fitVsDatCorrelation=0.802415211496103 cont.fitVsDatCorrelation=0.285339441377624 fstatistic=10932.8452121124,58,830 cont.fstatistic=4229.85202985409,58,830 residuals=-0.641101606757657,-0.0912837143053189,-0.00368123654434732,0.0828856507640495,0.878091226244105 cont.residuals=-0.649897316272449,-0.170917158970042,-0.0128609671701958,0.139696825500968,1.26003405277531 predictedValues: Include Exclude Both Lung 62.4715668249765 62.5119935143556 71.0090911116334 cerebhem 64.5553694317495 88.5827730833147 71.2029991588855 cortex 59.8560426763614 69.5076260425948 78.4212421663657 heart 60.5241723418482 59.6476267797803 73.4518913591621 kidney 68.7689314608533 72.9995549700732 90.0097947626205 liver 64.9623550291807 62.1809479463526 81.1400574138668 stomach 59.9471686040925 61.3252025749367 76.7653900605206 testicle 61.5920915261406 61.8820363698938 75.5077731458193 diffExp=-0.0404266893790606,-24.0274036515652,-9.65158336623337,0.876545562067868,-4.23062350921994,2.78140708282805,-1.37803397084419,-0.289944843753219 diffExpScore=1.17088459031613 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 66.4246210788256 68.0669517747701 66.3622823669736 cerebhem 69.0053614500594 68.1657614657021 63.3223055115089 cortex 65.8532919826664 75.3728491950737 66.113048151233 heart 67.9987471160715 69.942071869663 73.4389402802247 kidney 71.5312558375313 62.4981485290262 68.0461204754188 liver 70.9832496512298 85.1966785035178 71.0763300414507 stomach 74.0258957255917 78.8981968310928 68.3799564015021 testicle 71.1652549849446 74.7173584123473 68.5807406898835 cont.diffExp=-1.64233069594454,0.839599984357307,-9.51955721240726,-1.94332475359148,9.03310730850509,-14.2134288522879,-4.87230110550108,-3.55210342740278 cont.diffExpScore=1.69762479577017 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=0.457523375579431 cont.tran.correlation=0.284951237733708 tran.covariance=0.00307046936794522 cont.tran.covariance=0.00102281168179072 tran.mean=65.0822161985315 cont.tran.mean=71.2403559005071 weightedLogRatios: wLogRatio Lung -0.00267500429693629 cerebhem -1.36872210857946 cortex -0.622895416616059 heart 0.0597506894620723 kidney -0.254363432874719 liver 0.18168547192541 stomach -0.093291404309634 testicle -0.0193629306736087 cont.weightedLogRatios: wLogRatio Lung -0.102783103938482 cerebhem 0.0517590895557141 cortex -0.574491982327117 heart -0.119294085161327 kidney 0.567345592383343 liver -0.794631460638607 stomach -0.276409653860324 testicle -0.208924930504768 varWeightedLogRatios=0.258575927564143 cont.varWeightedLogRatios=0.166482088389714 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21027709908852 0.0735916987134775 57.2113047081688 2.92168599090738e-290 *** df.mm.trans1 -0.0933209252338658 0.0634593359534625 -1.47056258676111 0.141788430466018 df.mm.trans2 -0.113198665139362 0.0562065883124825 -2.01397502566834 0.0443343859579054 * df.mm.exp2 0.378663715464562 0.07236848279747 5.23243960391277 2.12011059033980e-07 *** df.mm.exp3 -0.0359780166062241 0.07236848279747 -0.497150350752992 0.619214692370486 df.mm.exp4 -0.112395506825102 0.07236848279747 -1.55310022374866 0.120780392697711 df.mm.exp5 0.0140248707469132 0.07236848279747 0.193798048608579 0.84638143276501 df.mm.exp6 -0.0995822112541264 0.07236848279747 -1.3760439269235 0.169179192991678 df.mm.exp7 -0.138361388139509 0.07236848279747 -1.91190118669098 0.056232915284844 . df.mm.exp8 -0.0857342393365191 0.07236848279747 -1.18469029641611 0.236479033419534 df.mm.trans1:exp2 -0.345851941539658 0.0666609522855672 -5.18822383541825 2.67041843819225e-07 *** df.mm.trans2:exp2 -0.0300847460462943 0.0495973356405971 -0.606579882925583 0.544295624833265 df.mm.trans1:exp3 -0.00679111523395663 0.0666609522855672 -0.101875460837468 0.918880133736447 df.mm.trans2:exp3 0.14205605582664 0.0495973356405971 2.86418723892826 0.00428626342444483 ** df.mm.trans1:exp4 0.0807268124768084 0.0666609522855672 1.21100598939818 0.226237808767841 df.mm.trans2:exp4 0.0654914342613425 0.0495973356405971 1.32046275098164 0.187044558253444 df.mm.trans1:exp5 0.0820156721670679 0.0666609522855672 1.23034054202711 0.218918188712414 df.mm.trans2:exp5 0.141070039520210 0.0495973356405971 2.84430680999604 0.00456026351617838 ** df.mm.trans1:exp6 0.138678637548865 0.0666609522855672 2.08035788259944 0.0377989596147926 * df.mm.trans2:exp6 0.0942724264032597 0.0495973356405971 1.90075586088730 0.0576801850842049 . df.mm.trans1:exp7 0.0971135167785938 0.0666609522855672 1.45682762470256 0.145542162239187 df.mm.trans2:exp7 0.119193847017335 0.0495973356405971 2.40323084854926 0.0164694010909661 * df.mm.trans1:exp8 0.0715561948885532 0.0666609522855672 1.07343493357274 0.283387997088146 df.mm.trans2:exp8 0.075605738306922 0.0495973356405971 1.52439112566031 0.127791954243612 df.mm.trans1:probe2 0.196101653554416 0.0456396340894759 4.29674026680322 1.93861525623724e-05 *** df.mm.trans1:probe3 -0.086880379103366 0.0456396340894759 -1.90361690746771 0.0573057375776337 . df.mm.trans1:probe4 -0.31386248987159 0.0456396340894759 -6.87697209088633 1.202672602838e-11 *** df.mm.trans1:probe5 -0.270924303051086 0.0456396340894759 -5.93616290875475 4.28487503812474e-09 *** df.mm.trans1:probe6 -0.0698507204800473 0.0456396340894759 -1.53048379711165 0.126278043024875 df.mm.trans1:probe7 0.205210647514154 0.0456396340894759 4.49632543310583 7.89699949984378e-06 *** df.mm.trans1:probe8 0.335185742399781 0.0456396340894759 7.34418119441215 4.94776900386993e-13 *** df.mm.trans1:probe9 -0.100147408373675 0.0456396340894759 -2.19430787234923 0.0284897427842097 * df.mm.trans1:probe10 0.116790764740145 0.0456396340894759 2.55897679878806 0.0106743613197930 * df.mm.trans1:probe11 -0.151390761007838 0.0456396340894759 -3.31708971879658 0.000949154446013998 *** df.mm.trans1:probe12 -0.193424801210423 0.0456396340894759 -4.23808834293493 2.50712071311834e-05 *** df.mm.trans1:probe13 -0.0649806804697952 0.0456396340894759 -1.42377741991536 0.154886722683112 df.mm.trans1:probe14 -0.0277291494174269 0.0456396340894759 -0.607567303520977 0.543640678523172 df.mm.trans1:probe15 0.133877719988396 0.0456396340894759 2.93336532291058 0.00344539500066392 ** df.mm.trans1:probe16 0.0755673560065232 0.0456396340894759 1.65573974275023 0.0981525330067088 . df.mm.trans1:probe17 0.149042842140235 0.0456396340894759 3.26564498409515 0.00113692888829449 ** df.mm.trans1:probe18 0.153443620583172 0.0456396340894759 3.36206947414057 0.000808995821850069 *** df.mm.trans1:probe19 0.209862651166437 0.0456396340894759 4.59825446354378 4.92414534623417e-06 *** df.mm.trans1:probe20 0.153784454107212 0.0456396340894759 3.36953740263823 0.000787678565020214 *** df.mm.trans1:probe21 0.100739046607234 0.0456396340894759 2.20727112776006 0.0275680605979555 * df.mm.trans2:probe2 0.256358483683452 0.0456396340894759 5.61701443926707 2.65121836168344e-08 *** df.mm.trans2:probe3 0.0499761058564217 0.0456396340894759 1.09501548058962 0.273827504597022 df.mm.trans2:probe4 0.0400612997050185 0.0456396340894759 0.877774340313045 0.380320201502921 df.mm.trans2:probe5 0.0624794283263422 0.0456396340894759 1.36897303347902 0.171378055298775 df.mm.trans2:probe6 0.203604692239401 0.0456396340894759 4.46113769975098 9.27566915230929e-06 *** df.mm.trans3:probe2 0.00108513391273596 0.0456396340894759 0.0237761308648655 0.98103689426694 df.mm.trans3:probe3 0.110634938636457 0.0456396340894759 2.42409784485912 0.0155591610292817 * df.mm.trans3:probe4 0.338183250870982 0.0456396340894759 7.40985894426713 3.11487414983582e-13 *** df.mm.trans3:probe5 0.417662175370633 0.0456396340894759 9.15130420528378 4.2749375034439e-19 *** df.mm.trans3:probe6 0.46649274739876 0.0456396340894759 10.2212201457226 3.51670848980444e-23 *** df.mm.trans3:probe7 0.153583097046473 0.0456396340894759 3.36512551229871 0.000800208320666983 *** df.mm.trans3:probe8 0.418579405524387 0.0456396340894759 9.17140143375751 3.60967281604078e-19 *** df.mm.trans3:probe9 0.0533341734512051 0.0456396340894759 1.16859336222206 0.242903032478938 df.mm.trans3:probe10 0.630949117337753 0.0456396340894759 13.8245875525817 2.84838829132159e-39 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32850079112929 0.118190746871419 36.6230090401097 1.69749620201781e-175 *** df.mm.trans1 -0.0798883626238935 0.101917830997566 -0.783850694642443 0.433351350474614 df.mm.trans2 -0.0731847930209608 0.0902696739969404 -0.810735098294931 0.417750214558816 df.mm.exp2 0.0864582320194885 0.116226220909587 0.74387888845448 0.457160277305927 df.mm.exp3 0.0970796770515047 0.116226220909587 0.83526485066587 0.403808966554038 df.mm.exp4 -0.050728415903891 0.116226220909587 -0.436462749170455 0.66261453079846 df.mm.exp5 -0.0363450204304542 0.116226220909587 -0.312709302135249 0.754580079469912 df.mm.exp6 0.222221272204597 0.116226220909587 1.91197193254235 0.0562238262703129 . df.mm.exp7 0.226062866945600 0.116226220909587 1.94502466978992 0.0521093952428696 . df.mm.exp8 0.129274665914588 0.116226220909587 1.11226765271110 0.266345138284880 df.mm.trans1:exp2 -0.048341815399303 0.107059734664724 -0.451540586670552 0.651717939593588 df.mm.trans2:exp2 -0.085007630238047 0.0796549915910702 -1.06719778057923 0.286192842003430 df.mm.trans1:exp3 -0.105718045101213 0.107059734664724 -0.987467841502576 0.323701109506582 df.mm.trans2:exp3 0.00487563714994943 0.0796549915910702 0.0612094365031108 0.951207150094417 df.mm.trans1:exp4 0.0741499090134143 0.107059734664724 0.692603145763695 0.488752365256783 df.mm.trans2:exp4 0.0779039649166109 0.0796549915910702 0.97801736414149 0.328350705512139 df.mm.trans1:exp5 0.110411732058227 0.107059734664724 1.03130959929987 0.302696075780387 df.mm.trans2:exp5 -0.0490098525149368 0.0796549915910702 -0.615276601453199 0.538540724393325 df.mm.trans1:exp6 -0.155845130535283 0.107059734664724 -1.45568388548075 0.145858149436187 df.mm.trans2:exp6 0.00224937045687203 0.0796549915910701 0.0282389139957451 0.977478388952673 df.mm.trans1:exp7 -0.117715679852398 0.107059734664724 -1.09953270686730 0.271854589911510 df.mm.trans2:exp7 -0.078396298920108 0.0796549915910702 -0.984198194666518 0.325304876436622 df.mm.trans1:exp8 -0.0603377455210765 0.107059734664724 -0.563589529808144 0.573185744014693 df.mm.trans2:exp8 -0.0360540314861294 0.0796549915910701 -0.452627396801726 0.650935349454432 df.mm.trans1:probe2 0.00694740657345948 0.0732987895954838 0.0947820095229448 0.924510853655462 df.mm.trans1:probe3 -0.0633786117493472 0.0732987895954838 -0.864661096030597 0.387474652621542 df.mm.trans1:probe4 -0.127167045628860 0.0732987895954838 -1.73491330935559 0.0831273722047744 . df.mm.trans1:probe5 -0.064379574789801 0.0732987895954838 -0.87831702467523 0.380025881419054 df.mm.trans1:probe6 -0.000133489920850961 0.0732987895954838 -0.00182117496874990 0.998547351030222 df.mm.trans1:probe7 -0.0299672909253620 0.0732987895954838 -0.408837459537099 0.682764412521037 df.mm.trans1:probe8 -0.0794154524830765 0.0732987895954838 -1.08344834780150 0.278924098635311 df.mm.trans1:probe9 -0.0900965661531875 0.0732987895954838 -1.22916853948621 0.219356972304006 df.mm.trans1:probe10 -0.194640624683245 0.0732987895954838 -2.65544118473735 0.00807232549345621 ** df.mm.trans1:probe11 -0.142834723929733 0.0732987895954838 -1.94866415554744 0.0516721733274869 . df.mm.trans1:probe12 -0.0557594362481996 0.0732987895954838 -0.760714284040989 0.44704379079147 df.mm.trans1:probe13 -0.0782645206578096 0.0732987895954838 -1.06774642650623 0.285945365124357 df.mm.trans1:probe14 -0.136957368684705 0.0732987895954838 -1.8684806316794 0.0620471956959399 . df.mm.trans1:probe15 -0.0502119473346396 0.0732987895954838 -0.685031057289564 0.493515579237175 df.mm.trans1:probe16 -0.143337788847609 0.0732987895954838 -1.95552736462159 0.0508560425493065 . df.mm.trans1:probe17 -0.150101967078843 0.0732987895954838 -2.04780962833377 0.0408928893393194 * df.mm.trans1:probe18 -0.114871855826082 0.0732987895954838 -1.56717261581028 0.117455497418331 df.mm.trans1:probe19 -0.00260607961350098 0.0732987895954838 -0.0355541971140755 0.971646379344794 df.mm.trans1:probe20 -0.0701256025502161 0.0732987895954838 -0.95670887523819 0.338992788759845 df.mm.trans1:probe21 -0.0415813420067856 0.0732987895954838 -0.567285520487607 0.570673606207676 df.mm.trans2:probe2 -0.0690959900646728 0.0732987895954838 -0.942662088228126 0.346128064497884 df.mm.trans2:probe3 -0.121803555622294 0.0732987895954838 -1.66174034106831 0.096942460316895 . df.mm.trans2:probe4 -0.0940697765196155 0.0732987895954838 -1.28337421448241 0.199719204009412 df.mm.trans2:probe5 -0.158788366284484 0.0732987895954838 -2.16631634929846 0.0305710073061151 * df.mm.trans2:probe6 -0.113429389828104 0.0732987895954838 -1.54749335499385 0.122125511593219 df.mm.trans3:probe2 0.0203398047937507 0.0732987895954838 0.277491687188842 0.781471687987475 df.mm.trans3:probe3 0.0456045433273468 0.0732987895954838 0.622173211577243 0.533998851142011 df.mm.trans3:probe4 0.0680733209942732 0.0732987895954838 0.928710028773346 0.353309382689817 df.mm.trans3:probe5 -0.0589497383580678 0.0732987895954838 -0.804238906036449 0.421489298909208 df.mm.trans3:probe6 -0.0140650597178441 0.0732987895954838 -0.191886657275862 0.847877923185667 df.mm.trans3:probe7 0.00595087433679105 0.0732987895954838 0.0811865294042687 0.935313184436649 df.mm.trans3:probe8 -0.0100019753041393 0.0732987895954838 -0.136454849518491 0.891494805229657 df.mm.trans3:probe9 0.107369003439011 0.0732987895954838 1.46481277564815 0.143350672091395 df.mm.trans3:probe10 0.0326914561094137 0.0732987895954838 0.44600267330237 0.655711635611242