chr1.401_chr1_172549961_172551011_+_1.R fitVsDatCorrelation=0.867749929233367 cont.fitVsDatCorrelation=0.288156481761483 fstatistic=6941.96676881249,38,370 cont.fstatistic=1862.89641154748,38,370 residuals=-0.63563129348109,-0.0794618215849293,-0.00197129940397782,0.0694152461671238,0.619685082851048 cont.residuals=-0.557754187375907,-0.218264671696510,-0.0374845428850035,0.146311262932269,1.26762766797482 predictedValues: Include Exclude Both chr1.401_chr1_172549961_172551011_+_1.R.tl.Lung 69.5868518450146 47.5370969927956 79.7403312320854 chr1.401_chr1_172549961_172551011_+_1.R.tl.cerebhem 104.224024915181 52.7347917574614 68.2684927469277 chr1.401_chr1_172549961_172551011_+_1.R.tl.cortex 65.0601455512114 46.6520044799948 78.9732762410352 chr1.401_chr1_172549961_172551011_+_1.R.tl.heart 64.4310123796576 47.7088944041434 76.654450035035 chr1.401_chr1_172549961_172551011_+_1.R.tl.kidney 69.6630791620992 45.6459100872641 76.1382448078864 chr1.401_chr1_172549961_172551011_+_1.R.tl.liver 65.521990012183 49.1175569152501 72.4856348615202 chr1.401_chr1_172549961_172551011_+_1.R.tl.stomach 66.7978910314073 49.976207741456 70.7301982395596 chr1.401_chr1_172549961_172551011_+_1.R.tl.testicle 69.9826715174584 50.0048636453503 71.5580577205009 diffExp=22.0497548522190,51.4892331577196,18.4081410712166,16.7221179755141,24.0171690748351,16.4044330969329,16.8216832899513,19.9778078721081 diffExpScore=0.99464926866787 diffExp1.5=0,1,0,0,1,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=1,1,0,0,1,0,0,0 diffExp1.4Score=0.75 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 59.7677468991718 69.5538057347303 71.8323783409418 cerebhem 60.3776126814661 62.1639778281396 60.2000333555217 cortex 57.9714785505896 68.9158141101542 69.418314124033 heart 62.0357977568212 55.493970398678 57.7326277970292 kidney 56.1297935642345 59.6818200815332 67.270473817456 liver 61.4523879065988 58.6562776002473 67.9526008787235 stomach 64.2059710778189 65.5921045251487 68.5269070283433 testicle 65.2712886388052 65.4586883637333 55.2915163047056 cont.diffExp=-9.78605883555846,-1.78636514667358,-10.9443355595646,6.54182735814322,-3.55202651729866,2.7961103063515,-1.38613344732985,-0.187399724928113 cont.diffExpScore=1.91564059007904 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=0.71547097040344 cont.tran.correlation=-0.000841438123335201 tran.covariance=0.00505463832157865 cont.tran.covariance=6.53506907902145e-06 tran.mean=60.2903120273705 cont.tran.mean=62.0455334823669 weightedLogRatios: wLogRatio Lung 1.54409275979210 cerebhem 2.93347479384327 cortex 1.33338308501868 heart 1.20652318637055 kidney 1.70467757827092 liver 1.16371428709211 stomach 1.17692158285991 testicle 1.37146157979775 cont.weightedLogRatios: wLogRatio Lung -0.631752176796633 cerebhem -0.119988084579406 cortex -0.717058934458214 heart 0.453771611828352 kidney -0.249023025686798 liver 0.190695335404408 stomach -0.0891268061216119 testicle -0.0119839188084540 varWeightedLogRatios=0.346142180727477 cont.varWeightedLogRatios=0.152519032392518 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.21612777420409 0.0863710047907766 37.2361972862858 3.34530150733133e-127 *** df.mm.trans1 1.02755214297101 0.0711299610388995 14.4461226740875 7.97482010795518e-38 *** df.mm.trans2 0.660911221634356 0.0711299610388996 9.29160106348036 1.33122375605129e-18 *** df.mm.exp2 0.663059198732813 0.0972326830506859 6.81930373542372 3.73901523529229e-11 *** df.mm.exp3 -0.0763920056482798 0.0972326830506859 -0.785661808884343 0.432568464910707 df.mm.exp4 -0.03390527721805 0.0972326830506859 -0.348702474870263 0.727511022772984 df.mm.exp5 0.00672323944097599 0.0972326830506859 0.0691458800686522 0.94491085478703 df.mm.exp6 0.0679034158971145 0.0972326830506859 0.698359993436749 0.48539076967609 df.mm.exp7 0.129035402931157 0.0972326830506859 1.32707849750369 0.185301056260926 df.mm.exp8 0.164548280032192 0.0972326830506859 1.69231450649588 0.0914278811447693 . df.mm.trans1:exp2 -0.259092169806658 0.0806210817596685 -3.21370247274791 0.00142539693099199 ** df.mm.trans2:exp2 -0.55929417140992 0.0806210817596685 -6.9373191130972 1.79418832975684e-11 *** df.mm.trans1:exp3 0.009128524653493 0.0806210817596685 0.113227513874164 0.909911591772204 df.mm.trans2:exp3 0.0575975049580051 0.0806210817596685 0.714422378128134 0.475416509658174 df.mm.trans1:exp4 -0.043075286115284 0.0806210817596685 -0.534293080359445 0.593459658997503 df.mm.trans2:exp4 0.0375127276763430 0.0806210817596685 0.465296754367157 0.641992694609281 df.mm.trans1:exp5 -0.00562841197287764 0.0806210817596685 -0.0698131537065694 0.944380093185867 df.mm.trans2:exp5 -0.0473196247938341 0.0806210817596685 -0.586938599197837 0.55760289817718 df.mm.trans1:exp6 -0.128093243444024 0.0806210817596685 -1.58883062157203 0.112952738795585 df.mm.trans2:exp6 -0.0351972659116818 0.0806210817596686 -0.436576453000282 0.662673192560803 df.mm.trans1:exp7 -0.169939533494864 0.0806210817596685 -2.10787959905393 0.0357135202916606 * df.mm.trans2:exp7 -0.0789987514732751 0.0806210817596686 -0.979877096027691 0.327787049121012 df.mm.trans1:exp8 -0.158876257600727 0.0806210817596685 -1.97065400430048 0.0495085472163323 * df.mm.trans2:exp8 -0.113938401976884 0.0806210817596686 -1.41325816387003 0.158420536017144 df.mm.trans1:probe2 -0.0606682386469233 0.0470725702702963 -1.28882358236568 0.198264658032766 df.mm.trans1:probe3 0.00662160866197684 0.0470725702702963 0.140668092350912 0.888208701675895 df.mm.trans1:probe4 0.136560830144371 0.0470725702702963 2.90107018504030 0.00394122290945571 ** df.mm.trans1:probe5 -0.0230705153916538 0.0470725702702963 -0.490105283377987 0.624349882631779 df.mm.trans1:probe6 -0.0715907422540552 0.0470725702702963 -1.52085900223788 0.129149317988771 df.mm.trans2:probe2 -0.0199833086560785 0.0470725702702963 -0.424521298525488 0.671432413489561 df.mm.trans2:probe3 -0.0430618392660417 0.0470725702702963 -0.914796855552512 0.360894060269797 df.mm.trans2:probe4 -0.0239474839968438 0.0470725702702963 -0.508735424034304 0.611240901388952 df.mm.trans2:probe5 0.0200628865342951 0.0470725702702963 0.426211834601163 0.67020133837567 df.mm.trans2:probe6 -0.103884857797713 0.0470725702702963 -2.20690854995157 0.0279339210505965 * df.mm.trans3:probe2 -0.711758398721076 0.0470725702702963 -15.1204490138115 1.47218424763497e-40 *** df.mm.trans3:probe3 -0.630453819644035 0.0470725702702963 -13.3932312602412 1.25678643388274e-33 *** df.mm.trans3:probe4 -0.495397693990609 0.0470725702702963 -10.5241267078890 7.82609772096375e-23 *** df.mm.trans3:probe5 -0.791468302462423 0.0470725702702963 -16.8137898125749 1.55807957454847e-47 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1169517061722 0.16641299242303 24.7393646747648 1.96450390147343e-80 *** df.mm.trans1 -0.0306517738379589 0.137047724477565 -0.223657663451221 0.823147128212734 df.mm.trans2 0.130753681001984 0.137047724477565 0.954074075293304 0.340669033380981 df.mm.exp2 0.0744897002641588 0.187340436608103 0.397616775175898 0.69114198601019 df.mm.exp3 -0.00554538617147661 0.187340436608103 -0.0296005831516075 0.976401559808382 df.mm.exp4 0.029932012210994 0.187340436608103 0.159773366353409 0.873146789128617 df.mm.exp5 -0.150258687615834 0.187340436608103 -0.802062226054057 0.423031795313983 df.mm.exp6 -0.0870845932894758 0.187340436608103 -0.464846751006819 0.642314639873723 df.mm.exp7 0.0600935972046464 0.187340436608103 0.320772163728624 0.748564150611921 df.mm.exp8 0.289120526768587 0.187340436608103 1.54328948946242 0.123615369325774 df.mm.trans1:exp2 -0.0643374809436842 0.155334484072610 -0.414186723107859 0.678977366165678 df.mm.trans2:exp2 -0.186814639157083 0.155334484072610 -1.20266044125629 0.229876792329118 df.mm.trans1:exp3 -0.0249696393012656 0.15533448407261 -0.160747560017605 0.872379957633877 df.mm.trans2:exp3 -0.00366957578047252 0.15533448407261 -0.0236237034061104 0.981165499876965 df.mm.trans1:exp4 0.00731342344396089 0.155334484072610 0.0470817763848386 0.962473453339128 df.mm.trans2:exp4 -0.255758275104795 0.155334484072610 -1.64650030308301 0.100509858658181 df.mm.trans1:exp5 0.0874592728048681 0.155334484072610 0.563038357690016 0.573749780632643 df.mm.trans2:exp5 -0.00281449588798786 0.15533448407261 -0.0181189380116797 0.985553736495795 df.mm.trans1:exp6 0.114881121945910 0.155334484072610 0.739572559382302 0.460028356827823 df.mm.trans2:exp6 -0.0833214386605614 0.15533448407261 -0.536400137793057 0.592004383009339 df.mm.trans1:exp7 0.0115364506658652 0.155334484072610 0.0742684455080339 0.940836919763583 df.mm.trans2:exp7 -0.118738902625551 0.15533448407261 -0.764407873335115 0.445111437554068 df.mm.trans1:exp8 -0.201034437057334 0.155334484072610 -1.29420352639380 0.196402318720913 df.mm.trans2:exp8 -0.349801931392958 0.15533448407261 -2.25192708162243 0.0249127131940761 * df.mm.trans1:probe2 0.053447437156122 0.0906957988818013 0.589304442047829 0.556016704406381 df.mm.trans1:probe3 -0.0338366490001439 0.0906957988818013 -0.373078460274012 0.709303690610578 df.mm.trans1:probe4 0.102669563687827 0.0906957988818013 1.13202116254172 0.258358643744423 df.mm.trans1:probe5 -0.0882642831282704 0.0906957988818013 -0.973190425758311 0.331094461805605 df.mm.trans1:probe6 0.0118125009823599 0.0906957988818013 0.130243088742782 0.896444884232372 df.mm.trans2:probe2 0.0161103579135558 0.0906957988818013 0.177630696373836 0.859110220017213 df.mm.trans2:probe3 -0.0258311666212004 0.0906957988818013 -0.284811060045513 0.77594824281801 df.mm.trans2:probe4 -0.0126383820558090 0.0906957988818013 -0.139349145292605 0.88925007380691 df.mm.trans2:probe5 -0.00641308943805213 0.0906957988818013 -0.070709884218672 0.94366685649643 df.mm.trans2:probe6 -0.0328799796453266 0.0906957988818013 -0.362530349263225 0.717162737174052 df.mm.trans3:probe2 0.167659098751564 0.0906957988818013 1.84858726444501 0.0653150892069996 . df.mm.trans3:probe3 0.0442446925411175 0.0906957988818013 0.487836185210509 0.62595481995415 df.mm.trans3:probe4 0.0600550883223567 0.0906957988818013 0.662159538399603 0.508281187781639 df.mm.trans3:probe5 0.0144496848521587 0.0906957988818013 0.159320332697991 0.873503433034975