fitVsDatCorrelation=0.955574807168097 cont.fitVsDatCorrelation=0.263993693828373 fstatistic=8453.41434541883,58,830 cont.fstatistic=776.448470605382,58,830 residuals=-0.953927830175368,-0.0997783913457672,-0.0071836334733376,0.0938179515320497,0.778677427950392 cont.residuals=-1.06811149110907,-0.399107964936894,-0.19108925635463,0.221972931664534,1.94157692242054 predictedValues: Include Exclude Both Lung 101.165283507594 43.1344080258358 59.4834556113147 cerebhem 79.3091841141895 43.1424566180847 60.6755648196551 cortex 88.5114025437372 43.4041883497181 65.4750635366848 heart 86.6267687084655 42.150008344326 58.7622515711817 kidney 102.194580907601 44.1378289649134 58.2742067473635 liver 93.661324412485 42.6247158464471 59.8205753360992 stomach 84.337080433749 42.0365922790458 61.8790061181387 testicle 89.57994653996 41.7782385211835 60.7819978233817 diffExp=58.0308754817586,36.1667274961048,45.1072141940191,44.4767603641395,58.0567519426873,51.0366085660379,42.3004881547031,47.8017080187764 diffExpScore=0.997395678255592 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 62.3493202275046 73.3194784115312 64.7441587735465 cerebhem 77.8872462510798 72.9895867264773 66.9028519998512 cortex 86.2299119628179 72.824974500959 71.6978445836695 heart 69.1150696745189 67.6485669274858 87.1009048181926 kidney 72.6618056026048 62.0969654503125 70.4504550326268 liver 74.4737765495168 61.8684682308809 78.1779372647937 stomach 79.8209780442128 71.5582654301806 72.9324559462473 testicle 77.9535836050086 68.9666627865842 71.173514367223 cont.diffExp=-10.9701581840266,4.89765952460243,13.4049374618589,1.46650274703309,10.5648401522923,12.6053083186359,8.26271261403217,8.9869208184244 cont.diffExpScore=1.41698225140495 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=0,0,0,0,0,1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.489729433419596 cont.tran.correlation=0.167017554510098 tran.covariance=0.000755353291966218 cont.tran.covariance=0.000901998290674178 tran.mean=66.7371255073335 cont.tran.mean=71.9852912738547 weightedLogRatios: wLogRatio Lung 3.57216003611708 cerebhem 2.4773536507905 cortex 2.94068745549891 heart 2.95455765534439 kidney 3.53211718877066 liver 3.26399004019471 stomach 2.84547897563938 testicle 3.13778974026360 cont.weightedLogRatios: wLogRatio Lung -0.682943785521794 cerebhem 0.280745451448013 cortex 0.738776030908429 heart 0.0906130359815878 kidney 0.66103890018018 liver 0.782120538110901 stomach 0.472627795424707 testicle 0.526079838402665 varWeightedLogRatios=0.133752158658505 cont.varWeightedLogRatios=0.23130233941246 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17965487608913 0.0864709029479634 48.3359689051052 1.55828748047574e-243 *** df.mm.trans1 0.381187594269563 0.0748620389544863 5.09186764871996 4.38940613052576e-07 *** df.mm.trans2 -0.420180117438714 0.0663233373878523 -6.33532831711321 3.87941359418163e-10 *** df.mm.exp2 -0.263057980573888 0.0857207797368315 -3.06877727175947 0.00221925335062917 ** df.mm.exp3 -0.223360473969567 0.0857207797368315 -2.60567478101924 0.00933360286979899 ** df.mm.exp4 -0.166034352357929 0.0857207797368314 -1.93692069609803 0.0530940933228933 . df.mm.exp5 0.0536578523862538 0.0857207797368315 0.625960852794235 0.531512709876703 df.mm.exp6 -0.0946084988250682 0.0857207797368315 -1.10368220069303 0.27005089535013 df.mm.exp7 -0.247197290734943 0.0857207797368314 -2.88374990864357 0.00403132688714795 ** df.mm.exp8 -0.175165019679640 0.0857207797368315 -2.04343707812047 0.0413244751453097 * df.mm.trans1:exp2 0.0196562676607100 0.0794652896246188 0.247356647834080 0.804693395168884 df.mm.trans2:exp2 0.263244556484023 0.0596591378215915 4.41247671515545 1.15663803003412e-05 *** df.mm.trans1:exp3 0.0897362103312163 0.0794652896246188 1.12925040297614 0.259118478513654 df.mm.trans2:exp3 0.22959540749387 0.0596591378215915 3.84845332797914 0.000127988637886555 *** df.mm.trans1:exp4 0.0108875779956027 0.0794652896246188 0.137010486553738 0.891055733133354 df.mm.trans2:exp4 0.142948226093627 0.0596591378215915 2.39608266752209 0.0167918159413627 * df.mm.trans1:exp5 -0.0435348500648537 0.0794652896246188 -0.547847371733059 0.58394400441821 df.mm.trans2:exp5 -0.030661646654968 0.0596591378215915 -0.513947196935037 0.60742570543829 df.mm.trans1:exp6 0.017538193467115 0.0794652896246188 0.220702567749550 0.825378309456507 df.mm.trans2:exp6 0.0827217593679678 0.0596591378215915 1.38657316194116 0.16594426247652 df.mm.trans1:exp7 0.0652632721731256 0.0794652896246187 0.821280240485108 0.411722490793139 df.mm.trans2:exp7 0.221416765764551 0.0596591378215915 3.71136382202991 0.000219894415621293 *** df.mm.trans1:exp8 0.0535408540182793 0.0794652896246188 0.67376403296581 0.500648925976001 df.mm.trans2:exp8 0.143219605425537 0.0596591378215915 2.40063149846098 0.0165860075438998 * df.mm.trans1:probe2 -0.200576626063355 0.0533069368357069 -3.7626740152325 0.000179928476522291 *** df.mm.trans1:probe3 -0.365448769175325 0.0533069368357069 -6.85555747278532 1.38612602574280e-11 *** df.mm.trans1:probe4 1.13251181145715 0.0533069368357069 21.2451113998086 2.5264131159576e-80 *** df.mm.trans1:probe5 -0.271827227692992 0.0533069368357069 -5.09928433011953 4.2258973697283e-07 *** df.mm.trans1:probe6 -0.428727512817103 0.0533069368357069 -8.04262143477593 3.02546884770093e-15 *** df.mm.trans1:probe7 0.0114271542387929 0.0533069368357069 0.214365238693261 0.830314899958569 df.mm.trans1:probe8 -0.256211795523169 0.0533069368357069 -4.80634999367566 1.82434998451756e-06 *** df.mm.trans1:probe9 -0.352702314476942 0.0533069368357069 -6.61644310127926 6.59245123887633e-11 *** df.mm.trans1:probe10 1.10232000004220 0.0533069368357069 20.6787346164642 6.04405359761502e-77 *** df.mm.trans1:probe11 -0.50248568519541 0.0533069368357069 -9.42627198302693 4.11903401606284e-20 *** df.mm.trans1:probe12 -0.634618250542057 0.0533069368357069 -11.9049843831388 2.77676101453419e-30 *** df.mm.trans1:probe13 -0.668083790403954 0.0533069368357069 -12.5327739701683 3.99587423988622e-33 *** df.mm.trans1:probe14 -0.495713796285634 0.0533069368357069 -9.29923619159426 1.22237296830001e-19 *** df.mm.trans1:probe15 -0.588779366866821 0.0533069368357069 -11.0450797178883 1.47634492266732e-26 *** df.mm.trans1:probe16 -0.567164040259163 0.0533069368357069 -10.6395916540313 7.17069673464724e-25 *** df.mm.trans1:probe17 0.722536409125913 0.0533069368357069 13.5542661427496 5.92366064730895e-38 *** df.mm.trans1:probe18 0.847652121124203 0.0533069368357069 15.9013473938051 6.7222301405027e-50 *** df.mm.trans1:probe19 0.897765405817074 0.0533069368357069 16.8414367642997 5.71514984022266e-55 *** df.mm.trans1:probe20 0.73796428503089 0.0533069368357069 13.8436820578401 2.29558203333908e-39 *** df.mm.trans1:probe21 0.817527744922102 0.0533069368357069 15.3362356468116 6.33725204771762e-47 *** df.mm.trans1:probe22 0.79594280172597 0.0533069368357069 14.9313175540189 7.88189165331797e-45 *** df.mm.trans2:probe2 -0.00180883475046398 0.0533069368357069 -0.033932445903595 0.972939178320334 df.mm.trans2:probe3 0.00540886477739978 0.0533069368357069 0.101466433797725 0.919204707886203 df.mm.trans2:probe4 0.00381896535102563 0.0533069368357069 0.0716410579507835 0.94290482314235 df.mm.trans2:probe5 0.0260585996507002 0.0533069368357069 0.488840687489011 0.625083628966752 df.mm.trans2:probe6 0.0392161554083653 0.0533069368357069 0.735667020771242 0.46214119998447 df.mm.trans3:probe2 0.0651666711631548 0.0533069368357069 1.22248013169468 0.221873138707112 df.mm.trans3:probe3 -0.00205141366287749 0.0533069368357069 -0.0384830527629077 0.969311797358505 df.mm.trans3:probe4 -0.268939468198006 0.0533069368357069 -5.0451120278564 5.57002900780588e-07 *** df.mm.trans3:probe5 -0.244648935305179 0.0533069368357069 -4.58943900789484 5.13133413899452e-06 *** df.mm.trans3:probe6 0.0776257611935873 0.0533069368357069 1.45620374760665 0.145714459078352 df.mm.trans3:probe7 -0.0685566724974274 0.0533069368357069 -1.28607413156604 0.198775840170272 df.mm.trans3:probe8 -0.126881806392586 0.0533069368357069 -2.38021191845329 0.0175275688240501 * df.mm.trans3:probe9 0.0736086398245434 0.0533069368357069 1.38084542451589 0.167698183719058 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21365974562093 0.282964098751174 14.8911461355606 1.26684339476138e-44 *** df.mm.trans1 -0.0398431811877344 0.244975693108921 -0.162641365280348 0.870840403577817 df.mm.trans2 0.012358290537517 0.217033970391374 0.0569417336614699 0.954605314247488 df.mm.exp2 0.185201812245480 0.280509424043799 0.660233833058538 0.509286962533675 df.mm.exp3 0.215479797046369 0.280509424043799 0.768173111405782 0.442602839920512 df.mm.exp4 -0.274103903403167 0.280509424043799 -0.977164686489708 0.328772343213846 df.mm.exp5 -0.0975322926553254 0.280509424043799 -0.347697026535895 0.728155884280675 df.mm.exp6 -0.180665347516496 0.280509424043799 -0.644061596619608 0.519713426289465 df.mm.exp7 0.103629025712916 0.280509424043799 0.369431530032071 0.7119003297997 df.mm.exp8 0.0674804533010875 0.280509424043799 0.240563943728861 0.809952504297047 df.mm.trans1:exp2 0.0373076381887649 0.260039195776212 0.143469287687197 0.885954388746586 df.mm.trans2:exp2 -0.189711337937466 0.19522629682863 -0.971750942466504 0.331457566697358 df.mm.trans1:exp3 0.108784557419023 0.260039195776212 0.418339078054381 0.675807438400373 df.mm.trans2:exp3 -0.222247153424985 0.195226296828630 -1.13840787350525 0.255278688683499 df.mm.trans1:exp4 0.377123925873833 0.260039195776212 1.45025800725204 0.147364359037937 df.mm.trans2:exp4 0.193603765083664 0.195226296828630 0.991688969307297 0.321638285112533 df.mm.trans1:exp5 0.250595399621088 0.260039195776212 0.963683181964416 0.335485466845936 df.mm.trans2:exp5 -0.0685968942163614 0.195226296828630 -0.351371179655044 0.725399076336746 df.mm.trans1:exp6 0.358359648773506 0.260039195776212 1.37809858896006 0.168544239300489 df.mm.trans2:exp6 0.0108496898049837 0.195226296828630 0.0555749403703927 0.955693803027034 df.mm.trans1:exp7 0.143404557414165 0.260039195776212 0.551472853875373 0.581458000070907 df.mm.trans2:exp7 -0.127943315825935 0.195226296828630 -0.655359026444289 0.512418221115299 df.mm.trans1:exp8 0.155880344729251 0.260039195776212 0.599449418630722 0.549036823195981 df.mm.trans2:exp8 -0.128683522560700 0.19522629682863 -0.659150558357715 0.509981919073855 df.mm.trans1:probe2 0.094318639303922 0.174439595570996 0.540695127130898 0.588862804373934 df.mm.trans1:probe3 0.00935093904118075 0.174439595570996 0.0536055991793157 0.95726229480447 df.mm.trans1:probe4 -0.119170027618663 0.174439595570996 -0.683159274868655 0.494696848506775 df.mm.trans1:probe5 -0.249012068167902 0.174439595570996 -1.42749739445799 0.15381268703806 df.mm.trans1:probe6 -0.148598749236066 0.174439595570996 -0.851863642251954 0.394535496185675 df.mm.trans1:probe7 0.366044132510109 0.174439595570996 2.09840048821445 0.0361714787582637 * df.mm.trans1:probe8 -0.277955285764574 0.174439595570996 -1.59341854041073 0.111447048404736 df.mm.trans1:probe9 -0.293751257469961 0.174439595570996 -1.68397121369389 0.0925632267404917 . df.mm.trans1:probe10 -0.0777771856619083 0.174439595570996 -0.445868871727884 0.655808250043261 df.mm.trans1:probe11 -0.0596123290310129 0.174439595570996 -0.341736225860206 0.732635910274144 df.mm.trans1:probe12 -0.0651944963801708 0.174439595570996 -0.373736800792094 0.708695586537049 df.mm.trans1:probe13 -0.0729930811456014 0.174439595570996 -0.418443306444686 0.675731275826428 df.mm.trans1:probe14 0.00332168637555413 0.174439595570996 0.0190420435491220 0.984812142030083 df.mm.trans1:probe15 -0.171695386748784 0.174439595570996 -0.98426842934811 0.325270371883979 df.mm.trans1:probe16 -0.104471165810678 0.174439595570996 -0.598895941421506 0.549405694069618 df.mm.trans1:probe17 -0.163295989150846 0.174439595570996 -0.93611767796369 0.349484887274501 df.mm.trans1:probe18 0.066233258527063 0.174439595570996 0.379691653779984 0.704271444067003 df.mm.trans1:probe19 0.212028096490597 0.174439595570996 1.21548147252097 0.224528125941249 df.mm.trans1:probe20 -0.118763050740529 0.174439595570996 -0.680826221545515 0.496171339942429 df.mm.trans1:probe21 -0.147632626423566 0.174439595570996 -0.846325204666506 0.397615285532584 df.mm.trans1:probe22 0.0456483054321276 0.174439595570996 0.261685457838321 0.793628877423601 df.mm.trans2:probe2 0.154014856421569 0.174439595570996 0.88291225347909 0.37753932495298 df.mm.trans2:probe3 0.139969351257654 0.174439595570996 0.802394380699464 0.422554546577987 df.mm.trans2:probe4 0.157367702821776 0.174439595570996 0.902132926338552 0.367247973964827 df.mm.trans2:probe5 0.200687119791539 0.174439595570996 1.15046769705368 0.250282637965932 df.mm.trans2:probe6 0.380085063571846 0.174439595570996 2.17889213929732 0.0296203000620907 * df.mm.trans3:probe2 0.0177648779286876 0.174439595570996 0.101839710591724 0.918908501995552 df.mm.trans3:probe3 -0.160734849772591 0.174439595570996 -0.921435579155383 0.357090806527268 df.mm.trans3:probe4 -0.177017265505473 0.174439595570996 -1.01477686259269 0.310507921809003 df.mm.trans3:probe5 -0.0327642405794854 0.174439595570996 -0.187825708218582 0.851059189967382 df.mm.trans3:probe6 0.0671757464243915 0.174439595570996 0.385094600824452 0.700265989533316 df.mm.trans3:probe7 0.0872836416187307 0.174439595570996 0.500365993930585 0.616950035043098 df.mm.trans3:probe8 0.0860116205206316 0.174439595570996 0.493073950550552 0.622090759096733 df.mm.trans3:probe9 -0.0293018647026376 0.174439595570996 -0.167977141925395 0.86664218232004