chr10.2016_chr10_80812135_80812991_-_0.R fitVsDatCorrelation=0.81042713680637 cont.fitVsDatCorrelation=0.347478216048890 fstatistic=8789.95430188326,36,324 cont.fstatistic=3425.56321779697,36,324 residuals=-0.333151657356048,-0.0744701381062843,-0.00110394102336318,0.0731860398485021,0.936029899878498 cont.residuals=-0.47368683988893,-0.126727951299275,-0.0273789125431434,0.0840913969203706,1.06052207219842 predictedValues: Include Exclude Both chr10.2016_chr10_80812135_80812991_-_0.R.tl.Lung 54.9802320282656 53.0913594188321 64.2964734103997 chr10.2016_chr10_80812135_80812991_-_0.R.tl.cerebhem 60.174718171881 48.4881944861141 67.1561956874238 chr10.2016_chr10_80812135_80812991_-_0.R.tl.cortex 50.7549552762054 46.8679482714399 56.1642489964422 chr10.2016_chr10_80812135_80812991_-_0.R.tl.heart 49.5864256590294 68.5362164645972 78.3416518745896 chr10.2016_chr10_80812135_80812991_-_0.R.tl.kidney 55.8331363291698 47.0614538563474 55.9727254811169 chr10.2016_chr10_80812135_80812991_-_0.R.tl.liver 54.7461490121126 44.5568008113785 59.0563328848405 chr10.2016_chr10_80812135_80812991_-_0.R.tl.stomach 53.1972054345796 45.4847332769991 62.8050818915561 chr10.2016_chr10_80812135_80812991_-_0.R.tl.testicle 60.174818701817 47.1492058097928 54.3570377946657 diffExp=1.88887260943346,11.6865236857669,3.88700700476551,-18.9497908055678,8.77168247282244,10.1893482007341,7.71247215758044,13.0256128920242 diffExpScore=1.94103431010245 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,-1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,0,-1,0,1,0,1 diffExp1.2Score=1.33333333333333 cont.predictedValues: Include Exclude Both Lung 53.5631444953682 57.6634939952084 56.2524389820946 cerebhem 55.3023104138767 55.8718248892818 51.5245089408744 cortex 52.479240813127 59.2006584110935 52.4512165434797 heart 52.178797691244 57.7008007241407 58.2289244909443 kidney 58.4091751202078 59.5618371780751 54.4230167741 liver 53.8727941607387 59.0411852350956 46.3913674659711 stomach 51.525098549257 59.554081874819 52.7199624343325 testicle 52.5150378693771 50.9112860901926 59.8648371328761 cont.diffExp=-4.10034949984024,-0.569514475405072,-6.72141759796648,-5.52200303289673,-1.15266205786731,-5.1683910743569,-8.02898332556198,1.60375177918456 cont.diffExpScore=1.07200047521444 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=-0.481833600405042 cont.tran.correlation=0.182489067920779 tran.covariance=-0.00470442300572632 cont.tran.covariance=0.000383668762244986 tran.mean=52.5427220630351 cont.tran.mean=55.584422969444 weightedLogRatios: wLogRatio Lung 0.139470756589906 cerebhem 0.861414354061308 cortex 0.309711248100615 heart -1.31579238628878 kidney 0.67287026795045 liver 0.80312031264718 stomach 0.61017873252181 testicle 0.969718463522071 cont.weightedLogRatios: wLogRatio Lung -0.296360992162189 cerebhem -0.0411659340268905 cortex -0.484551609203664 heart -0.402879574444102 kidney -0.0796777776820338 liver -0.369409545453413 stomach -0.581359686969751 testicle 0.122372280685848 varWeightedLogRatios=0.547972291718728 cont.varWeightedLogRatios=0.0590948671228405 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.87449538409121 0.0762586369232766 50.8072992176522 2.19389476064904e-156 *** df.mm.trans1 0.0872531583929225 0.0645902688656117 1.35087157748892 0.177679207194697 df.mm.trans2 0.105626183312078 0.0645902688656117 1.63532657731843 0.102951693249897 df.mm.exp2 -0.0439316405432205 0.0899497808133918 -0.488401863194756 0.625595605524861 df.mm.exp3 -0.0694202367499532 0.0899497808133918 -0.771766602677678 0.440815196878502 df.mm.exp4 -0.0454830921615793 0.0899497808133918 -0.505649838724317 0.613446310743641 df.mm.exp5 0.0334741722554365 0.0899497808133918 0.372142899657326 0.710029741014734 df.mm.exp6 -0.094503067817668 0.0899497808133918 -1.05062032350832 0.294215986174347 df.mm.exp7 -0.164136500507329 0.0899497808133918 -1.8247570924919 0.068958415371562 . df.mm.exp8 0.139513929130675 0.0899497808133918 1.55102022338562 0.121873049508899 df.mm.trans1:exp2 0.134210237767647 0.0778987952492394 1.72287950459615 0.085864469197206 . df.mm.trans2:exp2 -0.0467621959983641 0.0778987952492394 -0.600294213135738 0.548729809949166 df.mm.trans1:exp3 -0.0105442121428238 0.0778987952492394 -0.135357833315487 0.892413014648264 df.mm.trans2:exp3 -0.0552599193500949 0.0778987952492394 -0.709380923970508 0.478598283364665 df.mm.trans1:exp4 -0.0577734907515194 0.0778987952492394 -0.741648064859945 0.458837836333956 df.mm.trans2:exp4 0.300831213027641 0.0778987952492394 3.86182112399972 0.000135895310741123 *** df.mm.trans1:exp5 -0.0180803410559889 0.0778987952492394 -0.232100393826892 0.816606568614145 df.mm.trans2:exp5 -0.154034087956292 0.0778987952492394 -1.97736161982294 0.04884803796425 * df.mm.trans1:exp6 0.0902363934035977 0.0778987952492394 1.15837983263905 0.24756217888645 df.mm.trans2:exp6 -0.0807463264153884 0.0778987952492394 -1.03655423883050 0.300716638121914 df.mm.trans1:exp7 0.131168663091451 0.0778987952492394 1.68383429643261 0.0931768172597236 . df.mm.trans2:exp7 0.00949904558909441 0.0778987952492394 0.121940853625553 0.90302145597581 df.mm.trans1:exp8 -0.0492336612735456 0.0778987952492394 -0.632020830566392 0.527818970775907 df.mm.trans2:exp8 -0.258210956297814 0.0778987952492394 -3.31469768526793 0.00102137127906108 ** df.mm.trans1:probe2 -0.0293701648822955 0.0389493976246197 -0.754059540672609 0.451361134028949 df.mm.trans1:probe3 -0.0273041840597354 0.0389493976246197 -0.701016850706738 0.483795650447825 df.mm.trans1:probe4 0.291116084350695 0.0389493976246197 7.47421275051203 7.30683630159993e-13 *** df.mm.trans1:probe5 0.0867203857487572 0.0389493976246197 2.22648849629299 0.0266686819921872 * df.mm.trans1:probe6 0.0858643231124097 0.0389493976246197 2.20450965480748 0.0281910436677874 * df.mm.trans2:probe2 -0.0160735216369704 0.0389493976246197 -0.412677027559737 0.680116199313247 df.mm.trans2:probe3 0.0246152980090502 0.0389493976246197 0.631981481364196 0.527844650635122 df.mm.trans2:probe4 -0.0752462995310335 0.0389493976246197 -1.93189892835392 0.0542436876990343 . df.mm.trans2:probe5 0.059403478820487 0.0389493976246197 1.525144994359 0.128198441788445 df.mm.trans2:probe6 -0.0656653325099382 0.0389493976246197 -1.68591394256715 0.0927750751608843 . df.mm.trans3:probe2 -0.0719075477003904 0.0389493976246197 -1.84617868531394 0.0657782432030271 . df.mm.trans3:probe3 0.360297721912767 0.0389493976246197 9.2504054975429 3.10858191024396e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00363024475087 0.122058720211059 32.8008538663027 5.54460606814413e-105 *** df.mm.trans1 -0.0162881387428753 0.103382460976278 -0.157552244249755 0.874907777238835 df.mm.trans2 0.114412048598112 0.103382460976278 1.10668722254896 0.269250166949741 df.mm.exp2 0.0881812011970422 0.143972611789455 0.61248594507682 0.540645914986337 df.mm.exp3 0.0758306970980482 0.143972611789455 0.526702239790877 0.598760775386769 df.mm.exp4 -0.0600710429330687 0.143972611789455 -0.417239377590209 0.676779653003681 df.mm.exp5 0.152064756112769 0.143972611789455 1.05620613686684 0.291660951048333 df.mm.exp6 0.222111347275847 0.143972611789455 1.54273333320272 0.1238715939434 df.mm.exp7 0.0583235846327632 0.143972611789455 0.405101942014191 0.68566991950105 df.mm.exp8 -0.206541228708913 0.143972611789455 -1.4345869408201 0.15236933364585 df.mm.trans1:exp2 -0.0562277427766346 0.124683939258863 -0.450962193774592 0.652318454265848 df.mm.trans2:exp2 -0.119745262017702 0.124683939258863 -0.96039042982988 0.337574902013905 df.mm.trans1:exp3 -0.0962742476513768 0.124683939258863 -0.77214634237291 0.440590597215184 df.mm.trans2:exp3 -0.0495223203529477 0.124683939258863 -0.397182834030708 0.691494153691273 df.mm.trans1:exp4 0.0338860519419719 0.124683939258863 0.27177559630691 0.785967662610804 df.mm.trans2:exp4 0.0607178068269329 0.124683939258863 0.486973760917784 0.626606201758276 df.mm.trans1:exp5 -0.0654529992673 0.124683939258863 -0.524951326180108 0.59997603801969 df.mm.trans2:exp5 -0.119673989827315 0.124683939258863 -0.959818806966418 0.337862122435422 df.mm.trans1:exp6 -0.216346972213576 0.124683939258863 -1.73516311322509 0.0836627919867228 . df.mm.trans2:exp6 -0.198500378956973 0.124683939258863 -1.59202845319842 0.112353438855323 df.mm.trans1:exp7 -0.097115774079978 0.124683939258863 -0.778895619253343 0.436609701875965 df.mm.trans2:exp7 -0.0260630327355905 0.124683939258863 -0.209032798374133 0.834553994156886 df.mm.trans1:exp8 0.186779564027584 0.124683939258863 1.49802424544674 0.135100552503382 df.mm.trans2:exp8 0.0820015714468721 0.124683939258863 0.657675494809516 0.511213637702209 df.mm.trans1:probe2 0.0574388351314697 0.0623419696294313 0.921350985105115 0.357552731493115 df.mm.trans1:probe3 -0.000602831532508264 0.0623419696294313 -0.00966975435796418 0.992290723699078 df.mm.trans1:probe4 -0.105275092203325 0.0623419696294313 -1.68867125676481 0.0922445777059665 . df.mm.trans1:probe5 0.000896290249614398 0.0623419696294313 0.0143769960259848 0.988538060713997 df.mm.trans1:probe6 -0.0107850958759949 0.0623419696294313 -0.172998959450638 0.862760268213976 df.mm.trans2:probe2 -0.162903136920705 0.0623419696294313 -2.61305726926855 0.00939205233199854 ** df.mm.trans2:probe3 -0.102232229703311 0.0623419696294313 -1.63986204335527 0.102004350213007 df.mm.trans2:probe4 -0.148695321766576 0.0623419696294313 -2.3851559816034 0.0176463943532575 * df.mm.trans2:probe5 -0.0566858926351637 0.0623419696294313 -0.909273367076977 0.363881725660266 df.mm.trans2:probe6 -0.100245477086864 0.0623419696294313 -1.60799342213177 0.108810600325301 df.mm.trans3:probe2 -0.138738674083884 0.0623419696294313 -2.22544579371754 0.0267392592499220 * df.mm.trans3:probe3 -0.0769755902558135 0.0623419696294313 -1.23473144517837 0.21782517235691