chr18.11181_chr18_78182685_78183248_-_0.R fitVsDatCorrelation=0.911702473499723 cont.fitVsDatCorrelation=0.310278440969734 fstatistic=8294.4499132646,36,324 cont.fstatistic=1541.92220401145,36,324 residuals=-0.580121469025709,-0.073415254154608,-0.00844025511829274,0.0781803290856098,0.581990875463487 cont.residuals=-0.609173750268705,-0.200591685851034,-0.0526749603511347,0.119363129566298,1.15308495908118 predictedValues: Include Exclude Both chr18.11181_chr18_78182685_78183248_-_0.R.tl.Lung 49.213428707496 55.5652053066626 83.3386187415017 chr18.11181_chr18_78182685_78183248_-_0.R.tl.cerebhem 66.1653623507713 59.9008756376619 73.5844710652203 chr18.11181_chr18_78182685_78183248_-_0.R.tl.cortex 45.2301767595241 59.92370482236 94.0143124360214 chr18.11181_chr18_78182685_78183248_-_0.R.tl.heart 43.4675124130217 62.5956981716251 96.666713446803 chr18.11181_chr18_78182685_78183248_-_0.R.tl.kidney 44.8518654757149 56.9661477369195 89.6117401865894 chr18.11181_chr18_78182685_78183248_-_0.R.tl.liver 48.6107126668556 60.2707904201984 81.5631200047407 chr18.11181_chr18_78182685_78183248_-_0.R.tl.stomach 47.3464321389441 70.8706853753417 85.091699325175 chr18.11181_chr18_78182685_78183248_-_0.R.tl.testicle 54.578468504596 61.7705998088707 118.062319571826 diffExp=-6.35177659916658,6.26448671310941,-14.6935280628359,-19.1281857586034,-12.1142822612046,-11.6600777533429,-23.5242532363976,-7.19213130427475 diffExpScore=1.12895979742962 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,-1,0,0,-1,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,0,0,-1,0 diffExp1.3Score=0.75 diffExp1.2=0,0,-1,-1,-1,-1,-1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 68.3129948452868 58.3606766815465 57.3733148285043 cerebhem 64.4848082082852 59.2237124310163 61.6435278804117 cortex 58.3146137576564 56.5909338094666 53.9338606598188 heart 63.347719747248 57.3101215343827 66.6124876752033 kidney 59.9080936660459 60.2016130539196 72.2501940593277 liver 61.0025705734809 65.963047855817 51.9647944157222 stomach 60.364779296787 58.9814991710015 86.4031899623768 testicle 62.1106055909121 67.9139742815487 56.3072877577794 cont.diffExp=9.95231816374026,5.26109577726886,1.72367994818978,6.03759821286527,-0.293519387873673,-4.96047728233611,1.38328012578553,-5.80336869063654 cont.diffExpScore=2.47649193618572 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=-0.0822100424088647 cont.tran.correlation=-0.0907084578928315 tran.covariance=-0.000709104833376138 cont.tran.covariance=-0.000247120954797923 tran.mean=55.4579791435352 cont.tran.mean=61.399485281525 weightedLogRatios: wLogRatio Lung -0.480326128880296 cerebhem 0.412030254484284 cortex -1.11184561189956 heart -1.44208507848461 kidney -0.937936723627998 liver -0.858153906967702 stomach -1.63733048645176 testicle -0.502769370463653 cont.weightedLogRatios: wLogRatio Lung 0.652720572738132 cerebhem 0.350973401549306 cortex 0.121541460485545 heart 0.410518390737646 kidney -0.0200156886294474 liver -0.324442025347849 stomach 0.0947868612072933 testicle -0.372805234411485 varWeightedLogRatios=0.412781352475051 cont.varWeightedLogRatios=0.125985167067254 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79952872758713 0.0808596771105684 46.9891652225078 1.05572717484989e-146 *** df.mm.trans1 0.0653639644980797 0.0684873018411376 0.954395380470634 0.34059504718708 df.mm.trans2 0.216943000010432 0.0684873018411376 3.16763829466740 0.00168315543103692 ** df.mm.exp2 0.495602532602527 0.0953768717378846 5.1962548526916 3.6048398993216e-07 *** df.mm.exp3 -0.129422049271378 0.0953768717378846 -1.35695422709037 0.175740345990450 df.mm.exp4 -0.153370420720682 0.0953768717378846 -1.60804624775465 0.108799026724881 df.mm.exp5 -0.140475643817666 0.0953768717378846 -1.47284809470080 0.141762767133927 df.mm.exp6 0.0905026379369412 0.0953768717378846 0.948895012888043 0.343381233101413 df.mm.exp7 0.183807182150464 0.0953768717378846 1.92716723458497 0.0548329259953355 . df.mm.exp8 -0.138957318663772 0.0953768717378846 -1.45692887732422 0.146104458224774 df.mm.trans1:exp2 -0.199611961386343 0.0825987938584981 -2.41664499034093 0.0162164621500194 * df.mm.trans2:exp2 -0.420468610785641 0.0825987938584981 -5.09049334916385 6.06708039959382e-07 *** df.mm.trans1:exp3 0.0450200133027638 0.0825987938584981 0.545044439509475 0.586097932060452 df.mm.trans2:exp3 0.204937014525039 0.0825987938584982 2.48111388740277 0.0136042441948540 * df.mm.trans1:exp4 0.029217711244772 0.0825987938584982 0.353730482975641 0.723770941403627 df.mm.trans2:exp4 0.272509775639765 0.0825987938584981 3.29919800168762 0.00107750912013711 ** df.mm.trans1:exp5 0.0476742976530498 0.0825987938584981 0.577179101848892 0.564219299552026 df.mm.trans2:exp5 0.165375634349272 0.0825987938584982 2.00215555971169 0.0461011576109574 * df.mm.trans1:exp6 -0.102825233542481 0.0825987938584981 -1.2448757268615 0.214077091523223 df.mm.trans2:exp6 -0.00921225739653237 0.0825987938584982 -0.111530168495125 0.911265003662226 df.mm.trans1:exp7 -0.222482243637522 0.0825987938584981 -2.69352896385705 0.00743803920902203 ** df.mm.trans2:exp7 0.0594924999834334 0.0825987938584981 0.72025870117851 0.471885012978724 df.mm.trans1:exp8 0.242430246439798 0.0825987938584981 2.93503373493698 0.00357336883936053 ** df.mm.trans2:exp8 0.244827637160779 0.0825987938584982 2.96405825949710 0.0032611174600493 ** df.mm.trans1:probe2 0.162132248144491 0.0412993969292491 3.92577761903505 0.000105631820836728 *** df.mm.trans1:probe3 0.0756316575463357 0.0412993969292491 1.83130174215139 0.0679736280632312 . df.mm.trans1:probe4 -0.0320017677663275 0.0412993969292491 -0.774872519837285 0.438980117108904 df.mm.trans1:probe5 0.122408387831569 0.0412993969292491 2.96392676244812 0.00326247397190873 ** df.mm.trans1:probe6 -0.0467060073302529 0.0412993969292491 -1.13091257507382 0.258928302975167 df.mm.trans2:probe2 0.108088819284955 0.0412993969292491 2.61720091143521 0.00928117040734453 ** df.mm.trans2:probe3 -0.0288087332138205 0.0412993969292491 -0.697558205587684 0.485953781197996 df.mm.trans2:probe4 -0.0938098185837598 0.0412993969292491 -2.27145734705201 0.0237748149945361 * df.mm.trans2:probe5 0.0199192076129282 0.0412993969292491 0.482312311897732 0.629909771006575 df.mm.trans2:probe6 0.00437979022484629 0.0412993969292491 0.106049738022795 0.915608517548466 df.mm.trans3:probe2 0.889611892812996 0.0412993969292491 21.5405540748455 1.70553635709196e-64 *** df.mm.trans3:probe3 0.133617187142729 0.0412993969292491 3.23533022459459 0.00134044006447972 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23341250717029 0.187096766170428 22.6268609224066 1.16133696684909e-68 *** df.mm.trans1 -0.00655148332197321 0.158469006507325 -0.0413423638247545 0.967048424816646 df.mm.trans2 -0.117962186289936 0.158469006507325 -0.744386482188763 0.457182236513295 df.mm.exp2 -0.114779600047371 0.220687305555386 -0.520100600070829 0.60334863938758 df.mm.exp3 -0.127220005738929 0.220687305555386 -0.576471788527958 0.564696587097095 df.mm.exp4 -0.242938933320292 0.220687305555386 -1.10082876180352 0.271788351884235 df.mm.exp5 -0.330787281963361 0.220687305555386 -1.49889582969394 0.134874339380011 df.mm.exp6 0.108281217739809 0.220687305555386 0.490654491735746 0.624002969766757 df.mm.exp7 -0.522557995529142 0.220687305555386 -2.36786612720682 0.0184778315421139 * df.mm.exp8 0.0751715736319349 0.220687305555386 0.340624819550706 0.733606795335082 df.mm.trans1:exp2 0.0571092546953135 0.191120812903703 0.298812326233084 0.765274806690731 df.mm.trans2:exp2 0.129459291018791 0.191120812903703 0.677368880196315 0.498655311323468 df.mm.trans1:exp3 -0.0310272772762987 0.191120812903703 -0.162343790845699 0.871136330454453 df.mm.trans2:exp3 0.0964264795312165 0.191120812903703 0.504531547695966 0.614230846603582 df.mm.trans1:exp4 0.167477835163687 0.191120812903703 0.876293024392228 0.381519974194072 df.mm.trans2:exp4 0.224773863988592 0.191120812903703 1.17608260750672 0.240425274994833 df.mm.trans1:exp5 0.199498887833062 0.191120812903703 1.04383653879486 0.297339207947619 df.mm.trans2:exp5 0.361844110292869 0.191120812903703 1.89327423212241 0.0592122617385874 . df.mm.trans1:exp6 -0.221465223662873 0.191120812903703 -1.15877083347516 0.247402953997945 df.mm.trans2:exp6 0.0141711679803724 0.191120812903703 0.0741476962402444 0.940938619901982 df.mm.trans1:exp7 0.398863796404123 0.191120812903703 2.08697205889916 0.0376719371361504 * df.mm.trans2:exp7 0.533139498345698 0.191120812903703 2.78954180994574 0.00559063202660071 ** df.mm.trans1:exp8 -0.170354826569168 0.191120812903703 -0.891346285006653 0.373404869468001 df.mm.trans2:exp8 0.0764279280129822 0.191120812903703 0.399893276152455 0.689498629698347 df.mm.trans1:probe2 0.00614425506246105 0.0955604064518516 0.0642970796232103 0.948773329538252 df.mm.trans1:probe3 0.00349407421382735 0.0955604064518516 0.0365640367549907 0.970855135866957 df.mm.trans1:probe4 -0.105876293358327 0.0955604064518516 -1.10795147582041 0.268704578776768 df.mm.trans1:probe5 0.0597176138271379 0.0955604064518516 0.624920048422217 0.53246338050623 df.mm.trans1:probe6 0.0116712234361963 0.0955604064518516 0.122134510196719 0.902868208520395 df.mm.trans2:probe2 -0.0895668792902596 0.0955604064518516 -0.937280225313693 0.349312554373605 df.mm.trans2:probe3 -0.133176432291256 0.0955604064518516 -1.39363610135289 0.164382702671624 df.mm.trans2:probe4 -0.0921917022498356 0.0955604064518516 -0.964747908395373 0.335390604645407 df.mm.trans2:probe5 -0.0610313167110258 0.0955604064518516 -0.638667403971085 0.523490521269133 df.mm.trans2:probe6 -0.0633056905037796 0.0955604064518516 -0.662467781943522 0.508142428298823 df.mm.trans3:probe2 -0.138453145587142 0.0955604064518516 -1.44885471638196 0.148345269693127 df.mm.trans3:probe3 -0.0395054767821664 0.0955604064518516 -0.413408421426832 0.679580889194104