chr11.3596_chr11_91391155_91393505_+_1.R fitVsDatCorrelation=0.803105207333633 cont.fitVsDatCorrelation=0.32489848601133 fstatistic=7534.83495582144,39,393 cont.fstatistic=2984.65384103959,39,393 residuals=-0.587826512680496,-0.077202061317419,0.00666262981318997,0.0795225646601633,0.803984741454806 cont.residuals=-0.495221201983056,-0.154216119948908,-0.0451848289652226,0.0994298019738152,1.03380911619910 predictedValues: Include Exclude Both chr11.3596_chr11_91391155_91393505_+_1.R.tl.Lung 51.3790797626326 50.1325919744055 66.9744175253138 chr11.3596_chr11_91391155_91393505_+_1.R.tl.cerebhem 75.2165017845593 59.034018173035 70.2881449535734 chr11.3596_chr11_91391155_91393505_+_1.R.tl.cortex 49.6277951459137 55.9410486600651 74.656341064726 chr11.3596_chr11_91391155_91393505_+_1.R.tl.heart 51.4246893322133 52.5418832408838 75.3351678076442 chr11.3596_chr11_91391155_91393505_+_1.R.tl.kidney 52.2221859457142 52.411109370021 72.893705273461 chr11.3596_chr11_91391155_91393505_+_1.R.tl.liver 51.4372559479335 50.1498017874363 62.9967920452264 chr11.3596_chr11_91391155_91393505_+_1.R.tl.stomach 53.9881182907613 52.0465508650042 73.2460755081579 chr11.3596_chr11_91391155_91393505_+_1.R.tl.testicle 58.815205517125 56.3211137149495 72.3236304784057 diffExp=1.24648778822712,16.1824836115243,-6.31325351415136,-1.11719390867047,-0.188923424306743,1.28745416049718,1.9415674257571,2.49409180217553 diffExpScore=1.86124648046445 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 69.873342346557 63.7412969664214 59.0240778784362 cerebhem 68.728223819897 57.7819579465437 54.2185406224475 cortex 69.7802952311687 55.9314144118931 54.2873437615568 heart 64.7028006603668 55.5931593304183 56.674391653527 kidney 59.2020262316392 58.4823109048424 60.0934513553643 liver 67.2845932140205 52.2217176186326 60.588604699504 stomach 62.8839735002993 55.1178063195942 57.6421018432023 testicle 70.1018339678295 57.5289175922194 62.7252500844789 cont.diffExp=6.13204538013566,10.9462658733534,13.8488808192755,9.10964132994845,0.719715326796788,15.0628755953879,7.76616718070508,12.5729163756101 cont.diffExpScore=0.987039666428756 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=0,0,1,0,0,1,0,1 cont.diffExp1.2Score=0.75 tran.correlation=0.750364016988351 cont.tran.correlation=0.190049565005306 tran.covariance=0.00588919092121022 cont.tran.covariance=0.000586330852025675 tran.mean=54.5430593445408 cont.tran.mean=61.8097293788965 weightedLogRatios: wLogRatio Lung 0.0964449246476167 cerebhem 1.01729494214875 cortex -0.474729441038292 heart -0.0849129014960482 kidney -0.0142904786366595 liver 0.099559726572266 stomach 0.145419618208327 testicle 0.175608783784592 cont.weightedLogRatios: wLogRatio Lung 0.385846179578029 cerebhem 0.718814126986844 cortex 0.91470956007659 heart 0.62123100679196 kidney 0.0498411508687637 liver 1.03456726187806 stomach 0.537210794955676 testicle 0.820515115930393 varWeightedLogRatios=0.174796524535946 cont.varWeightedLogRatios=0.0990817120665642 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.62680977913109 0.0817254098294231 44.3779943924534 3.78193186989108e-155 *** df.mm.trans1 0.30268761860104 0.0667285177006412 4.53610583647255 7.62246059215323e-06 *** df.mm.trans2 0.275053574447801 0.0667285177006412 4.12197938641095 4.58451711074544e-05 *** df.mm.exp2 0.496289655347489 0.0906662017379366 5.4738110324945 7.86570940758769e-08 *** df.mm.exp3 -0.0336376653764922 0.0906662017379365 -0.371005564716599 0.710833295262526 df.mm.exp4 -0.0698097374371792 0.0906662017379366 -0.769964287673136 0.441783640489369 df.mm.exp5 -0.0239679830260083 0.0906662017379366 -0.264354109542228 0.791645645601502 df.mm.exp6 0.0627017930213292 0.0906662017379365 0.691567439899642 0.489617346341867 df.mm.exp7 -0.00251384961658562 0.0906662017379366 -0.0277264247139381 0.977894421668349 df.mm.exp8 0.174727292503931 0.0906662017379365 1.92714913776764 0.0546815946226973 . df.mm.trans1:exp2 -0.115150090941952 0.0740286437247286 -1.55548021884787 0.120636641362348 df.mm.trans2:exp2 -0.332847133389427 0.0740286437247287 -4.49619386013744 9.1170923098464e-06 *** df.mm.trans1:exp3 -0.00104235294548454 0.0740286437247287 -0.0140804004104205 0.988772982191506 df.mm.trans2:exp3 0.143264764138642 0.0740286437247287 1.93526123038757 0.0536753459821772 . df.mm.trans1:exp4 0.0706970506743263 0.0740286437247287 0.954995892363087 0.340166818825993 df.mm.trans2:exp4 0.116749030003733 0.0740286437247287 1.57707914301197 0.115581886488494 df.mm.trans1:exp5 0.0402443246355354 0.0740286437247287 0.543631797242992 0.58700311381416 df.mm.trans2:exp5 0.0684152277664197 0.0740286437247287 0.92417237874596 0.355963657526543 df.mm.trans1:exp6 -0.0615701403300056 0.0740286437247286 -0.831706988432082 0.406079413604995 df.mm.trans2:exp6 -0.0623585660090052 0.0740286437247286 -0.842357267017913 0.400100331375278 df.mm.trans1:exp7 0.0520467592161946 0.0740286437247287 0.703062444446875 0.482433102895957 df.mm.trans2:exp7 0.039981041686137 0.0740286437247286 0.540075296189463 0.589451007973037 df.mm.trans1:exp8 -0.0395579549014141 0.0740286437247287 -0.534360119422262 0.593394557673728 df.mm.trans2:exp8 -0.0583291410750994 0.0740286437247286 -0.787926647582428 0.431214586469046 df.mm.trans1:probe2 0.0528668986941699 0.0453331008689683 1.16618756892403 0.244245831965169 df.mm.trans1:probe3 -0.0655229452172119 0.0453331008689683 -1.44536649735479 0.149151876074836 df.mm.trans1:probe4 0.0127911117423564 0.0453331008689683 0.282158323546586 0.777970643247377 df.mm.trans1:probe5 -0.0201969403847115 0.0453331008689683 -0.445523028373664 0.656187138414971 df.mm.trans1:probe6 0.136866075598762 0.0453331008689683 3.01912009051316 0.00270045844975340 ** df.mm.trans2:probe2 0.0927149062866498 0.0453331008689683 2.04519224384484 0.0415014042820232 * df.mm.trans2:probe3 0.00926593515251884 0.0453331008689683 0.204396676488143 0.838149335933857 df.mm.trans2:probe4 0.0947054963622258 0.0453331008689683 2.08910254420858 0.037341231480252 * df.mm.trans2:probe5 0.00139917557673159 0.0453331008689683 0.0308643254026632 0.975393408518512 df.mm.trans2:probe6 -0.0443897364002592 0.0453331008689683 -0.979190382951394 0.328088300877627 df.mm.trans3:probe2 0.467374261751742 0.0453331008689683 10.3097792295888 3.22072489006550e-22 *** df.mm.trans3:probe3 -0.106738405362332 0.0453331008689683 -2.35453572149963 0.0190370485330656 * df.mm.trans3:probe4 -0.161138458330925 0.0453331008689683 -3.55454304343051 0.000424724812709379 *** df.mm.trans3:probe5 -0.190206522650374 0.0453331008689683 -4.19575363265246 3.36501233292314e-05 *** df.mm.trans3:probe6 -0.0107323975134619 0.0453331008689683 -0.236745276800788 0.81297775697802 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.41170976383007 0.129719510446276 34.0096084902911 3.84281104755297e-119 *** df.mm.trans1 -0.103444636680865 0.105915536759003 -0.976671032846103 0.329332839479084 df.mm.trans2 -0.289717896669521 0.105915536759003 -2.73536730809133 0.00651336467799146 ** df.mm.exp2 -0.0297578095462866 0.14391087579758 -0.206779434711682 0.836289208192288 df.mm.exp3 -0.0483846600825794 0.14391087579758 -0.336212672005663 0.736889935701694 df.mm.exp4 -0.173029219211428 0.14391087579758 -1.20233594752633 0.229957318332456 df.mm.exp5 -0.269792178446436 0.14391087579758 -1.87471709105514 0.0615731260804876 . df.mm.exp6 -0.263248500294904 0.14391087579758 -1.82924673924701 0.0681201955486347 . df.mm.exp7 -0.227060474228871 0.14391087579758 -1.57778536869059 0.115419475924702 df.mm.exp8 -0.160098791670118 0.14391087579758 -1.11248570188196 0.266609406750615 df.mm.trans1:exp2 0.0132335434133225 0.117502738047039 0.112623276982915 0.910386733760897 df.mm.trans2:exp2 -0.0683982663020318 0.117502738047039 -0.582099340311973 0.560833541304077 df.mm.trans1:exp3 0.0470521186497792 0.117502738047039 0.400434231846949 0.689054300770396 df.mm.trans2:exp3 -0.0823217989825414 0.117502738047039 -0.70059472954227 0.483970531399131 df.mm.trans1:exp4 0.096149498581875 0.117502738047039 0.818274537086825 0.413696302110483 df.mm.trans2:exp4 0.0362567227278379 0.117502738047039 0.308560662759395 0.757819242065253 df.mm.trans1:exp5 0.104063738568798 0.117502738047039 0.885628201507434 0.376359469589338 df.mm.trans2:exp5 0.183683852718593 0.117502738047039 1.56323040442735 0.118803263549489 df.mm.trans1:exp6 0.225495575686785 0.117502738047039 1.91906656333842 0.0556998407987835 . df.mm.trans2:exp6 0.0639142984488786 0.117502738047039 0.543938801011534 0.586792028826983 df.mm.trans1:exp7 0.121667604084288 0.117502738047039 1.03544484244769 0.301097918602608 df.mm.trans2:exp7 0.0817006453312957 0.117502738047039 0.695308438673057 0.487272939743664 df.mm.trans1:exp8 0.163363539466668 0.117502738047039 1.39029559805892 0.165225992996580 df.mm.trans2:exp8 0.0575538711562305 0.117502738047039 0.48980876627054 0.624542526508979 df.mm.trans1:probe2 -0.128529237769451 0.07195543789879 -1.78623383475528 0.0748322120021726 . df.mm.trans1:probe3 -0.0603858925315002 0.07195543789879 -0.839212355519771 0.401860337929055 df.mm.trans1:probe4 -0.145226875442177 0.07195543789879 -2.01828909229137 0.0442400515603606 * df.mm.trans1:probe5 -0.203302813038389 0.07195543789879 -2.82539887151195 0.00496305389030983 ** df.mm.trans1:probe6 -0.201526210196963 0.07195543789879 -2.80070855076196 0.00535090193331508 ** df.mm.trans2:probe2 0.0792822250369673 0.07195543789879 1.10182395315949 0.271212538552234 df.mm.trans2:probe3 0.0371614942049875 0.07195543789879 0.516451505128182 0.605829574424969 df.mm.trans2:probe4 0.137639362072882 0.07195543789879 1.9128416988648 0.0564948185061935 . df.mm.trans2:probe5 0.0974806794013315 0.07195543789879 1.35473679610489 0.176279700621887 df.mm.trans2:probe6 0.0425257119007902 0.07195543789879 0.591000668505491 0.554859712079421 df.mm.trans3:probe2 0.0960308221293893 0.07195543789879 1.33458741873634 0.182784106406082 df.mm.trans3:probe3 0.0917621564607768 0.07195543789879 1.27526367902654 0.202969270047508 df.mm.trans3:probe4 0.0444261141585065 0.07195543789879 0.617411490442108 0.537320905563188 df.mm.trans3:probe5 0.0712815865197134 0.07195543789879 0.990635156997802 0.322473287427473 df.mm.trans3:probe6 0.0528886995948376 0.07195543789879 0.73502018942931 0.462765574501943