chr7.21984_chr7_110282608_110285245_+_2.R fitVsDatCorrelation=0.939877164429618 cont.fitVsDatCorrelation=0.266162231524203 fstatistic=6726.3190529854,58,830 cont.fstatistic=831.79555082584,58,830 residuals=-0.92625722582234,-0.127838078846109,0.0141332269298894,0.128792710392034,0.83436129646515 cont.residuals=-1.13526351346326,-0.504898650831023,-0.0358742883361408,0.416291740791697,1.80539316103268 predictedValues: Include Exclude Both chr7.21984_chr7_110282608_110285245_+_2.R.tl.Lung 71.3942608576601 43.0130076777320 86.3330239226594 chr7.21984_chr7_110282608_110285245_+_2.R.tl.cerebhem 78.3995423891835 44.7612035004946 103.042965264429 chr7.21984_chr7_110282608_110285245_+_2.R.tl.cortex 108.063410949864 43.6780743872952 129.836598286566 chr7.21984_chr7_110282608_110285245_+_2.R.tl.heart 214.84168287974 43.1849861776868 283.564547057608 chr7.21984_chr7_110282608_110285245_+_2.R.tl.kidney 125.408742745790 43.4984735189572 158.375314723040 chr7.21984_chr7_110282608_110285245_+_2.R.tl.liver 105.595566132881 46.1380466917327 136.512731552142 chr7.21984_chr7_110282608_110285245_+_2.R.tl.stomach 103.490624091713 42.5476447554288 121.183510577462 chr7.21984_chr7_110282608_110285245_+_2.R.tl.testicle 121.457018253003 45.330084224038 170.601991868999 diffExp=28.3812531799280,33.6383388886888,64.385336562569,171.656696702053,81.9102692268331,59.4575194411483,60.9429793362841,76.1269340289654 diffExpScore=0.998268396251542 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 83.6315072416287 126.508268306779 87.1655938858637 cerebhem 110.421639013575 104.010135793648 95.4708327995179 cortex 102.403351362299 93.0254018755066 89.9858887144979 heart 104.494132555274 126.884828233371 96.3048208239686 kidney 95.5392967039054 102.582012221097 75.5436900267645 liver 83.1747988587698 117.909072078463 98.1081379941591 stomach 91.0368688459583 134.961568208813 98.6393917615982 testicle 82.9174847169103 89.9504840706872 103.628881907600 cont.diffExp=-42.87676106515,6.4115032199267,9.37794948679276,-22.3906956780978,-7.04271551719152,-34.7342732196928,-43.9246993628547,-7.0329993537769 cont.diffExpScore=1.21352091839964 cont.diffExp1.5=-1,0,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=-1,0,0,0,0,-1,-1,0 cont.diffExp1.4Score=0.75 cont.diffExp1.3=-1,0,0,0,0,-1,-1,0 cont.diffExp1.3Score=0.75 cont.diffExp1.2=-1,0,0,-1,0,-1,-1,0 cont.diffExp1.2Score=0.8 tran.correlation=-0.165585818291018 cont.tran.correlation=-0.147283912761318 tran.covariance=-0.000975984943363251 cont.tran.covariance=-0.00218795976020677 tran.mean=80.050148077075 cont.tran.mean=103.090678130418 weightedLogRatios: wLogRatio Lung 2.03438944635862 cerebhem 2.28762889741606 cortex 3.8316410560212 heart 7.32845215076992 kidney 4.55534449578074 liver 3.51528785312284 stomach 3.72879999392631 testicle 4.24470311257337 cont.weightedLogRatios: wLogRatio Lung -1.91769069234562 cerebhem 0.279612001274343 cortex 0.439980613914497 heart -0.921470497290077 kidney -0.326826272061246 liver -1.60366388995482 stomach -1.85370969428987 testicle -0.362985879651075 varWeightedLogRatios=2.65021504129086 cont.varWeightedLogRatios=0.876562392282338 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.48538879402889 0.102993934444753 43.5500286323648 1.42905638229040e-216 *** df.mm.trans1 -0.0316694771670010 0.089166825713834 -0.355171073024837 0.722551668092246 df.mm.trans2 -0.75247251902793 0.0789965321304949 -9.52538673197597 1.74873638092690e-20 *** df.mm.exp2 -0.043494049269097 0.102100476203901 -0.425992619096476 0.670223668987664 df.mm.exp3 0.0217798446498662 0.102100476203900 0.213317757758255 0.831131508501485 df.mm.exp4 -0.0835533565589151 0.102100476203900 -0.818344435456445 0.413395416751736 df.mm.exp5 -0.0321713061798237 0.102100476203900 -0.315094575225837 0.752769046822659 df.mm.exp6 0.00332854782873178 0.102100476203901 0.0326007081699058 0.97400084289072 df.mm.exp7 0.0212916372321924 0.102100476203900 0.208536120729465 0.834861545686215 df.mm.exp8 -0.0973098176624946 0.102100476203901 -0.953078979457073 0.340827526191062 df.mm.trans1:exp2 0.137094653765699 0.0946496746443892 1.44844294796344 0.147870864761887 df.mm.trans2:exp2 0.0833332461269757 0.0710589241045065 1.17273441973900 0.241238786557809 df.mm.trans1:exp3 0.392720862630631 0.0946496746443892 4.14920457049782 3.68028040210379e-05 *** df.mm.trans2:exp3 -0.00643617280662874 0.0710589241045065 -0.0905751513654085 0.92785203014476 df.mm.trans1:exp4 1.18523726877240 0.0946496746443892 12.5223596723970 4.46232899011398e-33 *** df.mm.trans2:exp4 0.0875436751373074 0.0710589241045065 1.23198706201288 0.218302818844407 df.mm.trans1:exp5 0.595532164844283 0.0946496746443892 6.29196209159485 5.06919669310707e-10 *** df.mm.trans2:exp5 0.0433945780588001 0.0710589241045065 0.610684422902036 0.54157570517801 df.mm.trans1:exp6 0.38807034921385 0.0946496746443892 4.10007060955972 4.53645728761714e-05 *** df.mm.trans2:exp6 0.066806795559597 0.0710589241045065 0.94016052735817 0.347408752559984 df.mm.trans1:exp7 0.349971896913916 0.0946496746443892 3.69754992004784 0.000232002124853963 *** df.mm.trans2:exp7 -0.0321697102253795 0.0710589241045065 -0.452718791211466 0.650869555726767 df.mm.trans1:exp8 0.628652772650933 0.0946496746443892 6.64189047678041 5.59696623982311e-11 *** df.mm.trans2:exp8 0.149778166551620 0.0710589241045065 2.10780234065100 0.035347389790237 * df.mm.trans1:probe2 0.217484926602154 0.0634929319659278 3.4253407405861 0.000644170426058866 *** df.mm.trans1:probe3 0.132142480406391 0.0634929319659278 2.08121559856934 0.0377202024534443 * df.mm.trans1:probe4 0.562328397851705 0.0634929319659278 8.85655112215431 4.93808045775061e-18 *** df.mm.trans1:probe5 -0.0395765483364864 0.0634929319659277 -0.623322110211014 0.533244113001334 df.mm.trans1:probe6 -0.717586707649353 0.0634929319659277 -11.3018360537269 1.19579921004307e-27 *** df.mm.trans1:probe7 -0.387650491585837 0.0634929319659277 -6.10541172982627 1.57328195671987e-09 *** df.mm.trans1:probe8 0.227663474450875 0.0634929319659278 3.58565067640988 0.000355884394857270 *** df.mm.trans1:probe9 -0.099663900804355 0.0634929319659277 -1.56968496679028 0.116869545905806 df.mm.trans1:probe10 0.0321090693700028 0.0634929319659277 0.505710925229182 0.613193870881859 df.mm.trans1:probe11 -0.815501731236126 0.0634929319659278 -12.8439765811059 1.43404185618983e-34 *** df.mm.trans1:probe12 -0.692790730312623 0.0634929319659278 -10.9113047525415 5.37848752381383e-26 *** df.mm.trans1:probe13 -0.76360972489717 0.0634929319659277 -12.0266886605102 7.951042540208e-31 *** df.mm.trans1:probe14 -0.819520183809543 0.0634929319659278 -12.9072663434305 7.24140154976793e-35 *** df.mm.trans1:probe15 -0.566725729220338 0.0634929319659278 -8.92580814388064 2.79497477251825e-18 *** df.mm.trans1:probe16 -0.713545984843511 0.0634929319659277 -11.2381955400394 2.23818729042564e-27 *** df.mm.trans1:probe17 -0.191420395977668 0.0634929319659278 -3.01482999840659 0.00264952664529759 ** df.mm.trans1:probe18 -0.29822050587904 0.0634929319659277 -4.69690872110103 3.09010982475274e-06 *** df.mm.trans1:probe19 -0.0527139874074718 0.0634929319659278 -0.830233945343077 0.406645261159683 df.mm.trans1:probe20 -0.231654336986164 0.0634929319659277 -3.64850590157777 0.000280242870044084 *** df.mm.trans1:probe21 -0.0214803195227282 0.0634929319659278 -0.33831040491649 0.735214840313588 df.mm.trans1:probe22 -0.510623827859206 0.0634929319659278 -8.04221528363539 3.03478858338470e-15 *** df.mm.trans2:probe2 0.115550441402580 0.0634929319659278 1.81989455872960 0.0691351418753073 . df.mm.trans2:probe3 0.0331272938135643 0.0634929319659278 0.521747740856281 0.601985177040087 df.mm.trans2:probe4 0.0772885898286157 0.0634929319659278 1.21727863929943 0.223844198342963 df.mm.trans2:probe5 0.0721436055504453 0.0634929319659278 1.13624624531688 0.256181481344103 df.mm.trans2:probe6 0.130684555616137 0.0634929319659278 2.05825359720773 0.0398774944257710 * df.mm.trans3:probe2 0.670974041235226 0.0634929319659277 10.5676965996040 1.41152488857193e-24 *** df.mm.trans3:probe3 1.24490154789252 0.0634929319659277 19.6069311866173 1.24020581372922e-70 *** df.mm.trans3:probe4 0.986698269735309 0.0634929319659278 15.5402851811096 5.42761589113702e-48 *** df.mm.trans3:probe5 0.139676394831023 0.0634929319659277 2.19987312770463 0.0280908474006613 * df.mm.trans3:probe6 0.924245660909234 0.0634929319659277 14.5566700464425 6.40151257046127e-43 *** df.mm.trans3:probe7 1.03578662921048 0.0634929319659277 16.3134162675983 4.19853044888943e-52 *** df.mm.trans3:probe8 0.600992029253958 0.0634929319659277 9.46549498732346 2.93710335243657e-20 *** df.mm.trans3:probe9 1.20941394848490 0.0634929319659278 19.0480091411420 2.17439203335501e-67 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.72942647282638 0.290702999772192 16.2689290324922 7.28585586023775e-52 *** df.mm.trans1 -0.392415279577017 0.25167563366637 -1.55921045617478 0.119327769576569 df.mm.trans2 0.16659807894415 0.222969721331052 0.747178038119333 0.455167709923562 df.mm.exp2 -0.00894462157070898 0.288181191160965 -0.0310381865474105 0.975246547640316 df.mm.exp3 -0.136779142040334 0.288181191160964 -0.474628970368632 0.635176304534439 df.mm.exp4 0.125974044122154 0.288181191160964 0.437134858158704 0.662127256413806 df.mm.exp5 0.0665707692213128 0.288181191160964 0.231003171834797 0.817369268025644 df.mm.exp6 -0.194130474398477 0.288181191160965 -0.673640335847058 0.500727543513284 df.mm.exp7 0.0258656314151019 0.288181191160964 0.0897547522477087 0.928503759012788 df.mm.exp8 -0.522629089260753 0.288181191160964 -1.81354337233215 0.0701090942371223 . df.mm.trans1:exp2 0.286830411795245 0.267151114237171 1.07366354287635 0.283285547196133 df.mm.trans2:exp2 -0.186874692622972 0.200565620772991 -0.931738410116083 0.351742667043736 df.mm.trans1:exp3 0.339278252288021 0.267151114237171 1.26998628943324 0.204445431636007 df.mm.trans2:exp3 -0.170655931796105 0.200565620772990 -0.850873300909638 0.395085136817991 df.mm.trans1:exp4 0.0967365479403187 0.267151114237171 0.362104227850751 0.717366316382051 df.mm.trans2:exp4 -0.123001901558418 0.200565620772990 -0.613275102105546 0.539862468445035 df.mm.trans1:exp5 0.0665465474700316 0.267151114237171 0.24909702383256 0.803347358800365 df.mm.trans2:exp5 -0.276215839477874 0.200565620772990 -1.37718437693022 0.168826537302205 df.mm.trans1:exp6 0.188654548040960 0.267151114237171 0.706171668344516 0.480279555239735 df.mm.trans2:exp6 0.123736558071415 0.200565620772991 0.6169380255426 0.537444794575692 df.mm.trans1:exp7 0.0589786152335423 0.267151114237171 0.220768741324366 0.82532679832643 df.mm.trans2:exp7 0.0388167584190964 0.200565620772990 0.193536450910652 0.846586213451961 df.mm.trans1:exp8 0.514054712545642 0.267151114237171 1.92420950222680 0.0546699504333558 . df.mm.trans2:exp8 0.181580763055269 0.200565620772991 0.90534341007919 0.365546207733877 df.mm.trans1:probe2 0.107248842083538 0.179210415509738 0.598452058595388 0.549701613019322 df.mm.trans1:probe3 0.155583261247605 0.179210415509738 0.868159703804442 0.385557853714700 df.mm.trans1:probe4 0.188010805760006 0.179210415509738 1.04910646641400 0.294434498201199 df.mm.trans1:probe5 0.167988281815307 0.179210415509738 0.93738012568906 0.348835733669716 df.mm.trans1:probe6 0.0858974216460876 0.179210415509738 0.479310431828222 0.631844061753292 df.mm.trans1:probe7 0.381731962729024 0.179210415509738 2.13007687998068 0.0334588127505548 * df.mm.trans1:probe8 0.255386963657120 0.179210415509738 1.42506763867886 0.154513564656007 df.mm.trans1:probe9 0.228749026148181 0.179210415509738 1.27642707315609 0.202161619541223 df.mm.trans1:probe10 -0.0501100472488417 0.179210415509738 -0.279615708195927 0.779842018452923 df.mm.trans1:probe11 0.209779128337543 0.179210415509738 1.17057442080505 0.242105860441883 df.mm.trans1:probe12 -0.00869968071080618 0.179210415509738 -0.0485445038786456 0.961293975758923 df.mm.trans1:probe13 0.113430483850584 0.179210415509738 0.632945822529053 0.526943365332009 df.mm.trans1:probe14 0.173142288046781 0.179210415509738 0.966139649608552 0.334255723909307 df.mm.trans1:probe15 0.140319565246718 0.179210415509738 0.782987779184591 0.433857633128005 df.mm.trans1:probe16 0.135897251905356 0.179210415509738 0.75831112560515 0.448480010204290 df.mm.trans1:probe17 -0.0272710650960605 0.179210415509738 -0.152173438237347 0.879087124970601 df.mm.trans1:probe18 0.192877923372307 0.179210415509738 1.07626514242319 0.282121426565473 df.mm.trans1:probe19 -0.0197788666242452 0.179210415509738 -0.110366724880287 0.912145221660062 df.mm.trans1:probe20 -0.0518075493726674 0.179210415509738 -0.289087825756714 0.772586358898156 df.mm.trans1:probe21 0.241026477051481 0.179210415509738 1.34493565212667 0.179013392366115 df.mm.trans1:probe22 0.152280764482928 0.179210415509738 0.84973166347384 0.39571932208578 df.mm.trans2:probe2 -0.0295778346616905 0.179210415509738 -0.165045288118777 0.86894852393475 df.mm.trans2:probe3 -0.304216362109249 0.179210415509738 -1.69753728455988 0.0899699940496286 . df.mm.trans2:probe4 -0.284601250863590 0.179210415509738 -1.58808432006635 0.112648114624232 df.mm.trans2:probe5 0.0717458763367038 0.179210415509738 0.400344344566319 0.689005929946364 df.mm.trans2:probe6 -0.289103683152818 0.179210415509738 -1.61320804000428 0.107079430003302 df.mm.trans3:probe2 0.0899704944988462 0.179210415509738 0.502038312019634 0.615773723189303 df.mm.trans3:probe3 0.0828770964464173 0.179210415509738 0.462456918090868 0.6438748966702 df.mm.trans3:probe4 0.0496176624518222 0.179210415509738 0.276868184869121 0.781950257299191 df.mm.trans3:probe5 -0.175404083482992 0.179210415509738 -0.978760542371827 0.327983500491644 df.mm.trans3:probe6 0.00188036442353969 0.179210415509738 0.0104924951944967 0.991630875075804 df.mm.trans3:probe7 0.000409505967170039 0.179210415509738 0.00228505673626870 0.99817733917629 df.mm.trans3:probe8 -0.103736890628843 0.179210415509738 -0.578855254220456 0.562843891513009 df.mm.trans3:probe9 -0.2678104116981 0.179210415509738 -1.49439088646914 0.135453548576862