883 0.0321939943794905 -0.918077342664805 0.359261291019253 df.mm.trans2:probe4 -0.0143347328432852 0.0321939943794905 -0.445261084235550 0.656428425648281 df.mm.trans2:probe5 0.134561209243090 0.0321939943794905 4.179699097196 3.75959664619216e-05 *** df.mm.trans2:probe6 -0.0519828241889857 0.0321939943794906 -1.61467457489841 0.107354568098282 df.mm.trans3:probe2 0.168799824021029 0.0321939943794905 5.24320847022836 2.85327352330967e-07 *** df.mm.trans3:probe3 0.149149523121253 0.0321939943794906 4.63283683792497 5.23231362997519e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.96143484663296 0.0740964697115845 53.4632063045996 8.57846406917919e-163 *** df.mm.trans1 -0.0462554106063854 0.0627589358236101 -0.73703306149726 0.46163561132886 df.mm.trans2 -0.0963900457480948 0.0627589358236101 -1.53587763213526 0.125544324574454 df.mm.exp2 -0.157934975960490 0.0873994275075849 -1.8070481748497 0.0716822876829087 . df.mm.exp3 -0.176591849419821 0.087399427507585 -2.02051494450002 0.0441523751062546 * df.mm.exp4 -0.0466145803576278 0.087399427507585 -0.533351094932313 0.594156230732321 df.mm.exp5 -0.085838587688679 0.0873994275075849 -0.982141303857278 0.326763049901983 df.mm.exp6 -0.193743973853009 0.0873994275075849 -2.21676479329565 0.0273331655654317 * df.mm.exp7 -0.159734803717161 0.0873994275075849 -1.82764130466757 0.0685229799959677 . df.mm.exp8 -0.0987599653025992 0.087399427507585 -1.12998412139517 0.259318748858659 df.mm.trans1:exp2 0.105604223245450 0.075690124497785 1.39521798842516 0.163905705059821 df.mm.trans2:exp2 0.105798096612357 0.075690124497785 1.39777939743584 0.163135568262371 df.mm.trans1:exp3 0.191061636910959 0.075690124497785 2.52426109982882 0.0120715271461138 * df.mm.trans2:exp3 0.156272820198117 0.075690124497785 2.06463949207390 0.0397525467468309 * df.mm.trans1:exp4 -0.00737723557762496 0.075690124497785 -0.0974662893815275 0.922416368689729 df.mm.trans2:exp4 0.0882788605319316 0.075690124497785 1.16631939923041 0.244343109600412 df.mm.trans1:exp5 0.0192916877190989 0.075690124497785 0.254877209505230 0.798979641996834 df.mm.trans2:exp5 0.0906026870822457 0.075690124497785 1.19702124528672 0.232173361645995 df.mm.trans1:exp6 0.130263914802170 0.075690124497785 1.72101599338739 0.0862025519818092 . df.mm.trans2:exp6 0.118254045944124 0.075690124497785 1.56234445020083 0.119183047485203 df.mm.trans1:exp7 0.107047716816772 0.075690124497785 1.41428908364267 0.158237253816658 df.mm.trans2:exp7 0.119246481639779 0.075690124497785 1.57545627558412 0.116127052989426 df.mm.trans1:exp8 0.0464088656923319 0.075690124497785 0.613142943022772 0.540211987826367 df.mm.trans2:exp8 0.0941454046186638 0.075690124497785 1.24382679039481 0.214462469403163 df.mm.trans1:probe2 -0.0495638023471349 0.0378450622488925 -1.30965043791374 0.191241996720430 df.mm.trans1:probe3 -0.00348815942772502 0.0378450622488925 -0.0921694725928769 0.926620353706955 df.mm.trans1:probe4 0.0129240406075470 0.0378450622488925 0.341498727695320 0.732949534019135 df.mm.trans1:probe5 -0.0361439671190884 0.0378450622488925 -0.955051067993582 0.340263884956708 df.mm.trans1:probe6 -0.046092039599705 0.0378450622488925 -1.21791422343489 0.224142756958419 df.mm.trans2:probe2 0.0364232805667900 0.0378450622488925 0.962431514242148 0.336550615445579 df.mm.trans2:probe3 0.0502980390459966 0.0378450622488925 1.32905156068196 0.184766102452315 df.mm.trans2:probe4 0.0278859218628631 0.0378450622488925 0.736844391468248 0.461750192930454 df.mm.trans2:probe5 0.098586767060187 0.0378450622488925 2.60501003834581 0.0096107896299168 ** df.mm.trans2:probe6 0.0692393720860468 0.0378450622488925 1.82954837359458 0.0682363147549565 . df.mm.trans3:probe2 -0.0179665250824291 0.0378450622488925 -0.474738949146658 0.635292880562387 df.mm.trans3:probe3 -0.0388511827678202 0.0378450622488925 -1.02658525205510 0.305381604226842