fitVsDatCorrelation=0.796691627151746 cont.fitVsDatCorrelation=0.206356657410766 fstatistic=11371.7826629791,69,1083 cont.fstatistic=4328.95907677091,69,1083 residuals=-0.527421629202054,-0.0847284548450679,-0.00652971960840354,0.0735285050705906,1.83649610738391 cont.residuals=-0.440843662989654,-0.151677849066251,-0.0468712283086451,0.100846660414065,1.87197607857997 predictedValues: Include Exclude Both Lung 48.7737846988411 47.9895028526764 59.6771778276954 cerebhem 54.6050932841311 43.9930732383829 69.9171354259169 cortex 50.0652027060317 45.4696386246378 60.0179402436898 heart 50.0292746651702 45.080654788527 59.010176650479 kidney 48.4523283189644 48.1588650872097 59.5938935089937 liver 52.0613794830102 47.2692782710459 62.470526034705 stomach 53.4273327064493 45.3139167533416 63.4461222077143 testicle 50.826558381212 46.4106544209549 61.3898418201579 diffExp=0.784281846164767,10.6120200457481,4.5955640813939,4.94861987664326,0.293463231754721,4.79210121196425,8.11341595310774,4.41590396025711 diffExpScore=0.97471898266238 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 53.7484117296325 57.1405501524233 52.7635053546339 cerebhem 55.4255194262954 51.6308197656312 53.7766888937829 cortex 55.3261210678443 49.5646804435324 49.4856497561249 heart 53.6477665360376 57.0490893328974 51.178643754023 kidney 53.2429351824014 48.2689366341049 54.4225292115032 liver 56.6086793858435 54.6643788234314 53.3961733424459 stomach 54.4409349889758 50.8922291221333 56.3582391434178 testicle 53.8485915919197 55.0526469756507 50.4852525530851 cont.diffExp=-3.39213842279076,3.79469966066417,5.761440624312,-3.40132279685983,4.97399854829643,1.94430056241216,3.54870586684245,-1.20405538373097 cont.diffExpScore=2.15119458715988 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=-0.70992378594038 cont.tran.correlation=-0.0887661981554018 tran.covariance=-0.000978030751676336 cont.tran.covariance=-0.000100929214672613 tran.mean=48.6204086425366 cont.tran.mean=53.7845181974222 weightedLogRatios: wLogRatio Lung 0.0628826223199208 cerebhem 0.841058829183123 cortex 0.372145433073573 heart 0.402094019814874 kidney 0.0235566854156851 liver 0.376994962643363 stomach 0.641698184457806 testicle 0.352923381093904 cont.weightedLogRatios: wLogRatio Lung -0.245712369974536 cerebhem 0.282237149901107 cortex 0.435276819162746 heart -0.246699059719448 kidney 0.385032922706072 liver 0.140453249867232 stomach 0.267158272338408 testicle -0.0883935981583873 varWeightedLogRatios=0.0728327718257694 cont.varWeightedLogRatios=0.0756515622874924 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.83490298170774 0.0693201098910432 55.3216517939082 0 *** df.mm.trans1 0.0600734271766241 0.0599945121134777 1.00131537136259 0.316898032134431 df.mm.trans2 -0.00359024404505364 0.0519660697743625 -0.0690882350857498 0.94493213941432 df.mm.exp2 -0.132377003858476 0.06574290941754 -2.01355560670027 0.0443032045776379 * df.mm.exp3 -0.0334980911093934 0.06574290941754 -0.509531619549167 0.610483447136304 df.mm.exp4 -0.0258739729127748 0.06574290941754 -0.393562943015596 0.693981211628361 df.mm.exp5 -0.00169308213709858 0.06574290941754 -0.0257530759149347 0.979459033742162 df.mm.exp6 0.00436351178067693 0.06574290941754 0.0663723558834887 0.947093628604253 df.mm.exp7 -0.0274800144933647 0.06574290941754 -0.417992065407941 0.676035780567222 df.mm.exp8 -0.0205219038093193 0.0657429094175401 -0.312153873187792 0.754983673678918 df.mm.trans1:exp2 0.245311196128943 0.0615317683458061 3.98674055246233 7.14777087799612e-05 *** df.mm.trans2:exp2 0.0454269025611332 0.0416125056671476 1.09166467706839 0.275223312522754 df.mm.trans1:exp3 0.0596313313961893 0.0615317683458061 0.969114540330834 0.332704418392896 df.mm.trans2:exp3 -0.0204393850634474 0.0416125056671476 -0.491183713543691 0.623396036368078 df.mm.trans1:exp4 0.0512893305881009 0.0615317683458061 0.833542281766661 0.404722758181331 df.mm.trans2:exp4 -0.0366551094096432 0.0416125056671475 -0.880867633947405 0.378584903987282 df.mm.trans1:exp5 -0.00491949428370201 0.0615317683458061 -0.0799504778743664 0.936291413463334 df.mm.trans2:exp5 0.0052160209726997 0.0416125056671475 0.125347437965450 0.900271761739401 df.mm.trans1:exp6 0.0608669156576592 0.0615317683458061 0.989194968615066 0.322788708330863 df.mm.trans2:exp6 -0.0194852317385892 0.0416125056671475 -0.468254228535258 0.639697019530852 df.mm.trans1:exp7 0.118609508223272 0.0615317683458061 1.9276141643889 0.0541644061442748 . df.mm.trans2:exp7 -0.0298880835222590 0.0416125056671476 -0.718247628761638 0.47275952950956 df.mm.trans1:exp8 0.0617479548378652 0.0615317683458061 1.00351341263011 0.315837366817408 df.mm.trans2:exp8 -0.0129313385984799 0.0416125056671475 -0.310756066984185 0.756045868160323 df.mm.trans1:probe2 -0.169605505286261 0.0440783068310067 -3.84782260209125 0.000126135617268071 *** df.mm.trans1:probe3 -0.130490758539276 0.0440783068310067 -2.96043037768145 0.00313901884988311 ** df.mm.trans1:probe4 0.165445162546132 0.0440783068310067 3.75343733552284 0.000183688700988243 *** df.mm.trans1:probe5 0.0907041351218548 0.0440783068310067 2.0577953565596 0.0398487086359555 * df.mm.trans1:probe6 0.00774781517900919 0.0440783068310067 0.175773883709141 0.860504447418143 df.mm.trans1:probe7 0.118712378696306 0.0440783068310067 2.69321548923015 0.00718594287664794 ** df.mm.trans1:probe8 -0.118412841897463 0.0440783068310067 -2.68641992877472 0.00733275531762492 ** df.mm.trans1:probe9 0.149523591245054 0.0440783068310067 3.39222628986902 0.000718327529837835 *** df.mm.trans1:probe10 -0.00581441246414028 0.0440783068310067 -0.131910975764844 0.89507920873469 df.mm.trans1:probe11 -0.0121511857296561 0.0440783068310067 -0.275672697144265 0.782852057514406 df.mm.trans1:probe12 -0.0911781680565743 0.0440783068310067 -2.06854969284881 0.0388252656547978 * df.mm.trans1:probe13 0.00471712092873899 0.0440783068310067 0.107016835896717 0.914795443714614 df.mm.trans1:probe14 -0.0980438182417096 0.0440783068310067 -2.22430999034521 0.0263331355396769 * df.mm.trans1:probe15 -0.0886099553755212 0.0440783068310067 -2.01028491668806 0.0446486143359686 * df.mm.trans1:probe16 -0.102424154085536 0.0440783068310067 -2.32368621776293 0.0203266943428275 * df.mm.trans1:probe17 -0.0362532728328779 0.0440783068310067 -0.822474260907311 0.410988076146201 df.mm.trans1:probe18 -0.0254512738453664 0.0440783068310067 -0.577410424201295 0.56378224833948 df.mm.trans1:probe19 -0.109166644363057 0.0440783068310067 -2.47665239914034 0.0134139516895727 * df.mm.trans1:probe20 0.0410097870167233 0.0440783068310067 0.930384807518857 0.352379217984184 df.mm.trans1:probe21 0.0833638343975185 0.0440783068310067 1.89126671124483 0.0588554117411247 . df.mm.trans1:probe22 -0.117226266579432 0.0440783068310067 -2.65950021694049 0.00794117332180475 ** df.mm.trans1:probe23 -0.19165965179583 0.0440783068310067 -4.34816274886965 1.50220214463964e-05 *** df.mm.trans1:probe24 0.113803852049584 0.0440783068310067 2.58185625155477 0.00995749589128544 ** df.mm.trans1:probe25 0.162393726879416 0.0440783068310067 3.68420972933518 0.000240756799418334 *** df.mm.trans1:probe26 -0.0719264284720539 0.0440783068310067 -1.63178746288543 0.103014986037521 df.mm.trans1:probe27 -0.12957559051089 0.0440783068310067 -2.9396680550293 0.00335539167128609 ** df.mm.trans1:probe28 0.230783522333947 0.0440783068310067 5.23576196378767 1.97284800243183e-07 *** df.mm.trans1:probe29 0.00288055685742318 0.0440783068310067 0.0653508962689299 0.947906680359362 df.mm.trans2:probe2 0.133068008021850 0.0440783068310067 3.01890017082608 0.00259632756606810 ** df.mm.trans2:probe3 0.202284564581743 0.0440783068310067 4.58920904918804 4.96935670571735e-06 *** df.mm.trans2:probe4 0.248447455575649 0.0440783068310067 5.63650179504807 2.21232551551030e-08 *** df.mm.trans2:probe5 0.120919973358260 0.0440783068310067 2.74329896159260 0.00618292384809405 ** df.mm.trans2:probe6 0.0490016148965887 0.0440783068310067 1.11169458220021 0.26651621427566 df.mm.trans3:probe2 -0.141836037529574 0.0440783068310067 -3.21781955176646 0.00132999573264351 ** df.mm.trans3:probe3 -0.0709812978715759 0.0440783068310067 -1.61034538245113 0.107613880392301 df.mm.trans3:probe4 0.31759580001195 0.0440783068310067 7.20526315199882 1.08359787892648e-12 *** df.mm.trans3:probe5 0.314778150244299 0.0440783068310067 7.14133942238611 1.69252764492475e-12 *** df.mm.trans3:probe6 0.4939427001156 0.0440783068310067 11.2060270828765 1.18295330441694e-27 *** df.mm.trans3:probe7 0.451845124035655 0.0440783068310067 10.2509637170956 1.3460969447803e-23 *** df.mm.trans3:probe8 0.168567236147134 0.0440783068310067 3.82426749723871 0.000138645937000058 *** df.mm.trans3:probe9 -0.0627667812285933 0.0440783068310067 -1.42398349077330 0.154739298893945 df.mm.trans3:probe10 -0.00903157449941793 0.0440783068310067 -0.204898398979897 0.83769000805611 df.mm.trans3:probe11 0.0191290905346013 0.0440783068310067 0.433979703620218 0.664389617885702 df.mm.trans3:probe12 0.84710157165176 0.0440783068310067 19.2181059698934 4.70674874121965e-71 *** df.mm.trans3:probe13 0.239981538253858 0.0440783068310067 5.44443640210437 6.4275547599088e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09149020363119 0.112226492470617 36.4574363286141 1.56138756704672e-190 *** df.mm.trans1 -0.0784060032128259 0.097128721702322 -0.807238084046065 0.419706576192917 df.mm.trans2 -0.0544779044434045 0.0841309938404118 -0.647536680081822 0.517421899252893 df.mm.exp2 -0.0896895547048314 0.106435147612155 -0.8426685800414 0.399599880921519 df.mm.exp3 -0.0491674593307163 0.106435147612155 -0.461947584362643 0.644211687886484 df.mm.exp4 0.0270212436651269 0.106435147612155 0.253875193217105 0.79964016920951 df.mm.exp5 -0.209133241993104 0.106435147612155 -1.96488891766446 0.0496824212477752 * df.mm.exp6 -0.00437285315538522 0.106435147612155 -0.0410846722486796 0.967235966697035 df.mm.exp7 -0.168910225815750 0.106435147612155 -1.58697788846267 0.112809554868980 df.mm.exp8 0.00877655213574569 0.106435147612155 0.0824591531335783 0.934296836000722 df.mm.trans1:exp2 0.120415564505050 0.0996174782154606 1.20877948992652 0.227011415468233 df.mm.trans2:exp2 -0.0117056937351342 0.0673689865939037 -0.173754932751111 0.862090535258662 df.mm.trans1:exp3 0.0780984910214354 0.0996174782154606 0.783983819109713 0.433221033548705 df.mm.trans2:exp3 -0.093068073330466 0.0673689865939037 -1.38146761641933 0.167420159613747 df.mm.trans1:exp4 -0.0288955230795985 0.0996174782154606 -0.290064791813952 0.771822197833765 df.mm.trans2:exp4 -0.0286231550030794 0.0673689865939037 -0.424871390386471 0.671014828056612 df.mm.trans1:exp5 0.199684247794839 0.0996174782154605 2.00451016600667 0.0452640264099136 * df.mm.trans2:exp5 0.0404074369777756 0.0673689865939037 0.599792857525812 0.548769759703117 df.mm.trans1:exp6 0.0562210552928118 0.0996174782154606 0.564369388785495 0.572619628014986 df.mm.trans2:exp6 -0.0399288842602758 0.0673689865939037 -0.592689400257195 0.553512766982895 df.mm.trans1:exp7 0.181712460765401 0.0996174782154606 1.82410219592569 0.068412077813708 . df.mm.trans2:exp7 0.0531064433057392 0.0673689865939037 0.78829215030147 0.430698346627053 df.mm.trans1:exp8 -0.00691442051232324 0.0996174782154606 -0.0694097123937245 0.944676311542614 df.mm.trans2:exp8 -0.0460006319100175 0.0673689865939037 -0.68281614784124 0.494869054919333 df.mm.trans1:probe2 -0.0103276091392457 0.0713610203079995 -0.144723395134640 0.884956194324508 df.mm.trans1:probe3 -0.120087958829893 0.0713610203079995 -1.68282289563103 0.0926976138797695 . df.mm.trans1:probe4 0.00675304826739439 0.0713610203079995 0.0946321708721053 0.924624501039429 df.mm.trans1:probe5 -0.0825892591086633 0.0713610203079995 -1.15734414603662 0.247386973375002 df.mm.trans1:probe6 0.0153847876766950 0.0713610203079995 0.215590915184412 0.829347232486838 df.mm.trans1:probe7 -0.0087386758326314 0.0713610203079995 -0.122457271419531 0.902559602933703 df.mm.trans1:probe8 0.00653352601055696 0.0713610203079995 0.0915559500460864 0.927067782554628 df.mm.trans1:probe9 -0.00335215619590895 0.0713610203079995 -0.0469746113696356 0.962542122870339 df.mm.trans1:probe10 -0.0524063240668032 0.0713610203079995 -0.734383054510902 0.462874280528801 df.mm.trans1:probe11 -0.00256756489424863 0.0713610203079995 -0.0359799353087558 0.971304988340357 df.mm.trans1:probe12 -0.0332429914898266 0.0713610203079995 -0.465842435356828 0.641421957962574 df.mm.trans1:probe13 -0.0765003353451928 0.0713610203079995 -1.07201851956449 0.283950539302653 df.mm.trans1:probe14 -0.0755070804251357 0.0713610203079995 -1.05809978752043 0.290245896205781 df.mm.trans1:probe15 -0.0435465010497225 0.0713610203079995 -0.610228116999624 0.54183872955939 df.mm.trans1:probe16 -0.0549377745550825 0.0713610203079995 -0.769856909528016 0.441552661187389 df.mm.trans1:probe17 -0.0460661445611406 0.0713610203079995 -0.645536517868097 0.51871630095545 df.mm.trans1:probe18 -0.0574977180203406 0.0713610203079995 -0.805730043827515 0.420575393735961 df.mm.trans1:probe19 -0.0910629012483818 0.0713610203079995 -1.27608743338236 0.202198212333296 df.mm.trans1:probe20 -0.0775313975411889 0.0713610203079995 -1.0864670545146 0.27751416571854 df.mm.trans1:probe21 -0.0296928826741854 0.0713610203079995 -0.416093863933401 0.677423759664289 df.mm.trans1:probe22 -0.0464335975022986 0.0713610203079995 -0.650685728733806 0.515387395843562 df.mm.trans1:probe23 0.043316936783392 0.0713610203079995 0.607011174958441 0.543970729570673 df.mm.trans1:probe24 -0.073944833842424 0.0713610203079995 -1.03620763160718 0.30033659513945 df.mm.trans1:probe25 -0.116010766582941 0.0713610203079995 -1.62568817096826 0.104306943150261 df.mm.trans1:probe26 -0.0626794908293251 0.0713610203079995 -0.878343534870938 0.379952138363023 df.mm.trans1:probe27 -0.0241618049093384 0.0713610203079995 -0.338585474325538 0.73498765726448 df.mm.trans1:probe28 -0.0477260496285112 0.0713610203079995 -0.6687971867908 0.503767389543037 df.mm.trans1:probe29 -0.0437199691247308 0.0713610203079995 -0.61265896894456 0.540230478125431 df.mm.trans2:probe2 0.0161944295252227 0.0713610203079995 0.226936630885129 0.820515845966269 df.mm.trans2:probe3 0.0699593789969937 0.0713610203079995 0.980358446320467 0.327128159875426 df.mm.trans2:probe4 0.0262587712524897 0.0713610203079995 0.367970793286795 0.712966916781918 df.mm.trans2:probe5 0.024426601789593 0.0713610203079995 0.342296139883734 0.732194456462531 df.mm.trans2:probe6 0.0246936070645805 0.0713610203079995 0.346037752235058 0.729381553499717 df.mm.trans3:probe2 -0.0267432508436378 0.0713610203079995 -0.374759928154221 0.707912389774554 df.mm.trans3:probe3 -0.0308211291919814 0.0713610203079995 -0.43190426732907 0.665896965036736 df.mm.trans3:probe4 0.00147405512812239 0.0713610203079995 0.0206563067870983 0.983523628638656 df.mm.trans3:probe5 0.0888412846723123 0.0713610203079995 1.24495535922646 0.213417477015714 df.mm.trans3:probe6 -0.00316045435337999 0.0713610203079995 -0.0442882450354441 0.964682805809046 df.mm.trans3:probe7 -0.0170805035828195 0.0713610203079995 -0.239353410434699 0.810876842173673 df.mm.trans3:probe8 0.0799960201181916 0.0713610203079995 1.12100443313342 0.262534482342985 df.mm.trans3:probe9 -0.0158942769972848 0.0713610203079995 -0.222730517706781 0.823787246780626 df.mm.trans3:probe10 0.0106453552571151 0.0713610203079995 0.149176051732009 0.881442489691476 df.mm.trans3:probe11 0.0362276928131467 0.0713610203079995 0.507667808795127 0.611789696673895 df.mm.trans3:probe12 -0.0204671075520106 0.0713610203079995 -0.286810747151219 0.774312094355839 df.mm.trans3:probe13 -0.00924258466524045 0.0713610203079995 -0.129518673154458 0.896971300377148