fitVsDatCorrelation=0.913511185925149 cont.fitVsDatCorrelation=0.243052513658523 fstatistic=5511.39558837843,59,853 cont.fstatistic=957.472545839765,59,853 residuals=-1.03576542931222,-0.137659398687575,-0.00324513194798559,0.124089755754093,0.92410219988223 cont.residuals=-1.13495099663667,-0.405248358750414,-0.0795910250773436,0.345966143554995,1.85539190619137 predictedValues: Include Exclude Both Lung 93.983312546803 48.2865497918088 65.7117977238464 cerebhem 119.547401790883 53.3927587636325 86.0900433364059 cortex 207.672564469233 49.5009821723555 168.474120152924 heart 131.012376205398 50.0133500688628 83.0754433954596 kidney 186.221275653693 49.5119591268644 114.566847014452 liver 143.964604381295 52.7258982429043 93.2976251351765 stomach 135.207367370102 50.1487591527701 98.0644247845133 testicle 162.562725698141 51.6294481586559 113.501412111174 diffExp=45.6967627549942,66.1546430272506,158.171582296878,80.9990261365348,136.709316526828,91.2387061383908,85.058608217332,110.933277539485 diffExpScore=0.998711276970137 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 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 96.0974602534658 108.098051397323 102.932550401363 cerebhem 101.246155918862 120.378524501728 109.72896345483 cortex 105.404847437342 100.999894815357 130.146137643050 heart 109.038001698645 90.1849912547846 100.047955952763 kidney 108.369303783869 95.4400571748554 90.1184406549345 liver 112.479229378598 99.5305714549711 112.573726933870 stomach 108.291902259851 111.818635408981 100.508019211789 testicle 85.7579669467752 113.029506244916 112.904574537804 cont.diffExp=-12.0005911438574,-19.1323685828661,4.40495262198489,18.8530104438604,12.9292466090136,12.9486579236265,-3.52673314913041,-27.2715392981406 cont.diffExpScore=8.05104491182902 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,-1 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,1,0,0,0,-1 cont.diffExp1.2Score=2 tran.correlation=-0.0870263956840263 cont.tran.correlation=-0.570329389614208 tran.covariance=6.46615260544241e-05 cont.tran.covariance=-0.00483387226717075 tran.mean=99.0863333495876 cont.tran.mean=104.135318745645 weightedLogRatios: wLogRatio Lung 2.80379924482811 cerebhem 3.53100511165662 cortex 6.62347581574168 heart 4.23122862707189 kidney 6.04679000820371 liver 4.4872645824286 stomach 4.37480237778649 testicle 5.18153429548372 cont.weightedLogRatios: wLogRatio Lung -0.544156578972915 cerebhem -0.814215425454363 cortex 0.197926711682399 heart 0.872622519031196 kidney 0.587211654792309 liver 0.57013310744123 stomach -0.150652284145728 testicle -1.26727689187154 varWeightedLogRatios=1.58079323226029 cont.varWeightedLogRatios=0.574380071301023 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.50468687690046 0.114886537913786 39.209875749593 4.60092287450355e-193 *** df.mm.trans1 0.257008504697531 0.0992132039230476 2.59046673764183 0.0097481729657596 ** df.mm.trans2 -0.585388813321428 0.0876544172303293 -6.67837208686475 4.35290030035525e-11 *** df.mm.exp2 0.0710025183808895 0.112751607194581 0.629725111220426 0.529043081984829 df.mm.exp3 -0.123818818190608 0.112751607194581 -1.09815568284475 0.272446390035758 df.mm.exp4 0.132840802154207 0.112751607194581 1.17817213837987 0.239056561990965 df.mm.exp5 0.152999579160686 0.112751607194581 1.35696140363345 0.175152438610657 df.mm.exp6 0.163887764506645 0.112751607194581 1.45352929846769 0.146444648568098 df.mm.exp7 0.00118695636685025 0.112751607194581 0.0105271791363635 0.99160314295501 df.mm.exp8 0.0683490525980778 0.112751607194581 0.606191382089342 0.544548953033666 df.mm.trans1:exp2 0.169593201632433 0.104218655197757 1.62728257537604 0.104046399190443 df.mm.trans2:exp2 0.0295195650542961 0.0769705779369173 0.383517518583392 0.701431648960906 df.mm.trans1:exp3 0.916664207827516 0.104218655197757 8.79558660671765 7.77319105301522e-18 *** df.mm.trans2:exp3 0.148658279775513 0.0769705779369173 1.93136499374279 0.0537689398005078 . df.mm.trans1:exp4 0.199333751022299 0.104218655197757 1.91264942580634 0.0561275720503171 . df.mm.trans2:exp4 -0.0977038806459894 0.0769705779369173 -1.26936659779357 0.204656562239864 df.mm.trans1:exp5 0.530818801114433 0.104218655197757 5.09331846690205 4.33239548479428e-07 *** df.mm.trans2:exp5 -0.127938389902412 0.0769705779369173 -1.66217265520945 0.0968455639278042 . df.mm.trans1:exp6 0.262562461534656 0.104218655197757 2.51934225246371 0.0119391400563123 * df.mm.trans2:exp6 -0.0759340515699698 0.0769705779369173 -0.9865334729876 0.324151141539987 df.mm.trans1:exp7 0.362505457727228 0.104218655197757 3.47831640160168 0.0005301094097076 *** df.mm.trans2:exp7 0.0366537653598793 0.0769705779369173 0.476204886884436 0.634050364563942 df.mm.trans1:exp8 0.479597638574931 0.104218655197757 4.60184059816245 4.82355719347205e-06 *** df.mm.trans2:exp8 -0.00140989180781639 0.0769705779369173 -0.0183172823383487 0.985390023989968 df.mm.trans1:probe2 -0.65003843773187 0.0713536354558306 -9.11009556246447 5.74394456166706e-19 *** df.mm.trans1:probe3 -0.386423375473027 0.0713536354558306 -5.41560879140112 7.94665639053008e-08 *** df.mm.trans1:probe4 0.0216838826913899 0.0713536354558306 0.303893173106963 0.761283391646927 df.mm.trans1:probe5 -0.208536030690566 0.0713536354558306 -2.92257050896383 0.0035632714755463 ** df.mm.trans1:probe6 -0.534349014819821 0.0713536354558306 -7.48874267451437 1.73591082813748e-13 *** df.mm.trans1:probe7 -0.364293071127693 0.0713536354558306 -5.10545915145695 4.07099218343921e-07 *** df.mm.trans1:probe8 -0.365718987280966 0.0713536354558306 -5.12544294267044 3.67357813911433e-07 *** df.mm.trans1:probe9 0.684533574800168 0.0713536354558306 9.5935346591262 9.08980375214813e-21 *** df.mm.trans1:probe10 0.483527758939447 0.0713536354558306 6.77649787359133 2.29492555948038e-11 *** df.mm.trans1:probe11 -0.129105901639380 0.0713536354558306 -1.80938085094907 0.0707437608224823 . df.mm.trans1:probe12 -0.371939439722415 0.0713536354558306 -5.21262073538851 2.33718802095752e-07 *** df.mm.trans1:probe13 0.126283937196397 0.0713536354558306 1.76983185775544 0.07711235270337 . df.mm.trans1:probe14 -0.0421789171821148 0.0713536354558306 -0.591124991917538 0.554593277418986 df.mm.trans1:probe15 -0.179701413441015 0.0713536354558306 -2.51846191568268 0.0119688007167919 * df.mm.trans1:probe16 -0.517199055984276 0.0713536354558306 -7.24839109710721 9.4511474549933e-13 *** df.mm.trans1:probe17 -0.735380616618718 0.0713536354558306 -10.3061408423112 1.48771485419531e-23 *** df.mm.trans1:probe18 -0.880698937937735 0.0713536354558306 -12.3427339379632 2.54683385158367e-32 *** df.mm.trans1:probe19 -0.74833121979768 0.0713536354558306 -10.4876396979228 2.74522924421249e-24 *** df.mm.trans1:probe20 -0.748751503054783 0.0713536354558306 -10.4935298428946 2.59775368907725e-24 *** df.mm.trans1:probe21 -0.755909088264118 0.0713536354558306 -10.5938412729095 1.01074887925652e-24 *** df.mm.trans1:probe22 -0.691974660795742 0.0713536354558306 -9.6978192684252 3.63517647055105e-21 *** df.mm.trans2:probe2 -0.100592110522509 0.0713536354558306 -1.40976854059213 0.158972531840373 df.mm.trans2:probe3 -0.233503935139823 0.0713536354558306 -3.27248827124396 0.00110890413423450 ** df.mm.trans2:probe4 -0.167611030449715 0.0713536354558306 -2.34901879040865 0.0190502435604959 * df.mm.trans2:probe5 -0.0704825593141128 0.0713536354558306 -0.98779212669189 0.3235345692788 df.mm.trans2:probe6 -0.102130587299831 0.0713536354558306 -1.43132983550715 0.152701974438282 df.mm.trans3:probe2 -0.0707562339971895 0.0713536354558306 -0.991627596059756 0.321660425844382 df.mm.trans3:probe3 0.422390544310782 0.0713536354558306 5.91967797593509 4.67107012798256e-09 *** df.mm.trans3:probe4 0.422823847666405 0.0713536354558306 5.92575059371911 4.50825013246454e-09 *** df.mm.trans3:probe5 -0.0476007614722008 0.0713536354558306 -0.667110528680304 0.504881951758944 df.mm.trans3:probe6 -0.0638227911765585 0.0713536354558306 -0.894457455024364 0.371329490503884 df.mm.trans3:probe7 -0.165994790961155 0.0713536354558306 -2.32636767420083 0.0202323960148522 * df.mm.trans3:probe8 -0.175818074126397 0.0713536354558306 -2.46403806902358 0.0139344060000235 * df.mm.trans3:probe9 -0.328872602780912 0.0713536354558306 -4.60905181186586 4.66319084672115e-06 *** df.mm.trans3:probe10 0.0973699696417201 0.0713536354558306 1.36461119352488 0.172735152634531 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.64310865919512 0.273937370135788 16.9495262982688 9.27897452756831e-56 *** df.mm.trans1 -0.0760038445312216 0.236565612115672 -0.321280188830058 0.748076771819754 df.mm.trans2 0.0768723035835127 0.209004649046702 0.367801883518561 0.713112207774237 df.mm.exp2 0.0958548686680579 0.268846805851572 0.356540849962631 0.72152373868346 df.mm.exp3 -0.210057516249040 0.268846805851572 -0.781327922359663 0.434826447048126 df.mm.exp4 -0.026417813275797 0.268846805851572 -0.0982634448347588 0.921746189755939 df.mm.exp5 0.128590775134915 0.268846805851572 0.478305013621434 0.632555689730884 df.mm.exp6 -0.0147025956601606 0.268846805851572 -0.0546876337756372 0.956400127150704 df.mm.exp7 0.177143431659163 0.268846805851572 0.658901009063736 0.510137157167005 df.mm.exp8 -0.161692759027956 0.268846805851572 -0.601430835362893 0.547712863780978 df.mm.trans1:exp2 -0.043663017098894 0.248500693313486 -0.175705816014818 0.860566767513962 df.mm.trans2:exp2 0.0117475818153790 0.183529925096129 0.0640090808582082 0.948977992826179 df.mm.trans1:exp3 0.302503254702089 0.248500693313486 1.21731352403302 0.223821611307771 df.mm.trans2:exp3 0.142138293101661 0.183529925096129 0.774469302633955 0.438867925480898 df.mm.trans1:exp4 0.152751386718155 0.248500693313486 0.614691994140468 0.538922075805798 df.mm.trans2:exp4 -0.154757866127767 0.183529925096129 -0.843229604364017 0.399336432083925 df.mm.trans1:exp5 -0.0084087891199528 0.248500693313486 -0.0338380911853032 0.9730141772397 df.mm.trans2:exp5 -0.253131096835461 0.183529925096129 -1.37923609298525 0.168183472564478 df.mm.trans1:exp6 0.172108285049747 0.248500693313486 0.6925867399196 0.488757436075669 df.mm.trans2:exp6 -0.0678712551194132 0.183529925096129 -0.369810291612464 0.711615639652606 df.mm.trans1:exp7 -0.0576759392882093 0.248500693313486 -0.232095687618266 0.816519364169003 df.mm.trans2:exp7 -0.143303898152201 0.183529925096129 -0.780820338030113 0.435124804293447 df.mm.trans1:exp8 0.0478588622952371 0.248500693313486 0.192590457825656 0.847325560522535 df.mm.trans2:exp8 0.206302962310787 0.183529925096129 1.12408350955698 0.261293833627349 df.mm.trans1:probe2 0.00391362274377779 0.170136794104337 0.0230028005663358 0.981653418772315 df.mm.trans1:probe3 0.31717652335271 0.170136794104337 1.86424415143382 0.0626307387867857 . df.mm.trans1:probe4 -0.0551311820731977 0.170136794104337 -0.324040325100921 0.745986941939907 df.mm.trans1:probe5 -0.0379420538735187 0.170136794104337 -0.223009103194049 0.823581812456716 df.mm.trans1:probe6 0.0336389581802447 0.170136794104337 0.197717127311189 0.843313494464464 df.mm.trans1:probe7 0.0167286446543988 0.170136794104337 0.0983246730518499 0.921697586660811 df.mm.trans1:probe8 -0.0612819515986702 0.170136794104337 -0.360192231911276 0.718792549636565 df.mm.trans1:probe9 -0.135253952178617 0.170136794104337 -0.794971792495819 0.42685104597456 df.mm.trans1:probe10 -0.0381361714596855 0.170136794104337 -0.224150053258312 0.822694222731831 df.mm.trans1:probe11 0.0480830180781493 0.170136794104337 0.282613871568911 0.777541407902195 df.mm.trans1:probe12 -0.167020271036061 0.170136794104337 -0.981682251128087 0.326534759104474 df.mm.trans1:probe13 -0.0760094486266662 0.170136794104337 -0.446754912873539 0.655165429717137 df.mm.trans1:probe14 0.0483323412156293 0.170136794104337 0.284079299072659 0.776418552754956 df.mm.trans1:probe15 0.0533036308102947 0.170136794104337 0.313298667057322 0.754130352465649 df.mm.trans1:probe16 -0.141634492906256 0.170136794104337 -0.832474207897666 0.405374335819782 df.mm.trans1:probe17 -0.262231888131866 0.170136794104337 -1.54130027847505 0.123614593596509 df.mm.trans1:probe18 0.0142083145328031 0.170136794104337 0.0835111217864478 0.933464728021476 df.mm.trans1:probe19 0.126568260647899 0.170136794104337 0.743920568823465 0.457129397620877 df.mm.trans1:probe20 0.0485473043005373 0.170136794104337 0.285342771127835 0.775450817513555 df.mm.trans1:probe21 -0.0301844860428150 0.170136794104337 -0.177413041086834 0.859226083247241 df.mm.trans1:probe22 0.238583608991406 0.170136794104337 1.40230459993912 0.161188120527103 df.mm.trans2:probe2 -0.154463611229120 0.170136794104337 -0.90787893378545 0.36419860232387 df.mm.trans2:probe3 -0.0512703337419795 0.170136794104337 -0.301347712656076 0.763222806082223 df.mm.trans2:probe4 -0.0716236973264041 0.170136794104337 -0.420977118461988 0.673877835609066 df.mm.trans2:probe5 -0.123346980616643 0.170136794104337 -0.724987097975997 0.468658770479727 df.mm.trans2:probe6 -0.190371604648186 0.170136794104337 -1.11893259568204 0.263483887901708 df.mm.trans3:probe2 -0.00239900260736459 0.170136794104337 -0.0141004338302824 0.988753151525764 df.mm.trans3:probe3 -0.05128202274671 0.170136794104337 -0.301416416223649 0.763170440460837 df.mm.trans3:probe4 0.161347023613984 0.170136794104337 0.948337039400409 0.343226487232866 df.mm.trans3:probe5 -0.0988275489314147 0.170136794104337 -0.580871112869381 0.561480757033139 df.mm.trans3:probe6 0.106807239136514 0.170136794104337 0.627772726639096 0.530320854668368 df.mm.trans3:probe7 -0.0874945587261782 0.170136794104337 -0.514260064595563 0.607203381154262 df.mm.trans3:probe8 -0.0268499252758537 0.170136794104337 -0.157813748737901 0.874640914153092 df.mm.trans3:probe9 0.0486316131822127 0.170136794104337 0.285838306982494 0.775071365390229 df.mm.trans3:probe10 -0.148964767627248 0.170136794104337 -0.87555880203018 0.381516406092653