chr8.22802_chr8_105116962_105117436_+_1.R fitVsDatCorrelation=0.958055232549642 cont.fitVsDatCorrelation=0.270134906013789 fstatistic=8044.4584322735,44,508 cont.fstatistic=702.17797022051,44,508 residuals=-0.857755711824234,-0.093980851385266,-0.00489025514070976,0.0850393188802078,0.827239142960651 cont.residuals=-1.22659190521062,-0.451223436247039,-0.0487560379553463,0.332843580471138,1.66533616028377 predictedValues: Include Exclude Both chr8.22802_chr8_105116962_105117436_+_1.R.tl.Lung 113.038000932741 200.487910254237 62.8949568026652 chr8.22802_chr8_105116962_105117436_+_1.R.tl.cerebhem 103.872220697199 104.935118029391 65.4602361876148 chr8.22802_chr8_105116962_105117436_+_1.R.tl.cortex 104.057210472722 123.703425429922 60.7802282619957 chr8.22802_chr8_105116962_105117436_+_1.R.tl.heart 104.089482916458 167.794915865899 59.6858251904467 chr8.22802_chr8_105116962_105117436_+_1.R.tl.kidney 113.730645070221 210.971521804343 62.9482037158717 chr8.22802_chr8_105116962_105117436_+_1.R.tl.liver 114.090507071507 211.862491497040 65.7768206214175 chr8.22802_chr8_105116962_105117436_+_1.R.tl.stomach 112.617156322077 180.625997326059 61.9622889480426 chr8.22802_chr8_105116962_105117436_+_1.R.tl.testicle 103.306026289620 193.961284446833 64.8174191563835 diffExp=-87.4499093214957,-1.06289733219191,-19.6462149571999,-63.7054329494412,-97.2408767341215,-97.7719844255331,-68.0088410039822,-90.655258157213 diffExpScore=0.9981008141587 diffExp1.5=-1,0,0,-1,-1,-1,-1,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,0,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,0,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,0,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 84.6549208885957 91.3039376833245 108.69645754166 cerebhem 104.258803615903 100.749721344871 94.9436530114362 cortex 101.666285963709 158.501910063567 117.436684894979 heart 108.430944981585 77.3439738210667 101.974067116338 kidney 97.5937747851693 111.317395165601 103.057000429726 liver 90.2670141158946 79.361274214015 109.437164442688 stomach 85.2649866759795 96.4921100085012 107.199116797197 testicle 109.525231617600 86.2062654445488 94.4309999260501 cont.diffExp=-6.64901679472885,3.50908227103174,-56.8356240998582,31.0869711605184,-13.7236203804318,10.9057399018796,-11.2271233325217,23.3189661730509 cont.diffExpScore=7.62837758839163 cont.diffExp1.5=0,0,-1,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,-1,1,0,0,0,0 cont.diffExp1.4Score=2 cont.diffExp1.3=0,0,-1,1,0,0,0,0 cont.diffExp1.3Score=2 cont.diffExp1.2=0,0,-1,1,0,0,0,1 cont.diffExp1.2Score=1.5 tran.correlation=0.707946535857612 cont.tran.correlation=0.07695679016293 tran.covariance=0.00837674943293908 cont.tran.covariance=0.00147173706383006 tran.mean=141.446494651642 cont.tran.mean=98.9336593993707 weightedLogRatios: wLogRatio Lung -2.87330883944034 cerebhem -0.0473226392464196 cortex -0.818279686540017 heart -2.33206717760978 kidney -3.11588345671312 liver -3.12348606139433 stomach -2.34337501208372 testicle -3.12000266907814 cont.weightedLogRatios: wLogRatio Lung -0.338462769366783 cerebhem 0.158508414856703 cortex -2.15096007445525 heart 1.52613584802346 kidney -0.611361608285853 liver 0.571494289006174 stomach -0.557579711462987 testicle 1.09565718981860 varWeightedLogRatios=1.36697358905330 cont.varWeightedLogRatios=1.32791176548269 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.38826398432147 0.0915940250648437 58.8276798678393 6.47511136206243e-229 *** df.mm.trans1 -0.663125754598253 0.0731064834653952 -9.0706832440127 2.58650673682873e-18 *** df.mm.trans2 -0.230835946977021 0.0731064834653952 -3.15753044100783 0.00168569263887842 ** df.mm.exp2 -0.771951163049997 0.097669292198038 -7.90372435058462 1.69386554491920e-14 *** df.mm.exp3 -0.531448742080209 0.097669292198038 -5.44130842069197 8.2393856935934e-08 *** df.mm.exp4 -0.208113139642878 0.097669292198038 -2.1307939779158 0.0335857276925517 * df.mm.exp5 0.0562318036597808 0.097669292198038 0.57573677861577 0.565048059977541 df.mm.exp6 0.0196499701267061 0.097669292198038 0.201188824905817 0.840631458391839 df.mm.exp7 -0.0931153256230393 0.097669292198038 -0.953373609324773 0.340854217715411 df.mm.exp8 -0.153232112850944 0.097669292198038 -1.56888730738670 0.117296842673300 df.mm.trans1:exp2 0.687388606024103 0.076103299359188 9.03231018644547 3.5006798045715e-18 *** df.mm.trans2:exp2 0.124539451739711 0.076103299359188 1.63645272660147 0.102364207146268 df.mm.trans1:exp3 0.448665537051669 0.076103299359188 5.89548075877871 6.81679216805831e-09 *** df.mm.trans2:exp3 0.0485817655807264 0.076103299359188 0.638366089115703 0.523523071069683 df.mm.trans1:exp4 0.125640027880482 0.076103299359188 1.65091433536270 0.099374102686137 . df.mm.trans2:exp4 0.030101687499247 0.076103299359188 0.395537220497823 0.692612507276305 df.mm.trans1:exp5 -0.0501229671544817 0.076103299359188 -0.658617531388675 0.510439790996983 df.mm.trans2:exp5 -0.00526259398938124 0.076103299359188 -0.069150668022199 0.944896900903306 df.mm.trans1:exp6 -0.0103819686606673 0.076103299359188 -0.136419429224310 0.891543759391947 df.mm.trans2:exp6 0.035533522138365 0.076103299359188 0.466911716542747 0.640763227733307 df.mm.trans1:exp7 0.0893853413079436 0.076103299359188 1.17452649307710 0.240734481998756 df.mm.trans2:exp7 -0.0112100409419221 0.076103299359188 -0.147300327795430 0.882953415353416 df.mm.trans1:exp8 0.0632037714061533 0.076103299359188 0.830499754128238 0.406646421555395 df.mm.trans2:exp8 0.120136740346767 0.076103299359188 1.57860094579806 0.115050069409895 df.mm.trans1:probe2 0.057128301551503 0.0530158829864565 1.07756955714756 0.281737319727121 df.mm.trans1:probe3 0.113527753305704 0.0530158829864565 2.14139135124292 0.0327175793061652 * df.mm.trans1:probe4 -0.135808953711274 0.0530158829864565 -2.56166541158935 0.0107046525135246 * df.mm.trans1:probe5 -0.110538716096032 0.0530158829864565 -2.08501131866974 0.0375669860588202 * df.mm.trans1:probe6 0.119650621182307 0.0530158829864565 2.25688254994967 0.0244394795352339 * df.mm.trans2:probe2 0.130540012027521 0.0530158829864565 2.46228120091612 0.0141365402413372 * df.mm.trans2:probe3 0.712111085225229 0.0530158829864565 13.4320329137430 2.05065105371082e-35 *** df.mm.trans2:probe4 0.203341187175905 0.0530158829864565 3.83547676132926 0.000141079792236313 *** df.mm.trans2:probe5 0.791658161069358 0.0530158829864565 14.9324714873012 4.59493063004468e-42 *** df.mm.trans2:probe6 0.598890017707379 0.0530158829864565 11.2964263532189 1.49002018912094e-26 *** df.mm.trans3:probe2 -0.718615217284896 0.0530158829864565 -13.5547156211370 6.02257580214626e-36 *** df.mm.trans3:probe3 -0.221575694655325 0.0530158829864565 -4.17942099939991 3.44118379817284e-05 *** df.mm.trans3:probe4 -0.656090304007496 0.0530158829864565 -12.3753537062676 6.2645839801425e-31 *** df.mm.trans3:probe5 -0.0364332811649335 0.0530158829864565 -0.687214455604574 0.492261201609311 df.mm.trans3:probe6 -0.431968710974004 0.0530158829864565 -8.14791127942461 2.89865158376283e-15 *** df.mm.trans3:probe7 -0.280977089774950 0.0530158829864565 -5.29986626548744 1.73066334184324e-07 *** df.mm.trans3:probe8 -0.568243989592463 0.0530158829864565 -10.7183726382078 2.64379858753824e-24 *** df.mm.trans3:probe9 0.0417151905601942 0.0530158829864565 0.7868432667782 0.431740537348462 df.mm.trans3:probe10 -0.276283012721221 0.0530158829864565 -5.21132530777241 2.73130601448954e-07 *** df.mm.trans3:probe11 -0.732727193379944 0.0530158829864565 -13.8208995513123 4.14688400116629e-37 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25404193140507 0.307724448843769 13.8241922193347 4.01128953903657e-37 *** df.mm.trans1 0.186927948227769 0.245612662129090 0.761068043509594 0.446969555839892 df.mm.trans2 0.256282656707893 0.245612662129090 1.04344236362373 0.297239748142631 df.mm.exp2 0.442014199333571 0.328135258706282 1.34704877822722 0.178565097882821 df.mm.exp3 0.657345100524447 0.328135258706282 2.00327481757407 0.0456784854095528 * df.mm.exp4 0.145439697108304 0.328135258706282 0.443230933736655 0.657787399738635 df.mm.exp5 0.393699057408113 0.328135258706282 1.19980723485896 0.230773473499103 df.mm.exp6 -0.0827858738781566 0.328135258706282 -0.252291918291716 0.800917477813636 df.mm.exp7 0.0763191806467096 0.328135258706282 0.232584516969034 0.816177725281309 df.mm.exp8 0.340810449639346 0.328135258706282 1.03862794563144 0.299471900528857 df.mm.trans1:exp2 -0.233721134129498 0.255680933706311 -0.91411248676823 0.361091398090011 df.mm.trans2:exp2 -0.343568680050831 0.255680933706311 -1.34373993035192 0.179632368042795 df.mm.trans1:exp3 -0.474232596275734 0.255680933706311 -1.85478279276180 0.064206453708241 . df.mm.trans2:exp3 -0.105772372137979 0.255680933706311 -0.413688930984095 0.679276522004317 df.mm.trans1:exp4 0.102090582403849 0.255680933706311 0.399288992432718 0.689848082625041 df.mm.trans2:exp4 -0.311370946793790 0.255680933706311 -1.21781058243259 0.223861303357307 df.mm.trans1:exp5 -0.251468588026444 0.255680933706311 -0.983524991015932 0.325817303446783 df.mm.trans2:exp5 -0.195507436275188 0.255680933706311 -0.764653951474375 0.444832682645161 df.mm.trans1:exp6 0.146974736318549 0.255680933706311 0.574836512789697 0.565656353456822 df.mm.trans2:exp6 -0.0573975228824398 0.255680933706311 -0.224488866066055 0.822467192412125 df.mm.trans1:exp7 -0.0691385222121666 0.255680933706311 -0.270409377852096 0.786955149859766 df.mm.trans2:exp7 -0.0210518529468617 0.255680933706311 -0.082336420794845 0.934411624313217 df.mm.trans1:exp8 -0.0832387402595398 0.255680933706311 -0.325557088097748 0.7448935321671 df.mm.trans2:exp8 -0.398261505372056 0.255680933706311 -1.55765038714041 0.119938890481111 df.mm.trans1:probe2 0.0933949280102112 0.178115148454535 0.524351403126449 0.600262863659524 df.mm.trans1:probe3 -0.077245164895342 0.178115148454535 -0.433681051643169 0.664704173445709 df.mm.trans1:probe4 -0.0357755832111724 0.178115148454535 -0.20085648818525 0.840891182817616 df.mm.trans1:probe5 0.0548730707041354 0.178115148454535 0.308076383060378 0.758150489691488 df.mm.trans1:probe6 -0.075820140272648 0.178115148454535 -0.425680470923008 0.670520976356896 df.mm.trans2:probe2 0.170271620352725 0.178115148454535 0.955963722513963 0.339545256316394 df.mm.trans2:probe3 -0.0280604895274871 0.178115148454535 -0.157541285909489 0.87488087053604 df.mm.trans2:probe4 -0.0278984939579133 0.178115148454535 -0.156631786796251 0.87559727753067 df.mm.trans2:probe5 0.0985551618258706 0.178115148454535 0.553322739143815 0.580285861184101 df.mm.trans2:probe6 -0.147089229161160 0.178115148454535 -0.825809766532607 0.409299664680906 df.mm.trans3:probe2 -0.152730302557754 0.178115148454535 -0.857480702135446 0.391583623740097 df.mm.trans3:probe3 0.0267800336449814 0.178115148454535 0.150352364059687 0.88054630034675 df.mm.trans3:probe4 0.116671570234903 0.178115148454535 0.65503451698093 0.512742019488111 df.mm.trans3:probe5 -0.0915289739716666 0.178115148454535 -0.513875292280543 0.607562626915526 df.mm.trans3:probe6 0.188389174415876 0.178115148454535 1.05768193244924 0.290703161423546 df.mm.trans3:probe7 -0.0767148096216299 0.178115148454535 -0.430703453845823 0.666866687303995 df.mm.trans3:probe8 -0.11220633755027 0.178115148454535 -0.629965157505462 0.529000622772554 df.mm.trans3:probe9 -0.0717809071401 0.178115148454535 -0.403002820158345 0.687115698650862 df.mm.trans3:probe10 -0.0799127836768549 0.178115148454535 -0.448657985411347 0.653869710164535 df.mm.trans3:probe11 0.157406683587064 0.178115148454535 0.883735521390775 0.377257037870714