chr5.18800_chr5_33282373_33309965_+_2.R fitVsDatCorrelation=0.893947679879191 cont.fitVsDatCorrelation=0.302529608633547 fstatistic=5515.12328192296,36,324 cont.fstatistic=1212.34436642765,36,324 residuals=-0.610284567798499,-0.121601111902974,-0.00887682146330226,0.0990089768203278,1.05151303918611 cont.residuals=-0.715493077510733,-0.259412655667127,-0.0597865187478977,0.148030073154448,1.75561550012866 predictedValues: Include Exclude Both chr5.18800_chr5_33282373_33309965_+_2.R.tl.Lung 78.8898806798543 91.8837666947269 178.745532962063 chr5.18800_chr5_33282373_33309965_+_2.R.tl.cerebhem 86.9971023218011 71.3288659755974 149.952117915143 chr5.18800_chr5_33282373_33309965_+_2.R.tl.cortex 68.2617633110166 96.709835156358 371.420599275369 chr5.18800_chr5_33282373_33309965_+_2.R.tl.heart 70.6205250235169 83.2569567357582 171.982712370858 chr5.18800_chr5_33282373_33309965_+_2.R.tl.kidney 85.3135654551055 91.7197790379048 117.938991409151 chr5.18800_chr5_33282373_33309965_+_2.R.tl.liver 77.9503172292819 86.7578079302856 85.8951699006988 chr5.18800_chr5_33282373_33309965_+_2.R.tl.stomach 68.2527655255203 71.2421185800698 117.543797669754 chr5.18800_chr5_33282373_33309965_+_2.R.tl.testicle 74.6099920300968 81.5077217645733 131.042967262108 diffExp=-12.9938860148726,15.6682363462037,-28.4480718453415,-12.6364317122413,-6.4062135827993,-8.80749070100364,-2.98935305454947,-6.89772973447646 diffExpScore=1.47025314701295 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,-1,0,0,0,0,0 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 87.7709958176592 81.0121086954262 111.723713482531 cerebhem 89.525684106163 107.845839723061 85.1924906160262 cortex 91.9435894430106 79.669233088078 103.934273834117 heart 80.5754569018816 89.0545461688093 82.2020297348755 kidney 86.9616818613153 91.0968680796001 84.5393831132468 liver 80.8803912995157 102.475240772196 78.7502532739154 stomach 88.5994292537927 99.729146191182 83.9447379140425 testicle 76.6777912637715 80.726440563584 104.801527233644 cont.diffExp=6.75888712223299,-18.3201556168977,12.2743563549325,-8.47908926692769,-4.13518621828489,-21.5948494726803,-11.1297169373892,-4.04864929981257 cont.diffExpScore=1.74618887124797 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,-1,0,0,0,-1,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.0672737480061171 cont.tran.correlation=0.116151392399883 tran.covariance=-0.000566384884478742 cont.tran.covariance=0.00086928711702414 tran.mean=80.3314227157167 cont.tran.mean=88.4090277018154 weightedLogRatios: wLogRatio Lung -0.677626951627112 cerebhem 0.867089585795632 cortex -1.53194783884879 heart -0.714349631143886 kidney -0.324556095572240 liver -0.472041764590562 stomach -0.181952243784076 testicle -0.385214971831689 cont.weightedLogRatios: wLogRatio Lung 0.355360643885437 cerebhem -0.854109346042079 cortex 0.6375808634881 heart -0.44416614685685 kidney -0.208526126825709 liver -1.06759738510830 stomach -0.537619325284902 testicle -0.224614372299875 varWeightedLogRatios=0.445039503237248 cont.varWeightedLogRatios=0.327957044417931 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.57227292587298 0.108226371339043 33.0074165998049 1.15707636814972e-105 *** df.mm.trans1 0.901217710312374 0.0916666059763342 9.83147243986587 3.91845999722886e-20 *** df.mm.trans2 0.967916765980907 0.0916666059763342 10.5590989834487 1.35361341131613e-22 *** df.mm.exp2 0.0202457335328274 0.127656863182199 0.158594947644387 0.87408681482406 df.mm.exp3 -0.824884377169061 0.127656863182199 -6.46173152470262 3.79712792840536e-10 *** df.mm.exp4 -0.170755594133116 0.127656863182199 -1.33761389616322 0.181960500479084 df.mm.exp5 0.492289907672028 0.127656863182199 3.85635284621874 0.000138832606021302 *** df.mm.exp6 0.663450340236371 0.127656863182199 5.1971380441133 3.58907536825037e-07 *** df.mm.exp7 0.0198769935518089 0.127656863182199 0.155706423112085 0.876361397791079 df.mm.exp8 0.134832790643095 0.127656863182199 1.05621262564360 0.291657991722727 df.mm.trans1:exp2 0.077576113302846 0.110554086483218 0.701702811452581 0.483368245204345 df.mm.trans2:exp2 -0.273469008437896 0.110554086483218 -2.4736218907603 0.0138873110493354 * df.mm.trans1:exp3 0.68018118796961 0.110554086483218 6.15247440964436 2.24852886158425e-09 *** df.mm.trans2:exp3 0.876075109594147 0.110554086483218 7.92440277390499 3.72106734806533e-14 *** df.mm.trans1:exp4 0.0600234544750898 0.110554086483218 0.542932933412652 0.58754927256518 df.mm.trans2:exp4 0.0721629111084802 0.110554086483218 0.652738522871647 0.514387726351236 df.mm.trans1:exp5 -0.414009398137947 0.110554086483218 -3.74485838839428 0.000213560369336074 *** df.mm.trans2:exp5 -0.494076231566527 0.110554086483218 -4.46909062598537 1.08727327334688e-05 *** df.mm.trans1:exp6 -0.675431639708307 0.110554086483218 -6.10951310072862 2.86405768393283e-09 *** df.mm.trans2:exp6 -0.720854293286913 0.110554086483218 -6.52037673339498 2.69061877110646e-10 *** df.mm.trans1:exp7 -0.164712004405497 0.110554086483218 -1.48987712390441 0.137229343901706 df.mm.trans2:exp7 -0.274317169728941 0.110554086483218 -2.48129380337815 0.0135975099305275 * df.mm.trans1:exp8 -0.19061131559879 0.110554086483218 -1.72414536325370 0.0856354285356905 . df.mm.trans2:exp8 -0.254659402094142 0.110554086483218 -2.30348248712451 0.0218844558099664 * df.mm.trans1:probe2 -0.0219621015587774 0.0552770432416092 -0.397309629293734 0.691400754360082 df.mm.trans1:probe3 -0.32483734282033 0.0552770432416092 -5.87653253088278 1.04042924337426e-08 *** df.mm.trans1:probe4 -0.163110539504903 0.0552770432416092 -2.95078263849907 0.00340070265549029 ** df.mm.trans1:probe5 -0.357303229094562 0.0552770432416092 -6.46386290114748 3.75003528129995e-10 *** df.mm.trans1:probe6 -0.0817258311251757 0.0552770432416092 -1.47847689262181 0.140251667055598 df.mm.trans2:probe2 0.0168722701782652 0.0552770432416092 0.305231054137947 0.760386174474386 df.mm.trans2:probe3 -0.131152706030832 0.0552770432416092 -2.37264329529313 0.0182447306041785 * df.mm.trans2:probe4 0.0155712823195163 0.0552770432416092 0.281695282641224 0.778357004445995 df.mm.trans2:probe5 -0.0280756509244619 0.0552770432416092 -0.507907971881684 0.611863475682635 df.mm.trans2:probe6 -0.0502030671411033 0.0552770432416092 -0.908208257841721 0.364443228752311 df.mm.trans3:probe2 -0.345543007988926 0.0552770432416092 -6.25111235560484 1.28399427011804e-09 *** df.mm.trans3:probe3 -0.420789625990364 0.0552770432416092 -7.61237579497774 2.96630069288402e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05156841977339 0.230168406912942 17.6026261558383 3.85988901757488e-49 *** df.mm.trans1 0.390562050216240 0.194950236283842 2.00339357192465 0.0459674965222573 * df.mm.trans2 0.386528923983789 0.194950236283842 1.98270559375478 0.0482445958747822 * df.mm.exp2 0.577014307002775 0.271491841282405 2.12534676650768 0.0343139688312729 * df.mm.exp3 0.101999234856207 0.271491841282405 0.375699079480285 0.707386477374126 df.mm.exp4 0.315962407953152 0.271491841282405 1.16380074797345 0.245361074613208 df.mm.exp5 0.386872753719006 0.271491841282405 1.42498850754260 0.155123075764599 df.mm.exp6 0.503010378653191 0.271491841282405 1.85276425353041 0.0648253347809995 . df.mm.exp7 0.503123932411429 0.271491841282405 1.85318251198599 0.0647652027069331 . df.mm.exp8 -0.0746908257985434 0.271491841282405 -0.275112598027763 0.783405088078257 df.mm.trans1:exp2 -0.557219851940822 0.235118831470775 -2.36994990343886 0.0183758340376231 * df.mm.trans2:exp2 -0.290910143137220 0.235118831470775 -1.23728984751006 0.216875461607886 df.mm.trans1:exp3 -0.0555551063968236 0.235118831470775 -0.236285226705582 0.813360652342247 df.mm.trans2:exp3 -0.118714391183881 0.235118831470775 -0.504912305157644 0.613963676754786 df.mm.trans1:exp4 -0.401499412054174 0.235118831470775 -1.70764463885182 0.0886602164704037 . df.mm.trans2:exp4 -0.221311981219892 0.235118831470775 -0.941277139884905 0.347264116867837 df.mm.trans1:exp5 -0.396136272764707 0.235118831470775 -1.68483430394194 0.0929834629882228 . df.mm.trans2:exp5 -0.269547962539613 0.235118831470775 -1.14643289460681 0.252462034523575 df.mm.trans1:exp6 -0.584770068230141 0.235118831470775 -2.48712561461853 0.0133808210260710 * df.mm.trans2:exp6 -0.267987796262353 0.235118831470775 -1.13979724459316 0.255212691333049 df.mm.trans1:exp7 -0.493729618955051 0.235118831470775 -2.09991524654383 0.0365092354332128 * df.mm.trans2:exp7 -0.295264592804632 0.235118831470775 -1.25581005552646 0.210089683093256 df.mm.trans1:exp8 -0.0604281633354862 0.235118831470775 -0.257011158814803 0.797333293536243 df.mm.trans2:exp8 0.0711583540383277 0.235118831470775 0.302648467556596 0.762351980972222 df.mm.trans1:probe2 0.138294434742308 0.117559415735388 1.17637905800411 0.240307013284886 df.mm.trans1:probe3 0.0717384972821268 0.117559415735388 0.610231829014884 0.542136019443855 df.mm.trans1:probe4 0.0895064627114525 0.117559415735388 0.761372129586974 0.446988630868726 df.mm.trans1:probe5 -0.0516622708982242 0.117559415735388 -0.439456683031752 0.660623618575612 df.mm.trans1:probe6 0.0455285669837954 0.117559415735388 0.387281330882716 0.698802215205146 df.mm.trans2:probe2 -0.0617217882781624 0.117559415735388 -0.525026327258132 0.599923958783692 df.mm.trans2:probe3 -0.0984525813783834 0.117559415735388 -0.83747082921872 0.402945285862979 df.mm.trans2:probe4 0.0271695788718004 0.117559415735388 0.231113592236252 0.817372432568326 df.mm.trans2:probe5 -0.0442441948604604 0.117559415735388 -0.376356028853093 0.706898561554663 df.mm.trans2:probe6 -0.214239406067306 0.117559415735388 -1.82239257253144 0.0693170931545502 . df.mm.trans3:probe2 -0.163269829614657 0.117559415735388 -1.388828181846 0.165838916945748 df.mm.trans3:probe3 -0.174621413155877 0.117559415735388 -1.48538857618116 0.138413227020360