fitVsDatCorrelation=0.860858859911426 cont.fitVsDatCorrelation=0.275504545138858 fstatistic=14082.7601327118,51,669 cont.fstatistic=3936.39439016862,51,669 residuals=-0.353839259101344,-0.0766749747097776,-0.00537025715386229,0.0722104981599185,0.838023866436389 cont.residuals=-0.439516176120342,-0.129494257025623,-0.0375940411818547,0.0533216869573351,1.1097527425338 predictedValues: Include Exclude Both Lung 43.8437309375588 41.7200881945267 81.5725727296354 cerebhem 44.472408146345 43.9685952336568 73.469065894886 cortex 44.4816434417233 44.5420235373338 75.8413148760195 heart 44.5579324876338 45.6557972103408 84.4265278883816 kidney 43.6336661880071 40.2683569542596 92.9401085987759 liver 45.1353096663735 47.1736520515562 88.845340426224 stomach 43.8825421438322 43.2911197481946 75.7021867753694 testicle 45.8368080039005 43.5862506713776 85.7093688479108 diffExp=2.12364274303204,0.503812912688254,-0.060380095610526,-1.09786472270699,3.36530923374757,-2.03834238518267,0.591422395637679,2.25055733252282 diffExpScore=1.81245051458436 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 47.8542226197175 49.6447212573881 53.3010505407345 cerebhem 46.7801103708057 46.8915275985919 45.143915841585 cortex 47.6618596519977 47.9145055005361 46.4084417515033 heart 50.085401087623 45.6540981256471 45.8706713898856 kidney 47.3161254097802 46.4432350145259 45.8943630440452 liver 45.2866346302399 48.5521166218748 47.3381031150832 stomach 46.9210120047483 49.6863538627876 44.0028836710929 testicle 45.5411869520390 45.5959701232779 45.0208721632493 cont.diffExp=-1.79049863767067,-0.111417227786269,-0.252645848538457,4.43130296197592,0.872890395254231,-3.26548199163488,-2.76534185803926,-0.0547831712388032 cont.diffExpScore=3.44117043235373 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=0.604855921887253 cont.tran.correlation=-0.176519739524112 tran.covariance=0.000497634068890769 cont.tran.covariance=-0.000186561275775151 tran.mean=44.1281202885388 cont.tran.mean=47.3643175519738 weightedLogRatios: wLogRatio Lung 0.186472067919828 cerebhem 0.0431712555971914 cortex -0.00514892462581354 heart -0.0927116020012061 kidney 0.299838434974199 liver -0.169250926597165 stomach 0.0512194916460207 testicle 0.191309312661114 cont.weightedLogRatios: wLogRatio Lung -0.142762601461646 cerebhem -0.0091507529803081 cortex -0.0204428552340595 heart 0.358262281100563 kidney 0.071642523842971 liver -0.267908049648254 stomach -0.222020979247644 testicle -0.00459151718732122 varWeightedLogRatios=0.0244519722593700 cont.varWeightedLogRatios=0.0383481396295163 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.30852363347646 0.0652072430783096 50.7385909492162 1.63763569559500e-231 *** df.mm.trans1 0.460147402302661 0.0585006754486405 7.86567674259826 1.47654305142104e-14 *** df.mm.trans2 0.417405265350380 0.0538868659948141 7.74595548738259 3.52473441780206e-14 *** df.mm.exp2 0.171358744500249 0.0738378087515593 2.32074525771500 0.0206003539376357 * df.mm.exp3 0.152745094897862 0.0738378087515593 2.06865693173264 0.0389619063185766 * df.mm.exp4 0.071917724343371 0.0738378087515593 0.973995918342475 0.330410603297999 df.mm.exp5 -0.170681705832581 0.0738378087515593 -2.31157598957020 0.0211039964419720 * df.mm.exp6 0.0664818673704361 0.0738378087515593 0.900377035755846 0.368243628334452 df.mm.exp7 0.112535647985709 0.0738378087515593 1.52409246547871 0.127958171691875 df.mm.exp8 0.0387456448777969 0.0738378087515593 0.524739906734823 0.599937844841712 df.mm.trans1:exp2 -0.157121531809917 0.0705544692529079 -2.22695363559044 0.0262824263573645 * df.mm.trans2:exp2 -0.11886585420172 0.061584989708359 -1.93011080727008 0.05401551565399 . df.mm.trans1:exp3 -0.138300240278993 0.0705544692529078 -1.96019106576006 0.0503881305079596 . df.mm.trans2:exp3 -0.0872947462823427 0.061584989708359 -1.41746790404179 0.156811728117298 df.mm.trans1:exp4 -0.0557592707145717 0.0705544692529078 -0.79030104407275 0.429632021195536 df.mm.trans2:exp4 0.0182301231011196 0.061584989708359 0.296015688034534 0.767309962182829 df.mm.trans1:exp5 0.165878975979219 0.0705544692529078 2.35107680258516 0.019008409320683 * df.mm.trans2:exp5 0.135264935031939 0.061584989708359 2.19639453822267 0.0284061088986772 * df.mm.trans1:exp6 -0.0374487502729345 0.0705544692529078 -0.530777860984209 0.595748873285909 df.mm.trans2:exp6 0.0563709060189071 0.061584989708359 0.91533515367716 0.360345512875774 df.mm.trans1:exp7 -0.111650822780161 0.0705544692529078 -1.58247697080591 0.114013334222202 df.mm.trans2:exp7 -0.0755708647502904 0.061584989708359 -1.22709876397094 0.220217117005486 df.mm.trans1:exp8 0.00571004916003136 0.0705544692529078 0.0809310766630992 0.935520970167394 df.mm.trans2:exp8 0.00501336007641451 0.0615849897083589 0.0814055519073027 0.935143781564733 df.mm.trans1:probe2 -0.0232518244790777 0.0352772346264539 -0.65911698366633 0.510047450387314 df.mm.trans1:probe3 -0.0142586286092234 0.0352772346264539 -0.404187821415317 0.686203786013141 df.mm.trans1:probe4 -0.0210876388787217 0.0352772346264539 -0.597769045732071 0.550196334892082 df.mm.trans1:probe5 -0.0301416888586591 0.0352772346264539 -0.85442323293834 0.393176380637425 df.mm.trans1:probe6 0.0691431270170149 0.0352772346264539 1.95999283246441 0.0504113485822793 . df.mm.trans1:probe7 0.0857548067220094 0.0352772346264539 2.43088234182911 0.0153238106477532 * df.mm.trans1:probe8 0.036303431791557 0.0352772346264539 1.02908950137303 0.303809530096284 df.mm.trans1:probe9 0.0542308199525833 0.0352772346264539 1.53727525773566 0.124698719670326 df.mm.trans1:probe10 0.00378617631852452 0.0352772346264539 0.107326335485643 0.914562269916045 df.mm.trans1:probe11 -0.00115819775345414 0.0352772346264539 -0.0328313079445752 0.973818904057587 df.mm.trans1:probe12 -0.033828078487749 0.0352772346264539 -0.958920925802438 0.337945000147685 df.mm.trans1:probe13 -0.00924124827315473 0.0352772346264539 -0.261960677218867 0.793432394577696 df.mm.trans1:probe14 -0.026873153066393 0.0352772346264539 -0.761770398132091 0.446465508082473 df.mm.trans1:probe15 0.0345848577795526 0.0352772346264539 0.980373267512809 0.327256293229328 df.mm.trans1:probe16 0.0125280903756552 0.0352772346264539 0.355132439044997 0.72260230714295 df.mm.trans1:probe17 0.0337979089427293 0.0352772346264539 0.95806571293388 0.338375720738335 df.mm.trans1:probe18 0.0169009877209620 0.0352772346264539 0.47909049277599 0.63203080897385 df.mm.trans1:probe19 0.0172974990663922 0.0352772346264539 0.490330357511101 0.624060918897489 df.mm.trans1:probe20 0.0335136599214051 0.0352772346264539 0.95000813630311 0.342451181630252 df.mm.trans1:probe21 0.0490560546120543 0.0352772346264539 1.39058673763696 0.164813171654895 df.mm.trans2:probe2 -0.00110371293953596 0.0352772346264539 -0.0312868327470401 0.975050122608928 df.mm.trans2:probe3 0.0158268204956489 0.0352772346264539 0.448641189232576 0.653835791631709 df.mm.trans2:probe4 0.00192417921182952 0.0352772346264539 0.054544502487352 0.956517637724575 df.mm.trans2:probe5 0.00849619965349612 0.0352772346264539 0.240840863618174 0.809752245967988 df.mm.trans2:probe6 0.0203411209749445 0.0352772346264539 0.576607582491485 0.564398525579804 df.mm.trans3:probe2 0.467446594903494 0.0352772346264539 13.250658671328 9.36298489088075e-36 *** df.mm.trans3:probe3 0.178803762588612 0.0352772346264539 5.06853114995952 5.19440825892136e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75676925433905 0.123188503871824 30.4961026091194 1.05412485237105e-128 *** df.mm.trans1 0.117555802123231 0.110518561187360 1.06367474259769 0.287859847038532 df.mm.trans2 0.134749991528304 0.101802224520833 1.32364486299342 0.186073146230149 df.mm.exp2 0.0863442291447049 0.139493233571535 0.618985071418721 0.536136905714974 df.mm.exp3 0.0989730146162646 0.139493233571535 0.709518390836566 0.478250047844282 df.mm.exp4 0.111901614784783 0.139493233571535 0.802201023803768 0.422721547816549 df.mm.exp5 0.0716442837790094 0.139493233571535 0.513604007482333 0.607698456955558 df.mm.exp6 0.0412388184781194 0.139493233571535 0.295633110096134 0.767602019117858 df.mm.exp7 0.172845361927776 0.139493233571535 1.23909495466056 0.215744870479126 df.mm.exp8 0.0342148684403209 0.139493233571535 0.245279771386007 0.80631503449968 df.mm.trans1:exp2 -0.109045469654098 0.133290399937602 -0.818104452422273 0.4135888337071 df.mm.trans2:exp2 -0.143399283982051 0.116345399452381 -1.23253076320171 0.218183847502223 df.mm.trans1:exp3 -0.103000885592864 0.133290399937602 -0.772755469569319 0.439940034401686 df.mm.trans2:exp3 -0.134446792974643 0.116345399452381 -1.1555832341241 0.248264207973819 df.mm.trans1:exp4 -0.0663314056359604 0.133290399937602 -0.497645784445183 0.618897254611283 df.mm.trans2:exp4 -0.195700304784759 0.116345399452381 -1.68206311298847 0.0930232613050093 . df.mm.trans1:exp5 -0.0829524896475015 0.133290399937602 -0.622344067437221 0.533927701991905 df.mm.trans2:exp5 -0.138305535039694 0.116345399452381 -1.18874949667693 0.234960043912176 df.mm.trans1:exp6 -0.0963862318188922 0.133290399937602 -0.723129586707022 0.46985301131452 df.mm.trans2:exp6 -0.0634930937011213 0.116345399452381 -0.545729302576406 0.585433942037862 df.mm.trans1:exp7 -0.192539130667915 0.133290399937602 -1.44450861245857 0.149064134190798 df.mm.trans2:exp7 -0.172007102444791 0.116345399452381 -1.47841773937259 0.139766651519928 df.mm.trans1:exp8 -0.0837571052212065 0.133290399937602 -0.62838062801534 0.529969086506542 df.mm.trans2:exp8 -0.119287596067826 0.116345399452381 -1.02528846545969 0.305597673098512 df.mm.trans1:probe2 -0.0269026253559992 0.0666451999688012 -0.403669362063483 0.686584864532222 df.mm.trans1:probe3 -0.045295495887701 0.0666451999688012 -0.679651286347783 0.496960380243908 df.mm.trans1:probe4 0.000434062084155697 0.0666451999688012 0.00651302846054772 0.994805333511535 df.mm.trans1:probe5 0.0121033262536871 0.0666451999688012 0.181608371786012 0.855945087827453 df.mm.trans1:probe6 -0.0448918629242127 0.0666451999688012 -0.673594841717453 0.500801576782066 df.mm.trans1:probe7 0.0368550268890874 0.0666451999688012 0.553003470712676 0.580445769189007 df.mm.trans1:probe8 -0.0894516999077296 0.0666451999688012 -1.34220769012029 0.179983972695763 df.mm.trans1:probe9 -0.08165302417189 0.0666451999688012 -1.22518987429124 0.220934864719120 df.mm.trans1:probe10 -0.0318946232323844 0.0666451999688012 -0.478573449360424 0.632398473193435 df.mm.trans1:probe11 0.000986518778569282 0.0666451999688012 0.0148025481059567 0.988194119941833 df.mm.trans1:probe12 -0.00860518421962679 0.0666451999688012 -0.129119339782237 0.897302027376588 df.mm.trans1:probe13 0.0755525159336836 0.0666451999688012 1.13365277572957 0.257346302908308 df.mm.trans1:probe14 -0.0302277814090888 0.0666451999688012 -0.453562768560068 0.650290641387147 df.mm.trans1:probe15 0.0333244178351943 0.0666451999688012 0.500027276544965 0.6172202986027 df.mm.trans1:probe16 -0.056307710992713 0.0666451999688012 -0.844887719131648 0.398475576991598 df.mm.trans1:probe17 0.162718171968044 0.0666451999688012 2.44155876258482 0.0148820642884005 * df.mm.trans1:probe18 0.0545042622917217 0.0666451999688012 0.81782727514115 0.413746996613401 df.mm.trans1:probe19 -0.0065996898578531 0.0666451999688012 -0.0990272346837076 0.92114631061991 df.mm.trans1:probe20 -0.0897219765745565 0.0666451999688012 -1.34626314598138 0.178673636863608 df.mm.trans1:probe21 -0.0129033180800834 0.0666451999688012 -0.193612114392692 0.846538380347617 df.mm.trans2:probe2 -0.0457447984608174 0.0666451999688012 -0.68639299577812 0.492703142267842 df.mm.trans2:probe3 0.119106006470055 0.0666451999688012 1.78716556519917 0.0743633492348722 . df.mm.trans2:probe4 0.0215156927743124 0.0666451999688012 0.322839346035193 0.746917786965075 df.mm.trans2:probe5 0.0144380746602114 0.0666451999688012 0.216640878367389 0.828554270536793 df.mm.trans2:probe6 0.0110404035276498 0.0666451999688012 0.165659395317565 0.868475050168572 df.mm.trans3:probe2 -0.0298294663235995 0.0666451999688012 -0.447586117793385 0.65459681248684 df.mm.trans3:probe3 -0.0695275548429974 0.0666451999688012 -1.04324924939149 0.297209699908597