fitVsDatCorrelation=0.92847891571477 cont.fitVsDatCorrelation=0.269945145839524 fstatistic=11825.8851336634,56,784 cont.fstatistic=1747.39199644153,56,784 residuals=-0.493250172442471,-0.0880597593976511,-0.0093083377016783,0.085392318870118,0.768185121467857 cont.residuals=-0.758979380775727,-0.284180588122157,-0.080908232232756,0.222918882859273,1.19773815504939 predictedValues: Include Exclude Both Lung 57.9010951605293 104.751305320451 68.0434011045314 cerebhem 58.9893858964006 83.749882949285 67.0378454454226 cortex 53.0468399975132 105.272790304713 74.6711508379457 heart 54.4599037671206 111.992922827968 67.9468463837121 kidney 58.5498641147477 112.282251877301 70.2939001617167 liver 59.0206440972573 134.988286271877 63.1554056780306 stomach 56.6283185840103 121.213041981020 66.5364294386821 testicle 57.473437709272 120.329599548675 68.1762418481864 diffExp=-46.8502101599221,-24.7604970528844,-52.2259503071993,-57.5330190608476,-53.7323877625533,-75.9676421746199,-64.58472339701,-62.856161839403 diffExpScore=0.997724741977188 diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.875 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 63.99132382619 77.0612758956644 62.0691152414003 cerebhem 72.588900411468 82.1897908723768 72.0051099393384 cortex 68.1559270380921 69.3268157120119 62.204657836126 heart 71.5627543070063 72.9853508537766 68.8869746332652 kidney 66.6727019764615 79.6787280249967 58.7558052286003 liver 66.1225887850014 71.3240200246243 75.350408532451 stomach 69.0145416963432 69.6306595223405 71.087593925407 testicle 63.2393128569278 72.3705697128323 69.3445300716304 cont.diffExp=-13.0699520694743,-9.60089046090879,-1.17088867391978,-1.42259654677027,-13.0060260485352,-5.20143123962295,-0.616117825997264,-9.13125685590452 cont.diffExpScore=0.981556335340803 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=-1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.0489968065257942 cont.tran.correlation=0.20285639486495 tran.covariance=5.82109091063969e-06 cont.tran.covariance=0.000563909200153285 tran.mean=84.4155981505088 cont.tran.mean=70.9947038447571 weightedLogRatios: wLogRatio Lung -2.58197003280045 cerebhem -1.49043818467877 cortex -2.95663554097157 heart -3.14195553974076 kidney -2.86203852792496 liver -3.71585234362586 stomach -3.36153822768300 testicle -3.26656538335479 cont.weightedLogRatios: wLogRatio Lung -0.790188052157533 cerebhem -0.539970564209923 cortex -0.0720577365528397 heart -0.0842557249612607 kidney -0.764312044691912 liver -0.320259588421487 stomach -0.0376730452382132 testicle -0.568406192828345 varWeightedLogRatios=0.451469375000766 cont.varWeightedLogRatios=0.0968195567728664 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36893305605363 0.0723143418903793 60.4158586228504 3.32540974418813e-297 *** df.mm.trans1 -0.468203042211497 0.0630284315358267 -7.42844190792468 2.87396942881245e-13 *** df.mm.trans2 0.245532778126526 0.0562460735382315 4.36533188329374 1.43936575567306e-05 *** df.mm.exp2 -0.190244582558912 0.0735873304637984 -2.58528990465965 0.00990915845051652 ** df.mm.exp3 -0.175543102362908 0.0735873304637984 -2.38550714173912 0.0172921381701901 * df.mm.exp4 0.00699510579447085 0.0735873304637984 0.095058561716845 0.924292602074715 df.mm.exp5 0.0480299840391412 0.0735873304637984 0.652693659851811 0.514145123332279 df.mm.exp6 0.347297277250742 0.0735873304637984 4.71952542729616 2.79932896427783e-06 *** df.mm.exp7 0.146129688610138 0.0735873304637984 1.98579956208667 0.0474034970230718 * df.mm.exp8 0.129281818539318 0.0735873304637984 1.75684887227861 0.0793339094759105 . df.mm.trans1:exp2 0.208865811129973 0.0687146190238277 3.0396124448794 0.00244757408115036 ** df.mm.trans2:exp2 -0.0335096643750651 0.0535970332051484 -0.625214911556825 0.532011948238589 df.mm.trans1:exp3 0.0879821000654263 0.0687146190238277 1.28039857188057 0.200783678485022 df.mm.trans2:exp3 0.180509066429029 0.0535970332051484 3.36789287828882 0.000794403744523095 *** df.mm.trans1:exp4 -0.0682666846779279 0.0687146190238276 -0.993481236565622 0.320782010319177 df.mm.trans2:exp4 0.0598515544504165 0.0535970332051484 1.11669528836285 0.264466748001623 df.mm.trans1:exp5 -0.0368875139290038 0.0687146190238277 -0.536821923093433 0.59154292826178 df.mm.trans2:exp5 0.0213968031432859 0.0535970332051484 0.399216185369578 0.689842638172495 df.mm.trans1:exp6 -0.328146293681917 0.0687146190238276 -4.77549462317659 2.13934196403294e-06 *** df.mm.trans2:exp6 -0.0936982909510673 0.0535970332051484 -1.74819920707975 0.0808209557915919 . df.mm.trans1:exp7 -0.168356799273780 0.0687146190238277 -2.45008706539434 0.0144997880203681 * df.mm.trans2:exp7 -0.000169033677723663 0.0535970332051484 -0.00315378795458077 0.99748444774488 df.mm.trans1:exp8 -0.136695229441039 0.0687146190238276 -1.98931801388055 0.0470131956402713 * df.mm.trans2:exp8 0.00936380194498425 0.0535970332051484 0.174707467652982 0.861354605680842 df.mm.trans1:probe2 -0.0540081538035935 0.0436673782965714 -1.23680779360720 0.216528801731820 df.mm.trans1:probe3 -0.0366091004432133 0.0436673782965714 -0.83836268334176 0.40208256350743 df.mm.trans1:probe4 -0.0300088181194629 0.0436673782965714 -0.68721364300039 0.492151337831857 df.mm.trans1:probe5 -0.0810164850241917 0.0436673782965714 -1.8553091159712 0.0639272572068663 . df.mm.trans1:probe6 -0.0110856315380524 0.0436673782965714 -0.253865287326461 0.799666194786042 df.mm.trans1:probe7 -0.0344929825860408 0.0436673782965714 -0.789902758800362 0.429823399332945 df.mm.trans1:probe8 -0.0611366204747423 0.0436673782965714 -1.40005246157731 0.161893201306291 df.mm.trans1:probe9 0.00675220504666355 0.0436673782965714 0.154628129969362 0.877154289334796 df.mm.trans1:probe10 -0.0481967679449691 0.0436673782965714 -1.10372479010844 0.270051187933183 df.mm.trans1:probe11 1.22412758811137 0.0436673782965714 28.0329993661994 2.51162599143240e-120 *** df.mm.trans1:probe12 0.711265671553572 0.0436673782965714 16.2882613818247 1.34777405580169e-51 *** df.mm.trans1:probe13 0.328100518421865 0.0436673782965714 7.513629881637 1.57032586371659e-13 *** df.mm.trans1:probe14 0.709711678674472 0.0436673782965714 16.2526743385965 2.08117131549263e-51 *** df.mm.trans1:probe15 0.46316727519446 0.0436673782965714 10.6067113085840 1.18388497071803e-24 *** df.mm.trans1:probe16 0.309022836534045 0.0436673782965714 7.0767435231693 3.27771200962513e-12 *** df.mm.trans1:probe17 0.259892909264377 0.0436673782965714 5.95164902044928 4.00103992516303e-09 *** df.mm.trans1:probe18 0.159848489427631 0.0436673782965714 3.66059277344301 0.000268502367760729 *** df.mm.trans1:probe19 0.148171495556998 0.0436673782965714 3.39318505797798 0.000725521462626697 *** df.mm.trans1:probe20 0.216528500690517 0.0436673782965714 4.95858714530426 8.7080028923823e-07 *** df.mm.trans1:probe21 0.170925480970884 0.0436673782965714 3.91426020151763 9.85654773464376e-05 *** df.mm.trans1:probe22 0.231222182666737 0.0436673782965714 5.29507819536059 1.54626213646131e-07 *** df.mm.trans2:probe2 0.180630976953356 0.0436673782965714 4.13651984615571 3.90713625049395e-05 *** df.mm.trans2:probe3 0.218098062137321 0.0436673782965714 4.99453071480696 7.27419959359e-07 *** df.mm.trans2:probe4 0.256997468897156 0.0436673782965713 5.88534230637186 5.88476989479516e-09 *** df.mm.trans2:probe5 -0.00562116109230879 0.0436673782965714 -0.128726782133155 0.897606856937285 df.mm.trans2:probe6 -0.167503931007447 0.0436673782965714 -3.83590537242304 0.000135153964759240 *** df.mm.trans3:probe2 -0.069582351964807 0.0436673782965714 -1.59346300783691 0.111459412828352 df.mm.trans3:probe3 -0.0498888370740014 0.0436673782965714 -1.14247383333106 0.253605894465217 df.mm.trans3:probe4 0.112331917896838 0.0436673782965714 2.57244474660064 0.0102812623559049 * df.mm.trans3:probe5 0.0659276618161292 0.0436673782965714 1.50976917753969 0.131505231184268 df.mm.trans3:probe6 0.314646827257789 0.0436673782965714 7.20553510496631 1.35976531646439e-12 *** df.mm.trans3:probe7 0.143745510109581 0.0436673782965714 3.29182826441557 0.00103993668358960 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43236587593656 0.187486574102411 23.6409774788219 1.05568522501781e-93 *** df.mm.trans1 -0.307316413408630 0.16341135645836 -1.88063069831342 0.0603926299041087 . df.mm.trans2 -0.0698365763639954 0.145827001376586 -0.478900174211551 0.632143182610137 df.mm.exp2 0.0420061520170543 0.190787001932670 0.220173028516265 0.825793719009645 df.mm.exp3 -0.0448998535361246 0.190787001932670 -0.235340212285374 0.814006143640172 df.mm.exp4 -0.0467334966525268 0.190787001932670 -0.244951155891739 0.806558349831317 df.mm.exp5 0.129308413422675 0.190787001932670 0.67776322345224 0.498121797239194 df.mm.exp6 -0.238505643482797 0.190787001932670 -1.25011474087196 0.211630648074518 df.mm.exp7 -0.161490500628517 0.190787001932670 -0.846443934820616 0.397563421393498 df.mm.exp8 -0.185461280769043 0.190787001932670 -0.972085513637316 0.331307819443632 df.mm.trans1:exp2 0.084058362276827 0.178153713008398 0.471830538119993 0.637179037187847 df.mm.trans2:exp2 0.0224240474660917 0.138958937812353 0.161371753549043 0.8718421873494 df.mm.trans1:exp3 0.107950469421097 0.178153713008398 0.605940048052821 0.544729948746207 df.mm.trans2:exp3 -0.0608692602358232 0.138958937812353 -0.438037748374415 0.661479578732314 df.mm.trans1:exp4 0.158560735075698 0.178153713008398 0.890022062398573 0.37372706228361 df.mm.trans2:exp4 -0.00760865180540117 0.138958937812353 -0.054754677354225 0.95634785812913 df.mm.trans1:exp5 -0.088260318947483 0.178153713008398 -0.495416668320141 0.620444858560003 df.mm.trans2:exp5 -0.0959066600482723 0.138958937812353 -0.690179858583712 0.490285343237796 df.mm.trans1:exp6 0.271268559543284 0.178153713008398 1.52266576408933 0.128245575035778 df.mm.trans2:exp6 0.161137904677511 0.138958937812353 1.15960806274375 0.246561450660989 df.mm.trans1:exp7 0.237060223289548 0.178153713008398 1.3306499162236 0.183691109635303 df.mm.trans2:exp7 0.0600945851183192 0.138958937812353 0.432462899216096 0.665523938241973 df.mm.trans1:exp8 0.173639918457207 0.178153713008398 0.974663483151892 0.330027838723583 df.mm.trans2:exp8 0.122660105607022 0.138958937812353 0.882707564825086 0.377664862502114 df.mm.trans1:probe2 0.109202153096477 0.113214708767851 0.964558000324833 0.335063664702203 df.mm.trans1:probe3 -0.0361629527526868 0.113214708767851 -0.319419209272883 0.749493738051493 df.mm.trans1:probe4 -0.0132540129124878 0.113214708767851 -0.117069708138943 0.906834796982034 df.mm.trans1:probe5 0.0410173958975981 0.113214708767851 0.362297411210985 0.717227428927304 df.mm.trans1:probe6 -0.0228497266520230 0.113214708767851 -0.201826484391501 0.840104731885606 df.mm.trans1:probe7 0.203997249852506 0.113214708767851 1.80186171984778 0.0719512041074748 . df.mm.trans1:probe8 -0.0994073051439207 0.113214708767851 -0.878042316460461 0.380189753765231 df.mm.trans1:probe9 0.0452582709018368 0.113214708767851 0.399756104082197 0.689445048651951 df.mm.trans1:probe10 -0.00851420773010038 0.113214708767851 -0.0752040774804173 0.940071524969809 df.mm.trans1:probe11 0.119782498739961 0.113214708767851 1.05801180821458 0.290375932940502 df.mm.trans1:probe12 0.121249193460279 0.113214708767851 1.07096679203497 0.284513941090268 df.mm.trans1:probe13 0.166002009905966 0.113214708767851 1.46625833085307 0.142978887234048 df.mm.trans1:probe14 0.147150589258171 0.113214708767851 1.29974798203921 0.194069380449144 df.mm.trans1:probe15 0.0155648598567949 0.113214708767851 0.137480898252461 0.89068598947048 df.mm.trans1:probe16 0.0473546005562423 0.113214708767851 0.418272511333697 0.675862429411453 df.mm.trans1:probe17 0.0808734485915397 0.113214708767851 0.714336939711359 0.475231479267393 df.mm.trans1:probe18 0.0122026689519007 0.113214708767851 0.107783423944697 0.914195068588924 df.mm.trans1:probe19 -0.0154230568038638 0.113214708767851 -0.136228383853277 0.891675709006426 df.mm.trans1:probe20 -0.0474306030971037 0.113214708767851 -0.418943824643501 0.675371938200656 df.mm.trans1:probe21 0.0217876415131443 0.113214708767851 0.192445325790841 0.847443227982021 df.mm.trans1:probe22 0.0888426308685814 0.113214708767851 0.784726930232667 0.432850737510406 df.mm.trans2:probe2 -0.0208476311979872 0.113214708767851 -0.184142426588189 0.85394934132844 df.mm.trans2:probe3 -0.0110325427445933 0.113214708767851 -0.0974479629428334 0.92239556334115 df.mm.trans2:probe4 -0.0212387170123049 0.113214708767851 -0.187596799421666 0.851241275137473 df.mm.trans2:probe5 -0.275653003275511 0.113214708767851 -2.43478083612565 0.0151228908884144 * df.mm.trans2:probe6 0.095702650813604 0.113214708767851 0.845319939919149 0.398190130922263 df.mm.trans3:probe2 -0.0193474865362407 0.113214708767851 -0.170891986975942 0.864352790972214 df.mm.trans3:probe3 0.0063421174691502 0.113214708767851 0.056018493870393 0.955341346985854 df.mm.trans3:probe4 0.114177423082207 0.113214708767851 1.00850343850930 0.31352398731595 df.mm.trans3:probe5 0.262372872599884 0.113214708767851 2.31748043567275 0.0207342438053056 * df.mm.trans3:probe6 0.0481566355984652 0.113214708767851 0.425356706054963 0.670693388775685 df.mm.trans3:probe7 0.0995479174609961 0.113214708767851 0.879284313358265 0.379516556285217