chr7.21048_chr7_117349315_117349744_+_1.R fitVsDatCorrelation=0.853082091294755 cont.fitVsDatCorrelation=0.252180374833860 fstatistic=5303.72777900332,36,324 cont.fstatistic=1535.62553131035,36,324 residuals=-0.684044126310418,-0.103550078573799,0.00357717818155900,0.09380963559664,0.983308433321912 cont.residuals=-0.845570182328275,-0.256239009945079,-0.0130471509138499,0.28586260347698,1.06919204414686 predictedValues: Include Exclude Both chr7.21048_chr7_117349315_117349744_+_1.R.tl.Lung 153.499883136022 96.9450516051082 80.8742219559007 chr7.21048_chr7_117349315_117349744_+_1.R.tl.cerebhem 87.5708219908735 60.5802059903172 74.4490324878808 chr7.21048_chr7_117349315_117349744_+_1.R.tl.cortex 125.637464573491 80.7911739807367 75.2398415893675 chr7.21048_chr7_117349315_117349744_+_1.R.tl.heart 143.786326006748 85.7452065819962 72.2238555055075 chr7.21048_chr7_117349315_117349744_+_1.R.tl.kidney 124.985134757226 102.058521141213 81.5709858234307 chr7.21048_chr7_117349315_117349744_+_1.R.tl.liver 114.045830335603 96.745141589906 80.3010065376063 chr7.21048_chr7_117349315_117349744_+_1.R.tl.stomach 129.913171372894 83.099572020879 75.138898084602 chr7.21048_chr7_117349315_117349744_+_1.R.tl.testicle 144.967448226396 81.1636544664836 85.6592750685421 diffExp=56.5548315309136,26.9906160005563,44.8462905927541,58.0411194247522,22.9266136160135,17.3006887456972,46.8135993520154,63.8037937599128 diffExpScore=0.997043847600692 diffExp1.5=1,0,1,1,0,0,1,1 diffExp1.5Score=0.833333333333333 diffExp1.4=1,1,1,1,0,0,1,1 diffExp1.4Score=0.857142857142857 diffExp1.3=1,1,1,1,0,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,0,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 96.2140392386762 91.6254532669088 86.6636050047338 cerebhem 81.4415078279308 98.2929112904481 101.759325342338 cortex 92.9647754840665 96.7548617201648 83.1091210192094 heart 101.675445882605 91.0152543369168 88.0710649868323 kidney 95.9246814889162 88.30825793894 91.467003117838 liver 88.0472599751041 94.7821246122815 93.9249635158334 stomach 89.675246598746 106.899784356041 83.1541844053328 testicle 90.9360915229773 97.5540360485153 98.5240326435427 cont.diffExp=4.58858597176744,-16.8514034625173,-3.79008623609836,10.6601915456878,7.61642354997622,-6.73486463717735,-17.2245377572948,-6.61794452553809 cont.diffExpScore=2.52384538728943 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,-1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.539694778064859 cont.tran.correlation=-0.579229096058306 tran.covariance=0.0184217648049604 cont.tran.covariance=-0.00230213881582756 tran.mean=106.970912985993 cont.tran.mean=93.8819832243274 weightedLogRatios: wLogRatio Lung 2.20766864675578 cerebhem 1.58011741343323 cortex 2.03662910533036 heart 2.43475052496995 kidney 0.957892871467946 liver 0.765733331431522 stomach 2.07481904380166 testicle 2.71835859609911 cont.weightedLogRatios: wLogRatio Lung 0.221957128523389 cerebhem -0.845157068922322 cortex -0.181905562733852 heart 0.50576931209604 kidney 0.374120082440824 liver -0.332767531619506 stomach -0.805402779231751 testicle -0.319303637686745 varWeightedLogRatios=0.479076354159747 cont.varWeightedLogRatios=0.259256744374442 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.6482936932435 0.111669272046168 50.5805544331687 7.94956862345088e-156 *** df.mm.trans1 -0.562907054935413 0.0945827069102455 -5.95147964489518 6.89891804742893e-09 *** df.mm.trans2 -0.90453712266176 0.0945827069102455 -9.56345141950866 2.99922889090311e-19 *** df.mm.exp2 -0.948647710474407 0.131717887303043 -7.20211757034835 4.17378492723782e-12 *** df.mm.exp3 -0.310361690948598 0.131717887303043 -2.35626077295448 0.0190550509371328 * df.mm.exp4 -0.0750108779633532 0.131717887303043 -0.569481332408378 0.569424137311006 df.mm.exp5 -0.162681452073288 0.131717887303043 -1.23507486647587 0.217697515855036 df.mm.exp6 -0.292050665919679 0.131717887303043 -2.21724377682858 0.0273001005606487 * df.mm.exp7 -0.247381521690973 0.131717887303043 -1.87811638006176 0.0612631734660045 . df.mm.exp8 -0.292349758108768 0.131717887303043 -2.21951447973168 0.0271438216827896 * df.mm.trans1:exp2 0.387395764938445 0.114071036537251 3.39609226582191 0.000768633843838683 *** df.mm.trans2:exp2 0.478471576674001 0.114071036537251 4.19450538189643 3.5341165504707e-05 *** df.mm.trans1:exp3 0.110062379627297 0.114071036537251 0.96485824069246 0.335335416723736 df.mm.trans2:exp3 0.128085077901547 0.114071036537251 1.12285363392590 0.262331013680998 df.mm.trans1:exp4 0.00963942267440187 0.114071036537251 0.0845036826789423 0.932708164236319 df.mm.trans2:exp4 -0.0477532771675467 0.114071036537251 -0.418627537867183 0.675765720667174 df.mm.trans1:exp5 -0.0428235453364892 0.114071036537251 -0.375411205477255 0.707600319248785 df.mm.trans2:exp5 0.214083497816075 0.114071036537251 1.87675596115202 0.0614500988016103 . df.mm.trans1:exp6 -0.00504875168477738 0.114071036537251 -0.0442597160334269 0.964724646776616 df.mm.trans2:exp6 0.289986440747617 0.114071036537251 2.54215662056266 0.0114823102738152 * df.mm.trans1:exp7 0.080548030779309 0.114071036537251 0.70612166965806 0.480619905641965 df.mm.trans2:exp7 0.0932767336731054 0.114071036537251 0.817707426044518 0.414125186962866 df.mm.trans1:exp8 0.235159174638086 0.114071036537251 2.06151519067939 0.0400512921919442 * df.mm.trans2:exp8 0.114672960282693 0.114071036537251 1.00527674476988 0.315513781713714 df.mm.trans1:probe2 -0.293666503409228 0.0570355182686253 -5.14883553834157 4.55731280750585e-07 *** df.mm.trans1:probe3 -0.169900380873981 0.0570355182686253 -2.97885223158289 0.00311175920295396 ** df.mm.trans1:probe4 0.0323372284086826 0.0570355182686253 0.566966504212008 0.571129511428176 df.mm.trans1:probe5 -0.0768673275112243 0.0570355182686253 -1.34770981038860 0.178693328031143 df.mm.trans1:probe6 0.0429154898959417 0.0570355182686253 0.752434468883385 0.452336123846359 df.mm.trans2:probe2 -0.277290796182415 0.0570355182686253 -4.86172133785888 1.8174371605373e-06 *** df.mm.trans2:probe3 -0.140345720929777 0.0570355182686253 -2.46067231770874 0.0143889148686133 * df.mm.trans2:probe4 -0.427096477763625 0.0570355182686253 -7.48825452505034 6.67050293691562e-13 *** df.mm.trans2:probe5 -0.306351337210404 0.0570355182686253 -5.3712378971916 1.49571726893634e-07 *** df.mm.trans2:probe6 -0.37542574588478 0.0570355182686253 -6.58231497286662 1.86545479121557e-10 *** df.mm.trans3:probe2 0.138979760483151 0.0570355182686253 2.4367230228116 0.0153590379549064 * df.mm.trans3:probe3 0.497157089915536 0.0570355182686253 8.7166226415973 1.51242176521494e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.7421705064129 0.207100364024458 22.8979341912352 1.07645783143404e-69 *** df.mm.trans1 -0.194480290974304 0.175411844929303 -1.10870671848122 0.268379018133365 df.mm.trans2 -0.237705623283562 0.175411844929303 -1.35512868802769 0.176320569321400 df.mm.exp2 -0.257023868299728 0.244282262337259 -1.05215935795157 0.293510508585376 df.mm.exp3 0.0619964113859763 0.244282262337259 0.253790065610181 0.799818722681113 df.mm.exp4 0.0324185370770620 0.244282262337259 0.132709337005831 0.894505638749935 df.mm.exp5 -0.093831722613763 0.244282262337259 -0.384111894641854 0.701147476989652 df.mm.exp6 -0.135292032362466 0.244282262337259 -0.553834859183022 0.580073881326482 df.mm.exp7 0.125139668787970 0.244282262337259 0.512274888854603 0.608807655823567 df.mm.exp8 -0.121987472817141 0.244282262337259 -0.499370980315891 0.617856974913736 df.mm.trans1:exp2 0.090333650461405 0.211554644878001 0.426999135440862 0.669663561526982 df.mm.trans2:exp2 0.327266672662656 0.211554644878001 1.54696046901444 0.122848950779338 df.mm.trans1:exp3 -0.0963510332713027 0.211554644878001 -0.455442769062652 0.649095793771531 df.mm.trans2:exp3 -0.00752493757409053 0.211554644878001 -0.0355697109767077 0.971647364068977 df.mm.trans1:exp4 0.0227920150219708 0.211554644878001 0.107735828892410 0.91427193086171 df.mm.trans2:exp4 -0.0391005217056747 0.211554644878001 -0.184824690227072 0.853482177394721 df.mm.trans1:exp5 0.0908197532676809 0.211554644878001 0.429296900193587 0.66799249607792 df.mm.trans2:exp5 0.0569562400700033 0.211554644878001 0.269227083635288 0.787926306720202 df.mm.trans1:exp6 0.0465904627994427 0.211554644878001 0.220228975952338 0.825831529654558 df.mm.trans2:exp6 0.169163757728671 0.211554644878001 0.799622044820732 0.424515650099736 df.mm.trans1:exp7 -0.195520180512805 0.211554644878001 -0.924206512343688 0.356066595683300 df.mm.trans2:exp7 0.02904302481849 0.211554644878001 0.137283796511480 0.8908917484661 df.mm.trans1:exp8 0.0655691566001633 0.211554644878001 0.309939574420479 0.756806161366272 df.mm.trans2:exp8 0.184684806009129 0.211554644878001 0.872988660285066 0.383315780971556 df.mm.trans1:probe2 0.101231923680529 0.105777322439000 0.95702860827194 0.339266362273483 df.mm.trans1:probe3 -0.0452679353262709 0.105777322439000 -0.427955012307821 0.668968193231035 df.mm.trans1:probe4 0.0474470565949326 0.105777322439000 0.448556037351903 0.654051785872634 df.mm.trans1:probe5 0.0340695571616824 0.105777322439000 0.322087536119375 0.747594094734674 df.mm.trans1:probe6 0.0324850244432247 0.105777322439000 0.307107645515967 0.758958729308329 df.mm.trans2:probe2 0.0804670530739558 0.105777322439000 0.76072121338068 0.447376856796023 df.mm.trans2:probe3 0.0128442798373793 0.105777322439000 0.121427537975224 0.903427678780525 df.mm.trans2:probe4 0.0737239293365238 0.105777322439000 0.696972920438961 0.486319505235754 df.mm.trans2:probe5 -0.00275345317278904 0.105777322439000 -0.0260306567542102 0.979248911209545 df.mm.trans2:probe6 -0.0450837926810362 0.105777322439001 -0.426214160478822 0.670234817272874 df.mm.trans3:probe2 0.219371698759433 0.105777322439000 2.07390103758714 0.0388780976300713 * df.mm.trans3:probe3 0.236776569340322 0.105777322439001 2.23844358961597 0.0258709602514344 *