fitVsDatCorrelation=0.66762681949963 cont.fitVsDatCorrelation=0.269807649699467 fstatistic=14631.9718871797,59,853 cont.fstatistic=8741.05123433837,59,853 residuals=-0.34580479842257,-0.0801388090162637,-0.00274310436679111,0.0654212958145018,0.580164990488191 cont.residuals=-0.453285016924851,-0.0970847615760598,-0.0171497140363393,0.0800768344404405,0.999343492094976 predictedValues: Include Exclude Both Lung 43.665240840746 41.6628696361140 50.2070619109146 cerebhem 54.2302825949935 50.427845878461 55.3085547070569 cortex 46.5354901432034 41.7714934127873 57.1160146246527 heart 45.8658521731532 44.0761031964262 50.836699062536 kidney 43.245090660078 41.4642367365394 51.4026911762015 liver 49.1772664179448 47.3414030693165 51.3010206687907 stomach 45.5337149149474 44.3600074183843 48.8875586293484 testicle 47.1759185112023 45.4169710354351 50.2668319053305 diffExp=2.00237120463196,3.80243671653248,4.76399673041612,1.78974897672695,1.7808539235386,1.83586334862831,1.17370749656303,1.75894747576718 diffExpScore=0.949768750075262 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 51.0212460106822 51.2748633660974 49.2554421478722 cerebhem 50.6583429461105 50.2199725043054 49.8897373475341 cortex 47.7604922169449 49.6645824245881 48.6301854881872 heart 51.9322275774834 49.5694503524035 50.0670030584573 kidney 49.2436570288355 50.7035477043151 49.2493832282354 liver 51.0078282262279 57.0511976182469 49.6807852634291 stomach 50.4013760046498 49.2040901668032 50.2695476243483 testicle 50.3383871745036 52.9370036166309 48.8268981674945 cont.diffExp=-0.253617355415237,0.438370441805176,-1.90409020764321,2.36277722507982,-1.45989067547953,-6.04336939201904,1.19728583784654,-2.59861644212729 cont.diffExpScore=1.75550731608608 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=0.936568537068859 cont.tran.correlation=0.235818532279547 tran.covariance=0.00463530501813636 cont.tran.covariance=0.00029943274338287 tran.mean=45.7468616649833 cont.tran.mean=50.8117665586768 weightedLogRatios: wLogRatio Lung 0.176177391430233 cerebhem 0.287649993429099 cortex 0.408915424356672 heart 0.151483486334154 kidney 0.157522394962387 liver 0.147482647003904 stomach 0.0993768008835114 testicle 0.145716855311613 cont.weightedLogRatios: wLogRatio Lung -0.0195103349115339 cerebhem 0.0340758148125613 cortex -0.151906520672181 heart 0.182844118375421 kidney -0.114272437376662 liver -0.446532151723527 stomach 0.0939549956341407 testicle -0.198516582030736 varWeightedLogRatios=0.0102573216013232 cont.varWeightedLogRatios=0.0386723030667461 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66634558564848 0.0582344055189361 62.958410118161 0 *** df.mm.trans1 0.0846456851561196 0.0490445989627053 1.72589208488556 0.084728959231445 . df.mm.trans2 0.0387811927926226 0.0439868417754872 0.881654404527728 0.378212087424789 df.mm.exp2 0.310848394159511 0.0560548092191754 5.54543666260797 3.91145773242653e-08 *** df.mm.exp3 -0.0626621670689691 0.0560548092191754 -1.11787316631404 0.263935902388097 df.mm.exp4 0.093013115592559 0.0560548092191754 1.65932445205327 0.0974180151284003 . df.mm.exp5 -0.0379825270262933 0.0560548092191754 -0.677596223328152 0.498211481226416 df.mm.exp6 0.22509904083638 0.0560548092191754 4.01569542331753 6.44927812429412e-05 *** df.mm.exp7 0.131261424138705 0.0560548092191754 2.34166213331439 0.0194273741418572 * df.mm.exp8 0.162416948843247 0.0560548092191754 2.89746680268224 0.00385819462916747 ** df.mm.trans1:exp2 -0.0941613037726306 0.049245739652121 -1.91207004784169 0.0562019441119271 . df.mm.trans2:exp2 -0.119915189812729 0.0367375112087628 -3.26410760737994 0.00114181101819308 ** df.mm.trans1:exp3 0.126325035939384 0.049245739652121 2.56519725019387 0.0104816551778017 * df.mm.trans2:exp3 0.06526598242933 0.0367375112087628 1.77654882657817 0.07599885598416 . df.mm.trans1:exp4 -0.0438446182698006 0.049245739652121 -0.890323073214563 0.373543471425509 df.mm.trans2:exp4 -0.0367056733890139 0.0367375112087628 -0.999133370261039 0.318013428918834 df.mm.trans1:exp5 0.0283138617137694 0.049245739652121 0.574950481275794 0.56547639889108 df.mm.trans2:exp5 0.0332035015966452 0.0367375112087628 0.903803782677728 0.366354625592652 df.mm.trans1:exp6 -0.106219970296751 0.049245739652121 -2.15693725075720 0.0312893030381611 * df.mm.trans2:exp6 -0.097324114803077 0.0367375112087628 -2.64917550484103 0.00821807680916566 ** df.mm.trans1:exp7 -0.0893607669334688 0.049245739652121 -1.81458878604984 0.0699382772103748 . df.mm.trans2:exp7 -0.0685334101736188 0.0367375112087628 -1.86548864957602 0.0624560439366521 . df.mm.trans1:exp8 -0.0850857689803588 0.049245739652121 -1.72777928773975 0.0843897417830078 . df.mm.trans2:exp8 -0.0761414180412595 0.0367375112087628 -2.07257964777695 0.0385112758670333 * df.mm.trans1:probe2 -0.0355078462251753 0.0366656880487485 -0.968421652907923 0.333108328064089 df.mm.trans1:probe3 0.0479167812389576 0.0366656880487485 1.30685618595921 0.191613762467350 df.mm.trans1:probe4 0.0756701889022814 0.0366656880487485 2.06378750622857 0.0393398376465623 * df.mm.trans1:probe5 0.057306074020443 0.0366656880487485 1.56293464189878 0.118438869274068 df.mm.trans1:probe6 0.0773683605873239 0.0366656880487485 2.11010251558513 0.0351401752362099 * df.mm.trans1:probe7 0.120250672107360 0.0366656880487485 3.27965131726104 0.00108147686999455 ** df.mm.trans1:probe8 0.112242987949994 0.0366656880487485 3.06125410222121 0.00227320561253042 ** df.mm.trans1:probe9 0.153209873818038 0.0366656880487485 4.17856262820814 3.23588669808673e-05 *** df.mm.trans1:probe10 0.0650048424001539 0.0366656880487485 1.77290665631932 0.0766009899802303 . df.mm.trans1:probe11 -0.0356970921118646 0.0366656880487485 -0.973583042118395 0.330539611246472 df.mm.trans1:probe12 -0.00650950258173318 0.0366656880487485 -0.177536626970658 0.85912904695406 df.mm.trans1:probe13 0.0604460685781395 0.0366656880487485 1.64857314276427 0.0996033427236901 . df.mm.trans1:probe14 0.0378485441983647 0.0366656880487485 1.03226057419252 0.302242645323343 df.mm.trans1:probe15 0.0884055893045714 0.0366656880487485 2.41112587842434 0.0161137975717041 * df.mm.trans2:probe2 0.152300990739254 0.0366656880487485 4.15377424628616 3.59953375539064e-05 *** df.mm.trans2:probe3 0.117490439354949 0.0366656880487485 3.20437023297479 0.00140387008091566 ** df.mm.trans2:probe4 0.0930712367459102 0.0366656880487485 2.53837420484700 0.0113136716120474 * df.mm.trans2:probe5 0.0574146700888901 0.0366656880487485 1.56589643190536 0.117743747656355 df.mm.trans2:probe6 0.142844021140107 0.0366656880487485 3.89585000969271 0.000105513579007151 *** df.mm.trans3:probe2 0.225353671385507 0.0366656880487485 6.14617325838508 1.21738674672281e-09 *** df.mm.trans3:probe3 0.00591566835878691 0.0366656880487485 0.161340715900975 0.871863261458203 df.mm.trans3:probe4 0.141435091459174 0.0366656880487485 3.85742362917425 0.0001232175338338 *** df.mm.trans3:probe5 0.473086444598773 0.0366656880487485 12.9027019476570 6.36173043497756e-35 *** df.mm.trans3:probe6 0.236766520112654 0.0366656880487485 6.45744107673266 1.78615267821208e-10 *** df.mm.trans3:probe7 0.0134628805667042 0.0366656880487485 0.36717926986137 0.713576374249821 df.mm.trans3:probe8 0.0613874466209391 0.0366656880487485 1.67424777463121 0.094448494035283 . df.mm.trans3:probe9 0.111702306176952 0.0366656880487485 3.04650784211222 0.00238644571314512 ** df.mm.trans3:probe10 0.130322700656559 0.0366656880487485 3.55435033656779 0.00039973616004906 *** df.mm.trans3:probe11 0.067356187126098 0.0366656880487485 1.83703595133808 0.0665523370118425 . df.mm.trans3:probe12 0.0566612400146381 0.0366656880487485 1.54534779053661 0.122633061167932 df.mm.trans3:probe13 0.0681678857218397 0.0366656880487485 1.85917377661665 0.063346675545577 . df.mm.trans3:probe14 0.104365029197617 0.0366656880487485 2.84639494720132 0.00452779800275144 ** df.mm.trans3:probe15 0.0808426260857857 0.0366656880487485 2.20485773997483 0.0277302869974082 * df.mm.trans3:probe16 0.18044879481065 0.0366656880487485 4.92146211931811 1.03101469005900e-06 *** df.mm.trans3:probe17 0.191240395395231 0.0366656880487485 5.21578635428767 2.29883363481746e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97520702351692 0.0753190499560327 52.7782417042892 6.43250346409326e-271 *** df.mm.trans1 -0.0389231748411748 0.0634331640621696 -0.613609228179553 0.539637173958374 df.mm.trans2 -0.0257242146243470 0.0568915764413311 -0.45216209909168 0.651267168830919 df.mm.exp2 -0.0407214879492398 0.0725000098864595 -0.561675619258711 0.574484589124247 df.mm.exp3 -0.0851765234807323 0.0725000098864594 -1.17484843952608 0.240383210722668 df.mm.exp4 -0.0324708048178095 0.0725000098864594 -0.447873108826624 0.654358499671835 df.mm.exp5 -0.046543305300699 0.0725000098864594 -0.641976537294124 0.521060898357933 df.mm.exp6 0.0978870284970708 0.0725000098864594 1.35016572618913 0.177320961746709 df.mm.exp7 -0.0738271605162581 0.0725000098864594 -1.01830552343203 0.308821512949014 df.mm.exp8 0.0271662814983572 0.0725000098864594 0.374707279914882 0.707971314552812 df.mm.trans1:exp2 0.0335832882527737 0.0636933148355705 0.527265511921804 0.598146322688727 df.mm.trans2:exp2 0.0199336549462634 0.0475154578695469 0.419519369906758 0.674942234253764 df.mm.trans1:exp3 0.0191331641417063 0.0636933148355705 0.300395170059842 0.763948942489906 df.mm.trans2:exp3 0.0532679360445401 0.0475154578695469 1.12106540551049 0.262575530184291 df.mm.trans1:exp4 0.0501682231066756 0.0636933148355705 0.787652883763218 0.431118580704323 df.mm.trans2:exp4 -0.00135511074998444 0.0475154578695469 -0.0285193663439986 0.977254593089086 df.mm.trans1:exp5 0.0110817389116071 0.0636933148355705 0.173985903233574 0.861917822613766 df.mm.trans2:exp5 0.0353385486664301 0.0475154578695469 0.743727415264558 0.457246205378301 df.mm.trans1:exp6 -0.0981500473401216 0.0636933148355705 -1.54097879178535 0.123692817570058 df.mm.trans2:exp6 0.00886140079530466 0.0475154578695469 0.186495115329279 0.852100852458447 df.mm.trans1:exp7 0.0616035024802559 0.0636933148355705 0.967189455271569 0.33372347091678 df.mm.trans2:exp7 0.0326032748033418 0.0475154578695468 0.686161435986869 0.492797772489184 df.mm.trans1:exp8 -0.0406404653717633 0.0636933148355705 -0.638064849924674 0.523602717157367 df.mm.trans2:exp8 0.00473567486108933 0.0475154578695469 0.0996659839433952 0.920632924904465 df.mm.trans1:probe2 -0.00417936848782361 0.0474225634349096 -0.0881303789821496 0.92979373688237 df.mm.trans1:probe3 -0.0204411334581422 0.0474225634349096 -0.431042355738507 0.666546476789591 df.mm.trans1:probe4 -0.00664185678234501 0.0474225634349096 -0.140056890671070 0.888648104485324 df.mm.trans1:probe5 0.0429261802878784 0.0474225634349096 0.905184730192775 0.365623121741489 df.mm.trans1:probe6 0.0043457076695286 0.0474225634349096 0.091637974726806 0.927007194366804 df.mm.trans1:probe7 0.0306518160874374 0.0474225634349096 0.646355107511404 0.518223266623237 df.mm.trans1:probe8 -0.074011468584054 0.0474225634349096 -1.56068046987041 0.118970072780628 df.mm.trans1:probe9 -0.0110408275149994 0.0474225634349096 -0.232818024064718 0.815958567916061 df.mm.trans1:probe10 -0.0252267883471449 0.0474225634349096 -0.531957501238208 0.5948939458912 df.mm.trans1:probe11 -0.0121622468478650 0.0474225634349096 -0.256465403110451 0.797653332431596 df.mm.trans1:probe12 -0.0222795374326151 0.0474225634349096 -0.469808796042735 0.638611707310702 df.mm.trans1:probe13 -0.025005750164595 0.0474225634349096 -0.527296467195767 0.598124838740979 df.mm.trans1:probe14 0.00523885172527442 0.0474225634349096 0.110471711055120 0.91206126962137 df.mm.trans1:probe15 -0.0115084343280687 0.0474225634349096 -0.242678452923885 0.808312839663886 df.mm.trans2:probe2 -0.0386763153320393 0.0474225634349096 -0.815567791587752 0.414975044072687 df.mm.trans2:probe3 -0.101909154801434 0.0474225634349096 -2.14895921729981 0.0319184367686214 * df.mm.trans2:probe4 -0.0790121813069433 0.0474225634349096 -1.66613054174923 0.0960545590163202 . df.mm.trans2:probe5 -0.0302284879752353 0.0474225634349096 -0.637428383995432 0.524016895303694 df.mm.trans2:probe6 -0.0326637628835324 0.0474225634349096 -0.68878104677672 0.49114835646825 df.mm.trans3:probe2 -0.0483928798125452 0.0474225634349096 -1.02046106973883 0.307799170924906 df.mm.trans3:probe3 -0.0137045130062671 0.0474225634349096 -0.288987182759056 0.772661402821025 df.mm.trans3:probe4 0.0442032522400758 0.0474225634349096 0.932114357351168 0.35154117442979 df.mm.trans3:probe5 -0.0403106303878154 0.0474225634349096 -0.850030607121108 0.395546590054966 df.mm.trans3:probe6 -0.0731581508442965 0.0474225634349096 -1.54268655140734 0.123277729351044 df.mm.trans3:probe7 -0.0371321876677213 0.0474225634349096 -0.783006758348008 0.433840473253973 df.mm.trans3:probe8 0.0380300253887338 0.0474225634349096 0.80193947003587 0.422811321051847 df.mm.trans3:probe9 0.0814397551658176 0.0474225634349096 1.71732081243560 0.08628354292033 . df.mm.trans3:probe10 -0.0796518969979207 0.0474225634349096 -1.67962023198615 0.0933974160609214 . df.mm.trans3:probe11 0.00404936469358714 0.0474225634349096 0.0853889878632383 0.93197218496669 df.mm.trans3:probe12 -0.0684896985675707 0.0474225634349096 -1.44424285839328 0.149037867244785 df.mm.trans3:probe13 0.0368020847353432 0.0474225634349096 0.77604587499485 0.437937012894498 df.mm.trans3:probe14 -0.0475469795442013 0.0474225634349096 -1.00262356355878 0.316326852196542 df.mm.trans3:probe15 -0.0380508282228995 0.0474225634349096 -0.802378139577515 0.422557741694801 df.mm.trans3:probe16 0.0147535938097615 0.0474225634349096 0.31110915861838 0.755793650596162 df.mm.trans3:probe17 -0.00301643623359236 0.0474225634349096 -0.0636076166091825 0.949297569079225