chr17.10073_chr17_36061233_36061698_-_0.R fitVsDatCorrelation=0.871905947976871 cont.fitVsDatCorrelation=0.238246107845669 fstatistic=5733.57691661001,37,347 cont.fstatistic=1450.53393107030,37,347 residuals=-0.631910309440757,-0.0876749607082673,-0.00820769817542798,0.0848106878915835,0.825122322876797 cont.residuals=-0.640474282394519,-0.225562941111181,-0.0744830797939456,0.109032989612560,1.09306688735778 predictedValues: Include Exclude Both chr17.10073_chr17_36061233_36061698_-_0.R.tl.Lung 68.7345474447546 84.5727620098507 95.5304473642136 chr17.10073_chr17_36061233_36061698_-_0.R.tl.cerebhem 88.85189812527 101.701609030863 107.243017062143 chr17.10073_chr17_36061233_36061698_-_0.R.tl.cortex 72.4231891402663 74.9175024758162 94.9529199281267 chr17.10073_chr17_36061233_36061698_-_0.R.tl.heart 66.6849908446148 77.4424583276126 106.570049221360 chr17.10073_chr17_36061233_36061698_-_0.R.tl.kidney 63.7835781359065 77.9171591061583 101.693976253787 chr17.10073_chr17_36061233_36061698_-_0.R.tl.liver 56.3736972578116 77.1518808801426 98.6039690622441 chr17.10073_chr17_36061233_36061698_-_0.R.tl.stomach 62.833428941551 83.4856599409159 112.896083008945 chr17.10073_chr17_36061233_36061698_-_0.R.tl.testicle 64.7984073588988 78.3837123336972 115.837038551908 diffExp=-15.8382145650961,-12.8497109055932,-2.49431333554986,-10.7574674829978,-14.1335809702518,-20.778183622331,-20.6522309993649,-13.5853049747984 diffExpScore=0.991078518509091 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,-1,-1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,0,0,-1,-1,-1,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 90.3842022982721 78.1107536696696 84.3092477256836 cerebhem 82.3401294707222 78.7726640129889 90.6860740329697 cortex 84.380098170612 73.2777285970175 81.176700497581 heart 82.5054970527144 78.4668154186035 77.5911865451636 kidney 77.5198523220502 78.735050625168 93.5852975298332 liver 80.1613688776822 87.3127648794731 71.5499699375856 stomach 87.960008263158 75.4614286996131 94.7393131960163 testicle 84.7282922680601 69.9935708443546 86.4950892362614 cont.diffExp=12.2734486286025,3.56746545773328,11.1023695735946,4.03868163411093,-1.21519830311776,-7.15139600179089,12.4985795635449,14.7347214237056 cont.diffExpScore=1.30941198655345 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=0.800902604097837 cont.tran.correlation=-0.415227736268702 tran.covariance=0.00978733669838861 cont.tran.covariance=-0.00131878337755855 tran.mean=75.0035300846332 cont.tran.mean=80.631889091885 weightedLogRatios: wLogRatio Lung -0.898685485826892 cerebhem -0.615187313508881 cortex -0.145583919314183 heart -0.639312581320878 kidney -0.851754397070303 liver -1.31436149647336 stomach -1.21705660467465 testicle -0.812055045609153 cont.weightedLogRatios: wLogRatio Lung 0.646682332856241 cerebhem 0.194387283352823 cortex 0.615762664122246 heart 0.220218333986587 kidney -0.067790758665226 liver -0.378289369650245 stomach 0.674385307658436 testicle 0.829890570811672 varWeightedLogRatios=0.134028397692887 cont.varWeightedLogRatios=0.17709428303884 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.66399279366629 0.102424253871245 45.5360192277238 2.01178504752968e-148 *** df.mm.trans1 -0.507607083690352 0.0853334593423869 -5.94851172801585 6.60940623572056e-09 *** df.mm.trans2 -0.267347390968153 0.0853334593423869 -3.13297261154578 0.00187775812548045 ** df.mm.exp2 0.325497412878225 0.117571580773842 2.76850418048171 0.00593380195995576 ** df.mm.exp3 -0.0628862901294315 0.117571580773842 -0.534876623377192 0.593077745956454 df.mm.exp4 -0.227706600237083 0.117571580773842 -1.93674864910674 0.0535876027957793 . df.mm.exp5 -0.219245292282191 0.117571580773842 -1.86478135990981 0.0630560474031835 . df.mm.exp6 -0.321752050212486 0.117571580773842 -2.73664816016551 0.00652631177348786 ** df.mm.exp7 -0.269724838818717 0.117571580773842 -2.29413296175335 0.0223797122742230 * df.mm.exp8 -0.327706365449056 0.117571580773842 -2.787292331124 0.00560761041043656 ** df.mm.trans1:exp2 -0.0687784420934793 0.0993661217261702 -0.692171948534095 0.489292257089353 df.mm.trans2:exp2 -0.141066541315877 0.0993661217261702 -1.4196643570796 0.156603149200011 df.mm.trans1:exp3 0.115160883387766 0.0993661217261702 1.15895519908810 0.247271375682868 df.mm.trans2:exp3 -0.0583384214438209 0.0993661217261702 -0.587105750233344 0.557514480524288 df.mm.trans1:exp4 0.197434556174974 0.0993661217261702 1.98694034491009 0.0477150149417168 * df.mm.trans2:exp4 0.139629534921794 0.0993661217261702 1.40520262335065 0.160855656939784 df.mm.trans1:exp5 0.144489106668540 0.0993661217261702 1.45410834355313 0.146820141255526 df.mm.trans2:exp5 0.137279239129179 0.0993661217261702 1.38154973490350 0.167998870408810 df.mm.trans1:exp6 0.123502792416004 0.0993661217261702 1.24290643803477 0.214741496989799 df.mm.trans2:exp6 0.229915755533980 0.0993661217261702 2.31382438541351 0.0212611027869451 * df.mm.trans1:exp7 0.179960131810276 0.0993661217261702 1.81108136942493 0.0709928670971713 . df.mm.trans2:exp7 0.256787465979695 0.0993661217261702 2.58425569518897 0.0101669128123846 * df.mm.trans1:exp8 0.268735443867041 0.0993661217261702 2.70449766176457 0.00717798490102148 ** df.mm.trans2:exp8 0.251710267663759 0.0993661217261702 2.53315982641864 0.0117437770489116 * df.mm.trans1:probe2 0.141974974006637 0.0544250663212276 2.60863208082601 0.00948360821694418 ** df.mm.trans1:probe3 0.193659254831973 0.0544250663212276 3.5582731987676 0.000425230693615538 *** df.mm.trans1:probe4 0.068475638370272 0.0544250663212276 1.2581636183246 0.209178520073870 df.mm.trans1:probe5 0.134960350286051 0.0544250663212276 2.47974617962776 0.0136216456942178 * df.mm.trans1:probe6 0.199592151180034 0.0544250663212276 3.66728356382703 0.000283565273490151 *** df.mm.trans2:probe2 0.0963020803473057 0.0544250663212276 1.76944350933652 0.0776981443801994 . df.mm.trans2:probe3 0.148616971969738 0.0544250663212276 2.73067139858997 0.00664325578706974 ** df.mm.trans2:probe4 0.0598071632759413 0.0544250663212276 1.09889003943417 0.27257788093157 df.mm.trans2:probe5 0.0232901162974244 0.0544250663212276 0.427929957126033 0.66896758022687 df.mm.trans2:probe6 0.0816521682558794 0.0544250663212276 1.50026768500294 0.134454371677011 df.mm.trans3:probe2 1.27850968256575 0.0544250663212276 23.4911920000195 1.04764572644782e-73 *** df.mm.trans3:probe3 1.05604270045441 0.0544250663212276 19.4036088853147 2.55113693762381e-57 *** df.mm.trans3:probe4 0.347074409916258 0.0544250663212276 6.37710587007383 5.76092613367366e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35932777502464 0.203145469640028 21.4591434539462 1.29093284997678e-65 *** df.mm.trans1 0.134412527388721 0.16924805423439 0.794174727719906 0.427636499676471 df.mm.trans2 -0.0683942251045525 0.16924805423439 -0.404106418912409 0.68638340736138 df.mm.exp2 -0.157684862545785 0.233188264399239 -0.676212685711383 0.499356069668913 df.mm.exp3 -0.0947456505237994 0.233188264399239 -0.406305397777594 0.684768676828383 df.mm.exp4 -0.00361879934886274 0.233188264399239 -0.0155187884698479 0.987627214008748 df.mm.exp5 -0.249956482580597 0.233188264399239 -1.07190849944597 0.284506063949319 df.mm.exp6 0.155436620860569 0.233188264399239 0.66657136996589 0.505488905112635 df.mm.exp7 -0.178330818935196 0.233188264399239 -0.764750401975109 0.444939949778316 df.mm.exp8 -0.199940350730953 0.233188264399239 -0.857420296197402 0.391804436061299 df.mm.trans1:exp2 0.0644739524885911 0.197080053809775 0.327146006113955 0.743754716504407 df.mm.trans2:exp2 0.166123157436251 0.197080053809775 0.842922224877187 0.399852578444157 df.mm.trans1:exp3 0.0260077218646316 0.197080053809775 0.131965266712048 0.895088248084634 df.mm.trans2:exp3 0.0308746357895401 0.197080053809775 0.156660378321902 0.875603621715419 df.mm.trans1:exp4 -0.0875857773775411 0.197080053809775 -0.444417259303575 0.657017979885703 df.mm.trans2:exp4 0.00816686283924422 0.197080053809775 0.041439317076334 0.966969502181124 df.mm.trans1:exp5 0.096421046341693 0.197080053809775 0.489248122667758 0.624975151016673 df.mm.trans2:exp5 0.257917170534510 0.197080053809775 1.30869240975272 0.191504639173418 df.mm.trans1:exp6 -0.275464405641718 0.197080053809775 -1.39772848807724 0.163087511229934 df.mm.trans2:exp6 -0.0440676885532395 0.197080053809775 -0.223602986204654 0.823197821990846 df.mm.trans1:exp7 0.151143579774687 0.197080053809775 0.766914646372959 0.443653723157042 df.mm.trans2:exp7 0.143824728096648 0.197080053809775 0.72977820594402 0.466018114790406 df.mm.trans1:exp8 0.135320426963204 0.197080053809775 0.686626699898399 0.492776572180947 df.mm.trans2:exp8 0.0902160050816433 0.197080053809775 0.457763245633784 0.647409044263259 df.mm.trans1:probe2 0.0834941763655669 0.107945191105946 0.773486762218238 0.439760965064762 df.mm.trans1:probe3 -0.0602357158093757 0.107945191105946 -0.558021299441267 0.57718968065473 df.mm.trans1:probe4 -0.0143697978124914 0.107945191105946 -0.133121241115668 0.894174661557818 df.mm.trans1:probe5 0.0984777750612554 0.107945191105946 0.912294230547073 0.362247220181716 df.mm.trans1:probe6 -0.00407447404563076 0.107945191105946 -0.0377457671239079 0.96991209062975 df.mm.trans2:probe2 0.201917923259092 0.107945191105946 1.87055969043507 0.0622477161982572 . df.mm.trans2:probe3 0.0733338826173987 0.107945191105946 0.67936220100276 0.497361299140466 df.mm.trans2:probe4 0.129880488317738 0.107945191105946 1.20320772965479 0.229716197769975 df.mm.trans2:probe5 0.165936225366306 0.107945191105946 1.53722665795685 0.125149127851007 df.mm.trans2:probe6 0.100873365238642 0.107945191105946 0.934486883622606 0.350702625514775 df.mm.trans3:probe2 0.0419729524291635 0.107945191105946 0.388835778594046 0.697636114408168 df.mm.trans3:probe3 -0.0341625948078029 0.107945191105946 -0.316480933127194 0.751827793463368 df.mm.trans3:probe4 0.0287715698523429 0.107945191105946 0.266538690214595 0.789982682434673