chr15.8182_chr15_76423884_76424433_-_1.R fitVsDatCorrelation=0.91173715333844 cont.fitVsDatCorrelation=0.345641380278906 fstatistic=13221.8463455827,39,393 cont.fstatistic=2525.54164272095,39,393 residuals=-0.348461262183153,-0.081358976829422,0.00203152229174641,0.077963082254033,0.571768919971914 cont.residuals=-0.674878608255091,-0.188890556393864,-0.0163049784654840,0.173106502734414,0.743661898145832 predictedValues: Include Exclude Both chr15.8182_chr15_76423884_76424433_-_1.R.tl.Lung 95.0438275793354 73.8767971369194 58.9472030595963 chr15.8182_chr15_76423884_76424433_-_1.R.tl.cerebhem 86.0708008994445 59.5804423553849 52.5520917437844 chr15.8182_chr15_76423884_76424433_-_1.R.tl.cortex 86.5729933137764 74.8221805068431 63.1910069578557 chr15.8182_chr15_76423884_76424433_-_1.R.tl.heart 89.5146574588587 81.731146695795 60.8122484136624 chr15.8182_chr15_76423884_76424433_-_1.R.tl.kidney 104.66271475482 79.2629738044395 62.1275586769121 chr15.8182_chr15_76423884_76424433_-_1.R.tl.liver 114.662124882992 73.996704005254 63.9018046674261 chr15.8182_chr15_76423884_76424433_-_1.R.tl.stomach 105.671633262803 114.755048492137 68.4792778633713 chr15.8182_chr15_76423884_76424433_-_1.R.tl.testicle 96.1783430126424 87.8485203864728 63.6498103112846 diffExp=21.167030442416,26.4903585440596,11.7508128069333,7.78351076306359,25.3997409503804,40.6654208777384,-9.08341522933483,8.3298226261696 diffExpScore=1.12858732931207 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,1,0,0,0,1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,1,0,0,1,1,0,0 diffExp1.3Score=0.75 diffExp1.2=1,1,0,0,1,1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 73.4591137248293 93.8031004560595 83.3671163095111 cerebhem 79.3994862918879 75.1136608848009 76.8551799131893 cortex 66.2218012129853 86.0164656802903 78.3837204644865 heart 82.0267798253237 84.9896156389524 79.5206363911484 kidney 77.2084856640934 78.0807717177666 71.1252972378635 liver 79.6763327434478 88.3467130801579 77.6401215248888 stomach 73.5036175092815 84.8142091978859 82.8470427946002 testicle 73.4360350170348 86.7390661737396 73.7565862732317 cont.diffExp=-20.3439867312301,4.285825407087,-19.7946644673050,-2.9628358136287,-0.872286053673264,-8.67038033671007,-11.3105916886044,-13.3030311567048 cont.diffExpScore=1.10235840380190 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=-1,0,-1,0,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.399776842765706 cont.tran.correlation=-0.326147323894673 tran.covariance=0.008427813277554 cont.tran.covariance=-0.00154831748775209 tran.mean=89.015681784245 cont.tran.mean=80.1772034261586 weightedLogRatios: wLogRatio Lung 1.11568016638270 cerebhem 1.57114833943179 cortex 0.640100551838639 heart 0.404705775545543 kidney 1.2541413685601 liver 1.98093698394194 stomach -0.387707093846169 testicle 0.409550220146831 cont.weightedLogRatios: wLogRatio Lung -1.08029928120045 cerebhem 0.241198625206239 cortex -1.13079238297034 heart -0.157006072555522 kidney -0.0488937783221376 liver -0.457563913278179 stomach -0.625313796830638 testicle -0.7291678438591 varWeightedLogRatios=0.570972140536431 cont.varWeightedLogRatios=0.239309589649046 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.60736085701108 0.0660145554148016 69.7931059000669 1.51193998360644e-223 *** df.mm.trans1 -0.0946249193257875 0.0539006587876494 -1.75554290901301 0.0799455333459099 . df.mm.trans2 -0.317936556896029 0.0539006587876494 -5.89856532456482 7.91412457318063e-09 *** df.mm.exp2 -0.199402056144647 0.0732365736846234 -2.72271142835445 0.00676327996637537 ** df.mm.exp3 -0.150154463193116 0.0732365736846234 -2.05026608480788 0.0410013926073812 * df.mm.exp4 0.00995157079681069 0.0732365736846234 0.135882528307029 0.891983721761946 df.mm.exp5 0.11422962613111 0.0732365736846234 1.55973471155292 0.1196274691061 df.mm.exp6 0.108567957611465 0.0732365736846234 1.48242813869185 0.139027817837673 df.mm.exp7 0.396510393148251 0.0732365736846234 5.41410354416267 1.07437679236856e-07 *** df.mm.exp8 0.108327084464009 0.0732365736846234 1.47913916522768 0.139904205878737 df.mm.trans1:exp2 0.10023415181799 0.0597974120123565 1.67622892772145 0.0944886353649341 . df.mm.trans2:exp2 -0.0156693741929595 0.0597974120123565 -0.262041009228318 0.793427116746041 df.mm.trans1:exp3 0.0568042464703721 0.0597974120123565 0.949944898261385 0.342724055989598 df.mm.trans2:exp3 0.162870032900255 0.0597974120123565 2.7236970199747 0.00674351131962501 ** df.mm.trans1:exp4 -0.0698873165898603 0.0597974120123565 -1.16873480369717 0.243218930356455 df.mm.trans2:exp4 0.0910847887683091 0.0597974120123565 1.52322292391998 0.128507178624673 df.mm.trans1:exp5 -0.0178248148783171 0.0597974120123565 -0.298086727810792 0.765794498911124 df.mm.trans2:exp5 -0.0438573216176216 0.0597974120123565 -0.733431768059778 0.463732405620089 df.mm.trans1:exp6 0.0790836735758512 0.0597974120123565 1.32252669328748 0.186761930335662 df.mm.trans2:exp6 -0.106946208025652 0.0597974120123565 -1.78847552806387 0.0744694849568267 . df.mm.trans1:exp7 -0.290512034711549 0.0597974120123565 -4.85827103439723 1.71347022143430e-06 *** df.mm.trans2:exp7 0.043890648241948 0.0597974120123565 0.73398909358951 0.463393047392796 df.mm.trans1:exp8 -0.0964610049003814 0.0597974120123565 -1.61313009466779 0.107518902775476 df.mm.trans2:exp8 0.0648880853715132 0.0597974120123565 1.08513200133318 0.278528388215514 df.mm.trans1:probe2 -0.198285941984245 0.0366182868423117 -5.41494316318286 1.06969654383970e-07 *** df.mm.trans1:probe3 0.126778907349358 0.0366182868423117 3.46217472967269 0.000594693746172599 *** df.mm.trans1:probe4 0.0706995808542098 0.0366182868423117 1.93071787215665 0.0542369888301734 . df.mm.trans1:probe5 0.418481097101287 0.0366182868423117 11.4281997654172 2.61119421021345e-26 *** df.mm.trans1:probe6 0.0815526423155769 0.0366182868423117 2.22710152079929 0.0265063356470533 * df.mm.trans2:probe2 0.05689047143539 0.0366182868423117 1.55360821985954 0.121082796026523 df.mm.trans2:probe3 0.0250893037724741 0.0366182868423117 0.68515777050181 0.493648279887715 df.mm.trans2:probe4 0.0235776670458752 0.0366182868423117 0.643876846216401 0.520030870689391 df.mm.trans2:probe5 0.100184910000788 0.0366182868423117 2.73592564371489 0.00650253379098375 ** df.mm.trans2:probe6 -0.0500483277528449 0.0366182868423117 -1.36675776145308 0.172482601937942 df.mm.trans3:probe2 -0.0682258242656164 0.0366182868423117 -1.86316264765240 0.0631851321931613 . df.mm.trans3:probe3 -0.304991521659115 0.0366182868423117 -8.3289402088222 1.37338665573274e-15 *** df.mm.trans3:probe4 -0.0237602437559294 0.0366182868423117 -0.648862789738021 0.516806050160484 df.mm.trans3:probe5 -0.243623167800686 0.0366182868423117 -6.65304657341817 9.654589005475e-11 *** df.mm.trans3:probe6 -0.0683424479756091 0.0366182868423117 -1.86634749653664 0.0627373440717292 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42570505610302 0.150802738503079 29.3476438162471 4.4557809114079e-101 *** df.mm.trans1 -0.168772879055942 0.123129920382302 -1.37068941920798 0.171254119239495 df.mm.trans2 0.148180612890456 0.123129920382302 1.20344927074081 0.229526927805770 df.mm.exp2 -0.063101469637101 0.16730061727792 -0.37717415909038 0.706247768551592 df.mm.exp3 -0.128740729343482 0.16730061727792 -0.769517360056226 0.44204849708725 df.mm.exp4 0.0588853498355527 0.16730061727792 0.351973296893055 0.725046942222125 df.mm.exp5 0.025137180928137 0.16730061727792 0.150251573109136 0.880643221283488 df.mm.exp6 0.0924843163575931 0.16730061727792 0.552803198591659 0.5807124653833 df.mm.exp7 -0.093871279068827 0.16730061727792 -0.561093441232723 0.575053615045753 df.mm.exp8 0.0438758891453643 0.16730061727792 0.262257783977435 0.793260118095559 df.mm.trans1:exp2 0.140864392406481 0.136600381994520 1.03121521587057 0.303074103419115 df.mm.trans2:exp2 -0.159093994909657 0.136600381994520 -1.16466727681654 0.244860182435041 df.mm.trans1:exp3 0.0250214859430552 0.136600381994520 0.18317288412897 0.854756830917592 df.mm.trans2:exp3 0.0420815593008569 0.136600381994520 0.308063262242892 0.75819740485663 df.mm.trans1:exp4 0.0514314517603448 0.136600381994520 0.376510306994662 0.706740743829624 df.mm.trans2:exp4 -0.157554179130435 0.136600381994520 -1.15339486486030 0.249449334805580 df.mm.trans1:exp5 0.0246432124034169 0.136600381994520 0.180403685872603 0.856928650227433 df.mm.trans2:exp5 -0.208591264560194 0.136600381994520 -1.52701816433107 0.127560880590423 df.mm.trans1:exp6 -0.0112407045338646 0.136600381994520 -0.0822889685207142 0.934458820377421 df.mm.trans2:exp6 -0.152413231046213 0.136600381994520 -1.11575991824333 0.265206680455193 df.mm.trans1:exp7 0.0944769263025008 0.136600381994520 0.691630030041139 0.489578072325349 df.mm.trans2:exp7 -0.00686354024278083 0.136600381994520 -0.0502453956758055 0.959952363228261 df.mm.trans1:exp8 -0.0441901092973847 0.136600381994520 -0.323499163414913 0.746489313263543 df.mm.trans2:exp8 -0.122169425973418 0.136600381994520 -0.894356400689418 0.371678619970132 df.mm.trans1:probe2 0.091308323509955 0.08365030863896 1.09154795715157 0.275700571696771 df.mm.trans1:probe3 0.0226024450567294 0.08365030863896 0.270201573962901 0.787147015313821 df.mm.trans1:probe4 0.172009018523514 0.08365030863896 2.05628671695542 0.0404147333613721 * df.mm.trans1:probe5 0.0324555720304871 0.08365030863896 0.387991061342850 0.698232718773332 df.mm.trans1:probe6 0.159186223362176 0.08365030863896 1.90299624654386 0.0577715240132425 . df.mm.trans2:probe2 -0.245838339723856 0.08365030863896 -2.93888144256478 0.00348833238523609 ** df.mm.trans2:probe3 0.0641858121844176 0.08365030863896 0.767311122083812 0.443357285592042 df.mm.trans2:probe4 -0.0833313921830988 0.08365030863896 -0.996187504134172 0.319772010138060 df.mm.trans2:probe5 -0.0139055660843206 0.08365030863896 -0.166234486286690 0.868057908865125 df.mm.trans2:probe6 -0.113363629693252 0.08365030863896 -1.35520874384979 0.176129454857911 df.mm.trans3:probe2 0.0196452123555005 0.08365030863896 0.234849251307494 0.814448052302632 df.mm.trans3:probe3 0.0449835723963364 0.08365030863896 0.537757398965356 0.59104892609936 df.mm.trans3:probe4 0.139412357758192 0.08365030863896 1.66660900630868 0.0963889023422866 . df.mm.trans3:probe5 -0.00951472284167194 0.08365030863896 -0.113744025532985 0.909498794722481 df.mm.trans3:probe6 -0.08567950274777 0.08365030863896 -1.02425805883835 0.306343445197918