chr6.20182_chr6_87908354_87911294_+_2.R fitVsDatCorrelation=0.914622194355566 cont.fitVsDatCorrelation=0.214504909332697 fstatistic=10056.8517277641,54,738 cont.fstatistic=1711.92150357528,54,738 residuals=-0.569749265254203,-0.0875905337851774,-0.00246754911489477,0.0813369636570032,0.991101653859233 cont.residuals=-0.65650783307034,-0.258757122195855,-0.103338893251949,0.205365505048331,1.81240626665192 predictedValues: Include Exclude Both chr6.20182_chr6_87908354_87911294_+_2.R.tl.Lung 51.4165737153107 108.658739255888 73.0112602945618 chr6.20182_chr6_87908354_87911294_+_2.R.tl.cerebhem 62.6086134776056 152.819231766156 77.2660107828959 chr6.20182_chr6_87908354_87911294_+_2.R.tl.cortex 49.1422383022559 99.108275044891 81.933980630147 chr6.20182_chr6_87908354_87911294_+_2.R.tl.heart 49.5735244385393 92.8590112374162 67.8106385016777 chr6.20182_chr6_87908354_87911294_+_2.R.tl.kidney 51.3704936648886 97.6524013538694 64.0646340296699 chr6.20182_chr6_87908354_87911294_+_2.R.tl.liver 50.9564653225837 100.764068555953 63.6157443573379 chr6.20182_chr6_87908354_87911294_+_2.R.tl.stomach 50.1956562102466 128.112477079403 104.243473735051 chr6.20182_chr6_87908354_87911294_+_2.R.tl.testicle 51.7840028560818 97.0603169873014 71.8181861070803 diffExp=-57.2421655405777,-90.2106182885508,-49.966036742635,-43.285486798877,-46.2819076889807,-49.8076032333689,-77.9168208691561,-45.2763141312195 diffExpScore=0.9978307412111 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 64.7436117600277 59.0323265092062 60.7955191523626 cerebhem 66.6698663985088 59.1674790877113 56.6083811423355 cortex 67.9233484203364 75.5262299964223 65.372961155096 heart 64.8138949725881 55.0165637271594 63.173991699127 kidney 61.9854063571254 54.8687903363232 50.9683122470767 liver 61.4369357902341 66.5396987153225 69.1055021636124 stomach 61.7239053898762 64.4442837812503 58.5221838361446 testicle 63.4530025092862 56.4772236875652 62.414149678398 cont.diffExp=5.71128525082149,7.50238731079742,-7.60288157608585,9.79733124542868,7.1166160208022,-5.10276292508843,-2.72037839137402,6.97577882172105 cont.diffExpScore=2.31638008316956 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=0.823493794281006 cont.tran.correlation=0.322729527219603 tran.covariance=0.0105001883983589 cont.tran.covariance=0.00118303126649435 tran.mean=80.8801305792744 cont.tran.mean=62.738910464934 weightedLogRatios: wLogRatio Lung -3.22802189530384 cerebhem -4.08972395106291 cortex -2.97816897848828 heart -2.64686563860012 kidney -2.73656583949160 liver -2.91260905761309 stomach -4.10811298321285 testicle -2.67710894419123 cont.weightedLogRatios: wLogRatio Lung 0.38087474075609 cerebhem 0.49424489769076 cortex -0.453199453442813 heart 0.670223257048084 kidney 0.495856534673204 liver -0.331748961215647 stomach -0.178739794149400 testicle 0.476570796328315 varWeightedLogRatios=0.362400476552773 cont.varWeightedLogRatios=0.193862127912407 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08000134811558 0.0785612893455939 51.9339916910924 1.17340260480938e-248 *** df.mm.trans1 -0.23129579413866 0.0692055008169938 -3.34215909729915 0.000873184300281133 *** df.mm.trans2 0.69199910665682 0.0624462367208937 11.0815181665749 1.66346675250592e-26 *** df.mm.exp2 0.481345416806876 0.0831597998111894 5.78819835905989 1.05189176904418e-08 *** df.mm.exp3 -0.252540978201542 0.0831597998111894 -3.03681561012562 0.00247511052495819 ** df.mm.exp4 -0.119738873154403 0.0831597998111895 -1.43986485569066 0.150329683118961 df.mm.exp5 0.023026697563501 0.0831597998111895 0.276896981664002 0.781936745363194 df.mm.exp6 0.0533334590580747 0.0831597998111894 0.641337030382058 0.521502854332404 df.mm.exp7 -0.215451182983245 0.0831597998111894 -2.59080930296150 0.00976413276137189 ** df.mm.exp8 -0.0892828709735988 0.0831597998111894 -1.07363018160591 0.283339380765504 df.mm.trans1:exp2 -0.284403118987110 0.078439834045943 -3.62574860651199 0.000307930609895556 *** df.mm.trans2:exp2 -0.140301824055069 0.0641780460375897 -2.1861342424307 0.0291192401539138 * df.mm.trans1:exp3 0.207299327443884 0.078439834045943 2.64278131086390 0.00839718033405186 ** df.mm.trans2:exp3 0.160541779613429 0.0641780460375897 2.50150619293392 0.0125821259796708 * df.mm.trans1:exp4 0.0832352165823692 0.078439834045943 1.06113453189635 0.288975900435134 df.mm.trans2:exp4 -0.0373909305998923 0.0641780460375897 -0.58261248056683 0.560332160492237 df.mm.trans1:exp5 -0.0239233094560963 0.078439834045943 -0.304989292074283 0.760460299710995 df.mm.trans2:exp5 -0.129824587510271 0.0641780460375897 -2.02288158530461 0.0434454552357784 * df.mm.trans1:exp6 -0.0623223783113423 0.078439834045943 -0.794524606908775 0.427145509756335 df.mm.trans2:exp6 -0.128763768111884 0.0641780460375897 -2.00635226626355 0.0451830766319765 * df.mm.trans1:exp7 0.191419109950855 0.078439834045943 2.44033037906148 0.0149080757843691 * df.mm.trans2:exp7 0.38014764982642 0.0641780460375897 5.92332851024732 4.83630504650452e-09 *** df.mm.trans1:exp8 0.0964035810607554 0.078439834045943 1.22901306757343 0.219458632784856 df.mm.trans2:exp8 -0.0235966575690477 0.0641780460375897 -0.367674914179016 0.713221066440684 df.mm.trans1:probe2 0.00772478361504272 0.0457989959887287 0.168667095168283 0.866104717266354 df.mm.trans1:probe3 0.144398799317278 0.0457989959887287 3.15288132850806 0.00168212354448838 ** df.mm.trans1:probe4 0.00226444996618671 0.0457989959887287 0.049443222876414 0.960579461236093 df.mm.trans1:probe5 -0.000591390493200912 0.0457989959887287 -0.0129127392518922 0.989700900853199 df.mm.trans1:probe6 0.0851141791001352 0.0457989959887287 1.85842892977571 0.0635060790735897 . df.mm.trans1:probe7 0.0663498095109971 0.0457989959887287 1.44871755545309 0.147841214903694 df.mm.trans1:probe8 0.229899438722288 0.0457989959887287 5.01974844118564 6.49136474504331e-07 *** df.mm.trans1:probe9 0.122589347374745 0.0457989959887287 2.67668198239355 0.00760059408039151 ** df.mm.trans1:probe10 0.0775066939776692 0.0457989959887287 1.69232299321024 0.0910065022590357 . df.mm.trans1:probe11 0.0471371193608158 0.0457989959887287 1.02921730800423 0.303714762258958 df.mm.trans1:probe12 0.02003036318596 0.0457989959887287 0.43735376187918 0.661982763794498 df.mm.trans1:probe13 -0.0615015766121964 0.0457989959887287 -1.34285862134035 0.179730608328765 df.mm.trans1:probe14 -0.115535675878789 0.0457989959887287 -2.52266831148924 0.0118559435916603 * df.mm.trans1:probe15 -0.0465934020551188 0.0457989959887287 -1.01734549086154 0.309322499025473 df.mm.trans1:probe16 -0.078488945902403 0.0457989959887287 -1.71377001193911 0.08699089249009 . df.mm.trans1:probe17 0.354990627343152 0.0457989959887287 7.75105697580174 3.02262166816857e-14 *** df.mm.trans1:probe18 0.3356704718513 0.0457989959887287 7.32921027207474 6.09095830135907e-13 *** df.mm.trans1:probe19 0.33176452442676 0.0457989959887287 7.24392570763797 1.09906432593933e-12 *** df.mm.trans1:probe20 0.244357524465212 0.0457989959887287 5.3354340895453 1.26914303763329e-07 *** df.mm.trans1:probe21 0.363742345962086 0.0457989959887287 7.94214672416844 7.41147997309957e-15 *** df.mm.trans1:probe22 0.333055845749928 0.0457989959887287 7.27212111444308 9.04800854493998e-13 *** df.mm.trans2:probe2 -0.108505115664481 0.0457989959887287 -2.36915926478354 0.0180850294043390 * df.mm.trans2:probe3 -0.0498635379410919 0.0457989959887287 -1.08874740296411 0.276620694994095 df.mm.trans2:probe4 -0.214531350011428 0.0457989959887287 -4.68419329681867 3.34592342308132e-06 *** df.mm.trans2:probe5 -0.332747266482418 0.0457989959887287 -7.26538342815 9.4790134100374e-13 *** df.mm.trans2:probe6 -0.216024209946718 0.0457989959887287 -4.71678920646825 2.86556415503629e-06 *** df.mm.trans3:probe2 -0.430450649662821 0.0457989959887287 -9.39869183526984 6.77022326996832e-20 *** df.mm.trans3:probe3 -0.697622583575688 0.0457989959887287 -15.2322680555568 9.26698459901504e-46 *** df.mm.trans3:probe4 -0.565411924377547 0.0457989959887287 -12.3455091573775 5.78154433929851e-32 *** df.mm.trans3:probe5 0.443030253764482 0.0457989959887287 9.67336170149915 6.41216671970322e-21 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14488047430165 0.189786771763872 21.8396700453845 5.9958333024363e-82 *** df.mm.trans1 0.0556093341968701 0.167185247311574 0.332621060118026 0.739514802489601 df.mm.trans2 -0.0788186838459496 0.150856353998035 -0.522475068216061 0.601496399181286 df.mm.exp2 0.102963924625719 0.200895760216805 0.512524129501791 0.608437592646805 df.mm.exp3 0.221746970048467 0.200895760216805 1.10379118906820 0.270043496879709 df.mm.exp4 -0.107742533502232 0.200895760216805 -0.536310638840546 0.591905492995446 df.mm.exp5 0.0596354213783963 0.200895760216805 0.296847585603789 0.766666399569207 df.mm.exp6 -0.0608285449347753 0.200895760216805 -0.302786603704974 0.762137816661844 df.mm.exp7 0.078062293009912 0.200895760216805 0.388571132241257 0.697705496416296 df.mm.exp8 -0.090659205447363 0.200895760216805 -0.451274856918455 0.651924036999394 df.mm.trans1:exp2 -0.0736458875555594 0.189493362510709 -0.388646264860054 0.697649933180488 df.mm.trans2:exp2 -0.100677074207936 0.155040023872399 -0.64936183375975 0.516306568000363 df.mm.trans1:exp3 -0.173802165421265 0.189493362510709 -0.91719394874025 0.35934064645665 df.mm.trans2:exp3 0.0246478422470794 0.155040023872399 0.158977286196532 0.873730263830974 df.mm.trans1:exp4 0.108827506827631 0.189493362510709 0.574307750866367 0.565934567855569 df.mm.trans2:exp4 0.0372916312759399 0.155040023872399 0.240529060461393 0.809986937540882 df.mm.trans1:exp5 -0.103171481012156 0.189493362510709 -0.544459603466717 0.586289705362305 df.mm.trans2:exp5 -0.132775917339492 0.155040023872399 -0.856397683792735 0.392055824339727 df.mm.trans1:exp6 0.00840472368902877 0.189493362510709 0.0443536574456732 0.96463449918144 df.mm.trans2:exp6 0.180542086846517 0.155040023872399 1.16448696496014 0.244603013146404 df.mm.trans1:exp7 -0.125826027230898 0.189493362510709 -0.664012847541226 0.506889478139732 df.mm.trans2:exp7 0.00965353934644323 0.155040023872399 0.0622648210786417 0.95036880178418 df.mm.trans1:exp8 0.0705236837238682 0.189493362510709 0.372169678079793 0.70987336258134 df.mm.trans2:exp8 0.0464114409555310 0.155040023872399 0.299351353259134 0.764756258415976 df.mm.trans1:probe2 -0.0518844737689797 0.110640286980153 -0.468947389645572 0.63924573671007 df.mm.trans1:probe3 0.00140799002637005 0.110640286980153 0.0127258349087853 0.98984996616814 df.mm.trans1:probe4 -0.0689598775020983 0.110640286980153 -0.623279994876266 0.533293110476644 df.mm.trans1:probe5 0.0289408611422374 0.110640286980153 0.261576157583801 0.79372121195705 df.mm.trans1:probe6 0.00503441773125868 0.110640286980153 0.0455025729656845 0.963719033428446 df.mm.trans1:probe7 -0.0237007278104446 0.110640286980153 -0.214214265502548 0.830439144067328 df.mm.trans1:probe8 0.0124610433881611 0.110640286980153 0.112626636537886 0.910357193916268 df.mm.trans1:probe9 -0.00372703265839006 0.110640286980153 -0.0336860357119160 0.973136623023406 df.mm.trans1:probe10 -0.135092360563309 0.110640286980153 -1.22100515328148 0.222474007397023 df.mm.trans1:probe11 -0.111921539824300 0.110640286980153 -1.01158034635590 0.312070281841328 df.mm.trans1:probe12 -0.0629119956528639 0.110640286980153 -0.568617430142325 0.569788776730346 df.mm.trans1:probe13 -0.0826621027454701 0.110640286980153 -0.747124804189077 0.455226192730742 df.mm.trans1:probe14 -0.00336999193348537 0.110640286980153 -0.0304589948694718 0.975709230610572 df.mm.trans1:probe15 0.0280902535710758 0.110640286980153 0.253888111986863 0.7996527156888 df.mm.trans1:probe16 -0.164574933037093 0.110640286980153 -1.48747745987513 0.137315872765537 df.mm.trans1:probe17 -0.0765884426155313 0.110640286980153 -0.692229247645299 0.489011110684705 df.mm.trans1:probe18 -0.0407765459112537 0.110640286980153 -0.368550615912343 0.712568406434486 df.mm.trans1:probe19 -0.00324724327685475 0.110640286980153 -0.0293495558036401 0.976593738934815 df.mm.trans1:probe20 -0.0201070569150752 0.110640286980153 -0.181733593285799 0.85584170394804 df.mm.trans1:probe21 -0.0366537959657613 0.110640286980153 -0.331287969022861 0.740521024533633 df.mm.trans1:probe22 -0.00123531297623879 0.110640286980153 -0.0111651280917265 0.991094719215822 df.mm.trans2:probe2 -0.0595394208674602 0.110640286980153 -0.53813509068483 0.590646039506467 df.mm.trans2:probe3 0.0793644330801436 0.110640286980153 0.717319479606739 0.473403962731281 df.mm.trans2:probe4 0.05570213562872 0.110640286980153 0.503452559181377 0.614796359105239 df.mm.trans2:probe5 0.0774475339130015 0.110640286980153 0.699993971697618 0.484151661234856 df.mm.trans2:probe6 -0.0207171680197258 0.110640286980153 -0.187247959899472 0.85151768891512 df.mm.trans3:probe2 -0.00994256016610576 0.110640286980153 -0.0898638320405775 0.928419804941529 df.mm.trans3:probe3 -0.009102235844498 0.110640286980153 -0.0822687295282485 0.934455327484195 df.mm.trans3:probe4 0.00550620557102419 0.110640286980153 0.0497667325466348 0.960321742993724 df.mm.trans3:probe5 -0.0572365923015383 0.110640286980153 -0.517321437459807 0.605086804534895