fitVsDatCorrelation=0.944388501720451 cont.fitVsDatCorrelation=0.209660324871165 fstatistic=6989.58899198419,69,1083 cont.fstatistic=776.616297369757,69,1083 residuals=-1.37442681798523,-0.116687401268528,-0.007760543978958,0.108265851082182,1.03554633748056 cont.residuals=-1.01138743502712,-0.458068945681332,-0.211180770485498,0.302486395264116,2.47848648080196 predictedValues: Include Exclude Both Lung 65.3112152868819 209.004673680302 80.0159757154991 cerebhem 63.0995973639918 179.941407673678 72.1877372004157 cortex 91.5765054137616 155.826909459508 108.162619102078 heart 58.9893833404622 149.378470928711 72.5747941062355 kidney 64.1121852144844 237.909288135526 79.3216024343661 liver 60.0891437195854 197.853979119351 76.3931772625926 stomach 106.278377972496 185.051081952762 122.078568497498 testicle 59.0983432042012 185.885510828208 75.4121933751753 diffExp=-143.69345839342,-116.841810309687,-64.2504040457463,-90.3890875882486,-173.797102921042,-137.764835399766,-78.7727039802658,-126.787167624007 diffExpScore=0.998928529224404 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 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 90.8938245262103 90.1531532298507 84.1238264824467 cerebhem 85.5049200780534 85.1399530589241 78.7127714725005 cortex 92.9600402291962 98.0994777617427 103.358189483486 heart 88.0410219953446 120.136808107133 80.2565329562511 kidney 91.11174511419 96.5052670930391 69.8286817895615 liver 86.9209489092756 87.7392648154454 85.7599240013306 stomach 83.7599929400326 93.3596129263037 84.5103866299966 testicle 84.843852898026 86.5787782597762 96.5522887294623 cont.diffExp=0.740671296359594,0.364967019129267,-5.13943753254651,-32.0957861117887,-5.39352197884912,-0.818315906169857,-9.59961998627105,-1.7349253617502 cont.diffExpScore=1.02215372974338 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,-1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.221921585329997 cont.tran.correlation=0.277229591070207 tran.covariance=-0.00683711528968883 cont.tran.covariance=0.00129502789252193 tran.mean=129.337879580869 cont.tran.mean=91.359291371409 weightedLogRatios: wLogRatio Lung -5.53768217920531 cerebhem -4.89238114928745 cortex -2.5424834102749 heart -4.22001478194802 kidney -6.31534764093368 liver -5.5910745706239 stomach -2.74143211128932 testicle -5.33104861770919 cont.weightedLogRatios: wLogRatio Lung 0.0368654366785276 cerebhem 0.0190196708770165 cortex -0.245334437476997 heart -1.44013494940524 kidney -0.261147767698793 liver -0.0418829273000198 stomach -0.486334535788986 testicle -0.0900966373993313 varWeightedLogRatios=1.89122895286236 cont.varWeightedLogRatios=0.237653630951878 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.46087444024518 0.0985766822763464 55.3972228943185 0 *** df.mm.trans1 -1.21328952298000 0.0840714166269347 -14.4316531308608 2.59272410096612e-43 *** df.mm.trans2 -0.206987360826425 0.0732302426558698 -2.82652840301402 0.00479211555017723 ** df.mm.exp2 -0.0812185873785682 0.0918108140503247 -0.88462985780792 0.376552649605329 df.mm.exp3 -0.257009317274295 0.0918108140503247 -2.79933600341916 0.00521172742952876 ** df.mm.exp4 -0.340071104194386 0.0918108140503247 -3.70404192264302 0.000222900972752415 *** df.mm.exp5 0.119719321588021 0.0918108140503247 1.30397843463624 0.19251801844272 df.mm.exp6 -0.0918289959898198 0.0918108140503248 -1.0001980370143 0.317438098012981 df.mm.exp7 -0.057265151315677 0.0918108140503247 -0.623729915784082 0.532936318000597 df.mm.exp8 -0.157929210885526 0.0918108140503247 -1.72015913941205 0.0856891895206955 . df.mm.trans1:exp2 0.0467692042691005 0.0834716128913219 0.560300713608983 0.575390221425045 df.mm.trans2:exp2 -0.0685067414675533 0.0554039924337561 -1.23649467228311 0.216542828706700 df.mm.trans1:exp3 0.595020293221798 0.0834716128913219 7.1284149498405 1.85143833947288e-12 *** df.mm.trans2:exp3 -0.0366014600559787 0.0554039924337561 -0.660628565707451 0.50899103684575 df.mm.trans1:exp4 0.238264816809386 0.0834716128913218 2.85444127118522 0.00439345412848314 ** df.mm.trans2:exp4 0.00419764913154335 0.0554039924337561 0.0757643799147197 0.939620548183948 df.mm.trans1:exp5 -0.138248650494669 0.0834716128913219 -1.65623552374225 0.097963808499741 . df.mm.trans2:exp5 0.00981352332107559 0.0554039924337561 0.177126645391289 0.859442032620377 df.mm.trans1:exp6 0.00849441255185871 0.0834716128913219 0.101764087905169 0.918962768733569 df.mm.trans2:exp6 0.0370016615961132 0.0554039924337561 0.667851899668681 0.504370425921101 df.mm.trans1:exp7 0.544163238541864 0.0834716128913219 6.51914129478188 1.08201618413317e-10 *** df.mm.trans2:exp7 -0.0644595568776547 0.0554039924337561 -1.16344606311045 0.244904836426965 df.mm.trans1:exp8 0.0579683294177938 0.0834716128913219 0.694467584965289 0.487537969648398 df.mm.trans2:exp8 0.0407035480772354 0.0554039924337561 0.73466814013273 0.46270066876094 df.mm.trans1:probe2 0.060835167952958 0.0634012413067576 0.959526449310605 0.337507764162353 df.mm.trans1:probe3 -0.000832529374208015 0.0634012413067576 -0.0131311210482448 0.98952560080069 df.mm.trans1:probe4 -0.409898573305708 0.0634012413067576 -6.46515060048231 1.52785057836101e-10 *** df.mm.trans1:probe5 -0.285931232832133 0.0634012413067576 -4.50986805524354 7.19380379592516e-06 *** df.mm.trans1:probe6 0.0959315321040933 0.0634012413067576 1.51308602366226 0.130549562436429 df.mm.trans1:probe7 -0.158992491681569 0.0634012413067576 -2.50771890904008 0.0122967432536485 * df.mm.trans1:probe8 -0.204539033854906 0.0634012413067576 -3.2261045626106 0.00129244711025185 ** df.mm.trans1:probe9 -0.335237652973791 0.0634012413067576 -5.2875566166251 1.49893492131722e-07 *** df.mm.trans1:probe10 -0.274617710234673 0.0634012413067576 -4.33142482031188 1.61897160625126e-05 *** df.mm.trans1:probe11 -0.179717061480427 0.0634012413067576 -2.83459846804721 0.00467361363507028 ** df.mm.trans1:probe12 -0.283642520693244 0.0634012413067576 -4.47376920147164 8.49642035760534e-06 *** df.mm.trans1:probe13 -0.235145897552027 0.0634012413067576 -3.70885321336704 0.000218760823978082 *** df.mm.trans1:probe14 -0.0969749535690412 0.0634012413067576 -1.52954345325578 0.126421790639199 df.mm.trans1:probe15 -0.268946696680992 0.0634012413067576 -4.24197840827331 2.40493534650773e-05 *** df.mm.trans1:probe16 -0.188094893637638 0.0634012413067576 -2.96673834393192 0.00307584974098134 ** df.mm.trans1:probe17 -0.0355758570013839 0.0634012413067576 -0.561122404989759 0.574830173914759 df.mm.trans1:probe18 -0.0937073703342056 0.0634012413067576 -1.47800529457801 0.139697115592852 df.mm.trans1:probe19 -0.093697404983073 0.0634012413067576 -1.47784811546089 0.139739185075981 df.mm.trans1:probe20 0.118355376879715 0.0634012413067576 1.86676750234385 0.0622041146128878 . df.mm.trans1:probe21 0.065426389905701 0.0634012413067576 1.03194178153619 0.302329781261393 df.mm.trans1:probe22 -0.068684699678111 0.0634012413067576 -1.08333367395427 0.278901469876333 df.mm.trans2:probe2 0.201172773548968 0.0634012413067576 3.17301001372548 0.00155113647149267 ** df.mm.trans2:probe3 0.528719913069752 0.0634012413067576 8.33926753123993 2.24953617254840e-16 *** df.mm.trans2:probe4 0.711868265512946 0.0634012413067576 11.2279862482294 9.47064626115068e-28 *** df.mm.trans2:probe5 0.198921456238266 0.0634012413067576 3.13750097219413 0.00174994261271952 ** df.mm.trans2:probe6 0.659525486101184 0.0634012413067576 10.4024065224554 3.19951584034431e-24 *** df.mm.trans3:probe2 -0.0474808994613867 0.0634012413067576 -0.748895423540011 0.454082856102536 df.mm.trans3:probe3 1.94962096090880 0.0634012413067576 30.750517193754 9.01406384269354e-150 *** df.mm.trans3:probe4 0.232768685139642 0.0634012413067576 3.67135848355753 0.000253035783572912 *** df.mm.trans3:probe5 0.789424562090644 0.0634012413067576 12.4512477330078 2.34223038319044e-33 *** df.mm.trans3:probe6 0.239075249338702 0.0634012413067576 3.77082915746036 0.000171501040530489 *** df.mm.trans3:probe7 -0.0824642693985276 0.0634012413067576 -1.30067278966253 0.193647167891491 df.mm.trans3:probe8 1.28786079526976 0.0634012413067576 20.3128640500687 5.62214681036284e-78 *** df.mm.trans3:probe9 1.28397229931737 0.0634012413067576 20.2515325071485 1.38793059935726e-77 *** df.mm.trans3:probe10 -0.093586475561667 0.0634012413067576 -1.47609847430057 0.140208141479508 df.mm.trans3:probe11 0.174946956469691 0.0634012413067576 2.75936169172518 0.00588907103630515 ** df.mm.trans3:probe12 0.315442149322216 0.0634012413067576 4.97533081089053 7.5712083140111e-07 *** df.mm.trans3:probe13 -0.00871193375331853 0.0634012413067576 -0.137409513974137 0.890732652508939 df.mm.trans3:probe14 0.146342685354785 0.0634012413067576 2.30819905633595 0.0211757642110053 * df.mm.trans3:probe15 0.249019331054279 0.0634012413067576 3.92767280137995 9.11978446087063e-05 *** df.mm.trans3:probe16 -0.230181203904169 0.0634012413067576 -3.63054727573031 0.000296042793383015 *** df.mm.trans3:probe17 -0.158548984951059 0.0634012413067576 -2.50072367170136 0.0125408407206129 * df.mm.trans3:probe18 0.981080217321115 0.0634012413067576 15.4741484094026 6.00324211470996e-49 *** df.mm.trans3:probe19 -0.0814546801116201 0.0634012413067576 -1.28474898018974 0.199154733411046 df.mm.trans3:probe20 -0.114550349601505 0.0634012413067576 -1.80675247424998 0.0710783407629747 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.45381226653083 0.293115637027904 15.1947276224872 2.05918159038095e-47 *** df.mm.trans1 0.0847560848562234 0.249984542707168 0.339045302314978 0.734641330617915 df.mm.trans2 0.0628469249526786 0.217748545904692 0.288621559751708 0.77292622664522 df.mm.exp2 -0.051847244921573 0.272997473895191 -0.189918405404287 0.849408659163511 df.mm.exp3 -0.0989609047046757 0.272997473895191 -0.362497510664398 0.717051010334496 df.mm.exp4 0.302293709284489 0.272997473895191 1.10731321052643 0.268404410365965 df.mm.exp5 0.256727320021704 0.272997473895191 0.940401815293964 0.347221190527203 df.mm.exp6 -0.0910953725167009 0.272997473895191 -0.333685770849566 0.738681283941484 df.mm.exp7 -0.0513722716171888 0.272997473895191 -0.188178560351484 0.850771931412307 df.mm.exp8 -0.247129590872402 0.272997473895191 -0.905244972952681 0.365536958222994 df.mm.trans1:exp2 -0.00927089788901795 0.248201039245741 -0.0373523733711644 0.970210930393144 df.mm.trans2:exp2 -0.00536627264730402 0.164742575638563 -0.0325736842859453 0.974020557621863 df.mm.trans1:exp3 0.121438568696094 0.248201039245741 0.489275021027851 0.624746078939024 df.mm.trans2:exp3 0.183433021103318 0.164742575638563 1.11345242959999 0.265761231089583 df.mm.trans1:exp4 -0.33418290625687 0.248201039245741 -1.34642025380885 0.178448745108425 df.mm.trans2:exp4 -0.0151724749553753 0.164742575638563 -0.092098080271993 0.926637146178154 df.mm.trans1:exp5 -0.25433266042368 0.248201039245741 -1.02470425263557 0.305731577681156 df.mm.trans2:exp5 -0.188639658520120 0.164742575638563 -1.14505711586048 0.252438478751325 df.mm.trans1:exp6 0.0464023830169432 0.248201039245741 0.186954829673379 0.851731065190342 df.mm.trans2:exp6 0.063954962464346 0.164742575638563 0.388211500375349 0.697935819366029 df.mm.trans1:exp7 -0.0303643079788097 0.248201039245741 -0.122337553747091 0.90265438845069 df.mm.trans2:exp7 0.0863211869244313 0.164742575638563 0.523976188850026 0.600402312259386 df.mm.trans1:exp8 0.178250071364167 0.248201039245741 0.718168110439227 0.472808532303368 df.mm.trans2:exp8 0.206674395044109 0.164742575638563 1.25452934217529 0.209920344649633 df.mm.trans1:probe2 -0.146206957001250 0.18852222254643 -0.775542294305605 0.438188594988763 df.mm.trans1:probe3 0.045787717038199 0.18852222254643 0.242877027544708 0.808146652633028 df.mm.trans1:probe4 0.0176036918015599 0.18852222254643 0.0933772770328141 0.925621113006772 df.mm.trans1:probe5 -0.0692745767481273 0.18852222254643 -0.367461065398091 0.713346923573392 df.mm.trans1:probe6 0.014323496424074 0.18852222254643 0.0759777612983866 0.939450823427324 df.mm.trans1:probe7 -0.0705402169545585 0.188522222546430 -0.374174545588043 0.708347704960972 df.mm.trans1:probe8 -0.104638373217255 0.18852222254643 -0.555045298129157 0.578978294282463 df.mm.trans1:probe9 -0.158026579416408 0.18852222254643 -0.838238470148997 0.402081745227832 df.mm.trans1:probe10 -0.0870847373578704 0.188522222546430 -0.46193353855895 0.644221757465956 df.mm.trans1:probe11 0.0952945255431227 0.188522222546430 0.505481657578343 0.613323434805766 df.mm.trans1:probe12 0.0670311593707289 0.188522222546430 0.355561049860953 0.722238557870352 df.mm.trans1:probe13 -0.0915504815915235 0.18852222254643 -0.485621696768274 0.62733363878087 df.mm.trans1:probe14 -0.000318978161261169 0.18852222254643 -0.00169199236542318 0.998650297648496 df.mm.trans1:probe15 -0.0142527597543907 0.18852222254643 -0.0756025446861072 0.939749274668924 df.mm.trans1:probe16 -0.115046635988303 0.18852222254643 -0.610255037492828 0.541820905871549 df.mm.trans1:probe17 -0.0713132519791181 0.18852222254643 -0.378275043736845 0.705300415776056 df.mm.trans1:probe18 -0.211127935717005 0.18852222254643 -1.11991007142411 0.263000384862271 df.mm.trans1:probe19 -0.150158685947097 0.18852222254643 -0.796503902398644 0.425913772921653 df.mm.trans1:probe20 0.0374072295062178 0.18852222254643 0.198423448445209 0.842751013363831 df.mm.trans1:probe21 -0.126612114760235 0.18852222254643 -0.671603130124633 0.501979613313416 df.mm.trans1:probe22 -0.0740996947961526 0.18852222254643 -0.393055491258613 0.694355852347468 df.mm.trans2:probe2 -0.229341261222231 0.18852222254643 -1.21652109827926 0.224051536563569 df.mm.trans2:probe3 0.0726445729570586 0.18852222254643 0.385336921959783 0.700063478760795 df.mm.trans2:probe4 -0.123003606666030 0.18852222254643 -0.652462107673994 0.514241569511761 df.mm.trans2:probe5 -0.175460383964392 0.18852222254643 -0.93071459478035 0.352208632320859 df.mm.trans2:probe6 0.0612797926750699 0.18852222254643 0.325053417296614 0.745203424916418 df.mm.trans3:probe2 -0.319945756730614 0.18852222254643 -1.69712489280576 0.0899603750243415 . df.mm.trans3:probe3 -0.313562239286872 0.18852222254643 -1.66326407068348 0.0965489134949471 . df.mm.trans3:probe4 0.0871106762607813 0.18852222254643 0.462071129250173 0.644123120117107 df.mm.trans3:probe5 -0.338275982482482 0.18852222254643 -1.79435600701753 0.073035184361179 . df.mm.trans3:probe6 -0.0878607976024029 0.18852222254643 -0.466050083728267 0.641273369330309 df.mm.trans3:probe7 -0.257762065090154 0.18852222254643 -1.36727682078260 0.171822255200510 df.mm.trans3:probe8 -0.240399687875222 0.18852222254643 -1.27517957632828 0.202519168408982 df.mm.trans3:probe9 -0.0674373152275799 0.18852222254643 -0.357715468853923 0.720625957023129 df.mm.trans3:probe10 -0.187036368655394 0.18852222254643 -0.992118415161 0.321361371980415 df.mm.trans3:probe11 0.0564348681016464 0.18852222254643 0.299353929416715 0.76472744778647 df.mm.trans3:probe12 -0.190376984318866 0.18852222254643 -1.00983842513303 0.312798268354332 df.mm.trans3:probe13 -0.148774665266020 0.18852222254643 -0.789162483109275 0.430189772868113 df.mm.trans3:probe14 -0.325288196538584 0.18852222254643 -1.72546340768113 0.084729250919969 . df.mm.trans3:probe15 -0.126229003013173 0.18852222254643 -0.669570946640442 0.503274061050189 df.mm.trans3:probe16 -0.141699361137083 0.18852222254643 -0.751632137702942 0.452435616206001 df.mm.trans3:probe17 -0.0770141695285681 0.18852222254643 -0.408515073121423 0.68297632580902 df.mm.trans3:probe18 -0.19515583778527 0.18852222254643 -1.03518744447863 0.300812469995708 df.mm.trans3:probe19 -0.280532302341386 0.18852222254643 -1.48805959611629 0.137026279546739 df.mm.trans3:probe20 -0.228703574697371 0.18852222254643 -1.21313854466703 0.225341384905179