fitVsDatCorrelation=0.757610292721259 cont.fitVsDatCorrelation=0.25681036812784 fstatistic=10439.8193297745,59,853 cont.fstatistic=4753.81815510368,59,853 residuals=-0.55114000876873,-0.090252336927498,-0.00388902409935691,0.0717235848951343,1.79158233633089 cont.residuals=-0.478867082318461,-0.146845280369097,-0.0280045097265274,0.126972357064970,1.90337775292078 predictedValues: Include Exclude Both Lung 58.1384545686541 46.636175938787 56.3017026481698 cerebhem 60.3032153388605 51.8780798670996 53.0534029128062 cortex 56.3670681220661 46.0420740675926 55.627205134645 heart 60.9858036112449 47.6542881209443 58.9531208977064 kidney 61.9707043538662 47.6876521886637 58.2842935183645 liver 72.0910649216642 49.9938672263807 67.2025437769877 stomach 55.8451404374717 45.9731782786662 53.7711929906375 testicle 59.0070767169378 49.5518469086486 56.9623309822451 diffExp=11.5022786298671,8.42513547176087,10.3249940544735,13.3315154903006,14.2830521652025,22.0971976952835,9.8719621588055,9.45522980828915 diffExpScore=0.990029051900192 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,1,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,1,1,1,1,1,0 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 57.0388992037041 58.5959552516211 56.2489915883878 cerebhem 57.4869412941977 59.1156774803755 56.6695751937664 cortex 59.7800330262092 70.8959003037645 56.1072202817863 heart 57.8806751953592 58.3766479193453 54.5509883526701 kidney 55.3668269463986 58.7183164344449 58.5444216506438 liver 59.6739446289542 53.3482138893438 59.6622402345867 stomach 57.8609122910642 62.5774368227442 58.8306464846673 testicle 58.919733567128 54.9735428014456 61.4545325658632 cont.diffExp=-1.55705604791702,-1.62873618617782,-11.1158672775553,-0.495972723986078,-3.35148948804622,6.32573073961033,-4.71652453167997,3.94619076568235 cont.diffExpScore=2.43771066208216 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=0.529375774769937 cont.tran.correlation=0.157971255203532 tran.covariance=0.00192183367615187 cont.tran.covariance=0.00025553806332409 tran.mean=54.3828556667218 cont.tran.mean=58.7881035660062 weightedLogRatios: wLogRatio Lung 0.871354122945533 cerebhem 0.605589247522115 cortex 0.795300517517094 heart 0.983542968460574 kidney 1.04682188519109 liver 1.49886087025936 stomach 0.763570445154923 testicle 0.696864118400137 cont.weightedLogRatios: wLogRatio Lung -0.109269285801817 cerebhem -0.113584097786236 cortex -0.71216892953555 heart -0.0346640647934303 kidney -0.237633036931807 liver 0.451902977241597 stomach -0.321069540531463 testicle 0.28017408211655 varWeightedLogRatios=0.0778612697883378 cont.varWeightedLogRatios=0.128033164776179 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79579638324516 0.0727509426261217 52.1752192648876 1.14752574435995e-267 *** df.mm.trans1 0.238201387897877 0.0628259345039695 3.79144997648745 0.000160318916605970 *** df.mm.trans2 0.0292590551327403 0.0555064291660981 0.527129119496864 0.598240987562626 df.mm.exp2 0.202503679887111 0.0713990155415038 2.83622509850150 0.00467310935914287 ** df.mm.exp3 -0.0317107451789371 0.0713990155415038 -0.444134207431808 0.657058204106852 df.mm.exp4 0.023392076356861 0.0713990155415038 0.327624634309746 0.743275885132214 df.mm.exp5 0.0515225301040628 0.0713990155415038 0.72161401264858 0.470729570388869 df.mm.exp6 0.107640268093111 0.0713990155415038 1.50758756653361 0.132030370913809 df.mm.exp7 -0.00857630532347153 0.0713990155415038 -0.120117977235781 0.904417977165789 df.mm.exp8 0.0638076412512822 0.0713990155415038 0.893676765251633 0.37174692775304 df.mm.trans1:exp2 -0.165945568777339 0.0659955948063584 -2.51449463050147 0.0121032816129459 * df.mm.trans2:exp2 -0.0959838798265188 0.0487409769766954 -1.96926458557472 0.0492459997301844 * df.mm.trans1:exp3 0.000768521031938524 0.0659955948063584 0.0116450353117278 0.990711539042923 df.mm.trans2:exp3 0.0188898296106689 0.0487409769766954 0.387555416045532 0.698441750930457 df.mm.trans1:exp4 0.0244217194757080 0.0659955948063584 0.370050751832227 0.71143653483374 df.mm.trans2:exp4 -0.00179600592061636 0.0487409769766954 -0.036847967193499 0.97061484905522 df.mm.trans1:exp5 0.0123119192803458 0.0659955948063584 0.186556683313045 0.852052590600217 df.mm.trans2:exp5 -0.0292265773500690 0.0487409769766954 -0.599630519594287 0.548911741357156 df.mm.trans1:exp6 0.107462528756854 0.0659955948063584 1.62832881606971 0.103824438249237 df.mm.trans2:exp6 -0.0381164733200761 0.0487409769766954 -0.78202111825335 0.434419178821597 df.mm.trans1:exp7 -0.0316684974853164 0.0659955948063584 -0.479857747751753 0.631451563531385 df.mm.trans2:exp7 -0.00574209680369569 0.0487409769766954 -0.117808405983351 0.906247203098672 df.mm.trans1:exp8 -0.0489775737543772 0.0659955948063584 -0.742133984822551 0.458210457508492 df.mm.trans2:exp8 -0.00316465519416352 0.0487409769766954 -0.0649280213582227 0.948246522702335 df.mm.trans1:probe2 0.162823194640384 0.0451840949642666 3.60355108958477 0.000332079027730094 *** df.mm.trans1:probe3 0.24317380348123 0.0451840949642666 5.38184517524456 9.53231311882293e-08 *** df.mm.trans1:probe4 -0.0643295793408108 0.0451840949642666 -1.42372176297180 0.154892704958043 df.mm.trans1:probe5 0.165341479545879 0.0451840949642666 3.65928496911663 0.000268456154738618 *** df.mm.trans1:probe6 0.0597742306257366 0.0451840949642666 1.3229042359487 0.186221752367292 df.mm.trans1:probe7 0.0348821950985629 0.0451840949642666 0.772001633011553 0.440327284819145 df.mm.trans1:probe8 0.179535413406688 0.0451840949642666 3.97342059299124 7.68362867141598e-05 *** df.mm.trans1:probe9 -0.0243408573530619 0.0451840949642666 -0.538704103121058 0.590231595292739 df.mm.trans1:probe10 0.425460686239571 0.0451840949642666 9.41616041166791 4.24396924541153e-20 *** df.mm.trans1:probe11 -0.146081778894322 0.0451840949642666 -3.23303540792063 0.00127186873579614 ** df.mm.trans1:probe12 0.0113236570642723 0.0451840949642666 0.250611571908821 0.802174784654285 df.mm.trans1:probe13 0.0517534898978444 0.0451840949642666 1.14539175651904 0.252368160655113 df.mm.trans1:probe14 -0.0560559848961336 0.0451840949642666 -1.24061320560841 0.215089784312818 df.mm.trans1:probe15 -0.0471304807464995 0.0451840949642666 -1.04307679026817 0.29720825185718 df.mm.trans1:probe16 -0.243418024039192 0.0451840949642666 -5.38725018685661 9.25930776422011e-08 *** df.mm.trans1:probe17 0.092383317402181 0.0451840949642666 2.04459815949045 0.0412009976974608 * df.mm.trans1:probe18 -0.0226314921656722 0.0451840949642666 -0.50087297717416 0.616589737787911 df.mm.trans1:probe19 -0.0633763816424936 0.0451840949642666 -1.40262589507689 0.161092268863339 df.mm.trans1:probe20 0.102334234343908 0.0451840949642666 2.26482868418275 0.0237733466501785 * df.mm.trans1:probe21 0.108674521658604 0.0451840949642666 2.40514990384444 0.0163778863208823 * df.mm.trans1:probe22 -0.0475502866336390 0.0451840949642666 -1.05236780046704 0.292928855189143 df.mm.trans2:probe2 0.0482218913297175 0.0451840949642666 1.06723154171514 0.286169244517793 df.mm.trans2:probe3 -0.0084611922991992 0.0451840949642666 -0.187260413335503 0.851500990783448 df.mm.trans2:probe4 0.0415145329077566 0.0451840949642666 0.918786421208347 0.358467023567774 df.mm.trans2:probe5 0.0775205877644144 0.0451840949642666 1.71566096047117 0.0865872438169719 . df.mm.trans2:probe6 0.118341928809788 0.0451840949642666 2.61910588014163 0.00897256720945171 ** df.mm.trans3:probe2 0.0425839595385262 0.0451840949642666 0.94245463082094 0.346226766600527 df.mm.trans3:probe3 0.195143437830663 0.0451840949642666 4.31885241886533 1.75275766630519e-05 *** df.mm.trans3:probe4 -0.109878972175337 0.0451840949642666 -2.43180641910022 0.0152286043731103 * df.mm.trans3:probe5 0.145555345742295 0.0451840949642666 3.22138455705279 0.00132407103094937 ** df.mm.trans3:probe6 -0.0794938427204848 0.0451840949642666 -1.75933241073771 0.0788795336751714 . df.mm.trans3:probe7 -0.293921824972617 0.0451840949642666 -6.50498422520275 1.32274721825377e-10 *** df.mm.trans3:probe8 -0.276696167606563 0.0451840949642666 -6.12375146222106 1.39344414729505e-09 *** df.mm.trans3:probe9 -0.210024522658321 0.0451840949642666 -4.64819584910171 3.87813911572377e-06 *** df.mm.trans3:probe10 0.261412095334074 0.0451840949642666 5.78548924219483 1.01480922050662e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10971823176894 0.107722132591549 38.1511035188264 1.33422918772229e-186 *** df.mm.trans1 -0.0780401305604204 0.093026198734016 -0.838904863602518 0.40175772725437 df.mm.trans2 -0.0315743473343797 0.0821882261105845 -0.384171174249416 0.70094732790698 df.mm.exp2 0.00920546486757998 0.105720337653829 0.087073736916376 0.930633336618132 df.mm.exp3 0.240008774688314 0.105720337653829 2.27022330816043 0.0234427318299457 * df.mm.exp4 0.0415526473665692 0.105720337653829 0.393043082236733 0.694385868306246 df.mm.exp5 -0.0676645097243433 0.105720337653829 -0.640033045920681 0.522322985241123 df.mm.exp6 -0.107574378409474 0.105720337653829 -1.01753721939212 0.309186450614712 df.mm.exp7 0.0351728929531785 0.105720337653829 0.332697508669983 0.739444376501353 df.mm.exp8 -0.119880529185852 0.105720337653829 -1.13394008992280 0.257138256720148 df.mm.trans1:exp2 -0.00138112803121207 0.0977195065461062 -0.0141335960447203 0.988726702299008 df.mm.trans2:exp2 -0.000374976428863772 0.0721706385511493 -0.00519569227031317 0.995855670835859 df.mm.trans1:exp3 -0.193070542617187 0.0977195065461062 -1.97576256206423 0.0485035837446147 * df.mm.trans2:exp3 -0.0494618376552235 0.0721706385511492 -0.685345711887647 0.493311992889224 df.mm.trans1:exp4 -0.0269025574836777 0.0977195065461062 -0.275303861373722 0.783149470520911 df.mm.trans2:exp4 -0.0453023730851367 0.0721706385511492 -0.627711961465184 0.530360648691622 df.mm.trans1:exp5 0.0379116553020238 0.0977195065461062 0.387964047732233 0.69813943594247 df.mm.trans2:exp5 0.0697505513531594 0.0721706385511492 0.966467149985452 0.334084404239729 df.mm.trans1:exp6 0.152736387918076 0.0977195065461062 1.56300817837236 0.118421571582208 df.mm.trans2:exp6 0.0137492052933282 0.0721706385511492 0.190509680520337 0.848955088016454 df.mm.trans1:exp7 -0.0208643035614444 0.0977195065461062 -0.213512166596955 0.830978521333106 df.mm.trans2:exp7 0.0305662148485194 0.0721706385511492 0.423527011290836 0.672017562920395 df.mm.trans1:exp8 0.152323121580191 0.0977195065461062 1.55877907046452 0.119419597128899 df.mm.trans2:exp8 0.0560668875168716 0.0721706385511492 0.776865615192465 0.43745343400737 df.mm.trans1:probe2 0.0328449256234482 0.0669039725544701 0.490926388514634 0.623604785016903 df.mm.trans1:probe3 0.0512972816014351 0.0669039725544701 0.766729980939042 0.443454218332811 df.mm.trans1:probe4 0.0651501915874555 0.0669039725544701 0.973786594427008 0.330438571361556 df.mm.trans1:probe5 -0.0132619429294325 0.0669039725544701 -0.198223549709776 0.842917393949435 df.mm.trans1:probe6 -0.0143834586228500 0.0669039725544701 -0.214986615497901 0.829829142005043 df.mm.trans1:probe7 0.0565362263944247 0.0669039725544701 0.845035417715973 0.398328018888792 df.mm.trans1:probe8 0.0176731584273682 0.0669039725544701 0.264157085933568 0.791722684253832 df.mm.trans1:probe9 0.036256713545688 0.0669039725544701 0.541921685086329 0.588013984802855 df.mm.trans1:probe10 -0.093564231432864 0.0669039725544701 -1.39848543906251 0.162330794397433 df.mm.trans1:probe11 -0.011510507087832 0.0669039725544701 -0.172045196246915 0.863442805626447 df.mm.trans1:probe12 -0.0108456705728477 0.0669039725544701 -0.162108020775861 0.87125918109732 df.mm.trans1:probe13 -0.00591797978983506 0.0669039725544701 -0.0884548340536418 0.929535943018498 df.mm.trans1:probe14 0.178552226722450 0.0669039725544701 2.66878362980735 0.00775722916745097 ** df.mm.trans1:probe15 0.0557823436907162 0.0669039725544701 0.83376728706656 0.404645545188083 df.mm.trans1:probe16 0.0193502760317103 0.0669039725544701 0.289224619897663 0.772479771228924 df.mm.trans1:probe17 0.0414898409115363 0.0669039725544701 0.620140169969087 0.535331137584113 df.mm.trans1:probe18 0.0160935979876251 0.0669039725544701 0.240547718964258 0.809963474937106 df.mm.trans1:probe19 0.000730731412444314 0.0669039725544701 0.0109220930319106 0.991288157728663 df.mm.trans1:probe20 -0.0115141924902736 0.0669039725544701 -0.17210028120377 0.86339951350719 df.mm.trans1:probe21 -0.0119256695225340 0.0669039725544701 -0.178250544283071 0.858568540156419 df.mm.trans1:probe22 -0.0130618282553774 0.0669039725544701 -0.195232476587889 0.845257448474293 df.mm.trans2:probe2 -0.0693232834487712 0.0669039725544701 -1.03616094533596 0.300420709045438 df.mm.trans2:probe3 0.0192043099439704 0.0669039725544701 0.287042894625355 0.774149187759296 df.mm.trans2:probe4 -0.0824128756325765 0.0669039725544701 -1.23180840368604 0.218360126496294 df.mm.trans2:probe5 0.010746358655598 0.0669039725544701 0.160623625851945 0.872427876605647 df.mm.trans2:probe6 0.00213407858310569 0.0669039725544701 0.0318976362930949 0.974561145496026 df.mm.trans3:probe2 0.0722754709613774 0.0669039725544701 1.08028668854505 0.280319876186824 df.mm.trans3:probe3 0.0451731811139761 0.0669039725544701 0.675194302957095 0.499735303679128 df.mm.trans3:probe4 0.0187184305309805 0.0669039725544701 0.279780554372026 0.779713700234731 df.mm.trans3:probe5 0.0119061646233496 0.0669039725544701 0.177959008542521 0.858797420468743 df.mm.trans3:probe6 0.0134527328056190 0.0669039725544701 0.201075246984272 0.840687672313044 df.mm.trans3:probe7 5.89175290991823e-05 0.0669039725544701 0.000880628262413182 0.999297566286384 df.mm.trans3:probe8 0.0657430693354913 0.0669039725544701 0.982648216919651 0.326059230466308 df.mm.trans3:probe9 0.0259149242424289 0.0669039725544701 0.387345074634099 0.698597384964138 df.mm.trans3:probe10 0.0436007061791638 0.0669039725544701 0.651690841581434 0.514776174288588