fitVsDatCorrelation=0.90195266216744 cont.fitVsDatCorrelation=0.236386520736512 fstatistic=11568.4260182723,58,830 cont.fstatistic=2273.49375326146,58,830 residuals=-0.431478535609415,-0.0841638305029467,-0.00043095280033503,0.0774028735235168,0.818774451587868 cont.residuals=-0.619260640550999,-0.23993285320665,-0.0929428692732329,0.203614072876753,1.13630245138815 predictedValues: Include Exclude Both Lung 55.1623477096734 88.5788266421341 57.461448723239 cerebhem 48.2140799327377 64.0275399425481 53.4932613986677 cortex 49.7961130134971 80.0241330476238 52.1632980531683 heart 50.9149182068673 106.568573148861 53.5224735278959 kidney 48.8534998619795 113.770451572340 53.1812650890055 liver 50.8778324453042 112.352853211965 56.4387906384198 stomach 59.4014481849787 87.9248594512537 63.8868742133433 testicle 50.8116665029211 113.476568404461 61.0359376205597 diffExp=-33.4164789324607,-15.8134600098104,-30.2280200341268,-55.6536549419939,-64.9169517103601,-61.475020766661,-28.5234112662749,-62.6649019015398 diffExpScore=0.997172680513082 diffExp1.5=-1,0,-1,-1,-1,-1,0,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 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 58.6325648117493 62.29348938501 58.4509743089364 cerebhem 58.5045139378187 57.8554351523073 70.8729648920631 cortex 60.104763001098 55.7025395463412 53.4363657389994 heart 55.586722858283 64.8080757881739 60.4089061286684 kidney 55.2709811820247 62.2993567585653 57.8074180100427 liver 56.569624590867 61.1469941184377 55.9886739877044 stomach 61.5317611269879 57.0030978243434 68.3045664515535 testicle 56.5853766796597 61.171000558605 57.109978227701 cont.diffExp=-3.66092457326066,0.649078785511414,4.40222345475685,-9.22135292989086,-7.02837557654053,-4.57736952757068,4.52866330264442,-4.58562387894525 cont.diffExpScore=1.88612344146826 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=-0.0699919540492508 cont.tran.correlation=-0.826919739674126 tran.covariance=0.000125128713081218 cont.tran.covariance=-0.00164740917763129 tran.mean=73.7972319549466 cont.tran.mean=59.066643582517 weightedLogRatios: wLogRatio Lung -2.01147225371373 cerebhem -1.13960787572094 cortex -1.96641493465402 heart -3.17573079221983 kidney -3.64475965017937 liver -3.42676337289048 stomach -1.67861890501429 testicle -3.47891523125695 cont.weightedLogRatios: wLogRatio Lung -0.248418614223143 cerebhem 0.0453347681308503 cortex 0.308669443388197 heart -0.628476547105374 kidney -0.487442870032819 liver -0.317021249165748 stomach 0.312008944368725 testicle -0.317512312740108 varWeightedLogRatios=0.942694387406964 cont.varWeightedLogRatios=0.124032766707359 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.54376172712729 0.0732448480921615 62.0352399585842 0 *** df.mm.trans1 -0.669894030165495 0.0647922278923888 -10.3391109081493 1.18828413530803e-23 *** df.mm.trans2 -0.0612372374196573 0.0580778820845919 -1.05439859756703 0.292007296059143 df.mm.exp2 -0.387650495187588 0.0771932788892893 -5.0218166758217 6.2674282881351e-07 *** df.mm.exp3 -0.107173163816492 0.0771932788892893 -1.38837429059335 0.165395595941038 df.mm.exp4 0.175783794822527 0.0771932788892893 2.27719041543289 0.0230285167849201 * df.mm.exp5 0.206243482393073 0.0771932788892893 2.67178030730976 0.0076928838370925 ** df.mm.exp6 0.174855811522334 0.0771932788892892 2.26516885975413 0.0237593198006063 * df.mm.exp7 -0.0393719427451064 0.0771932788892893 -0.510043663277651 0.610156471903232 df.mm.exp8 0.105200321626859 0.0771932788892893 1.36281711491667 0.173309778254818 df.mm.trans1:exp2 0.253020974540403 0.0734376826025064 3.44538342678814 0.000598868651965911 *** df.mm.trans2:exp2 0.0630709454916768 0.0592232749335038 1.06496889208665 0.287199713688237 df.mm.trans1:exp3 0.00482947902946641 0.0734376826025064 0.0657629551793834 0.947582386108223 df.mm.trans2:exp3 0.0056085639940975 0.0592232749335038 0.0947020238309148 0.924574367747665 df.mm.trans1:exp4 -0.255908439498157 0.0734376826025064 -3.48470200078757 0.00051850065788521 *** df.mm.trans2:exp4 0.00911201035721135 0.0592232749335038 0.153858603183332 0.877758625767926 df.mm.trans1:exp5 -0.327698075227932 0.0734376826025064 -4.46226056725744 9.22831943147948e-06 *** df.mm.trans2:exp5 0.0440465012191096 0.0592232749335038 0.743736331173263 0.457246487241734 df.mm.trans1:exp6 -0.255709108542387 0.0734376826025064 -3.48198771367085 0.000523707694899569 *** df.mm.trans2:exp6 0.0628957304312539 0.0592232749335038 1.06201034140502 0.288539896460369 df.mm.trans1:exp7 0.113409935201397 0.0734376826025064 1.54430166070526 0.122896439619534 df.mm.trans2:exp7 0.0319616704962537 0.0592232749335038 0.539680903025717 0.589561863219718 df.mm.trans1:exp8 -0.187354951673525 0.0734376826025064 -2.55121001962459 0.0109132826252837 * df.mm.trans2:exp8 0.14250319610565 0.0592232749335038 2.40620256589412 0.0163369746522204 * df.mm.trans1:probe2 0.0489008854421057 0.0428783687757902 1.14045582512262 0.254425418649203 df.mm.trans1:probe3 0.08251861129861 0.0428783687757902 1.92448112310656 0.0546358723443106 . df.mm.trans1:probe4 0.136541119220304 0.0428783687757902 3.18438231487476 0.00150475493414861 ** df.mm.trans1:probe5 0.00184048039514778 0.0428783687757902 0.0429232838770431 0.965773011543502 df.mm.trans1:probe6 0.0793529405853314 0.0428783687757902 1.85065203856671 0.0645747770511668 . df.mm.trans1:probe7 0.0017408695210001 0.0428783687757902 0.0406001807135682 0.967624403951968 df.mm.trans1:probe8 0.111401119194837 0.0428783687757902 2.59807269668655 0.00954105347597537 ** df.mm.trans1:probe9 0.110342011217087 0.0428783687757902 2.57337241055188 0.0102438172789730 * df.mm.trans1:probe10 0.0392187084696417 0.0428783687757902 0.914650197509034 0.360640930994751 df.mm.trans1:probe11 0.0655009372608724 0.0428783687757902 1.52759862678954 0.126993198881589 df.mm.trans1:probe12 0.0592437759164617 0.0428783687757902 1.38167046946785 0.167444685884040 df.mm.trans1:probe13 0.0287683867082451 0.0428783687757902 0.670930064030053 0.502451741181173 df.mm.trans1:probe14 0.0523818919719106 0.0428783687757902 1.22163910305950 0.222190992162802 df.mm.trans1:probe15 0.00991509267158332 0.0428783687757902 0.231237636940646 0.817187182849849 df.mm.trans1:probe16 0.0763477781560618 0.0428783687757902 1.78056629335137 0.0753490306285433 . df.mm.trans1:probe17 0.381768622290879 0.0428783687757902 8.9035248585863 3.35796430793373e-18 *** df.mm.trans1:probe18 0.377841718930255 0.0428783687757902 8.81194247164528 7.11129151077083e-18 *** df.mm.trans1:probe19 0.365510390130328 0.0428783687757902 8.5243539007179 7.20355715540165e-17 *** df.mm.trans1:probe20 0.650279109536635 0.0428783687757902 15.1656681003171 4.87639091100127e-46 *** df.mm.trans1:probe21 0.548750654792697 0.0428783687757902 12.7978435388272 2.35644125327121e-34 *** df.mm.trans1:probe22 0.66813474671312 0.0428783687757902 15.5820933908838 3.27328626733442e-48 *** df.mm.trans1:probe23 0.0625753894416138 0.0428783687757902 1.45936963621025 0.144841747282789 df.mm.trans1:probe24 0.142526914841947 0.0428783687757902 3.32398173977224 0.00092630867092449 *** df.mm.trans1:probe25 0.0912757243594521 0.0428783687757902 2.12871261117069 0.0335719460612353 * df.mm.trans1:probe26 0.0361225439603011 0.0428783687757902 0.842442121555157 0.39978320630165 df.mm.trans2:probe2 -0.252359668263202 0.0428783687757902 -5.88547735066097 5.75678925944343e-09 *** df.mm.trans2:probe3 0.0430867509924401 0.0428783687757902 1.00485984477953 0.315257198188771 df.mm.trans2:probe4 0.142064221750832 0.0428783687757902 3.31319091203497 0.000962308727044946 *** df.mm.trans2:probe5 0.152085427911628 0.0428783687757902 3.54690330471475 0.000411635631574998 *** df.mm.trans2:probe6 -0.0698247460710945 0.0428783687757902 -1.62843755638668 0.103811621429622 df.mm.trans3:probe2 0.608968187386521 0.0428783687757902 14.2022237499472 3.86146283097951e-41 *** df.mm.trans3:probe3 0.097252074853272 0.0428783687757902 2.26809175884933 0.0235798035162454 * df.mm.trans3:probe4 0.209671377673719 0.0428783687757902 4.88991031282195 1.21134561026110e-06 *** df.mm.trans3:probe5 0.276527392172624 0.0428783687757902 6.44911175652643 1.90846090332866e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.122526141947 0.164806088327083 25.0144044057718 2.31727450988492e-103 *** df.mm.trans1 -0.0570882159100606 0.145787095080128 -0.391586209181845 0.695464490603865 df.mm.trans2 -0.00715069833603154 0.130679342151677 -0.0547194240366766 0.956375162592335 df.mm.exp2 -0.268796548211479 0.173690336866803 -1.54756190275346 0.122108996068002 df.mm.exp3 0.00266449337613892 0.173690336866803 0.0153404813658818 0.987764233422122 df.mm.exp4 -0.0467207642930518 0.173690336866803 -0.268988851860425 0.788005110393839 df.mm.exp5 -0.0478768026097908 0.173690336866803 -0.275644595280542 0.782889663969059 df.mm.exp6 -0.0113553568032757 0.173690336866803 -0.0653770210140343 0.94788956243534 df.mm.exp7 -0.196276341272572 0.173690336866803 -1.13003604468272 0.258787490146168 df.mm.exp8 -0.0305138469706214 0.173690336866803 -0.175679588865219 0.860588522082861 df.mm.trans1:exp2 0.266610205060267 0.165239979613258 1.61347275450085 0.107021940835435 df.mm.trans2:exp2 0.194887033862049 0.133256557067727 1.46249489068668 0.143984177108737 df.mm.trans1:exp3 0.0221343405485209 0.165239979613258 0.133952694743282 0.89347245373567 df.mm.trans2:exp3 -0.114495670269667 0.133256557067727 -0.859212280349366 0.390471487763765 df.mm.trans1:exp4 -0.0066251162224478 0.165239979613258 -0.0400939060749935 0.968027902058752 df.mm.trans2:exp4 0.0862940701677631 0.133256557067727 0.647578416151817 0.517436734023253 df.mm.trans1:exp5 -0.0111654349206666 0.165239979613258 -0.0675710257699087 0.946143394356057 df.mm.trans2:exp5 0.0479709873682457 0.133256557067727 0.359989695245275 0.718946419675938 df.mm.trans1:exp6 -0.0244627257937636 0.165239979613258 -0.148043626312581 0.882344295581821 df.mm.trans2:exp6 -0.0072208542225677 0.133256557067727 -0.054187609086266 0.956798732531774 df.mm.trans1:exp7 0.244539568121999 0.165239979613258 1.47990558153263 0.139277896248338 df.mm.trans2:exp7 0.107525039346826 0.133256557067727 0.80690242726425 0.419953853804505 df.mm.trans1:exp8 -0.00502581949425199 0.165239979613258 -0.0304152754437205 0.975743174326762 df.mm.trans2:exp8 0.0123301610272719 0.133256557067727 0.092529488218768 0.926299692002496 df.mm.trans1:probe2 -0.07917841053606 0.0964793622466446 -0.820677175846628 0.412065809674823 df.mm.trans1:probe3 0.0863796647639135 0.0964793622466446 0.895317534781048 0.370876937627425 df.mm.trans1:probe4 0.103004686655636 0.0964793622466446 1.06763440654085 0.285995882025487 df.mm.trans1:probe5 -0.0535376889852759 0.0964793622466446 -0.554913379800433 0.579103416187258 df.mm.trans1:probe6 -0.0447420540026639 0.0964793622466446 -0.463747406292789 0.64295031985864 df.mm.trans1:probe7 -0.00907056875510257 0.0964793622466446 -0.094015637581788 0.925119424980757 df.mm.trans1:probe8 -0.00719833059514016 0.0964793622466446 -0.0746100557416414 0.9405429508652 df.mm.trans1:probe9 -0.0504423746785145 0.0964793622466446 -0.522830722590819 0.601231588983992 df.mm.trans1:probe10 0.0251891702868483 0.0964793622466446 0.261083507397711 0.79409287617992 df.mm.trans1:probe11 0.0138075695737235 0.0964793622466446 0.143114229325285 0.886234702943921 df.mm.trans1:probe12 -0.0228569904717431 0.0964793622466446 -0.236910671251230 0.812784549527317 df.mm.trans1:probe13 -0.00673929215500796 0.0964793622466446 -0.0698521631784765 0.944328153921773 df.mm.trans1:probe14 -0.00460481538441312 0.0964793622466446 -0.0477285014865785 0.961944100820443 df.mm.trans1:probe15 0.0428429545987583 0.0964793622466446 0.444063410050664 0.657112487808879 df.mm.trans1:probe16 0.0224856492355206 0.0964793622466446 0.233061752398789 0.815770916369573 df.mm.trans1:probe17 0.0699660118332369 0.0964793622466446 0.725191483484026 0.468538967024192 df.mm.trans1:probe18 0.0321539829604792 0.0964793622466446 0.333273170673321 0.739012258913895 df.mm.trans1:probe19 0.130129826705423 0.0964793622466446 1.34878406816945 0.177774209740802 df.mm.trans1:probe20 -0.0543861837715616 0.0964793622466446 -0.56370795271766 0.573105171407443 df.mm.trans1:probe21 -0.0960996392088575 0.0964793622466446 -0.996064204520587 0.319509244754448 df.mm.trans1:probe22 0.0140074540267285 0.0964793622466446 0.145186013884702 0.884599256873783 df.mm.trans1:probe23 0.0587391642158334 0.0964793622466446 0.608826207470876 0.542806230119487 df.mm.trans1:probe24 0.0751853265656307 0.0964793622466446 0.779289216002726 0.436031500123052 df.mm.trans1:probe25 0.0643446873655868 0.0964793622466446 0.666926955850857 0.505004146827497 df.mm.trans1:probe26 -0.127957579371285 0.0964793622466446 -1.32626891794919 0.185115466725552 df.mm.trans2:probe2 0.0692939908616849 0.0964793622466446 0.718226045944814 0.472820037710058 df.mm.trans2:probe3 -0.0200833273029156 0.0964793622466446 -0.208161899449269 0.835153624735024 df.mm.trans2:probe4 0.129467095129434 0.0964793622466446 1.34191491438820 0.179990563706823 df.mm.trans2:probe5 -0.0337448447372340 0.0964793622466446 -0.349762311352837 0.726605813744146 df.mm.trans2:probe6 0.0363632831974638 0.0964793622466446 0.376902192870046 0.70634262968152 df.mm.trans3:probe2 -0.00421352441144396 0.0964793622466446 -0.0436728053889111 0.96517572083112 df.mm.trans3:probe3 0.0156979428868991 0.0964793622466446 0.162707780413889 0.870788125080462 df.mm.trans3:probe4 0.0609378883211369 0.0964793622466446 0.631615787067003 0.527811881434179 df.mm.trans3:probe5 -0.0229166609018290 0.0964793622466446 -0.237529149946532 0.812304926428167