chr6.19204_chr6_83997713_83998329_+_0.R fitVsDatCorrelation=0.871143981061923 cont.fitVsDatCorrelation=0.249953557997551 fstatistic=9319.8655369057,41,439 cont.fstatistic=2388.88903114978,41,439 residuals=-0.559165895710488,-0.100373884249859,-0.00828214270559628,0.0813372459637178,0.93129107219754 cont.residuals=-0.647513450222398,-0.232334844601563,-0.0289396693744403,0.189353326575002,0.892955269197074 predictedValues: Include Exclude Both chr6.19204_chr6_83997713_83998329_+_0.R.tl.Lung 62.984393259088 77.2984362062688 80.6012682790292 chr6.19204_chr6_83997713_83998329_+_0.R.tl.cerebhem 74.0423662504124 94.5405080670922 93.7912103590632 chr6.19204_chr6_83997713_83998329_+_0.R.tl.cortex 58.9190623125715 78.7924933265205 92.6312041268859 chr6.19204_chr6_83997713_83998329_+_0.R.tl.heart 62.0727624666261 90.3263657326194 108.148702248837 chr6.19204_chr6_83997713_83998329_+_0.R.tl.kidney 65.7001816103496 86.6273777993759 103.227288045283 chr6.19204_chr6_83997713_83998329_+_0.R.tl.liver 69.2660259430628 92.019440331896 106.988929011533 chr6.19204_chr6_83997713_83998329_+_0.R.tl.stomach 64.0964068158436 78.5449984823646 85.8623822661034 chr6.19204_chr6_83997713_83998329_+_0.R.tl.testicle 66.0139208486258 99.656524788898 115.903700103284 diffExp=-14.3140429471809,-20.4981418166798,-19.8734310139490,-28.2536032659933,-20.9271961890263,-22.7534143888331,-14.4485916665211,-33.6426039402723 diffExpScore=0.994308837486437 diffExp1.5=0,0,0,0,0,0,0,-1 diffExp1.5Score=0.5 diffExp1.4=0,0,0,-1,0,0,0,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 93.9764665032336 83.3471662857071 79.7155666347385 cerebhem 94.6800621694205 88.315881601099 76.332004408111 cortex 82.3615753998495 96.4450745199133 84.4751050209805 heart 91.7965612776455 88.1854054241452 80.2320026022031 kidney 90.6020735051218 81.629763251939 85.0707957485266 liver 83.4809809532377 81.3752041810907 83.7713184025653 stomach 84.6710048311685 87.6383828399762 86.8896080764935 testicle 87.7902151762849 86.8457818812112 77.9981939445865 cont.diffExp=10.6293002175265,6.36418056832144,-14.0834991200638,3.61115585350029,8.97231025318288,2.10577677214698,-2.96737800880766,0.94443329507365 cont.diffExpScore=2.99693505391222 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=0.623842029540941 cont.tran.correlation=-0.311457247605521 tran.covariance=0.00423579404338527 cont.tran.covariance=-0.000907929306038105 tran.mean=76.306329015101 cont.tran.mean=87.6963499875652 weightedLogRatios: wLogRatio Lung -0.869377188715939 cerebhem -1.08187791754645 cortex -1.22698930687222 heart -1.6189776473415 kidney -1.19547017180042 liver -1.24411203811332 stomach -0.866399877256539 testicle -1.81047024696947 cont.weightedLogRatios: wLogRatio Lung 0.538097061666344 cerebhem 0.314218980794573 cortex -0.708774739105073 heart 0.180580381341958 kidney 0.464512174639809 liver 0.112714793813311 stomach -0.153490561452904 testicle 0.0483431307364794 varWeightedLogRatios=0.110452194815936 cont.varWeightedLogRatios=0.156911924019478 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79813636516329 0.080256363596751 47.3250493163018 1.62995506118695e-174 *** df.mm.trans1 0.321243103767017 0.0647548364695781 4.96091290289857 1.00505358410332e-06 *** df.mm.trans2 0.526941419114284 0.064754836469578 8.13748358953608 4.19421529903522e-15 *** df.mm.exp2 0.211548390301774 0.0872253413553178 2.42530882670919 0.0156977295747569 * df.mm.exp3 -0.186689965640084 0.0872253413553178 -2.14031797112253 0.03287951475627 * df.mm.exp4 -0.152816780546608 0.0872253413553178 -1.75197687016322 0.080475990899833 . df.mm.exp5 -0.0912619509318528 0.0872253413553178 -1.04627794530596 0.296008454915192 df.mm.exp6 -0.0138172693844542 0.0872253413553178 -0.158408888629838 0.874207452349245 df.mm.exp7 -0.0297321222712592 0.0872253413553178 -0.34086564534203 0.733367797336434 df.mm.exp8 -0.0622108214850931 0.0872253413553178 -0.713219581814802 0.476088845547679 df.mm.trans1:exp2 -0.0497979136815578 0.069581751279185 -0.715674911396699 0.474572580884188 df.mm.trans2:exp2 -0.0101937160527011 0.069581751279185 -0.146499848958969 0.883594058587534 df.mm.trans1:exp3 0.119967672865367 0.069581751279185 1.72412551653115 0.0853891588887938 . df.mm.trans2:exp3 0.205833970414198 0.069581751279185 2.95816024503787 0.00326202313557296 ** df.mm.trans1:exp4 0.138237095990669 0.069581751279185 1.9866860699728 0.0475784254134137 * df.mm.trans2:exp4 0.308572452473967 0.069581751279185 4.43467499453804 1.16685341025114e-05 *** df.mm.trans1:exp5 0.133476670999128 0.069581751279185 1.91827122119383 0.0557249490647111 . df.mm.trans2:exp5 0.205204132210466 0.069581751279185 2.94910847223604 0.00335716218530427 ** df.mm.trans1:exp6 0.108884839582393 0.069581751279185 1.56484764439904 0.118339167419748 df.mm.trans2:exp6 0.188143406868331 0.069581751279185 2.70391882080457 0.00711889973997932 ** df.mm.trans1:exp7 0.0472334590554706 0.069581751279185 0.678819635711011 0.497609847730984 df.mm.trans2:exp7 0.0457300867031319 0.069581751279185 0.657213793306921 0.511387864902307 df.mm.trans1:exp8 0.109189493563342 0.069581751279185 1.56922600474997 0.117315752909842 df.mm.trans2:exp8 0.316266617860882 0.069581751279185 4.54525234054422 7.10392513389581e-06 *** df.mm.trans1:probe2 0.120539292243513 0.045551948889208 2.6461939649759 0.00843274857300193 ** df.mm.trans1:probe3 0.0149124797433004 0.045551948889208 0.327373034676754 0.743541810092507 df.mm.trans1:probe4 0.159296235009473 0.045551948889208 3.4970234840427 0.000518413060032687 *** df.mm.trans1:probe5 0.0420805068123295 0.045551948889208 0.92379157947069 0.356102311338958 df.mm.trans1:probe6 -0.00772350364686302 0.045551948889208 -0.169553747648607 0.8654392019116 df.mm.trans2:probe2 0.158026470323417 0.045551948889208 3.46914839379915 0.000573681485985741 *** df.mm.trans2:probe3 -0.0658853257177139 0.045551948889208 -1.44637775823733 0.148784855753374 df.mm.trans2:probe4 0.0644857802691304 0.045551948889208 1.41565359642402 0.157585990569442 df.mm.trans2:probe5 0.128993759767731 0.045551948889208 2.83179453159009 0.00484162061501447 ** df.mm.trans2:probe6 0.0307224882458232 0.045551948889208 0.674449480099014 0.500380598456807 df.mm.trans3:probe2 -0.0114031285388260 0.0455519488892080 -0.250332396678809 0.802447414332469 df.mm.trans3:probe3 -0.426067191453751 0.045551948889208 -9.35343496477037 4.38321416292583e-19 *** df.mm.trans3:probe4 -0.103087342448028 0.045551948889208 -2.26307205205991 0.0241189202247997 * df.mm.trans3:probe5 -0.771127515640695 0.045551948889208 -16.9285296116801 7.61844976376435e-50 *** df.mm.trans3:probe6 -0.656637067368624 0.0455519488892080 -14.4151256615980 7.63002645003951e-39 *** df.mm.trans3:probe7 -0.0510268543531112 0.045551948889208 -1.12019036720513 0.263245104959828 df.mm.trans3:probe8 -0.0351029256688136 0.045551948889208 -0.77061303686899 0.441350850006981 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.57823710318791 0.158257585669412 28.9290215304529 9.25983871524097e-104 *** df.mm.trans1 -0.0316516947175746 0.127690112295443 -0.247878979418082 0.804343943551302 df.mm.trans2 -0.156864596571859 0.127690112295443 -1.22847880506921 0.219925447463369 df.mm.exp2 0.10873696372519 0.171999718320676 0.632192684888363 0.527590077146458 df.mm.exp3 -0.0439583186159557 0.171999718320676 -0.255572038402993 0.79840101160606 df.mm.exp4 0.0264997224630661 0.171999718320676 0.154068406168318 0.877626599650127 df.mm.exp5 -0.122406883962475 0.171999718320676 -0.711669095493868 0.477047703940495 df.mm.exp6 -0.191995367175233 0.171999718320676 -1.11625396279590 0.26492404593865 df.mm.exp7 -0.140240286149448 0.171999718320676 -0.815351836146525 0.415313076842383 df.mm.exp8 -0.00519582158223995 0.171999718320676 -0.0302083144842882 0.975914645832408 df.mm.trans1:exp2 -0.101277917029917 0.137208309355037 -0.738132533707217 0.460828306871569 df.mm.trans2:exp2 -0.0508316235528814 0.137208309355037 -0.370470445935973 0.711210676089053 df.mm.trans1:exp3 -0.0879670657686868 0.137208309355037 -0.641120542787721 0.521778918738199 df.mm.trans2:exp3 0.189917378112417 0.137208309355037 1.38415362017902 0.167014652815436 df.mm.trans1:exp4 -0.0499692789644415 0.137208309355037 -0.36418551616391 0.715894824840831 df.mm.trans2:exp4 0.0299271446293013 0.137208309355037 0.218114666451159 0.827441120895296 df.mm.trans1:exp5 0.0858395885424754 0.137208309355037 0.625615088080118 0.531892534104073 df.mm.trans2:exp5 0.101586214316622 0.137208309355037 0.740379462396553 0.459465561244075 df.mm.trans1:exp6 0.0735698054956691 0.137208309355037 0.536190598379153 0.59209840787273 df.mm.trans2:exp6 0.168051365991740 0.137208309355037 1.22479000566135 0.221311140018344 df.mm.trans1:exp7 0.0359691067736089 0.137208309355037 0.26214962448474 0.793329052847609 df.mm.trans2:exp7 0.190444737554407 0.137208309355037 1.38799711511361 0.165841925794951 df.mm.trans1:exp8 -0.0628985230255752 0.137208309355037 -0.458416281938294 0.646880243132977 df.mm.trans2:exp8 0.0463151343812899 0.137208309355037 0.337553422230763 0.735861087815673 df.mm.trans1:probe2 0.0107486236250076 0.0898239233709106 0.119663261430067 0.904804645778184 df.mm.trans1:probe3 0.040593996404148 0.0898239233709106 0.451928560685587 0.651543651961647 df.mm.trans1:probe4 -0.0787252285318491 0.0898239233709106 -0.876439433699288 0.381270514228359 df.mm.trans1:probe5 -0.0700130722558342 0.0898239233709106 -0.779447942467718 0.436135877649741 df.mm.trans1:probe6 0.0478214862524476 0.0898239233709106 0.532391421547888 0.594724274749928 df.mm.trans2:probe2 -0.0318740526714046 0.0898239233709106 -0.354850372542589 0.722872096170487 df.mm.trans2:probe3 0.09771243863515 0.0898239233709106 1.08782198514827 0.277270566423678 df.mm.trans2:probe4 0.0320340813607662 0.0898239233709106 0.356631954590623 0.721538699022929 df.mm.trans2:probe5 -0.059751589732394 0.0898239233709106 -0.665207970104594 0.506266800968846 df.mm.trans2:probe6 -0.0151314183601505 0.0898239233709106 -0.168456440025095 0.866301792815888 df.mm.trans3:probe2 -0.0276515222355918 0.0898239233709105 -0.30784139901583 0.75834901822126 df.mm.trans3:probe3 -0.0800900367140422 0.0898239233709106 -0.891633695216428 0.373077739017062 df.mm.trans3:probe4 -0.0289576002042881 0.0898239233709106 -0.322381823433756 0.747316923148138 df.mm.trans3:probe5 0.049186220478966 0.0898239233709106 0.547584859724519 0.584255271627048 df.mm.trans3:probe6 0.0786526722149929 0.0898239233709106 0.875631672090428 0.381709140921398 df.mm.trans3:probe7 -0.0384080382689445 0.0898239233709106 -0.427592525772293 0.669157472344964 df.mm.trans3:probe8 -0.0427791337271961 0.0898239233709106 -0.476255457586148 0.634129331308632