fitVsDatCorrelation=0.87832156132856 cont.fitVsDatCorrelation=0.192627630614679 fstatistic=9736.6291894188,69,1083 cont.fstatistic=2299.10172562826,69,1083 residuals=-0.932160948374345,-0.0927487778540749,-0.00888087659785712,0.0850278200836528,1.68998322339555 cont.residuals=-0.648150409658882,-0.24838612237386,-0.0804622282690963,0.162808766521574,2.37388654968738 predictedValues: Include Exclude Both Lung 52.2072697802723 43.3222669769219 65.5741683276395 cerebhem 54.5005051537688 46.874549247827 76.509280123332 cortex 59.777182964709 45.4168515429913 73.1013091609404 heart 64.3014888876448 46.28124703048 83.457506219312 kidney 56.2006347403866 45.1021907118118 68.7470182781262 liver 50.8901121548372 49.1141660442782 65.7704664448932 stomach 51.1697529030518 50.7486027790205 66.6725719657644 testicle 52.5758561693197 45.7029993727117 67.3157706807044 diffExp=8.88500280335036,7.62595590594177,14.3603314217176,18.0202418571647,11.0984440285748,1.77594611055903,0.421150124031293,6.87285679660804 diffExpScore=0.985726505670372 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=0,0,1,1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=1,0,1,1,1,0,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 57.6003748316337 67.5097881798145 58.4504643063903 cerebhem 59.7326040962033 65.280166942655 62.508829486744 cortex 59.386451972198 63.086498255864 61.4183687296289 heart 58.0345974306925 62.7469528797398 63.2672420540484 kidney 62.6514237873495 59.4499284206727 63.0352002495624 liver 57.7359018560082 66.239281174679 63.8371534428917 stomach 61.8578496458248 62.7628550613336 58.2027315890254 testicle 65.543021898276 64.3429475014015 64.6798214783304 cont.diffExp=-9.90941334818086,-5.54756284645165,-3.700046283666,-4.71235544904734,3.20149536667680,-8.50337931867077,-0.905005415508825,1.20007439687450 cont.diffExpScore=1.26118252595804 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=-0.326140870277408 cont.tran.correlation=-0.468494828521505 tran.covariance=-0.00136075496806075 cont.tran.covariance=-0.000861884169266532 tran.mean=50.886604778752 cont.tran.mean=62.1225402458966 weightedLogRatios: wLogRatio Lung 0.72046501500349 cerebhem 0.591310053406192 cortex 1.08612012979433 heart 1.31510746516331 kidney 0.862153625388235 liver 0.138955423234608 stomach 0.0324879150801965 testicle 0.545271747035622 cont.weightedLogRatios: wLogRatio Lung -0.656071235917006 cerebhem -0.367166778383709 cortex -0.248670162779367 heart -0.320095605973869 kidney 0.215649027170181 liver -0.56669396038041 stomach -0.0600163467849651 testicle 0.0771232411404761 varWeightedLogRatios=0.191198342074794 cont.varWeightedLogRatios=0.0918684246787469 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.92374368800820 0.0769911133460316 37.9750799922534 2.64667006896334e-201 *** df.mm.trans1 0.95355621850363 0.0656620999735038 14.5221706111808 8.6035402694739e-44 *** df.mm.trans2 0.826576825343378 0.0571948434708919 14.4519466298399 2.02545796604447e-43 *** df.mm.exp2 -0.0324338653998562 0.0717067832646678 -0.452312374411550 0.65113454486104 df.mm.exp3 0.0739543103124548 0.0717067832646677 1.03134329759978 0.302610121642627 df.mm.exp4 0.033275377019988 0.0717067832646677 0.464047827904502 0.642706738628014 df.mm.exp5 0.0667188440891574 0.0717067832646678 0.930439786190101 0.352350776096028 df.mm.exp6 0.096938590621153 0.0717067832646678 1.35187476285689 0.176697641465169 df.mm.exp7 0.121532364691505 0.0717067832646678 1.69485171637016 0.0903910285034142 . df.mm.exp8 0.0343197286541713 0.0717067832646677 0.478612023739764 0.632311270046971 df.mm.trans1:exp2 0.0754220828895512 0.0651936366784572 1.15689332168325 0.247571056873052 df.mm.trans2:exp2 0.11124198188039 0.0432720493608409 2.57075834224432 0.0102802568753698 * df.mm.trans1:exp3 0.0614479690460664 0.0651936366784572 0.942545502548587 0.34612362888365 df.mm.trans2:exp3 -0.0267378464915777 0.0432720493608409 -0.617901090577284 0.536770449112202 df.mm.trans1:exp4 0.175085656271310 0.0651936366784572 2.68562493506619 0.00735010539035874 ** df.mm.trans2:exp4 0.032794718568148 0.0432720493608409 0.757873016243728 0.448691879234567 df.mm.trans1:exp5 0.00698745404659598 0.0651936366784572 0.107180001033827 0.914666031819202 df.mm.trans2:exp5 -0.0264547759363976 0.0432720493608409 -0.611359441652371 0.541089947736726 df.mm.trans1:exp6 -0.122491699167984 0.0651936366784572 -1.87889041643937 0.0605278574707193 . df.mm.trans2:exp6 0.0285421649897561 0.0432720493608409 0.65959817968745 0.509651957914286 df.mm.trans1:exp7 -0.141605523840930 0.0651936366784572 -2.17207585058256 0.0300663478618646 * df.mm.trans2:exp7 0.0366849696860726 0.0432720493608409 0.847775185782413 0.396750503638074 df.mm.trans1:exp8 -0.0272844754878899 0.0651936366784572 -0.418514396158941 0.675654041528413 df.mm.trans2:exp8 0.0191774471628674 0.0432720493608409 0.443183242904646 0.657721711277383 df.mm.trans1:probe2 0.264597880145727 0.0495181217607174 5.34345550148938 1.11134166067527e-07 *** df.mm.trans1:probe3 -0.0120655417681626 0.0495181217607174 -0.243659115878142 0.80754098626583 df.mm.trans1:probe4 0.246065494183083 0.0495181217607174 4.96920087906657 7.80897317055897e-07 *** df.mm.trans1:probe5 0.35353551234683 0.0495181217607174 7.13951781239184 1.71408571405113e-12 *** df.mm.trans1:probe6 0.295412923697105 0.0495181217607174 5.96575381280829 3.29399153112536e-09 *** df.mm.trans1:probe7 0.379080152610747 0.0495181217607174 7.65538229504234 4.25309832017379e-14 *** df.mm.trans1:probe8 0.253873296616762 0.0495181217607174 5.12687653710968 3.48746483042631e-07 *** df.mm.trans1:probe9 0.236452191936462 0.0495181217607174 4.77506382570509 2.04305043049039e-06 *** df.mm.trans1:probe10 0.454167818846408 0.0495181217607174 9.1717497089459 2.28392740796953e-19 *** df.mm.trans1:probe11 0.0328150377339804 0.0495181217607174 0.662687447891298 0.507671755164469 df.mm.trans1:probe12 0.527975969774609 0.0495181217607174 10.6622777884409 2.61407376010704e-25 *** df.mm.trans1:probe13 0.0581593998015635 0.0495181217607174 1.17450738706534 0.240450040321464 df.mm.trans1:probe14 0.331842763723688 0.0495181217607174 6.70144084477247 3.31258033847128e-11 *** df.mm.trans1:probe15 0.426126348599368 0.0495181217607174 8.60546267603818 2.63682583743796e-17 *** df.mm.trans1:probe16 -0.0764014592785047 0.0495181217607174 -1.54289897439353 0.123147420920640 df.mm.trans1:probe17 -0.057963213396606 0.0495181217607174 -1.17054547578959 0.242039027971513 df.mm.trans1:probe18 -0.119366051490887 0.0495181217607174 -2.41055288945914 0.0160941506623806 * df.mm.trans1:probe19 -0.0218313577929684 0.0495181217607174 -0.440876128106442 0.659390668240107 df.mm.trans1:probe20 -0.0903897310938974 0.0495181217607174 -1.82538690644772 0.0682179650030703 . df.mm.trans1:probe21 -0.141286707450493 0.0495181217607174 -2.85323236073496 0.00441007651253239 ** df.mm.trans1:probe22 -0.0680831746938794 0.0495181217607174 -1.37491431970850 0.169442384178464 df.mm.trans2:probe2 0.0617662534713436 0.0495181217607174 1.24734645166495 0.212540152253167 df.mm.trans2:probe3 0.164234254273923 0.0495181217607174 3.3166495100024 0.000941291239932815 *** df.mm.trans2:probe4 0.103086175505526 0.0495181217607174 2.08178686590056 0.0375963226740257 * df.mm.trans2:probe5 0.0130474060207383 0.0495181217607174 0.263487498249353 0.79222498079035 df.mm.trans2:probe6 0.134868108241229 0.0495181217607174 2.72361114367265 0.00656109195280878 ** df.mm.trans3:probe2 -0.330226650977272 0.0495181217607174 -6.66880405062616 4.10314272177974e-11 *** df.mm.trans3:probe3 -0.909869341860209 0.0495181217607174 -18.3744720015210 7.57855099824419e-66 *** df.mm.trans3:probe4 -0.652066749239201 0.0495181217607174 -13.1682447971297 7.67597494472262e-37 *** df.mm.trans3:probe5 -0.749382434239448 0.0495181217607174 -15.1334987595174 4.44362946523032e-47 *** df.mm.trans3:probe6 -0.521626451365316 0.0495181217607174 -10.5340516323687 9.05146050693504e-25 *** df.mm.trans3:probe7 0.235745652830768 0.0495181217607174 4.76079553198611 2.18975133215806e-06 *** df.mm.trans3:probe8 -0.930202739653508 0.0495181217607174 -18.7850973861338 2.29035773776567e-68 *** df.mm.trans3:probe9 -0.33270502125279 0.0495181217607174 -6.71885381397328 2.95400060353772e-11 *** df.mm.trans3:probe10 0.0936448742214356 0.0495181217607174 1.89112330782554 0.058874567975828 . df.mm.trans3:probe11 -0.602174523458021 0.0495181217607174 -12.1606899059674 5.50601139483606e-32 *** df.mm.trans3:probe12 -0.806181062166027 0.0495181217607174 -16.2805258660996 1.78089000449829e-53 *** df.mm.trans3:probe13 -0.753959085850272 0.0495181217607174 -15.2259225318272 1.39051014568373e-47 *** df.mm.trans3:probe14 -0.437499688668815 0.0495181217607174 -8.83514303678381 3.96197788001376e-18 *** df.mm.trans3:probe15 -0.873566710663062 0.0495181217607174 -17.6413539044217 2.01895570086728e-61 *** df.mm.trans3:probe16 -0.377353029150026 0.0495181217607174 -7.62050368092472 5.49929816419148e-14 *** df.mm.trans3:probe17 -0.829969210358987 0.0495181217607174 -16.7609186465024 3.07445226340048e-56 *** df.mm.trans3:probe18 -0.787229625258818 0.0495181217607174 -15.8978086661462 2.6132302031117e-51 *** df.mm.trans3:probe19 -0.852614098462895 0.0495181217607174 -17.2182237158129 6.52432830355747e-59 *** df.mm.trans3:probe20 0.00333811660464288 0.0495181217607174 0.0674120198010216 0.946266143420281 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14670873272368 0.158029508157767 26.2400913668848 7.21196589518376e-118 *** df.mm.trans1 -0.134161286789416 0.134775935980848 -0.99543947376838 0.319744922018645 df.mm.trans2 0.0726751607173943 0.117396314847958 0.619058279738315 0.536008160212225 df.mm.exp2 -0.0643635880539372 0.147183060465187 -0.437302960344142 0.661978834637344 df.mm.exp3 -0.0867581044922815 0.147183060465187 -0.589457130583327 0.555677604257949 df.mm.exp4 -0.144840375897693 0.147183060465187 -0.98408319163843 0.325294402789073 df.mm.exp5 -0.118594519302334 0.147183060465187 -0.805762014511071 0.420556963705343 df.mm.exp6 -0.104804550214177 0.147183060465187 -0.712069377297437 0.476575178552814 df.mm.exp7 0.00264809436353036 0.147183060465187 0.0179918419630682 0.985648675490499 df.mm.exp8 -0.0201372337034839 0.147183060465187 -0.136817604144377 0.891200397585484 df.mm.trans1:exp2 0.100712516466943 0.133814383135476 0.752628485123156 0.451836750999857 df.mm.trans2:exp2 0.0307792586019895 0.0888188309050447 0.346539785407616 0.729004406888888 df.mm.trans1:exp3 0.117295148395951 0.133814383135476 0.876551127371704 0.380924878794944 df.mm.trans2:exp3 0.0189922799488327 0.0888188309050447 0.213831681359746 0.830718567609949 df.mm.trans1:exp4 0.152350640855696 0.133814383135476 1.13852216246032 0.255154294242255 df.mm.trans2:exp4 0.0716777956082392 0.0888188309050447 0.807011248378953 0.419837194027964 df.mm.trans1:exp5 0.202651851266926 0.133814383135476 1.51442503054218 0.130209854217821 df.mm.trans2:exp5 -0.00854365902415625 0.0888188309050447 -0.0961919779521777 0.923385898279335 df.mm.trans1:exp6 0.107154670933239 0.133814383135476 0.80077095169025 0.423439890594741 df.mm.trans2:exp6 0.0858056109638948 0.0888188309050447 0.966074537229934 0.334222559895908 df.mm.trans1:exp7 0.0686618354890297 0.133814383135476 0.513112521092109 0.60797726141573 df.mm.trans2:exp7 -0.0755572732345658 0.0888188309050447 -0.8506897970245 0.395129730301016 df.mm.trans1:exp8 0.149314908431085 0.133814383135476 1.11583601801546 0.264739853937907 df.mm.trans2:exp8 -0.027908031635136 0.0888188309050447 -0.314213003602493 0.753419783504145 df.mm.trans1:probe2 0.0996219995317118 0.101639320262485 0.980152162317068 0.327229913153031 df.mm.trans1:probe3 0.125246358301708 0.101639320262485 1.23226284845527 0.218118369009386 df.mm.trans1:probe4 0.111638579478889 0.101639320262485 1.09837983164961 0.272282792231410 df.mm.trans1:probe5 0.166556900515484 0.101639320262485 1.63870537588552 0.101565085844717 df.mm.trans1:probe6 0.0811190091112031 0.101639320262485 0.798106568419703 0.424983615744343 df.mm.trans1:probe7 0.0790010805476662 0.101639320262485 0.777268879245205 0.437169896319048 df.mm.trans1:probe8 0.0727820976123534 0.101639320262485 0.716082097207975 0.474095027824787 df.mm.trans1:probe9 0.200056900899924 0.101639320262485 1.96830223168823 0.0492880347819427 * df.mm.trans1:probe10 0.149989533665704 0.101639320262485 1.47570382484214 0.140314086769441 df.mm.trans1:probe11 -0.00569659217184935 0.101639320262485 -0.0560471297637353 0.955314599974373 df.mm.trans1:probe12 0.0357208395196431 0.101639320262485 0.351447052453652 0.72532134995601 df.mm.trans1:probe13 -0.0350902378434711 0.101639320262485 -0.345242744174695 0.72997892821993 df.mm.trans1:probe14 0.0944043445399132 0.101639320262485 0.928817157534235 0.353190816061416 df.mm.trans1:probe15 0.158902210479872 0.101639320262485 1.56339308517122 0.118252306761101 df.mm.trans1:probe16 0.0329555912652833 0.101639320262485 0.324240571268826 0.745818519197246 df.mm.trans1:probe17 0.0303158154321617 0.101639320262485 0.298268577100582 0.765555398996673 df.mm.trans1:probe18 0.0564806076618704 0.101639320262485 0.555696432404392 0.57853316941039 df.mm.trans1:probe19 0.0194727205440987 0.101639320262485 0.191586489301681 0.848102039287905 df.mm.trans1:probe20 0.137324903980624 0.101639320262485 1.35110018077630 0.176945527466251 df.mm.trans1:probe21 0.0574784262035518 0.101639320262485 0.56551368166486 0.571841557905558 df.mm.trans1:probe22 0.0529473388058487 0.101639320262485 0.520933617709283 0.602519507226722 df.mm.trans2:probe2 0.00366267775561638 0.101639320262485 0.0360360315885373 0.971260269342257 df.mm.trans2:probe3 -0.00574366636003986 0.101639320262485 -0.0565102791440041 0.95494573069321 df.mm.trans2:probe4 -0.0953169196645975 0.101639320262485 -0.937795721364922 0.348558486299654 df.mm.trans2:probe5 -0.0383574568594069 0.101639320262485 -0.377387971115393 0.70595924549463 df.mm.trans2:probe6 -0.0491383309808844 0.101639320262485 -0.483457886711402 0.628868382144527 df.mm.trans3:probe2 -0.132836156968349 0.101639320262485 -1.30693669167895 0.191511646072049 df.mm.trans3:probe3 0.0291013025285893 0.101639320262485 0.286319334421312 0.77468831133414 df.mm.trans3:probe4 0.0478995514824169 0.101639320262485 0.471269891993725 0.637542929140021 df.mm.trans3:probe5 0.00979293771555978 0.101639320262485 0.0963498938232701 0.92326051165862 df.mm.trans3:probe6 0.0267004782017133 0.101639320262485 0.262698315305129 0.792833078853494 df.mm.trans3:probe7 0.0146020461097187 0.101639320262485 0.143665326293099 0.885791479281898 df.mm.trans3:probe8 -0.0143494248527465 0.101639320262485 -0.141179858500519 0.887754112550606 df.mm.trans3:probe9 -0.09627125184129 0.101639320262485 -0.94718512080431 0.343755745528518 df.mm.trans3:probe10 -0.0412101881267517 0.101639320262485 -0.405455172469923 0.685223045758058 df.mm.trans3:probe11 -0.035679121956383 0.101639320262485 -0.351036605363369 0.725629160962039 df.mm.trans3:probe12 0.0734859026817701 0.101639320262485 0.723006632590533 0.469831919107728 df.mm.trans3:probe13 -0.0967545129867144 0.101639320262485 -0.951939788035225 0.341339919039276 df.mm.trans3:probe14 -0.0305061436403696 0.101639320262485 -0.30014116152673 0.764127083264769 df.mm.trans3:probe15 0.0173004446245015 0.101639320262485 0.170214092142911 0.86487356082363 df.mm.trans3:probe16 -0.0693584950704862 0.101639320262485 -0.682398257794 0.49513308284811 df.mm.trans3:probe17 -0.0226989343580949 0.101639320262485 -0.223328277869967 0.82332213677342 df.mm.trans3:probe18 0.0194952033000659 0.101639320262485 0.191807690662622 0.847928802272696 df.mm.trans3:probe19 -0.0399357093340796 0.101639320262485 -0.392915942677942 0.694458891178572 df.mm.trans3:probe20 0.000362570204728313 0.101639320262485 0.00356722382432281 0.997154430181779