fitVsDatCorrelation=0.961945135698877 cont.fitVsDatCorrelation=0.255562798935199 fstatistic=5855.87980268124,49,623 cont.fstatistic=456.060885217931,49,623 residuals=-1.20216016866867,-0.118344580001261,-0.00968254268683229,0.097642506896497,0.676073142332168 cont.residuals=-1.08864783824627,-0.439904990117065,-0.198349340623918,0.132685430376695,3.16455541936789 predictedValues: Include Exclude Both Lung 54.4060088612471 79.9092811235036 105.270659422207 cerebhem 59.6321180249434 55.2265763261857 58.0693375168599 cortex 52.1450832864048 50.4327506897238 62.741041754839 heart 51.8347942761909 79.0304382206511 100.989686136705 kidney 54.948032327399 73.4678276804216 92.4773596005294 liver 54.5174580283174 175.459418813633 192.784981191139 stomach 55.8631804288373 52.6148989252194 74.9407480973434 testicle 63.123331502131 875.554102670264 864.839993039957 diffExp=-25.5032722622565,4.40554169875773,1.71233259668099,-27.1956439444603,-18.5197953530226,-120.941960785315,3.24828150361787,-812.430771168133 diffExpScore=1.01779949958789 diffExp1.5=0,0,0,-1,0,-1,0,-1 diffExp1.5Score=0.75 diffExp1.4=-1,0,0,-1,0,-1,0,-1 diffExp1.4Score=0.8 diffExp1.3=-1,0,0,-1,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,0,-1,-1,-1,0,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 83.2560326782848 86.7060001100749 115.034239868268 cerebhem 94.9152009709595 65.2851615366713 82.5850294405687 cortex 99.8729854546881 76.0941691169487 89.0011315293152 heart 82.157296214663 69.2738198870941 85.5023547519971 kidney 107.521728884204 84.2602590512085 86.312726481988 liver 92.0356498841048 122.984690323839 100.611759033346 stomach 92.143321838102 96.0118765800192 77.942678117025 testicle 74.7235870857857 75.2683888884978 74.6032689593507 cont.diffExp=-3.44996743179007,29.6300394342883,23.7788163377395,12.8834763275689,23.2614698329953,-30.9490404397345,-3.86855474191722,-0.544801802712072 cont.diffExpScore=2.48091611888371 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,1,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,1,1,0,0,-1,0,0 cont.diffExp1.3Score=1.5 cont.diffExp1.2=0,1,1,0,1,-1,0,0 cont.diffExp1.2Score=1.33333333333333 tran.correlation=0.754912627769444 cont.tran.correlation=0.123328167352240 tran.covariance=0.0412199230528558 cont.tran.covariance=0.00348409171911781 tran.mean=118.010331324067 cont.tran.mean=87.6568855315716 weightedLogRatios: wLogRatio Lung -1.61020274762890 cerebhem 0.310823750701463 cortex 0.131464152115595 heart -1.75412523038172 kidney -1.20587772378692 liver -5.35696658638649 stomach 0.239202467791094 testicle -14.3584558777067 cont.weightedLogRatios: wLogRatio Lung -0.180365294329341 cerebhem 1.63379354443533 cortex 1.21495492730647 heart 0.737427991910815 kidney 1.11062528514418 liver -1.35292224736879 stomach -0.186875906057374 testicle -0.0313637166014933 varWeightedLogRatios=24.6446989160462 cont.varWeightedLogRatios=0.96245594643849 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.59882458311807 0.103873910364805 34.6460874581405 2.07419405586674e-147 *** df.mm.trans1 0.357076212911732 0.0874936626283137 4.08116659178672 5.06398452976999e-05 *** df.mm.trans2 0.76438078142962 0.0806242555972875 9.4807793977987 5.2110277441682e-20 *** df.mm.exp2 0.317168975905395 0.105293665199784 3.01223226775893 0.00269848888501003 ** df.mm.exp3 0.0148230014574517 0.105293665199784 0.140777713733552 0.888091054322063 df.mm.exp4 -0.017955561721043 0.105293665199784 -0.170528413907657 0.864649955392699 df.mm.exp5 0.0554397423721822 0.105293665199784 0.526524955390155 0.598710917612377 df.mm.exp6 0.183521417057732 0.105293665199784 1.74294832181517 0.0818360121122299 . df.mm.exp7 -0.0516248198523471 0.105293665199784 -0.490293691974672 0.624098713311279 df.mm.exp8 0.436571023355902 0.105293665199784 4.14622306600832 3.84850382527311e-05 *** df.mm.trans1:exp2 -0.225449258719027 0.0919078459004471 -2.45299252213167 0.0144403199475250 * df.mm.trans2:exp2 -0.686616688569298 0.0762059600353967 -9.01001297339963 2.50007976826446e-18 *** df.mm.trans1:exp3 -0.0572677094478622 0.0919078459004471 -0.62309924562799 0.533447333255226 df.mm.trans2:exp3 -0.475074227415471 0.0762059600353967 -6.23408231055425 8.37977378742788e-10 *** df.mm.trans1:exp4 -0.0304574151024175 0.0919078459004472 -0.331390805692567 0.740460749337098 df.mm.trans2:exp4 0.0068966286445949 0.0762059600353967 0.090499859084401 0.92791909408043 df.mm.trans1:exp5 -0.0455264753524594 0.0919078459004471 -0.495349171840811 0.620528334739143 df.mm.trans2:exp5 -0.139484155882992 0.0762059600353967 -1.83035757069661 0.0676740667904725 . df.mm.trans1:exp6 -0.181475040658863 0.0919078459004471 -1.97453262973254 0.048762398385283 * df.mm.trans2:exp6 0.602994362009719 0.0762059600353967 7.9126929406786 1.15460451515551e-14 *** df.mm.trans1:exp7 0.0780557096897844 0.0919078459004471 0.84928233193859 0.396050434990261 df.mm.trans2:exp7 -0.366267856251792 0.0762059600353967 -4.8062888530197 1.92919284653907e-06 *** df.mm.trans1:exp8 -0.287955172529301 0.0919078459004471 -3.13308586125725 0.00181097946433256 ** df.mm.trans2:exp8 1.95739391750256 0.0762059600353968 25.6855752042671 8.81384008169607e-100 *** df.mm.trans1:probe2 -0.0330693960475514 0.0629250005142295 -0.525536682992529 0.599397152641182 df.mm.trans1:probe3 -0.0188024342089628 0.0629250005142295 -0.298807056898012 0.765186908314614 df.mm.trans1:probe4 0.0370563014068982 0.0629250005142295 0.588896322670963 0.556144281291331 df.mm.trans1:probe5 0.00963551161308192 0.0629250005142295 0.153126921483346 0.878347768515078 df.mm.trans1:probe6 0.0284612059655221 0.0629250005142295 0.45230362706292 0.651207708743417 df.mm.trans1:probe7 0.0923615057446769 0.0629250005142295 1.46780301930694 0.142662399534789 df.mm.trans1:probe8 0.0553520627145108 0.0629250005142295 0.879651366899771 0.379387374741367 df.mm.trans1:probe9 0.0805989857862671 0.0629250005142295 1.28087382006522 0.200714598900642 df.mm.trans1:probe10 0.257721177056875 0.0629250005142295 4.09568811999605 4.76458114365682e-05 *** df.mm.trans1:probe11 0.317341510938994 0.0629250005142295 5.04317057362967 6.01263315465576e-07 *** df.mm.trans1:probe12 0.0659673613181364 0.0629250005142295 1.04834899926968 0.294884365383576 df.mm.trans2:probe2 0.0561905501953214 0.0629250005142295 0.892976555202646 0.372214524929955 df.mm.trans2:probe3 0.0347321668141921 0.0629250005142295 0.551961327458996 0.581172787125695 df.mm.trans2:probe4 0.0283318319363449 0.0629250005142295 0.450247623437653 0.652688534257248 df.mm.trans2:probe5 -0.0356988342613848 0.0629250005142295 -0.567323543419153 0.570698747039611 df.mm.trans2:probe6 0.199430536858280 0.0629250005142295 3.16933707156953 0.0016027392965511 ** df.mm.trans3:probe2 -0.322174446853955 0.0629250005142295 -5.11997527566329 4.07791056248227e-07 *** df.mm.trans3:probe3 0.0231235488808684 0.0629250005142295 0.367477929152171 0.713387351702208 df.mm.trans3:probe4 0.0108043696174945 0.0629250005142295 0.171702336578468 0.863727305316507 df.mm.trans3:probe5 0.0333654293896609 0.0629250005142295 0.530241225538263 0.596133622682361 df.mm.trans3:probe6 0.137065664093413 0.0629250005142295 2.17823858519346 0.0297625165634363 * df.mm.trans3:probe7 -0.0358666063857022 0.0629250005142295 -0.569989767065501 0.568890120821335 df.mm.trans3:probe8 -0.314923327740705 0.0629250005142295 -5.00474096411791 7.28818532214624e-07 *** df.mm.trans3:probe9 -0.131727585458775 0.0629250005142295 -2.09340618803789 0.0367166508411972 * df.mm.trans3:probe10 -0.0371353966624404 0.0629250005142295 -0.590153299308163 0.555301867109642 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30592199815739 0.367528664567145 11.7158807279119 8.63213051913725e-29 *** df.mm.trans1 0.164409453158175 0.30957175744072 0.531086732579788 0.595547957909299 df.mm.trans2 0.200698808056671 0.285266289555526 0.703548983545794 0.481976540238161 df.mm.exp2 0.178706368725101 0.372552068390877 0.479681590541068 0.631622170165927 df.mm.exp3 0.30800793473703 0.372552068390877 0.826751374827617 0.408694522112279 df.mm.exp4 0.0589449395689891 0.372552068390877 0.158219332464274 0.874335240696609 df.mm.exp5 0.51441237650814 0.372552068390877 1.38077981617438 0.167841802262951 df.mm.exp6 0.583752883990716 0.372552068390877 1.56690281310759 0.117645170153849 df.mm.exp7 0.592629592917491 0.372552068390877 1.59072957365979 0.112177722817064 df.mm.exp8 0.183457862491679 0.372552068390877 0.492435495752496 0.622585000195689 df.mm.trans1:exp2 -0.0476430879456988 0.325190105469249 -0.146508418135754 0.883567439457293 df.mm.trans2:exp2 -0.462464680351357 0.2696333913444 -1.71516101194105 0.086812961551928 . df.mm.trans1:exp3 -0.126029292359616 0.325190105469249 -0.387555741210993 0.698477195865579 df.mm.trans2:exp3 -0.438559380946596 0.2696333913444 -1.62650248457703 0.104348469580119 df.mm.trans1:exp4 -0.0722298741089954 0.325190105469250 -0.222115842069572 0.824296497464027 df.mm.trans2:exp4 -0.283400970970527 0.2696333913444 -1.05106036591937 0.293638384185655 df.mm.trans1:exp5 -0.258640011049526 0.325190105469249 -0.79535018655106 0.426712739277678 df.mm.trans2:exp5 -0.543025132348887 0.2696333913444 -2.01393873971376 0.0444453589527969 * df.mm.trans1:exp6 -0.483497474098796 0.325190105469250 -1.48681483835773 0.137569620947767 df.mm.trans2:exp6 -0.234216092090839 0.2696333913444 -0.868646464456908 0.385375031226514 df.mm.trans1:exp7 -0.491204972824684 0.325190105469249 -1.51051635508982 0.131418733365752 df.mm.trans2:exp7 -0.490680781540215 0.2696333913444 -1.81980718001456 0.0692681335660874 . df.mm.trans1:exp8 -0.291582653525789 0.325190105469249 -0.896652907397152 0.370250499007489 df.mm.trans2:exp8 -0.324920704918830 0.2696333913444 -1.20504624185738 0.228642987036244 df.mm.trans1:probe2 -0.191609278819874 0.22264244530374 -0.86061432966419 0.389781566102642 df.mm.trans1:probe3 -0.123277872937893 0.22264244530374 -0.553703373001095 0.579980530613868 df.mm.trans1:probe4 -0.348797062184341 0.22264244530374 -1.56662428724449 0.117710300446508 df.mm.trans1:probe5 -0.201708661975403 0.22264244530374 -0.905975775195166 0.365298921340925 df.mm.trans1:probe6 -0.209843114092183 0.22264244530374 -0.94251172010757 0.346296048371421 df.mm.trans1:probe7 -0.0775982511706859 0.22264244530374 -0.348533052917302 0.727557642847834 df.mm.trans1:probe8 -0.217268041761031 0.22264244530374 -0.975860831319128 0.329512195984188 df.mm.trans1:probe9 -0.111704097098300 0.22264244530374 -0.501719683081578 0.616041969356087 df.mm.trans1:probe10 0.0707105602974202 0.22264244530374 0.317596944288648 0.750897108080795 df.mm.trans1:probe11 0.217646840023209 0.22264244530374 0.977562206192463 0.328670333740742 df.mm.trans1:probe12 0.128410049237733 0.22264244530374 0.576754576435546 0.564313586927274 df.mm.trans2:probe2 -0.24075683161829 0.22264244530374 -1.08136088466796 0.279955011323777 df.mm.trans2:probe3 0.0128427207429914 0.22264244530374 0.0576831642568006 0.954019500166373 df.mm.trans2:probe4 -0.248700821343977 0.22264244530374 -1.11704136650442 0.264407242523753 df.mm.trans2:probe5 -0.174368749170061 0.22264244530374 -0.783178377924203 0.433819982383116 df.mm.trans2:probe6 -0.0545798290154195 0.22264244530374 -0.245145659179942 0.806424377688445 df.mm.trans3:probe2 0.214693395429661 0.22264244530374 0.964296790473916 0.335271296381236 df.mm.trans3:probe3 0.108245503522088 0.22264244530374 0.486185387401824 0.627006696717505 df.mm.trans3:probe4 0.00851351912412014 0.22264244530374 0.0382385268563933 0.969509752982236 df.mm.trans3:probe5 -0.0269774714411467 0.22264244530374 -0.121169489511951 0.903595859461242 df.mm.trans3:probe6 0.224574975519878 0.22264244530374 1.00867997211180 0.313519734241466 df.mm.trans3:probe7 0.078996579411095 0.22264244530374 0.354813653359421 0.722849274966603 df.mm.trans3:probe8 -0.113951657260029 0.22264244530374 -0.511814614255473 0.608962127559553 df.mm.trans3:probe9 0.154847073956478 0.22264244530374 0.695496645957277 0.487003451860741 df.mm.trans3:probe10 0.0930537083457946 0.22264244530374 0.417951339956072 0.676126709118104