fitVsDatCorrelation=0.94121693078909 cont.fitVsDatCorrelation=0.225555231896608 fstatistic=8373.5731674056,57,807 cont.fstatistic=994.276122172507,57,807 residuals=-1.0047251283064,-0.104225518267124,0.00881507559636256,0.118549856918558,0.732023405270636 cont.residuals=-1.09481248232620,-0.424718745570233,-0.0914071368674998,0.318045424095668,1.9190844511676 predictedValues: Include Exclude Both Lung 69.2003922148145 48.5529270124834 96.3488089080354 cerebhem 75.3836384682116 61.0815491045305 88.6095226433063 cortex 85.7054893444637 47.2912755656878 138.088638522789 heart 166.788772918194 47.3838453724184 281.625641820725 kidney 100.847294413114 46.6586740857467 169.961605531727 liver 68.7538704133264 54.4588868989821 94.6279114716977 stomach 85.1606202219129 49.2293966410714 141.773136477530 testicle 87.8505601077591 50.679014366477 141.274634847911 diffExp=20.6474652023311,14.3020893636811,38.4142137787758,119.404927545776,54.1886203273668,14.2949835143443,35.9312235808414,37.1715457412821 diffExpScore=0.997018085926598 diffExp1.5=0,0,1,1,1,0,1,1 diffExp1.5Score=0.833333333333333 diffExp1.4=1,0,1,1,1,0,1,1 diffExp1.4Score=0.857142857142857 diffExp1.3=1,0,1,1,1,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 94.632165872906 97.3618792848421 82.3288173348001 cerebhem 94.9097851774098 75.3846003547002 82.9669818333272 cortex 90.879327392745 85.6396844323874 94.3644884112559 heart 85.3641279774907 97.8560920894213 97.5254101673828 kidney 87.8078031661295 111.569536332464 94.8716660767125 liver 82.3142665917871 98.8710281947619 93.3837984968537 stomach 94.186343408497 85.6711169088933 100.148386927175 testicle 99.726103477186 95.8119712669493 83.7230525042958 cont.diffExp=-2.72971341193607,19.5251848227096,5.2396429603577,-12.4919641119306,-23.7617331663344,-16.5567616029747,8.51522649960363,3.91413221023674 cont.diffExpScore=4.79346773762226 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=0,1,0,0,-1,-1,0,0 cont.diffExp1.2Score=1.5 tran.correlation=-0.424096150284932 cont.tran.correlation=-0.428091713218584 tran.covariance=-0.0125029020256489 cont.tran.covariance=-0.00327434101297389 tran.mean=71.5641379468246 cont.tran.mean=92.3741144955357 weightedLogRatios: wLogRatio Lung 1.43860925803508 cerebhem 0.887258364407549 cortex 2.46970606925922 heart 5.64728611916135 kidney 3.25890471842851 liver 0.958917412936989 stomach 2.28564319393610 testicle 2.31084226343656 cont.weightedLogRatios: wLogRatio Lung -0.129794041054665 cerebhem 1.02212285992454 cortex 0.266029850161511 heart -0.616651464050216 kidney -1.10046765245776 liver -0.825122759533252 stomach 0.426218116225217 testicle 0.183478795160204 varWeightedLogRatios=2.37106284225577 cont.varWeightedLogRatios=0.506410709128849 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.37476017251198 0.0915077244949477 36.8795114416631 2.79030268669703e-175 *** df.mm.trans1 0.701898717570463 0.0794624124147119 8.83309097019702 6.27417924793531e-18 *** df.mm.trans2 0.503055309179566 0.0706306695734186 7.12233527188432 2.3509647631692e-12 *** df.mm.exp2 0.398874786722759 0.0917982771825159 4.3451227949486 1.56957793138347e-05 *** df.mm.exp3 -0.172339060310149 0.0917982771825159 -1.87736704434550 0.060828306537264 . df.mm.exp4 -0.217255122387555 0.0917982771825159 -2.36665794888070 0.0181847121092346 * df.mm.exp5 -0.230792255184275 0.0917982771825159 -2.51412403661354 0.0121263794325735 * df.mm.exp6 0.126340620892448 0.0917982771825159 1.37628531569557 0.169115079179556 df.mm.exp7 -0.164884068140805 0.091798277182516 -1.79615645523475 0.0728435664907807 . df.mm.exp8 -0.101242642737657 0.0917982771825159 -1.10288172986475 0.270407308814416 df.mm.trans1:exp2 -0.313291062150502 0.0853856502175263 -3.66913013313560 0.000259361523591026 *** df.mm.trans2:exp2 -0.169319426338884 0.0652748352336566 -2.59394643790047 0.00966015387261901 ** df.mm.trans1:exp3 0.386249406438454 0.0853856502175263 4.5235868726707 6.99275601021638e-06 *** df.mm.trans2:exp3 0.146010408327179 0.0652748352336566 2.23685602276164 0.0255681415221047 * df.mm.trans1:exp4 1.09697677095125 0.0853856502175263 12.8473199906146 1.66448445999606e-34 *** df.mm.trans2:exp4 0.192881996530412 0.0652748352336566 2.9549212317423 0.0032184698612044 ** df.mm.trans1:exp5 0.607393160940631 0.0853856502175263 7.11352738303511 2.49661544901237e-12 *** df.mm.trans2:exp5 0.190996623326331 0.0652748352336566 2.92603761683416 0.00352942463646294 ** df.mm.trans1:exp6 -0.132814119460970 0.0853856502175263 -1.55546182669589 0.120228181402396 df.mm.trans2:exp6 -0.0115490542682917 0.0652748352336566 -0.176929657914125 0.859608036791151 df.mm.trans1:exp7 0.372416660707035 0.0853856502175263 4.36158370590697 1.45851327218758e-05 *** df.mm.trans2:exp7 0.178720524376389 0.0652748352336566 2.73796975107856 0.0063181255007905 ** df.mm.trans1:exp8 0.339873302627354 0.0853856502175263 3.98044989716073 7.4983415037288e-05 *** df.mm.trans2:exp8 0.144100068286514 0.0652748352336566 2.20758991992391 0.0275535098754553 * df.mm.trans1:probe2 0.482642538778507 0.055898029340691 8.6343390718994 3.12681168016672e-17 *** df.mm.trans1:probe3 0.804714181248288 0.055898029340691 14.3961100371475 5.43795066615721e-42 *** df.mm.trans1:probe4 0.0224065152580788 0.055898029340691 0.400846246680971 0.688639430758998 df.mm.trans1:probe5 0.322922834965358 0.055898029340691 5.77699855923698 1.08543816282609e-08 *** df.mm.trans1:probe6 -0.378333549057560 0.055898029340691 -6.76828062670452 2.50597738129533e-11 *** df.mm.trans1:probe7 -0.0876906095570934 0.055898029340691 -1.56876030499449 0.117095795700802 df.mm.trans1:probe8 0.713518296434634 0.055898029340691 12.7646413451508 4.03402700241129e-34 *** df.mm.trans1:probe9 -0.257862280512006 0.055898029340691 -4.61308356579747 4.61250440813607e-06 *** df.mm.trans1:probe10 0.93013808772301 0.055898029340691 16.6399083955884 1.12371379128907e-53 *** df.mm.trans1:probe11 -0.0198754644313388 0.055898029340691 -0.355566460316525 0.722258183603762 df.mm.trans1:probe12 -0.0395065886034711 0.055898029340691 -0.706761742219637 0.479918566752684 df.mm.trans1:probe13 -0.0114322144761524 0.055898029340691 -0.204519096844624 0.837999438707362 df.mm.trans1:probe14 -0.0121442806211820 0.055898029340691 -0.217257759610169 0.828062389006426 df.mm.trans1:probe15 0.120489289963956 0.055898029340691 2.15551945900616 0.0314162502701071 * df.mm.trans1:probe16 -0.062751507063825 0.055898029340691 -1.12260678603467 0.261938410126732 df.mm.trans1:probe17 0.49791336973789 0.055898029340691 8.90752993639857 3.41221056159486e-18 *** df.mm.trans1:probe18 0.214390386365135 0.055898029340691 3.83538362432162 0.000135141523557688 *** df.mm.trans1:probe19 0.448410528652742 0.055898029340691 8.0219380529453 3.65712181972753e-15 *** df.mm.trans1:probe20 0.350012167633537 0.055898029340691 6.26161909036649 6.18691601859197e-10 *** df.mm.trans1:probe21 0.404481255281525 0.055898029340691 7.23605572597678 1.07582831506478e-12 *** df.mm.trans1:probe22 0.367986253357872 0.055898029340691 6.5831704211797 8.28875199742613e-11 *** df.mm.trans2:probe2 0.127737864572245 0.055898029340691 2.28519441702855 0.0225600395355931 * df.mm.trans2:probe3 0.0198241577111519 0.055898029340691 0.354648597544045 0.722945519321499 df.mm.trans2:probe4 0.00183989658073074 0.055898029340691 0.0329152315820799 0.973750325330735 df.mm.trans2:probe5 -0.0354949766114583 0.055898029340691 -0.634995133640959 0.525611579182329 df.mm.trans2:probe6 -0.0461609443786445 0.055898029340691 -0.825806292692356 0.409158044830793 df.mm.trans3:probe2 -0.411341320751767 0.055898029340691 -7.35878036495163 4.57380956903576e-13 *** df.mm.trans3:probe3 0.54819634746696 0.055898029340691 9.807078244669 1.58159368785975e-21 *** df.mm.trans3:probe4 0.442032820395692 0.055898029340691 7.90784264864799 8.60257053829375e-15 *** df.mm.trans3:probe5 -0.701073392632571 0.055898029340691 -12.5420055215117 4.29450073996946e-33 *** df.mm.trans3:probe6 0.343176572141798 0.055898029340691 6.13933221241455 1.29890242990859e-09 *** df.mm.trans3:probe7 0.0330296205247611 0.055898029340691 0.590890607671515 0.554759169264366 df.mm.trans3:probe8 -0.347933980070576 0.055898029340691 -6.22444090023219 7.76212948105934e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70730187482092 0.263910251769126 17.8367526205043 3.02978459303792e-60 *** df.mm.trans1 -0.192806586656001 0.229171311845992 -0.841320779215033 0.400417477544229 df.mm.trans2 -0.122893072824512 0.203700374942359 -0.603303125285301 0.546476573931787 df.mm.exp2 -0.260623886597808 0.264748211988895 -0.984421706344667 0.325203252433973 df.mm.exp3 -0.305194480213779 0.264748211988895 -1.1527725831311 0.249345083002327 df.mm.exp4 -0.267400037042517 0.264748211988895 -1.01001640401535 0.312790164341676 df.mm.exp5 -0.0804376758752821 0.264748211988895 -0.303827078834648 0.761337953484155 df.mm.exp6 -0.250068162673409 0.264748211988895 -0.944550902892964 0.345170905825768 df.mm.exp7 -0.328573008259679 0.264748211988895 -1.24107734587254 0.214937818704997 df.mm.exp8 0.0195897506308458 0.264748211988895 0.0739938920972487 0.94103358187137 df.mm.trans1:exp2 0.263553259240554 0.246254057466177 1.07024940808033 0.28482705187495 df.mm.trans2:exp2 0.00479215049881678 0.188254033151635 0.0254557653750600 0.979697724278241 df.mm.trans1:exp3 0.264729596048730 0.246254057466177 1.07502633163757 0.282684243943556 df.mm.trans2:exp3 0.176908508187192 0.188254033151635 0.939732898283757 0.347635771167125 df.mm.trans1:exp4 0.1643285644282 0.246254057466177 0.667313124173682 0.504762910784443 df.mm.trans2:exp4 0.272463237493816 0.188254033151635 1.44731686717358 0.148196533380570 df.mm.trans1:exp5 0.00559060883439458 0.246254057466177 0.0227026059668578 0.981893109228598 df.mm.trans2:exp5 0.216650965701921 0.188254033151635 1.15084368751565 0.250137425831066 df.mm.trans1:exp6 0.110615165895321 0.246254057466177 0.449191241896651 0.653414310044438 df.mm.trans2:exp6 0.265449667064285 0.188254033151635 1.41006098313161 0.158906967776860 df.mm.trans1:exp7 0.323850766837733 0.246254057466177 1.31510834854859 0.188847045890336 df.mm.trans2:exp7 0.200654000564735 0.188254033151635 1.06586826962221 0.286801974481396 df.mm.trans1:exp8 0.0328402742382061 0.246254057466177 0.133359322384837 0.89394246128088 df.mm.trans2:exp8 -0.035636863353763 0.188254033151635 -0.189301991341977 0.849903734108342 df.mm.trans1:probe2 0.0621381592486507 0.161211122646966 0.385445856516528 0.700008689145378 df.mm.trans1:probe3 0.126765242450345 0.161211122646966 0.786330622657759 0.431904649505281 df.mm.trans1:probe4 -0.0654093167619802 0.161211122646966 -0.405736996852377 0.685043405678047 df.mm.trans1:probe5 0.287036566403541 0.161211122646966 1.78050100818489 0.0753701371676037 . df.mm.trans1:probe6 0.103048496264774 0.161211122646966 0.639214556494582 0.522864755889199 df.mm.trans1:probe7 -0.0557874981078057 0.161211122646966 -0.346052413703327 0.729393521897344 df.mm.trans1:probe8 -0.0180013512251519 0.161211122646966 -0.111663208652004 0.911118218680759 df.mm.trans1:probe9 -0.0751374851086876 0.161211122646966 -0.466081272030034 0.641283101545784 df.mm.trans1:probe10 -0.00975831178702978 0.161211122646966 -0.0605312563227996 0.951747509423428 df.mm.trans1:probe11 0.0543245821318604 0.161211122646966 0.336977878696528 0.736221192922377 df.mm.trans1:probe12 -0.0702641369341808 0.161211122646966 -0.435851669416454 0.663060913026096 df.mm.trans1:probe13 -0.0255304346534751 0.161211122646966 -0.15836645905248 0.874207661869246 df.mm.trans1:probe14 -0.0132054828925808 0.161211122646966 -0.0819142170574628 0.934735240518312 df.mm.trans1:probe15 0.23973440608633 0.161211122646966 1.48708353462262 0.137383342905544 df.mm.trans1:probe16 0.0465065416226940 0.161211122646966 0.288482214248567 0.773051722874165 df.mm.trans1:probe17 0.186716008574978 0.161211122646966 1.15820797913470 0.247121815763021 df.mm.trans1:probe18 0.125094130491773 0.161211122646966 0.775964638405968 0.437997209649709 df.mm.trans1:probe19 0.0160407078420352 0.161211122646966 0.0995012476723615 0.920765008638446 df.mm.trans1:probe20 -0.167050113546193 0.161211122646966 -1.03621952879774 0.300410133397574 df.mm.trans1:probe21 0.139195103271365 0.161211122646966 0.863433620372376 0.388155639408761 df.mm.trans1:probe22 0.178608683551950 0.161211122646966 1.10791786955720 0.268227384488513 df.mm.trans2:probe2 -0.0913630253071402 0.161211122646966 -0.566729043300659 0.57105586942464 df.mm.trans2:probe3 -0.134988822358843 0.161211122646966 -0.837341866630712 0.402648335083348 df.mm.trans2:probe4 0.0209269932284835 0.161211122646966 0.129811100405964 0.89674822844469 df.mm.trans2:probe5 0.128891151061938 0.161211122646966 0.799517731442113 0.424225532626642 df.mm.trans2:probe6 -0.00710301161542695 0.161211122646966 -0.0440603073708614 0.964867231687379 df.mm.trans3:probe2 -0.00439825722694433 0.161211122646966 -0.0272825916396353 0.97824108662633 df.mm.trans3:probe3 0.000290192675015708 0.161211122646966 0.00180007849490136 0.998564190804072 df.mm.trans3:probe4 -0.0465381477606942 0.161211122646966 -0.288678268574604 0.772901727879179 df.mm.trans3:probe5 0.0820917664643816 0.161211122646966 0.509218998766935 0.610737931118827 df.mm.trans3:probe6 -0.088106736103798 0.161211122646966 -0.546530131774728 0.584852651785844 df.mm.trans3:probe7 0.0231035492975545 0.161211122646966 0.143312377695853 0.886079256718941 df.mm.trans3:probe8 0.186509480098628 0.161211122646966 1.15692687350774 0.247644575806724