chr5.18249_chr5_87272692_87273432_+_1.R fitVsDatCorrelation=0.62397595359461 cont.fitVsDatCorrelation=0.339112432885113 fstatistic=12213.3271849341,36,324 cont.fstatistic=8424.43484817738,36,324 residuals=-0.321770237475383,-0.0655789832390635,-0.00462324447567746,0.0577466325924278,0.409943546407701 cont.residuals=-0.321569780281528,-0.0809450746177255,-0.00301596248617787,0.0725629477800066,0.543076133043175 predictedValues: Include Exclude Both chr5.18249_chr5_87272692_87273432_+_1.R.tl.Lung 43.4686036886791 49.0883472055232 43.5045204577116 chr5.18249_chr5_87272692_87273432_+_1.R.tl.cerebhem 50.9172950755956 43.9145497367909 52.1651209980492 chr5.18249_chr5_87272692_87273432_+_1.R.tl.cortex 41.212043379565 46.8629055655614 41.5009112041898 chr5.18249_chr5_87272692_87273432_+_1.R.tl.heart 42.9805058107451 45.8227362609851 42.5593755600498 chr5.18249_chr5_87272692_87273432_+_1.R.tl.kidney 41.3980865157433 45.7167508272233 43.4379374944706 chr5.18249_chr5_87272692_87273432_+_1.R.tl.liver 46.9113497826526 46.581839761234 44.0284619068714 chr5.18249_chr5_87272692_87273432_+_1.R.tl.stomach 44.054351273666 48.4770287309999 43.1947247606052 chr5.18249_chr5_87272692_87273432_+_1.R.tl.testicle 49.1361507924298 42.402207870499 46.3839132621854 diffExp=-5.61974351684412,7.00274533880476,-5.65086218599644,-2.84223045024000,-4.31866431147999,0.329510021418656,-4.42267745733392,6.73394292193071 diffExpScore=3.7720119537384 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 43.639863203359 49.0166759528564 44.3420330568909 cerebhem 45.8131321490631 47.6839549071118 47.5589132497016 cortex 43.6664435968367 44.1322582212018 45.3923458154774 heart 42.6509374851165 44.9116373926416 46.759245436298 kidney 46.3091412249140 44.4463240622923 45.0389382690174 liver 45.1116547188092 48.3840284273724 45.3733685309786 stomach 44.5712598969616 46.8979941680463 45.9844924951265 testicle 44.2458124809458 46.0040956999975 48.6250904261776 cont.diffExp=-5.37681274949747,-1.87082275804873,-0.465814624365045,-2.26069990752504,1.86281716262170,-3.27237370856322,-2.32673427108466,-1.75828321905171 cont.diffExpScore=1.16550367306814 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=-0.632073149684044 cont.tran.correlation=0.120398567023092 tran.covariance=-0.0023783911999289 cont.tran.covariance=0.000139687002349462 tran.mean=45.5590470173683 cont.tran.mean=45.4678258492204 weightedLogRatios: wLogRatio Lung -0.466006069833532 cerebhem 0.570555193965 cortex -0.486097253998822 heart -0.242864807711939 kidney -0.374380289565657 liver 0.0271010975432033 stomach -0.366712079465661 testicle 0.563179690209069 cont.weightedLogRatios: wLogRatio Lung -0.445478339190769 cerebhem -0.153876307763756 cortex -0.0401298435601594 heart -0.195170071218232 kidney 0.156625130241245 liver -0.269202865345673 stomach -0.194512036746205 testicle -0.148445141181863 varWeightedLogRatios=0.193627327602245 cont.varWeightedLogRatios=0.0300899323194716 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81413173798266 0.0618746863167796 61.6428456453979 4.93604345471647e-181 *** df.mm.trans1 -0.0584431834837277 0.0524072129586716 -1.11517442321949 0.265602146447732 df.mm.trans2 0.0345010094034327 0.0524072129586717 0.65832559023202 0.510796443995483 df.mm.exp2 -0.134761496772247 0.072983398296133 -1.8464678258122 0.0657361634805658 . df.mm.exp3 -0.0525541694494457 0.0729833982961331 -0.720083891355744 0.471992483860794 df.mm.exp4 -0.0581688712224668 0.072983398296133 -0.797015110017819 0.426025916776079 df.mm.exp5 -0.118429515924558 0.072983398296133 -1.62269116935369 0.105628010575728 df.mm.exp6 0.0118383691724290 0.072983398296133 0.162206329779197 0.871244486069353 df.mm.exp7 0.0080000525949242 0.072983398296133 0.109614690213022 0.912782819505463 df.mm.exp8 -0.0879529871129198 0.072983398296133 -1.20510950663118 0.229040481508994 df.mm.trans1:exp2 0.292925224768525 0.0632054769789691 4.63449116705492 5.19323496992617e-06 *** df.mm.trans2:exp2 0.0233855121829333 0.0632054769789691 0.369991863058237 0.711630284780484 df.mm.trans1:exp3 -0.00075422509418581 0.0632054769789692 -0.0119329072453131 0.990486487687843 df.mm.trans2:exp3 0.0061589270031438 0.0632054769789692 0.09744293212427 0.922434902219296 df.mm.trans1:exp4 0.0468766076605145 0.0632054769789692 0.741654203101926 0.45883412148772 df.mm.trans2:exp4 -0.0106724147671643 0.0632054769789691 -0.168852689312279 0.866017876497137 df.mm.trans1:exp5 0.0696252530398054 0.0632054769789691 1.10156993298179 0.271466332221009 df.mm.trans2:exp5 0.0472726067106345 0.0632054769789692 0.747919467902503 0.455051242659459 df.mm.trans1:exp6 0.0643823534113566 0.0632054769789691 1.01861984892194 0.309143479377223 df.mm.trans2:exp6 -0.0642492875340144 0.0632054769789692 -1.01651455862587 0.310142878621406 df.mm.trans1:exp7 0.00538515202036756 0.0632054769789692 0.0852007180035901 0.932154442602077 df.mm.trans2:exp7 -0.0205316801080945 0.0632054769789691 -0.324840205144345 0.74551165464739 df.mm.trans1:exp8 0.210509096464118 0.0632054769789691 3.33055150480334 0.000966773644625233 *** df.mm.trans2:exp8 -0.0584682584314859 0.0632054769789692 -0.925050505527242 0.355628096522791 df.mm.trans1:probe2 -0.00226031298855654 0.0316027384894846 -0.0715226938104946 0.94302589186215 df.mm.trans1:probe3 0.0476281122891383 0.0316027384894846 1.5070881374722 0.132762437168023 df.mm.trans1:probe4 0.00144009530762483 0.0316027384894846 0.0455686872865148 0.963682099789489 df.mm.trans1:probe5 0.0493584297531141 0.0316027384894846 1.56184027436539 0.119301808801409 df.mm.trans1:probe6 0.050986994140738 0.0316027384894846 1.61337265622418 0.107637074107666 df.mm.trans2:probe2 0.0935443459766803 0.0316027384894846 2.96000759579130 0.00330314158385652 ** df.mm.trans2:probe3 -0.0296132672548617 0.0316027384894846 -0.937047505067168 0.349432061577093 df.mm.trans2:probe4 0.0252815072693614 0.0316027384894846 0.799978371424158 0.42430946512079 df.mm.trans2:probe5 0.156508408795910 0.0316027384894846 4.95236856919807 1.18231729254337e-06 *** df.mm.trans2:probe6 0.159179388334335 0.0316027384894846 5.03688591377295 7.87376086818331e-07 *** df.mm.trans3:probe2 -0.0206441657410664 0.0316027384894846 -0.65323977375997 0.514064992189723 df.mm.trans3:probe3 -0.031329972175343 0.0316027384894846 -0.99136890259582 0.322245260826337 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86810670910919 0.0744882404951498 51.929092208334 4.01132457270866e-159 *** df.mm.trans1 -0.109283738055999 0.0630907615847974 -1.73216704491855 0.0841955180330728 . df.mm.trans2 0.0124723724251510 0.0630907615847974 0.197689362306833 0.84341208079247 df.mm.exp2 -0.0490020145211445 0.0878615351131305 -0.557718624629646 0.577421666704835 df.mm.exp3 -0.127771113830545 0.0878615351131305 -1.45423265899038 0.146849823831158 df.mm.exp4 -0.163464336490062 0.0878615351131305 -1.86047667252325 0.0637239508394319 . df.mm.exp5 -0.0541043113501185 0.0878615351131305 -0.615790644682611 0.538465037335129 df.mm.exp6 -0.0028134734065915 0.0878615351131305 -0.0320216736819914 0.974474483312482 df.mm.exp7 -0.0594385967421502 0.0878615351131305 -0.676503052964157 0.499203958995446 df.mm.exp8 -0.141847035753734 0.0878615351131305 -1.61443839526696 0.107405773347164 df.mm.trans1:exp2 0.0976017663899528 0.0760903214234695 1.28270934547332 0.200510795669848 df.mm.trans2:exp2 0.0214364149341679 0.0760903214234695 0.281723280085344 0.77833555384606 df.mm.trans1:exp3 0.128380013582996 0.0760903214234695 1.68720556282731 0.0925262664578916 . df.mm.trans2:exp3 0.022801542040318 0.0760903214234695 0.299664157198382 0.764625489468625 df.mm.trans1:exp4 0.140542564781724 0.0760903214234695 1.84704916673376 0.0656516261795307 . df.mm.trans2:exp4 0.0760007165655552 0.0760903214234695 0.998822388232327 0.31862610638661 df.mm.trans1:exp5 0.113472661319862 0.0760903214234695 1.49128902595044 0.136858572416669 df.mm.trans2:exp5 -0.0437739941523139 0.0760903214234695 -0.575289909851953 0.565494541501429 df.mm.trans1:exp6 0.0359830797182963 0.0760903214234694 0.472899562587438 0.636603250182147 df.mm.trans2:exp6 -0.0101773242813959 0.0760903214234695 -0.133753203968682 0.89368077197012 df.mm.trans1:exp7 0.0805568248279632 0.0760903214234695 1.05870002019884 0.290525063223783 df.mm.trans2:exp7 0.0152529372263470 0.0760903214234695 0.200458309821810 0.8412479281689 df.mm.trans1:exp8 0.155636743232548 0.0760903214234695 2.04542102492083 0.0416207010276445 * df.mm.trans2:exp8 0.0784168994542269 0.0760903214234695 1.03057653046055 0.303508136493412 df.mm.trans1:probe2 0.025738080897862 0.0380451607117347 0.676513922306111 0.499197069436763 df.mm.trans1:probe3 0.0245612504992720 0.0380451607117347 0.645581462656203 0.51900737099548 df.mm.trans1:probe4 0.0298324325705978 0.0380451607117347 0.784132121208158 0.433535472773988 df.mm.trans1:probe5 0.0828588839688638 0.0380451607117347 2.17790863328662 0.0301337014815283 * df.mm.trans1:probe6 -0.00865815073196278 0.0380451607117347 -0.227575611982952 0.820119720063142 df.mm.trans2:probe2 0.0406266177095225 0.0380451607117347 1.0678524403497 0.286382063948298 df.mm.trans2:probe3 -0.00589953244939427 0.0380451607117347 -0.155066566654681 0.87686539543933 df.mm.trans2:probe4 0.0722640744538871 0.0380451607117347 1.89942881307367 0.0583960246578058 . df.mm.trans2:probe5 -0.00876188216918891 0.0380451607117347 -0.230302146324917 0.81800233298778 df.mm.trans2:probe6 0.00600408067968976 0.0380451607117347 0.157814570036442 0.874701224801825 df.mm.trans3:probe2 0.0318816079060143 0.0380451607117347 0.837993776595631 0.402651950126876 df.mm.trans3:probe3 0.0300315256634261 0.0380451607117347 0.789365193932881 0.430475856626847