fitVsDatCorrelation=0.893347903723241 cont.fitVsDatCorrelation=0.244048482162318 fstatistic=1867.45651505757,53,715 cont.fstatistic=390.3827380098,53,715 residuals=-2.36208137096215,-0.134911684956602,-0.00597631001454895,0.113187834640344,2.09161571333613 cont.residuals=-1.25706271414967,-0.414409602033397,-0.174538808428613,0.162461628286850,4.48219617096587 predictedValues: Include Exclude Both Lung 67.2939440698217 97.6021714986339 61.6518904298889 cerebhem 64.7113834770986 125.358923903891 59.7038214494773 cortex 62.1863120859848 89.9736141423384 71.6155733908267 heart 65.4860094886094 83.9396776573571 60.1828479467074 kidney 71.4261295373945 98.8177284333921 67.3705739881881 liver 70.4966942816616 94.49314773962 64.1748331265328 stomach 68.052675227203 129.455956236069 76.7408988841594 testicle 80.6445746047377 2842.94071387425 891.967885074997 diffExp=-30.3082274288121,-60.6475404267923,-27.7873020563535,-18.4536681687477,-27.3915988959977,-23.9964534579585,-61.4032810088656,-2762.29613926951 diffExpScore=0.999668136182958 diffExp1.5=0,-1,0,0,0,0,-1,-1 diffExp1.5Score=0.75 diffExp1.4=-1,-1,-1,0,0,0,-1,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,-1,-1,0,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 87.6449118851303 68.1296498595891 99.8150022027505 cerebhem 100.778591890378 66.7445900832824 99.4779321147055 cortex 93.2389913666243 66.4076653511133 76.1216128926233 heart 73.8803039710764 68.7825321896011 85.9480535913113 kidney 76.7594990943674 76.9306254964113 69.0723956193632 liver 89.7581623154961 63.3224499224857 70.9007489339585 stomach 74.6899199143817 70.1991052146386 73.5485361762852 testicle 87.8366398228725 67.1809244071241 80.3971199070125 cont.diffExp=19.5152620255412,34.0340018070957,26.8313260155111,5.09777178147529,-0.171126402043939,26.4357123930104,4.49081469974313,20.6557154157484 cont.diffExpScore=0.99522989566201 cont.diffExp1.5=0,1,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,1,1,0,0,1,0,0 cont.diffExp1.4Score=0.75 cont.diffExp1.3=0,1,1,0,0,1,0,1 cont.diffExp1.3Score=0.8 cont.diffExp1.2=1,1,1,0,0,1,0,1 cont.diffExp1.2Score=0.833333333333333 tran.correlation=0.846685098317835 cont.tran.correlation=-0.61390154576782 tran.covariance=0.0776442027943144 cont.tran.covariance=-0.00399127022350718 tran.mean=257.054978516129 cont.tran.mean=77.0177851740358 weightedLogRatios: wLogRatio Lung -1.63418504534062 cerebhem -2.97596716706467 cortex -1.59381694893774 heart -1.06900763773995 kidney -1.43835176208786 liver -1.28963017542151 stomach -2.92065121206807 testicle -21.9856014923428 cont.weightedLogRatios: wLogRatio Lung 1.09501586321002 cerebhem 1.81587489808611 cortex 1.48144407452851 heart 0.305053761733633 kidney -0.00966874498008855 liver 1.50809226798209 stomach 0.265546115632716 testicle 1.16389235588281 varWeightedLogRatios=51.2178104397552 cont.varWeightedLogRatios=0.45939083352531 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.76166089767814 0.189222644515684 25.1643290889715 1.44639741289368e-100 *** df.mm.trans1 -0.416575655818997 0.164174322570458 -2.53739835375424 0.0113793149719667 * df.mm.trans2 -0.165783710504598 0.147612807499706 -1.12309841749286 0.261772661821792 df.mm.exp2 0.243256015641408 0.193605780903369 1.25645016644839 0.209363147791094 df.mm.exp3 -0.310127289606811 0.193605780903369 -1.60184932577813 0.109630609380422 df.mm.exp4 -0.153918549497390 0.193605780903369 -0.79501009101692 0.426871450324242 df.mm.exp5 -0.0167336439095082 0.193605780903369 -0.0864315302540482 0.931147581211754 df.mm.exp6 -0.0259840837737402 0.193605780903369 -0.134211301194096 0.893273258042214 df.mm.exp7 0.0747218622943921 0.193605780903369 0.385948508075214 0.699649613224066 df.mm.exp8 0.88074956327254 0.193605780903369 4.549190417574 6.32560843717097e-06 *** df.mm.trans1:exp2 -0.282389135649774 0.178985511046892 -1.57772064340890 0.115072100270908 df.mm.trans2:exp2 0.00702525618974133 0.142243260496459 0.0493890266942821 0.960623065855926 df.mm.trans1:exp3 0.231191953710076 0.178985511046892 1.29167971394906 0.196885463060137 df.mm.trans2:exp3 0.228743998623338 0.142243260496459 1.60811835882399 0.108250705860027 df.mm.trans1:exp4 0.126684825231878 0.178985511046892 0.707793745375782 0.479304000536606 df.mm.trans2:exp4 0.00311722510712318 0.142243260496459 0.0219147472874525 0.982522075151443 df.mm.trans1:exp5 0.0763271577173575 0.178985511046892 0.426443220297093 0.669913271154719 df.mm.trans2:exp5 0.0291109280183181 0.142243260496459 0.204655938824201 0.837899190492928 df.mm.trans1:exp6 0.0724796543726079 0.178985511046892 0.404947048220116 0.685637556151275 df.mm.trans2:exp6 -0.00638833718039597 0.142243260496459 -0.0449113522714491 0.964190507986474 df.mm.trans1:exp7 -0.0635100697976799 0.178985511046892 -0.354833580808901 0.722818904331422 df.mm.trans2:exp7 0.207719112577802 0.142243260496459 1.46030899357073 0.144644420180336 df.mm.trans1:exp8 -0.69976828032729 0.178985511046892 -3.90963646294229 0.000101230771258577 *** df.mm.trans2:exp8 2.49094495265989 0.142243260496459 17.5118662491704 2.09460389202499e-57 *** df.mm.trans1:probe2 0.116153722689335 0.113743307196094 1.02119171274918 0.307509009593388 df.mm.trans1:probe3 -0.440225548182866 0.113743307196094 -3.87034243187527 0.000118623153080905 *** df.mm.trans1:probe4 -0.266327632435420 0.113743307196094 -2.34147959120153 0.0194811784292099 * df.mm.trans1:probe5 -0.270199951799906 0.113743307196094 -2.37552396233811 0.0177867177025741 * df.mm.trans1:probe6 0.0361891135329888 0.113743307196094 0.318164773164179 0.750452857511628 df.mm.trans1:probe7 -0.315016033427905 0.113743307196094 -2.76953467587166 0.00575920309290328 ** df.mm.trans1:probe8 -0.427104511349991 0.113743307196094 -3.75498587018980 0.000187443399260919 *** df.mm.trans1:probe9 -0.551200647426501 0.113743307196094 -4.84600510583211 1.54573118050939e-06 *** df.mm.trans1:probe10 -0.511538161961261 0.113743307196094 -4.49730339807481 8.02861753541036e-06 *** df.mm.trans1:probe11 -0.531053142107489 0.113743307196094 -4.66887375792539 3.61715701118923e-06 *** df.mm.trans1:probe12 -0.366704655720578 0.113743307196094 -3.22396688438448 0.00132172754326252 ** df.mm.trans1:probe13 -0.486681279536871 0.113743307196094 -4.27876849666267 2.13514095269094e-05 *** df.mm.trans1:probe14 0.178495019616273 0.113743307196094 1.56927931863760 0.117025285682303 df.mm.trans1:probe15 0.0367578450267477 0.113743307196094 0.323164904668870 0.746664846429877 df.mm.trans1:probe16 0.180509933862728 0.113743307196094 1.58699389276178 0.112956137275306 df.mm.trans1:probe17 -0.103213538806106 0.113743307196094 -0.907425160657284 0.364487731081941 df.mm.trans1:probe18 -0.162568002946245 0.113743307196094 -1.42925335084532 0.153368176257203 df.mm.trans1:probe19 0.347337643588110 0.113743307196094 3.0536974187791 0.00234428877756511 ** df.mm.trans2:probe2 -0.00472501534927161 0.113743307196094 -0.0415410406620731 0.966876172456966 df.mm.trans2:probe3 0.156352848565291 0.113743307196094 1.37461141599951 0.169682595460000 df.mm.trans2:probe4 0.050474903630901 0.113743307196094 0.443761526503551 0.657349264770206 df.mm.trans2:probe5 -0.404930605208144 0.113743307196094 -3.5600389613258 0.000395339770483737 *** df.mm.trans2:probe6 0.0081210828336148 0.113743307196094 0.0713983357246155 0.943100699949296 df.mm.trans3:probe2 0.0847228131617885 0.113743307196094 0.744859765820999 0.45660125418923 df.mm.trans3:probe3 0.148420676064142 0.113743307196094 1.30487392817112 0.192355582622351 df.mm.trans3:probe4 0.00579365188305454 0.113743307196094 0.0509362003433421 0.959390585349517 df.mm.trans3:probe5 -0.694106746055207 0.113743307196094 -6.10239637975853 1.71205611063963e-09 *** df.mm.trans3:probe6 -0.191835994283224 0.113743307196094 -1.68656951351430 0.0921223815679833 . df.mm.trans3:probe7 0.242422131080751 0.113743307196094 2.13130897154955 0.0334041379271602 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68150016907266 0.408355888671409 9.01542079153178 1.77344286865837e-18 *** df.mm.trans1 0.770481301458826 0.354299833203785 2.17465894491591 0.0299828165594522 * df.mm.trans2 0.486645722234276 0.318558908951448 1.52764750430648 0.127042317452383 df.mm.exp2 0.122475795727581 0.417815007897559 0.293134026812196 0.76950476413215 df.mm.exp3 0.307258687246967 0.417815007897559 0.735394089343727 0.462340670853355 df.mm.exp4 -0.0117345660189834 0.417815007897559 -0.0280855541260512 0.977601752707615 df.mm.exp5 0.357038457142242 0.417815007897559 0.854537176485968 0.393093644246102 df.mm.exp6 0.292690326829521 0.417815007897559 0.700526121123165 0.483826686413919 df.mm.exp7 0.175347592335184 0.417815007897559 0.419677582233179 0.674847073750433 df.mm.exp8 0.204502144305074 0.417815007897559 0.489456195779388 0.62466888108915 df.mm.trans1:exp2 0.0171565960005791 0.386263428512657 0.0444168273104243 0.964584550095196 df.mm.trans2:exp2 -0.143015054647015 0.306971045649537 -0.465891023514616 0.641435394299667 df.mm.trans1:exp3 -0.245386250054642 0.386263428512658 -0.63528212080425 0.525447684002596 df.mm.trans2:exp3 -0.33285870086647 0.306971045649537 -1.08433256355548 0.2785828616814 df.mm.trans1:exp4 -0.159112723582159 0.386263428512658 -0.411928005182465 0.680515654478865 df.mm.trans2:exp4 0.0212718806552838 0.306971045649537 0.0692960491119724 0.944773345630942 df.mm.trans1:exp5 -0.489654870944496 0.386263428512657 -1.26767080391213 0.205328206938052 df.mm.trans2:exp5 -0.235546914456681 0.306971045649537 -0.76732616249938 0.443140921418027 df.mm.trans1:exp6 -0.268864917889874 0.386263428512658 -0.696066202604692 0.486613565963876 df.mm.trans2:exp6 -0.365862906906884 0.306971045649537 -1.19184826090922 0.233716186877665 df.mm.trans1:exp7 -0.335296009519902 0.386263428512658 -0.868050104590511 0.385658155319443 df.mm.trans2:exp7 -0.145424533088681 0.306971045649537 -0.473740227782622 0.635829780856777 df.mm.trans1:exp8 -0.202316979971081 0.386263428512657 -0.523779796472374 0.600594015305361 df.mm.trans2:exp8 -0.218525305535729 0.306971045649537 -0.711875952578327 0.476773795811053 df.mm.trans1:probe2 -0.113306330958833 0.245466124888853 -0.461596609349407 0.644511025588842 df.mm.trans1:probe3 -0.0689921544021667 0.245466124888853 -0.281065888148135 0.77874117873562 df.mm.trans1:probe4 -0.0525788400431469 0.245466124888853 -0.214199984079084 0.830452179393488 df.mm.trans1:probe5 -0.204849999460288 0.245466124888853 -0.834534702305842 0.404258477864843 df.mm.trans1:probe6 0.0170431257182002 0.245466124888853 0.0694316811572973 0.944665425045306 df.mm.trans1:probe7 0.525806264413541 0.245466124888853 2.14207261654383 0.0325248113546253 * df.mm.trans1:probe8 0.00333797718733202 0.245466124888853 0.013598524801919 0.989154074810403 df.mm.trans1:probe9 0.226142048278996 0.245466124888853 0.921275994320571 0.35721708948015 df.mm.trans1:probe10 0.141968190132896 0.245466124888853 0.578361638279738 0.563202106777211 df.mm.trans1:probe11 -0.200193199025943 0.245466124888853 -0.815563447366066 0.415021456134501 df.mm.trans1:probe12 -0.232025401567546 0.245466124888853 -0.945244080716254 0.344853592254927 df.mm.trans1:probe13 0.106403621403952 0.245466124888853 0.433475785924152 0.664799899453382 df.mm.trans1:probe14 -0.0151504535982321 0.245466124888853 -0.0617211584901672 0.950802138734955 df.mm.trans1:probe15 0.18900797104803 0.245466124888853 0.769996149707469 0.441556496427037 df.mm.trans1:probe16 0.0784491356985399 0.245466124888853 0.319592512954940 0.749370607447366 df.mm.trans1:probe17 0.0957635060777055 0.245466124888853 0.390129212823427 0.696557193790227 df.mm.trans1:probe18 -0.111082569913582 0.245466124888853 -0.452537269506657 0.651019249782841 df.mm.trans1:probe19 0.168371417328563 0.245466124888853 0.685925267304402 0.49298258420156 df.mm.trans2:probe2 0.276163150020655 0.245466124888853 1.12505605466213 0.260942805581530 df.mm.trans2:probe3 0.147975424392484 0.245466124888853 0.602834401119448 0.54681001191726 df.mm.trans2:probe4 0.102211281104201 0.245466124888853 0.416396686713828 0.677244716335119 df.mm.trans2:probe5 0.0493868508296738 0.245466124888853 0.201196197039555 0.840602386498148 df.mm.trans2:probe6 0.116729277822331 0.245466124888853 0.475541290575982 0.634546456167015 df.mm.trans3:probe2 -0.589991577830812 0.245466124888853 -2.40355600227102 0.0164900843230748 * df.mm.trans3:probe3 -0.424769499119222 0.245466124888853 -1.73046076851361 0.0839794538035643 . df.mm.trans3:probe4 -0.299715954951256 0.245466124888853 -1.22100740005150 0.222485696010570 df.mm.trans3:probe5 -0.144300754687891 0.245466124888853 -0.587864230770051 0.556809043652144 df.mm.trans3:probe6 -0.445396257596898 0.245466124888853 -1.81449174625856 0.0700210043373728 . df.mm.trans3:probe7 -0.442986842323632 0.245466124888853 -1.8046760730191 0.0715460200784197 .