fitVsDatCorrelation=0.74853830873336 cont.fitVsDatCorrelation=0.267593693245137 fstatistic=11563.5000765298,53,715 cont.fstatistic=5469.41190818833,53,715 residuals=-0.531932993678821,-0.0873983764252825,-0.00338873169240999,0.0842814431078483,0.516669826505077 cont.residuals=-0.527922949413418,-0.138759309050094,-0.0360195628623735,0.0964587292753186,0.697979099953706 predictedValues: Include Exclude Both Lung 52.9459952251942 60.7675946570833 49.423131297435 cerebhem 59.106026276851 56.8639975959844 68.0071260084314 cortex 55.7146798920419 49.7486814252939 56.0808389158892 heart 51.7485415684224 53.9442857276368 51.8217586731654 kidney 52.1943091173953 63.320919700611 47.917438929653 liver 51.8045321174109 58.2431431451003 52.2250664149437 stomach 51.4543953958413 53.0037267171541 47.726343135693 testicle 51.2470792546589 59.7082514682701 54.7080970088813 diffExp=-7.82159943188918,2.24202868086660,5.96599846674805,-2.19574415921444,-11.1266105832157,-6.43861102768946,-1.54933132131283,-8.46117221361119 diffExpScore=1.50735669556065 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,-1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 54.481565034479 51.8980191565071 61.1861621274863 cerebhem 53.0843171780411 56.1351638748584 59.379932679124 cortex 54.2942970476315 61.1932422767053 53.1180864363557 heart 50.6805640333409 52.8369006841294 48.5716718908437 kidney 53.951011901942 57.2493224421479 49.7445088933394 liver 54.5402598988108 53.6696777877925 57.4560980066436 stomach 52.7551608384767 58.4518583875825 54.545771522069 testicle 54.3431638733893 51.5070242978549 54.709709354912 cont.diffExp=2.58354587797184,-3.0508466968173,-6.89894522907377,-2.15633665078850,-3.29831054020590,0.870582111018265,-5.69669754910576,2.83613957553435 cont.diffExpScore=1.73244140184391 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=-0.253446532564095 cont.tran.correlation=0.0488979792373352 tran.covariance=-0.00102858071408846 cont.tran.covariance=7.34710373033138e-05 tran.mean=55.1135099553094 cont.tran.mean=54.4419717946056 weightedLogRatios: wLogRatio Lung -0.556395385994778 cerebhem 0.157002045582717 cortex 0.448917734225681 heart -0.164857973583382 kidney -0.782939426798935 liver -0.469303917549257 stomach -0.117346059753363 testicle -0.613242796511162 cont.weightedLogRatios: wLogRatio Lung 0.193043118274965 cerebhem -0.223513586670867 cortex -0.484956892692481 heart -0.164434991078017 kidney -0.238410481732819 liver 0.0642173107077329 stomach -0.411903808831665 testicle 0.212715105976098 varWeightedLogRatios=0.176531490637081 cont.varWeightedLogRatios=0.0695223546961361 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03225097472524 0.0752460038299461 53.5875763427652 1.37023248010305e-252 *** df.mm.trans1 0.0127677662876338 0.0676505812138086 0.188731065698926 0.850357157818576 df.mm.trans2 -0.0288341710245482 0.0621213136817565 -0.464159067405814 0.642675071928418 df.mm.exp2 -0.275527738388540 0.0850631120133809 -3.23909779300337 0.00125453554711295 ** df.mm.exp3 -0.275477118447166 0.0850631120133808 -3.23850270612992 0.00125711731096593 ** df.mm.exp4 -0.189372635844735 0.0850631120133808 -2.22626037729428 0.0263076231384198 * df.mm.exp5 0.0577991792572379 0.0850631120133808 0.679485829864134 0.497049992119581 df.mm.exp6 -0.119369110657782 0.0850631120133809 -1.40330053571288 0.16096135714875 df.mm.exp7 -0.130335937713350 0.0850631120133808 -1.53222630383953 0.125908918655763 df.mm.exp8 -0.151793412421737 0.0850631120133808 -1.78447988592116 0.0747696367842596 . df.mm.trans1:exp2 0.385588189015397 0.0815896708129706 4.72594367857284 2.75869369290268e-06 *** df.mm.trans2:exp2 0.209133483811559 0.0708291952348136 2.95264520679978 0.00325389190502183 ** df.mm.trans1:exp3 0.326448347429609 0.0815896708129705 4.00109896481790 6.96196039112866e-05 *** df.mm.trans2:exp3 0.075404413413698 0.0708291952348136 1.06459508912556 0.287418587208312 df.mm.trans1:exp4 0.166496449294102 0.0815896708129706 2.04065597562913 0.0416518434832257 * df.mm.trans2:exp4 0.0702677397358043 0.0708291952348136 0.992073106334924 0.321497475158069 df.mm.trans1:exp5 -0.0720981470976683 0.0815896708129705 -0.883667581683718 0.377172710152325 df.mm.trans2:exp5 -0.0166400839761312 0.0708291952348136 -0.234932557414579 0.814328343847559 df.mm.trans1:exp6 0.0975743126637968 0.0815896708129706 1.19591501830505 0.232126173800817 df.mm.trans2:exp6 0.0769388176548832 0.0708291952348136 1.08625853223116 0.277730703901647 df.mm.trans1:exp7 0.101759390732393 0.0815896708129705 1.24720923271841 0.212729157214452 df.mm.trans2:exp7 -0.00635850006117798 0.0708291952348136 -0.089772304204477 0.928493297961135 df.mm.trans1:exp8 0.119179602616979 0.0815896708129705 1.46071924827564 0.144531781543486 df.mm.trans2:exp8 0.134206974545678 0.0708291952348136 1.89479739393839 0.0585224395848896 . df.mm.trans1:probe2 -0.0907798487689451 0.0407948354064853 -2.22527797610659 0.0263737730912717 * df.mm.trans1:probe3 -0.108021130434098 0.0407948354064853 -2.64791190742066 0.00827759758035051 ** df.mm.trans1:probe4 -0.131592544480807 0.0407948354064853 -3.22571578410847 0.00131379452178083 ** df.mm.trans1:probe5 -0.166804959987526 0.0407948354064853 -4.08887444514626 4.82695494444856e-05 *** df.mm.trans1:probe6 -0.159747850116990 0.0407948354064853 -3.91588416830808 9.86982553903938e-05 *** df.mm.trans1:probe7 -0.139180154319535 0.0407948354064853 -3.41171015724723 0.000681887831980156 *** df.mm.trans1:probe8 -0.0338922478900548 0.0407948354064853 -0.830797515233188 0.406365359514434 df.mm.trans1:probe9 -0.0650715083699611 0.0407948354064853 -1.59509182281482 0.111133597180381 df.mm.trans1:probe10 -0.095097986860706 0.0407948354064853 -2.3311280928857 0.0200236682985302 * df.mm.trans1:probe11 -0.046188142508986 0.0407948354064853 -1.13220563457999 0.257927545527487 df.mm.trans1:probe12 -0.140688531501132 0.0407948354064853 -3.44868486658403 0.000596343175488881 *** df.mm.trans1:probe13 -0.173457443031318 0.0407948354064853 -4.25194614227424 2.40050920131052e-05 *** df.mm.trans1:probe14 -0.140726103733126 0.0407948354064853 -3.44960587120678 0.000594346037280063 *** df.mm.trans1:probe15 -0.0871223508743998 0.0407948354064853 -2.13562207093866 0.0330493654504642 * df.mm.trans1:probe16 -0.208740700124734 0.0407948354064853 -5.11684133652737 3.9962084854767e-07 *** df.mm.trans1:probe17 -0.172508972360408 0.0407948354064853 -4.2286963690748 2.65570832204321e-05 *** df.mm.trans1:probe18 0.195664425745054 0.0407948354064853 4.79630384080307 1.96760268715992e-06 *** df.mm.trans1:probe19 -0.196376030136887 0.0407948354064853 -4.81374733296922 1.80826209701172e-06 *** df.mm.trans1:probe20 0.286508469548021 0.0407948354064853 7.02315542379841 5.05738012874799e-12 *** df.mm.trans1:probe21 0.0466492604976023 0.0407948354064853 1.14350897687863 0.253210039619715 df.mm.trans1:probe22 -0.154780280462663 0.0407948354064853 -3.79411459613482 0.000160711665457806 *** df.mm.trans1:probe23 -0.187449298959995 0.0407948354064853 -4.59492720321641 5.1166452930828e-06 *** df.mm.trans2:probe2 0.38928275582285 0.0407948354064853 9.5424519291225 2.14739081982217e-20 *** df.mm.trans2:probe3 -0.083658679047586 0.0407948354064853 -2.05071740611280 0.0406584750690869 * df.mm.trans2:probe4 0.335359988234355 0.0407948354064853 8.22064815050195 9.51427857354803e-16 *** df.mm.trans2:probe5 0.279592111271613 0.0407948354064853 6.85361537767513 1.55636813567678e-11 *** df.mm.trans2:probe6 0.0121825688662612 0.0407948354064853 0.298630175728677 0.765309006820062 df.mm.trans3:probe2 -0.240699847943113 0.0407948354064853 -5.90025294978513 5.60159899131936e-09 *** df.mm.trans3:probe3 -0.106598183555481 0.0407948354064853 -2.6130313431424 0.00916293343374368 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81495647381566 0.109339232469933 34.8910120149665 1.62451505408194e-156 *** df.mm.trans1 0.142325263731533 0.0983023981284034 1.44783104421956 0.148102636968589 df.mm.trans2 0.161829534160862 0.0902678735383428 1.79276998357696 0.0734323093248553 . df.mm.exp2 0.0824656710011556 0.123604376387435 0.667174362359702 0.504876018418717 df.mm.exp3 0.302716520498000 0.123604376387435 2.44907607113475 0.0145613263517458 * df.mm.exp4 0.176489873666597 0.123604376387435 1.42786104201839 0.153768477867153 df.mm.exp5 0.295370215425011 0.123604376387435 2.38964205036867 0.0171228965635948 * df.mm.exp6 0.0975442258546843 0.123604376387435 0.789164823330642 0.430277280068375 df.mm.exp7 0.201602935450794 0.123604376387435 1.63103395966236 0.103323520909811 df.mm.exp8 0.101773866687542 0.123604376387435 0.823384006797091 0.410564197792660 df.mm.trans1:exp2 -0.108446519622064 0.118557152939654 -0.914719330998642 0.360647362481435 df.mm.trans2:exp2 -0.00398387069770625 0.102921211084375 -0.0387079655955491 0.969134027501703 df.mm.trans1:exp3 -0.306159714020481 0.118557152939654 -2.5823807879084 0.0100098261876189 * df.mm.trans2:exp3 -0.137960380293959 0.102921211084375 -1.34044653031588 0.180525971503077 df.mm.trans1:exp4 -0.248809777166921 0.118557152939654 -2.0986483817941 0.0361981056564577 * df.mm.trans2:exp4 -0.158560673363738 0.102921211084375 -1.54060248313392 0.123855992222629 df.mm.trans1:exp5 -0.305156155804533 0.118557152939654 -2.57391602478729 0.0102557063683194 * df.mm.trans2:exp5 -0.197235031034543 0.102921211084375 -1.91636912310378 0.0557165191494491 . df.mm.trans1:exp6 -0.0964674713600939 0.118557152939654 -0.813679048190335 0.416099722059161 df.mm.trans2:exp6 -0.0639766661674464 0.102921211084375 -0.621608174771652 0.534397682120452 df.mm.trans1:exp7 -0.23380372027928 0.118557152939654 -1.9720760365955 0.0489859345612124 * df.mm.trans2:exp7 -0.08268007645647 0.102921211084375 -0.803333691717722 0.422048860198514 df.mm.trans1:exp8 -0.104317428929260 0.118557152939654 -0.879891481388372 0.379213732638042 df.mm.trans2:exp8 -0.109336297087774 0.102921211084375 -1.06233006720199 0.288444541951469 df.mm.trans1:probe2 0.0708949512538064 0.0592785764698271 1.19596244504778 0.232107676484406 df.mm.trans1:probe3 0.0656245537083468 0.0592785764698271 1.10705346883203 0.268643112654582 df.mm.trans1:probe4 -0.00197489396980072 0.0592785764698271 -0.0333154756306596 0.973432310737902 df.mm.trans1:probe5 0.0184033552222750 0.0592785764698271 0.310455417761968 0.756305131235903 df.mm.trans1:probe6 -0.00290382258920597 0.0592785764698271 -0.048986037825723 0.960944102904626 df.mm.trans1:probe7 0.0942001677285514 0.0592785764698271 1.58910981569369 0.112477659671341 df.mm.trans1:probe8 0.0140788306536353 0.0592785764698271 0.237502846594189 0.812334748293854 df.mm.trans1:probe9 0.118833716988911 0.0592785764698271 2.00466549748201 0.0453753960858727 * df.mm.trans1:probe10 0.0679986227533338 0.0592785764698271 1.14710282875880 0.251722824435738 df.mm.trans1:probe11 0.0374044942790066 0.0592785764698271 0.6309951504663 0.528245341504232 df.mm.trans1:probe12 -0.00277368447984489 0.0592785764698271 -0.0467906728707748 0.962693128528654 df.mm.trans1:probe13 0.0254601269512702 0.0592785764698271 0.429499634901479 0.667689053808067 df.mm.trans1:probe14 -0.0273218198718369 0.0592785764698271 -0.460905465328502 0.645006590335763 df.mm.trans1:probe15 0.0256967583839101 0.0592785764698271 0.433491489070926 0.664788499069366 df.mm.trans1:probe16 0.128027778922625 0.0592785764698271 2.15976473368572 0.031122633730847 * df.mm.trans1:probe17 0.0265734842771718 0.0592785764698271 0.448281417329543 0.654085917896136 df.mm.trans1:probe18 0.0531484947969215 0.0592785764698271 0.896588581609651 0.370240261919331 df.mm.trans1:probe19 0.0949574044651274 0.0592785764698271 1.60188402151426 0.109622934191653 df.mm.trans1:probe20 0.051636162565071 0.0592785764698271 0.871076291640604 0.384004871446536 df.mm.trans1:probe21 0.0496680031596752 0.0592785764698271 0.837874424750336 0.402381224690068 df.mm.trans1:probe22 0.0478541442714079 0.0592785764698271 0.807275530574958 0.419776220532135 df.mm.trans1:probe23 0.0996100874911266 0.0592785764698271 1.68037246207875 0.0933216614498435 . df.mm.trans2:probe2 -0.0207786280843266 0.0592785764698271 -0.350525085481817 0.726047909621188 df.mm.trans2:probe3 -0.0297071721420816 0.0592785764698271 -0.501145167634087 0.616423207469511 df.mm.trans2:probe4 -0.0250197453913519 0.0592785764698271 -0.422070617773471 0.673100351431548 df.mm.trans2:probe5 -0.0780196667049898 0.0592785764698271 -1.31615283887092 0.188544531880293 df.mm.trans2:probe6 -0.0940232533461972 0.0592785764698271 -1.58612535835868 0.113153005304631 df.mm.trans3:probe2 -0.00603796381186187 0.0592785764698271 -0.101857436049181 0.918898392770991 df.mm.trans3:probe3 -0.00953268654096572 0.0592785764698271 -0.160811664325608 0.872287162162782