chr6.19702_chr6_116635557_116639976_-_2.R fitVsDatCorrelation=0.883174703287742 cont.fitVsDatCorrelation=0.232732840623138 fstatistic=10982.1746510702,52,692 cont.fstatistic=2544.25456327219,52,692 residuals=-0.580675472138839,-0.076112344187346,-6.94830937468055e-05,0.0755208839160675,1.38207412431966 cont.residuals=-0.52222212951326,-0.208778447761741,-0.0566428755525959,0.140875311737565,1.26237810973529 predictedValues: Include Exclude Both chr6.19702_chr6_116635557_116639976_-_2.R.tl.Lung 57.8013204879219 60.4406329017073 65.1821006974409 chr6.19702_chr6_116635557_116639976_-_2.R.tl.cerebhem 55.4714316452773 58.2685304092392 75.5009526986054 chr6.19702_chr6_116635557_116639976_-_2.R.tl.cortex 57.8880940953391 58.1996929724169 75.2867196710969 chr6.19702_chr6_116635557_116639976_-_2.R.tl.heart 60.3120691534609 60.1898238966056 67.318612196434 chr6.19702_chr6_116635557_116639976_-_2.R.tl.kidney 57.5830947568956 63.4166630259158 71.5134286423777 chr6.19702_chr6_116635557_116639976_-_2.R.tl.liver 59.1541784172312 58.3080054234507 74.949991456267 chr6.19702_chr6_116635557_116639976_-_2.R.tl.stomach 59.7393002517764 57.1482878096941 67.2615842522238 chr6.19702_chr6_116635557_116639976_-_2.R.tl.testicle 58.1761400427018 54.9900668992728 68.9183310159775 diffExp=-2.63931241378542,-2.79709876396187,-0.311598877077842,0.122245256855329,-5.83356826902027,0.846172993780513,2.59101244208231,3.18607314342899 diffExpScore=3.14030984330715 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 60.9559462223143 58.1090977449661 56.5841739536155 cerebhem 56.3234130902828 54.360623383557 51.6211855464654 cortex 58.5439229890528 63.8001176717742 55.6900863753156 heart 59.4021636042889 62.1735014225031 53.6111454970808 kidney 57.1992517606238 58.8757436153088 55.8626986702972 liver 61.0399092764833 56.6362809297633 54.1152238078027 stomach 59.0354525642043 57.5031780210571 52.2379853809169 testicle 60.271628144387 58.8713616608562 55.7025764760384 cont.diffExp=2.84684847734817,1.96278970672581,-5.25619468272146,-2.77133781821418,-1.67649185468502,4.40362834672003,1.53227454314720,1.40026648353074 cont.diffExpScore=6.34840448443701 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=-0.071582384385513 cont.tran.correlation=0.142892859440154 tran.covariance=-7.51996292641393e-05 cont.tran.covariance=0.000245129690841886 tran.mean=58.5679582618067 cont.tran.mean=58.943849506339 weightedLogRatios: wLogRatio Lung -0.182142329649782 cerebhem -0.198766712879346 cortex -0.0218018993265473 heart 0.00831562918099445 kidney -0.395782849472333 liver 0.0586822778719162 stomach 0.180369850904295 testicle 0.227279932358468 cont.weightedLogRatios: wLogRatio Lung 0.195441319416600 cerebhem 0.142355440608078 cortex -0.353606347705738 heart -0.187277875772637 kidney -0.117315218859302 liver 0.305060092920139 stomach 0.106900834192799 testicle 0.0960745411491898 varWeightedLogRatios=0.0434851563143147 cont.varWeightedLogRatios=0.0488078145112769 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11528192035327 0.0783325770080048 52.5360211235324 1.07872594120519e-243 *** df.mm.trans1 -0.0309406844547369 0.070354109432523 -0.439785034652627 0.660230121154778 df.mm.trans2 0.190130319201923 0.0647001585874667 2.938637606968 0.0034063026919624 ** df.mm.exp2 -0.224703299221019 0.0886231942242841 -2.53549086317458 0.0114479775227922 * df.mm.exp3 -0.180400267729569 0.0886231942242841 -2.03558751530687 0.0421722525186164 * df.mm.exp4 0.006110453097147 0.0886231942242841 0.0689486894557524 0.945050380252053 df.mm.exp5 -0.0484178918484187 0.0886231942242842 -0.546334312052492 0.58501225157847 df.mm.exp6 -0.152422811452882 0.0886231942242842 -1.71989751426852 0.0858981840948604 . df.mm.exp7 -0.0544380585748043 0.0886231942242841 -0.614264234676924 0.539242625575965 df.mm.exp8 -0.143782650335024 0.0886231942242841 -1.62240428810481 0.105172277973710 df.mm.trans1:exp2 0.183559821087346 0.0848502485262846 2.16333863807699 0.0308567909990796 * df.mm.trans2:exp2 0.188103850771394 0.0738526618535701 2.54701517928159 0.0110802149380289 * df.mm.trans1:exp3 0.181900381184240 0.0848502485262846 2.14378136002621 0.0323982638744669 * df.mm.trans2:exp3 0.142618738145035 0.0738526618535702 1.93112522373005 0.0538756829204148 . df.mm.trans1:exp4 0.0364101611778141 0.0848502485262846 0.429110837153707 0.667976123699324 df.mm.trans2:exp4 -0.0102687622533002 0.0738526618535701 -0.139043901676291 0.889455911709725 df.mm.trans1:exp5 0.0446353014394649 0.0848502485262846 0.526047975282452 0.599023415745808 df.mm.trans2:exp5 0.0964829335921172 0.0738526618535701 1.30642459148482 0.191842244110672 df.mm.trans1:exp6 0.175558419188335 0.0848502485262846 2.06903836155473 0.0389132270100224 * df.mm.trans2:exp6 0.116500600758494 0.0738526618535702 1.57747328037387 0.115143665912422 df.mm.trans1:exp7 0.0874165368372866 0.0848502485262846 1.03024491213137 0.303254996993154 df.mm.trans2:exp7 -0.00157412013856410 0.0738526618535701 -0.0213143317932826 0.983001055524026 df.mm.trans1:exp8 0.150246334882632 0.0848502485262846 1.77072356878353 0.0770468534626388 . df.mm.trans2:exp8 0.0492736085056308 0.0738526618535701 0.667187983059121 0.504874476982251 df.mm.trans1:probe2 -0.0534034806444751 0.0424251242631423 -1.25877016442520 0.208537841907746 df.mm.trans1:probe3 0.240453676610465 0.0424251242631423 5.66771885260838 2.12465914308426e-08 *** df.mm.trans1:probe4 -0.203403687699884 0.0424251242631423 -4.79441583808384 1.99836937664588e-06 *** df.mm.trans1:probe5 0.827358644428815 0.0424251242631423 19.5016198254863 7.78232539723095e-68 *** df.mm.trans1:probe6 -0.25588518319424 0.0424251242631423 -6.03145394712598 2.64522427788374e-09 *** df.mm.trans1:probe7 -0.140668298305615 0.0424251242631423 -3.31568382529932 0.00096191422186298 *** df.mm.trans1:probe8 -0.215975454476379 0.0424251242631423 -5.09074418113164 4.6018973293271e-07 *** df.mm.trans1:probe9 0.336747086337573 0.0424251242631423 7.9374449029046 8.33417242355068e-15 *** df.mm.trans1:probe10 0.489682509434959 0.0424251242631423 11.5422763737284 2.61429732635320e-28 *** df.mm.trans1:probe11 0.281954490937717 0.0424251242631423 6.64593199984261 6.10888422695307e-11 *** df.mm.trans1:probe12 0.0389824125972253 0.0424251242631423 0.91885205463245 0.35849311991599 df.mm.trans1:probe13 -0.0179857073010505 0.0424251242631423 -0.423940002850528 0.671741316507634 df.mm.trans1:probe14 -0.120468453265480 0.0424251242631423 -2.83955451770212 0.00465024712915897 ** df.mm.trans1:probe15 -0.265475042131827 0.0424251242631423 -6.25749592352907 6.85617344919294e-10 *** df.mm.trans1:probe16 -0.234225431207233 0.0424251242631423 -5.52091326249152 4.77311625329882e-08 *** df.mm.trans1:probe17 -0.204150090057759 0.0424251242631423 -4.81200924224795 1.83541851565595e-06 *** df.mm.trans1:probe18 -0.147085761134307 0.0424251242631423 -3.46694944773777 0.000558949338208437 *** df.mm.trans1:probe19 -0.157390221110637 0.0424251242631423 -3.70983524136364 0.000224029990917028 *** df.mm.trans1:probe20 -0.269741165173143 0.0424251242631423 -6.35805244788609 3.70984252918093e-10 *** df.mm.trans1:probe21 -0.306437840442833 0.0424251242631423 -7.22302752826718 1.34469034711804e-12 *** df.mm.trans1:probe22 -0.306123370935931 0.0424251242631423 -7.21561518682178 1.41459977280396e-12 *** df.mm.trans2:probe2 -0.306995753692917 0.0424251242631423 -7.23617806724083 1.22892472766225e-12 *** df.mm.trans2:probe3 -0.182291522421457 0.0424251242631423 -4.29678228614703 1.98126675483650e-05 *** df.mm.trans2:probe4 -0.41647943605304 0.0424251242631423 -9.8168112241657 2.19725552561171e-21 *** df.mm.trans2:probe5 -0.445467619508997 0.0424251242631423 -10.5000899171439 4.94355540002449e-24 *** df.mm.trans2:probe6 -0.482521344040426 0.0424251242631423 -11.3734809837582 1.34473440167726e-27 *** df.mm.trans3:probe2 -0.0828128333221771 0.0424251242631423 -1.95197621127824 0.0513439911073186 . df.mm.trans3:probe3 -0.209047125792526 0.0424251242631423 -4.9274369709776 1.04346755588686e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2194302865558 0.162418857070270 25.9786970716724 2.22299468089253e-104 *** df.mm.trans1 -0.0518751828723863 0.145875885623671 -0.355611776755299 0.722239695229546 df.mm.trans2 -0.144664412411428 0.134152688592993 -1.07835641557901 0.281250470783156 df.mm.exp2 -0.0539261076653873 0.18375596036313 -0.293465896609944 0.7692540514335 df.mm.exp3 0.0689858634721639 0.183755960363130 0.375421092931283 0.707462363986477 df.mm.exp4 0.0957582365082507 0.18375596036313 0.521116356275016 0.602452433794785 df.mm.exp5 -0.0376711863100687 0.183755960363130 -0.205006608959103 0.837627247131926 df.mm.exp6 0.0203178233396485 0.183755960363130 0.110569601658076 0.911989713230053 df.mm.exp7 0.0374241355301199 0.18375596036313 0.203662158528975 0.838677390633948 df.mm.exp8 0.0174455163765243 0.183755960363130 0.0949385061689933 0.924391154947917 df.mm.trans1:exp2 -0.0251149907654113 0.17593293766346 -0.142753205277874 0.886526649573126 df.mm.trans2:exp2 -0.0127560747210787 0.153129966969275 -0.0833022756652079 0.933635281505082 df.mm.trans1:exp3 -0.109359980822690 0.17593293766346 -0.621600379525769 0.534409389985453 df.mm.trans2:exp3 0.0244469319697143 0.153129966969275 0.15964825470523 0.873204750540264 df.mm.trans1:exp4 -0.121578996677824 0.17593293766346 -0.691053069950957 0.489763936445971 df.mm.trans2:exp4 -0.0281515889749271 0.153129966969275 -0.183841148353252 0.854191930354355 df.mm.trans1:exp5 -0.0259394066871656 0.17593293766346 -0.147439172173574 0.882828334792194 df.mm.trans2:exp5 0.0507781296831876 0.153129966969275 0.331601519207380 0.740290572467922 df.mm.trans1:exp6 -0.0189413328501481 0.17593293766346 -0.107662232562618 0.914294835949415 df.mm.trans2:exp6 -0.0459902769531287 0.153129966969275 -0.300334923747201 0.764011898602527 df.mm.trans1:exp7 -0.0694373913577952 0.17593293766346 -0.394681020393243 0.693199923945883 df.mm.trans2:exp7 -0.0479061586150545 0.153129966969275 -0.31284639814927 0.754491621088959 df.mm.trans1:exp8 -0.0287354452599089 0.17593293766346 -0.163331810640692 0.87030487655008 df.mm.trans2:exp8 -0.00441300293430281 0.153129966969275 -0.0288186761980316 0.97701751462904 df.mm.trans1:probe2 -0.117586024241746 0.08796646883173 -1.33671415714862 0.181755255903918 df.mm.trans1:probe3 -0.0591358272523556 0.08796646883173 -0.672254190008193 0.501646277019526 df.mm.trans1:probe4 0.0949064220555904 0.08796646883173 1.07889316595322 0.281011267600259 df.mm.trans1:probe5 -0.0649470265836786 0.08796646883173 -0.738315717866486 0.460572929710423 df.mm.trans1:probe6 -0.0728079568391544 0.08796646883173 -0.827678521215031 0.408137921913133 df.mm.trans1:probe7 -0.0842412717881817 0.08796646883173 -0.957652079331794 0.338572676392748 df.mm.trans1:probe8 -0.174997856584461 0.08796646883173 -1.98937002824578 0.047053618108991 * df.mm.trans1:probe9 -0.0896352061243453 0.08796646883173 -1.01897015209065 0.308573244101792 df.mm.trans1:probe10 -0.0277088063494134 0.08796646883173 -0.314992823031435 0.752862060560652 df.mm.trans1:probe11 -0.0590412312195166 0.08796646883173 -0.671178825336912 0.502330585313275 df.mm.trans1:probe12 -0.118132691151357 0.08796646883173 -1.34292864906663 0.179735374630382 df.mm.trans1:probe13 -0.084991157473839 0.08796646883173 -0.966176755786544 0.334293273576311 df.mm.trans1:probe14 -0.0119109131842562 0.08796646883173 -0.13540287955676 0.892332723519699 df.mm.trans1:probe15 -0.0940443979376536 0.08796646883173 -1.06909370339225 0.28540022626797 df.mm.trans1:probe16 -0.0436160163708572 0.08796646883173 -0.495825477026817 0.620174977433557 df.mm.trans1:probe17 -0.134496693139427 0.08796646883173 -1.52895409950699 0.126732780779916 df.mm.trans1:probe18 -0.0432993380639698 0.08796646883173 -0.492225488177735 0.622716065412859 df.mm.trans1:probe19 0.00248113447370375 0.08796646883173 0.0282054572231367 0.977506416792686 df.mm.trans1:probe20 -0.069964462170774 0.08796646883173 -0.795353764905673 0.426680465280992 df.mm.trans1:probe21 -0.210384868739203 0.08796646883173 -2.39164844892939 0.0170389267022572 * df.mm.trans1:probe22 0.0284618513358803 0.08796646883173 0.323553414316591 0.746373917681848 df.mm.trans2:probe2 0.0534170685764341 0.08796646883173 0.607243524559511 0.543888506984175 df.mm.trans2:probe3 -0.0661018899543551 0.08796646883173 -0.751444167672577 0.45264086663586 df.mm.trans2:probe4 -0.0219922020318546 0.08796646883173 -0.250006648259613 0.802656406816209 df.mm.trans2:probe5 -0.0324625842039436 0.08796646883173 -0.369033617412117 0.712215550628739 df.mm.trans2:probe6 -0.0448531060545653 0.08796646883173 -0.509888672925638 0.610292035952016 df.mm.trans3:probe2 0.0559749017208198 0.08796646883173 0.636320889814203 0.524777699723222 df.mm.trans3:probe3 -0.0174589735864540 0.08796646883173 -0.198473052497436 0.84273325984994