chr15.8561_chr15_82757968_82762243_-_2.R fitVsDatCorrelation=0.924549785133895 cont.fitVsDatCorrelation=0.271015385898762 fstatistic=6284.7009960096,56,784 cont.fstatistic=973.123414625309,56,784 residuals=-0.903958158038568,-0.119900417937911,-0.00700523386483821,0.109771484162723,1.43326724706586 cont.residuals=-1.08419709061082,-0.349287681236916,-0.0791889583332046,0.241717057091854,2.48879585616228 predictedValues: Include Exclude Both chr15.8561_chr15_82757968_82762243_-_2.R.tl.Lung 79.1164056649771 92.1286978569564 57.8273436882223 chr15.8561_chr15_82757968_82762243_-_2.R.tl.cerebhem 90.1429189370215 85.089982032516 86.5253409224915 chr15.8561_chr15_82757968_82762243_-_2.R.tl.cortex 106.509699983653 76.9041550008132 66.0289129458101 chr15.8561_chr15_82757968_82762243_-_2.R.tl.heart 84.020003117726 76.5265209666616 57.3641151525264 chr15.8561_chr15_82757968_82762243_-_2.R.tl.kidney 83.5232909249965 92.9962917037772 54.5414226731555 chr15.8561_chr15_82757968_82762243_-_2.R.tl.liver 86.915202622432 89.0253158037289 55.3869815848286 chr15.8561_chr15_82757968_82762243_-_2.R.tl.stomach 91.9896659366245 80.0785410937886 57.8931383872157 chr15.8561_chr15_82757968_82762243_-_2.R.tl.testicle 101.74378023288 74.5812157323472 63.8702349271708 diffExp=-13.0122921919793,5.05293690450543,29.6055449828399,7.49348215106428,-9.47300077878073,-2.11011318129682,11.9111248428360,27.1625645005327 diffExpScore=1.83620693334906 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=0,0,1,0,0,0,0,1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,1,0,0,0,0,1 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 82.160325056493 98.9109277214503 95.4413553028954 cerebhem 99.1625459149423 76.3279644452726 117.780833892596 cortex 87.1753484095654 81.0877060572042 103.852079692590 heart 92.2952202027427 70.17997858236 86.6561951307637 kidney 99.3800351793715 101.730949691920 88.4647029945568 liver 85.984591078947 79.5734665997334 110.446132265920 stomach 95.7170431098012 87.8826804461314 110.399644968131 testicle 98.0240632463674 106.778786009249 93.1753507430983 cont.diffExp=-16.7506026649573,22.8345814696696,6.08764235236119,22.1152416203826,-2.35091451254888,6.41112447921354,7.83436266366974,-8.75472276288163 cont.diffExpScore=2.42381369924509 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=0,0,0,1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,1,0,1,0,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=-0.722493111385041 cont.tran.correlation=0.165867735982594 tran.covariance=-0.00650343034664335 cont.tran.covariance=0.00146998403805043 tran.mean=86.9557304756812 cont.tran.mean=90.148226984472 weightedLogRatios: wLogRatio Lung -0.677136060156094 cerebhem 0.258008560336043 cortex 1.46730068924632 heart 0.409574842351231 kidney -0.48118038499861 liver -0.107391696519334 stomach 0.617398828549209 testicle 1.38736559314413 cont.weightedLogRatios: wLogRatio Lung -0.83523073841419 cerebhem 1.16881980035727 cortex 0.320813696634840 heart 1.20200931392589 kidney -0.107798363861755 liver 0.34213977289032 stomach 0.385868127868095 testicle -0.395907007111498 varWeightedLogRatios=0.623199287078626 cont.varWeightedLogRatios=0.500663279374146 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.84512886973924 0.106889006422032 35.9730995585966 4.04361957561646e-168 *** df.mm.trans1 -0.0959320764056924 0.0931633511014485 -1.02971903942387 0.303459478883004 df.mm.trans2 0.624135823824072 0.0831382372912387 7.5072053986144 1.64388992150226e-13 *** df.mm.exp2 -0.351976630609022 0.108770631563634 -3.23595280774943 0.00126326025799871 ** df.mm.exp3 -0.0159417627394397 0.108770631563635 -0.146563116443001 0.883514545706417 df.mm.exp4 -0.117371681997372 0.108770631563634 -1.07907511715334 0.280886108171214 df.mm.exp5 0.122079673738165 0.108770631563634 1.12235878364597 0.262053526310113 df.mm.exp6 0.102864152028269 0.108770631563634 0.945697846464097 0.344594086482249 df.mm.exp7 0.00944028249616866 0.108770631563634 0.0867907298179636 0.930860017439312 df.mm.exp8 -0.059152084220785 0.108770631563634 -0.543824039361021 0.586717172752779 df.mm.trans1:exp2 0.482452772231614 0.101568197429763 4.7500377523707 2.41847624951484e-06 *** df.mm.trans2:exp2 0.272499450048045 0.0792226476340117 3.43966603220486 0.000613210733505136 *** df.mm.trans1:exp3 0.313257566023352 0.101568197429763 3.08420917128098 0.00211255081216633 ** df.mm.trans2:exp3 -0.164684820252495 0.0792226476340118 -2.07875935948640 0.0379641201688247 * df.mm.trans1:exp4 0.177506327468773 0.101568197429763 1.74765656928709 0.0809149967156045 . df.mm.trans2:exp4 -0.0681774472105925 0.0792226476340117 -0.860580266460606 0.389732352526685 df.mm.trans1:exp5 -0.0678744049192887 0.101568197429763 -0.668264344911958 0.504161557345945 df.mm.trans2:exp5 -0.112706544796674 0.0792226476340117 -1.42265561884966 0.155233765696412 df.mm.trans1:exp6 -0.00885144856311508 0.101568197429763 -0.0871478355145184 0.930576255700956 df.mm.trans2:exp6 -0.137129864774501 0.0792226476340117 -1.73094271486615 0.0838554236803921 . df.mm.trans1:exp7 0.141315704091145 0.101568197429763 1.39133811239358 0.164517590874877 df.mm.trans2:exp7 -0.149618855028525 0.0792226476340118 -1.8885869066094 0.0593161199084792 . df.mm.trans1:exp8 0.310689521365935 0.101568197429763 3.05892522687316 0.00229692163164957 ** df.mm.trans2:exp8 -0.152145729374725 0.0792226476340117 -1.92048276494872 0.0551594055944208 . df.mm.trans1:probe2 0.570662525070466 0.0645454630044347 8.84124922972909 6.17282017213259e-18 *** df.mm.trans1:probe3 0.107375371708270 0.0645454630044347 1.66356187887122 0.0965996518656687 . df.mm.trans1:probe4 1.11431594251761 0.0645454630044347 17.2640475511199 7.59529877577539e-57 *** df.mm.trans1:probe5 -0.0591096792368614 0.0645454630044347 -0.915783642806934 0.36006197340139 df.mm.trans1:probe6 1.58811244553614 0.0645454630044347 24.6045557908079 1.65906671207675e-99 *** df.mm.trans1:probe7 0.577415758843279 0.0645454630044347 8.94587678151146 2.62351029557517e-18 *** df.mm.trans1:probe8 0.76337798366606 0.0645454630044347 11.8269812955499 8.17694528146212e-30 *** df.mm.trans1:probe9 2.81774822179331 0.0645454630044347 43.6552484192376 4.48869317744308e-212 *** df.mm.trans1:probe10 0.862061037248894 0.0645454630044347 13.3558734746339 8.2869827511406e-37 *** df.mm.trans1:probe11 1.04519582436059 0.0645454630044347 16.1931726214247 4.29881348673052e-51 *** df.mm.trans1:probe12 1.07259573537333 0.0645454630044347 16.6176782293690 2.36349332346421e-53 *** df.mm.trans1:probe13 0.659730193833415 0.0645454630044347 10.2211706776058 4.15812727584908e-23 *** df.mm.trans1:probe14 0.900588310791742 0.0645454630044347 13.9527748174936 1.10625659414456e-39 *** df.mm.trans1:probe15 0.796258719927872 0.0645454630044347 12.3364010863655 4.40684498662319e-32 *** df.mm.trans1:probe16 1.06724558440239 0.0645454630044347 16.5347885773019 6.56154521464886e-53 *** df.mm.trans1:probe17 0.678797487476221 0.0645454630044347 10.5165794136388 2.74420958931444e-24 *** df.mm.trans1:probe18 0.792315376952288 0.0645454630044347 12.2753070482716 8.30918816252683e-32 *** df.mm.trans1:probe19 0.559368816394146 0.0645454630044347 8.66627630133683 2.53596565033199e-17 *** df.mm.trans1:probe20 0.713416009212773 0.0645454630044347 11.0529226378585 1.70820936139679e-26 *** df.mm.trans1:probe21 0.847709735038844 0.0645454630044347 13.1335293850259 9.31890246326327e-36 *** df.mm.trans1:probe22 0.554799056343159 0.0645454630044347 8.5954772112339 4.46332920739788e-17 *** df.mm.trans2:probe2 -0.0319611613505036 0.0645454630044347 -0.495172857437055 0.620616857262124 df.mm.trans2:probe3 0.217771997386015 0.0645454630044347 3.37393191169846 0.00077742497133187 *** df.mm.trans2:probe4 0.0986968526861215 0.0645454630044347 1.52910596798013 0.126641526160890 df.mm.trans2:probe5 0.198973332272705 0.0645454630044347 3.082685025577 0.00212326773356785 ** df.mm.trans2:probe6 0.217502323112528 0.0645454630044347 3.36975386012157 0.000789135177838713 *** df.mm.trans3:probe2 -0.165390265214927 0.0645454630044347 -2.56238405484152 0.0105813538826518 * df.mm.trans3:probe3 -0.284478934622845 0.0645454630044347 -4.40741953626235 1.19178497173129e-05 *** df.mm.trans3:probe4 -0.50748905194489 0.0645454630044347 -7.86250540816513 1.24485508800974e-14 *** df.mm.trans3:probe5 -0.512039308395458 0.0645454630044347 -7.93300232985048 7.37330317201374e-15 *** df.mm.trans3:probe6 -0.206709516212131 0.0645454630044347 -3.20254138076179 0.00141720235241443 ** df.mm.trans3:probe7 -0.534989077680071 0.0645454630044347 -8.28856208907067 4.95655268794048e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43746944111486 0.270005623628251 16.4347296974246 2.24338309958053e-52 *** df.mm.trans1 -0.099562257162293 0.235334105493747 -0.423067693284 0.67236189339241 df.mm.trans2 0.14975712102795 0.210010293467817 0.713094194360998 0.475999664530493 df.mm.exp2 -0.28140606717874 0.274758679034033 -1.02419355111212 0.306059771700795 df.mm.exp3 -0.223894896010776 0.274758679034033 -0.814878339049822 0.415389414410927 df.mm.exp4 -0.130273358834737 0.274758679034033 -0.474137374996628 0.635533966661066 df.mm.exp5 0.294298915212731 0.274758679034033 1.07111781235590 0.284446082463802 df.mm.exp6 -0.318059370372564 0.274758679034033 -1.15759535418777 0.247381747339353 df.mm.exp7 -0.111088067076437 0.274758679034033 -0.404311403253899 0.686094011342338 df.mm.exp8 0.277108787187307 0.274758679034033 1.00855335366124 0.313500052575918 df.mm.trans1:exp2 0.469493926837754 0.256565061326715 1.82992151936031 0.0676412355812001 . df.mm.trans2:exp2 0.0222257196549002 0.200119367706027 0.111062312007449 0.911595359852972 df.mm.trans1:exp3 0.2831439632366 0.256565061326715 1.10359517298436 0.270107400390748 df.mm.trans2:exp3 0.0252065305594529 0.200119367706027 0.125957476522118 0.899797878265617 df.mm.trans1:exp4 0.246593191553945 0.256565061326715 0.961133173312043 0.33678153931841 df.mm.trans2:exp4 -0.212883301350050 0.200119367706027 -1.06378160090318 0.287755180511867 df.mm.trans1:exp5 -0.104020197046452 0.256565061326715 -0.405433992097586 0.6852691415257 df.mm.trans2:exp5 -0.266187060192426 0.200119367706027 -1.33014142131137 0.183858484936481 df.mm.trans1:exp6 0.363554955075688 0.256565061326715 1.41700882105974 0.156877593659057 df.mm.trans2:exp6 0.100520348322237 0.200119367706027 0.502301948454589 0.615596186535046 df.mm.trans1:exp7 0.263811916604762 0.256565061326715 1.02824568255932 0.304151397568823 df.mm.trans2:exp7 -0.00712890982289576 0.200119367706027 -0.0356232877637714 0.971591806993003 df.mm.trans1:exp8 -0.100568317323561 0.256565061326715 -0.391979784010792 0.695179677629296 df.mm.trans2:exp8 -0.200569238421810 0.200119367706027 -1.00224801187881 0.316533054906209 df.mm.trans1:probe2 0.180397599487703 0.163044250987577 1.10643336637149 0.268878369318522 df.mm.trans1:probe3 0.183915784949763 0.163044250987577 1.12801146827174 0.259660153174316 df.mm.trans1:probe4 0.127126779627507 0.163044250987577 0.779707219711743 0.435798537442187 df.mm.trans1:probe5 -0.00585971704390256 0.163044250987577 -0.0359394275382885 0.9713398059478 df.mm.trans1:probe6 0.259812077844184 0.163044250987577 1.59350652519468 0.111449656778633 df.mm.trans1:probe7 -0.00076092119016457 0.163044250987577 -0.00466696118112467 0.996277504469275 df.mm.trans1:probe8 0.127591342451838 0.163044250987577 0.782556524863666 0.434123876088650 df.mm.trans1:probe9 0.161063700011143 0.163044250987577 0.98785267824877 0.32352957223967 df.mm.trans1:probe10 0.125608221675085 0.163044250987577 0.770393441745183 0.441298623187674 df.mm.trans1:probe11 0.123105164256914 0.163044250987577 0.755041429006251 0.450450878356053 df.mm.trans1:probe12 -0.0631614962308492 0.163044250987577 -0.387388674229683 0.698573627574777 df.mm.trans1:probe13 -0.170700153673909 0.163044250987577 -1.04695598060011 0.29544254208506 df.mm.trans1:probe14 -0.0787257347518557 0.163044250987577 -0.48284888473531 0.629337834079462 df.mm.trans1:probe15 0.219359027899193 0.163044250987577 1.34539566142634 0.178886484267676 df.mm.trans1:probe16 0.321038648203612 0.163044250987577 1.96902771032432 0.0493017141829146 * df.mm.trans1:probe17 -0.0564758373188259 0.163044250987577 -0.346383493908834 0.72914746807183 df.mm.trans1:probe18 0.23719393014646 0.163044250987577 1.45478254344909 0.146129714870462 df.mm.trans1:probe19 0.040587590607191 0.163044250987577 0.248936042585662 0.803475452791167 df.mm.trans1:probe20 0.148248086085446 0.163044250987577 0.909250618697013 0.363497278835942 df.mm.trans1:probe21 0.069274052782717 0.163044250987577 0.424878843400588 0.67104157787694 df.mm.trans1:probe22 0.103556657448452 0.163044250987577 0.635144488819433 0.525519504216713 df.mm.trans2:probe2 -0.121282573097831 0.163044250987577 -0.74386292287651 0.457182280872854 df.mm.trans2:probe3 0.0507021107413348 0.163044250987577 0.310971472064955 0.755904970032571 df.mm.trans2:probe4 0.0478266992766153 0.163044250987577 0.293335698664158 0.769343197544855 df.mm.trans2:probe5 0.0504404335605499 0.163044250987577 0.309366526295938 0.757124930965233 df.mm.trans2:probe6 0.0632244487204425 0.163044250987577 0.387774781002611 0.698287966712011 df.mm.trans3:probe2 0.163716906850626 0.163044250987577 1.00412560307386 0.31562788369287 df.mm.trans3:probe3 0.164857296932845 0.163044250987577 1.01111996242913 0.312270965594223 df.mm.trans3:probe4 -0.151224984881602 0.163044250987577 -0.927508844780575 0.353947846127015 df.mm.trans3:probe5 0.26385778488271 0.163044250987577 1.61832007742986 0.105995840452756 df.mm.trans3:probe6 -0.0369125202384824 0.163044250987577 -0.226395717818317 0.820952669904935 df.mm.trans3:probe7 0.091639229438417 0.163044250987577 0.562051276775157 0.5742417313285