fitVsDatCorrelation=0.841562908075418 cont.fitVsDatCorrelation=0.258669754782749 fstatistic=10559.1975717113,62,922 cont.fstatistic=3291.57954484265,62,922 residuals=-0.730137261014385,-0.0850892536600923,-0.00903636298661877,0.0732716036471237,1.13623650696307 cont.residuals=-0.555012843283025,-0.181204895435144,-0.0493316143045821,0.110748470740398,1.51788761052587 predictedValues: Include Exclude Both Lung 52.5816341789311 56.6891228408542 64.019482634667 cerebhem 55.2095000318485 69.6075400862842 66.6035728261525 cortex 55.6004794468291 52.8936297632829 60.9364258713075 heart 59.9071744038892 53.7972192983937 60.2247913050816 kidney 55.8515631435775 54.4424920735341 66.468100886089 liver 56.1921452606738 52.6966706270106 63.4765640584417 stomach 56.2022338319204 55.2782591726386 61.3664514177315 testicle 56.0855070377046 57.4508694589282 59.611661883398 diffExp=-4.10748866192314,-14.3980400544357,2.70684968354614,6.1099551054955,1.40907107004341,3.49547463366319,0.923974659281797,-1.3653624212236 diffExpScore=5.54426960853259 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,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 61.238186826499 53.1865700573977 62.7623814602529 cerebhem 60.1741927288062 61.7007188330236 62.0675179221562 cortex 59.7354945462072 70.288837172273 61.3547548621293 heart 58.4725166958153 55.7230846435948 56.3664177909097 kidney 57.3444125015682 58.297934536508 62.4115388879983 liver 59.3441533275978 55.5003114841831 58.8626522470841 stomach 59.2215028501856 58.0714454040184 57.506083285297 testicle 64.7208646253834 63.4216217473011 63.5099271231049 cont.diffExp=8.05161676910122,-1.52652610421740,-10.5533426260658,2.74943205222048,-0.953522034939802,3.84384184341464,1.15005744616715,1.29924287808235 cont.diffExpScore=5.95312607139593 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=-0.268506535100833 cont.tran.correlation=0.246200550619043 tran.covariance=-0.000889216816843586 cont.tran.covariance=0.000801147303027976 tran.mean=56.2803775410188 cont.tran.mean=59.7776154987727 weightedLogRatios: wLogRatio Lung -0.300859918085437 cerebhem -0.956383049430202 cortex 0.199298136795526 heart 0.434494340220751 kidney 0.102463753385128 liver 0.256684608838736 stomach 0.0666498757598629 testicle -0.0971465443172231 cont.weightedLogRatios: wLogRatio Lung 0.570103285019652 cerebhem -0.102958001266310 cortex -0.678609707410876 heart 0.194791399159929 kidney -0.0669103107997031 liver 0.271199503593318 stomach 0.0798442756382295 testicle 0.084358643114198 varWeightedLogRatios=0.187584640202730 cont.varWeightedLogRatios=0.130016927929043 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.62761037929479 0.0729307769026853 49.7404598354309 2.79405198167183e-263 *** df.mm.trans1 0.188332936474524 0.0626350558245475 3.00682954609444 0.00271147709981903 ** df.mm.trans2 0.421142775641433 0.0549976155068667 7.65747336061964 4.78283669329768e-14 *** df.mm.exp2 0.214487913617087 0.069977437755158 3.06510098822927 0.00223931846486797 ** df.mm.exp3 0.0358818129633997 0.069977437755158 0.512762600553417 0.608240215899408 df.mm.exp4 0.139172166872327 0.069977437755158 1.98881484285367 0.0470168534361749 * df.mm.exp5 -0.0176414808146298 0.069977437755158 -0.252102411585218 0.801018091399204 df.mm.exp6 0.00189669206636224 0.069977437755158 0.0271043371579068 0.97838238075529 df.mm.exp7 0.0837110991221225 0.069977437755158 1.19625841996412 0.231903263587165 df.mm.exp8 0.149194551044718 0.069977437755158 2.13203792294783 0.033267497600794 * df.mm.trans1:exp2 -0.165719771792369 0.0642399714499367 -2.57969871486505 0.0100424514872171 * df.mm.trans2:exp2 -0.00919737288009 0.0455330608553079 -0.201993292507105 0.83996652669069 df.mm.trans1:exp3 0.0199431127236481 0.0642399714499367 0.310447098177653 0.756291145433695 df.mm.trans2:exp3 -0.105181257012739 0.0455330608553079 -2.30999750592164 0.0211083643500091 * df.mm.trans1:exp4 -0.00874279458000242 0.0642399714499367 -0.136095866524720 0.891775203802964 df.mm.trans2:exp4 -0.191532742252475 0.0455330608553079 -4.20645435766149 2.84731334551906e-05 *** df.mm.trans1:exp5 0.077972095508145 0.0642399714499368 1.21376292280749 0.225149091703596 df.mm.trans2:exp5 -0.0227959212424221 0.0455330608553079 -0.500645482957132 0.616740131588636 df.mm.trans1:exp6 0.064513392343711 0.0642399714499367 1.00425624245471 0.315518603231632 df.mm.trans2:exp6 -0.074926769781445 0.0455330608553079 -1.64554651881503 0.100197987741696 df.mm.trans1:exp7 -0.0171214937901806 0.0642399714499367 -0.26652399438757 0.789895276545258 df.mm.trans2:exp7 -0.108913766555525 0.0455330608553079 -2.39197111965798 0.0169576116761627 * df.mm.trans1:exp8 -0.0846840121426108 0.0642399714499367 -1.31824485956701 0.187749084688543 df.mm.trans2:exp8 -0.135846767988134 0.0455330608553079 -2.98347542283221 0.00292505785052655 ** df.mm.trans1:probe2 -0.112386748418466 0.0460183292063378 -2.44221705474234 0.0147842874460894 * df.mm.trans1:probe3 -0.0451930978559282 0.0460183292063378 -0.982067333502932 0.326324302007148 df.mm.trans1:probe4 0.125861926117179 0.0460183292063378 2.73503902223040 0.00635698241571543 ** df.mm.trans1:probe5 0.053101240764077 0.0460183292063378 1.15391500908216 0.248834034993849 df.mm.trans1:probe6 0.108482237417012 0.0460183292063378 2.35737018896531 0.0186130332535906 * df.mm.trans1:probe7 0.974723433699472 0.0460183292063378 21.1811999807509 1.90459229335481e-81 *** df.mm.trans1:probe8 0.104190661447052 0.0460183292063378 2.26411221884829 0.0237987978465180 * df.mm.trans1:probe9 0.994815707385252 0.0460183292063378 21.6178145652503 3.65424561529222e-84 *** df.mm.trans1:probe10 0.160812057748356 0.0460183292063378 3.49452186817353 0.00049751672633077 *** df.mm.trans1:probe11 0.346159941346324 0.0460183292063378 7.52221880534182 1.27741929276913e-13 *** df.mm.trans1:probe12 0.536019782436318 0.0460183292063378 11.6479627070532 2.38128393854236e-29 *** df.mm.trans1:probe13 0.191204877187516 0.0460183292063378 4.15497217054944 3.55576888807058e-05 *** df.mm.trans1:probe14 0.369623870370469 0.0460183292063378 8.03210105071706 2.91358204190692e-15 *** df.mm.trans1:probe15 0.174470351041959 0.0460183292063378 3.79132302391219 0.000159592810318753 *** df.mm.trans1:probe16 0.140398904590385 0.0460183292063378 3.0509344213881 0.00234659578168272 ** df.mm.trans1:probe17 0.040836858981978 0.0460183292063378 0.88740420798141 0.375092741017077 df.mm.trans1:probe18 0.0634104161807825 0.0460183292063378 1.37793825361329 0.168556676770163 df.mm.trans1:probe19 0.312634581455460 0.0460183292063378 6.79369692136504 1.95836309124086e-11 *** df.mm.trans1:probe20 0.261895000030704 0.0460183292063378 5.69110188369541 1.69531913063152e-08 *** df.mm.trans1:probe21 0.097061987911863 0.0460183292063378 2.10920278041071 0.0351961689452115 * df.mm.trans1:probe22 0.226701411921321 0.0460183292063378 4.92632861364509 9.92895852477729e-07 *** df.mm.trans2:probe2 0.150966000688099 0.0460183292063378 3.28056240397592 0.00107485391005510 ** df.mm.trans2:probe3 0.00452661534559247 0.0460183292063378 0.0983654866150389 0.921663459710187 df.mm.trans2:probe4 -0.155894521512152 0.0460183292063378 -3.38766148621237 0.000734742662073062 *** df.mm.trans2:probe5 -0.0490405218238201 0.0460183292063378 -1.06567367111334 0.286850166490093 df.mm.trans2:probe6 -0.162802765621435 0.0460183292063378 -3.53778088925083 0.000423630783519351 *** df.mm.trans3:probe2 -0.124509244459248 0.0460183292063378 -2.70564461175832 0.00694301075600392 ** df.mm.trans3:probe3 0.302323484963419 0.0460183292063378 6.56963193096073 8.42081680632837e-11 *** df.mm.trans3:probe4 0.130215927651290 0.0460183292063378 2.82965352930190 0.00476101327286095 ** df.mm.trans3:probe5 0.153964883953267 0.0460183292063378 3.34572955186870 0.000853800077464165 *** df.mm.trans3:probe6 -0.193309035066218 0.0460183292063378 -4.20069651376207 2.91930341020134e-05 *** df.mm.trans3:probe7 -0.299629530128815 0.0460183292063378 -6.51109102169553 1.22409531634180e-10 *** df.mm.trans3:probe8 -0.167176256324131 0.0460183292063378 -3.63281890514849 0.000295813826046099 *** df.mm.trans3:probe9 -0.260865582099666 0.0460183292063378 -5.66873214648869 1.92365340221916e-08 *** df.mm.trans3:probe10 -0.166178488577811 0.0460183292063378 -3.61113694138475 0.000321301781951115 *** df.mm.trans3:probe11 0.0725899096679004 0.0460183292063378 1.57741297695578 0.115043548992677 df.mm.trans3:probe12 -0.326470763779441 0.0460183292063378 -7.09436368964214 2.58866299058295e-12 *** df.mm.trans3:probe13 -0.133637602525410 0.0460183292063378 -2.90400813828340 0.00377211899916701 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86803157116653 0.130422009379924 29.6578130451803 2.87520109997783e-136 *** df.mm.trans1 0.228517174125641 0.112010185345501 2.04014637973116 0.041620263476774 * df.mm.trans2 0.094791767297219 0.0983521611881513 0.963799535791378 0.335399149663392 df.mm.exp2 0.142095346247142 0.125140556989599 1.13548596606416 0.256466838366676 df.mm.exp3 0.276645706003783 0.125140556989599 2.21067983600852 0.0273028500076827 * df.mm.exp4 0.107856786971009 0.125140556989599 0.861885143918392 0.388974826666244 df.mm.exp5 0.0316708869871754 0.125140556989599 0.253082515765114 0.800260865698398 df.mm.exp6 0.0753144234860059 0.125140556989599 0.601838646860629 0.547429505564268 df.mm.exp7 0.141847019102702 0.125140556989599 1.13350158026299 0.257298325209728 df.mm.exp8 0.219471234160698 0.125140556989599 1.75379780496693 0.0797973993352661 . df.mm.trans1:exp2 -0.159622740744257 0.114880253780777 -1.38947064870575 0.165025119255598 df.mm.trans2:exp2 0.00639431310202466 0.081426710946623 0.0785284463499487 0.937424742315768 df.mm.trans1:exp3 -0.301490276451080 0.114880253780777 -2.62438727743766 0.00882406538259106 ** df.mm.trans2:exp3 0.00216136981049633 0.081426710946623 0.0265437444957485 0.978829386694628 df.mm.trans1:exp4 -0.154070905879952 0.114880253780777 -1.34114350211971 0.180204143534243 df.mm.trans2:exp4 -0.061268201820261 0.081426710946623 -0.752433705205454 0.451982335609794 df.mm.trans1:exp5 -0.0973664388886982 0.114880253780777 -0.847547212722043 0.396910137351324 df.mm.trans2:exp5 0.0600898556029209 0.081426710946623 0.737962456107445 0.460725043104114 df.mm.trans1:exp6 -0.106731781718192 0.114880253780777 -0.929069863667485 0.353096047437201 df.mm.trans2:exp6 -0.0327317123702434 0.081426710946623 -0.401977581922716 0.68779368870953 df.mm.trans1:exp7 -0.175333282026435 0.114880253780777 -1.52622645107504 0.127296425412157 df.mm.trans2:exp7 -0.0539788712585973 0.081426710946623 -0.662913565230231 0.507551601547916 df.mm.trans1:exp8 -0.164158564706797 0.114880253780777 -1.42895370879017 0.153356234691778 df.mm.trans2:exp8 -0.043472315782251 0.081426710946623 -0.533882742859994 0.593551350789687 df.mm.trans1:probe2 0.0162528419983720 0.0822945156803399 0.197496052610646 0.84348290077316 df.mm.trans1:probe3 -0.0144569668689603 0.0822945156803399 -0.175673515415245 0.860589017261476 df.mm.trans1:probe4 0.0449528984669991 0.0822945156803399 0.546244158500325 0.585030266307989 df.mm.trans1:probe5 0.00463514909522500 0.0822945156803399 0.0563239124370025 0.955095972782134 df.mm.trans1:probe6 -0.0481986495165209 0.0822945156803399 -0.58568483109787 0.558230657570883 df.mm.trans1:probe7 0.162110428478238 0.0822945156803399 1.96988131150721 0.0491509343113928 * df.mm.trans1:probe8 -0.048286165942154 0.0822945156803399 -0.586748285021982 0.557516405788543 df.mm.trans1:probe9 0.0199197956926660 0.0822945156803399 0.242054959896007 0.808791405129568 df.mm.trans1:probe10 -0.0234262098996577 0.0822945156803399 -0.284663075127061 0.775966205938515 df.mm.trans1:probe11 0.0217106644545852 0.0822945156803399 0.263816662326768 0.791980146419121 df.mm.trans1:probe12 -0.0243935860466656 0.0822945156803399 -0.296418125132647 0.766977540437634 df.mm.trans1:probe13 -0.0512538882414299 0.0822945156803399 -0.622810497366769 0.533563069207991 df.mm.trans1:probe14 0.0903529300457586 0.0822945156803399 1.09792164518861 0.272525343430485 df.mm.trans1:probe15 0.161436810824996 0.0822945156803399 1.96169586138732 0.0500984496426214 . df.mm.trans1:probe16 0.123267396746829 0.0822945156803399 1.49788106446415 0.134506420332494 df.mm.trans1:probe17 0.102564440731871 0.0822945156803399 1.24630954911098 0.212967333818601 df.mm.trans1:probe18 -0.0381486646066587 0.0822945156803399 -0.463562660175816 0.643070522923447 df.mm.trans1:probe19 0.0103885511227543 0.0822945156803399 0.126236250822679 0.89957243883793 df.mm.trans1:probe20 0.0721947946079475 0.0822945156803399 0.877273461191227 0.380566649662676 df.mm.trans1:probe21 0.0442043793358047 0.0822945156803399 0.537148544716025 0.591294611898345 df.mm.trans1:probe22 0.0119506582546512 0.0822945156803399 0.145218161330114 0.884570366335924 df.mm.trans2:probe2 0.086016243943083 0.0822945156803399 1.04522449925095 0.296193171998157 df.mm.trans2:probe3 -0.0133433169579468 0.0822945156803399 -0.162141022978698 0.871230327672939 df.mm.trans2:probe4 0.092467408160111 0.0822945156803399 1.1236156795586 0.261468571631221 df.mm.trans2:probe5 0.0381308714567246 0.0822945156803399 0.46334644710515 0.643225411010161 df.mm.trans2:probe6 0.00539787960830873 0.08229451568034 0.0655922155162312 0.947716706000656 df.mm.trans3:probe2 -0.0212367571548322 0.0822945156803399 -0.258057988181412 0.796419743676024 df.mm.trans3:probe3 0.0449468707641418 0.0822945156803399 0.546170913001431 0.585080588457225 df.mm.trans3:probe4 -0.104536663769609 0.0822945156803399 -1.27027497404158 0.204307157988286 df.mm.trans3:probe5 -0.0969235742464862 0.0822945156803399 -1.17776468389425 0.239194342053985 df.mm.trans3:probe6 -0.0927289734061143 0.0822945156803399 -1.12679408390111 0.260122726708380 df.mm.trans3:probe7 0.0112429940000875 0.0822945156803399 0.136618994681968 0.891361786583695 df.mm.trans3:probe8 -0.142616855950500 0.0822945156803399 -1.73300559303943 0.0834290300323752 . df.mm.trans3:probe9 -0.0520284525654676 0.0822945156803399 -0.63222259873992 0.527398171597656 df.mm.trans3:probe10 -0.0236489132074112 0.0822945156803399 -0.287369249480386 0.773894177585754 df.mm.trans3:probe11 -0.088367216944922 0.0822945156803399 -1.07379229605252 0.283196727330431 df.mm.trans3:probe12 -0.0831484192866715 0.0822945156803399 -1.01037619091956 0.312580167938993 df.mm.trans3:probe13 -0.0267543573709864 0.0822945156803399 -0.325104986034665 0.745175370613272