fitVsDatCorrelation=0.78256004976277 cont.fitVsDatCorrelation=0.259396199496270 fstatistic=10674.5455925441,52,692 cont.fstatistic=4428.15151577837,52,692 residuals=-0.595645927524806,-0.0835116224385947,-0.0089253934805169,0.0620767526937597,0.90197588977136 cont.residuals=-0.382754594800692,-0.138781432285120,-0.0525828501939165,0.083562458923835,0.911289714427714 predictedValues: Include Exclude Both Lung 55.7837784232796 44.2861352278234 68.0057311944312 cerebhem 62.3219644534847 51.8458522640021 53.215743983315 cortex 53.4532400221481 42.8491406147748 53.3979387417047 heart 55.4406651105049 44.2456218033852 58.5553993835747 kidney 53.8468921448705 45.0473924254097 63.1527848068927 liver 56.4590910643336 48.6215080777945 60.712156569221 stomach 56.9260181362354 45.5520555479619 54.5402618203074 testicle 57.0077300207019 47.9395330991966 59.9035135738779 diffExp=11.4976431954562,10.4761121894826,10.6040994073733,11.1950433071197,8.79949971946085,7.83758298653908,11.3739625882735,9.06819692150527 diffExpScore=0.987782848485708 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=1,1,1,1,0,0,1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 52.2425291216425 51.498076580701 46.8224793690063 cerebhem 55.7409866018131 49.0225201627479 53.1394872651932 cortex 48.9859836743117 51.1181154178963 46.9913407057717 heart 53.0726997791314 52.911957181994 49.0418495268899 kidney 51.8762475936659 53.636932709152 52.81750075912 liver 49.9678686752117 54.3412552235235 45.7482310197441 stomach 54.9603494974089 51.8178956661498 48.2191309007732 testicle 55.7773546405409 50.4838964854119 50.1622373481389 cont.diffExp=0.744452540941495,6.71846643906527,-2.13213174358462,0.160742597137450,-1.76068511548610,-4.3733865483118,3.14245383125909,5.29345815512901 cont.diffExpScore=2.76637700209881 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=0.887686092864185 cont.tran.correlation=-0.574805626945415 tran.covariance=0.002607155897759 cont.tran.covariance=-0.000924775396516769 tran.mean=51.3516636522442 cont.tran.mean=52.3409168132064 weightedLogRatios: wLogRatio Lung 0.901567441216423 cerebhem 0.743571692180107 cortex 0.8553530864854 heart 0.880243778761358 kidney 0.695327596192517 liver 0.591642675756804 stomach 0.876052005407981 testicle 0.685460348303185 cont.weightedLogRatios: wLogRatio Lung 0.0566737698155465 cerebhem 0.508156485611231 cortex -0.166705360806790 heart 0.0120427255764320 kidney -0.132357531813753 liver -0.331698508046757 stomach 0.234161838713390 testicle 0.396013487566599 varWeightedLogRatios=0.0132328097840668 cont.varWeightedLogRatios=0.0838654536121928 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10425290361292 0.0771790660332877 53.1783178335267 1.27319890858575e-246 *** df.mm.trans1 0.406840939836423 0.0693180878889121 5.86918872442672 6.7928410818379e-09 *** df.mm.trans2 -0.327913842236046 0.063747396073488 -5.14395665444949 3.50694338489412e-07 *** df.mm.exp2 0.513671780079792 0.0873181455324513 5.88276098796543 6.2828671783089e-09 *** df.mm.exp3 0.166157910256876 0.0873181455324513 1.90290241786141 0.0574680402511684 . df.mm.exp4 0.142533684396539 0.0873181455324513 1.63234896397986 0.103060919278044 df.mm.exp5 0.0557400557372744 0.0873181455324514 0.638355927022738 0.523453209791717 df.mm.exp6 0.218875571518297 0.0873181455324514 2.50664475503495 0.0124165306094001 * df.mm.exp7 0.26910628192299 0.0873181455324513 3.08190560257579 0.00213843799780827 ** df.mm.exp8 0.227829414504289 0.0873181455324513 2.60918750753379 0.00927183868576333 ** df.mm.trans1:exp2 -0.402840974837795 0.0836007595317819 -4.81862816909755 1.77749009827681e-06 *** df.mm.trans2:exp2 -0.356068497035472 0.0727651212770428 -4.89339522543764 1.23404540088223e-06 *** df.mm.trans1:exp3 -0.208833774672111 0.0836007595317819 -2.49798896375721 0.0127210176143804 * df.mm.trans2:exp3 -0.199143974601840 0.0727651212770428 -2.73680536920468 0.00636312359713016 ** df.mm.trans1:exp4 -0.148703450507038 0.0836007595317819 -1.77873324763882 0.0757224318918546 . df.mm.trans2:exp4 -0.143448913657195 0.0727651212770428 -1.97139661337241 0.0490765165439818 * df.mm.trans1:exp5 -0.0910784848504378 0.083600759531782 -1.08944566246211 0.276336642279704 df.mm.trans2:exp5 -0.038696608670154 0.0727651212770428 -0.531801610318523 0.595034084262656 df.mm.trans1:exp6 -0.20684236543973 0.0836007595317819 -2.47416849557565 0.0135934175543365 * df.mm.trans2:exp6 -0.125481238991628 0.0727651212770428 -1.72446959187873 0.0850696611784595 . df.mm.trans1:exp7 -0.248836902423179 0.0836007595317819 -2.9764909292311 0.00301740666407653 ** df.mm.trans2:exp7 -0.240922185470473 0.0727651212770428 -3.31095697007356 0.000978050027464628 *** df.mm.trans1:exp8 -0.206125659329175 0.0836007595317819 -2.46559553386370 0.0139201201135314 * df.mm.trans2:exp8 -0.148560578342744 0.0727651212770428 -2.04164544407369 0.0415654411671671 * df.mm.trans1:probe2 -0.447199717047625 0.0418003797658910 -10.6984606252917 7.97244711917856e-25 *** df.mm.trans1:probe3 -0.6293882017216 0.0418003797658910 -15.0569972150153 1.58541006579826e-44 *** df.mm.trans1:probe4 -0.66730667188888 0.0418003797658910 -15.9641294080635 4.44547860477785e-49 *** df.mm.trans1:probe5 -0.553536333435935 0.0418003797658910 -13.2423757041466 7.6923547001951e-36 *** df.mm.trans1:probe6 -0.679464179755874 0.0418003797658910 -16.2549762361326 1.45356189289076e-50 *** df.mm.trans1:probe7 -0.648292957645242 0.041800379765891 -15.5092599941938 8.839226537269e-47 *** df.mm.trans1:probe8 -0.538202416742468 0.041800379765891 -12.8755389246880 3.67119355749786e-34 *** df.mm.trans1:probe9 -0.0278945500903412 0.041800379765891 -0.667327671340994 0.504785320394045 df.mm.trans1:probe10 -0.593895873837811 0.0418003797658910 -14.2079061760685 2.18476191263906e-40 *** df.mm.trans1:probe11 -0.672437255564457 0.041800379765891 -16.0868695291894 1.05315762748353e-49 *** df.mm.trans1:probe12 -0.644758551186415 0.0418003797658910 -15.4247055839559 2.34542101353565e-46 *** df.mm.trans1:probe13 -0.61177134765688 0.0418003797658910 -14.6355452051678 1.86317804158372e-42 *** df.mm.trans1:probe14 -0.684534259946592 0.0418003797658910 -16.3762689186181 3.46290062069465e-51 *** df.mm.trans1:probe15 -0.613014262457922 0.0418003797658910 -14.6652797388731 1.33410181928693e-42 *** df.mm.trans1:probe16 -0.716899764947724 0.0418003797658910 -17.1505562619963 3.28611285789961e-55 *** df.mm.trans1:probe17 -0.656435176670629 0.041800379765891 -15.7040481533203 9.24528737862912e-48 *** df.mm.trans1:probe18 -0.543380868271065 0.041800379765891 -12.9994241993577 1.00232563590726e-34 *** df.mm.trans1:probe19 -0.594811294855528 0.041800379765891 -14.2298060014491 1.71488430876664e-40 *** df.mm.trans1:probe20 -0.547044145714083 0.041800379765891 -13.0870616194848 3.98319300160909e-35 *** df.mm.trans1:probe21 -0.636978621279072 0.041800379765891 -15.2385845498668 1.99141032988579e-45 *** df.mm.trans1:probe22 -0.533021688944516 0.041800379765891 -12.7515991943083 1.33535351756493e-33 *** df.mm.trans2:probe2 0.0202600212117907 0.041800379765891 0.484685099160819 0.628053116445473 df.mm.trans2:probe3 -0.00278696535580023 0.041800379765891 -0.0666732065930748 0.946861122713587 df.mm.trans2:probe4 -0.0101328917540955 0.041800379765891 -0.242411475944627 0.808533169331731 df.mm.trans2:probe5 0.0118369294560209 0.041800379765891 0.283177557771371 0.777125421674543 df.mm.trans2:probe6 0.109816235758167 0.041800379765891 2.62715880509242 0.00880078281448478 ** df.mm.trans3:probe2 0.107652281209983 0.041800379765891 2.57539002786351 0.0102191748610835 * df.mm.trans3:probe3 0.00158366683694409 0.041800379765891 0.0378864222242393 0.9697891655011 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09067999190007 0.119724193894600 34.1675300441056 1.13375887627070e-150 *** df.mm.trans1 -0.0794372595015707 0.107529834466196 -0.738746226997525 0.460311591501946 df.mm.trans2 -0.161115264127239 0.0988882866823807 -1.62926540172270 0.103711947911676 df.mm.exp2 -0.111002722206203 0.135452462999944 -0.81949578285815 0.412785757713109 df.mm.exp3 -0.0753681314904146 0.135452462999944 -0.556417578692873 0.578105304284403 df.mm.exp4 -0.00345993131043923 0.135452462999944 -0.0255435097584063 0.979628808091852 df.mm.exp5 -0.0868216277562127 0.135452462999944 -0.640974891362801 0.521751207898004 df.mm.exp6 0.0324328104629072 0.135452462999944 0.239440536883561 0.810834900961953 df.mm.exp7 0.0275137835065691 0.135452462999944 0.203125014467847 0.839097031785841 df.mm.exp8 -0.0233180797356380 0.135452462999944 -0.172149543974314 0.863370296079273 df.mm.trans1:exp2 0.175821546867787 0.129685859888508 1.35574955526332 0.175621135798273 df.mm.trans2:exp2 0.0617380508687587 0.112877052499953 0.546949530497214 0.584589733406497 df.mm.trans1:exp3 0.0110054439146528 0.129685859888508 0.0848623275052055 0.932395386975128 df.mm.trans2:exp3 0.0679626160083486 0.112877052499953 0.602094176833478 0.547308671610759 df.mm.trans1:exp4 0.0192257015917282 0.129685859888508 0.148248248561999 0.882190042585313 df.mm.trans2:exp4 0.0305448192943296 0.112877052499953 0.270602559314191 0.786777415979133 df.mm.trans1:exp5 0.0797857587122744 0.129685859888509 0.615223269374676 0.538609544648215 df.mm.trans2:exp5 0.127515042608286 0.112877052499953 1.12968083223416 0.259002163318356 df.mm.trans1:exp6 -0.0769495354074223 0.129685859888508 -0.593353319117259 0.553138600680153 df.mm.trans2:exp6 0.0213064336829721 0.112877052499953 0.188757884894106 0.850337924799999 df.mm.trans1:exp7 0.0232013262064818 0.129685859888508 0.178904054971206 0.858065404353634 df.mm.trans2:exp7 -0.0213226767484739 0.112877052499953 -0.188901785404812 0.8502251812376 df.mm.trans1:exp8 0.088789138632629 0.129685859888508 0.684647799759831 0.493795432664658 df.mm.trans2:exp8 0.00342802466176585 0.112877052499953 0.0303695444365654 0.975781090229767 df.mm.trans1:probe2 -0.0374473736281455 0.0648429299442542 -0.577508969140339 0.563783528425459 df.mm.trans1:probe3 -0.103903240511646 0.0648429299442542 -1.60238349193925 0.10952716801313 df.mm.trans1:probe4 -0.0700875620182597 0.0648429299442542 -1.08088209583550 0.280126106518436 df.mm.trans1:probe5 -0.0174229863699749 0.0648429299442542 -0.268695236087474 0.788244302518625 df.mm.trans1:probe6 -0.0429311764453676 0.0648429299442542 -0.662079527903439 0.508140673583414 df.mm.trans1:probe7 -0.0172226994188202 0.0648429299442542 -0.265606434404285 0.790621436869298 df.mm.trans1:probe8 -0.0901679507386644 0.0648429299442542 -1.39055947681855 0.164806076669090 df.mm.trans1:probe9 -0.0799242784542735 0.0648429299442542 -1.23258277383494 0.218150059514871 df.mm.trans1:probe10 -0.0662117605442034 0.0648429299442542 -1.02110994369203 0.307559191814143 df.mm.trans1:probe11 -0.0847402907197708 0.0648429299442542 -1.30685474565419 0.191696163776515 df.mm.trans1:probe12 -0.135054263418980 0.0648429299442542 -2.08279088460510 0.0376369309085832 * df.mm.trans1:probe13 -0.0147769638804889 0.0648429299442542 -0.227888590062057 0.819800170355305 df.mm.trans1:probe14 -0.0834820951132449 0.0648429299442542 -1.28745100175169 0.198367553885964 df.mm.trans1:probe15 -0.101805045100282 0.0648429299442542 -1.57002537034961 0.116866318346888 df.mm.trans1:probe16 -0.118774552482837 0.0648429299442542 -1.83172710710248 0.067421777928538 . df.mm.trans1:probe17 -0.046863205005877 0.0648429299442543 -0.722718807527135 0.470096836687555 df.mm.trans1:probe18 -0.08324561932138 0.0648429299442542 -1.28380410004524 0.199640201104975 df.mm.trans1:probe19 -0.0499717853083102 0.0648429299442542 -0.770658965461172 0.441172130990109 df.mm.trans1:probe20 -0.000146347354603909 0.0648429299442543 -0.00225695160181263 0.99819986515462 df.mm.trans1:probe21 -0.0871086756401478 0.0648429299442542 -1.34337969791056 0.179589424731544 df.mm.trans1:probe22 -0.052358005887625 0.0648429299442542 -0.807458977141184 0.419679553959925 df.mm.trans2:probe2 0.0611647233962918 0.0648429299442542 0.943275133447476 0.345869391394773 df.mm.trans2:probe3 -0.00748563264071615 0.0648429299442542 -0.115442541648744 0.90812785106798 df.mm.trans2:probe4 0.0286993526198644 0.0648429299442543 0.442598023324013 0.658194822245824 df.mm.trans2:probe5 0.00810651148927831 0.0648429299442542 0.125017661852225 0.900545876316517 df.mm.trans2:probe6 0.0173326264019763 0.0648429299442543 0.267301715343172 0.789316509511436 df.mm.trans3:probe2 -0.0155213323317421 0.0648429299442542 -0.239368152319549 0.810891001365416 df.mm.trans3:probe3 0.00422917551920402 0.0648429299442542 0.0652218448925713 0.948016205287576