fitVsDatCorrelation=0.86341966240683 cont.fitVsDatCorrelation=0.252918863898587 fstatistic=13157.5064440404,55,761 cont.fstatistic=3567.44291790737,55,761 residuals=-0.516633328020218,-0.0755389115519126,-0.0046917884152829,0.0663078310225291,1.10652908777126 cont.residuals=-0.508983882115601,-0.159923392071411,-0.0443270252845235,0.100096124386106,1.54943264453644 predictedValues: Include Exclude Both Lung 47.8925454696371 43.4676993573769 68.2398302868552 cerebhem 56.2612150404728 51.1410176807656 66.8108671079844 cortex 57.665584618122 41.3598667625936 78.1860098519695 heart 48.0259311095838 42.1282441560975 63.6770794002378 kidney 49.1444964176383 42.529761877231 70.9778708590623 liver 49.7119497779105 46.7220813207699 65.5137250640031 stomach 47.9917208613878 44.3280223960861 62.9404030119206 testicle 52.29428237486 43.7964850897232 68.5193884809409 diffExp=4.42484611226015,5.12019735970713,16.3057178555284,5.89768695348638,6.61473454040726,2.98986845714058,3.66369846530179,8.49779728513678 diffExpScore=0.981656272417184 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 56.913088434989 54.4842808755788 52.3878053043385 cerebhem 55.3370337835847 53.655617709077 55.2379928460784 cortex 52.4394491003916 53.9240913018206 55.0315374300054 heart 54.3075520855533 56.9006070981624 54.5183879442658 kidney 54.7385491878665 53.7034384108598 54.7883611037037 liver 51.2437367909344 62.9523052733878 52.8203386150545 stomach 52.8116739575407 57.2012468168704 59.5232660364221 testicle 49.9082497821981 57.4405121006385 54.6707738750003 cont.diffExp=2.42880755941020,1.68141607450777,-1.48464220142893,-2.59305501260904,1.03511077700676,-11.7085684824534,-4.38957285932972,-7.5322623184404 cont.diffExpScore=1.39429448304831 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,-1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.286809569461593 cont.tran.correlation=-0.613460400052384 tran.covariance=0.00138584350924565 cont.tran.covariance=-0.00144873599676359 tran.mean=47.778806519391 cont.tran.mean=54.8725895443409 weightedLogRatios: wLogRatio Lung 0.370364875768276 cerebhem 0.379984843772573 cortex 1.29233643156843 heart 0.498702491360087 kidney 0.552581242692928 liver 0.240374602836861 stomach 0.304251001132264 testicle 0.685964953895317 cont.weightedLogRatios: wLogRatio Lung 0.175312414084867 cerebhem 0.123363524574311 cortex -0.110936219186477 heart -0.187409703481675 kidney 0.0762314739720953 liver -0.831261376609583 stomach -0.319905003347668 testicle -0.559508623012156 varWeightedLogRatios=0.112701562985883 cont.varWeightedLogRatios=0.124541027999867 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.61928829502794 0.0649702975204417 55.7068142390636 9.93512715469316e-271 *** df.mm.trans1 0.140347345058890 0.0568909854035538 2.46695226077281 0.0138460336452481 * df.mm.trans2 0.130527839001913 0.0510181090378802 2.55846093599820 0.0107061389629770 * df.mm.exp2 0.344776952747878 0.0672806897783106 5.12445627242996 3.78644944257516e-07 *** df.mm.exp3 -6.87467586276084e-05 0.0672806897783106 -0.00102179033618901 0.999184997201033 df.mm.exp4 0.0406852500506276 0.0672806897783106 0.604709169669413 0.545552508742738 df.mm.exp5 -0.0353487429621529 0.0672806897783106 -0.525392101041573 0.599463685621423 df.mm.exp6 0.150252997128125 0.0672806897783106 2.23322616969605 0.0258240036512306 * df.mm.exp7 0.102507688094040 0.0672806897783106 1.52358259750014 0.128028444346493 df.mm.exp8 0.0913742898563692 0.0672806897783106 1.35810572331299 0.174832639548528 df.mm.trans1:exp2 -0.183731418406364 0.0631148815335958 -2.91106334895938 0.0037072342138115 ** df.mm.trans2:exp2 -0.182208201949274 0.0503482579792524 -3.61895742300277 0.000315355361049694 *** df.mm.trans1:exp3 0.185769423197598 0.0631148815335958 2.94335374928516 0.00334554148225749 ** df.mm.trans2:exp3 -0.0496383632177817 0.0503482579792524 -0.98590031135212 0.324495382038078 df.mm.trans1:exp4 -0.0379040189326066 0.0631148815335958 -0.600555970503254 0.548314578603964 df.mm.trans2:exp4 -0.0719849704998484 0.0503482579792524 -1.42974103551929 0.153201755699039 df.mm.trans1:exp5 0.0611537426924991 0.0631148815335958 0.96892747330829 0.332889214922172 df.mm.trans2:exp5 0.0135347345114469 0.0503482579792524 0.268822300009352 0.788139292918393 df.mm.trans1:exp6 -0.112967520105894 0.0631148815335958 -1.78987137994963 0.0738720793071461 . df.mm.trans2:exp6 -0.078054229546656 0.0503482579792524 -1.55028659737981 0.121488493572631 df.mm.trans1:exp7 -0.100439039484367 0.0631148815335958 -1.59136858128939 0.111941936933846 df.mm.trans2:exp7 -0.0829087700575477 0.0503482579792524 -1.64670583223978 0.100031381426165 df.mm.trans1:exp8 -0.00344711374525534 0.0631148815335958 -0.0546164971159845 0.956458330438288 df.mm.trans2:exp8 -0.0838388435512592 0.0503482579792524 -1.66517863608722 0.0962887162313076 . df.mm.trans1:probe2 0.157084557936426 0.0386498137333796 4.0643031042802 5.31833848742802e-05 *** df.mm.trans1:probe3 -0.0119336777516182 0.0386498137333796 -0.308764172421141 0.75758543120158 df.mm.trans1:probe4 0.0812562075319386 0.0386498137333796 2.10236996463873 0.0358488568640376 * df.mm.trans1:probe5 0.68255428413415 0.0386498137333796 17.6599630943284 9.3549119011674e-59 *** df.mm.trans1:probe6 0.124717557376061 0.0386498137333796 3.22686050278038 0.00130513463829347 ** df.mm.trans1:probe7 0.272340459364034 0.0386498137333796 7.04635890984462 4.11861386421002e-12 *** df.mm.trans1:probe8 0.141731609205384 0.0386498137333796 3.66707095105555 0.000262417203068085 *** df.mm.trans1:probe9 -0.0419992691532592 0.0386498137333796 -1.08666161868167 0.277530453027563 df.mm.trans1:probe10 0.00404289297718381 0.0386498137333796 0.104603168467335 0.916718262917411 df.mm.trans1:probe11 0.188791339512590 0.0386498137333796 4.8846636316268 1.26341057552612e-06 *** df.mm.trans1:probe12 0.168096773989418 0.0386498137333796 4.34922597943189 1.55224855006901e-05 *** df.mm.trans1:probe13 0.238513615922470 0.0386498137333796 6.17114528850834 1.10144498995855e-09 *** df.mm.trans1:probe14 0.129285603082162 0.0386498137333796 3.34505112945747 0.00086299039653429 *** df.mm.trans1:probe15 0.269797857296978 0.0386498137333796 6.9805732870575 6.40675657401101e-12 *** df.mm.trans1:probe16 0.198346263861904 0.0386498137333796 5.13188149444052 3.64485543357483e-07 *** df.mm.trans1:probe17 0.0912317869065556 0.0386498137333796 2.36047158043 0.0185030494552547 * df.mm.trans1:probe18 0.0359612952406681 0.0386498137333796 0.930439031058264 0.35243887442319 df.mm.trans1:probe19 0.169102486504750 0.0386498137333796 4.375247127225 1.38216165956447e-05 *** df.mm.trans1:probe20 0.0796803714226234 0.0386498137333796 2.06159781188824 0.0395851340237254 * df.mm.trans1:probe21 0.0320646452612769 0.0386498137333796 0.829619658259427 0.407014016296119 df.mm.trans1:probe22 0.0504116472247236 0.0386498137333796 1.30431798643278 0.192519526741204 df.mm.trans2:probe2 0.0713784054481 0.0386498137333796 1.8467981745137 0.0651644543632681 . df.mm.trans2:probe3 0.126653714346035 0.0386498137333796 3.27695536179652 0.00109691795674843 ** df.mm.trans2:probe4 0.0406824710439924 0.0386498137333796 1.05259164570973 0.292862290487881 df.mm.trans2:probe5 0.00878929517463546 0.0386498137333796 0.227408474340063 0.820167210914404 df.mm.trans2:probe6 0.0189199307980034 0.0386498137333796 0.489521914090451 0.624613314727908 df.mm.trans3:probe2 0.252382705988407 0.0386498137333796 6.52998505321226 1.20198274000042e-10 *** df.mm.trans3:probe3 0.915370004810991 0.0386498137333796 23.6836847681995 2.48408809048912e-93 *** df.mm.trans3:probe4 0.218541257264234 0.0386498137333796 5.65439354434695 2.21276124865221e-08 *** df.mm.trans3:probe5 0.428977543488259 0.0386498137333796 11.099084369397 1.23584852299649e-26 *** df.mm.trans3:probe6 0.181918110974364 0.0386498137333796 4.70683021215318 2.98902643903188e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14764588999051 0.124597918456200 33.2882438276731 1.21419406663648e-150 *** df.mm.trans1 -0.0915808912944277 0.109103677076045 -0.83939326105935 0.401512270535807 df.mm.trans2 -0.103168101652095 0.0978408662464762 -1.05444795830199 0.292012532936762 df.mm.exp2 -0.0963861803673235 0.129028713406111 -0.747013419129069 0.455286194458916 df.mm.exp3 -0.141433668844686 0.129028713406111 -1.09614104574949 0.273363813183445 df.mm.exp4 -0.0433324641869512 0.129028713406111 -0.335835823229242 0.737087243300407 df.mm.exp5 -0.0981962883692335 0.129028713406111 -0.761042141528344 0.446867647774876 df.mm.exp6 0.0313107299114099 0.129028713406111 0.242664823083689 0.808330446894227 df.mm.exp7 -0.153823036965316 0.129028713406111 -1.19216128646626 0.233569627359764 df.mm.exp8 -0.121156847325665 0.129028713406111 -0.938991361901985 0.34803310802486 df.mm.trans1:exp2 0.0683032135266043 0.121039662165952 0.564304396627913 0.572713264715774 df.mm.trans2:exp2 0.0810601185243048 0.0965562477243824 0.839511895239436 0.401445765032028 df.mm.trans1:exp3 0.0595674824530077 0.121039662165953 0.492131929212898 0.622768015956062 df.mm.trans2:exp3 0.131098774063882 0.0965562477243824 1.35774511907402 0.174947026340691 df.mm.trans1:exp4 -0.00352957767876643 0.121039662165953 -0.0291605050411259 0.976744225696233 df.mm.trans2:exp4 0.0867262390602001 0.0965562477243824 0.898193965736512 0.369366234839566 df.mm.trans1:exp5 0.0592391480378961 0.121039662165953 0.489419310809673 0.624685904262932 df.mm.trans2:exp5 0.083761082066003 0.0965562477243825 0.86748484991978 0.385949863446737 df.mm.trans1:exp6 -0.136242668218421 0.121039662165953 -1.1256035069862 0.26068824168226 df.mm.trans2:exp6 0.113154414847948 0.0965562477243824 1.17190153423262 0.241603345765206 df.mm.trans1:exp7 0.0790299610197075 0.121039662165953 0.652926153340983 0.514001083616137 df.mm.trans2:exp7 0.202486496845636 0.0965562477243824 2.09708332311783 0.0363156420932422 * df.mm.trans1:exp8 -0.0101821771852210 0.121039662165953 -0.0841226504024821 0.932981039991397 df.mm.trans2:exp8 0.173994451661376 0.0965562477243825 1.80200096588301 0.0719408800154441 . df.mm.trans1:probe2 0.0265679994515302 0.0741213527363604 0.358439214486937 0.720114058718544 df.mm.trans1:probe3 -0.0442442649330713 0.0741213527363604 -0.596916587456817 0.550740613299577 df.mm.trans1:probe4 0.00696992066630297 0.0741213527363604 0.0940339107287211 0.925106971260738 df.mm.trans1:probe5 -0.0980134697231138 0.0741213527363604 -1.32233784334366 0.186452958617565 df.mm.trans1:probe6 0.0216496537429780 0.0741213527363604 0.292083899493617 0.770302084465866 df.mm.trans1:probe7 -0.0807533684716451 0.0741213527363604 -1.08947510387289 0.276289300342591 df.mm.trans1:probe8 -0.0079552272280864 0.0741213527363604 -0.107327064798481 0.914557802150144 df.mm.trans1:probe9 -0.0255528297638255 0.0741213527363604 -0.344743165369815 0.730382716353671 df.mm.trans1:probe10 -0.0117660141596304 0.0741213527363605 -0.158739873535234 0.873915937703357 df.mm.trans1:probe11 -0.057787652102843 0.0741213527363604 -0.779635691598693 0.435847734779323 df.mm.trans1:probe12 -0.0096963059284674 0.0741213527363605 -0.130816634755113 0.895954936623962 df.mm.trans1:probe13 0.0147125165283009 0.0741213527363604 0.198492283062228 0.842712936096764 df.mm.trans1:probe14 -0.0149121532378988 0.0741213527363604 -0.201185659562087 0.840607164548628 df.mm.trans1:probe15 -0.0126930691156391 0.0741213527363604 -0.171247132533949 0.864074971382784 df.mm.trans1:probe16 -0.0515910665809056 0.0741213527363605 -0.696035146098965 0.48661933410398 df.mm.trans1:probe17 0.0293437408374750 0.0741213527363604 0.395887821176802 0.692298750519448 df.mm.trans1:probe18 -0.0757399146312708 0.0741213527363604 -1.02183664807990 0.307182941933344 df.mm.trans1:probe19 -0.0948319911066365 0.0741213527363604 -1.27941527786117 0.201140799268756 df.mm.trans1:probe20 -0.0194180482716563 0.0741213527363604 -0.261976442074981 0.793410498204561 df.mm.trans1:probe21 0.102534747732016 0.0741213527363604 1.3833361635576 0.166967489600409 df.mm.trans1:probe22 -0.00393364999342786 0.0741213527363605 -0.0530704020934334 0.957689741829252 df.mm.trans2:probe2 -0.0991218327414748 0.0741213527363605 -1.33729119993314 0.18152716702735 df.mm.trans2:probe3 -0.0986343633922278 0.0741213527363604 -1.33071456133642 0.183681528409542 df.mm.trans2:probe4 -0.194114329424677 0.0741213527363604 -2.61887192095799 0.00899767942683756 ** df.mm.trans2:probe5 -0.0522939038304521 0.0741213527363604 -0.705517396808102 0.480704207615369 df.mm.trans2:probe6 -0.114622201484902 0.0741213527363605 -1.54641270367255 0.122420662997353 df.mm.trans3:probe2 -0.007801601576108 0.0741213527363604 -0.105254441373422 0.916201648567106 df.mm.trans3:probe3 0.079741583853288 0.0741213527363604 1.07582472404299 0.282346604046710 df.mm.trans3:probe4 -0.0436926831960951 0.0741213527363604 -0.589474983700096 0.555717646267415 df.mm.trans3:probe5 0.0514002741348122 0.0741213527363604 0.693461090998109 0.488231832062101 df.mm.trans3:probe6 -0.0449859092212145 0.0741213527363604 -0.606922399018044 0.544083438857271