fitVsDatCorrelation=0.806992028747653 cont.fitVsDatCorrelation=0.201122831608795 fstatistic=14380.3729883000,63,945 cont.fstatistic=5217.23176237561,63,945 residuals=-0.48150962361967,-0.0808340927994169,-0.00373019405352143,0.0742003674631739,1.04803861084175 cont.residuals=-0.560040489188718,-0.151859928786191,-0.0282822248901050,0.119468621751644,0.887870627034153 predictedValues: Include Exclude Both Lung 54.7383974222672 76.0980306105145 66.8705091316855 cerebhem 53.4115452484045 65.1975068332145 64.5178565441692 cortex 54.5762294772237 72.2570825754008 64.3722238411672 heart 55.1908748595445 76.4152541031861 66.6721590494581 kidney 55.8642853815155 82.9418352041166 70.6455945052744 liver 54.879210639673 94.4865878677646 65.1130423595695 stomach 55.4408072199006 77.284166991604 70.5543052423671 testicle 53.5443529423866 78.7597560866457 62.5825455746914 diffExp=-21.3596331882474,-11.78596158481,-17.6808530981771,-21.2243792436416,-27.0775498226010,-39.6073772280916,-21.8433597717034,-25.2154031442591 diffExpScore=0.994646523807958 diffExp1.5=0,0,0,0,0,-1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,-1,-1,0,-1 diffExp1.4Score=0.75 diffExp1.3=-1,0,-1,-1,-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 56.7991282912572 57.3957254383148 54.9792804966831 cerebhem 56.0523987713528 57.1386923939229 54.9344995733783 cortex 57.4089959517813 59.653977533194 55.8883477089377 heart 58.295678914726 60.1431615801083 54.9344484789188 kidney 56.6968241478243 59.8268458723755 62.3602791588738 liver 57.7253573044882 62.148199666149 54.3204701207696 stomach 58.2434126918881 61.0019542891928 60.713654783915 testicle 55.7914251641947 55.1087779728147 57.8853850482034 cont.diffExp=-0.596597147057537,-1.08629362257012,-2.24498158141279,-1.84748266538222,-3.13002172455119,-4.42284236166076,-2.75854159730468,0.68264719137995 cont.diffExpScore=1.02226846227133 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=0.464309132202653 cont.tran.correlation=0.847568110008468 tran.covariance=0.00083371131219838 cont.tran.covariance=0.000558124271820997 tran.mean=66.3178702164602 cont.tran.mean=58.089409748974 weightedLogRatios: wLogRatio Lung -1.37294397576586 cerebhem -0.813074953116695 cortex -1.16179212622972 heart -1.35798982026046 kidney -1.66801480040252 liver -2.32368303909053 stomach -1.38894982838323 testicle -1.61050262454726 cont.weightedLogRatios: wLogRatio Lung -0.0422629469704563 cerebhem -0.07746704901252 cortex -0.156100689414783 heart -0.127330266912438 kidney -0.218415791207069 liver -0.302138122190908 stomach -0.189161084314880 testicle 0.0494350808287239 varWeightedLogRatios=0.191758082672833 cont.varWeightedLogRatios=0.0120318654226259 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42215840885087 0.0683172917971579 64.7297088704977 0 *** df.mm.trans1 -0.290855766896678 0.0611566930843803 -4.75591063263301 2.28347113145669e-06 *** df.mm.trans2 0.0254831892650036 0.0549459241586037 0.463786707662707 0.642907374947452 df.mm.exp2 -0.143323638719537 0.0738358389134897 -1.94111207820721 0.0525417085683805 . df.mm.exp3 -0.0166832147328725 0.0738358389134897 -0.225950093861864 0.821289094311671 df.mm.exp4 0.0153627433684875 0.0738358389134897 0.208066212757294 0.835222100887075 df.mm.exp5 0.0515593421394326 0.0738358389134897 0.698296963888261 0.485163291576876 df.mm.exp6 0.245637861629877 0.0738358389134897 3.32681073641867 0.000912326346186454 *** df.mm.exp7 -0.0254074390413851 0.0738358389134897 -0.344107135711614 0.73084227062621 df.mm.exp8 0.0785963169297594 0.0738358389134897 1.06447381226138 0.287386021807383 df.mm.trans1:exp2 0.118785137460233 0.0712136066699224 1.66801181705073 0.095644668642034 . df.mm.trans2:exp2 -0.0112775173978572 0.0585079481616253 -0.192751886747142 0.8471947010954 df.mm.trans1:exp3 0.0137162181175498 0.0712136066699224 0.192606704799055 0.847308376826723 df.mm.trans2:exp3 -0.0351088199183184 0.0585079481616253 -0.600069238820887 0.548603954125736 df.mm.trans1:exp4 -0.00713054127678623 0.0712136066699224 -0.100128916512213 0.920263216221168 df.mm.trans2:exp4 -0.0112027916536592 0.0585079481616253 -0.191474697125116 0.84819483426677 df.mm.trans1:exp5 -0.0311994951144473 0.0712136066699224 -0.438111430854191 0.66140563310118 df.mm.trans2:exp5 0.0345578537578159 0.0585079481616253 0.590652293297853 0.55489464559985 df.mm.trans1:exp6 -0.243068688714133 0.0712136066699225 -3.41323379169328 0.000669229091478683 *** df.mm.trans2:exp6 -0.0292023500925606 0.0585079481616253 -0.499117658542573 0.617812666591858 df.mm.trans1:exp7 0.0381579270499909 0.0712136066699224 0.535823543200869 0.592206595264492 df.mm.trans2:exp7 0.0408741626262863 0.0585079481616253 0.6986087174579 0.484968477854163 df.mm.trans1:exp8 -0.100651406530906 0.0712136066699224 -1.41337324757933 0.157875309827567 df.mm.trans2:exp8 -0.0442165456473093 0.0585079481616253 -0.755735708337665 0.449995999989578 df.mm.trans1:probe2 -0.240879826673248 0.0390052987744169 -6.1755667625148 9.77915750806737e-10 *** df.mm.trans1:probe3 -0.23071672136234 0.0390052987744169 -5.91500971949136 4.63462526843971e-09 *** df.mm.trans1:probe4 -0.293263086153406 0.0390052987744169 -7.51854479693804 1.28547375386397e-13 *** df.mm.trans1:probe5 -0.190114572205115 0.0390052987744169 -4.87407040014289 1.28170848923380e-06 *** df.mm.trans1:probe6 -0.135101262286704 0.0390052987744169 -3.46366433617258 0.000556790239600218 *** df.mm.trans1:probe7 -0.0756734096582972 0.0390052987744169 -1.94008024642874 0.0526671490967173 . df.mm.trans1:probe8 -0.236399880717939 0.0390052987744169 -6.06071195826838 1.95572195790149e-09 *** df.mm.trans1:probe9 -0.244411842985171 0.0390052987744169 -6.26611898036474 5.61695235268728e-10 *** df.mm.trans1:probe10 -0.172557150708108 0.0390052987744169 -4.42394126259807 1.08187464061675e-05 *** df.mm.trans1:probe11 -0.137656544995742 0.0390052987744169 -3.52917550489395 0.00043694778075096 *** df.mm.trans1:probe12 -0.315944278594305 0.0390052987744169 -8.10003482915323 1.68737132372235e-15 *** df.mm.trans1:probe13 -0.192416424438971 0.0390052987744169 -4.93308423431883 9.56090793118275e-07 *** df.mm.trans1:probe14 -0.141108504390986 0.0390052987744169 -3.61767526015048 0.000313007104987552 *** df.mm.trans1:probe15 -0.150186111743251 0.0390052987744169 -3.85040280326622 0.000125863560696354 *** df.mm.trans1:probe16 -0.007695820627999 0.0390052987744169 -0.197301927425476 0.843633699148258 df.mm.trans1:probe17 -0.0780999988238706 0.0390052987744169 -2.00229202897673 0.0455386846709159 * df.mm.trans1:probe18 0.0390867706332432 0.0390052987744169 1.00208873823266 0.316557256490887 df.mm.trans1:probe19 0.093919646759661 0.0390052987744169 2.40786892321568 0.0162366446031813 * df.mm.trans1:probe20 -0.0668986293619846 0.0390052987744169 -1.71511644479090 0.0866517047438781 . df.mm.trans1:probe21 0.118527946776579 0.0390052987744169 3.03876525756343 0.00244084717288974 ** df.mm.trans1:probe22 -0.0501304372338888 0.0390052987744169 -1.2852212086315 0.199029886501452 df.mm.trans1:probe23 -0.0341815952424226 0.0390052987744169 -0.876332096316153 0.381072223614127 df.mm.trans1:probe24 -0.191564291337912 0.0390052987744169 -4.91123763583518 1.06605231782272e-06 *** df.mm.trans1:probe25 -0.121580032459410 0.0390052987744169 -3.11701323357516 0.00188215248307290 ** df.mm.trans1:probe26 -0.179299601703519 0.0390052987744169 -4.59680113567339 4.87249207723314e-06 *** df.mm.trans1:probe27 -0.150234498932299 0.0390052987744169 -3.85164333187561 0.000125237403478193 *** df.mm.trans1:probe28 -0.262788173388985 0.0390052987744169 -6.73724292970534 2.80183904565001e-11 *** df.mm.trans1:probe29 -0.282288976268013 0.0390052987744169 -7.23719558977365 9.4741915941948e-13 *** df.mm.trans1:probe30 -0.202215831051215 0.0390052987744169 -5.18431693654521 2.65174761492365e-07 *** df.mm.trans1:probe31 -0.164807352039470 0.0390052987744169 -4.22525547086863 2.61786371016477e-05 *** df.mm.trans1:probe32 -0.337859246755567 0.0390052987744169 -8.661880753934 1.98374850493055e-17 *** df.mm.trans2:probe2 -0.331664815225645 0.0390052987744169 -8.50307075312495 7.14278842061627e-17 *** df.mm.trans2:probe3 -0.193028038177545 0.0390052987744169 -4.94876450745585 8.8399682463515e-07 *** df.mm.trans2:probe4 -0.264470471498834 0.0390052987744169 -6.78037291877629 2.10934185235598e-11 *** df.mm.trans2:probe5 -0.337236909870295 0.0390052987744169 -8.64592556566917 2.25824123003480e-17 *** df.mm.trans2:probe6 -0.0297918907413796 0.0390052987744169 -0.763790861177039 0.445182532995716 df.mm.trans3:probe2 -0.311823819371855 0.0390052987744169 -7.99439638125209 3.7835247670006e-15 *** df.mm.trans3:probe3 0.132435983934849 0.0390052987744169 3.39533314949794 0.000713986202822918 *** df.mm.trans3:probe4 0.363276709721187 0.0390052987744169 9.31352203766367 8.43648557323385e-20 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10844735401951 0.11331792580679 36.2559350144171 4.49525668636871e-181 *** df.mm.trans1 -0.0980326063799436 0.101440637168418 -0.966403693001099 0.334089466164545 df.mm.trans2 -0.00148688998593405 0.091138831669103 -0.0163145605303839 0.986986885152945 df.mm.exp2 -0.0169075142430346 0.12247154264726 -0.138052594729955 0.890228260874631 df.mm.exp3 0.0328715194352746 0.12247154264726 0.268401285104658 0.788449029117211 df.mm.exp4 0.0735806769965947 0.12247154264726 0.600798156095088 0.548118498599981 df.mm.exp5 -0.0862902486828164 0.12247154264726 -0.704573869305687 0.481249062882099 df.mm.exp6 0.107782843655558 0.12247154264726 0.880064391496983 0.379048173243612 df.mm.exp7 -0.0131661410633968 0.12247154264726 -0.107503676191274 0.91441222793075 df.mm.exp8 -0.110070149025918 0.12247154264726 -0.898740610648952 0.369019776154510 df.mm.trans1:exp2 0.0036734812121368 0.118122044723557 0.0310990316899263 0.975197128599998 df.mm.trans2:exp2 0.0124191956965079 0.0970471626505896 0.127970724308779 0.898199368098472 df.mm.trans1:exp3 -0.0221914831154643 0.118122044723557 -0.18786910747608 0.85101959167839 df.mm.trans2:exp3 0.00571947724234529 0.0970471626505896 0.0589350279403613 0.953016318199735 df.mm.trans1:exp4 -0.0475736831122158 0.118122044723557 -0.402750250586613 0.687223054585848 df.mm.trans2:exp4 -0.0268227613170437 0.0970471626505896 -0.276388928686322 0.782309794485717 df.mm.trans1:exp5 0.0844874677211716 0.118122044723557 0.715255716398229 0.474627717423196 df.mm.trans2:exp5 0.127774905696495 0.0970471626505896 1.31662690805849 0.188282931189061 df.mm.trans1:exp6 -0.091607277288328 0.118122044723557 -0.775530744516976 0.43822013307338 df.mm.trans2:exp6 -0.0282308245151012 0.0970471626505896 -0.290897989637718 0.771193169732448 df.mm.trans1:exp7 0.0382761616554376 0.118122044723557 0.324039105020709 0.745980132898951 df.mm.trans2:exp7 0.0741022114145238 0.0970471626505896 0.763569066736374 0.445314674457263 df.mm.trans1:exp8 0.0921693570876008 0.118122044723557 0.780289211072379 0.435416057332658 df.mm.trans2:exp8 0.069409331511023 0.0970471626505896 0.715212373193492 0.474654482897296 df.mm.trans1:probe2 0.000497357806125434 0.0646981084337264 0.00768736240001364 0.993868055124455 df.mm.trans1:probe3 0.0417513375374646 0.0646981084337264 0.645325474704298 0.518872931754301 df.mm.trans1:probe4 0.0878615761358844 0.0646981084337264 1.35802387833155 0.174780249250175 df.mm.trans1:probe5 0.0629998995329782 0.0646981084337264 0.973751799830627 0.330428973598360 df.mm.trans1:probe6 -0.0117487771433463 0.0646981084337264 -0.181593827513228 0.855940435407338 df.mm.trans1:probe7 0.0659481633628695 0.0646981084337264 1.01932135203649 0.308311312426534 df.mm.trans1:probe8 0.0670641959016699 0.0646981084337264 1.03657120007407 0.300200907638230 df.mm.trans1:probe9 -0.034174118159802 0.0646981084337264 -0.528208922750938 0.597478350278879 df.mm.trans1:probe10 0.0241972666654321 0.0646981084337264 0.374002691133059 0.708486210319652 df.mm.trans1:probe11 0.0686264329316444 0.0646981084337264 1.06071776429047 0.289089200885399 df.mm.trans1:probe12 0.00768711836021754 0.0646981084337264 0.118815194853677 0.905447021569095 df.mm.trans1:probe13 0.0120640283618410 0.0646981084337264 0.186466477210826 0.852118985713883 df.mm.trans1:probe14 0.0545575640524168 0.0646981084337264 0.843263665247693 0.39929438828791 df.mm.trans1:probe15 -0.0250631679416888 0.0646981084337264 -0.387386409718026 0.698557393091252 df.mm.trans1:probe16 0.0196951358567639 0.0646981084337264 0.304415945590413 0.760878043431133 df.mm.trans1:probe17 0.00995866048268532 0.0646981084337264 0.153925064020790 0.877701685649352 df.mm.trans1:probe18 0.0370314706689601 0.0646981084337264 0.572373312998685 0.567205230571213 df.mm.trans1:probe19 0.0438740595207448 0.0646981084337264 0.678135119911385 0.497852034693564 df.mm.trans1:probe20 -0.00833738968037751 0.0646981084337264 -0.128866050062622 0.897491061256446 df.mm.trans1:probe21 -0.000768145576990708 0.0646981084337264 -0.0118727671579079 0.990529630901336 df.mm.trans1:probe22 0.066315644262601 0.0646981084337264 1.02500128470574 0.305624883614959 df.mm.trans1:probe23 0.0520537644625841 0.0646981084337264 0.804563931199094 0.421273723737912 df.mm.trans1:probe24 0.0357084810832181 0.0646981084337264 0.55192465355917 0.581130470473317 df.mm.trans1:probe25 0.060840708283237 0.0646981084337264 0.940378470965024 0.347263769115043 df.mm.trans1:probe26 0.094219862235954 0.0646981084337264 1.45630010701887 0.145641828290327 df.mm.trans1:probe27 0.0164411224760624 0.0646981084337264 0.254120605286380 0.799457666859448 df.mm.trans1:probe28 0.0213744536383806 0.0646981084337264 0.330372157020256 0.741192005008355 df.mm.trans1:probe29 0.0800028943051548 0.0646981084337264 1.23655692943644 0.216558850242921 df.mm.trans1:probe30 0.00687120461323427 0.0646981084337264 0.106204103637323 0.91544295510703 df.mm.trans1:probe31 0.0725381659724407 0.0646981084337264 1.12117908434286 0.262496465485361 df.mm.trans1:probe32 0.0177353454466056 0.0646981084337264 0.274124636345015 0.784048749665722 df.mm.trans2:probe2 -0.172915364907985 0.0646981084337264 -2.67264946524845 0.00765511762369416 ** df.mm.trans2:probe3 -0.0596704708400851 0.0646981084337264 -0.922290810112458 0.356612311022666 df.mm.trans2:probe4 -0.142642163187398 0.0646981084337264 -2.20473467680302 0.0277130099468139 * df.mm.trans2:probe5 -0.113241333268094 0.0646981084337264 -1.75030361798124 0.0803904518690573 . df.mm.trans2:probe6 -0.0814369997648142 0.0646981084337264 -1.25872304053888 0.208441248590538 df.mm.trans3:probe2 -0.0416970578485335 0.0646981084337264 -0.644486505988748 0.519416410919211 df.mm.trans3:probe3 0.0116427396944839 0.0646981084337264 0.179954870031636 0.8572265782003 df.mm.trans3:probe4 0.0221684715963106 0.0646981084337264 0.342644818109619 0.731941879294253