chr14.7403_chr14_110267166_110274311_-_0.R fitVsDatCorrelation=0.671080831502553 cont.fitVsDatCorrelation=0.34651861120124 fstatistic=5172.02795589805,39,393 cont.fstatistic=3226.95703745308,39,393 residuals=-0.329483961558082,-0.0763240516297079,-0.0012449295973762,0.0708314743032417,2.63675432618176 cont.residuals=-0.453029277627694,-0.12966092848815,-0.0327801318342547,0.0891526982017329,2.57252346182647 predictedValues: Include Exclude Both chr14.7403_chr14_110267166_110274311_-_0.R.tl.Lung 40.6374530647983 43.3978613160755 57.0530626928969 chr14.7403_chr14_110267166_110274311_-_0.R.tl.cerebhem 43.4855719886046 48.0553771563856 59.2861930251631 chr14.7403_chr14_110267166_110274311_-_0.R.tl.cortex 41.3175641676125 44.5849461995503 65.9504523315708 chr14.7403_chr14_110267166_110274311_-_0.R.tl.heart 43.6065893454068 52.3297196967518 52.6575269184671 chr14.7403_chr14_110267166_110274311_-_0.R.tl.kidney 41.3726142994116 42.027629308288 55.0610867988209 chr14.7403_chr14_110267166_110274311_-_0.R.tl.liver 46.7002929929645 48.3220930991221 53.860445126924 chr14.7403_chr14_110267166_110274311_-_0.R.tl.stomach 44.3041034063907 43.4389289001308 57.3203196571781 chr14.7403_chr14_110267166_110274311_-_0.R.tl.testicle 42.6498736883331 45.3966310865572 55.8699499181259 diffExp=-2.76040825127714,-4.56980516778098,-3.26738203193782,-8.72313035134503,-0.655015008876418,-1.62180010615759,0.865174506259834,-2.74675739822410 diffExpScore=1.02983558636364 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=0,0,0,-1,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 48.9672569003652 50.4689499438749 58.6194350103867 cerebhem 43.0769960538356 44.0910724370283 46.8375441657249 cortex 46.0426588270011 49.9881096885813 53.5559740963034 heart 45.4089567982413 45.5223754312066 52.2781146626035 kidney 47.8736346074538 49.2559539178785 50.1203592567309 liver 48.6866421831157 44.755871068118 48.2142048380299 stomach 45.8561094155463 46.630157053773 45.0573900208899 testicle 50.22030316961 48.0455615833063 47.7920045824658 cont.diffExp=-1.50169304350975,-1.01407638319266,-3.94545086158013,-0.113418632965320,-1.38231931042473,3.93077111499774,-0.77404763822669,2.17474158630371 cont.diffExpScore=4.09227596943420 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=0.538737225043821 cont.tran.correlation=0.492006546686747 tran.covariance=0.00182302747203829 cont.tran.covariance=0.00130677100936385 tran.mean=44.476703107274 cont.tran.mean=47.1806630674335 weightedLogRatios: wLogRatio Lung -0.24563191073049 cerebhem -0.381951840616707 cortex -0.28611818311633 heart -0.705059499400863 kidney -0.0585985382883373 liver -0.131802327469089 stomach 0.0745704404914063 testicle -0.236187428979179 cont.weightedLogRatios: wLogRatio Lung -0.117994084285962 cerebhem -0.0878286975442926 cortex -0.318234804856595 heart -0.00952178356468189 kidney -0.110525094906791 liver 0.323538105978715 stomach -0.0641754410527743 testicle 0.172398537995623 varWeightedLogRatios=0.0545498965197494 cont.varWeightedLogRatios=0.0382995396511149 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.22382802895222 0.0936701264633814 34.4168215702422 1.15484234700280e-120 *** df.mm.trans1 0.458612786293635 0.0764813379924186 5.99640119187109 4.57473155271298e-09 *** df.mm.trans2 0.539064289982914 0.0764813379924186 7.04831144607264 8.18176199544885e-12 *** df.mm.exp2 0.131288262176968 0.103917675059359 1.26338721590888 0.207199017669530 df.mm.exp3 -0.101338305770957 0.103917675059359 -0.975178724053163 0.330071474114957 df.mm.exp4 0.337845011962862 0.103917675059359 3.25108324228654 0.00124882389059479 ** df.mm.exp5 0.0213846434193358 0.103917675059359 0.205784467436560 0.837065829783192 df.mm.exp6 0.304124423977073 0.103917675059359 2.92658995501345 0.0036260726896638 ** df.mm.exp7 0.0826596062270165 0.103917675059359 0.795433560073395 0.426841495508847 df.mm.exp8 0.114317009540771 0.103917675059359 1.10007281702050 0.271973773378617 df.mm.trans1:exp2 -0.0635491881793581 0.0848484263839253 -0.748973091048364 0.454321461187119 df.mm.trans2:exp2 -0.0293443869615109 0.0848484263839253 -0.345844798920988 0.729644414445793 df.mm.trans1:exp3 0.117935867183054 0.0848484263839253 1.38995939240421 0.165327994093420 df.mm.trans2:exp3 0.128324417202426 0.0848484263839254 1.51239595913988 0.131236864256876 df.mm.trans1:exp4 -0.267326871908436 0.0848484263839253 -3.15064030414454 0.00175386789635298 ** df.mm.trans2:exp4 -0.150690709507986 0.0848484263839254 -1.77599887151866 0.0765067741151047 . df.mm.trans1:exp5 -0.00345560247841034 0.0848484263839253 -0.0407267715581936 0.967534400480002 df.mm.trans2:exp5 -0.0534675622054692 0.0848484263839253 -0.630153845912677 0.528960148712752 df.mm.trans1:exp6 -0.165064116039311 0.0848484263839253 -1.94539985093445 0.0524395845299005 . df.mm.trans2:exp6 -0.196645715300664 0.0848484263839253 -2.31761181298607 0.0209833599606990 * df.mm.trans1:exp7 0.00372756356146478 0.0848484263839253 0.0439320293884787 0.964980896683052 df.mm.trans2:exp7 -0.081713749361169 0.0848484263839253 -0.963055566775364 0.336111826511581 df.mm.trans1:exp8 -0.065982827850724 0.0848484263839253 -0.777655292652835 0.437240135133496 df.mm.trans2:exp8 -0.0692892738494159 0.0848484263839253 -0.816624147345918 0.414637978614227 df.mm.trans1:probe2 0.109609779676894 0.0519588375296797 2.10955026879274 0.0355289161140092 * df.mm.trans1:probe3 0.0349382052968736 0.0519588375296797 0.672420842304571 0.501710959942885 df.mm.trans1:probe4 0.0204703130789555 0.0519588375296797 0.39397172939565 0.69381558633813 df.mm.trans1:probe5 0.00639100989743036 0.0519588375296797 0.123001402673409 0.902168864493745 df.mm.trans1:probe6 0.0955824769799204 0.0519588375296797 1.83958074360925 0.0665838825080592 . df.mm.trans2:probe2 -0.0320500763253939 0.0519588375296797 -0.61683590028523 0.537700140334379 df.mm.trans2:probe3 -0.0336913327966333 0.0519588375296797 -0.648423529055828 0.51708973789513 df.mm.trans2:probe4 0.149643928736179 0.0519588375296797 2.88004766562956 0.00419380997142319 ** df.mm.trans2:probe5 -0.0714603623601321 0.0519588375296797 -1.37532642679530 0.169813708976583 df.mm.trans2:probe6 0.077771953052765 0.0519588375296797 1.49679932712776 0.135248138671162 df.mm.trans3:probe2 -0.102036669404076 0.0519588375296797 -1.96379815745091 0.0502580470094017 . df.mm.trans3:probe3 -0.298814200358369 0.0519588375296797 -5.75097932450244 1.78498830816684e-08 *** df.mm.trans3:probe4 -0.148828622657896 0.0519588375296797 -2.86435628150615 0.00440275014956938 ** df.mm.trans3:probe5 -0.381335838063772 0.0519588375296797 -7.33919110191692 1.24437740803131e-12 *** df.mm.trans3:probe6 -0.134124745508987 0.0519588375296797 -2.58136540164842 0.0102024718463949 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68191652081470 0.118516980385786 31.0665738262118 7.13238323470162e-108 *** df.mm.trans1 0.203645753662159 0.0967687092668724 2.10445871609734 0.0359729974078337 * df.mm.trans2 0.207150426656368 0.0967687092668724 2.14067572282153 0.0329150029407283 * df.mm.exp2 -0.0388825095583629 0.131482784552034 -0.295723198218206 0.767597666551 df.mm.exp3 0.0191823059695828 0.131482784552034 0.145892148808208 0.884081329973714 df.mm.exp4 -0.0641083268528296 0.131482784552034 -0.48757962550952 0.626119504051258 df.mm.exp5 0.109724035511240 0.131482784552034 0.834512562880935 0.404499186453914 df.mm.exp6 0.0695298082442966 0.131482784552034 0.528813018990941 0.597233689228497 df.mm.exp7 0.118375059844363 0.131482784552034 0.900308433896274 0.368507480844571 df.mm.exp8 0.180266947793847 0.131482784552034 1.37103080382746 0.171147761557200 df.mm.trans1:exp2 -0.089280218293129 0.107355244037593 -0.8316335088565 0.406120850308932 df.mm.trans2:exp2 -0.0962184621374488 0.107355244037593 -0.896262339115507 0.370661325168997 df.mm.trans1:exp3 -0.0807658216387333 0.107355244037593 -0.752323022156715 0.452307182320453 df.mm.trans2:exp3 -0.0287554296355996 0.107355244037593 -0.267853050807004 0.788952957423997 df.mm.trans1:exp4 -0.0113341494779908 0.107355244037593 -0.105576113953240 0.915972542178 df.mm.trans2:exp4 -0.0390459949349594 0.107355244037593 -0.363708315182877 0.716271410042775 df.mm.trans1:exp5 -0.132310956590490 0.107355244037593 -1.23245918517179 0.21851439900589 df.mm.trans2:exp5 -0.134052078051789 0.107355244037593 -1.24867750293453 0.212526362968934 df.mm.trans1:exp6 -0.0752769518251824 0.107355244037593 -0.701194920658209 0.483596356760332 df.mm.trans2:exp6 -0.189665469661501 0.107355244037593 -1.76670894246289 0.0780531549923923 . df.mm.trans1:exp7 -0.184018470357798 0.107355244037593 -1.71410788552965 0.0872973740156457 . df.mm.trans2:exp7 -0.197485875530865 0.107355244037593 -1.83955499613704 0.066587674381493 . df.mm.trans1:exp8 -0.154999405477095 0.107355244037593 -1.44379910703588 0.149592170080164 df.mm.trans2:exp8 -0.229475482024267 0.107355244037593 -2.13753397965275 0.0331711241828490 * df.mm.trans1:probe2 -0.00266798780908875 0.065741392276017 -0.0405830743268583 0.967648886478367 df.mm.trans1:probe3 0.0371651012283574 0.065741392276017 0.565322697644107 0.572176876798481 df.mm.trans1:probe4 -0.0212393538423318 0.065741392276017 -0.323074293181346 0.746810805417664 df.mm.trans1:probe5 0.0466868741384514 0.065741392276017 0.71015949802881 0.478026406627091 df.mm.trans1:probe6 0.00713025125760816 0.065741392276017 0.108459085071877 0.913686887023964 df.mm.trans2:probe2 0.0788787368694928 0.065741392276017 1.19983368375161 0.230926750001224 df.mm.trans2:probe3 0.0584495205100763 0.065741392276017 0.88908248649001 0.374502620079603 df.mm.trans2:probe4 0.174896264115700 0.065741392276017 2.66036751064524 0.00812517416361428 ** df.mm.trans2:probe5 0.0492886975624658 0.065741392276017 0.749736138162786 0.453862202977596 df.mm.trans2:probe6 0.0259829463142409 0.065741392276017 0.395229632575330 0.692887860332662 df.mm.trans3:probe2 0.00414881162585705 0.065741392276017 0.0631080584426651 0.949712541844589 df.mm.trans3:probe3 0.0712087133482383 0.065741392276017 1.08316405970331 0.279399710045196 df.mm.trans3:probe4 -0.0526179121901824 0.065741392276017 -0.800377210894237 0.423975837584594 df.mm.trans3:probe5 -0.0469804316193462 0.065741392276017 -0.714624835173821 0.475265155266136 df.mm.trans3:probe6 -0.10563760557517 0.065741392276017 -1.60686596249206 0.108886884944093