chr16.9511_chr16_38295896_38304765_+_2.R fitVsDatCorrelation=0.821569945382018 cont.fitVsDatCorrelation=0.233303056041235 fstatistic=12296.2674966858,47,577 cont.fstatistic=4218.56734750709,47,577 residuals=-0.389869482761131,-0.0762550887484922,-0.00711888993296845,0.0696678198009099,0.731774197496148 cont.residuals=-0.520285010349581,-0.154842313894086,-0.0320343669504021,0.133729893223206,0.782500218455293 predictedValues: Include Exclude Both chr16.9511_chr16_38295896_38304765_+_2.R.tl.Lung 50.0597997311931 71.5932876788699 51.2014656143492 chr16.9511_chr16_38295896_38304765_+_2.R.tl.cerebhem 52.0481357689572 52.7765499364357 48.7283019077411 chr16.9511_chr16_38295896_38304765_+_2.R.tl.cortex 48.5479302892738 62.7273021934844 49.4983121040443 chr16.9511_chr16_38295896_38304765_+_2.R.tl.heart 51.2093294872904 79.7053111612138 51.403807524298 chr16.9511_chr16_38295896_38304765_+_2.R.tl.kidney 49.9946782704131 70.6291770201553 48.9221955763452 chr16.9511_chr16_38295896_38304765_+_2.R.tl.liver 53.9138832717936 79.1435390648535 51.4563154329252 chr16.9511_chr16_38295896_38304765_+_2.R.tl.stomach 51.1806368127173 68.6744987941704 49.8368509414814 chr16.9511_chr16_38295896_38304765_+_2.R.tl.testicle 51.3877911443121 63.3580233293528 48.3411382483681 diffExp=-21.5334879476769,-0.728414167478448,-14.1793719042106,-28.4959816739234,-20.6344987497422,-25.2296557930599,-17.4938619814531,-11.9702321850407 diffExpScore=0.992921130999185 diffExp1.5=0,0,0,-1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=-1,0,0,-1,-1,-1,0,0 diffExp1.4Score=0.8 diffExp1.3=-1,0,0,-1,-1,-1,-1,0 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 52.1636265459356 53.0592388126542 53.9269319951546 cerebhem 53.0047198294577 55.3512624715197 56.003685861855 cortex 53.8287437583851 49.8682831113016 58.1183653114376 heart 52.0403367050214 53.5328893851562 48.7383560763587 kidney 54.2534591445342 56.4617593702452 57.8828801297698 liver 52.9067452391123 51.1157388349056 55.2129880861957 stomach 54.7878713265218 50.7303629923687 51.8603601493897 testicle 54.7584381935079 50.1477593215006 56.412710361252 cont.diffExp=-0.895612266718622,-2.34654264206204,3.96046064708355,-1.49255268013483,-2.20830022571104,1.79100640420662,4.05750833415313,4.61067887200727 cont.diffExpScore=2.52017849466422 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=0.240990838679506 cont.tran.correlation=-0.314075488053905 tran.covariance=0.000812483538213299 cont.tran.covariance=-0.000311702713804350 tran.mean=59.8093671221554 cont.tran.mean=53.000702190133 weightedLogRatios: wLogRatio Lung -1.46408747188933 cerebhem -0.0550237574287606 cortex -1.02771613230726 heart -1.83917410057405 kidney -1.41136625346307 liver -1.60433917226489 stomach -1.20028467759671 testicle -0.846838534795943 cont.weightedLogRatios: wLogRatio Lung -0.0674626197640672 cerebhem -0.172929213238018 cortex 0.30168480410498 heart -0.112151421781934 kidney -0.160130523175870 liver 0.136076809519019 stomach 0.305083645213732 testicle 0.348220153115945 varWeightedLogRatios=0.30699578133277 cont.varWeightedLogRatios=0.050674608308902 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07851809521038 0.0674139249120153 60.4996386211518 5.49020344853756e-252 *** df.mm.trans1 -0.0920351403875454 0.0593001367372462 -1.55202239744143 0.121205160714715 df.mm.trans2 0.198606362274786 0.0547265060507848 3.62907074846848 0.000309693128276596 *** df.mm.exp2 -0.216475519537967 0.0740497510231867 -2.92337943810485 0.00359835550331643 ** df.mm.exp3 -0.129041662785774 0.0740497510231867 -1.74263466119377 0.0819302056394943 . df.mm.exp4 0.126094258343342 0.0740497510231868 1.70283163145085 0.0891381309137601 . df.mm.exp5 0.0306772551959887 0.0740497510231867 0.414278978282897 0.678823767992119 df.mm.exp6 0.169466519101255 0.0740497510231868 2.28854947869022 0.0224661385582956 * df.mm.exp7 0.00753306728910654 0.0740497510231867 0.101729812524930 0.91900647915744 df.mm.exp8 -0.0385322048527582 0.0740497510231867 -0.520355630104589 0.603015326928881 df.mm.trans1:exp2 0.255426212378993 0.0690517488781197 3.69905493385598 0.000237093668261795 *** df.mm.trans2:exp2 -0.0884588404667123 0.0598005687041953 -1.47923075622033 0.139624375340333 df.mm.trans1:exp3 0.0983749406952301 0.0690517488781198 1.42465530987300 0.154797492021301 df.mm.trans2:exp3 -0.00316286474041256 0.0598005687041953 -0.0528902117312251 0.957837689277822 df.mm.trans1:exp4 -0.103390811768835 0.0690517488781197 -1.49729461525046 0.134863427867770 df.mm.trans2:exp4 -0.0187593574247207 0.0598005687041953 -0.313698645869310 0.75386330232545 df.mm.trans1:exp5 -0.031978975444322 0.0690517488781198 -0.463116082704389 0.643455784065173 df.mm.trans2:exp5 -0.0442352459002949 0.0598005687041953 -0.739712796363282 0.459775125167223 df.mm.trans1:exp6 -0.0952967851572891 0.0690517488781198 -1.38007779246103 0.168097170807491 df.mm.trans2:exp6 -0.0692046871737019 0.0598005687041953 -1.15725801063907 0.247645808218564 df.mm.trans1:exp7 0.0146099205537295 0.0690517488781197 0.211579298006149 0.832509971396834 df.mm.trans2:exp7 -0.0491564556329291 0.0598005687041953 -0.822006490876072 0.411412739756839 df.mm.trans1:exp8 0.0647145373045929 0.0690517488781197 0.937188968505603 0.349053462968558 df.mm.trans2:exp8 -0.0836675676513299 0.0598005687041953 -1.39910989919165 0.162317349584156 df.mm.trans1:probe2 -0.171267745740000 0.0378212004957282 -4.52835297386566 7.22543099108842e-06 *** df.mm.trans1:probe3 -0.146425627149087 0.0378212004957282 -3.87152245909343 0.000120520567203284 *** df.mm.trans1:probe4 -0.161277061757085 0.0378212004957282 -4.26419731904862 2.343824119161e-05 *** df.mm.trans1:probe5 -0.142660331463038 0.0378212004957282 -3.77196729858301 0.000178677253258647 *** df.mm.trans1:probe6 -0.204246411328344 0.0378212004957282 -5.40031539589583 9.73597577778568e-08 *** df.mm.trans1:probe7 0.0146625167190789 0.0378212004957282 0.387679833714822 0.698395951005083 df.mm.trans1:probe8 -0.173536978110397 0.0378212004957282 -4.5883519252647 5.48462134302823e-06 *** df.mm.trans1:probe9 0.0999861050673682 0.0378212004957282 2.64365233670098 0.00842467720810437 ** df.mm.trans1:probe10 0.0741136437188065 0.0378212004957282 1.95957935621788 0.0505259918753175 . df.mm.trans1:probe11 -0.0544305076681238 0.0378212004957282 -1.43915335723602 0.150649322639274 df.mm.trans1:probe12 -0.244347646325789 0.0378212004957282 -6.46059995777731 2.21855435863773e-10 *** df.mm.trans1:probe13 0.0120817158412405 0.0378212004957282 0.319442949533162 0.74950626719216 df.mm.trans1:probe14 -0.0715587881691919 0.0378212004957282 -1.89202847163125 0.058987592417156 . df.mm.trans1:probe15 -0.126466867052548 0.0378212004957282 -3.34380890598203 0.000879949914481808 *** df.mm.trans1:probe16 -0.169919404636121 0.0378212004957282 -4.49270256916653 8.49887708954515e-06 *** df.mm.trans2:probe2 -0.222459945250411 0.0378212004957282 -5.8818848247701 6.87415599642038e-09 *** df.mm.trans2:probe3 0.137917771065422 0.0378212004957282 3.64657306636788 0.000289796674180826 *** df.mm.trans2:probe4 -0.0257779108243415 0.0378212004957282 -0.681573045975974 0.495782430125725 df.mm.trans2:probe5 0.0481321445009864 0.0378212004957282 1.27262339296773 0.203664278289358 df.mm.trans2:probe6 0.000956586358759765 0.0378212004957282 0.0252923319784048 0.979830534723797 df.mm.trans3:probe2 -0.365201903730469 0.0378212004957282 -9.65601035778114 1.50313667902017e-20 *** df.mm.trans3:probe3 -0.277003091724095 0.0378212004957282 -7.32401637423915 8.13943922730194e-13 *** df.mm.trans3:probe4 -0.355079645271834 0.0378212004957282 -9.38837584787769 1.39236279536324e-19 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88821989775122 0.114984540124578 33.8151537027377 5.30789300386994e-139 *** df.mm.trans1 0.0382146183898991 0.101145259839953 0.377819172647023 0.705703872518472 df.mm.trans2 0.079093501183771 0.0933442480776384 0.84733127978047 0.397161815555189 df.mm.exp2 0.0204984969377682 0.126302934873668 0.162296283599992 0.871129359461869 df.mm.exp3 -0.105453351713630 0.126302934873668 -0.834924000927669 0.4041060584598 df.mm.exp4 0.107684598145059 0.126302934873669 0.852589832950187 0.394240556179747 df.mm.exp5 0.0306442499512634 0.126302934873669 0.242625002989159 0.808382128673653 df.mm.exp6 -0.0467393528502574 0.126302934873669 -0.370057535852253 0.711475411806263 df.mm.exp7 0.0432742992186756 0.126302934873668 0.342623069384411 0.73200682067786 df.mm.exp8 -0.052953607453094 0.126302934873668 -0.419258725112442 0.675183096983305 df.mm.trans1:exp2 -0.00450297650387688 0.117778094064559 -0.0382327167003451 0.969515359776552 df.mm.trans2:exp2 0.0217919680151582 0.101998821469221 0.213649213797377 0.830896089438725 df.mm.trans1:exp3 0.136875504272392 0.117778094064559 1.16214738708002 0.245656068707493 df.mm.trans2:exp3 0.0434295405723086 0.101998821469221 0.425784729144284 0.670423487830519 df.mm.trans1:exp4 -0.110050916941344 0.117778094064559 -0.934392068537127 0.350492549692323 df.mm.trans2:exp4 -0.0987973810865404 0.101998821469221 -0.96861296692877 0.333144158728389 df.mm.trans1:exp5 0.00863706093021092 0.117778094064559 0.0733333392666092 0.941566276188611 df.mm.trans2:exp5 0.0315103309242763 0.101998821469221 0.308928382410621 0.757487518698936 df.mm.trans1:exp6 0.0608847502926702 0.117778094064559 0.516944604820118 0.605392916662409 df.mm.trans2:exp6 0.0094228004987167 0.101998821469221 0.092381464442313 0.926427030715071 df.mm.trans1:exp7 0.00580910154147891 0.117778094064559 0.0493224278047385 0.960679414408177 df.mm.trans2:exp7 -0.0881586951086456 0.101998821469221 -0.864310918879077 0.387776275713388 df.mm.trans1:exp8 0.101499643990735 0.117778094064559 0.861787115820529 0.389162675847413 df.mm.trans2:exp8 -0.00348156149032646 0.101998821469221 -0.0341333501718649 0.972782618715345 df.mm.trans1:probe2 0.0231197188694983 0.0645097188991243 0.358391251179551 0.72018162086628 df.mm.trans1:probe3 0.0160863292629911 0.0645097188991243 0.249362879539838 0.80316883907171 df.mm.trans1:probe4 0.0436053442140249 0.0645097188991243 0.675949995724084 0.499343222763542 df.mm.trans1:probe5 0.0307164538443052 0.0645097188991243 0.476152343685412 0.634146087754006 df.mm.trans1:probe6 0.0506690858019081 0.0645097188991243 0.785448869822868 0.432512835550894 df.mm.trans1:probe7 0.0399701133185555 0.0645097188991243 0.619598317907073 0.535766856974298 df.mm.trans1:probe8 0.0331252861115266 0.0645097188991243 0.513492953880726 0.607803100460757 df.mm.trans1:probe9 0.137832657146781 0.0645097188991243 2.13661847391265 0.0330490238915244 * df.mm.trans1:probe10 0.0315134747239123 0.0645097188991243 0.48850739488093 0.625376092092509 df.mm.trans1:probe11 0.0192458197395518 0.0645097188991243 0.29833984813431 0.765551197106183 df.mm.trans1:probe12 0.132207834828237 0.0645097188991243 2.04942506469412 0.0408721646864863 * df.mm.trans1:probe13 -0.0241688238130223 0.0645097188991243 -0.374653993622508 0.708055467185249 df.mm.trans1:probe14 0.0244456968088491 0.0645097188991243 0.378945951494154 0.704867401614512 df.mm.trans1:probe15 0.0152125327386765 0.0645097188991243 0.235817687602464 0.813657820278289 df.mm.trans1:probe16 -0.0145629956341449 0.0645097188991243 -0.225748862073286 0.821476690369403 df.mm.trans2:probe2 -0.0189621593105334 0.0645097188991243 -0.293942674594275 0.7689074129817 df.mm.trans2:probe3 -0.0100130056034515 0.0645097188991243 -0.155217008759705 0.876704521662056 df.mm.trans2:probe4 0.00476210892027035 0.0645097188991243 0.0738200228048893 0.941179178141195 df.mm.trans2:probe5 0.0502825207154885 0.0645097188991243 0.779456515600645 0.436030422002554 df.mm.trans2:probe6 0.0148865741463802 0.0645097188991243 0.230764827384518 0.817579305816034 df.mm.trans3:probe2 -0.0473073484830033 0.0645097188991243 -0.733336763674001 0.463650893249651 df.mm.trans3:probe3 -0.0473559437113201 0.0645097188991243 -0.734090064558675 0.463192039462388 df.mm.trans3:probe4 0.0230712992220405 0.0645097188991243 0.357640672068619 0.72074301810908