chr15.8313_chr15_90423746_90425217_-_2.R fitVsDatCorrelation=0.79819030171268 cont.fitVsDatCorrelation=0.243069083461831 fstatistic=14068.0121576601,59,853 cont.fstatistic=5416.85751744519,59,853 residuals=-0.507679363937365,-0.0807056442709763,0.00109986175283096,0.0696963975562237,1.01900991704637 cont.residuals=-0.411538438335715,-0.140024735215456,-0.0276975939701944,0.106982041309447,0.93119182334051 predictedValues: Include Exclude Both chr15.8313_chr15_90423746_90425217_-_2.R.tl.Lung 51.9609964574364 45.6259752834637 67.9792702486861 chr15.8313_chr15_90423746_90425217_-_2.R.tl.cerebhem 57.7145060389741 50.6025473246647 69.2184113996882 chr15.8313_chr15_90423746_90425217_-_2.R.tl.cortex 59.2337590538729 44.784791442899 81.0493246799656 chr15.8313_chr15_90423746_90425217_-_2.R.tl.heart 48.3928396684606 46.2004325863543 59.6305734574052 chr15.8313_chr15_90423746_90425217_-_2.R.tl.kidney 50.2848199603728 45.9031416981055 59.4281142507075 chr15.8313_chr15_90423746_90425217_-_2.R.tl.liver 50.2352856647395 48.4951597116382 54.2383526121996 chr15.8313_chr15_90423746_90425217_-_2.R.tl.stomach 50.0751790197745 45.0303986743043 63.4451480256975 chr15.8313_chr15_90423746_90425217_-_2.R.tl.testicle 51.3647993563285 48.4110480577919 58.2982920271336 diffExp=6.33502117397264,7.11195871430947,14.4489676109739,2.19240708210627,4.3816782622673,1.74012595310136,5.04478034547017,2.95375129853664 diffExpScore=0.977880359058158 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 51.9621681152553 52.3950605604926 56.4080202683349 cerebhem 56.466936139801 51.5724919003267 55.1106396098058 cortex 54.4404660702012 52.1869254908123 56.8100235022944 heart 54.0062605713941 54.5024634295507 53.8247978446048 kidney 52.7807460362313 53.3181842959206 51.9268043392216 liver 53.6224522620731 55.8783267576664 59.3118209770001 stomach 54.6428404709002 52.188488765978 58.1964567465573 testicle 52.7203679098253 47.2615963255058 55.6819528385099 cont.diffExp=-0.432892445237329,4.89444423947438,2.25354057938891,-0.49620285815665,-0.537438259689239,-2.25587449559327,2.45435170492218,5.45877158431954 cont.diffExpScore=1.52232537394824 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=0.178225145674199 cont.tran.correlation=0.0768981297779705 tran.covariance=0.000539505203840017 cont.tran.covariance=0.000117393614553674 tran.mean=49.6447299999488 cont.tran.mean=53.1216109438709 weightedLogRatios: wLogRatio Lung 0.505175958249794 cerebhem 0.524679261061687 cortex 1.10218453266955 heart 0.178782479125183 kidney 0.35301979635163 liver 0.137457332888349 stomach 0.409930234435303 testicle 0.231530879782055 cont.weightedLogRatios: wLogRatio Lung -0.0328094960408073 cerebhem 0.361608531401908 cortex 0.168087087913192 heart -0.0365258325273293 kidney -0.0402322528457852 liver -0.164940896614587 stomach 0.182806613925790 testicle 0.427416188127239 varWeightedLogRatios=0.094408124915086 cont.varWeightedLogRatios=0.0446763988339237 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74310780222167 0.0616024594313463 60.7623110631358 0 *** df.mm.trans1 0.188906983805271 0.0531983771180385 3.55099147829486 0.000404796486593678 *** df.mm.trans2 0.082898345319129 0.0470005257314112 1.76377485206993 0.0781278311130154 . df.mm.exp2 0.190475828544217 0.0604577040456696 3.1505633823 0.00168639249482472 ** df.mm.exp3 -0.0634654246434425 0.0604577040456696 -1.04974917002308 0.294130769225468 df.mm.exp4 0.0724048560648856 0.0604577040456695 1.19761173878173 0.231400887198647 df.mm.exp5 0.107701637184647 0.0604577040456695 1.78143776520673 0.0751966894185682 . df.mm.exp6 0.253025656344786 0.0604577040456695 4.18516813264446 3.14505084312465e-05 *** df.mm.exp7 0.0189197750516439 0.0604577040456695 0.31294233465022 0.75440096897076 df.mm.exp8 0.201340609759035 0.0604577040456695 3.33027217849595 0.000904857991970521 *** df.mm.trans1:exp2 -0.0854606511520514 0.0558823130663674 -1.52929695394956 0.126561577032568 df.mm.trans2:exp2 -0.086951098883364 0.041271823408278 -2.10679082489784 0.0354271891314113 * df.mm.trans1:exp3 0.194463689275244 0.0558823130663673 3.47987902799037 0.000527070581655608 *** df.mm.trans2:exp3 0.0448568419580037 0.041271823408278 1.08686358521796 0.277404125668175 df.mm.trans1:exp4 -0.143546363034260 0.0558823130663673 -2.56872622405196 0.0103763516263295 * df.mm.trans2:exp4 -0.0598928824301343 0.041271823408278 -1.45118091434074 0.147097139916245 df.mm.trans1:exp5 -0.140491764692330 0.0558823130663674 -2.51406495156129 0.0121179268540110 * df.mm.trans2:exp5 -0.101645263628796 0.041271823408278 -2.46282464002811 0.0139813085552034 * df.mm.trans1:exp6 -0.286801343892119 0.0558823130663673 -5.13223823701618 3.54719434917931e-07 *** df.mm.trans2:exp6 -0.192038850891821 0.041271823408278 -4.65302560034948 3.79056773870037e-06 *** df.mm.trans1:exp7 -0.0558876874865765 0.0558823130663673 -1.00009617390395 0.317547581925847 df.mm.trans2:exp7 -0.0320591750008302 0.041271823408278 -0.776781163354173 0.437503239375119 df.mm.trans1:exp8 -0.212880878345063 0.0558823130663673 -3.80945001493119 0.000149267322530069 *** df.mm.trans2:exp8 -0.142089744178341 0.041271823408278 -3.44277844893671 0.000603795385263618 *** df.mm.trans1:probe2 0.163638981902575 0.0382600042900188 4.27702466163249 2.10819962934075e-05 *** df.mm.trans1:probe3 0.0328913442131381 0.0382600042900189 0.859679574623536 0.390207233861859 df.mm.trans1:probe4 0.0471445418618908 0.0382600042900189 1.23221475629028 0.218208412662926 df.mm.trans1:probe5 -0.0373999247338347 0.0382600042900189 -0.977520139578015 0.328588856081182 df.mm.trans1:probe6 0.104627492872128 0.0382600042900189 2.73464404444469 0.00637428630256924 ** df.mm.trans1:probe7 0.105447296938744 0.0382600042900188 2.7560712262192 0.00597482647745658 ** df.mm.trans1:probe8 0.0593617826192074 0.0382600042900188 1.55153622485854 0.121144154816147 df.mm.trans1:probe9 0.137483941374420 0.0382600042900189 3.59341155145366 0.000345076409278194 *** df.mm.trans1:probe10 0.208924344485863 0.0382600042900189 5.46064613328772 6.22465298920334e-08 *** df.mm.trans1:probe11 0.101026194139718 0.0382600042900189 2.64051706251568 0.00842927523896265 ** df.mm.trans1:probe12 0.00259101429135219 0.0382600042900188 0.0677212232312303 0.946023424628054 df.mm.trans1:probe13 0.065726379974147 0.0382600042900188 1.717887417783 0.0861800694414145 . df.mm.trans1:probe14 -0.035824688446488 0.0382600042900189 -0.936348260050608 0.349358922448689 df.mm.trans1:probe15 0.134692321325145 0.0382600042900188 3.52044710460953 0.000453644721643936 *** df.mm.trans1:probe16 -0.0257692847067808 0.0382600042900188 -0.67353062773972 0.500792220880379 df.mm.trans1:probe17 -0.135951656943236 0.0382600042900188 -3.55336230264622 0.000401218513916517 *** df.mm.trans1:probe18 -0.04393968550708 0.0382600042900188 -1.14844957083664 0.251104972611066 df.mm.trans1:probe19 -0.0136798962500774 0.0382600042900189 -0.357550828964392 0.720767929060293 df.mm.trans1:probe20 -0.101543898317204 0.0382600042900189 -2.65404827316485 0.00810131672139748 ** df.mm.trans1:probe21 -0.104333862635652 0.0382600042900189 -2.72696944424729 0.0065231159148391 ** df.mm.trans1:probe22 -0.0737980828924023 0.0382600042900189 -1.92885715153081 0.0540800995995764 . df.mm.trans2:probe2 -0.00522200707210518 0.0382600042900188 -0.136487362430001 0.891468219852004 df.mm.trans2:probe3 0.00460808548467399 0.0382600042900188 0.120441321693112 0.904161922302768 df.mm.trans2:probe4 -0.0760603331174019 0.0382600042900188 -1.98798548324377 0.0471325773311013 * df.mm.trans2:probe5 0.00493415386082216 0.0382600042900188 0.128963756078547 0.897416728636963 df.mm.trans2:probe6 -0.016723255807014 0.0382600042900188 -0.437094979923374 0.662153098729964 df.mm.trans3:probe2 0.432981395498358 0.0382600042900188 11.3168151319657 9.22147499674247e-28 *** df.mm.trans3:probe3 0.284927158728834 0.0382600042900189 7.44712824831451 2.33562852970573e-13 *** df.mm.trans3:probe4 0.0511691719138261 0.0382600042900189 1.33740632975242 0.181446537334158 df.mm.trans3:probe5 0.284126465258424 0.0382600042900188 7.42620055932785 2.71010279907581e-13 *** df.mm.trans3:probe6 0.0649114137902502 0.0382600042900188 1.69658668353009 0.0901396679965007 . df.mm.trans3:probe7 0.0160563439395475 0.0382600042900188 0.419663934636209 0.674836648839068 df.mm.trans3:probe8 0.246512921899728 0.0382600042900189 6.44309707942289 1.95483973709805e-10 *** df.mm.trans3:probe9 0.247363266898006 0.0382600042900188 6.46532250814559 1.69962001456506e-10 *** df.mm.trans3:probe10 0.414848642773253 0.0382600042900189 10.8428801949057 9.42481259351631e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88839270496676 0.0991938996064467 39.1999177408492 5.28927947819864e-193 *** df.mm.trans1 0.0540490063996464 0.0856614253356812 0.630960857677124 0.528235137557039 df.mm.trans2 0.0644781031412423 0.0756814821013378 0.85196670771994 0.394471696737247 df.mm.exp2 0.0905840109447997 0.0973505843906426 0.93049272905553 0.352379285500177 df.mm.exp3 0.0355100640496857 0.0973505843906426 0.364764775393572 0.715377414262278 df.mm.exp4 0.124894701496773 0.0973505843906426 1.28293735757767 0.199862497860752 df.mm.exp5 0.115871900869937 0.0973505843906426 1.19025377808698 0.234277811108820 df.mm.exp6 0.0456189396354069 0.0973505843906426 0.468604681943665 0.639471956923944 df.mm.exp7 0.0151387871171431 0.0973505843906426 0.155507922339686 0.876457700080863 df.mm.exp8 -0.0756730590940055 0.0973505843906426 -0.77732516520239 0.437182472251278 df.mm.trans1:exp2 -0.00744466270825104 0.0899831695560621 -0.0827339461921576 0.934082502296574 df.mm.trans2:exp2 -0.106407906085019 0.0664569750222107 -1.60115482309354 0.109712884698380 df.mm.trans1:exp3 0.0110817573154796 0.0899831695560621 0.123153667181898 0.90201442510364 df.mm.trans2:exp3 -0.0394903928296098 0.0664569750222107 -0.594224952556321 0.552519227448149 df.mm.trans1:exp4 -0.0863106426987262 0.0899831695560621 -0.959186513706345 0.337736585165641 df.mm.trans2:exp4 -0.085461123077992 0.0664569750222107 -1.28596167745267 0.198805399941164 df.mm.trans1:exp5 -0.100241352619883 0.0899831695560621 -1.11400113059398 0.265592495081906 df.mm.trans2:exp5 -0.0984067818750939 0.0664569750222107 -1.48075927082455 0.139039993795921 df.mm.trans1:exp6 -0.0141669913336535 0.0899831695560621 -0.157440456960422 0.874934990195765 df.mm.trans2:exp6 0.018745328041907 0.0664569750222107 0.282067127425558 0.777960459291188 df.mm.trans1:exp7 0.0351634944329734 0.089983169556062 0.390778571220094 0.696058493013961 df.mm.trans2:exp7 -0.0190891610427184 0.0664569750222107 -0.287240895877951 0.773997637494527 df.mm.trans1:exp8 0.090159010273195 0.089983169556062 1.00195415118183 0.316649877740353 df.mm.trans2:exp8 -0.0274412149706908 0.0664569750222107 -0.412917003241866 0.679771192217555 df.mm.trans1:probe2 0.0759664929204363 0.0616072647020842 1.23307686662911 0.217886790652227 df.mm.trans1:probe3 -0.00568891793257445 0.0616072647020842 -0.0923416736659953 0.926448261057758 df.mm.trans1:probe4 0.088405839188259 0.0616072647020842 1.43499049366605 0.151656347107231 df.mm.trans1:probe5 0.0340346223240507 0.0616072647020842 0.552444950910138 0.580788264428934 df.mm.trans1:probe6 -0.000744423469687208 0.0616072647020842 -0.0120833715518297 0.990361924450673 df.mm.trans1:probe7 0.0299519689870588 0.0616072647020842 0.486175926360281 0.62696731091291 df.mm.trans1:probe8 -0.0555411939932275 0.0616072647020842 -0.901536438304954 0.367557645758146 df.mm.trans1:probe9 0.0287469374285189 0.0616072647020842 0.466616032500894 0.640893762085464 df.mm.trans1:probe10 0.0768086405355587 0.0616072647020842 1.24674648204208 0.212832708138581 df.mm.trans1:probe11 0.00665443028349868 0.0616072647020842 0.108013727206973 0.914010204126704 df.mm.trans1:probe12 -0.0284925393091238 0.0616072647020842 -0.46248667988923 0.643850299620323 df.mm.trans1:probe13 0.0124738023750176 0.0616072647020842 0.202472913467875 0.839595313106672 df.mm.trans1:probe14 -0.00226552641190443 0.0616072647020842 -0.0367736893182953 0.970674056683671 df.mm.trans1:probe15 0.0448596616485522 0.0616072647020842 0.728155386633074 0.466718307939187 df.mm.trans1:probe16 -0.0225643312671114 0.0616072647020842 -0.366260884592528 0.714261236133428 df.mm.trans1:probe17 -0.0247225047634097 0.0616072647020842 -0.401292037277762 0.688305632325129 df.mm.trans1:probe18 0.0589880702689233 0.0616072647020842 0.957485623719433 0.338593490490099 df.mm.trans1:probe19 -0.0199023286123753 0.0616072647020842 -0.323051651596894 0.746735299232764 df.mm.trans1:probe20 -0.0207231447125622 0.0616072647020842 -0.336375017017452 0.736670817522309 df.mm.trans1:probe21 -0.00971908441902359 0.0616072647020842 -0.157758739428254 0.874684248924055 df.mm.trans1:probe22 -0.00815187308489396 0.0616072647020842 -0.132319997070381 0.894762396354351 df.mm.trans2:probe2 0.106759349255081 0.0616072647020842 1.73290195192629 0.0834745097693629 . df.mm.trans2:probe3 -0.0245047042406085 0.0616072647020842 -0.397756731435919 0.690909034151987 df.mm.trans2:probe4 0.000796795815590262 0.0616072647020842 0.0129334717170669 0.989683894545663 df.mm.trans2:probe5 0.0211953905848667 0.0616072647020842 0.344040442103082 0.730900663326402 df.mm.trans2:probe6 -0.00918259680508168 0.0616072647020842 -0.149050551903030 0.881548974260418 df.mm.trans3:probe2 -0.0136640629076513 0.0616072647020842 -0.221793046221529 0.824528079697854 df.mm.trans3:probe3 0.0515341155351545 0.0616072647020842 0.83649413400123 0.403111246145425 df.mm.trans3:probe4 0.0478519675858919 0.0616072647020842 0.776726053612197 0.437535742040239 df.mm.trans3:probe5 0.0499641904176376 0.0616072647020842 0.811011341913177 0.417585381898216 df.mm.trans3:probe6 0.0247327513070702 0.0616072647020842 0.40145835765751 0.688183244652976 df.mm.trans3:probe7 0.0294978171675306 0.0616072647020842 0.478804201260582 0.632200635158899 df.mm.trans3:probe8 0.0102704494052674 0.0616072647020842 0.166708414258164 0.867638993365807 df.mm.trans3:probe9 0.00572845341423566 0.0616072647020842 0.0929834077512918 0.925938576824685 df.mm.trans3:probe10 0.0509996903318458 0.0616072647020842 0.827819423220075 0.40800431096848