fitVsDatCorrelation=0.915848378526855 cont.fitVsDatCorrelation=0.248348163230612 fstatistic=9103.2251040072,51,669 cont.fstatistic=1553.24320337604,51,669 residuals=-0.526672389984958,-0.0935925671344411,-0.000839426943314173,0.0945102491513625,0.577105260846557 cont.residuals=-0.715199587513605,-0.272805521236909,-0.106718049362544,0.158332231334312,1.14759649045098 predictedValues: Include Exclude Both Lung 60.2385808865812 47.2081404783799 53.9984000712739 cerebhem 60.6754583262607 63.62270119856 60.2306825503763 cortex 56.7095580383673 52.5248207242711 58.23868898179 heart 54.4893056385578 51.5651392577183 57.9647499318792 kidney 62.7799914769095 48.360536911653 50.8242228112792 liver 66.843603396884 52.3506832108596 53.5941349244055 stomach 57.5718026793087 55.3662187645959 60.0001955505506 testicle 55.8296056523011 58.7254324992947 62.1160912207362 diffExp=13.0304404082012,-2.94724287229938,4.1847373140962,2.92416638083948,14.4194545652565,14.4929201860244,2.20558391471280,-2.89582684699356 diffExpScore=1.23023410571304 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=1,0,0,0,1,1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 58.178641928935 54.2073065031684 61.868691986107 cerebhem 63.0529149485134 61.08609251129 75.4437875377555 cortex 62.3042021481884 71.3033808906663 65.7118441236907 heart 54.7973212581459 58.4935803526181 65.7642306466664 kidney 59.366851306359 62.3188843289796 61.8132473178936 liver 61.4152799316085 64.7751482445238 59.6842207078685 stomach 64.4686384231239 56.0013516100961 60.0136023566527 testicle 63.4321088850262 62.00070171762 57.7451190573564 cont.diffExp=3.97133542576666,1.96682243722346,-8.99917874247786,-3.69625909447215,-2.95203302262068,-3.35986831291528,8.46728681302781,1.43140716740612 cont.diffExpScore=8.35494470228917 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=-0.182579099925826 cont.tran.correlation=0.273961148605551 tran.covariance=-0.00129261081251133 cont.tran.covariance=0.00131494394683680 tran.mean=56.5538486962814 cont.tran.mean=61.0751503118039 weightedLogRatios: wLogRatio Lung 0.969243971849886 cerebhem -0.195854844016850 cortex 0.306598173734774 heart 0.219003121413750 kidney 1.04619954420226 liver 0.997152925952402 stomach 0.157561760814356 testicle -0.20468088626996 cont.weightedLogRatios: wLogRatio Lung 0.284801898932828 cerebhem 0.130820712901635 cortex -0.566573151060586 heart -0.263470947789449 kidney -0.199355038335590 liver -0.220738795278824 stomach 0.576697956600305 testicle 0.0944604310256241 varWeightedLogRatios=0.274001823147485 cont.varWeightedLogRatios=0.130754846239145 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97465971814572 0.0784469940291762 50.6668198996567 3.4728748272786e-231 *** df.mm.trans1 -0.145906143128847 0.0677976496771184 -2.1520826138327 0.0317479622651967 * df.mm.trans2 -0.124104069037852 0.0613493259564288 -2.02290843628816 0.0434798478070153 * df.mm.exp2 0.196402796506492 0.0806322946284625 2.43578329764118 0.0151196127030336 * df.mm.exp3 -0.0292462440349769 0.0806322946284625 -0.362711295390237 0.716935121390758 df.mm.exp4 -0.0829097152242567 0.0806322946284625 -1.02824452170545 0.304206435259211 df.mm.exp5 0.126022522574952 0.0806322946284626 1.56292863989099 0.118542268199249 df.mm.exp6 0.214956008859631 0.0806322946284625 2.66587984194305 0.00786414159759902 ** df.mm.exp7 0.00872977458359266 0.0806322946284625 0.108266478385834 0.913816775132018 df.mm.exp8 0.00224716479146033 0.0806322946284626 0.0278692898647473 0.977774713325717 df.mm.trans1:exp2 -0.18917651668597 0.0739140980467842 -2.55941047357745 0.0107037323022318 * df.mm.trans2:exp2 0.102001201754034 0.0596640034186173 1.70959365630175 0.0878047605025545 . df.mm.trans1:exp3 -0.0311240128872314 0.0739140980467842 -0.421083578230656 0.673829296217288 df.mm.trans2:exp3 0.135965732126796 0.0596640034186173 2.27885700483132 0.0229898191897038 * df.mm.trans1:exp4 -0.0173988547314744 0.0739140980467842 -0.235392911382910 0.813975823771316 df.mm.trans2:exp4 0.17118921813051 0.0596640034186172 2.8692211102464 0.00424463327378145 ** df.mm.trans1:exp5 -0.0846991324768701 0.0739140980467842 -1.14591308985817 0.252240837063667 df.mm.trans2:exp5 -0.101904739981433 0.0596640034186173 -1.70797690638431 0.0881045360750327 . df.mm.trans1:exp6 -0.110913421354831 0.0739140980467842 -1.50057193804392 0.133938094864484 df.mm.trans2:exp6 -0.111557366282924 0.0596640034186173 -1.86975998744520 0.0619537224180553 . df.mm.trans1:exp7 -0.05400988911605 0.0739140980467842 -0.730711603649201 0.465211148189706 df.mm.trans2:exp7 0.150673518109484 0.0596640034186173 2.52536721433730 0.0117876281943329 * df.mm.trans1:exp8 -0.0782558944141678 0.0739140980467842 -1.05874111275274 0.290099814590513 df.mm.trans2:exp8 0.216059385067544 0.0596640034186173 3.62126864923258 0.000315335719687653 *** df.mm.trans1:probe2 0.137210920689309 0.0469715895498099 2.92114705941144 0.00360494018307250 ** df.mm.trans1:probe3 0.0703007310017723 0.0469715895498099 1.49666493460315 0.134952074454833 df.mm.trans1:probe4 0.409359882769720 0.0469715895498099 8.71505279453282 2.27734204034099e-17 *** df.mm.trans1:probe5 0.0444700370839213 0.0469715895498099 0.946743286955705 0.344111435552928 df.mm.trans1:probe6 0.990860864533294 0.0469715895498099 21.0948974482237 4.07373339789487e-76 *** df.mm.trans1:probe7 0.879589414310886 0.0469715895498099 18.7259878309663 3.13299414221066e-63 *** df.mm.trans1:probe8 1.04371287248955 0.0469715895498099 22.2200884086064 2.37819198272607e-82 *** df.mm.trans1:probe9 0.895327710520324 0.0469715895498099 19.0610477333516 4.99209760217390e-65 *** df.mm.trans1:probe10 1.05659332338903 0.0469715895498099 22.4943063140026 7.06793949043001e-84 *** df.mm.trans1:probe11 0.899691689193686 0.0469715895498099 19.1539545034904 1.57818740077544e-65 *** df.mm.trans1:probe12 0.0102961479493804 0.0469715895498099 0.219199478835225 0.826561520004915 df.mm.trans1:probe13 0.0443715984712724 0.0469715895498099 0.94464758158162 0.345179860829476 df.mm.trans1:probe14 0.0162991556522708 0.0469715895498099 0.347000299723447 0.728700218664032 df.mm.trans1:probe15 0.011021359290781 0.0469715895498099 0.234638840124703 0.814560855120691 df.mm.trans1:probe16 -0.0426010125552368 0.0469715895498099 -0.906952755134283 0.364758333865423 df.mm.trans1:probe17 0.00292211811252747 0.0469715895498099 0.0622103305537228 0.950413916962714 df.mm.trans2:probe2 0.0646261224252734 0.0469715895498099 1.37585555534037 0.169326779509080 df.mm.trans2:probe3 -0.0529044147521066 0.0469715895498099 -1.12630667301573 0.260439587534245 df.mm.trans2:probe4 0.0266829084460631 0.0469715895498099 0.568064838805761 0.570181545936642 df.mm.trans2:probe5 0.0960628246861741 0.0469715895498099 2.04512611999870 0.0412331489716077 * df.mm.trans2:probe6 -0.0823283879294386 0.0469715895498099 -1.75272731279693 0.0801068722813991 . df.mm.trans3:probe2 0.357168459972022 0.0469715895498099 7.60392533859796 9.75800941451941e-14 *** df.mm.trans3:probe3 0.267415195807327 0.0469715895498099 5.69312638491302 1.86769216889488e-08 *** df.mm.trans3:probe4 0.392422477090418 0.0469715895498099 8.35446449335683 3.79122181753268e-16 *** df.mm.trans3:probe5 0.234609575601799 0.0469715895498099 4.99471228992607 7.52797385649036e-07 *** df.mm.trans3:probe6 0.335852883133542 0.0469715895498099 7.15012811685657 2.27821320369783e-12 *** df.mm.trans3:probe7 0.402665791250987 0.0469715895498099 8.57253916910753 6.9972977524552e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.8622821330535 0.189252105283302 20.4081329889135 2.42094706776875e-72 *** df.mm.trans1 0.224553548745614 0.163560734142119 1.37290621690716 0.170241511494832 df.mm.trans2 0.114037756610723 0.148004257379798 0.770503218147892 0.441273454165688 df.mm.exp2 0.00155092452488761 0.194524107661596 0.00797291679438356 0.99364097704277 df.mm.exp3 0.282373684832078 0.194524107661596 1.45161280124369 0.147078066045126 df.mm.exp4 -0.0448375099848351 0.194524107661596 -0.230498474064905 0.817774915531131 df.mm.exp5 0.160563063582894 0.194524107661596 0.825414728863417 0.409430407181294 df.mm.exp6 0.268193254437129 0.194524107661596 1.37871473958226 0.168443540270582 df.mm.exp7 0.165663715505769 0.194524107661596 0.85163591031075 0.394720943329375 df.mm.exp8 0.289757310278409 0.194524107661596 1.48957018110314 0.136808555622004 df.mm.trans1:exp2 0.0789050589303265 0.178316566983659 0.442499876848556 0.658270544877267 df.mm.trans2:exp2 0.117917591578922 0.143938443994475 0.819222358576067 0.412951298277314 df.mm.trans1:exp3 -0.213863121320371 0.178316566983659 -1.19934521473806 0.230818343292972 df.mm.trans2:exp3 -0.0082456464061735 0.143938443994475 -0.0572859215185766 0.954334540234574 df.mm.trans1:exp4 -0.0150394896253173 0.178316566983659 -0.0843415162131035 0.932810136560318 df.mm.trans2:exp4 0.120938814947391 0.143938443994475 0.840212048923037 0.401089677226024 df.mm.trans1:exp5 -0.140345362021307 0.178316566983659 -0.787057335139073 0.431527032096501 df.mm.trans2:exp5 -0.0211142701671482 0.143938443994475 -0.146689581887927 0.883421234141663 df.mm.trans1:exp6 -0.214052901641995 0.178316566983659 -1.20040950351860 0.230405221360325 df.mm.trans2:exp6 -0.0900869450760553 0.143938443994475 -0.625871327881754 0.531612801814599 df.mm.trans1:exp7 -0.0630031461670435 0.178316566983659 -0.353321888329181 0.723958443263338 df.mm.trans2:exp7 -0.133103594858877 0.143938443994475 -0.924725814487658 0.355441973273613 df.mm.trans1:exp8 -0.203305437965483 0.178316566983659 -1.14013768549118 0.254636959261091 df.mm.trans2:exp8 -0.1554273129628 0.143938443994475 -1.07981793223196 0.280612398359819 df.mm.trans1:probe2 0.0342555008821595 0.113318200662966 0.302294783024689 0.762521312339149 df.mm.trans1:probe3 0.0315761000287932 0.113318200662966 0.278649853633907 0.78059965738212 df.mm.trans1:probe4 0.0389460079212252 0.113318200662966 0.343687136694479 0.731189571892607 df.mm.trans1:probe5 -0.0158247037451985 0.113318200662966 -0.139648385278061 0.888979839360411 df.mm.trans1:probe6 -0.119390700259326 0.113318200662966 -1.0535880340566 0.292451939401247 df.mm.trans1:probe7 0.0705100385169385 0.113318200662966 0.622230481109132 0.534002332102893 df.mm.trans1:probe8 -0.0716536444296473 0.113318200662966 -0.632322469033562 0.527392222902261 df.mm.trans1:probe9 -0.158650013799609 0.113318200662966 -1.40004000126573 0.161964907997126 df.mm.trans1:probe10 -0.0425304445050106 0.113318200662966 -0.375318741880713 0.707542391393366 df.mm.trans1:probe11 -0.180729291014297 0.113318200662966 -1.59488316931387 0.111210641984406 df.mm.trans1:probe12 -0.0585232700875854 0.113318200662966 -0.516450753234663 0.605710290796474 df.mm.trans1:probe13 -0.0439975303744938 0.113318200662966 -0.388265345876365 0.697943199414719 df.mm.trans1:probe14 -0.108277059623763 0.113318200662966 -0.95551340376294 0.339663268529542 df.mm.trans1:probe15 0.127617802429391 0.113318200662966 1.12618980607498 0.260489005142838 df.mm.trans1:probe16 -0.0321905000125686 0.113318200662966 -0.284071753912776 0.776443290222678 df.mm.trans1:probe17 -0.0307552098561755 0.113318200662966 -0.271405737791835 0.786162715220785 df.mm.trans2:probe2 -0.0563479417567181 0.113318200662966 -0.497254116523696 0.619173243065455 df.mm.trans2:probe3 0.0932267218647748 0.113318200662966 0.822698571980085 0.410972565721206 df.mm.trans2:probe4 -0.00639120343257787 0.113318200662966 -0.0564005022598864 0.955039596922565 df.mm.trans2:probe5 0.0676632982192526 0.113318200662966 0.597108830032508 0.550636749158007 df.mm.trans2:probe6 0.116294733460735 0.113318200662966 1.02626703195387 0.305136653196284 df.mm.trans3:probe2 -0.0799896623166317 0.113318200662966 -0.705885390419665 0.480505142789956 df.mm.trans3:probe3 -0.117179890360874 0.113318200662966 -1.03407828288232 0.301473221676048 df.mm.trans3:probe4 -0.0547986313222829 0.113318200662966 -0.48358190477509 0.628840856247908 df.mm.trans3:probe5 -0.069354094367481 0.113318200662966 -0.612029611851637 0.540726109356904 df.mm.trans3:probe6 -0.0517437152941321 0.113318200662966 -0.456623163723095 0.648090140198527 df.mm.trans3:probe7 -0.157948199259564 0.113318200662966 -1.39384669307747 0.163826709303489