fitVsDatCorrelation=0.802360347184916 cont.fitVsDatCorrelation=0.290195933518026 fstatistic=14550.5537695385,57,807 cont.fstatistic=5651.14906552615,57,807 residuals=-0.425449255858765,-0.0790128985583086,-0.00274024120415268,0.0680230092293433,0.732914096846979 cont.residuals=-0.469736534040006,-0.141495866471809,-0.0245627250362242,0.108342961911461,0.780381366028373 predictedValues: Include Exclude Both Lung 62.7058896614696 50.6148426475501 53.7385523116592 cerebhem 63.8969037744866 53.2985631894867 50.2022852436397 cortex 57.9923977591141 52.9744974080172 51.2422822844209 heart 62.6199100422067 51.8148693759127 50.2080121603276 kidney 61.4124747883767 50.2569727301976 54.444090610028 liver 62.6601962783451 53.1893303173709 52.4776639574334 stomach 64.2389914896553 53.9150378221428 54.0599299192168 testicle 61.836187132484 54.3213577095653 51.2155136125012 diffExp=12.0910470139196,10.5983405850000,5.01790035109696,10.8050406662940,11.1555020581791,9.4708659609742,10.3239536675125,7.51482942291876 diffExpScore=0.987175784553243 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=1,0,0,1,1,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 58.4112820832846 54.9592749878048 60.602997663006 cerebhem 55.3562549134554 60.1074529845985 60.9891590893855 cortex 57.8948319114387 56.4686801889955 65.3190775798136 heart 58.5679424244826 55.1161689032803 66.8120310263042 kidney 58.604657972797 58.9531731526928 60.2870189643161 liver 56.314112519226 55.5754816989838 52.1361034017422 stomach 56.1651713547989 59.7788395240549 59.3332493199526 testicle 56.1414271505655 57.1849125464153 58.6666303200615 cont.diffExp=3.45200709547972,-4.75119807114308,1.42615172244324,3.45177352120235,-0.348515179895863,0.738630820242228,-3.61366816925604,-1.04348539584974 cont.diffExpScore=11.1505000299817 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=0.120240583501228 cont.tran.correlation=-0.526632121610247 tran.covariance=0.000100845528068509 cont.tran.covariance=-0.000444237594667106 tran.mean=57.3592763828988 cont.tran.mean=57.2249790198047 weightedLogRatios: wLogRatio Lung 0.863557569542317 cerebhem 0.737523018648984 cortex 0.363368313231085 heart 0.765651543703211 kidney 0.805339043360885 liver 0.664616974927344 stomach 0.713945551363658 testicle 0.526020879084705 cont.weightedLogRatios: wLogRatio Lung 0.24592341154704 cerebhem -0.333902517561633 cortex 0.100919130768121 heart 0.245396034201409 kidney -0.0241545442483184 liver 0.0531335813937549 stomach -0.253128138708567 testicle -0.0743473269573829 varWeightedLogRatios=0.0264860677623380 cont.varWeightedLogRatios=0.0450256417626557 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0177002883335 0.062721927013677 64.0557533804312 0 *** df.mm.trans1 0.165093224093451 0.0547368548285955 3.0161255083148 0.00264059979761994 ** df.mm.trans2 -0.133411480345501 0.0487448051028530 -2.7369374041808 0.00633780014800142 ** df.mm.exp2 0.138550151914830 0.063728555953419 2.17406702289159 0.0299900596295839 * df.mm.exp3 0.0149879491156866 0.0637285559534189 0.235184194768853 0.814125437027682 df.mm.exp4 0.0900162428623505 0.0637285559534189 1.41249462686941 0.158189888888968 df.mm.exp5 -0.0409816090508781 0.0637285559534189 -0.64306508185801 0.520364543544531 df.mm.exp6 0.0726270262224807 0.063728555953419 1.13963081598092 0.254778282850016 df.mm.exp7 0.0813569803839995 0.0637285559534189 1.2766173525643 0.202104606670776 df.mm.exp8 0.104794157216094 0.0637285559534189 1.6443830500834 0.100486564853649 df.mm.trans1:exp2 -0.119734623160755 0.0596750075237322 -2.00644504507415 0.0451419883871989 * df.mm.trans2:exp2 -0.0868856443869118 0.0463012953051093 -1.87652729398532 0.060943566938158 . df.mm.trans1:exp3 -0.0931313975009053 0.0596750075237322 -1.56064324690479 0.118999998323217 df.mm.trans2:exp3 0.0305778014156546 0.0463012953051093 0.660409200523605 0.509179726408079 df.mm.trans1:exp4 -0.091388340753692 0.0596750075237322 -1.53143408850594 0.126054037864994 df.mm.trans2:exp4 -0.0665839474351185 0.0463012953051093 -1.43805798512447 0.150805265536631 df.mm.trans1:exp5 0.0201392187942961 0.0596750075237322 0.337481629747377 0.735841617755782 df.mm.trans2:exp5 0.033886040928372 0.0463012953051093 0.731859458900122 0.464466826174385 df.mm.trans1:exp6 -0.0733559854909355 0.0596750075237322 -1.22925808533434 0.219333350518643 df.mm.trans2:exp6 -0.0230140741687258 0.0463012953051093 -0.49705033125037 0.619288941480247 df.mm.trans1:exp7 -0.0572019871933103 0.0596750075237322 -0.958558525033475 0.338068279349838 df.mm.trans2:exp7 -0.0181924127711758 0.0463012953051093 -0.392913689591062 0.694486995772756 df.mm.trans1:exp8 -0.118760789037824 0.0596750075237322 -1.99012608403265 0.0469140478965013 * df.mm.trans2:exp8 -0.0341215458600048 0.0463012953051093 -0.736945816205698 0.461369489258048 df.mm.trans1:probe2 0.268960517954245 0.0379228054438597 7.0923159509496 2.88474633125708e-12 *** df.mm.trans1:probe3 -0.0617407815671653 0.0379228054438597 -1.62806471843349 0.103901477052006 df.mm.trans1:probe4 -0.182057040539917 0.0379228054438597 -4.80072711944878 1.88387291256772e-06 *** df.mm.trans1:probe5 -0.215431830893337 0.0379228054438597 -5.68079888531081 1.87162236264753e-08 *** df.mm.trans1:probe6 -0.215352441637786 0.0379228054438597 -5.67870544167915 1.89377717037532e-08 *** df.mm.trans1:probe7 0.0290830336800954 0.0379228054438597 0.76690090144173 0.443364728702120 df.mm.trans1:probe8 0.0849908897321325 0.0379228054438597 2.24115512387267 0.0252871998236801 * df.mm.trans1:probe9 0.413743964353812 0.0379228054438597 10.9101623551114 6.02884499189524e-26 *** df.mm.trans1:probe10 -0.208376353282678 0.0379228054438597 -5.49475047649507 5.24814831391147e-08 *** df.mm.trans1:probe11 -0.181759911291855 0.0379228054438597 -4.7928920121938 1.95686051706611e-06 *** df.mm.trans1:probe12 -0.346984610693305 0.0379228054438597 -9.1497611168819 4.57154961957964e-19 *** df.mm.trans1:probe13 -0.208323075895886 0.0379228054438597 -5.49334558605596 5.28856747436417e-08 *** df.mm.trans1:probe14 -0.318621678686763 0.0379228054438597 -8.40184883363774 1.97141068125796e-16 *** df.mm.trans1:probe15 -0.235782288042292 0.0379228054438597 -6.21742735756563 8.10041207987017e-10 *** df.mm.trans1:probe16 0.158697141616346 0.0379228054438597 4.18474160228675 3.16835918976773e-05 *** df.mm.trans1:probe17 -0.0325044552890727 0.0379228054438597 -0.857121589730269 0.391632147387236 df.mm.trans1:probe18 -0.048629633939289 0.0379228054438597 -1.28233218429157 0.200094369760029 df.mm.trans1:probe19 -0.0175210618587934 0.0379228054438597 -0.462019137395605 0.64419212251843 df.mm.trans1:probe20 0.0470469492183814 0.0379228054438597 1.2405978056668 0.215114910474898 df.mm.trans1:probe21 -0.122523652400522 0.0379228054438597 -3.23086994663157 0.00128415137964748 ** df.mm.trans1:probe22 0.187455416579984 0.0379228054438597 4.94307882515417 9.35472688480324e-07 *** df.mm.trans1:probe23 -0.124513153262257 0.0379228054438597 -3.28333180535876 0.00107003979169392 ** df.mm.trans2:probe2 0.0152157537463747 0.0379228054438597 0.401229644491885 0.688357273483152 df.mm.trans2:probe3 0.145828889702699 0.0379228054438597 3.84541407197792 0.000129826569089846 *** df.mm.trans2:probe4 0.153367798249819 0.0379228054438597 4.04421024380336 5.75355760945429e-05 *** df.mm.trans2:probe5 0.192546124222803 0.0379228054438597 5.07731751301587 4.75608395048855e-07 *** df.mm.trans2:probe6 0.0124701912926602 0.0379228054438597 0.3288309276359 0.742368798230233 df.mm.trans3:probe2 -0.103551629310677 0.0379228054438597 -2.7305898943572 0.00645999085164544 ** df.mm.trans3:probe3 -0.0512053629663047 0.0379228054438597 -1.35025250286686 0.177313553505608 df.mm.trans3:probe4 -0.127548237479846 0.0379228054438597 -3.36336502500234 0.000806279132465759 *** df.mm.trans3:probe5 -0.123613508164918 0.0379228054438597 -3.25960874249964 0.00116239589395788 ** df.mm.trans3:probe6 -0.0462014543511987 0.0379228054438597 -1.21830264956517 0.22346503358123 df.mm.trans3:probe7 -0.00463938376477587 0.0379228054438597 -0.122337567341739 0.902662138905499 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89926858119889 0.100567722602881 38.7725651956563 1.67408141160197e-186 *** df.mm.trans1 0.128256255822249 0.0877645361781697 1.46136767089930 0.144303819222503 df.mm.trans2 0.0970142426696897 0.0781569424173826 1.24127479490693 0.214864932380289 df.mm.exp2 0.0294702188940521 0.102181741571940 0.288409831743804 0.773107102597612 df.mm.exp3 -0.0567270286928651 0.10218174157194 -0.5551581703364 0.578940322918201 df.mm.exp4 -0.0920097160766378 0.10218174157194 -0.900451633150716 0.368148580994542 df.mm.exp5 0.0786836725913789 0.10218174157194 0.770036519058372 0.441503577908792 df.mm.exp6 0.125072469686001 0.102181741571940 1.22401974914417 0.221302006267172 df.mm.exp7 0.0660215810733413 0.10218174157194 0.646119160406553 0.518385877007352 df.mm.exp8 0.0325358464967693 0.10218174157194 0.318411547858212 0.750255164484654 df.mm.trans1:exp2 -0.0831896170913867 0.0956823217766078 -0.869435602593463 0.384867429307272 df.mm.trans2:exp2 0.060071168492379 0.074239042773398 0.809158715525683 0.418662354176114 df.mm.trans1:exp3 0.0478460928844953 0.0956823217766078 0.50005154553213 0.61717510573818 df.mm.trans2:exp3 0.0838207240145658 0.074239042773398 1.12906525843031 0.259205816385892 df.mm.trans1:exp4 0.0946881478326006 0.0956823217766078 0.989609638169856 0.32266157433124 df.mm.trans2:exp4 0.094860379515755 0.074239042773398 1.27776943198608 0.201698171372416 df.mm.trans1:exp5 -0.0753785491107031 0.0956823217766078 -0.787800167377748 0.431044930215644 df.mm.trans2:exp5 -0.00853267538405557 0.074239042773398 -0.114935148209010 0.908525115989922 df.mm.trans1:exp6 -0.161636357069552 0.0956823217766078 -1.68930220408875 0.0915477981116799 . df.mm.trans2:exp6 -0.113922798518492 0.074239042773398 -1.53454024004892 0.125288737787076 df.mm.trans1:exp7 -0.105233800600364 0.0956823217766078 -1.09982490648645 0.271736392852962 df.mm.trans2:exp7 0.0180377069560553 0.074239042773398 0.242967935498742 0.80809197832246 df.mm.trans1:exp8 -0.0721709121415282 0.0956823217766078 -0.754276346993624 0.450903336730677 df.mm.trans2:exp8 0.00716179423211872 0.074239042773398 0.0964693773595501 0.92317174516525 df.mm.trans1:probe2 0.0738039567652466 0.0608050543053227 1.21377996629445 0.225186863665070 df.mm.trans1:probe3 0.127560200139317 0.0608050543053227 2.09785521280507 0.0362283826363086 * df.mm.trans1:probe4 0.149547519433641 0.0608050543053227 2.45945869372491 0.0141234304905180 * df.mm.trans1:probe5 0.0339411772190171 0.0608050543053227 0.558196643466298 0.576864938014625 df.mm.trans1:probe6 0.0192967503904409 0.0608050543053227 0.317354381324049 0.751056806475241 df.mm.trans1:probe7 -0.0416498182569438 0.0608050543053227 -0.684972963724463 0.49355766804563 df.mm.trans1:probe8 0.0409584801139606 0.0608050543053227 0.673603215750687 0.500756480485469 df.mm.trans1:probe9 0.128906879083118 0.0608050543053227 2.12000269641785 0.0343104276159967 * df.mm.trans1:probe10 0.0671880138300814 0.0608050543053227 1.10497416041613 0.269500114028279 df.mm.trans1:probe11 -0.0173641799410087 0.0608050543053227 -0.285571325268740 0.775279748754494 df.mm.trans1:probe12 0.0709307570837417 0.0608050543053227 1.16652732069894 0.243745914997795 df.mm.trans1:probe13 0.0306730154039371 0.0608050543053227 0.504448450122542 0.614083981184066 df.mm.trans1:probe14 0.0516713292437896 0.0608050543053227 0.849786746087428 0.395695691431611 df.mm.trans1:probe15 0.0200584436128015 0.0608050543053227 0.329881189022237 0.741575347138102 df.mm.trans1:probe16 0.098805056811705 0.0608050543053227 1.62494808927514 0.104564077573332 df.mm.trans1:probe17 0.0354798486028295 0.0608050543053227 0.583501634990296 0.559718692784939 df.mm.trans1:probe18 0.07905897507214 0.0608050543053227 1.30020400401516 0.193902269385363 df.mm.trans1:probe19 0.0263673548270353 0.0608050543053227 0.433637550829836 0.664667553964058 df.mm.trans1:probe20 0.127204248423003 0.0608050543053227 2.09200123042843 0.0367503747497958 * df.mm.trans1:probe21 0.0182013232439891 0.0608050543053227 0.299338985088215 0.764758485850829 df.mm.trans1:probe22 -0.0147022749116732 0.0608050543053227 -0.241793631790017 0.809001500985337 df.mm.trans1:probe23 0.073589559187009 0.0608050543053227 1.21025398345162 0.226535810427331 df.mm.trans2:probe2 0.0712886734060201 0.0608050543053227 1.17241361298776 0.241377011324958 df.mm.trans2:probe3 0.0164205882740835 0.0608050543053227 0.270053015521213 0.787188510604878 df.mm.trans2:probe4 0.0521722360269236 0.0608050543053227 0.858024659676303 0.391133601803152 df.mm.trans2:probe5 0.0340763166621800 0.0608050543053227 0.560419146919453 0.575349117594513 df.mm.trans2:probe6 -0.0399325941483887 0.0608050543053227 -0.656731493863548 0.51154080857908 df.mm.trans3:probe2 -0.0381470336216707 0.0608050543053227 -0.627366163183106 0.530596702683723 df.mm.trans3:probe3 -0.0392348174649183 0.0608050543053227 -0.645255857644779 0.518944795525618 df.mm.trans3:probe4 0.0169656741445779 0.0608050543053227 0.279017498436684 0.780302881870408 df.mm.trans3:probe5 -0.0983788500126915 0.0608050543053227 -1.61793869171957 0.106066569447547 df.mm.trans3:probe6 0.0116160330451883 0.0608050543053227 0.191037294150915 0.848544425196914 df.mm.trans3:probe7 0.00581594374031902 0.0608050543053227 0.0956490181081856 0.923823078671948