fitVsDatCorrelation=0.7869736458444 cont.fitVsDatCorrelation=0.218504713420831 fstatistic=11418.7229219925,65,991 cont.fstatistic=4555.5822671463,65,991 residuals=-0.549340654957061,-0.0867259872410653,-0.00456051546578999,0.0741195525242577,1.20271737640548 cont.residuals=-0.578554696589558,-0.164638366903073,-0.0286150387054623,0.123635924448420,1.56939332725108 predictedValues: Include Exclude Both Lung 65.460576600417 63.7157853745578 54.9406022829643 cerebhem 65.0202164032387 95.109414639921 54.1799946714059 cortex 57.9148416237352 56.7883034301454 52.6879123506361 heart 61.1293609075261 53.0586962821564 54.9920023846283 kidney 62.5456637962593 63.0889261716717 55.7930128252762 liver 60.8116857819427 60.623667355045 56.1154329398424 stomach 67.8241987314401 63.8507349169002 54.9834014268298 testicle 60.5480529528056 63.7251774805271 53.5774234784472 diffExp=1.74479122585918,-30.0891982366823,1.12653819358983,8.0706646253697,-0.543262375412354,0.188018426897642,3.97346381453992,-3.17712452772159 diffExpScore=2.48212682623217 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,-1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 62.2279763950863 60.9645212520859 59.681753424943 cerebhem 63.3305541620019 69.9740347319381 64.0894374114067 cortex 62.1731794379829 59.7977651721842 64.2753361003476 heart 59.7091045146843 62.3570564796316 59.5423264689898 kidney 58.6037048099585 67.7773077929464 62.0572189061309 liver 59.7813689095219 63.8778666208467 62.6866350412303 stomach 60.9049636872167 59.5265416638858 62.4019601816405 testicle 61.681805321097 61.7366465006195 61.2717062292713 cont.diffExp=1.26345514300038,-6.64348056993621,2.37541426579867,-2.64795196494730,-9.17360298298789,-4.09649771132483,1.37842202333093,-0.0548411795225263 cont.diffExpScore=1.48575421033564 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=0.449595134400596 cont.tran.correlation=-0.0552834855083942 tran.covariance=0.00430388854329143 cont.tran.covariance=-0.000121212877246331 tran.mean=63.8259564030181 cont.tran.mean=62.1515248407305 weightedLogRatios: wLogRatio Lung 0.112599978663705 cerebhem -1.66008715633852 cortex 0.079538727338362 heart 0.572348660036433 kidney -0.0358060683847955 liver 0.0127153780495226 stomach 0.252757092333484 testicle -0.211167495982821 cont.weightedLogRatios: wLogRatio Lung 0.0845231760951356 cerebhem -0.418801662276265 cortex 0.160124147990933 heart -0.178393699637638 kidney -0.602589097647815 liver -0.273323025517829 stomach 0.093809984899996 testicle -0.00366361948209226 varWeightedLogRatios=0.44517655556176 cont.varWeightedLogRatios=0.0749522999385663 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.38371379469293 0.0703623317876355 62.3019971527332 0 *** df.mm.trans1 -0.248149450437213 0.0602083156478859 -4.12151457430658 4.07860594778127e-05 *** df.mm.trans2 -0.249715470043571 0.0526463880198851 -4.74325930867756 2.41145678896079e-06 *** df.mm.exp2 0.407786692471978 0.0664784792605306 6.13411583730508 1.2357981118758e-09 *** df.mm.exp3 -0.195709773598748 0.0664784792605306 -2.94395683799801 0.00331607134873925 ** df.mm.exp4 -0.252424470620723 0.0664784792605305 -3.79708551441838 0.000155305166085052 *** df.mm.exp5 -0.0708342492581125 0.0664784792605305 -1.06552150479423 0.286899528370015 df.mm.exp6 -0.144571307819111 0.0664784792605305 -2.17470840830358 0.0298878986503862 * df.mm.exp7 0.0368080154676645 0.0664784792605305 0.5536831750229 0.57992059045346 df.mm.exp8 -0.052738501031696 0.0664784792605305 -0.793316899218058 0.427783108733123 df.mm.trans1:exp2 -0.414536526818482 0.0607281093115143 -6.82610625488195 1.51816742124825e-11 *** df.mm.trans2:exp2 -0.00719107051193239 0.0416806518971779 -0.172527784106450 0.863057847992731 df.mm.trans1:exp3 0.0732353798731698 0.0607281093115143 1.20595521091390 0.228122599163278 df.mm.trans2:exp3 0.0806078126380729 0.0416806518971779 1.93393838553496 0.0534051664292965 . df.mm.trans1:exp4 0.183968682488055 0.0607281093115142 3.02938267918666 0.00251407310280007 ** df.mm.trans2:exp4 0.0693909084377866 0.0416806518971779 1.66482301210085 0.0962641668917465 . df.mm.trans1:exp5 0.0252830824939752 0.0607281093115142 0.416332449348649 0.677256900988136 df.mm.trans2:exp5 0.0609471669603923 0.0416806518971779 1.4622412123194 0.143992159229768 df.mm.trans1:exp6 0.070905201210767 0.0607281093115142 1.16758453399311 0.243255232779228 df.mm.trans2:exp6 0.0948243351061038 0.0416806518971779 2.27502044209928 0.0231176029190296 * df.mm.trans1:exp7 -0.00133704822993765 0.0607281093115142 -0.0220169579638822 0.982438860464365 df.mm.trans2:exp7 -0.0346922629587016 0.0416806518971779 -0.83233494150437 0.405420440903135 df.mm.trans1:exp8 -0.0252722629824898 0.0607281093115142 -0.416154286194747 0.677387214997464 df.mm.trans2:exp8 0.0528858964338631 0.0416806518971779 1.26883563540051 0.204797634242908 df.mm.trans1:probe2 -0.00286290947701204 0.0448506811170611 -0.0638320178358004 0.949116856434438 df.mm.trans1:probe3 0.186772861619019 0.0448506811170611 4.16432609198372 3.39466223535145e-05 *** df.mm.trans1:probe4 0.00853547995840486 0.0448506811170611 0.190308814622616 0.849106122641122 df.mm.trans1:probe5 -0.115799120913725 0.0448506811170611 -2.58188098886362 0.00996893187447213 ** df.mm.trans1:probe6 0.118784548479819 0.0448506811170611 2.64844469518286 0.00821484125911314 ** df.mm.trans1:probe7 0.0508186675345578 0.0448506811170611 1.13306345118640 0.2574616568441 df.mm.trans1:probe8 0.223533892034459 0.0448506811170611 4.98395757805843 7.34936769822164e-07 *** df.mm.trans1:probe9 -0.222220763756659 0.0448506811170611 -4.95467980021661 8.51572873677526e-07 *** df.mm.trans1:probe10 -0.0442006318939795 0.0448506811170611 -0.985506368980552 0.324615948633966 df.mm.trans1:probe11 -0.116264916251067 0.0448506811170611 -2.59226645739477 0.00967493951052498 ** df.mm.trans1:probe12 -0.0772094340171031 0.0448506811170611 -1.72147740221793 0.0854763098106048 . df.mm.trans1:probe13 -0.0123445609309340 0.0448506811170611 -0.275236866497399 0.783191639387281 df.mm.trans1:probe14 -0.139112290770648 0.0448506811170611 -3.10167621329011 0.00197871821252057 ** df.mm.trans1:probe15 0.0880480162684385 0.0448506811170611 1.96313665869715 0.0499096501937292 * df.mm.trans1:probe16 -0.0598152015638366 0.0448506811170611 -1.33365202208898 0.182624363182028 df.mm.trans1:probe17 0.141613249574536 0.0448506811170611 3.15743810456128 0.00163972428918250 ** df.mm.trans1:probe18 0.309842021234507 0.0448506811170611 6.90830135724837 8.76103516972215e-12 *** df.mm.trans1:probe19 0.185542734404123 0.0448506811170611 4.13689892289155 3.8189933562993e-05 *** df.mm.trans1:probe20 0.417316970523453 0.0448506811170611 9.30458490550562 8.37812162681115e-20 *** df.mm.trans1:probe21 0.332581843740975 0.0448506811170611 7.41531311136458 2.60088545270761e-13 *** df.mm.trans1:probe22 0.470021406468445 0.0448506811170611 10.4796938365703 1.92188830683557e-24 *** df.mm.trans2:probe2 0.288571020830065 0.0448506811170611 6.43403876246359 1.93107162991791e-10 *** df.mm.trans2:probe3 0.0103986351833309 0.0448506811170611 0.231850106271301 0.816702303617393 df.mm.trans2:probe4 0.0607759936235372 0.0448506811170611 1.35507403923055 0.175702762386224 df.mm.trans2:probe5 0.126092058907157 0.0448506811170611 2.81137444887525 0.00503032074784382 ** df.mm.trans2:probe6 -0.0362893717848335 0.0448506811170611 -0.809115288352424 0.418643046760617 df.mm.trans3:probe2 -0.0565231933883096 0.0448506811170611 -1.26025273152002 0.207874873201167 df.mm.trans3:probe3 0.0726034534396227 0.0448506811170611 1.61878151304160 0.105812467710268 df.mm.trans3:probe4 0.0688736895968213 0.0448506811170611 1.53562193218559 0.124950321052032 df.mm.trans3:probe5 -0.00450736483955892 0.0448506811170611 -0.100497132424692 0.919969986433049 df.mm.trans3:probe6 0.373908063349228 0.0448506811170611 8.33673099352317 2.53083478494106e-16 *** df.mm.trans3:probe7 -0.0114961545619038 0.0448506811170611 -0.256320623802761 0.79775647225145 df.mm.trans3:probe8 0.189263091295936 0.0448506811170611 4.21984876443584 2.6689550862441e-05 *** df.mm.trans3:probe9 0.237617527792705 0.0448506811170611 5.2979692141691 1.44293790565832e-07 *** df.mm.trans3:probe10 0.0792592622871448 0.0448506811170611 1.76718079442933 0.077505690468942 . df.mm.trans3:probe11 0.126589579557343 0.0448506811170611 2.82246727149899 0.00486090838188475 ** df.mm.trans3:probe12 -0.0271443766346614 0.0448506811170611 -0.605216597799576 0.545173651422551 df.mm.trans3:probe13 0.224542447088977 0.0448506811170611 5.00644452874455 6.55979215536328e-07 *** df.mm.trans3:probe14 0.0733155802281087 0.0448506811170611 1.63465923821209 0.102438088778566 df.mm.trans3:probe15 0.352677883534639 0.0448506811170611 7.86337854299567 9.72842305815013e-15 *** df.mm.trans3:probe16 0.227480814508274 0.0448506811170611 5.07195897236309 4.69855564779135e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19368418094621 0.111286189069255 37.6837792364012 1.54247405456435e-193 *** df.mm.trans1 -0.0553239306063342 0.0952264347769874 -0.580972402630618 0.56139115207041 df.mm.trans2 -0.0713338718110346 0.0832663691231428 -0.85669487648175 0.391820643046125 df.mm.exp2 0.0841421542020485 0.105143425808466 0.800260725338408 0.423751547118928 df.mm.exp3 -0.0943544058117085 0.105143425808466 -0.897387593053971 0.369730085287467 df.mm.exp4 -0.0163964947895053 0.105143425808466 -0.155944079845504 0.876108859135553 df.mm.exp5 0.00689809703548165 0.105143425808466 0.0656065463193819 0.947704309249075 df.mm.exp6 -0.0425517059795093 0.105143425808466 -0.404701536518538 0.685784247062872 df.mm.exp7 -0.089930139780692 0.105143425808466 -0.855309203492308 0.392586728438407 df.mm.exp8 -0.0225218951011330 0.105143425808466 -0.21420164815782 0.830433875699765 df.mm.trans1:exp2 -0.066578933220444 0.0960485487470322 -0.693180002082032 0.488359016231484 df.mm.trans2:exp2 0.0536900104847756 0.0659227855261932 0.814437831414797 0.415589836071347 df.mm.trans1:exp3 0.0934734339223318 0.0960485487470322 0.973189445772027 0.330696633864539 df.mm.trans2:exp3 0.0750306182181394 0.0659227855261932 1.13815909960794 0.255329133213878 df.mm.trans1:exp4 -0.0249236719013255 0.0960485487470322 -0.259490353851865 0.795310818796214 df.mm.trans2:exp4 0.0389812596573132 0.0659227855261932 0.591316937628867 0.554442932266143 df.mm.trans1:exp5 -0.0669048603333401 0.0960485487470322 -0.696573360098868 0.486233181453353 df.mm.trans2:exp5 0.0990372724199739 0.0659227855261932 1.50232232496643 0.133332453314610 df.mm.trans1:exp6 0.00244108179992407 0.0960485487470322 0.0254150825990434 0.979728997440246 df.mm.trans2:exp6 0.0892325559514068 0.0659227855261932 1.35359201282464 0.176175214409276 df.mm.trans1:exp7 0.068440136817026 0.0960485487470322 0.712557739912137 0.476287201443451 df.mm.trans2:exp7 0.066060355104316 0.0659227855261932 1.00208682896247 0.316546273999745 df.mm.trans1:exp8 0.013706213181315 0.0960485487470322 0.142700887833441 0.886555423019054 df.mm.trans2:exp8 0.0351075200722758 0.0659227855261932 0.532555167261955 0.59446092869288 df.mm.trans1:probe2 -0.0149329098730901 0.07093655443004 -0.210510786618731 0.83331230834145 df.mm.trans1:probe3 -0.0292505241515459 0.0709365544300399 -0.412347687120803 0.680173783607424 df.mm.trans1:probe4 0.0557480862103241 0.0709365544300399 0.785886580737507 0.432121798193112 df.mm.trans1:probe5 -0.0630479678298607 0.0709365544300399 -0.88879377263863 0.374329604609882 df.mm.trans1:probe6 -0.0415691911176632 0.0709365544300399 -0.586005219053318 0.558005443110286 df.mm.trans1:probe7 -0.0262072217427116 0.0709365544300399 -0.369445935925152 0.711874290567564 df.mm.trans1:probe8 -0.0189720919831712 0.0709365544300399 -0.267451557742097 0.78917715736088 df.mm.trans1:probe9 -0.0502379185066914 0.0709365544300399 -0.70820917241248 0.478981909348411 df.mm.trans1:probe10 -0.0535631369812411 0.0709365544300399 -0.755085123764602 0.450377319503752 df.mm.trans1:probe11 -0.0320336340015729 0.0709365544300399 -0.451581476700643 0.651669325459699 df.mm.trans1:probe12 -0.00952490248093358 0.0709365544300399 -0.134273542850568 0.893213537327665 df.mm.trans1:probe13 0.0433019470381954 0.0709365544300399 0.610432059833147 0.541715596762957 df.mm.trans1:probe14 0.0875890347932605 0.0709365544300399 1.23475175101215 0.217215592377106 df.mm.trans1:probe15 0.0128968169287458 0.0709365544300399 0.181807772203893 0.855770764737967 df.mm.trans1:probe16 -0.0437662567475216 0.0709365544300399 -0.616977482190588 0.537391297071714 df.mm.trans1:probe17 0.0199116803625136 0.0709365544300399 0.280697033038885 0.77900137331953 df.mm.trans1:probe18 -0.00284989353922626 0.0709365544300399 -0.0401752462059166 0.9679615044356 df.mm.trans1:probe19 -0.0516986187030843 0.0709365544300399 -0.728800815298567 0.466295703451689 df.mm.trans1:probe20 0.0092180692748139 0.0709365544300399 0.129948083169236 0.89663387290172 df.mm.trans1:probe21 -0.0351794481973191 0.0709365544300399 -0.495928347239001 0.620054929301283 df.mm.trans1:probe22 -0.0429435956446855 0.0709365544300399 -0.605380342895537 0.545064915391003 df.mm.trans2:probe2 -0.0710284302699069 0.0709365544300399 -1.00129518328886 0.316928543427548 df.mm.trans2:probe3 -0.088178898145736 0.0709365544300399 -1.24306711616084 0.214137116740053 df.mm.trans2:probe4 -0.0305778553295238 0.0709365544300399 -0.431059213056094 0.666519061068662 df.mm.trans2:probe5 -0.0718236910630104 0.0709365544300399 -1.01250605756226 0.311543291365806 df.mm.trans2:probe6 -0.003672250883155 0.0709365544300399 -0.0517681033799957 0.958723903911135 df.mm.trans3:probe2 0.041025394149628 0.0709365544300399 0.578339256526601 0.563166495862765 df.mm.trans3:probe3 -0.0306585653351523 0.0709365544300399 -0.432196990416117 0.665692270314968 df.mm.trans3:probe4 0.0332609712232704 0.0709365544300399 0.468883377414017 0.639256125435726 df.mm.trans3:probe5 -0.00312013465164726 0.0709365544300399 -0.0439848633291662 0.964925330325752 df.mm.trans3:probe6 -0.0161583036042160 0.0709365544300399 -0.227785289742990 0.81986015009593 df.mm.trans3:probe7 0.057932775713908 0.0709365544300399 0.816684376332986 0.41430509670056 df.mm.trans3:probe8 0.0637594683761225 0.0709365544300399 0.898823869983766 0.368964817187769 df.mm.trans3:probe9 0.0551328778593491 0.0709365544300399 0.777213924503805 0.437218052764285 df.mm.trans3:probe10 0.133233739582308 0.0709365544300399 1.87820991099458 0.0606458799812155 . df.mm.trans3:probe11 -0.0146909675005264 0.0709365544300399 -0.207100099780222 0.835974229836446 df.mm.trans3:probe12 -0.070369404634828 0.0709365544300399 -0.992004830235006 0.321437316739226 df.mm.trans3:probe13 -0.00198151631542376 0.0709365544300399 -0.0279336419895895 0.977720700518723 df.mm.trans3:probe14 0.0988503250844088 0.0709365544300399 1.39350333376988 0.163780073596247 df.mm.trans3:probe15 0.00908906097861443 0.0709365544300399 0.128129439773937 0.898072534513411 df.mm.trans3:probe16 -0.0033064028487083 0.0709365544300399 -0.0466107055138855 0.962832890453727