chr1.1222_chr1_134236609_134238972_-_1.R fitVsDatCorrelation=0.951979051080204 cont.fitVsDatCorrelation=0.284165505119941 fstatistic=8231.5060977307,39,393 cont.fstatistic=830.317004399331,39,393 residuals=-0.587844886858501,-0.0961389109257714,-0.00339419774679054,0.0875847421194091,0.627014799486092 cont.residuals=-1.16159527954819,-0.367617498566384,-0.0729296548187672,0.389396795988192,1.31159358982246 predictedValues: Include Exclude Both chr1.1222_chr1_134236609_134238972_-_1.R.tl.Lung 122.743071325704 265.745471550789 84.2984879835578 chr1.1222_chr1_134236609_134238972_-_1.R.tl.cerebhem 90.2043627643745 141.235483056137 60.4366620327367 chr1.1222_chr1_134236609_134238972_-_1.R.tl.cortex 94.662149042146 155.127805021843 56.7886064285885 chr1.1222_chr1_134236609_134238972_-_1.R.tl.heart 106.014520441272 258.274510596011 61.1539955833282 chr1.1222_chr1_134236609_134238972_-_1.R.tl.kidney 127.068939237727 229.896168895348 64.048942604886 chr1.1222_chr1_134236609_134238972_-_1.R.tl.liver 126.157344999338 223.756115288845 59.0314670900335 chr1.1222_chr1_134236609_134238972_-_1.R.tl.stomach 108.383879774239 224.636977038857 67.9375489664481 chr1.1222_chr1_134236609_134238972_-_1.R.tl.testicle 114.064655320322 236.232344280616 56.9450769440586 diffExp=-143.002400225085,-51.0311202917621,-60.4656559796965,-152.259990154739,-102.827229657621,-97.5987702895063,-116.253097264617,-122.167688960294 diffExpScore=0.99881881293574 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 115.902182494646 105.376299283096 101.929258516999 cerebhem 145.452828417812 83.9307487932863 117.702938770514 cortex 111.663732552363 109.543212176665 126.543227093888 heart 128.853896114988 96.2905784972544 95.857719805175 kidney 128.956256609374 111.370608836334 111.347464948236 liver 109.297448792275 104.481671377347 113.122716994805 stomach 110.139107022728 112.913238402225 89.8167822058055 testicle 110.896785502969 111.790840022762 85.5116200959654 cont.diffExp=10.5258832115499,61.5220796245262,2.12052037569802,32.5633176177334,17.5856477730399,4.81577741492852,-2.77413137949750,-0.894054519793755 cont.diffExpScore=1.05010374244662 cont.diffExp1.5=0,1,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,1,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,1,0,1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,1,0,1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.747351564567506 cont.tran.correlation=-0.807186546383203 tran.covariance=0.0238718407792120 cont.tran.covariance=-0.008452671487281 tran.mean=164.012737414598 cont.tran.mean=112.303714681008 weightedLogRatios: wLogRatio Lung -4.01387184187156 cerebhem -2.11901938225375 cortex -2.36954584780507 heart -4.54911493866836 kidney -3.04819460682477 liver -2.93621407962492 stomach -3.68053193499384 testicle -3.71363261527986 cont.weightedLogRatios: wLogRatio Lung 0.447971102389195 cerebhem 2.58704739214143 cortex 0.0902256868270681 heart 1.37294511244127 kidney 0.701699149919373 liver 0.210506197111665 stomach -0.117267849485404 testicle -0.0378409636445271 varWeightedLogRatios=0.691698641429079 cont.varWeightedLogRatios=0.840890839402951 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.92511457723815 0.08987300541685 65.927633662146 1.38263545300215e-214 *** df.mm.trans1 -1.04716161825757 0.073381001640557 -14.2702006629304 1.60934453624775e-37 *** df.mm.trans2 -0.240119455635984 0.073381001640557 -3.27222919103999 0.00116136984895066 ** df.mm.exp2 -0.607357892376853 0.0997051474801556 -6.09153998290546 2.66657347206487e-09 *** df.mm.exp3 -0.403040529674899 0.0997051474801556 -4.04232419148787 6.36939178491989e-05 *** df.mm.exp4 0.145935515396133 0.0997051474801556 1.46367082426891 0.144083269355837 df.mm.exp5 0.164441648149437 0.0997051474801556 1.64927942343364 0.0998894486024901 . df.mm.exp6 0.211747575387708 0.0997051474801556 2.12373764784664 0.0343161936875894 * df.mm.exp7 -0.0766922222055237 0.0997051474801556 -0.769190198738614 0.442242436038117 df.mm.exp8 0.201225661232433 0.0997051474801556 2.01820734754425 0.0442486007609719 * df.mm.trans1:exp2 0.299342366281995 0.0814089120184417 3.67702207117305 0.000268787745564903 *** df.mm.trans2:exp2 -0.0247524945185360 0.0814089120184417 -0.304051410402447 0.761249644777078 df.mm.trans1:exp3 0.143261436973762 0.0814089120184417 1.75977584544194 0.0792237921242864 . df.mm.trans2:exp3 -0.13524912144766 0.0814089120184417 -1.66135522628065 0.0974395931676521 . df.mm.trans1:exp4 -0.292452765040424 0.0814089120184417 -3.59239250088706 0.000369228219323015 *** df.mm.trans2:exp4 -0.174451478373784 0.0814089120184417 -2.14290394071678 0.0327343858538378 * df.mm.trans1:exp5 -0.129805199995247 0.0814089120184417 -1.5944839057159 0.111631551267068 df.mm.trans2:exp5 -0.309352857557647 0.0814089120184417 -3.79998761668217 0.000167637190000176 *** df.mm.trans1:exp6 -0.1843109973345 0.0814089120184417 -2.26401499252008 0.0241175809121959 * df.mm.trans2:exp6 -0.383729864522795 0.0814089120184417 -4.71361003370083 3.38412946885389e-06 *** df.mm.trans1:exp7 -0.0477217300869425 0.0814089120184417 -0.586197860943431 0.558079075758025 df.mm.trans2:exp7 -0.0913610907779305 0.0814089120184417 -1.12224925395434 0.262441648355942 df.mm.trans1:exp8 -0.274553541344909 0.0814089120184417 -3.37252438999202 0.00081875882481689 *** df.mm.trans2:exp8 -0.31894880767142 0.0814089120184417 -3.91786107642819 0.000105347106543408 *** df.mm.trans1:probe2 -0.0959025877552655 0.0498525737400778 -1.92372390350966 0.0551111883692304 . df.mm.trans1:probe3 0.0135188110136453 0.0498525737400778 0.271175788919745 0.78639821041017 df.mm.trans1:probe4 -0.201647913156291 0.0498525737400778 -4.04488470761101 6.3029415013918e-05 *** df.mm.trans1:probe5 -0.217941210152816 0.0498525737400778 -4.37171431286822 1.58032472314036e-05 *** df.mm.trans1:probe6 -0.312342771679183 0.0498525737400778 -6.26532891376242 9.7803858195558e-10 *** df.mm.trans2:probe2 -0.345315064775034 0.0498525737400778 -6.92672491846547 1.76800207316308e-11 *** df.mm.trans2:probe3 -0.131744788449415 0.0498525737400778 -2.64268780055987 0.00855386254542643 ** df.mm.trans2:probe4 -0.575052459919788 0.0498525737400778 -11.5350606152855 1.03481843626175e-26 *** df.mm.trans2:probe5 -0.127057385519850 0.0498525737400778 -2.54866250601833 0.0111930781500551 * df.mm.trans2:probe6 -0.0503040387646892 0.0498525737400778 -1.00905600234333 0.313568631119505 df.mm.trans3:probe2 -0.476413503110104 0.0498525737400778 -9.55644748842933 1.35128411056223e-19 *** df.mm.trans3:probe3 -0.252590567755102 0.0498525737400778 -5.06675079750271 6.23935288413406e-07 *** df.mm.trans3:probe4 -0.145554914728927 0.0498525737400778 -2.91970712460752 0.00370535400993727 ** df.mm.trans3:probe5 -0.428418077996995 0.0498525737400778 -8.59370030182771 2.01227026143777e-16 *** df.mm.trans3:probe6 0.0821596157766791 0.0498525737400778 1.64805163731454 0.100141270861839 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.91621347637855 0.281444736580995 17.4677755075507 5.6011559283637e-51 *** df.mm.trans1 -0.136673701690953 0.229798665138487 -0.594754114905704 0.552350104651674 df.mm.trans2 -0.248419146161979 0.229798665138487 -1.08102954389344 0.280346885034266 df.mm.exp2 -0.144325410465640 0.312234901216072 -0.462233433557654 0.644169720331962 df.mm.exp3 -0.214778194256769 0.312234901216072 -0.687873756009547 0.491938068114806 df.mm.exp4 0.077179347986069 0.312234901216072 0.247183603387949 0.804895211817548 df.mm.exp5 0.0736757834321487 0.312234901216072 0.23596267792358 0.813584552630386 df.mm.exp6 -0.171393803100006 0.312234901216072 -0.54892583254618 0.58336807679856 df.mm.exp7 0.144586757472101 0.312234901216072 0.463070454036286 0.643570185242779 df.mm.exp8 0.190571961048482 0.312234901216072 0.61034804343222 0.541984055994921 df.mm.trans1:exp2 0.371430660187014 0.254938729289229 1.45694089408293 0.145931205594849 df.mm.trans2:exp2 -0.0832202961736339 0.254938729289229 -0.326432536969384 0.744270890018897 df.mm.trans1:exp3 0.177523580407119 0.254938729289229 0.696338217822204 0.486628668084781 df.mm.trans2:exp3 0.253559551082355 0.254938729289229 0.994590158150084 0.320547609642154 df.mm.trans1:exp4 0.0287532452460127 0.254938729289229 0.112784924150901 0.910258658081424 df.mm.trans2:exp4 -0.167346615222315 0.254938729289229 -0.65641895873914 0.511938751335858 df.mm.trans1:exp5 0.0330508863611812 0.254938729289229 0.129642469205551 0.89691562716396 df.mm.trans2:exp5 -0.0183500715873952 0.254938729289229 -0.0719783598143577 0.94265574542962 df.mm.trans1:exp6 0.112720275665921 0.254938729289229 0.442146534503352 0.658626385571238 df.mm.trans2:exp6 0.162867719268718 0.254938729289229 0.638850439565595 0.523292373505984 df.mm.trans1:exp7 -0.195589162337912 0.254938729289229 -0.767200663795633 0.443422870231309 df.mm.trans2:exp7 -0.0755047817554082 0.254938729289229 -0.29616834588419 0.767257960706292 df.mm.trans1:exp8 -0.234718633668982 0.254938729289229 -0.920686450126192 0.357778951496213 df.mm.trans2:exp8 -0.131480081836648 0.254938729289229 -0.515732082776184 0.606331550312558 df.mm.trans1:probe2 -0.0322452082728116 0.156117450608036 -0.206544548013211 0.836472534563495 df.mm.trans1:probe3 -0.127584094809729 0.156117450608036 -0.817231477408984 0.414291301430649 df.mm.trans1:probe4 0.0391817639167001 0.156117450608036 0.250976196216999 0.801963680309702 df.mm.trans1:probe5 -0.0635721547617532 0.156117450608036 -0.407207230928744 0.684077492416409 df.mm.trans1:probe6 -0.137298630192609 0.156117450608036 -0.879457291019468 0.379690804700638 df.mm.trans2:probe2 -0.0298440832410916 0.156117450608036 -0.191164300498483 0.848495591686054 df.mm.trans2:probe3 0.0827973289588123 0.156117450608036 0.530352812170189 0.596166866473373 df.mm.trans2:probe4 -0.0945220587305985 0.156117450608036 -0.605454792929684 0.545226325369274 df.mm.trans2:probe5 0.120214896373984 0.156117450608036 0.770028564428761 0.44174555653776 df.mm.trans2:probe6 -0.201725089943531 0.156117450608036 -1.29213671602928 0.197069080253535 df.mm.trans3:probe2 0.0762509814356933 0.156117450608036 0.488420616264973 0.625524353575817 df.mm.trans3:probe3 0.0211462822406978 0.156117450608036 0.135451111700445 0.892324564363784 df.mm.trans3:probe4 0.173045989367189 0.156117450608036 1.1084346349061 0.268352028897050 df.mm.trans3:probe5 0.00802997992555758 0.156117450608036 0.0514355050910898 0.95900462595659 df.mm.trans3:probe6 0.280477168199664 0.156117450608036 1.79657794248676 0.0731704452705072 .