chr7.21455_chr7_110307784_110313508_-_0.R fitVsDatCorrelation=0.76683528040598 cont.fitVsDatCorrelation=0.263879084288912 fstatistic=14575.2362644025,47,577 cont.fstatistic=6447.02358158648,47,577 residuals=-0.381770376019327,-0.073760448653357,-0.00145469748649561,0.0646909207524273,0.695477896846292 cont.residuals=-0.45636079428157,-0.120567156606051,-0.0101948323804376,0.106361119337532,0.911035841099613 predictedValues: Include Exclude Both chr7.21455_chr7_110307784_110313508_-_0.R.tl.Lung 52.5703673213169 55.9022769618074 55.3845439030498 chr7.21455_chr7_110307784_110313508_-_0.R.tl.cerebhem 58.0080961449439 63.9395416669434 56.088667178077 chr7.21455_chr7_110307784_110313508_-_0.R.tl.cortex 51.2282534569691 53.7125550611175 52.1745808958152 chr7.21455_chr7_110307784_110313508_-_0.R.tl.heart 52.6822274741945 52.8724409170387 49.6990663423105 chr7.21455_chr7_110307784_110313508_-_0.R.tl.kidney 50.7361259266825 53.513478637996 56.0242559064942 chr7.21455_chr7_110307784_110313508_-_0.R.tl.liver 51.3891161664321 53.0615909468248 53.0167128178067 chr7.21455_chr7_110307784_110313508_-_0.R.tl.stomach 53.8815401610404 52.641207036936 55.2469280942768 chr7.21455_chr7_110307784_110313508_-_0.R.tl.testicle 50.9806317036447 53.9137648205396 55.3327470776456 diffExp=-3.33190964049052,-5.93144552199949,-2.48430160414834,-0.190213442844204,-2.77735271131341,-1.67247478039273,1.24033312410446,-2.93313311689494 diffExpScore=1.07760102865011 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 52.4608614545883 49.2864084454798 50.6481111509224 cerebhem 55.0467053989226 55.6546443531451 52.9768848282272 cortex 53.1860366366056 52.5062016876173 52.4103667394455 heart 52.7256450826579 48.6747700086892 54.3621976071844 kidney 52.436675342511 49.3873769595802 53.7299741734424 liver 51.0581995046422 51.9964659633073 53.5795063068053 stomach 51.5389283270273 46.7645610913164 51.2654214126356 testicle 54.0502305754493 52.3779154442538 52.0014553744774 cont.diffExp=3.17445300910850,-0.60793895422254,0.679834948988344,4.05087507396874,3.04929838293076,-0.938266458665062,4.77436723571093,1.67231513119544 cont.diffExpScore=1.12414230061035 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=0.849370338429008 cont.tran.correlation=0.697628164302051 tran.covariance=0.00238630730965177 cont.tran.covariance=0.000918502597117941 tran.mean=53.8145759002767 cont.tran.mean=51.8219766422371 weightedLogRatios: wLogRatio Lung -0.245372427949361 cerebhem -0.400058620650156 cortex -0.187526584853536 heart -0.0142940638667936 kidney -0.210691792309209 liver -0.126680595257013 stomach 0.0925760032058875 testicle -0.221489847849284 cont.weightedLogRatios: wLogRatio Lung 0.245235549341168 cerebhem -0.0440842532002002 cortex 0.0510385561924218 heart 0.313779458384512 kidney 0.235430839949829 liver -0.0717835556236182 stomach 0.378516408833945 testicle 0.124904193892489 varWeightedLogRatios=0.0225742008192675 cont.varWeightedLogRatios=0.0275078680061955 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36463974650531 0.0615662074637569 70.8934320678225 5.33657157606462e-287 *** df.mm.trans1 -0.189776540777716 0.0541562373910044 -3.50424161500626 0.000493422642274755 *** df.mm.trans2 -0.349980429355600 0.0499793392787407 -7.00250212200119 7.01949889976286e-12 *** df.mm.exp2 0.220129633627298 0.0676264190237128 3.2550833949985 0.00119997828563241 ** df.mm.exp3 -0.00611459116178809 0.0676264190237128 -0.0904171956767968 0.927987070199148 df.mm.exp4 0.054717099216752 0.0676264190237128 0.809108333794309 0.418786339771294 df.mm.exp5 -0.0906701293021018 0.0676264190237128 -1.34075011823868 0.180529191172262 df.mm.exp6 -0.0311846158234807 0.0676264190237128 -0.461130668659922 0.644878629146466 df.mm.exp7 -0.0329827360843333 0.0676264190237128 -0.487719689442201 0.625933663354534 df.mm.exp8 -0.0659904425368811 0.0676264190237128 -0.975808618725501 0.329568234928169 df.mm.trans1:exp2 -0.121699646203632 0.0630619609036864 -1.92984240356087 0.0541162389797161 . df.mm.trans2:exp2 -0.0857967702963274 0.0546132601550535 -1.57098788925511 0.116733637473107 df.mm.trans1:exp3 -0.0197468057480146 0.0630619609036864 -0.313133392381717 0.754292472667125 df.mm.trans2:exp3 -0.0338437467304088 0.0546132601550535 -0.619698341287856 0.535701036658142 df.mm.trans1:exp4 -0.0525915422134266 0.0630619609036864 -0.833966173264877 0.404645158313046 df.mm.trans2:exp4 -0.110439973854555 0.0546132601550535 -2.02221902777829 0.0436146189799312 * df.mm.trans1:exp5 0.0551557270065601 0.0630619609036864 0.874627528484225 0.382140505319902 df.mm.trans2:exp5 0.0469985765311691 0.0546132601550535 0.860570791740588 0.38983191746536 df.mm.trans1:exp6 0.00845841604101455 0.0630619609036864 0.134128655687269 0.893347610427398 df.mm.trans2:exp6 -0.020967164096737 0.0546132601550535 -0.383920755457717 0.701178613926204 df.mm.trans1:exp7 0.0576180701538901 0.0630619609036864 0.913673937952696 0.361269824418966 df.mm.trans2:exp7 -0.0271231592293758 0.0546132601550535 -0.496640543933285 0.619631710825017 df.mm.trans1:exp8 0.0352836305262658 0.0630619609036864 0.559507348338787 0.576032632576443 df.mm.trans2:exp8 0.0297711527522614 0.0546132601550535 0.545126818427202 0.585876941769245 df.mm.trans1:probe2 -0.114638202027966 0.0345404585074579 -3.31895426354037 0.000960504936742822 *** df.mm.trans1:probe3 -0.07980366049817 0.0345404585074579 -2.31044010261007 0.0212156182618216 * df.mm.trans1:probe4 -0.0498058784493169 0.0345404585074579 -1.44195765202604 0.149856856801734 df.mm.trans1:probe5 -0.119776382093641 0.0345404585074579 -3.46771256866144 0.000564013130208461 *** df.mm.trans1:probe6 -0.139201784333779 0.0345404585074579 -4.03010817889758 6.32241776993734e-05 *** df.mm.trans1:probe7 -0.22718929654873 0.0345404585074579 -6.57748351834055 1.07381603000660e-10 *** df.mm.trans1:probe8 -0.347193651928595 0.0345404585074579 -10.0517962682409 5.16042343940111e-22 *** df.mm.trans1:probe9 -0.252127324305635 0.0345404585074579 -7.29947821194083 9.62034885020233e-13 *** df.mm.trans1:probe10 -0.295167034182816 0.0345404585074579 -8.54554475931708 1.14497142052747e-16 *** df.mm.trans1:probe11 -0.375155564971096 0.0345404585074579 -10.8613371443837 3.93484424440246e-25 *** df.mm.trans1:probe12 -0.369790265328697 0.0345404585074579 -10.7060033742416 1.60501258614931e-24 *** df.mm.trans1:probe13 -0.343017019097963 0.0345404585074579 -9.93087625122 1.46005703265245e-21 *** df.mm.trans1:probe14 -0.317967800548417 0.0345404585074579 -9.20566241122021 6.2029793502684e-19 *** df.mm.trans1:probe15 -0.390978776686596 0.0345404585074579 -11.3194437358779 5.77767632849969e-27 *** df.mm.trans1:probe16 -0.406978224527827 0.0345404585074579 -11.7826526373398 7.25557326925454e-29 *** df.mm.trans2:probe2 0.0345491205777614 0.0345404585074579 1.00025078040877 0.317608443381343 df.mm.trans2:probe3 -0.0436494045699887 0.0345404585074579 -1.26371815708712 0.206841525379776 df.mm.trans2:probe4 0.101789002962904 0.0345404585074579 2.94694996422604 0.00333891293452000 ** df.mm.trans2:probe5 0.0715922885068149 0.0345404585074579 2.07270811102165 0.0386428622618222 * df.mm.trans2:probe6 -0.074823057966034 0.0345404585074579 -2.16624391219006 0.0307012902663875 * df.mm.trans3:probe2 0.0571877644226799 0.0345404585074579 1.65567473316349 0.0983314285527085 . df.mm.trans3:probe3 0.389282519123857 0.0345404585074579 11.2703344409804 9.1316474715846e-27 *** df.mm.trans3:probe4 0.311200866598878 0.0345404585074579 9.0097491477041 3.00654366481893e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85052224990489 0.092520994779921 41.6178215448731 6.79878775650084e-176 *** df.mm.trans1 0.0938410880316379 0.0813853762212484 1.15304606784059 0.249368911388906 df.mm.trans2 0.0211511876333563 0.0751083813508317 0.281608886424526 0.778344391125108 df.mm.exp2 0.124678051629762 0.101628211631535 1.22680552602654 0.220396046382041 df.mm.exp3 0.0428089624296413 0.101628211631535 0.421231090682282 0.673743207356163 df.mm.exp4 -0.0782200379672496 0.101628211631535 -0.769668546868122 0.441811501367287 df.mm.exp5 -0.0574837119761958 0.101628211631535 -0.565627506903395 0.571866907014088 df.mm.exp6 -0.0298385570038462 0.101628211631535 -0.293605058327990 0.769165284692249 df.mm.exp7 -0.0823672018969248 0.101628211631535 -0.810475758400203 0.418000937200660 df.mm.exp8 0.0643133354453711 0.101628211631535 0.632829550111012 0.52709566718551 df.mm.trans1:exp2 -0.0765634331571141 0.0947687959992707 -0.80789707571787 0.419482772178817 df.mm.trans2:exp2 -0.00316087318781363 0.0820721848214335 -0.0385133306087906 0.969291724256375 df.mm.trans1:exp3 -0.0290804652439525 0.0947687959992707 -0.306856966339177 0.759062952209536 df.mm.trans2:exp3 0.0204739752967304 0.0820721848214335 0.249463022597438 0.803091421059042 df.mm.trans1:exp4 0.0832546036367828 0.0947687959992707 0.878502282939455 0.380036874751813 df.mm.trans2:exp4 0.065732511896677 0.0820721848214335 0.800910954663787 0.423512859714404 df.mm.trans1:exp5 0.0570225741221634 0.0947687959992707 0.601702000335661 0.547608855051814 df.mm.trans2:exp5 0.059530224105546 0.0820721848214335 0.725339824144653 0.468537644183128 df.mm.trans1:exp6 0.00273731037592532 0.0947687959992707 0.0288840893994938 0.976967022482689 df.mm.trans2:exp6 0.0833659587537422 0.0820721848214335 1.01576385391864 0.310167583635409 df.mm.trans1:exp7 0.0646372183968641 0.0947687959992707 0.682051699774276 0.495479951747003 df.mm.trans2:exp7 0.0298445240518146 0.0820721848214335 0.363637499315366 0.716261916252803 df.mm.trans1:exp8 -0.0344669208775884 0.0947687959992707 -0.363694827122776 0.7162191252605 df.mm.trans2:exp8 -0.00347664622344355 0.0820721848214335 -0.0423608343192981 0.96622570423175 df.mm.trans1:probe2 0.0249238871446837 0.0519070073164059 0.48016420967514 0.631292610866365 df.mm.trans1:probe3 -0.0359918194342154 0.0519070073164059 -0.693390378197352 0.488343616423536 df.mm.trans1:probe4 -0.0204071912762138 0.0519070073164059 -0.393149062742515 0.694354603187823 df.mm.trans1:probe5 -0.0324563347354398 0.0519070073164059 -0.625278481912819 0.532035510076282 df.mm.trans1:probe6 -0.00767286583732245 0.0519070073164059 -0.147819460878403 0.882536890085888 df.mm.trans1:probe7 0.0390862536668877 0.0519070073164059 0.753005339503247 0.451753818864015 df.mm.trans1:probe8 0.0523893178395476 0.0519070073164059 1.00929181912187 0.31325775614583 df.mm.trans1:probe9 0.00351421733021862 0.0519070073164059 0.0677021757158384 0.946046170568155 df.mm.trans1:probe10 0.0192216287536645 0.0519070073164059 0.370308937991678 0.711288207463498 df.mm.trans1:probe11 0.0420795599888139 0.0519070073164059 0.810672049195834 0.417888265822068 df.mm.trans1:probe12 -0.00253173696331216 0.0519070073164059 -0.0487744737021657 0.96111590286541 df.mm.trans1:probe13 0.0652572924887728 0.0519070073164059 1.25719620264348 0.209191233605992 df.mm.trans1:probe14 0.0709523678389498 0.0519070073164059 1.36691309145315 0.172184871118589 df.mm.trans1:probe15 0.0550225165699814 0.0519070073164059 1.06002097625441 0.289578572425329 df.mm.trans1:probe16 0.0092859423012911 0.0519070073164059 0.17889573645977 0.858082367686932 df.mm.trans2:probe2 0.0523554774394795 0.0519070073164059 1.00863987631458 0.313570158916065 df.mm.trans2:probe3 -0.00981757940586624 0.0519070073164059 -0.189137843105111 0.850051291552188 df.mm.trans2:probe4 0.0567821321793589 0.0519070073164059 1.09392036094927 0.274446368559928 df.mm.trans2:probe5 0.0514741902958747 0.0519070073164059 0.991661684175069 0.321778257551006 df.mm.trans2:probe6 0.108954926887759 0.0519070073164059 2.09904081396198 0.0362472192974433 * df.mm.trans3:probe2 -0.053660044351223 0.0519070073164059 -1.03377264699795 0.301675543083816 df.mm.trans3:probe3 -0.0705431929351269 0.0519070073164059 -1.35903024624636 0.1746679653608 df.mm.trans3:probe4 -0.0382471137285628 0.0519070073164059 -0.736839122614459 0.461519676362697