chr6.19935_chr6_123378996_123380010_-_1.R fitVsDatCorrelation=0.813608880224405 cont.fitVsDatCorrelation=0.265016719638898 fstatistic=6439.87560073714,36,324 cont.fstatistic=2335.65595509517,36,324 residuals=-0.508243052123397,-0.104947881532990,-0.0206237409039987,0.0979565426326722,1.34680891657882 cont.residuals=-0.712993816762999,-0.152967042299016,0.0235459902545122,0.157106017593696,1.21216769757474 predictedValues: Include Exclude Both chr6.19935_chr6_123378996_123380010_-_1.R.tl.Lung 83.4981230410975 84.003689691148 62.6053665722468 chr6.19935_chr6_123378996_123380010_-_1.R.tl.cerebhem 67.5276917858637 81.8207867007093 62.3319371248426 chr6.19935_chr6_123378996_123380010_-_1.R.tl.cortex 83.198917535523 84.4906598468483 82.3582810290305 chr6.19935_chr6_123378996_123380010_-_1.R.tl.heart 80.7436786603339 91.5203865953724 61.0659003371298 chr6.19935_chr6_123378996_123380010_-_1.R.tl.kidney 83.6321144954125 96.183605861936 63.1636744654684 chr6.19935_chr6_123378996_123380010_-_1.R.tl.liver 85.5239168091787 116.514734126364 49.4425542562819 chr6.19935_chr6_123378996_123380010_-_1.R.tl.stomach 94.9235683919204 84.4329055647105 68.108725776864 chr6.19935_chr6_123378996_123380010_-_1.R.tl.testicle 82.3259857432413 101.113555805705 60.8006414147093 diffExp=-0.505566650050525,-14.2930949148457,-1.29174231132536,-10.7767079350385,-12.5514913665235,-30.9908173171853,10.4906628272099,-18.7875700624633 diffExpScore=1.25068681776495 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,-1,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,-1,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 86.2256920972747 82.6648730073566 91.6047315962946 cerebhem 78.5230675686503 76.387192667048 78.9844637880443 cortex 85.371940604664 78.8189544284769 86.5409787586692 heart 83.5248603420118 81.0667009514194 85.3539757641033 kidney 80.6360051353569 85.1452186099224 97.8147508971288 liver 76.7967206814937 74.3709167089306 77.0570025566038 stomach 83.2700782693294 76.0308661787629 67.5096970430872 testicle 84.3679332359122 81.785856174279 81.3839408009628 cont.diffExp=3.56081908991808,2.13587490160236,6.55298617618702,2.45815939059243,-4.5092134745655,2.42580397256306,7.23921209056653,2.58207706163321 cont.diffExpScore=1.34199961527412 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=0.197367845456721 cont.tran.correlation=0.502520724469498 tran.covariance=0.00266250783416816 cont.tran.covariance=0.00100892284198492 tran.mean=87.5908950409602 cont.tran.mean=80.9366797913056 weightedLogRatios: wLogRatio Lung -0.026728979580462 cerebhem -0.827210907397232 cortex -0.0682351214723992 heart -0.557996431566157 kidney -0.628729680023701 liver -1.42347318189893 stomach 0.526374281425093 testicle -0.92777622521431 cont.weightedLogRatios: wLogRatio Lung 0.187076317537564 cerebhem 0.119950714927044 cortex 0.351967370533475 heart 0.131741882797491 kidney -0.240350431682873 liver 0.138823074794033 stomach 0.398052585542101 testicle 0.137376162369958 varWeightedLogRatios=0.375157063932046 cont.varWeightedLogRatios=0.0367876776017638 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.98108413777521 0.098297431158295 50.6735942036352 4.68470893034071e-156 *** df.mm.trans1 -0.54906224222449 0.0832568973623405 -6.5947958621966 1.73220837965386e-10 *** df.mm.trans2 -0.585276472288031 0.0832568973623406 -7.02976559096194 1.22995436942300e-11 *** df.mm.exp2 -0.234238712372612 0.115945324279843 -2.02025147480082 0.0441798395180952 * df.mm.exp3 -0.272037552667645 0.115945324279843 -2.34625720663872 0.0195652502590398 * df.mm.exp4 0.0770539401460042 0.115945324279843 0.664571345369897 0.506797405337237 df.mm.exp5 0.128123278723866 0.115945324279843 1.10503187187291 0.269965687237064 df.mm.exp6 0.587168454805044 0.115945324279843 5.06418398889349 6.89691693714241e-07 *** df.mm.exp7 0.0490900163469348 0.115945324279843 0.423389357456557 0.672292116056368 df.mm.exp8 0.200496790267783 0.115945324279843 1.72923566787280 0.0847194076494521 . df.mm.trans1:exp2 0.0219523217471445 0.100411596276369 0.218623371813788 0.827081088550976 df.mm.trans2:exp2 0.207909317071603 0.100411596276369 2.07057077849218 0.0391906223645732 * df.mm.trans1:exp3 0.268447736965391 0.100411596276369 2.67347345247383 0.00788731912789935 ** df.mm.trans2:exp3 0.277817823791314 0.100411596276369 2.76679023234189 0.00598620684623164 ** df.mm.trans1:exp4 -0.110598417006095 0.100411596276369 -1.10145064023969 0.271518144090101 df.mm.trans2:exp4 0.0086470888660217 0.100411596276369 0.0861164366137733 0.931427050150746 df.mm.trans1:exp5 -0.126519840770353 0.100411596276369 -1.26001224422452 0.208571730433764 df.mm.trans2:exp5 0.0072749244162713 0.100411596276369 0.0724510383865237 0.942287672550101 df.mm.trans1:exp6 -0.563196542271233 0.100411596276369 -5.60887948361178 4.36873702263534e-08 *** df.mm.trans2:exp6 -0.260011439381936 0.100411596276369 -2.58945628815911 0.0100465393200503 * df.mm.trans1:exp7 0.0791578551376202 0.100411596276369 0.788333798814924 0.431077883407248 df.mm.trans2:exp7 -0.0439935371411965 0.100411596276369 -0.43813203626512 0.661582530665885 df.mm.trans1:exp8 -0.214634141329574 0.100411596276369 -2.13754336440209 0.0333019951289189 * df.mm.trans2:exp8 -0.0151133128604819 0.100411596276369 -0.150513620148859 0.880453064820796 df.mm.trans1:probe2 -0.105504122489870 0.0502057981381843 -2.10143302969679 0.0363749249638847 * df.mm.trans1:probe3 0.00161547050534255 0.0502057981381843 0.032176970892808 0.97435073360446 df.mm.trans1:probe4 0.096195674062219 0.0502057981381843 1.91602718469795 0.0562413972692197 . df.mm.trans1:probe5 -0.130882429667910 0.0502057981381843 -2.60691861341740 0.0095585025673015 ** df.mm.trans1:probe6 0.0737957251235914 0.0502057981381843 1.46986459453307 0.142568792887447 df.mm.trans2:probe2 0.115871171497791 0.0502057981381843 2.30792410029758 0.0216329379391992 * df.mm.trans2:probe3 0.247351006517208 0.0502057981381843 4.92674184436645 1.33597951186893e-06 *** df.mm.trans2:probe4 -0.253434868380225 0.0502057981381843 -5.04792031555163 7.46376790328271e-07 *** df.mm.trans2:probe5 0.06429613739016 0.0502057981381843 1.28065163336701 0.201232042376256 df.mm.trans2:probe6 0.14139406854978 0.0502057981381843 2.816289627756 0.00515597237797399 ** df.mm.trans3:probe2 0.457827701585238 0.0502057981381843 9.11902048295563 8.187728246185e-18 *** df.mm.trans3:probe3 0.412489035895029 0.0502057981381843 8.21596411553326 5.09611495525914e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.31810041679447 0.163021395147458 26.4879368311663 4.54229712100813e-83 *** df.mm.trans1 0.139532034873998 0.138077418745567 1.01053478651068 0.312993204069435 df.mm.trans2 0.0460472916746881 0.138077418745567 0.333488937532491 0.738980953919785 df.mm.exp2 -0.0243237092204232 0.192289547165133 -0.126495223370278 0.899418391748794 df.mm.exp3 -0.000727075679021215 0.192289547165134 -0.00378115030036875 0.996985412727484 df.mm.exp4 0.0193295506165423 0.192289547165133 0.100523148041648 0.919991179742449 df.mm.exp5 -0.103051896131726 0.192289547165133 -0.535920426518179 0.592381254512001 df.mm.exp6 -0.0485985600424539 0.192289547165133 -0.252736359094541 0.800632217301763 df.mm.exp7 0.186677404627069 0.192289547165133 0.970814104974489 0.332365030079556 df.mm.exp8 0.0858337267344445 0.192289547165133 0.446377496852352 0.65562276773496 df.mm.trans1:exp2 -0.0692520402137261 0.166527632727212 -0.41585915249974 0.677788373259882 df.mm.trans2:exp2 -0.0546560036773479 0.166527632727212 -0.32820981588611 0.742965029982947 df.mm.trans1:exp3 -0.0092236272972259 0.166527632727212 -0.0553879686282162 0.955863519395397 df.mm.trans2:exp3 -0.0469141777192516 0.166527632727212 -0.281720078229308 0.778338006980448 df.mm.trans1:exp4 -0.0511534198587457 0.166527632727212 -0.307176767128732 0.758906167086869 df.mm.trans2:exp4 -0.0388520260420848 0.166527632727212 -0.233306781618209 0.815670521241919 df.mm.trans1:exp5 0.0360289743020785 0.166527632727212 0.216354329380863 0.828847716791609 df.mm.trans2:exp5 0.132615389338758 0.166527632727212 0.796356659654163 0.426407872191128 df.mm.trans1:exp6 -0.0672076856087679 0.166527632727212 -0.403582783878641 0.68678576821202 df.mm.trans2:exp6 -0.0571312387612424 0.166527632727212 -0.343073625833791 0.731765558980071 df.mm.trans1:exp7 -0.221556309866953 0.166527632727212 -1.33044772353117 0.184306443207559 df.mm.trans2:exp7 -0.270332772629533 0.166527632727212 -1.62335084095241 0.105486926322833 df.mm.trans1:exp8 -0.107614520723856 0.166527632727212 -0.646226208596497 0.518590328434669 df.mm.trans2:exp8 -0.0965241652506783 0.166527632727212 -0.579628519723176 0.562567967818757 df.mm.trans1:probe2 0.0086705387067418 0.0832638163636058 0.104133332885900 0.91712796831814 df.mm.trans1:probe3 0.0640235215909986 0.0832638163636058 0.768923698037254 0.442498745463816 df.mm.trans1:probe4 0.0462985259560461 0.0832638163636058 0.556046167207428 0.578563080687124 df.mm.trans1:probe5 -0.0848092151650173 0.0832638163636058 -1.01856026866055 0.309171733200759 df.mm.trans1:probe6 -0.0401617672036172 0.0832638163636058 -0.482343579211338 0.629887586968289 df.mm.trans2:probe2 0.107048559029840 0.0832638163636058 1.28565520660702 0.199481546801223 df.mm.trans2:probe3 0.0550869031649358 0.0832638163636058 0.661594742719649 0.508701203357373 df.mm.trans2:probe4 0.159320969726206 0.0832638163636058 1.91344784186285 0.0565717911591333 . df.mm.trans2:probe5 0.057504924867721 0.0832638163636058 0.690635228832198 0.490289262911667 df.mm.trans2:probe6 0.0768621049173164 0.0832638163636058 0.923115325169174 0.356634032612307 df.mm.trans3:probe2 0.0202677783602698 0.0832638163636058 0.243416399168664 0.80783688409844 df.mm.trans3:probe3 0.0211417675924532 0.0832638163636058 0.253913026279374 0.799723807368383