chr16.9390_chr16_20035926_20036660_-_0.R fitVsDatCorrelation=0.830112180627698 cont.fitVsDatCorrelation=0.282272477239839 fstatistic=8011.0247368383,46,554 cont.fstatistic=2698.40101014503,46,554 residuals=-0.442990263154826,-0.0982664865093432,-0.0118475149548712,0.0935747324416638,1.22887630827453 cont.residuals=-0.759740733564308,-0.219405066025684,0.0085033439062157,0.186043396155792,1.18105510514683 predictedValues: Include Exclude Both chr16.9390_chr16_20035926_20036660_-_0.R.tl.Lung 57.5917379342766 104.861910425473 68.7561133641291 chr16.9390_chr16_20035926_20036660_-_0.R.tl.cerebhem 69.9056278476725 119.126872126868 68.5211559726473 chr16.9390_chr16_20035926_20036660_-_0.R.tl.cortex 60.3417132612189 89.4348653652543 81.3461682251007 chr16.9390_chr16_20035926_20036660_-_0.R.tl.heart 61.5519411749574 87.3570654915608 95.1051275977862 chr16.9390_chr16_20035926_20036660_-_0.R.tl.kidney 60.0697944212636 114.628240213188 81.778609655645 chr16.9390_chr16_20035926_20036660_-_0.R.tl.liver 58.8452923474624 105.129341869764 68.3045091958022 chr16.9390_chr16_20035926_20036660_-_0.R.tl.stomach 62.0409065704707 97.4182875019448 79.9437932277622 chr16.9390_chr16_20035926_20036660_-_0.R.tl.testicle 62.4763902719594 94.5799348876758 86.9817616565073 diffExp=-47.270172491196,-49.2212442791954,-29.0931521040354,-25.8051243166034,-54.5584457919242,-46.2840495223019,-35.3773809314741,-32.1035446157164 diffExpScore=0.996881948519771 diffExp1.5=-1,-1,0,0,-1,-1,-1,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 75.9958752782395 81.1684044337276 70.6118862397248 cerebhem 81.284854981495 83.3686333684328 81.5781404972307 cortex 68.7269015825773 91.8723497627144 83.750713830158 heart 73.0716262759834 79.5442899688905 84.8188545768882 kidney 74.5322981774066 83.3113148829595 79.3637860011266 liver 71.7745783126492 79.4448578825246 67.2050464881448 stomach 71.7890328349009 84.3884909130358 74.5880549865095 testicle 82.1777686827817 81.705457470138 74.3633332331034 cont.diffExp=-5.17252915548812,-2.08377838693782,-23.1454481801371,-6.47266369290709,-8.77901670555289,-7.6702795698754,-12.5994580781349,0.472311212643675 cont.diffExpScore=0.999166638737523 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,-1,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.370492912850185 cont.tran.correlation=-0.387615682947973 tran.covariance=0.00211079383105466 cont.tran.covariance=-0.00113673739249875 tran.mean=81.5849951069381 cont.tran.mean=79.0097959255286 weightedLogRatios: wLogRatio Lung -2.60860856399486 cerebhem -2.40597856105280 cortex -1.69072188179755 heart -1.50375618928528 kidney -2.85524262249583 liver -2.53295033842228 stomach -1.96434292540131 testicle -1.80048757102855 cont.weightedLogRatios: wLogRatio Lung -0.287330126424993 cerebhem -0.111643390636881 cortex -1.26996339768307 heart -0.367832749361999 kidney -0.486263157074569 liver -0.439058038515947 stomach -0.704132805001102 testicle 0.0253962573007427 varWeightedLogRatios=0.24311082937472 cont.varWeightedLogRatios=0.158835654421722 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42369131360209 0.0871035858192544 50.7865580044148 1.35740300010300e-210 *** df.mm.trans1 -0.356520049669427 0.0691914156759262 -5.15266303177362 3.57699335450161e-07 *** df.mm.trans2 0.228542984947259 0.0691914156759262 3.30305403805716 0.00101807837279753 ** df.mm.exp2 0.324734882123857 0.0920954714811192 3.52606786089837 0.000456706349185163 *** df.mm.exp3 -0.280637314701753 0.0920954714811192 -3.04724336808773 0.00241957557802736 ** df.mm.exp4 -0.440555385612861 0.0920954714811192 -4.78368130948958 2.20976271341846e-06 *** df.mm.exp5 -0.0422721994193759 0.0920954714811192 -0.459004104539953 0.646411251166316 df.mm.exp6 0.0306696606712015 0.0920954714811193 0.333020290552388 0.739244954429361 df.mm.exp7 -0.149973712813916 0.0920954714811192 -1.62845914573186 0.103996063868817 df.mm.exp8 -0.25692219235826 0.0920954714811193 -2.78973752157762 0.00545666587909431 ** df.mm.trans1:exp2 -0.130967842055348 0.0709643267510302 -1.84554476948443 0.0654917390309008 . df.mm.trans2:exp2 -0.19719015042096 0.0709643267510302 -2.77872220380217 0.00564248696557022 ** df.mm.trans1:exp3 0.327281822714462 0.0709643267510302 4.61192035066705 4.9594313025048e-06 *** df.mm.trans2:exp3 0.121503567955196 0.0709643267510302 1.71217812551758 0.0874234570733414 . df.mm.trans1:exp4 0.507057657153146 0.0709643267510302 7.14524720190889 2.84373147100778e-12 *** df.mm.trans2:exp4 0.257914960261889 0.0709643267510302 3.63443115816137 0.000304530159198188 *** df.mm.trans1:exp5 0.0844002071835143 0.0709643267510302 1.18933288100685 0.234818190157611 df.mm.trans2:exp5 0.131322051742908 0.0709643267510302 1.85053614619125 0.0647684786883696 . df.mm.trans1:exp6 -0.00913694303291984 0.0709643267510302 -0.128754029682769 0.897599009160546 df.mm.trans2:exp6 -0.0281225870578923 0.0709643267510302 -0.396291888409750 0.692042299343807 df.mm.trans1:exp7 0.224388544919814 0.0709643267510302 3.16199075215713 0.00165268825205609 ** df.mm.trans2:exp7 0.0763433167333052 0.0709643267510302 1.07579850649674 0.282485656412018 df.mm.trans1:exp8 0.338331803311819 0.0709643267510302 4.76763211604635 2.38567771477481e-06 *** df.mm.trans2:exp8 0.153723195338756 0.0709643267510302 2.16620381502492 0.0307213667266018 * df.mm.trans1:probe2 0.071960998638333 0.05083532381206 1.41557077327520 0.157463121718228 df.mm.trans1:probe3 -0.0096367425911749 0.0508353238120601 -0.189567841188585 0.849717221875791 df.mm.trans1:probe4 -0.0628415680089604 0.0508353238120601 -1.23617916237315 0.216915829420577 df.mm.trans1:probe5 -0.142272571914607 0.0508353238120601 -2.79869510501386 0.00530966059218459 ** df.mm.trans1:probe6 -0.119260874166522 0.0508353238120601 -2.34602369422162 0.0193264752604470 * df.mm.trans2:probe2 -0.0776617677863237 0.0508353238120601 -1.52771266046110 0.127154611443335 df.mm.trans2:probe3 0.145673867709064 0.0508353238120601 2.86560322203563 0.00432021320447405 ** df.mm.trans2:probe4 -0.164885948312968 0.0508353238120601 -3.24353099279069 0.00125167841056884 ** df.mm.trans2:probe5 -0.015296268306391 0.0508353238120601 -0.300898413924574 0.76360485254037 df.mm.trans2:probe6 0.119961014430407 0.0508353238120601 2.35979640601695 0.0186307797188930 * df.mm.trans3:probe2 -0.0774681098376461 0.0508353238120601 -1.52390314506599 0.128103520376442 df.mm.trans3:probe3 -0.127769916220701 0.05083532381206 -2.51340813118593 0.0122396936153305 * df.mm.trans3:probe4 0.0734140906533587 0.05083532381206 1.44415507069008 0.149260643227425 df.mm.trans3:probe5 -0.101127476874628 0.0508353238120601 -1.98931509216897 0.0471577303524349 * df.mm.trans3:probe6 -0.0586202727251484 0.05083532381206 -1.15314053947742 0.249349984264205 df.mm.trans3:probe7 0.100540012898961 0.05083532381206 1.97775887630147 0.0484510363174615 * df.mm.trans3:probe8 0.0663156655656302 0.05083532381206 1.30451938913189 0.192598215747350 df.mm.trans3:probe9 0.0854671938037904 0.0508353238120601 1.68125601244846 0.0932768103788685 . df.mm.trans3:probe10 0.0221224555984024 0.0508353238120601 0.43517880755889 0.663602124768053 df.mm.trans3:probe11 -0.170766659139383 0.0508353238120601 -3.35921257766967 0.000835411046281265 *** df.mm.trans3:probe12 -0.0635781584595318 0.0508353238120601 -1.25066889894480 0.211583253732004 df.mm.trans3:probe13 -0.595460555407333 0.05083532381206 -11.7135194733641 1.81484990011714e-28 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43003327937406 0.149860099957332 29.5611258809741 5.50033989051469e-116 *** df.mm.trans1 -0.125231661734957 0.119042544251853 -1.05199080313682 0.29326240287693 df.mm.trans2 -0.0140956944628094 0.119042544251853 -0.118408881055060 0.905786609019014 df.mm.exp2 -0.0503361254678589 0.158448546428583 -0.317681206943396 0.750846468810088 df.mm.exp3 -0.147310518885128 0.158448546428583 -0.929705713340356 0.352928392513231 df.mm.exp4 -0.242770383745331 0.158448546428583 -1.53217173156432 0.126050882039616 df.mm.exp5 -0.110231870590044 0.158448546428583 -0.695695057320884 0.486911547578726 df.mm.exp6 -0.0291614599190102 0.158448546428583 -0.184043720035981 0.854046501580056 df.mm.exp7 -0.0728242654765253 0.158448546428583 -0.459608289996836 0.645977709809077 df.mm.exp8 0.0330359835875935 0.158448546428583 0.208496602412719 0.834917866972422 df.mm.trans1:exp2 0.117616772919067 0.122092804794304 0.963339101900575 0.335797670305711 df.mm.trans2:exp2 0.0770822019033926 0.122092804794304 0.631341069060185 0.528077934951439 df.mm.trans1:exp3 0.04677215583943 0.122092804794304 0.383086914238962 0.701802268700363 df.mm.trans2:exp3 0.271184566395489 0.122092804794304 2.22113470857162 0.0267453507881304 * df.mm.trans1:exp4 0.203531459553445 0.122092804794304 1.66702255629514 0.096075094469425 . df.mm.trans2:exp4 0.222558293319844 0.122092804794304 1.82286166408249 0.0688631702246554 . df.mm.trans1:exp5 0.0907853684513236 0.122092804794304 0.743576729228835 0.457447812989774 df.mm.trans2:exp5 0.136290179980601 0.122092804794304 1.11628347149707 0.264784740374239 df.mm.trans1:exp6 -0.0279872553784321 0.122092804794304 -0.229229358974786 0.818775254671559 df.mm.trans2:exp6 0.00769856589248403 0.122092804794305 0.063055033467813 0.949745424295547 df.mm.trans1:exp7 0.0158769176730456 0.122092804794304 0.130039748859846 0.896582180338248 df.mm.trans2:exp7 0.111729230703986 0.122092804794304 0.915117241283968 0.360528218011094 df.mm.trans1:exp8 0.0451697617557364 0.122092804794304 0.369962520165181 0.711551803423928 df.mm.trans2:exp8 -0.0264412485389524 0.122092804794304 -0.216566804108557 0.828625680901342 df.mm.trans1:probe2 0.0730916727041362 0.0874612294796555 0.835703695671671 0.403681892241545 df.mm.trans1:probe3 0.150386197898933 0.0874612294796555 1.71946128351551 0.086088959304704 . df.mm.trans1:probe4 0.0661460058697704 0.0874612294796555 0.756289458349733 0.449797139167970 df.mm.trans1:probe5 0.0757223513198785 0.0874612294796555 0.865781921548363 0.386984537252093 df.mm.trans1:probe6 0.126325294081 0.0874612294796555 1.44435762946123 0.149203707177297 df.mm.trans2:probe2 -0.0410798556829878 0.0874612294796555 -0.46969218163739 0.638759769645611 df.mm.trans2:probe3 -0.0149224459554318 0.0874612294796555 -0.170617838832268 0.86458656266651 df.mm.trans2:probe4 -0.135867124170004 0.0874612294796555 -1.55345545653012 0.120885417759243 df.mm.trans2:probe5 -0.063001049396516 0.0874612294796555 -0.720331165835838 0.471624929080625 df.mm.trans2:probe6 -0.113948431865983 0.0874612294796555 -1.30284507254142 0.193168932724571 df.mm.trans3:probe2 -0.126138671162228 0.0874612294796555 -1.44222385064423 0.149804313705311 df.mm.trans3:probe3 0.0105766632204214 0.0874612294796555 0.120929734047263 0.903790537013612 df.mm.trans3:probe4 0.0202683085425716 0.0874612294796555 0.231740494195617 0.816825148642711 df.mm.trans3:probe5 0.0153217782911336 0.0874612294796555 0.175183660031873 0.860999330155185 df.mm.trans3:probe6 -0.0399781974745367 0.0874612294796555 -0.45709622094709 0.647781068483657 df.mm.trans3:probe7 -0.0818609511078832 0.0874612294796555 -0.93596844676103 0.349697242850239 df.mm.trans3:probe8 -0.0658235072094273 0.0874612294796555 -0.752602125547967 0.452008700934141 df.mm.trans3:probe9 0.0142254024410954 0.0874612294796555 0.162648095913223 0.870854824261696 df.mm.trans3:probe10 -0.0827419230582304 0.0874612294796555 -0.946041160757718 0.344540026368743 df.mm.trans3:probe11 -0.00375798257728043 0.0874612294796555 -0.0429674108132059 0.965742993692483 df.mm.trans3:probe12 -0.121601950908966 0.0874612294796555 -1.39035263547549 0.164980118344676 df.mm.trans3:probe13 0.0259144083464814 0.0874612294796555 0.296295953082953 0.767115114580988