chr9.24533_chr9_58289112_58289437_-_0.R fitVsDatCorrelation=0.909377858811753 cont.fitVsDatCorrelation=0.292030343896438 fstatistic=7094.65436970232,36,324 cont.fstatistic=1334.75677420597,36,324 residuals=-0.517683440145012,-0.0935850517728046,0.00189834501583355,0.0904734111870514,0.632394287927056 cont.residuals=-0.722574311197584,-0.302322922526229,-0.0248459923938715,0.265009643932109,0.91390653735417 predictedValues: Include Exclude Both chr9.24533_chr9_58289112_58289437_-_0.R.tl.Lung 51.6994325890758 112.548348276309 107.017991826478 chr9.24533_chr9_58289112_58289437_-_0.R.tl.cerebhem 57.8255704136463 93.4563050205222 86.2576767667533 chr9.24533_chr9_58289112_58289437_-_0.R.tl.cortex 51.1951817961807 117.020065680796 107.706657307444 chr9.24533_chr9_58289112_58289437_-_0.R.tl.heart 47.7994209451561 100.306971065431 114.607179399685 chr9.24533_chr9_58289112_58289437_-_0.R.tl.kidney 53.3734150773879 79.8782295961002 78.487128669445 chr9.24533_chr9_58289112_58289437_-_0.R.tl.liver 51.0175734738191 84.3223148123414 89.9236303532794 chr9.24533_chr9_58289112_58289437_-_0.R.tl.stomach 56.6689736372377 102.571715523392 100.741116549565 chr9.24533_chr9_58289112_58289437_-_0.R.tl.testicle 53.5414054149443 104.271551541500 110.334470997510 diffExp=-60.8489156872335,-35.6307346068759,-65.8248838846151,-52.5075501202749,-26.5048145187123,-33.3047413385223,-45.9027418861539,-50.7301461265556 diffExpScore=0.99731366598838 diffExp1.5=-1,-1,-1,-1,0,-1,-1,-1 diffExp1.5Score=0.875 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 73.4741244572345 68.701520215837 83.5873888365991 cerebhem 78.8764004227451 81.0431971155166 71.3557565907441 cortex 83.9785979774524 78.3392712571097 78.3771399577275 heart 75.4085211542684 73.7229944999283 76.4902732548834 kidney 84.5605457001032 78.8209228151676 66.2580646552074 liver 89.6066430081015 79.7580966841152 60.4926181267323 stomach 90.9758333481252 73.2093454238244 64.0870539225342 testicle 70.0981029837279 71.0852230096198 86.4750916636064 cont.diffExp=4.77260424139753,-2.1667966927715,5.63932672034271,1.68552665434014,5.73962288493563,9.84854632398623,17.7664879243008,-0.987120025891898 cont.diffExpScore=1.12258785997447 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=0,0,0,0,0,0,1,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.156291619946472 cont.tran.correlation=0.547988492990259 tran.covariance=-0.00114034841595947 cont.tran.covariance=0.00326575235670297 tran.mean=76.09352967899 cont.tran.mean=78.2287087545548 weightedLogRatios: wLogRatio Lung -3.37189772015752 cerebhem -2.06305245067017 cortex -3.59531451335402 heart -3.14101900238899 kidney -1.68489631474118 liver -2.10206390527001 stomach -2.57145287069376 testicle -2.87528570074929 cont.weightedLogRatios: wLogRatio Lung 0.286335134629578 cerebhem -0.118737840203203 cortex 0.305565427982422 heart 0.0974663965140984 kidney 0.309436279351758 liver 0.516630114943634 stomach 0.956417136251147 testicle -0.0595272486909793 varWeightedLogRatios=0.469099013221867 cont.varWeightedLogRatios=0.117474911844467 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04791549894589 0.09224176808092 43.8837587696153 2.19454527754245e-138 *** df.mm.trans1 -0.122772527476385 0.0781278139941086 -1.57143175010174 0.117058404377429 df.mm.trans2 0.731833553415811 0.0781278139941086 9.36713208782466 1.30637032755439e-18 *** df.mm.exp2 0.141753285759090 0.108802453800298 1.30285008111373 0.193551150170434 df.mm.exp3 0.0227467180912502 0.108802453800298 0.209064385009192 0.83452935703031 df.mm.exp4 -0.262094459707933 0.108802453800298 -2.40890210241946 0.0165582454840213 * df.mm.exp5 -0.000951253263780848 0.108802453800298 -0.00874293943339582 0.993029613206512 df.mm.exp6 -0.127976799992439 0.108802453800298 -1.17623082497142 0.240366142096520 df.mm.exp7 0.059402322706247 0.108802453800298 0.545964917439063 0.585465766299477 df.mm.exp8 -0.0718952714528171 0.108802453800298 -0.660787224383541 0.509218330709668 df.mm.trans1:exp2 -0.0297690195966045 0.0942256889851404 -0.315933159175935 0.752256662480935 df.mm.trans2:exp2 -0.327642176458111 0.0942256889851404 -3.47720648144882 0.000575817642267138 *** df.mm.trans1:exp3 -0.0325481024052631 0.0942256889851404 -0.34542705663204 0.729997495527461 df.mm.trans2:exp3 0.0162158117679383 0.0942256889851404 0.172095443849665 0.863469937352925 df.mm.trans1:exp4 0.183661178625969 0.0942256889851404 1.9491624906551 0.0521386800647846 . df.mm.trans2:exp4 0.146946762629405 0.0942256889851404 1.55951911004417 0.119849773661495 df.mm.trans1:exp5 0.0328172238833047 0.0942256889851404 0.348283193646692 0.72785369958691 df.mm.trans2:exp5 -0.341928293507024 0.0942256889851404 -3.62882242825464 0.000330704480225478 *** df.mm.trans1:exp6 0.114700144893524 0.0942256889851404 1.21729165505612 0.224379132116312 df.mm.trans2:exp6 -0.160759554662417 0.0942256889851404 -1.70611174504406 0.0889455525649938 . df.mm.trans1:exp7 0.0323777297046711 0.0942256889851404 0.343618922327829 0.731355765960042 df.mm.trans2:exp7 -0.15222299691456 0.0942256889851404 -1.61551481930332 0.107172555230076 df.mm.trans1:exp8 0.106903752624763 0.0942256889851404 1.13454997014266 0.257402599324275 df.mm.trans2:exp8 -0.00448905159810881 0.0942256889851404 -0.0476414834049846 0.962031322969923 df.mm.trans1:probe2 -0.0657755897671833 0.0471128444925702 -1.39612860305126 0.163631596589678 df.mm.trans1:probe3 0.0435519180912213 0.0471128444925702 0.92441707904284 0.355957163079488 df.mm.trans1:probe4 0.246245911635967 0.0471128444925702 5.22672562627375 3.09792456262014e-07 *** df.mm.trans1:probe5 0.0179822456461605 0.0471128444925702 0.381684566912456 0.702945544780425 df.mm.trans1:probe6 -0.0592699713403621 0.0471128444925702 -1.25804272653733 0.209282180208325 df.mm.trans2:probe2 -0.0919402664249979 0.0471128444925702 -1.95149045690708 0.0518601452745651 . df.mm.trans2:probe3 -0.203074670518172 0.0471128444925702 -4.3103886573904 2.16477892811049e-05 *** df.mm.trans2:probe4 -0.0623664607767651 0.0471128444925702 -1.32376767840032 0.186513434118324 df.mm.trans2:probe5 0.00125634019531471 0.0471128444925702 0.0266666173279527 0.978742055989677 df.mm.trans2:probe6 -0.151170387718581 0.0471128444925702 -3.20868734093143 0.00146677805737053 ** df.mm.trans3:probe2 0.325716843324662 0.0471128444925702 6.91354654622961 2.52286416424182e-11 *** df.mm.trans3:probe3 -0.277655513267688 0.0471128444925702 -5.89341433866247 9.48789738925708e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07243193950158 0.212084142129131 19.2019634217725 2.09174407038687e-55 *** df.mm.trans1 0.266020367693432 0.179633052922716 1.48090990697509 0.139602368770103 df.mm.trans2 0.160363017595655 0.179633052922716 0.892725559057595 0.372666719602851 df.mm.exp2 0.394374398354644 0.250160806279618 1.57648355959418 0.115890259691420 df.mm.exp3 0.329266737749415 0.250160806279619 1.31622032502316 0.189030505771904 df.mm.exp4 0.185259481552841 0.250160806279618 0.74056157840236 0.459495641514563 df.mm.exp5 0.510277120558076 0.250160806279618 2.03979643392943 0.0421813749822959 * df.mm.exp6 0.671094411254018 0.250160806279618 2.68265209580392 0.00767875450807824 ** df.mm.exp7 0.542862643678712 0.250160806279618 2.17005474099698 0.0307289276476773 * df.mm.exp8 -0.0468931672820572 0.250160806279618 -0.187452095232065 0.851423513568534 df.mm.trans1:exp2 -0.323425618721779 0.216645613269347 -1.49287868718429 0.13644205038459 df.mm.trans2:exp2 -0.229163415419628 0.216645613269347 -1.0577800859264 0.290943716680695 df.mm.trans1:exp3 -0.195638053386658 0.216645613269347 -0.903032609035238 0.367179459811336 df.mm.trans2:exp3 -0.197989039171195 0.216645613269347 -0.91388436711637 0.361457178384461 df.mm.trans1:exp4 -0.159272496302684 0.216645613269347 -0.735175265721474 0.462764567912991 df.mm.trans2:exp4 -0.114716056971131 0.216645613269347 -0.529510176735075 0.596814206014905 df.mm.trans1:exp5 -0.369742621644214 0.216645613269347 -1.70667024392747 0.0888415064493723 . df.mm.trans2:exp5 -0.372869968286075 0.216645613269347 -1.72110555417755 0.0861862789678482 . df.mm.trans1:exp6 -0.472598249340386 0.216645613269347 -2.18143465823526 0.0298697274661473 * df.mm.trans2:exp6 -0.521867476229991 0.216645613269347 -2.40885318818421 0.0165604246865893 * df.mm.trans1:exp7 -0.329202036068586 0.216645613269347 -1.51954166576778 0.12960141801516 df.mm.trans2:exp7 -0.479310888443312 0.216645613269347 -2.21241908022113 0.0276347467049407 * df.mm.trans1:exp8 -0.000144396651021037 0.216645613269347 -0.000666510846178612 0.999468611514874 df.mm.trans2:exp8 0.0810013212823681 0.216645613269347 0.373888582648855 0.708731756487584 df.mm.trans1:probe2 -0.0734592050616277 0.108322806634674 -0.678150865397848 0.498160068644885 df.mm.trans1:probe3 -0.0392897941322812 0.108322806634674 -0.362710267144285 0.717057821852898 df.mm.trans1:probe4 -0.0943260081598005 0.108322806634674 -0.870786227667841 0.384515610000989 df.mm.trans1:probe5 -0.0404520109054518 0.108322806634674 -0.373439464524577 0.709065612628895 df.mm.trans1:probe6 -0.126144082186499 0.108322806634674 -1.16452006835392 0.245070041986487 df.mm.trans2:probe2 0.0678288734108084 0.108322806634674 0.626173522622674 0.531642012297711 df.mm.trans2:probe3 -0.137222286435322 0.108322806634674 -1.26679035282121 0.206140136128503 df.mm.trans2:probe4 -0.0296369655797642 0.108322806634674 -0.273598575410966 0.78456745663204 df.mm.trans2:probe5 0.0973988168847748 0.108322806634674 0.899153372320376 0.369238721358817 df.mm.trans2:probe6 -0.0255811063911985 0.108322806634674 -0.236156236954537 0.813460653941252 df.mm.trans3:probe2 -0.0870004925343233 0.108322806634674 -0.803159512176773 0.422471336618879 df.mm.trans3:probe3 -0.13176750941385 0.108322806634674 -1.21643367179587 0.224705182225247