chr11.3817_chr11_94718312_94722709_-_2.R fitVsDatCorrelation=0.913511185925149 cont.fitVsDatCorrelation=0.213290165030636 fstatistic=5511.39558837843,59,853 cont.fstatistic=943.642803306906,59,853 residuals=-1.03576542931222,-0.137659398687575,-0.00324513194798559,0.124089755754093,0.92410219988223 cont.residuals=-1.30890437202219,-0.397081127936001,-0.0743494794685323,0.345400718790332,1.72506292123412 predictedValues: Include Exclude Both chr11.3817_chr11_94718312_94722709_-_2.R.tl.Lung 93.983312546803 48.2865497918088 65.7117977238464 chr11.3817_chr11_94718312_94722709_-_2.R.tl.cerebhem 119.547401790883 53.3927587636325 86.0900433364059 chr11.3817_chr11_94718312_94722709_-_2.R.tl.cortex 207.672564469233 49.5009821723555 168.474120152924 chr11.3817_chr11_94718312_94722709_-_2.R.tl.heart 131.012376205398 50.0133500688628 83.0754433954596 chr11.3817_chr11_94718312_94722709_-_2.R.tl.kidney 186.221275653693 49.5119591268644 114.566847014452 chr11.3817_chr11_94718312_94722709_-_2.R.tl.liver 143.964604381295 52.7258982429043 93.2976251351765 chr11.3817_chr11_94718312_94722709_-_2.R.tl.stomach 135.207367370102 50.1487591527701 98.0644247845133 chr11.3817_chr11_94718312_94722709_-_2.R.tl.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 99.1591720601917 103.629529470081 107.359843790633 cerebhem 108.487253506163 108.720383319992 107.730210367638 cortex 93.4342254424303 90.7935590353416 98.77269103211 heart 93.1305616534253 90.5980786958253 122.285265605074 kidney 98.7809554277663 117.572498702459 109.320920126958 liver 108.994075203141 97.7352528579937 105.766424787188 stomach 104.071477205193 79.1650386420435 115.763941600160 testicle 87.570495188739 105.739499009733 92.8995715004924 cont.diffExp=-4.47035740988895,-0.233129813829393,2.64066640708867,2.53248295760001,-18.7915432746931,11.2588223451471,24.906438563149,-18.1690038209943 cont.diffExpScore=62.6138646296322 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=0,0,0,0,0,0,1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,0,0,1,-1 cont.diffExp1.2Score=2 tran.correlation=-0.0870263956840263 cont.tran.correlation=-0.0103730268303678 tran.covariance=6.46615260544241e-05 cont.tran.covariance=-0.000259255555843077 tran.mean=99.0863333495876 cont.tran.mean=99.2238784637824 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 -0.20366931214543 cerebhem -0.0100626705454480 cortex 0.129669354720358 heart 0.124619649196721 kidney -0.815020041946379 liver 0.505553476105119 stomach 1.23321642943125 testicle -0.860982108274765 varWeightedLogRatios=1.58079323226029 cont.varWeightedLogRatios=0.464935297421651 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) 4.50312317484174 0.275907367074635 16.3211414852276 2.55482311529071e-52 *** df.mm.trans1 0.0297080575876484 0.238266853284314 0.124683971681948 0.900803123684578 df.mm.trans2 0.120202969025295 0.210507687929724 0.57101462757704 0.568140129464053 df.mm.exp2 0.134419470742761 0.270780194437014 0.49641544508909 0.619729225306317 df.mm.exp3 -0.108337557180087 0.270780194437014 -0.400094096266288 0.689187384500718 df.mm.exp4 -0.327283616303758 0.270780194437014 -1.20866896112628 0.227124989913035 df.mm.exp5 0.104309740957331 0.270780194437014 0.385219240920495 0.700171025237051 df.mm.exp6 0.0509602613090804 0.270780194437014 0.188197890229871 0.850766287861476 df.mm.exp7 -0.296302879625215 0.270780194437014 -1.09425610038159 0.2741515696262 df.mm.exp8 0.0405411457307513 0.270780194437014 0.149719760025439 0.881021109690076 df.mm.trans1:exp2 -0.044513141467305 0.250287764587794 -0.177847852613231 0.858884690443886 df.mm.trans2:exp2 -0.0864624978579696 0.184849764701975 -0.467744700662018 0.640086646881861 df.mm.trans1:exp3 0.0488689171208399 0.250287764587794 0.195250923277546 0.845243012563401 df.mm.trans2:exp3 -0.0238964181688879 0.184849764701975 -0.129274809775469 0.897170678449287 df.mm.trans1:exp4 0.264559656075093 0.250287764587794 1.05702193038004 0.290800837795640 df.mm.trans2:exp4 0.192894299973946 0.184849764701975 1.04351931572616 0.297003481842732 df.mm.trans1:exp5 -0.108131271210118 0.250287764587794 -0.432027795638362 0.665830395690336 df.mm.trans2:exp5 0.0219230898819884 0.184849764701975 0.118599501153459 0.905620583538107 df.mm.trans1:exp6 0.0436069059102756 0.250287764587794 0.174227078107846 0.861728346339112 df.mm.trans2:exp6 -0.109520262433928 0.184849764701975 -0.592482563396839 0.553684515583502 df.mm.trans1:exp7 0.344654465918479 0.250287764587794 1.37703281854829 0.168863363205038 df.mm.trans2:exp7 0.0270153269927516 0.184849764701975 0.146147478393101 0.88383949654129 df.mm.trans1:exp8 -0.164823375034515 0.250287764587794 -0.658535487365784 0.510371805331966 df.mm.trans2:exp8 -0.0203849555853569 0.184849764701975 -0.110278504374743 0.912214444096534 df.mm.trans1:probe2 0.121894395626382 0.171360318165347 0.71133385448529 0.47707184841649 df.mm.trans1:probe3 0.0346648049925202 0.171360318165347 0.20229190377128 0.839736765515736 df.mm.trans1:probe4 -0.0430968997015176 0.171360318165347 -0.251498714305217 0.801489132169233 df.mm.trans1:probe5 -0.147219074228401 0.171360318165347 -0.859119986497392 0.390515683825153 df.mm.trans1:probe6 0.11315201541482 0.171360318165347 0.660316324259149 0.509229123229966 df.mm.trans1:probe7 0.167559383019272 0.171360318165347 0.977819047100466 0.328441059262252 df.mm.trans1:probe8 0.164249765022222 0.171360318165347 0.958505252445532 0.338079635662736 df.mm.trans1:probe9 -0.00776064398839985 0.171360318165347 -0.0452884545937383 0.963887989733814 df.mm.trans1:probe10 0.0989480184482532 0.171360318165347 0.577426673267365 0.563803633168557 df.mm.trans1:probe11 0.243862826475539 0.171360318165347 1.42309975311924 0.155072863531078 df.mm.trans1:probe12 0.155760592392073 0.171360318165347 0.908965354754876 0.363625158377681 df.mm.trans1:probe13 0.202405962402002 0.171360318165347 1.18117172382173 0.237863731808805 df.mm.trans1:probe14 0.274852329621086 0.171360318165347 1.60394385680283 0.109096599160089 df.mm.trans1:probe15 0.187591321125699 0.171360318165347 1.09471856223266 0.273948966935836 df.mm.trans1:probe16 0.00407666515689271 0.171360318165347 0.0237900186025513 0.98102566577085 df.mm.trans1:probe17 0.0448203121946275 0.171360318165347 0.26155595807998 0.793726943555949 df.mm.trans1:probe18 0.0404949499466676 0.171360318165347 0.236314628615440 0.813245268455559 df.mm.trans1:probe19 0.0544801684774135 0.171360318165347 0.317927563748132 0.750617698010152 df.mm.trans1:probe20 0.164222133882929 0.171360318165347 0.958344006600582 0.338160864135736 df.mm.trans1:probe21 -0.0287251826333363 0.171360318165347 -0.167630306367774 0.866913860588302 df.mm.trans1:probe22 0.198410161747566 0.171360318165347 1.15785360270001 0.247247875440922 df.mm.trans2:probe2 0.148921248681161 0.171360318165347 0.869053292358305 0.385062438877295 df.mm.trans2:probe3 0.00765321731281126 0.171360318165347 0.0446615493875695 0.964387535056792 df.mm.trans2:probe4 0.0459405363926168 0.171360318165347 0.268093201999593 0.788692394956323 df.mm.trans2:probe5 0.0372469977437871 0.171360318165347 0.217360694369436 0.827979240156564 df.mm.trans2:probe6 0.0401768612937035 0.171360318165347 0.234458372415816 0.814685411021365 df.mm.trans3:probe2 0.151863734165040 0.171360318165347 0.886224627679006 0.375746268671275 df.mm.trans3:probe3 0.0179377698084949 0.171360318165347 0.104678667736755 0.916655397755662 df.mm.trans3:probe4 0.115396878940550 0.171360318165347 0.673416577280177 0.50086471951091 df.mm.trans3:probe5 0.0891101361640796 0.171360318165347 0.520016168959821 0.603187230965165 df.mm.trans3:probe6 0.0765759672796595 0.171360318165347 0.446871061512449 0.655081593923208 df.mm.trans3:probe7 -0.0211463597703958 0.171360318165347 -0.123402897454891 0.901817133034616 df.mm.trans3:probe8 -0.0980634969145283 0.171360318165347 -0.572264909195058 0.567293305034105 df.mm.trans3:probe9 -0.0935441101657365 0.171360318165347 -0.54589131933961 0.585283377091448 df.mm.trans3:probe10 -0.0066103518836696 0.171360318165347 -0.0385757446907354 0.969237667012445