chr9.24431_chr9_58065225_58066778_+_1.R fitVsDatCorrelation=0.96766477410091 cont.fitVsDatCorrelation=0.298368417088400 fstatistic=6694.1277760603,41,439 cont.fstatistic=457.575301554857,41,439 residuals=-0.917496187180253,-0.109474801478428,0.00213717026207007,0.126099366937313,0.673867214988864 cont.residuals=-1.37689735018771,-0.580740133072417,-0.152816283433145,0.494344758241515,1.99624446394109 predictedValues: Include Exclude Both chr9.24431_chr9_58065225_58066778_+_1.R.tl.Lung 113.880370572438 46.656482347825 67.8995528277861 chr9.24431_chr9_58065225_58066778_+_1.R.tl.cerebhem 159.813072912118 51.1343197591826 80.58888378261 chr9.24431_chr9_58065225_58066778_+_1.R.tl.cortex 290.281849097329 48.966338733271 110.314629926232 chr9.24431_chr9_58065225_58066778_+_1.R.tl.heart 655.298723740942 55.6321397907194 268.155473118157 chr9.24431_chr9_58065225_58066778_+_1.R.tl.kidney 308.004542562808 47.2857429118228 135.183782971258 chr9.24431_chr9_58065225_58066778_+_1.R.tl.liver 165.885799242567 47.9447709567228 83.716250026611 chr9.24431_chr9_58065225_58066778_+_1.R.tl.stomach 282.797094643738 47.9141944464406 111.645800445614 chr9.24431_chr9_58065225_58066778_+_1.R.tl.testicle 183.291230688088 46.5694689313663 87.0874225230473 diffExp=67.2238882246128,108.678753152935,241.315510364058,599.666583950223,260.718799650985,117.941028285844,234.882900197297,136.721761756721 diffExpScore=0.99943443687584 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 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 122.382650036159 89.9113596889743 119.572058149396 cerebhem 125.504143105759 120.552747844635 94.4068421558323 cortex 127.226064736333 107.102766801339 121.075989945286 heart 121.866095352138 100.97216098114 142.651974819712 kidney 139.356390425525 89.7607959110146 108.399584673584 liver 114.910352064528 161.890240063143 101.146679097100 stomach 112.089572908033 128.064969160833 112.415456553100 testicle 89.9142311987823 101.439499363939 93.2988889551422 cont.diffExp=32.471290347185,4.95139526112423,20.1232979349943,20.8939343709980,49.5955945145107,-46.9798879986152,-15.9753962527999,-11.5252681651572 cont.diffExpScore=3.71214761774087 cont.diffExp1.5=0,0,0,0,1,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,0,1,-1,0,0 cont.diffExp1.4Score=2 cont.diffExp1.3=1,0,0,0,1,-1,0,0 cont.diffExp1.3Score=1.5 cont.diffExp1.2=1,0,0,1,1,-1,0,0 cont.diffExp1.2Score=1.33333333333333 tran.correlation=0.800096959631997 cont.tran.correlation=-0.227286770817228 tran.covariance=0.0222172265513577 cont.tran.covariance=-0.00520465711819269 tran.mean=159.459758833586 cont.tran.mean=115.809002477642 weightedLogRatios: wLogRatio Lung 3.82721418424476 cerebhem 5.13279096735217 cortex 8.50882476530305 heart 12.9529835847339 kidney 8.981932994871 liver 5.57404926799057 stomach 8.44531264804067 testicle 6.20122833159261 cont.weightedLogRatios: wLogRatio Lung 1.43464810922337 cerebhem 0.193698180087616 cortex 0.819540089442312 heart 0.8856375416479 kidney 2.07498392267255 liver -1.68487988087887 stomach -0.63767282927664 testicle -0.549863464480258 varWeightedLogRatios=8.33056953535999 cont.varWeightedLogRatios=1.51444554798778 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.627634864802 0.101031960642368 45.8036727722513 2.48323316139699e-169 *** df.mm.trans1 0.114046429450417 0.0815176242281459 1.39904015273105 0.162507063667307 df.mm.trans2 -0.837274467069748 0.0815176242281458 -10.2710852407382 2.52990084169684e-22 *** df.mm.exp2 0.259169065213504 0.109804965735880 2.36026725637251 0.0186982757000256 * df.mm.exp3 0.498717944407916 0.109804965735880 4.54185237494185 7.21424200595458e-06 *** df.mm.exp4 0.552354408216905 0.109804965735880 5.03032266815248 7.15150285237677e-07 *** df.mm.exp5 0.319757212786021 0.109804965735880 2.91204692468235 0.00377372160617949 ** df.mm.exp6 0.193985291503337 0.109804965735880 1.76663496230162 0.0779842317858423 . df.mm.exp7 0.438879149544817 0.109804965735880 3.99689710391128 7.52684696866741e-05 *** df.mm.exp8 0.225178052556852 0.109804965735880 2.0507091919548 0.0408884368233908 * df.mm.trans1:exp2 0.0796872562679782 0.087594060353742 0.909733558944149 0.363462235280269 df.mm.trans2:exp2 -0.167525048606482 0.087594060353742 -1.91251607620363 0.0564604760723692 . df.mm.trans1:exp3 0.43698588356374 0.087594060353742 4.98876158724696 8.7719364615427e-07 *** df.mm.trans2:exp3 -0.450396721713364 0.087594060353742 -5.14186372791113 4.10542062431803e-07 *** df.mm.trans1:exp4 1.19758827376628 0.087594060353742 13.6720260361251 1.05932822057605e-35 *** df.mm.trans2:exp4 -0.37640519489982 0.087594060353742 -4.29715432050685 2.13241315396223e-05 *** df.mm.trans1:exp5 0.675208802194049 0.087594060353742 7.70838569952425 8.5942708605933e-14 *** df.mm.trans2:exp5 -0.306360255799108 0.087594060353742 -3.49750033919989 0.00051751243677617 *** df.mm.trans1:exp6 0.182165787267266 0.087594060353742 2.0796591290734 0.0381365544067331 * df.mm.trans2:exp6 -0.166747422838564 0.087594060353742 -1.90363846778158 0.0576109546216905 . df.mm.trans1:exp7 0.470701994422545 0.087594060353742 5.37367479623219 1.25479200406625e-07 *** df.mm.trans2:exp7 -0.412279228748364 0.087594060353742 -4.70670302396539 3.38008472280792e-06 *** df.mm.trans1:exp8 0.250749743370236 0.087594060353742 2.86263409137106 0.00440241275315067 ** df.mm.trans2:exp8 -0.227044774012249 0.0875940603537419 -2.59201106896228 0.00985981949220418 ** df.mm.trans1:probe2 -0.0681969912898907 0.0573437731427933 -1.18926585315674 0.234977912737358 df.mm.trans1:probe3 -0.0354371784674613 0.0573437731427933 -0.61797779471571 0.536910428803276 df.mm.trans1:probe4 0.124562685158078 0.0573437731427933 2.17220943672299 0.0303743601262070 * df.mm.trans1:probe5 0.00628814431076311 0.0573437731427933 0.109656968248407 0.912731536855296 df.mm.trans1:probe6 -0.118675548731229 0.0573437731427933 -2.06954551866881 0.0390794028465135 * df.mm.trans2:probe2 0.0746177799062105 0.0573437731427933 1.30123596367478 0.193860203543162 df.mm.trans2:probe3 0.225418580487907 0.0573437731427933 3.93100363184309 9.82623867236472e-05 *** df.mm.trans2:probe4 0.151376783517653 0.0573437731427933 2.6398120531885 0.00859061407215853 ** df.mm.trans2:probe5 0.132355844058748 0.0573437731427933 2.30811188041577 0.0214569242367990 * df.mm.trans2:probe6 0.150551689866882 0.0573437731427933 2.62542350486756 0.00895630065880882 ** df.mm.trans3:probe2 0.105369375538371 0.0573437731427933 1.83750335500225 0.0668112830268831 . df.mm.trans3:probe3 0.697640987589436 0.0573437731427933 12.1659414676502 1.51362243722469e-29 *** df.mm.trans3:probe4 0.0204146479526710 0.0573437731427933 0.356004616261227 0.722008123950398 df.mm.trans3:probe5 0.421336854392954 0.0573437731427933 7.34756070800873 9.92452694736578e-13 *** df.mm.trans3:probe6 0.263675670024977 0.0573437731427933 4.59815696759945 5.58248565411095e-06 *** df.mm.trans3:probe7 1.04461844227311 0.0573437731427933 18.2167720228642 1.23924864341098e-55 *** df.mm.trans3:probe8 -0.044075004864254 0.0573437731427933 -0.76861012885395 0.442538069126437 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.66972025232933 0.382295171077586 12.2149600769653 9.65720388890465e-30 *** df.mm.trans1 0.224517042180647 0.308454808775321 0.727876615287865 0.467077153665624 df.mm.trans2 -0.177888307393343 0.308454808775321 -0.576707842875348 0.564432418225802 df.mm.exp2 0.554754900518895 0.415491374157925 1.33517790024694 0.182510051874677 df.mm.exp3 0.201278269829643 0.415491374157925 0.484434292378689 0.628319284038585 df.mm.exp4 -0.064697931419481 0.415491374157925 -0.155714258931618 0.876329831605349 df.mm.exp5 0.226300945038844 0.415491374157925 0.544658587672218 0.586264909988752 df.mm.exp6 0.692441402703251 0.415491374157924 1.66656023631446 0.0963151435104074 . df.mm.exp7 0.32757685674828 0.415491374157925 0.788408321140672 0.430883430210245 df.mm.exp8 0.0604528672636752 0.415491374157925 0.145497285921266 0.884385038331612 df.mm.trans1:exp2 -0.529568741657501 0.331447455591309 -1.59774568404135 0.110819081648362 df.mm.trans2:exp2 -0.261491794580155 0.331447455591309 -0.78893891073518 0.430573565077953 df.mm.trans1:exp3 -0.162465340426368 0.331447455591310 -0.490169218938569 0.62425903916316 df.mm.trans2:exp3 -0.0263137515573194 0.331447455591309 -0.079390416530352 0.936758266965134 df.mm.trans1:exp4 0.0604681823304503 0.331447455591310 0.182436706966339 0.855324236799604 df.mm.trans2:exp4 0.180718483756269 0.331447455591309 0.54524022045625 0.585865213448947 df.mm.trans1:exp5 -0.0964189453102704 0.331447455591310 -0.290902656465583 0.771263153004991 df.mm.trans2:exp5 -0.227976928862831 0.331447455591309 -0.687822232504742 0.491927984333548 df.mm.trans1:exp6 -0.755441737570372 0.331447455591309 -2.27922020467059 0.0231331872605505 * df.mm.trans2:exp6 -0.104347120233173 0.331447455591309 -0.314822510998057 0.753045996883086 df.mm.trans1:exp7 -0.415431158942275 0.331447455591310 -1.25338466756710 0.210732863037106 df.mm.trans2:exp7 0.0261365573090878 0.331447455591310 0.078855809173311 0.93718324368202 df.mm.trans1:exp8 -0.36874924995454 0.331447455591310 -1.11254210504251 0.266513983115935 df.mm.trans2:exp8 0.0601853954602859 0.331447455591309 0.181583519333144 0.855993393115663 df.mm.trans1:probe2 -0.298774523396797 0.21698329344967 -1.37694713102923 0.169230334195998 df.mm.trans1:probe3 -0.252853514301209 0.21698329344967 -1.16531328417623 0.244524982708357 df.mm.trans1:probe4 -0.364937875728746 0.21698329344967 -1.68187084787426 0.0933050323551415 . df.mm.trans1:probe5 -0.11247816593751 0.21698329344967 -0.518372470752453 0.60445958282127 df.mm.trans1:probe6 -0.190141476329451 0.21698329344967 -0.876295466376794 0.381348667905475 df.mm.trans2:probe2 -0.0214003400865447 0.21698329344967 -0.0986266718801952 0.921479709201247 df.mm.trans2:probe3 -0.0738228737619382 0.21698329344967 -0.34022376832925 0.733850753607957 df.mm.trans2:probe4 0.0524627260517197 0.21698329344967 0.241782329033956 0.809061731200286 df.mm.trans2:probe5 0.0729798335873045 0.21698329344967 0.336338491443501 0.73677633416642 df.mm.trans2:probe6 0.0676735223510086 0.21698329344967 0.311883561518093 0.755277085533028 df.mm.trans3:probe2 0.157926275079427 0.21698329344967 0.72782688735462 0.467107566922009 df.mm.trans3:probe3 -0.135720467318852 0.21698329344967 -0.625488097083992 0.531975775071303 df.mm.trans3:probe4 -0.229947821864863 0.21698329344967 -1.05974897057317 0.289841531072775 df.mm.trans3:probe5 -0.0411766505473445 0.21698329344967 -0.189768760040024 0.849578048687605 df.mm.trans3:probe6 0.257853691563499 0.21698329344967 1.18835734984044 0.235335124632615 df.mm.trans3:probe7 -0.0403804663566126 0.21698329344967 -0.18609942597253 0.852452752639039 df.mm.trans3:probe8 0.572007036175358 0.21698329344967 2.63618008133901 0.00868163490982835 **