chr9.24219_chr9_21069246_21072178_+_2.R fitVsDatCorrelation=0.980622190941909 cont.fitVsDatCorrelation=0.259074289573378 fstatistic=6392.91213588733,42,462 cont.fstatistic=252.466654300862,42,462 residuals=-0.62196959240599,-0.137037055141347,-0.00556983559923187,0.117907470468515,0.87787803456909 cont.residuals=-1.79436382203563,-0.83271556973386,-0.305667598225854,0.82030736779004,2.74233326580954 predictedValues: Include Exclude Both chr9.24219_chr9_21069246_21072178_+_2.R.tl.Lung 98.5753941169531 1066.17214105835 70.1488739655355 chr9.24219_chr9_21069246_21072178_+_2.R.tl.cerebhem 118.826990359519 862.859525053137 84.9850949863625 chr9.24219_chr9_21069246_21072178_+_2.R.tl.cortex 82.7655703052174 448.003911574983 84.4357942246542 chr9.24219_chr9_21069246_21072178_+_2.R.tl.heart 85.5841875664716 455.559074938559 118.230738458447 chr9.24219_chr9_21069246_21072178_+_2.R.tl.kidney 102.381010602168 1145.15688830406 85.9509048239293 chr9.24219_chr9_21069246_21072178_+_2.R.tl.liver 103.435666206056 635.935073520143 76.3081045825367 chr9.24219_chr9_21069246_21072178_+_2.R.tl.stomach 93.4366750361059 537.530746820805 69.9958190175618 chr9.24219_chr9_21069246_21072178_+_2.R.tl.testicle 100.310625224359 638.75413560508 97.120730558087 diffExp=-967.596746941399,-744.032534693618,-365.238341269765,-369.974887372088,-1042.77587770189,-532.499407314087,-444.094071784700,-538.443510380721 diffExpScore=0.999800225959521 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 108.344507984202 191.393073129126 250.630423882194 cerebhem 154.095739939691 162.163070092166 172.552075035148 cortex 186.922289388298 200.537010789853 180.485731313449 heart 190.956627673039 121.367388981731 139.293967080963 kidney 175.919894394786 229.356963599208 200.815830543361 liver 158.435513273669 186.020575353002 143.816980781031 stomach 228.073272963826 137.22831348365 151.897588731357 testicle 193.329217475021 171.20546822255 109.487843177059 cont.diffExp=-83.0485651449243,-8.06733015247565,-13.6147214015544,69.5892386913076,-53.4370692044218,-27.5850620793334,90.844959480176,22.1237492524714 cont.diffExpScore=87.8017179238709 cont.diffExp1.5=-1,0,0,1,0,0,1,0 cont.diffExp1.5Score=1.5 cont.diffExp1.4=-1,0,0,1,0,0,1,0 cont.diffExp1.4Score=1.5 cont.diffExp1.3=-1,0,0,1,-1,0,1,0 cont.diffExp1.3Score=4 cont.diffExp1.2=-1,0,0,1,-1,0,1,0 cont.diffExp1.2Score=4 tran.correlation=0.590937068457759 cont.tran.correlation=-0.417076008438044 tran.covariance=0.0289045225665537 cont.tran.covariance=-0.0186961199407268 tran.mean=410.955476018248 cont.tran.mean=174.709307921489 weightedLogRatios: wLogRatio Lung -13.7653854572144 cerebhem -11.4374443986351 cortex -8.88372182029969 heart -8.83750908239794 kidney -14.0915781601661 liver -10.0742074398366 stomach -9.46962349050113 testicle -10.2446131555602 cont.weightedLogRatios: wLogRatio Lung -2.82789493990759 cerebhem -0.258360975737330 cortex -0.370219805583848 heart 2.27764794427387 kidney -1.40653345524070 liver -0.825918767071408 stomach 2.62934162140034 testicle 0.632397723554909 varWeightedLogRatios=4.3060780542034 cont.varWeightedLogRatios=3.33707573711809 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 7.47605231275548 0.122803052044248 60.8783917688113 8.3075769507341e-223 *** df.mm.trans1 -2.50921167885146 0.108211063533059 -23.1881251040924 1.84814944688091e-79 *** df.mm.trans2 -0.79239700974404 0.102311794840234 -7.74492335885042 6.10630943984757e-14 *** df.mm.exp2 -0.216587410763754 0.140964160411112 -1.536471469997 0.125107333633377 df.mm.exp3 -1.22720929111454 0.140964160411112 -8.70582485317878 5.62147040277371e-17 *** df.mm.exp4 -1.51364418175618 0.140964160411112 -10.7377944673436 3.66431990670588e-24 *** df.mm.exp5 -0.093810078224206 0.140964160411112 -0.665488858661772 0.506069965490284 df.mm.exp6 -0.55276484138949 0.140964160411112 -3.92131474963132 0.000101422377803476 *** df.mm.exp7 -0.736197613837688 0.140964160411112 -5.22258715754855 2.67372106289216e-07 *** df.mm.exp8 -0.820195607827767 0.140964160411112 -5.81846907352708 1.11108599746451e-08 *** df.mm.trans1:exp2 0.403434305567872 0.130507453056170 3.09127406995090 0.00211347625149315 ** df.mm.trans2:exp2 0.00500923859869055 0.119136459935685 0.0420462266664191 0.966480011916823 df.mm.trans1:exp3 1.05239977056105 0.130507453056170 8.0639055158643 6.4021674870777e-15 *** df.mm.trans2:exp3 0.360181179827595 0.119136459935685 3.02326575778765 0.00263973384943745 ** df.mm.trans1:exp4 1.37232304525065 0.130507453056170 10.5152848600915 2.49075012407085e-23 *** df.mm.trans2:exp4 0.663339507699434 0.119136459935685 5.56789674678546 4.38022987164713e-08 *** df.mm.trans1:exp5 0.131689652395901 0.130507453056170 1.00905848142805 0.313474769117876 df.mm.trans2:exp5 0.165276930321034 0.119136459935685 1.38729093016746 0.166021907373102 df.mm.trans1:exp6 0.600893000383441 0.130507453056170 4.60428110664928 5.35738661191701e-06 *** df.mm.trans2:exp6 0.0360312390003913 0.119136459935685 0.302436710137624 0.762455285147127 df.mm.trans1:exp7 0.682659870439929 0.130507453056170 5.23081137861234 2.56381896479276e-07 *** df.mm.trans2:exp7 0.0513535007330273 0.119136459935685 0.431047730986386 0.66663470930739 df.mm.trans1:exp8 0.837645553726504 0.130507453056170 6.4183733121049 3.41980892431842e-10 *** df.mm.trans2:exp8 0.30788514899525 0.119136459935685 2.58430667791758 0.0100632570324425 * df.mm.trans1:probe2 -0.0340940939058393 0.0652537265280849 -0.522485009207334 0.60158312442877 df.mm.trans1:probe3 0.133895348866145 0.0652537265280849 2.05191880970226 0.0407406225438114 * df.mm.trans1:probe4 -0.0140683478435483 0.0652537265280849 -0.215594550565528 0.829398950284155 df.mm.trans1:probe5 -0.0712029826094601 0.0652537265280849 -1.09117113148802 0.275766323693527 df.mm.trans1:probe6 -0.0636130303096908 0.0652537265280849 -0.974856666343984 0.330141509721324 df.mm.trans1:probe7 -1.02080197573863 0.0652537265280849 -15.6435812949209 1.42508755023781e-44 *** df.mm.trans1:probe8 -0.859749321390658 0.0652537265280849 -13.1754823384780 7.02458872360975e-34 *** df.mm.trans1:probe9 -0.99411914014133 0.0652537265280849 -15.234672302025 9.3305855046999e-43 *** df.mm.trans1:probe10 -0.799965330967604 0.0652537265280849 -12.2593049244981 4.13680719907588e-30 *** df.mm.trans1:probe11 -0.885863316313165 0.0652537265280849 -13.5756739644884 1.45109958792031e-35 *** df.mm.trans1:probe12 -1.03070214972496 0.0652537265280849 -15.7952994344522 2.99446256260050e-45 *** df.mm.trans2:probe2 0.223698131760549 0.0652537265280849 3.42812807272042 0.000662164743205976 *** df.mm.trans2:probe3 0.84915116423501 0.0652537265280849 13.0130677497712 3.34273685053724e-33 *** df.mm.trans2:probe4 0.605794731262455 0.0652537265280849 9.28368023551458 6.39820544077751e-19 *** df.mm.trans2:probe5 0.583874955595135 0.0652537265280849 8.94776416093015 8.83786263366887e-18 *** df.mm.trans2:probe6 0.33105396376567 0.0652537265280849 5.0733342198194 5.67286449817549e-07 *** df.mm.trans3:probe2 0.215970327613682 0.0652537265280849 3.30970105624127 0.00100703424386352 ** df.mm.trans3:probe3 0.0125580637356027 0.0652537265280849 0.192449755803568 0.847474485370744 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63885000798613 0.605437078311616 7.66198532293811 1.08559298946814e-13 *** df.mm.trans1 0.185533255650894 0.533496432343078 0.347768503035804 0.728172432891091 df.mm.trans2 0.709732492628731 0.504412171470863 1.40704870494928 0.160085308333913 df.mm.exp2 0.559811568913029 0.694974009238811 0.805514395460885 0.420937281580703 df.mm.exp3 0.920374176612028 0.694974009238811 1.32432891644407 0.186048598473569 df.mm.exp4 0.69861608146633 0.694974009238811 1.00524058767537 0.315307213335407 df.mm.exp5 0.887254219244025 0.69497400923881 1.27667251933035 0.202359213873539 df.mm.exp6 0.90699753004016 0.694974009238811 1.30508122315764 0.192514812778192 df.mm.exp7 0.91244058752142 0.694974009238811 1.31291325343346 0.189863942254225 df.mm.exp8 1.29577952006457 0.694974009238811 1.86450069044137 0.0628853314450313 . df.mm.trans1:exp2 -0.207553510712577 0.643420906572806 -0.322578126685555 0.747160690039957 df.mm.trans2:exp2 -0.725538422277072 0.587360240833916 -1.23525286840487 0.217364199613573 df.mm.trans1:exp3 -0.374997249862236 0.643420906572806 -0.58281794394849 0.560300214117655 df.mm.trans2:exp3 -0.873704642248434 0.587360240833916 -1.4875106987289 0.137562152151596 df.mm.trans1:exp4 -0.131885798417562 0.643420906572806 -0.204975929551394 0.837681206702062 df.mm.trans2:exp4 -1.15412315127569 0.587360240833916 -1.96493237206028 0.0500208608457726 . df.mm.trans1:exp5 -0.402541512265558 0.643420906572806 -0.62562703224815 0.531868526647168 df.mm.trans2:exp5 -0.706303924152935 0.587360240833916 -1.20250550692731 0.229783665943670 df.mm.trans1:exp6 -0.526965914794225 0.643420906572806 -0.819006515658808 0.413204960653477 df.mm.trans2:exp6 -0.935469530083515 0.587360240833916 -1.59266743822388 0.111918733202435 df.mm.trans1:exp7 -0.168089676513115 0.643420906572806 -0.261243728321556 0.794020974172373 df.mm.trans2:exp7 -1.24512381483739 0.587360240833916 -2.11986397490849 0.0345500499187059 * df.mm.trans1:exp8 -0.716701033306501 0.643420906572806 -1.11389142936623 0.265904928100529 df.mm.trans2:exp8 -1.40724440413077 0.587360240833916 -2.39587957491438 0.0169774188891684 * df.mm.trans1:probe2 0.0638883633000636 0.321710453286403 0.198589640614466 0.842671116910557 df.mm.trans1:probe3 -0.370568219329418 0.321710453286403 -1.15186875509923 0.249970722668562 df.mm.trans1:probe4 -0.299052512602372 0.321710453286403 -0.929570393337952 0.353078995806043 df.mm.trans1:probe5 -0.155874385087138 0.321710453286403 -0.484517626004434 0.628248177794817 df.mm.trans1:probe6 -0.0660524448119267 0.321710453286403 -0.205316439478960 0.837415332111392 df.mm.trans1:probe7 0.0536211138680131 0.321710453286403 0.166675074807957 0.867698640247036 df.mm.trans1:probe8 0.0359593566446602 0.321710453286403 0.111775530690162 0.91104993469636 df.mm.trans1:probe9 -0.714823169543948 0.321710453286403 -2.22194573487351 0.0267706353423273 * df.mm.trans1:probe10 -0.108369948135769 0.321710453286403 -0.336855538975267 0.736378768178912 df.mm.trans1:probe11 -0.373766300121893 0.321710453286403 -1.16180962198685 0.245912659811447 df.mm.trans1:probe12 -0.150970224255142 0.321710453286403 -0.469273605233898 0.639095460198469 df.mm.trans2:probe2 -0.225128660680273 0.321710453286403 -0.699786588780354 0.484412789685484 df.mm.trans2:probe3 -0.138763592049364 0.321710453286403 -0.431330690786815 0.666429130836231 df.mm.trans2:probe4 -0.272696579530122 0.321710453286403 -0.847646002000915 0.397074056033309 df.mm.trans2:probe5 -0.374092821614659 0.321710453286403 -1.16282457655059 0.245500956945314 df.mm.trans2:probe6 0.162402738684921 0.321710453286403 0.504810263471114 0.613932802844778 df.mm.trans3:probe2 0.116777675260192 0.321710453286403 0.362989993229194 0.716778373676536 df.mm.trans3:probe3 -0.147186626301841 0.321710453286403 -0.457512725490483 0.647517632042815