fitVsDatCorrelation=0.845624464971 cont.fitVsDatCorrelation=0.241069600383448 fstatistic=10823.1484930216,64,968 cont.fstatistic=3263.44026141139,64,968 residuals=-0.59457144067955,-0.0897446109712246,0.00399796175781949,0.0867249303365712,1.0252288619852 cont.residuals=-0.843001327585713,-0.181909835919577,-0.0542305601476275,0.121782445787383,1.36233735584806 predictedValues: Include Exclude Both Lung 56.9620426561399 63.6272278236974 78.1829784096259 cerebhem 58.7027272210803 57.5334690249998 81.6737845233973 cortex 77.311819908746 56.2359462231207 111.980938866968 heart 61.8470923638141 61.1579801562123 82.6737858822627 kidney 61.9584129699095 64.9764991116445 83.539333313806 liver 56.7040499353978 57.5989227865627 75.0202220591064 stomach 54.7894616815189 56.0426983005408 75.9770363709718 testicle 54.2542549062779 60.2411090313865 72.3123944724188 diffExp=-6.6651851675575,1.16925819608048,21.0758736856252,0.689112207601809,-3.01808614173504,-0.89487285116492,-1.25323661902186,-5.98685412510861 diffExpScore=6.6632468596863 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 68.816563529267 69.6426575297001 70.4793897433497 cerebhem 70.1075311928675 71.3601471418097 69.7081916346249 cortex 77.3761613679571 65.2730217356552 67.7242277076328 heart 68.0402267770977 66.1571970725428 62.5722107084521 kidney 70.220680491181 73.2097608109837 68.9264494249486 liver 67.8102530542803 62.3899757809434 64.8540817287601 stomach 70.4167784666507 69.6869872678312 63.153046552628 testicle 65.203878335306 67.2149078222955 70.4066616770298 cont.diffExp=-0.826094000433116,-1.25261594894224,12.1031396323019,1.88302970455496,-2.98908031980274,5.42027727333688,0.729791198819527,-2.01102948698956 cont.diffExpScore=1.93599261705615 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=-0.187840485810121 cont.tran.correlation=0.000592043763826084 tran.covariance=-0.00105073134356261 cont.tran.covariance=4.36453208180538e-05 tran.mean=59.9964821313156 cont.tran.mean=68.932920523523 weightedLogRatios: wLogRatio Lung -0.453438045055656 cerebhem 0.0817333306313944 cortex 1.33322468734805 heart 0.0461529926105613 kidney -0.197394971714846 liver -0.0633481069252064 stomach -0.0907988474553943 testicle -0.423511181117899 cont.weightedLogRatios: wLogRatio Lung -0.0505642196619875 cerebhem -0.0754219850025688 cortex 0.725242968726363 heart 0.118045005517658 kidney -0.178102394614488 liver 0.347819116940778 stomach 0.0442682361202117 testicle -0.127358148923059 varWeightedLogRatios=0.316532067521568 cont.varWeightedLogRatios=0.0911782762874748 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66605887551206 0.0740342881150186 49.518391664907 1.48467576500603e-267 *** df.mm.trans1 0.34487260042887 0.0634185736678065 5.43803779371247 6.82233790938467e-08 *** df.mm.trans2 0.479608298977814 0.0555219402393937 8.63817613199195 2.32784652211623e-17 *** df.mm.exp2 -0.114254701367253 0.07026851630153 -1.62597287349819 0.104280910293794 df.mm.exp3 -0.177300310122396 0.07026851630153 -2.52318277735624 0.0117889801851311 * df.mm.exp4 -0.0131517921518909 0.07026851630153 -0.187164790778491 0.851570650782368 df.mm.exp5 0.038796814827704 0.07026851630153 0.552122299853644 0.580992011342342 df.mm.exp6 -0.0627828635337582 0.07026851630153 -0.893470743915383 0.371827205226518 df.mm.exp7 -0.137194061786215 0.07026851630153 -1.95242576629198 0.0511756202836055 . df.mm.exp8 -0.0253337916963908 0.07026851630153 -0.360528342276087 0.71853072087761 df.mm.trans1:exp2 0.144355759747971 0.0642853825351739 2.24554562880583 0.0249581003126733 * df.mm.trans2:exp2 0.0135800606650745 0.0445623137448489 0.304743168023772 0.760627274924499 df.mm.trans1:exp3 0.482762035884033 0.0642853825351739 7.50967042344173 1.34439624692586e-13 *** df.mm.trans2:exp3 0.0538149858997406 0.0445623137448489 1.20763446458076 0.22748287134992 df.mm.trans1:exp4 0.0954317510918431 0.0642853825351739 1.4845015667384 0.13800145450383 df.mm.trans2:exp4 -0.0264293420698467 0.0445623137448489 -0.593087293922249 0.55326126929585 df.mm.trans1:exp5 0.0452814589092455 0.064285382535174 0.704381884707143 0.481364404528887 df.mm.trans2:exp5 -0.0178126516047851 0.0445623137448489 -0.399724567866365 0.689447577100265 df.mm.trans1:exp6 0.0582433714495738 0.0642853825351739 0.906012675863067 0.365154600180900 df.mm.trans2:exp6 -0.0367547597298921 0.0445623137448489 -0.82479468952935 0.409691357934917 df.mm.trans1:exp7 0.0983068044662862 0.0642853825351739 1.52922485002710 0.126535408481568 df.mm.trans2:exp7 0.0102664424731211 0.0445623137448489 0.230383963721090 0.817842099180569 df.mm.trans1:exp8 -0.0233699152056327 0.0642853825351739 -0.363533890349735 0.716285478517594 df.mm.trans2:exp8 -0.029352703934302 0.0445623137448489 -0.658688956376171 0.510252165078275 df.mm.trans1:probe2 0.075525230376143 0.0470520821292004 1.60514108958575 0.108788925812999 df.mm.trans1:probe3 0.245434214584473 0.0470520821292004 5.21622430885278 2.23338717884361e-07 *** df.mm.trans1:probe4 0.0342199188819358 0.0470520821292004 0.72727746219543 0.467231854538955 df.mm.trans1:probe5 -0.273571127642512 0.0470520821292004 -5.81421937697278 8.26752460093945e-09 *** df.mm.trans1:probe6 0.0531197736114672 0.0470520821292004 1.12895691768975 0.259195708781449 df.mm.trans1:probe7 0.095007792629713 0.0470520821292004 2.01920485407704 0.0437411592365678 * df.mm.trans1:probe8 -0.138513038661995 0.0470520821292004 -2.94382378832144 0.00331929282095368 ** df.mm.trans1:probe9 0.126182848398174 0.0470520821292005 2.68176970472186 0.0074480483992306 ** df.mm.trans1:probe10 0.0799018191742615 0.0470520821292004 1.69815692650665 0.0897995277048061 . df.mm.trans1:probe11 0.0956907686051255 0.0470520821292004 2.03372017294300 0.0422521167346253 * df.mm.trans1:probe12 0.0905220611624769 0.0470520821292004 1.92386940314165 0.0546639176854896 . df.mm.trans1:probe13 0.0928916613400996 0.0470520821292004 1.97423062140009 0.0486394613504208 * df.mm.trans1:probe14 0.157670965521955 0.0470520821292004 3.35098806231370 0.00083637166521839 *** df.mm.trans1:probe15 0.000435480250525501 0.0470520821292004 0.00925528118670104 0.99261736648687 df.mm.trans1:probe16 0.183849483261200 0.0470520821292004 3.90736126737955 9.97986643005464e-05 *** df.mm.trans1:probe17 0.0704103580928436 0.0470520821292004 1.49643448082709 0.134866429701469 df.mm.trans1:probe18 -0.00225932677985814 0.0470520821292004 -0.0480175728175907 0.961712142534447 df.mm.trans1:probe19 -0.0263882758568873 0.0470520821292004 -0.56083120369525 0.575042400684618 df.mm.trans1:probe20 -0.0344283409613383 0.0470520821292004 -0.73170706594453 0.464524481071869 df.mm.trans1:probe21 0.0745471731464821 0.0470520821292004 1.58435439566272 0.113439713641641 df.mm.trans1:probe22 0.163535675919310 0.0470520821292004 3.47563101395294 0.000532272816587692 *** df.mm.trans2:probe2 0.0371106299806600 0.0470520821292004 0.788713874101422 0.430472373677873 df.mm.trans2:probe3 0.00383924586175586 0.0470520821292004 0.0815956635290584 0.934985117331196 df.mm.trans2:probe4 0.0513512436432075 0.0470520821292004 1.09137027139845 0.275381555306586 df.mm.trans2:probe5 -0.0662279817465526 0.0470520821292004 -1.40754624980669 0.159586499128706 df.mm.trans2:probe6 0.128787470510973 0.0470520821292004 2.73712585465048 0.00631143258468512 ** df.mm.trans3:probe2 -0.329431459158759 0.0470520821292004 -7.00142149404084 4.73320210896932e-12 *** df.mm.trans3:probe3 -0.137142270939883 0.0470520821292004 -2.9146908007876 0.00364242375762102 ** df.mm.trans3:probe4 -0.20797204692692 0.0470520821292004 -4.42003918882589 1.09836239080610e-05 *** df.mm.trans3:probe5 -0.0162231056436904 0.0470520821292004 -0.344790387790775 0.730326857402415 df.mm.trans3:probe6 0.139822563228583 0.0470520821292004 2.97165517233953 0.0030352418747836 ** df.mm.trans3:probe7 -0.270419135375469 0.0470520821292004 -5.74722994474346 1.21490972589554e-08 *** df.mm.trans3:probe8 -0.130470603666427 0.0470520821292004 -2.77289755867057 0.00566267371964611 ** df.mm.trans3:probe9 0.250830475586396 0.0470520821292004 5.33091128459821 1.21628236542421e-07 *** df.mm.trans3:probe10 -0.0691954772601931 0.0470520821292004 -1.47061456430746 0.141720351080157 df.mm.trans3:probe11 -0.265024276004896 0.0470520821292004 -5.63257275793162 2.32648116830463e-08 *** df.mm.trans3:probe12 -0.481968759806668 0.0470520821292004 -10.2433035478266 1.89559690544602e-23 *** df.mm.trans3:probe13 -0.521521636574908 0.0470520821292004 -11.0839226018279 5.8126316052222e-27 *** df.mm.trans3:probe14 0.478468210489038 0.0470520821292004 10.1689062170556 3.78678929067609e-23 *** df.mm.trans3:probe15 -0.412069754794451 0.0470520821292004 -8.75773687682827 8.763308607412e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23500644864918 0.134608052567096 31.4617615208289 3.16087302425078e-150 *** df.mm.trans1 0.0279549461814069 0.115306716865352 0.242439876369483 0.808490694949805 df.mm.trans2 0.0121970007203660 0.100949174236138 0.120823184663552 0.903856159857322 df.mm.exp2 0.0539505185327441 0.127761181703176 0.422276295612902 0.67291713106905 df.mm.exp3 0.0923121463926887 0.127761181703176 0.722536729561212 0.470139071965222 df.mm.exp4 0.0563101062317042 0.127761181703176 0.440745032889003 0.659496001507953 df.mm.exp5 0.09243021526126 0.127761181703176 0.723460866822603 0.469571567873352 df.mm.exp6 -0.0415232404832604 0.127761181703176 -0.325006703364173 0.745246226033473 df.mm.exp7 0.133382661486509 0.127761181703176 1.04399990441849 0.296746077004881 df.mm.exp8 -0.0883752854908763 0.127761181703176 -0.691722511585687 0.489277476733248 df.mm.trans1:exp2 -0.0353647602077660 0.116882735985073 -0.302566156667331 0.762285511437373 df.mm.trans2:exp2 -0.029588243436571 0.0810225427137079 -0.365185323066455 0.715052847985228 df.mm.trans1:exp3 0.0249221291656401 0.116882735985073 0.213223355490436 0.83119764916021 df.mm.trans2:exp3 -0.157110614020805 0.0810225427137078 -1.93909754938146 0.0527797807930478 . df.mm.trans1:exp4 -0.0676554706486826 0.116882735985073 -0.578832024066605 0.562837176138821 df.mm.trans2:exp4 -0.107653697444647 0.0810225427137078 -1.32868821242800 0.184263980191687 df.mm.trans1:exp5 -0.0722318186581066 0.116882735985073 -0.61798535129543 0.536730357727279 df.mm.trans2:exp5 -0.0424787339095008 0.0810225427137079 -0.524282903087834 0.600201813339174 df.mm.trans1:exp6 0.0267921840498313 0.116882735985073 0.229222766082861 0.81874419990164 df.mm.trans2:exp6 -0.0684494167205131 0.0810225427137078 -0.84481940985706 0.398420426211162 df.mm.trans1:exp7 -0.110395561162334 0.116882735985073 -0.944498434537264 0.345150682656028 df.mm.trans2:exp7 -0.132746332589076 0.0810225427137078 -1.63838764056238 0.101665824501023 df.mm.trans1:exp8 0.0344497713889184 0.116882735985073 0.294737893484268 0.768257310277423 df.mm.trans2:exp8 0.0528930758719571 0.0810225427137078 0.65281925375823 0.514027814842227 df.mm.trans1:probe2 -0.0644401989421777 0.0855494029306929 -0.753251299654111 0.451482075028839 df.mm.trans1:probe3 -0.0456402556993505 0.0855494029306929 -0.533495899864147 0.593812819759788 df.mm.trans1:probe4 -0.126563387358142 0.0855494029306929 -1.47941871038745 0.139353789620871 df.mm.trans1:probe5 -0.0294101027155603 0.0855494029306929 -0.343779169790193 0.731087023966231 df.mm.trans1:probe6 -0.173781078740423 0.0855494029306929 -2.03135349619226 0.0424919289233417 * df.mm.trans1:probe7 -0.0362141816682765 0.0855494029306929 -0.423313084927256 0.67216088060877 df.mm.trans1:probe8 -0.0463288961959936 0.0855494029306929 -0.541545523509107 0.588256244921155 df.mm.trans1:probe9 -0.0151027247512343 0.085549402930693 -0.176538049756696 0.859908185898595 df.mm.trans1:probe10 -0.126189243722238 0.0855494029306929 -1.47504528844543 0.140525532180991 df.mm.trans1:probe11 -0.188128379141105 0.0855494029306929 -2.19906127566449 0.0281094642091379 * df.mm.trans1:probe12 0.0148424525696163 0.0855494029306929 0.173495688586404 0.862298089167703 df.mm.trans1:probe13 -0.0421397996710803 0.0855494029306929 -0.492578536231509 0.62242212060974 df.mm.trans1:probe14 0.00290272619721654 0.0855494029306929 0.0339304086034143 0.972939639577713 df.mm.trans1:probe15 -0.0394088661151356 0.0855494029306929 -0.460656237975878 0.645148725872822 df.mm.trans1:probe16 -0.0707256044587062 0.0855494029306929 -0.8267223620018 0.408598175611994 df.mm.trans1:probe17 0.0137016251818661 0.0855494029306929 0.160160383503393 0.872788150156208 df.mm.trans1:probe18 0.00949103665825739 0.0855494029306929 0.110942172979822 0.911685200520307 df.mm.trans1:probe19 -0.0908945216424041 0.0855494029306929 -1.06247990668084 0.288282863779464 df.mm.trans1:probe20 0.0269485479717292 0.0855494029306929 0.315005681495654 0.752825229705583 df.mm.trans1:probe21 -0.130389752291945 0.0855494029306929 -1.52414567285267 0.127798946137869 df.mm.trans1:probe22 -0.0086558015714055 0.0855494029306929 -0.101178982843608 0.919429332995101 df.mm.trans2:probe2 -0.0539181959919054 0.0855494029306929 -0.630258004671134 0.528674543295321 df.mm.trans2:probe3 -0.0599233645885494 0.0855494029306929 -0.700453334982312 0.483812555913897 df.mm.trans2:probe4 0.0752245976686561 0.0855494029306929 0.879311778827944 0.379450476263534 df.mm.trans2:probe5 0.045249435163578 0.0855494029306929 0.528927539099676 0.596976974909342 df.mm.trans2:probe6 -0.0869821311084284 0.0855494029306929 -1.01674737787353 0.309527674947521 df.mm.trans3:probe2 -0.0320049598917035 0.0855494029306929 -0.37411085051794 0.70840377573242 df.mm.trans3:probe3 0.00634336119359337 0.0855494029306929 0.0741485150835288 0.940907531295686 df.mm.trans3:probe4 -0.0629384130818218 0.0855494029306929 -0.735696696010968 0.462093521176602 df.mm.trans3:probe5 -0.0133872647612514 0.0855494029306929 -0.15648577666985 0.875682759305284 df.mm.trans3:probe6 0.0189068408533357 0.0855494029306929 0.221004942239666 0.825135191964394 df.mm.trans3:probe7 -0.0515087759511811 0.0855494029306929 -0.602093926861307 0.547252615407847 df.mm.trans3:probe8 0.020888357807114 0.0855494029306929 0.244167195696696 0.807153073532904 df.mm.trans3:probe9 0.0174581454029016 0.0855494029306929 0.204070920483748 0.8383409730304 df.mm.trans3:probe10 -0.0232193759257177 0.0855494029306929 -0.271414821498271 0.786129873600003 df.mm.trans3:probe11 0.000291630232115005 0.0855494029306929 0.00340891019837119 0.99728079082496 df.mm.trans3:probe12 0.00965334517596227 0.0855494029306929 0.112839421962802 0.910181259565068 df.mm.trans3:probe13 -0.0245107998122116 0.0855494029306929 -0.286510471990889 0.774548482825399 df.mm.trans3:probe14 -0.127298982445561 0.0855494029306929 -1.48801719339515 0.137072035927882 df.mm.trans3:probe15 -0.0362441968888114 0.0855494029306929 -0.423663937411396 0.671905038456966