fitVsDatCorrelation=0.851784369552619 cont.fitVsDatCorrelation=0.238302051965895 fstatistic=9937.26005645372,66,1014 cont.fstatistic=2880.73017257902,66,1014 residuals=-0.480135278468699,-0.101692129131004,-0.00952234737290737,0.081808657363151,1.20111818567547 cont.residuals=-0.67729913417451,-0.239530405363587,-0.0135673770337102,0.208665963371189,1.34672226722964 predictedValues: Include Exclude Both Lung 62.1665557897903 50.3707572468302 71.1825101273512 cerebhem 63.6293929165555 53.3021876450984 71.045151289848 cortex 62.3768811350087 52.579997425324 76.097103438155 heart 61.4493079592411 55.9424809519223 68.9276610869199 kidney 63.3598831736833 56.0573146901695 86.7670536867369 liver 63.8779805038891 56.897267178146 70.074367125639 stomach 64.3610942227614 53.8524353927202 84.2066715636254 testicle 63.3187493894414 58.6180391199431 69.90114985779 diffExp=11.7957985429601,10.3272052714571,9.79688370968465,5.50682700731878,7.30256848351376,6.98071332574314,10.5086588300413,4.7007102694983 diffExpScore=0.985276658674319 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=1,0,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 69.0927292282863 78.3326622821285 68.4386158845439 cerebhem 69.0404761801388 75.3207452851298 61.5690478704174 cortex 66.9222404498437 77.4217972897994 70.7465844592518 heart 68.205038968979 67.7045131220042 67.0543851801118 kidney 65.4864367968663 70.6660788005804 69.6663529303164 liver 68.8365002361327 70.6478978997817 71.0889704194987 stomach 66.9324572618606 65.2159320741604 68.1822320705894 testicle 68.928082277812 64.2661584577928 61.8015330275237 cont.diffExp=-9.2399330538422,-6.28026910499104,-10.4995568399557,0.50052584697471,-5.17964200371408,-1.81139766364906,1.7165251877002,4.66192382001917 cont.diffExpScore=1.47022086675944 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=0.254502877538119 cont.tran.correlation=0.109106518465291 tran.covariance=0.000197369659907179 cont.tran.covariance=0.000150082893695329 tran.mean=58.8850202962828 cont.tran.mean=69.563734163206 weightedLogRatios: wLogRatio Lung 0.846804432451832 cerebhem 0.719820081721885 cortex 0.691596885603232 heart 0.3822454252529 kidney 0.500552546837798 liver 0.47437944914805 stomach 0.726483876924992 testicle 0.317011359475864 cont.weightedLogRatios: wLogRatio Lung -0.539490126339162 cerebhem -0.372473562641009 cortex -0.623229705670315 heart 0.0310742813633419 kidney -0.321230166254063 liver -0.110253340473653 stomach 0.108875125665016 testicle 0.293991386730554 varWeightedLogRatios=0.0357626462546625 cont.varWeightedLogRatios=0.105590911226233 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.42227601887608 0.0792867203135432 43.1632939960503 7.35058918243308e-232 *** df.mm.trans1 0.429115189962972 0.0689828959038107 6.22060272101837 7.23156066203136e-10 *** df.mm.trans2 0.485483447848302 0.060165644253874 8.06911409108767 1.99022903555361e-15 *** df.mm.exp2 0.0817564658688556 0.0770059114412061 1.06169077592539 0.288628883651968 df.mm.exp3 -0.0204605353809351 0.0770059114412061 -0.265700840338169 0.790523626195333 df.mm.exp4 0.12549823027125 0.0770059114412061 1.62972202941936 0.103470838980948 df.mm.exp5 -0.0720022524270981 0.0770059114412061 -0.935022403858848 0.349999365800172 df.mm.exp6 0.164684138555186 0.0770059114412061 2.13859086235116 0.0327072318413081 * df.mm.exp7 -0.0664980174242735 0.0770059114412061 -0.86354431990646 0.388042471328735 df.mm.exp8 0.188161052815584 0.0770059114412061 2.44346244715569 0.0147167286127109 * df.mm.trans1:exp2 -0.0584981163783744 0.0724631839157036 -0.807280514287439 0.419694205876426 df.mm.trans2:exp2 -0.0251898848731168 0.0514595934436933 -0.489508042862394 0.624587936420136 df.mm.trans1:exp3 0.0238380806030303 0.0724631839157036 0.328968164451083 0.742247681437773 df.mm.trans2:exp3 0.0633855123839666 0.0514595934436933 1.23175307347351 0.218326907157191 df.mm.trans1:exp4 -0.137102823045788 0.0724631839157036 -1.89203421154223 0.0587711174717783 . df.mm.trans2:exp4 -0.0205849865817474 0.0514595934436933 -0.40002233216769 0.689224320754542 df.mm.trans1:exp5 0.0910159892099504 0.0724631839157036 1.25603077717133 0.209394126900547 df.mm.trans2:exp5 0.178966103045452 0.0514595934436933 3.47779861963494 0.000527006687560467 *** df.mm.trans1:exp6 -0.137526596482827 0.0724631839157036 -1.89788233212071 0.057995381635705 . df.mm.trans2:exp6 -0.0428476204117632 0.0514595934436933 -0.832645917784926 0.405240516876186 df.mm.trans1:exp7 0.101190174128673 0.0724631839157036 1.39643566098872 0.162888934665677 df.mm.trans2:exp7 0.133334852163711 0.0514595934436933 2.59105918334946 0.00970551409436525 ** df.mm.trans1:exp8 -0.169796735510275 0.0724631839157036 -2.34321384094577 0.0193108912382388 * df.mm.trans2:exp8 -0.0365293621245866 0.0514595934436933 -0.709864957727596 0.477951088199419 df.mm.trans1:probe2 0.582693972358522 0.0496121505240955 11.7449851740557 5.91562648331538e-30 *** df.mm.trans1:probe3 0.304428666005315 0.0496121505240955 6.13617153841095 1.21061237825470e-09 *** df.mm.trans1:probe4 0.296083416116935 0.0496121505240955 5.96796173899242 3.3172316002808e-09 *** df.mm.trans1:probe5 0.0435953887741903 0.0496121505240955 0.87872402856266 0.379759101585275 df.mm.trans1:probe6 0.628265283414452 0.0496121505240955 12.6635365888709 3.15893357535686e-34 *** df.mm.trans1:probe7 0.616091274326644 0.0496121505240955 12.4181529689470 4.61093741752312e-33 *** df.mm.trans1:probe8 -0.0128588782989259 0.0496121505240955 -0.259188085238931 0.795542731486548 df.mm.trans1:probe9 -0.00960954941724762 0.0496121505240955 -0.193693466534584 0.84645470066489 df.mm.trans1:probe10 0.0350241172310122 0.0496121505240955 0.705958456971177 0.480376130977757 df.mm.trans1:probe11 0.626549562065805 0.0496121505240955 12.6289539043768 4.61967451716031e-34 *** df.mm.trans1:probe12 0.640052501687985 0.0496121505240955 12.9011239167535 2.27408019721161e-35 *** df.mm.trans1:probe13 0.51149065324175 0.0496121505240955 10.3097859665110 9.04924252621715e-24 *** df.mm.trans1:probe14 0.798394615668944 0.0496121505240955 16.0927234001111 4.57482296998737e-52 *** df.mm.trans1:probe15 0.693724521067522 0.0496121505240955 13.9829560649784 9.29395334741167e-41 *** df.mm.trans1:probe16 0.611845609084718 0.0496121505240955 12.3325758432414 1.16398392742423e-32 *** df.mm.trans1:probe17 0.364349281408646 0.0496121505240955 7.34395259144611 4.25363882957164e-13 *** df.mm.trans1:probe18 0.357079440295665 0.0496121505240955 7.19741911051082 1.19273817356884e-12 *** df.mm.trans1:probe19 0.189276492086433 0.0496121505240955 3.81512371640705 0.000144344786437595 *** df.mm.trans1:probe20 0.502145371497945 0.0496121505240955 10.1214191723873 5.26554072583075e-23 *** df.mm.trans1:probe21 0.456666588962402 0.0496121505240955 9.2047327950561 1.90708129719884e-19 *** df.mm.trans1:probe22 0.390535113551069 0.0496121505240955 7.87176345765126 8.94459280333199e-15 *** df.mm.trans1:probe23 0.0120649734888802 0.0496121505240955 0.243185859944139 0.807910605580891 df.mm.trans1:probe24 0.602644103562655 0.0496121505240955 12.1471070533410 8.52266315471656e-32 *** df.mm.trans1:probe25 0.313995088586149 0.0496121505240955 6.32899572522358 3.69835307801589e-10 *** df.mm.trans1:probe26 0.671055735276098 0.0496121505240955 13.5260360251907 1.90712630326950e-38 *** df.mm.trans1:probe27 0.617605826685304 0.0496121505240955 12.4486808203435 3.31009780558246e-33 *** df.mm.trans1:probe28 0.0304249911898868 0.0496121505240955 0.613256850760985 0.539844047016182 df.mm.trans1:probe29 -0.0150017966366394 0.0496121505240955 -0.302381502881101 0.762423266743228 df.mm.trans2:probe2 -0.0221833779408038 0.0496121505240955 -0.447135987988059 0.654872366678738 df.mm.trans2:probe3 0.0372003374878841 0.0496121505240955 0.749823119838693 0.453535172053627 df.mm.trans2:probe4 0.0473056978889555 0.0496121505240955 0.953510327394096 0.340558806187557 df.mm.trans2:probe5 0.136260632232998 0.0496121505240955 2.74651735096262 0.0061298297027714 ** df.mm.trans2:probe6 -0.0121620639491718 0.0496121505240955 -0.245142849497422 0.806395427975031 df.mm.trans3:probe2 -0.0513487361154553 0.0496121505240955 -1.03500323152725 0.300914169585130 df.mm.trans3:probe3 -0.0755462395519653 0.0496121505240955 -1.52273664321957 0.128136390205130 df.mm.trans3:probe4 -0.127501817687177 0.0496121505240955 -2.56997159647922 0.0103125501513317 * df.mm.trans3:probe5 -0.0946331481581465 0.0496121505240955 -1.90745910343446 0.0567434643217318 . df.mm.trans3:probe6 -0.478135659431886 0.0496121505240955 -9.63747094977602 4.30977452215847e-21 *** df.mm.trans3:probe7 0.716370937100188 0.0496121505240955 14.4394252120206 4.04986782946467e-43 *** df.mm.trans3:probe8 -0.184813566947061 0.0496121505240955 -3.72516742359922 0.000205951736575403 *** df.mm.trans3:probe9 -0.107935878628484 0.0496121505240955 -2.17559362954972 0.0298160334705449 * df.mm.trans3:probe10 -0.312731017816950 0.0496121505240955 -6.30351666906807 4.33372965302602e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35461070766192 0.146981587233844 29.6269130685999 1.80272788764316e-139 *** df.mm.trans1 -0.133768664964101 0.127880375072056 -1.04604529732359 0.295789405373798 df.mm.trans2 0.000564295365937558 0.111534968963405 0.00505935825492278 0.995964228535823 df.mm.exp2 0.0658123299313157 0.142753427626441 0.461021013824859 0.644882393766747 df.mm.exp3 -0.0767814824168862 0.142753427626441 -0.537860867465885 0.590791132012883 df.mm.exp4 -0.138309658710889 0.142753427626441 -0.968871017744104 0.332840582496351 df.mm.exp5 -0.174385681405749 0.142753427626441 -1.22158664982870 0.222147904806441 df.mm.exp6 -0.144966649160778 0.142753427626441 -1.01550380660651 0.310107972885157 df.mm.exp7 -0.21127313570434 0.142753427626441 -1.47998642986844 0.139187480933804 df.mm.exp8 -0.0983082475697748 0.142753427626441 -0.688657703036237 0.491196226179003 df.mm.trans1:exp2 -0.0665688902454759 0.134332127067801 -0.495554501358242 0.620316137426017 df.mm.trans2:exp2 -0.105021390102795 0.095395706782317 -1.10090268886464 0.271200276542186 df.mm.trans1:exp3 0.0448633332719184 0.134332127067801 0.333973221828571 0.738468821209772 df.mm.trans2:exp3 0.0650851832684849 0.0953957067823169 0.682265328952407 0.495227010448039 df.mm.trans1:exp4 0.125378601777951 0.134332127067801 0.933347848461223 0.350862587591604 df.mm.trans2:exp4 -0.00750215887591186 0.095395706782317 -0.0786425210206891 0.937332481391806 df.mm.trans1:exp5 0.120779226572437 0.134332127067801 0.899109015905603 0.368808052079157 df.mm.trans2:exp5 0.0713866896871079 0.095395706782317 0.748321828046255 0.454439588092631 df.mm.trans1:exp6 0.141251275879898 0.134332127067801 1.05150777377779 0.293275960285235 df.mm.trans2:exp6 0.0417103453018448 0.095395706782317 0.437235036132428 0.662034004626865 df.mm.trans1:exp7 0.179507641822301 0.134332127067801 1.33629717432896 0.181751944996524 df.mm.trans2:exp7 0.0280122729240677 0.095395706782317 0.293642909821810 0.769090873002934 df.mm.trans1:exp8 0.0959224185519956 0.134332127067801 0.714069081207813 0.475348796487894 df.mm.trans2:exp8 -0.0996232255958145 0.095395706782317 -1.04431560870076 0.296588287446462 df.mm.trans1:probe2 0.00139131648646342 0.0919709202408558 0.0151277869441754 0.987933208587995 df.mm.trans1:probe3 -0.0730446101936572 0.0919709202408559 -0.794214192946706 0.427256576181722 df.mm.trans1:probe4 0.0108960221165029 0.0919709202408558 0.118472470297874 0.90571677145054 df.mm.trans1:probe5 0.135165145408613 0.0919709202408558 1.46965089676867 0.141966515080365 df.mm.trans1:probe6 0.0381930080736743 0.0919709202408559 0.415272653287077 0.678030171982694 df.mm.trans1:probe7 -0.0557007589012323 0.0919709202408559 -0.60563446310379 0.54489304157353 df.mm.trans1:probe8 0.0432801908961534 0.0919709202408559 0.470585602305708 0.638037896846787 df.mm.trans1:probe9 0.0926909563981906 0.0919709202408558 1.00782895458096 0.313777014216473 df.mm.trans1:probe10 -0.00741975216132473 0.0919709202408559 -0.0806749801121233 0.93571635448934 df.mm.trans1:probe11 -0.0272468797713508 0.0919709202408559 -0.296255378330411 0.767095723723702 df.mm.trans1:probe12 0.00659621121434855 0.0919709202408559 0.0717206177460682 0.942838390892741 df.mm.trans1:probe13 0.045389049710047 0.0919709202408558 0.493515228413296 0.621755452145865 df.mm.trans1:probe14 -0.00525367129015386 0.0919709202408559 -0.057123178461142 0.954458326827229 df.mm.trans1:probe15 0.0286752522798658 0.0919709202408559 0.311786075476578 0.755267201770084 df.mm.trans1:probe16 0.120179481764291 0.0919709202408559 1.30671174594711 0.191606906051328 df.mm.trans1:probe17 -0.145223419591443 0.0919709202408558 -1.57901453210567 0.114644604340795 df.mm.trans1:probe18 0.0707831059663584 0.0919709202408559 0.769624852953409 0.441701701706505 df.mm.trans1:probe19 -0.090868698194112 0.0919709202408559 -0.988015537477963 0.323380734329844 df.mm.trans1:probe20 0.0893376936351299 0.0919709202408558 0.97136892184149 0.331596246424795 df.mm.trans1:probe21 0.0303527334676839 0.0919709202408559 0.330025331791781 0.741448989215284 df.mm.trans1:probe22 -0.0264096911584425 0.0919709202408558 -0.287152624865339 0.774054127468226 df.mm.trans1:probe23 0.0743586390237142 0.0919709202408558 0.808501631047964 0.418991511235568 df.mm.trans1:probe24 0.0867062810384542 0.0919709202408559 0.942757567407018 0.346029476444583 df.mm.trans1:probe25 0.069974956100103 0.0919709202408559 0.760837837838859 0.446930817431025 df.mm.trans1:probe26 -0.0599647018720287 0.0919709202408559 -0.651996323566096 0.514551300398513 df.mm.trans1:probe27 -0.00971377252401411 0.0919709202408558 -0.105617868110653 0.915906436505826 df.mm.trans1:probe28 0.0703059438669442 0.0919709202408558 0.764436668490705 0.444784915988652 df.mm.trans1:probe29 0.0562609679125126 0.0919709202408559 0.611725616805561 0.540856436072497 df.mm.trans2:probe2 -0.0193045802041925 0.0919709202408559 -0.209898739227978 0.83378887145237 df.mm.trans2:probe3 0.0732497025962603 0.0919709202408559 0.796444163051017 0.425960342685148 df.mm.trans2:probe4 0.0453991738369773 0.0919709202408559 0.493625308065688 0.621677721077366 df.mm.trans2:probe5 -0.0691162412047633 0.0919709202408559 -0.751501029061794 0.452525561911973 df.mm.trans2:probe6 0.0624064377816387 0.0919709202408559 0.678545323002174 0.497580822000302 df.mm.trans3:probe2 0.0569761312610215 0.0919709202408559 0.619501589326397 0.535725143565388 df.mm.trans3:probe3 0.162600442838258 0.0919709202408559 1.76795494067511 0.0773691118459837 . df.mm.trans3:probe4 -0.0389059592695352 0.0919709202408559 -0.423024573067739 0.672367030517021 df.mm.trans3:probe5 0.0159538310430771 0.0919709202408559 0.173466036887494 0.862319743071804 df.mm.trans3:probe6 -0.0762561285859258 0.0919709202408559 -0.829133038858633 0.407224261605843 df.mm.trans3:probe7 -0.00682587521380204 0.0919709202408559 -0.0742177548721516 0.940851756599567 df.mm.trans3:probe8 -0.0144736768199556 0.0919709202408559 -0.15737231705469 0.874982759546773 df.mm.trans3:probe9 -0.0439657593508556 0.0919709202408559 -0.478039789486904 0.632724924163263 df.mm.trans3:probe10 -0.00979413613809905 0.0919709202408559 -0.10649166184757 0.915213334560783