fitVsDatCorrelation=0.773833695862448 cont.fitVsDatCorrelation=0.255420922918798 fstatistic=12316.9244314022,65,991 cont.fstatistic=5277.48865797107,65,991 residuals=-0.612426862726377,-0.0789373106140506,-0.0043182883004536,0.0682661366909001,0.844721142745013 cont.residuals=-0.475149840053256,-0.133287140253359,-0.0383426024354740,0.0756400320859244,1.26480946062736 predictedValues: Include Exclude Both Lung 45.3763940804184 66.5370317369522 47.324048161345 cerebhem 56.5605874736318 107.249226360109 52.1381682776785 cortex 45.2641655422636 63.0479777715481 46.2259827241046 heart 45.4780914314840 61.442332605838 46.9300796090981 kidney 44.8246916138611 63.5883213473989 45.8724940174894 liver 47.4228083282675 67.5502894995089 51.80033716316 stomach 46.6709493774354 68.0544646261108 47.9446741171791 testicle 47.7917496097667 73.3282707591063 48.5842474205342 diffExp=-21.1606376565338,-50.6886388864768,-17.7838122292845,-15.9642411743541,-18.7636297335378,-20.1274811712414,-21.3835152486754,-25.5365211493397 diffExpScore=0.994802723797333 diffExp1.5=0,-1,0,0,0,0,0,-1 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,-1,0,0,-1,-1,-1,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 50.126520073775 52.2290670518414 51.1848930137371 cerebhem 52.512718073648 54.2632408270614 49.4304892553749 cortex 52.6248368765241 58.1517836788986 50.6913665889819 heart 51.4929283140105 50.1525086007054 52.9563202748927 kidney 52.9415374433285 47.3507643946429 48.1816150778841 liver 53.7631716230176 50.4334229058357 50.2832036127449 stomach 54.4204042029688 50.6084919939311 48.5365410350593 testicle 56.0889951272712 50.7103728009499 50.6078990347562 cont.diffExp=-2.10254697806646,-1.7505227534134,-5.52694680237445,1.34041971330510,5.59077304868565,3.32974871718196,3.81191220903778,5.37862232632131 cont.diffExpScore=2.60412754061056 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=0.988552236419829 cont.tran.correlation=-0.198568006323315 tran.covariance=0.0134353812581658 cont.tran.covariance=-0.000413488428850875 tran.mean=59.3867095102313 cont.tran.mean=52.3669227492756 weightedLogRatios: wLogRatio Lung -1.53350684782073 cerebhem -2.78666563077353 cortex -1.31829910778872 heart -1.19374478813444 kidney -1.39084819630722 liver -1.42780757397867 stomach -1.52070824389508 testicle -1.7470053691858 cont.weightedLogRatios: wLogRatio Lung -0.161688984565541 cerebhem -0.130427163761205 cortex -0.400783949739602 heart 0.103611492745379 kidney 0.436754474611733 liver 0.252709197982612 stomach 0.287605473387911 testicle 0.400871105252129 varWeightedLogRatios=0.250932856480361 cont.varWeightedLogRatios=0.0906860317766172 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2422719012114 0.064399474607584 65.8743247062424 0 *** df.mm.trans1 -0.481946128784795 0.0545260236342661 -8.83882771312014 4.33948860929289e-18 *** df.mm.trans2 0.0133703780820678 0.0481199793287393 0.277855025471352 0.781181605537178 df.mm.exp2 0.600838383125363 0.0606820164421965 9.90142415088827 4.18732389753393e-22 *** df.mm.exp3 -0.0328624687834777 0.0606820164421965 -0.541552023321132 0.588248876135136 df.mm.exp4 -0.0690611618953158 0.0606820164421965 -1.13808284471069 0.255360954903864 df.mm.exp5 -0.0264088044587186 0.0606820164421965 -0.435199850088611 0.66351213269645 df.mm.exp6 -0.0311530992111725 0.0606820164421965 -0.513382729145921 0.607798055308072 df.mm.exp7 0.0376504210488668 0.0606820164421965 0.620454349679221 0.53510135397465 df.mm.exp8 0.122767798818762 0.0606820164421965 2.02313314580945 0.0433274894644643 * df.mm.trans1:exp2 -0.380517990506202 0.054137010174328 -7.02879581419229 3.87300946759813e-12 *** df.mm.trans2:exp2 -0.123441699661896 0.0377866824802583 -3.26680437549363 0.00112501571390086 ** df.mm.trans1:exp3 0.0303861249485896 0.054137010174328 0.561281918797187 0.574732260588893 df.mm.trans2:exp3 -0.0210002036222673 0.0377866824802583 -0.555756744012628 0.578502602319021 df.mm.trans1:exp4 0.0712998492416972 0.054137010174328 1.31702598669751 0.188134366422496 df.mm.trans2:exp4 -0.0105984452892124 0.0377866824802583 -0.280480968255142 0.779167065446834 df.mm.trans1:exp5 0.0141759284977914 0.054137010174328 0.261852814777601 0.793489333899107 df.mm.trans2:exp5 -0.0189200295629431 0.0377866824802583 -0.500706289122573 0.616689028064381 df.mm.trans1:exp6 0.0752643848898331 0.054137010174328 1.39025750863360 0.164762853512625 df.mm.trans2:exp6 0.0462667884221306 0.0377866824802583 1.22442049381559 0.22108462865033 df.mm.trans1:exp7 -0.0095205345933325 0.054137010174328 -0.175859999705842 0.860439858843348 df.mm.trans2:exp7 -0.0151007469714458 0.0377866824802583 -0.399631456911709 0.689514097196052 df.mm.trans1:exp8 -0.0709067921734296 0.054137010174328 -1.30976557340535 0.19057869557247 df.mm.trans2:exp8 -0.0255802395137201 0.0377866824802583 -0.676964418008502 0.498586516302925 df.mm.trans1:probe2 -0.0289340063775394 0.0411200110648132 -0.703647825676256 0.481817407387664 df.mm.trans1:probe3 -0.0189990645724504 0.0411200110648132 -0.46203938375659 0.644154491384277 df.mm.trans1:probe4 0.0170781970334115 0.0411200110648132 0.415325691583422 0.677993401590812 df.mm.trans1:probe5 0.328502154391382 0.0411200110648132 7.98886347266782 3.7633839360212e-15 *** df.mm.trans1:probe6 0.0157149143793572 0.0411200110648132 0.382171939462453 0.702415773593032 df.mm.trans1:probe7 0.0682260242928063 0.0411200110648132 1.65919275131684 0.0973933663215804 . df.mm.trans1:probe8 0.232075637884069 0.0411200110648132 5.64386127032583 2.16996776240582e-08 *** df.mm.trans1:probe9 0.250065237487413 0.0411200110648132 6.08135141533064 1.69929233640488e-09 *** df.mm.trans1:probe10 0.169299871948047 0.0411200110648132 4.11721367684453 4.15413150258177e-05 *** df.mm.trans1:probe11 0.0863498952167293 0.0411200110648132 2.0999482485699 0.0359857104325259 * df.mm.trans1:probe12 0.078702661462643 0.0411200110648132 1.91397471509899 0.0559111835362377 . df.mm.trans1:probe13 0.13043612448149 0.0411200110648132 3.17208388577272 0.00156003012903439 ** df.mm.trans1:probe14 0.126009326135454 0.0411200110648132 3.06442831294084 0.00223985914954814 ** df.mm.trans1:probe15 0.26597690860692 0.0411200110648132 6.46830829368426 1.55431862501598e-10 *** df.mm.trans1:probe16 0.159980119369340 0.0411200110648132 3.89056605838895 0.000106688109661867 *** df.mm.trans1:probe17 0.0883560279407376 0.0411200110648132 2.14873550985848 0.0318970876918549 * df.mm.trans1:probe18 0.108477212750248 0.0411200110648132 2.63806380254293 0.0084687698094751 ** df.mm.trans2:probe2 -0.345096339257076 0.0411200110648132 -8.39241844349547 1.62811745401665e-16 *** df.mm.trans2:probe3 -0.270102984159623 0.0411200110648132 -6.56865057098082 8.18518575186423e-11 *** df.mm.trans2:probe4 -0.352582347005399 0.0411200110648132 -8.57447111212253 3.78370165329234e-17 *** df.mm.trans2:probe5 -0.300268173434893 0.0411200110648132 -7.30223960693036 5.80587234291292e-13 *** df.mm.trans2:probe6 -0.236924234553045 0.0411200110648132 -5.76177458171414 1.11041301221908e-08 *** df.mm.trans3:probe2 0.0461727291098598 0.0411200110648132 1.12287735129941 0.26176154703248 df.mm.trans3:probe3 0.137491639849431 0.0411200110648132 3.34366738454221 0.000857749123185924 *** df.mm.trans3:probe4 -0.00261060552769425 0.0411200110648132 -0.0634874714303805 0.949391138451353 df.mm.trans3:probe5 0.0934872207433724 0.0411200110648132 2.27352129346508 0.0232080331035229 * df.mm.trans3:probe6 -0.0327815659694544 0.0411200110648132 -0.79721685672176 0.425516059777022 df.mm.trans3:probe7 -0.00132471082573835 0.0411200110648132 -0.0322157215291199 0.974306505728946 df.mm.trans3:probe8 0.0556046253685262 0.0411200110648132 1.35225219859213 0.176603146739454 df.mm.trans3:probe9 0.0477497275753437 0.0411200110648132 1.16122847097684 0.245828624100000 df.mm.trans3:probe10 -0.0228870760671823 0.0411200110648132 -0.556592167037792 0.577931768777542 df.mm.trans3:probe11 0.137494910845602 0.0411200110648132 3.34374693209308 0.000857506000231505 *** df.mm.trans3:probe12 0.684549369040668 0.0411200110648132 16.6475969075418 4.64731660941815e-55 *** df.mm.trans3:probe13 0.00403419573903743 0.0411200110648132 0.0981078466316253 0.921866496117011 df.mm.trans3:probe14 0.359310181863778 0.0411200110648132 8.73808572904893 9.96881768449324e-18 *** df.mm.trans3:probe15 0.0017187371737679 0.0411200110648132 0.0417980717723746 0.966668091676593 df.mm.trans3:probe16 0.00521861538178886 0.0411200110648132 0.126911818519779 0.899035937568907 df.mm.trans3:probe17 0.0797625202691981 0.0411200110648132 1.93974948458737 0.0526936097510426 . df.mm.trans3:probe18 0.0156193923815003 0.0411200110648132 0.379848934303085 0.704138943321972 df.mm.trans3:probe19 0.0857707256851692 0.0411200110648132 2.08586339021110 0.037246298068567 * df.mm.trans3:probe20 -0.0279319611149423 0.0411200110648132 -0.679279027209308 0.497119700467769 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01906767156211 0.0983019241445388 40.884933906814 6.63473874113726e-215 *** df.mm.trans1 -0.056520284021643 0.0832306951548908 -0.679079802426974 0.49724586271611 df.mm.trans2 -0.0597086719655156 0.0734522538675097 -0.812890943730819 0.416475828428979 df.mm.exp2 0.119590036622252 0.0926274478726255 1.29108638280419 0.196974810850060 df.mm.exp3 0.165744172027313 0.0926274478726255 1.78936347523287 0.073861585034127 . df.mm.exp4 -0.047699369576048 0.0926274478726255 -0.514959341659081 0.60669626853575 df.mm.exp5 0.0170487216138375 0.0926274478726255 0.184056907594838 0.854006465952231 df.mm.exp6 0.05282674084925 0.0926274478726255 0.570314113823944 0.568593946381591 df.mm.exp7 0.103796650681108 0.0926274478726255 1.12058199880279 0.262737314228173 df.mm.exp8 0.094217477255279 0.0926274478726255 1.01716585546911 0.309322795540515 df.mm.trans1:exp2 -0.0730848581686289 0.0826368895087569 -0.884409597252378 0.376689661768684 df.mm.trans2:exp2 -0.081382183421422 0.0576790978106937 -1.41094757911303 0.158573909701092 df.mm.trans1:exp3 -0.117106190591345 0.0826368895087569 -1.41711760071676 0.156762843697412 df.mm.trans2:exp3 -0.0583267997615687 0.0576790978106937 -1.01122940502642 0.312153476804510 df.mm.trans1:exp4 0.0745936426979445 0.0826368895087569 0.902667599680648 0.366921683444013 df.mm.trans2:exp4 0.0071287246969209 0.0576790978106937 0.123592860628954 0.901662707275125 df.mm.trans1:exp5 0.03758930517673 0.0826368895087569 0.454873185573457 0.649300101711416 df.mm.trans2:exp5 -0.115104938598069 0.0576790978106937 -1.99560920623013 0.0462498453903688 * df.mm.trans1:exp6 0.0172117387610543 0.0826368895087569 0.208281541855837 0.835051941080982 df.mm.trans2:exp6 -0.0878118126513575 0.0576790978106937 -1.52242000975052 0.128222887422308 df.mm.trans1:exp7 -0.0216077007109101 0.0826368895087569 -0.261477662571271 0.793778505558363 df.mm.trans2:exp7 -0.135316442486153 0.0576790978106937 -2.34602217479666 0.0191712641718719 * df.mm.trans1:exp8 0.0181719399128963 0.0826368895087569 0.219901063809652 0.825993496030338 df.mm.trans2:exp8 -0.123726175833744 0.0576790978106937 -2.14507820909094 0.0321891178967187 * df.mm.trans1:probe2 -0.127090344934209 0.062767223384146 -2.02478838607205 0.0431568404897951 * df.mm.trans1:probe3 -0.0215361885316590 0.062767223384146 -0.343112015643164 0.731586962252863 df.mm.trans1:probe4 -0.128673099939609 0.062767223384146 -2.05000465214317 0.0406266839779356 * df.mm.trans1:probe5 -0.198839542501573 0.062767223384146 -3.16788813939788 0.00158248802519954 ** df.mm.trans1:probe6 -0.0740775558008977 0.062767223384146 -1.18019488208887 0.238205921043026 df.mm.trans1:probe7 -0.09087087555364 0.062767223384146 -1.44774407173462 0.14800491334311 df.mm.trans1:probe8 -0.127218459255755 0.062767223384146 -2.02682948833274 0.042947194155178 * df.mm.trans1:probe9 -0.0917961609502848 0.062767223384146 -1.46248560954301 0.143925234262427 df.mm.trans1:probe10 -0.0286141863284347 0.062767223384146 -0.45587784174092 0.648577701144547 df.mm.trans1:probe11 -0.139710000758651 0.062767223384146 -2.22584325426669 0.0262489739419271 * df.mm.trans1:probe12 -0.0945982706303168 0.062767223384146 -1.50712849047598 0.132096418981227 df.mm.trans1:probe13 -0.146777862388084 0.062767223384146 -2.33844759214182 0.0195621020235315 * df.mm.trans1:probe14 -0.101908955731400 0.062767223384146 -1.62360146326213 0.104778911104377 df.mm.trans1:probe15 -0.102154460184451 0.062767223384146 -1.62751281125259 0.103946102648888 df.mm.trans1:probe16 -0.133200423360241 0.062767223384146 -2.12213343491446 0.0340740796163123 * df.mm.trans1:probe17 -0.129747810782458 0.062767223384146 -2.0671268185368 0.0389814097533958 * df.mm.trans1:probe18 -0.0870785174320358 0.062767223384146 -1.38732466942341 0.165654685419810 df.mm.trans2:probe2 -0.0972393667199532 0.062767223384146 -1.54920612187721 0.121651451999341 df.mm.trans2:probe3 -0.0478415855878184 0.062767223384146 -0.762206499003785 0.446118134455223 df.mm.trans2:probe4 -0.0193567961610878 0.062767223384146 -0.308390193439352 0.75785025456006 df.mm.trans2:probe5 0.073270657502869 0.062767223384146 1.1673394735727 0.243354098088848 df.mm.trans2:probe6 -0.00554821895424301 0.062767223384146 -0.0883935700052713 0.929581730614965 df.mm.trans3:probe2 0.0479111654422818 0.062767223384146 0.763315037038637 0.445457207317865 df.mm.trans3:probe3 -0.00573715731093345 0.062767223384146 -0.091403713620102 0.927190283074013 df.mm.trans3:probe4 0.0441457788650757 0.062767223384146 0.703325342191674 0.482018220311171 df.mm.trans3:probe5 -0.0158492652440333 0.062767223384146 -0.252508624557647 0.800700311505376 df.mm.trans3:probe6 0.0566710645475407 0.062767223384146 0.90287671641462 0.366810730446605 df.mm.trans3:probe7 -0.0213334685392985 0.062767223384146 -0.339882304634285 0.734017185427061 df.mm.trans3:probe8 0.023108783449219 0.062767223384146 0.368166412393126 0.712827778160495 df.mm.trans3:probe9 0.0253619940567712 0.062767223384146 0.404064298042173 0.686252618568833 df.mm.trans3:probe10 0.0148065432704389 0.062767223384146 0.235896101056125 0.813562035996338 df.mm.trans3:probe11 0.0887313427555417 0.062767223384146 1.41365728753829 0.157776593878490 df.mm.trans3:probe12 0.0680577048171301 0.062767223384146 1.08428732621492 0.278501138173834 df.mm.trans3:probe13 0.0864770581743092 0.062767223384146 1.37774229146724 0.168593902543335 df.mm.trans3:probe14 0.07968633512961 0.062767223384146 1.26955329283751 0.204541842589785 df.mm.trans3:probe15 0.07990207329207 0.062767223384146 1.27299040779701 0.203319992090526 df.mm.trans3:probe16 -0.0230820572542611 0.062767223384146 -0.367740613807226 0.71314517861075 df.mm.trans3:probe17 0.0385555311878979 0.062767223384146 0.6142621755296 0.53918308191769 df.mm.trans3:probe18 0.0296202839734432 0.062767223384146 0.471906870758996 0.637097149695038 df.mm.trans3:probe19 0.012799885910367 0.062767223384146 0.203926272666700 0.838452982391538 df.mm.trans3:probe20 0.0222806956126457 0.062767223384146 0.354973414648025 0.722685038158435