chr17.10431_chr17_56303816_56305244_-_0.R fitVsDatCorrelation=0.793199998118546 cont.fitVsDatCorrelation=0.348901399347639 fstatistic=4169.26607305841,37,347 cont.fstatistic=1754.98372418764,37,347 residuals=-0.420882198717195,-0.113845176158194,-0.00438992062221229,0.103477286963235,2.4698079485982 cont.residuals=-0.65086282510208,-0.213560957058393,-0.0503643140576794,0.181428076991578,2.29887746198997 predictedValues: Include Exclude Both chr17.10431_chr17_56303816_56305244_-_0.R.tl.Lung 80.2046052844668 62.6503255260824 86.9114162173327 chr17.10431_chr17_56303816_56305244_-_0.R.tl.cerebhem 67.7810201364624 61.4280899502906 75.2982328170891 chr17.10431_chr17_56303816_56305244_-_0.R.tl.cortex 74.7790660904946 59.407320033559 83.6706717551314 chr17.10431_chr17_56303816_56305244_-_0.R.tl.heart 89.4256565670005 65.2738984624645 97.8229386471284 chr17.10431_chr17_56303816_56305244_-_0.R.tl.kidney 67.401905614553 60.1738116716208 77.5507814238322 chr17.10431_chr17_56303816_56305244_-_0.R.tl.liver 68.1111144873117 58.2269908254536 84.9298476683096 chr17.10431_chr17_56303816_56305244_-_0.R.tl.stomach 96.9948075375962 66.1364216499473 115.637611136323 chr17.10431_chr17_56303816_56305244_-_0.R.tl.testicle 104.392203833433 73.8274952320955 116.470779491004 diffExp=17.5542797583843,6.35293018617183,15.3717460569356,24.1517581045359,7.22809394293214,9.88412366185813,30.8583858876489,30.5647086013372 diffExpScore=0.993005331220423 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,1,1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,1,0,0,1,1 diffExp1.3Score=0.75 diffExp1.2=1,0,1,1,0,0,1,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 72.8087363617594 72.4013525302573 71.106524879705 cerebhem 87.9418999349314 69.1746443489694 68.4943359214074 cortex 69.7276738868293 74.9078228560273 69.5948905552725 heart 80.4778797255944 97.872472803188 67.8447334738563 kidney 81.732619085247 64.1048675605275 78.5481688693541 liver 70.5333346408052 72.8306685038674 83.4621573595937 stomach 75.5120468859823 68.0830997714277 65.9779591155985 testicle 82.5873120648234 76.842978746461 67.4803915330603 cont.diffExp=0.407383831502131,18.7672555859620,-5.18014896919792,-17.3945930775937,17.6277515247196,-2.29733386306212,7.42894711455457,5.74433331836238 cont.diffExpScore=2.86733478476569 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=0,1,0,-1,1,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=0.921682446639677 cont.tran.correlation=0.0276927492381703 tran.covariance=0.0120237612068408 cont.tran.covariance=7.44885561115021e-05 tran.mean=72.263420806427 cont.tran.mean=76.0962131066686 weightedLogRatios: wLogRatio Lung 1.05253688385481 cerebhem 0.410102638414068 cortex 0.966386033767182 heart 1.36503950179315 kidney 0.471208298249976 liver 0.649546746893712 stomach 1.67848875966544 testicle 1.55022666499339 cont.weightedLogRatios: wLogRatio Lung 0.0240431876716027 cerebhem 1.04578020051154 cortex -0.306739939244221 heart -0.877799345269938 kidney 1.04023536409003 liver -0.136928460401649 stomach 0.442474829735581 testicle 0.315604905155699 varWeightedLogRatios=0.235440282617072 cont.varWeightedLogRatios=0.436580147500372 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11180928928456 0.117193888516989 35.085526568977 3.63843641846321e-116 *** df.mm.trans1 0.30248901986835 0.0976385918662602 3.09804775024493 0.00210693793532894 ** df.mm.trans2 0.0408049299643278 0.0976385918662602 0.417918050479672 0.676265525406612 df.mm.exp2 -0.0445676450821652 0.134525468423687 -0.33129522316027 0.740621480294728 df.mm.exp3 -0.0851935039916097 0.134525468423687 -0.633289034335815 0.526962068668896 df.mm.exp4 0.0315803528236019 0.134525468423687 0.234753710160963 0.814538359554133 df.mm.exp5 -0.100282857093584 0.134525468423687 -0.745456293656929 0.456500562666667 df.mm.exp6 -0.213596596350346 0.134525468423687 -1.58777812746673 0.113247260961654 df.mm.exp7 -0.0413446266787328 0.134525468423687 -0.307336797731997 0.758771396812183 df.mm.exp8 0.134985383886664 0.134525468423687 1.00341879845033 0.316358034171938 df.mm.trans1:exp2 -0.123731073875394 0.113694772007626 -1.08827408411616 0.277229401435726 df.mm.trans2:exp2 0.0248659893895925 0.113694772007626 0.218708291951407 0.827005864931594 df.mm.trans1:exp3 0.0151505489787106 0.113694772007626 0.133256338098769 0.894067901257275 df.mm.trans2:exp3 0.0320420783802344 0.113694772007626 0.281825433258139 0.77824536830933 df.mm.trans1:exp4 0.0772463386678142 0.113694772007626 0.679418563437845 0.497325640405254 df.mm.trans2:exp4 0.0094430090079115 0.113694772007626 0.0830557891199972 0.933855070115077 df.mm.trans1:exp5 -0.0736247878983288 0.113694772007625 -0.647565289047686 0.517694344648495 df.mm.trans2:exp5 0.0599512154170862 0.113694772007626 0.527299666980864 0.598322465568377 df.mm.trans1:exp6 0.0501560687959048 0.113694772007626 0.441146658815067 0.659381563060468 df.mm.trans2:exp6 0.140376726196252 0.113694772007626 1.23468057253184 0.217784831816433 df.mm.trans1:exp7 0.231421137485613 0.113694772007626 2.03545979642839 0.0425640338318480 * df.mm.trans2:exp7 0.0954953528313348 0.113694772007626 0.839927387557713 0.401527425178497 df.mm.trans1:exp8 0.128588677125068 0.113694772007626 1.13099903236047 0.258836565195248 df.mm.trans2:exp8 0.029176965179895 0.113694772007626 0.256625389757922 0.797620057040332 df.mm.trans1:probe2 -0.0738255425895329 0.0622731912989834 -1.18551082816817 0.236626733397799 df.mm.trans1:probe3 -0.0134197398705543 0.0622731912989834 -0.215497866587952 0.829505783185473 df.mm.trans1:probe4 -0.158123822985907 0.0622731912989834 -2.53919575482698 0.0115467889558569 * df.mm.trans1:probe5 0.0475224953794352 0.0622731912989834 0.763129275827092 0.445904794647567 df.mm.trans1:probe6 -0.0993271242280936 0.0622731912989834 -1.59502222635754 0.111617612938984 df.mm.trans2:probe2 -0.210982715617154 0.0622731912989834 -3.38801836257584 0.000784867029836577 *** df.mm.trans2:probe3 -0.0460638712334002 0.0622731912989834 -0.73970628889469 0.459978361856911 df.mm.trans2:probe4 -0.0448674261221462 0.0622731912989834 -0.720493444871496 0.47170630355089 df.mm.trans2:probe5 0.0118977466721791 0.0622731912989834 0.191057282018135 0.848592452089534 df.mm.trans2:probe6 0.139562846567893 0.0622731912989834 2.24113850047975 0.0256490063897027 * df.mm.trans3:probe2 0.0335584442849006 0.0622731912989834 0.538890710189899 0.590307821042403 df.mm.trans3:probe3 -0.445560185576656 0.0622731912989834 -7.1549277671904 5.00915108197502e-12 *** df.mm.trans3:probe4 0.451146366958286 0.0622731912989834 7.24463220123505 2.82626421072519e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.45296743985737 0.18035526046181 24.6899781489894 2.08658470629892e-78 *** df.mm.trans1 -0.165283337219851 0.150260682446857 -1.09997728300154 0.272104536424110 df.mm.trans2 -0.185259855497611 0.150260682446857 -1.23292302737366 0.218439091352391 df.mm.exp2 0.180677867697861 0.207027654797748 0.87272334642621 0.38341738425144 df.mm.exp3 0.0122826273223788 0.207027654797747 0.0593284377122378 0.952724663109793 df.mm.exp4 0.448544100345837 0.207027654797747 2.16659025956718 0.0309462769283938 * df.mm.exp5 -0.105620408640912 0.207027654797747 -0.510175361567496 0.610252967856683 df.mm.exp6 -0.186052587338009 0.207027654797747 -0.898684707218319 0.369443790562669 df.mm.exp7 0.0498186555209973 0.207027654797747 0.240637684707712 0.809978049694002 df.mm.exp8 0.237901274127054 0.207027654797747 1.14912799625474 0.251294654336933 df.mm.trans1:exp2 0.0081625477498676 0.174970303298783 0.0466510464688917 0.962818183614758 df.mm.trans2:exp2 -0.226268463935583 0.174970303298783 -1.29318209815984 0.196808343975770 df.mm.trans1:exp3 -0.055521298475755 0.174970303298783 -0.31731841020442 0.751192852557302 df.mm.trans2:exp3 0.0217507211782827 0.174970303298783 0.124310930301931 0.901141072251134 df.mm.trans1:exp4 -0.348397692671219 0.174970303298783 -1.99118185259294 0.0472446512940471 * df.mm.trans2:exp4 -0.147103747599633 0.174970303298783 -0.840735512405413 0.401075069594414 df.mm.trans1:exp5 0.221237632263581 0.174970303298783 1.26442960943944 0.206924484678611 df.mm.trans2:exp5 -0.0160842739081446 0.174970303298783 -0.0919257359957752 0.926810089258344 df.mm.trans1:exp6 0.154302064219136 0.174970303298783 0.881875731538548 0.378454470407689 df.mm.trans2:exp6 0.191964743914275 0.174970303298783 1.09712757133690 0.273346394003327 df.mm.trans1:exp7 -0.0133624035626900 0.174970303298783 -0.0763695513510776 0.939169105463275 df.mm.trans2:exp7 -0.111314621559026 0.174970303298783 -0.636191510561328 0.525071001575669 df.mm.trans1:exp8 -0.111881165334119 0.174970303298783 -0.639429453025915 0.522965490524044 df.mm.trans2:exp8 -0.178362151981575 0.174970303298783 -1.01938528206698 0.308730277346844 df.mm.trans1:probe2 0.0085187615593728 0.095835182010264 0.0888897102366899 0.929220841041171 df.mm.trans1:probe3 -0.0666874497566724 0.095835182010264 -0.695855617507255 0.486985037798064 df.mm.trans1:probe4 0.00322586537312474 0.095835182010264 0.0336605545631379 0.973167187575235 df.mm.trans1:probe5 0.0742755199685461 0.095835182010264 0.775033953194675 0.438847412926012 df.mm.trans1:probe6 -0.0178141938011691 0.095835182010264 -0.185883653867962 0.852644613786253 df.mm.trans2:probe2 0.0457609214914114 0.095835182010264 0.477496056578788 0.633309549196121 df.mm.trans2:probe3 0.0908309132894487 0.095835182010264 0.947782551085683 0.34389973262853 df.mm.trans2:probe4 0.0519709036901943 0.095835182010264 0.542294620827539 0.587963646888367 df.mm.trans2:probe5 0.0125411849261304 0.095835182010264 0.130862013960460 0.895960298151486 df.mm.trans2:probe6 -0.0559299612516038 0.095835182010264 -0.583605728902499 0.559864743666558 df.mm.trans3:probe2 0.248320044298563 0.095835182010264 2.59111569561133 0.00997029911253628 ** df.mm.trans3:probe3 0.201710209521472 0.095835182010264 2.10476158431952 0.0360307254059677 * df.mm.trans3:probe4 0.196989169854435 0.095835182010264 2.05549951199903 0.0405787094561312 *