chr3.15164_chr3_90282325_90285803_+_1.R fitVsDatCorrelation=0.794592022222353 cont.fitVsDatCorrelation=0.321561002535906 fstatistic=9171.82543166833,46,554 cont.fstatistic=3763.77143411694,46,554 residuals=-0.640807617519596,-0.0879374526222571,0.00163918215584185,0.0800769380919029,0.73860201181088 cont.residuals=-0.68107911363541,-0.178070236341741,-0.0287311923326650,0.139256561585846,0.824430074508091 predictedValues: Include Exclude Both chr3.15164_chr3_90282325_90285803_+_1.R.tl.Lung 48.2384423324819 63.825801305032 79.058029665007 chr3.15164_chr3_90282325_90285803_+_1.R.tl.cerebhem 68.5109610237212 67.9691502758648 67.9969168091232 chr3.15164_chr3_90282325_90285803_+_1.R.tl.cortex 49.5558089131600 61.2355508050439 73.135317628761 chr3.15164_chr3_90282325_90285803_+_1.R.tl.heart 53.1384683421306 62.7846274739888 78.4487812749709 chr3.15164_chr3_90282325_90285803_+_1.R.tl.kidney 48.3518886039939 54.7975834088808 70.2926167282402 chr3.15164_chr3_90282325_90285803_+_1.R.tl.liver 54.8808021916497 51.8572835314298 62.5630149141273 chr3.15164_chr3_90282325_90285803_+_1.R.tl.stomach 54.4577070827902 61.4573766110916 79.7206531103061 chr3.15164_chr3_90282325_90285803_+_1.R.tl.testicle 57.6701207290214 61.7199193629409 74.1156908600097 diffExp=-15.58735897255,0.541810747856402,-11.6797418918840,-9.64615913185825,-6.44569480488688,3.02351866021997,-6.9996695283014,-4.04979863391952 diffExpScore=1.11825410861353 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=-1,0,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=-1,0,-1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 80.0085655692688 70.3990424661854 64.8612676014948 cerebhem 66.615705831755 66.7918928387119 67.5224592366527 cortex 75.4200973607455 67.7279560600994 61.70225861784 heart 64.3397993226345 70.9885160129183 65.4813127447432 kidney 66.4435906561457 69.5736868547434 64.2149557281284 liver 60.3949238359962 65.2344552590329 65.0127701214355 stomach 67.6958291505254 69.9979529124648 62.8927826469168 testicle 64.0331729143978 84.9364436429692 66.9633076921296 cont.diffExp=9.60952310308338,-0.176187006956894,7.69214130064603,-6.6487166902838,-3.13009619859771,-4.83953142303664,-2.30212376193938,-20.9032707285714 cont.diffExpScore=2.54866457635628 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,-1 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,0,0,0,-1 cont.diffExp1.2Score=0.5 tran.correlation=0.475818836799016 cont.tran.correlation=-0.124988527355086 tran.covariance=0.0042746545095021 cont.tran.covariance=-0.000758782640946246 tran.mean=57.5282182495763 cont.tran.mean=69.4126019180371 weightedLogRatios: wLogRatio Lung -1.12452897276335 cerebhem 0.0335300389572623 cortex -0.84840010912508 heart -0.676628868232673 kidney -0.493189413264359 liver 0.225359779657777 stomach -0.490677269901447 testicle -0.277488093650156 cont.weightedLogRatios: wLogRatio Lung 0.552525578843212 cerebhem -0.0110942983582708 cortex 0.45926740844441 heart -0.414339466559175 kidney -0.194230703265640 liver -0.31907981134418 stomach -0.141515602324004 testicle -1.21494277638777 varWeightedLogRatios=0.198275254597331 cont.varWeightedLogRatios=0.302538414928554 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.50152803613797 0.0781866212432387 44.7842352113503 2.95098283016072e-186 *** df.mm.trans1 0.368014051106033 0.062108155018588 5.92537406715579 5.47781636996054e-09 *** df.mm.trans2 0.634013952763676 0.062108155018588 10.2082239051204 1.55526009332104e-22 *** df.mm.exp2 0.564453669954901 0.0826674777988308 6.82800159124831 2.26936557143447e-11 *** df.mm.exp3 0.0633843680892358 0.0826674777988307 0.766738864871141 0.443563394004472 df.mm.exp4 0.0880337867339282 0.0826674777988308 1.06491439049533 0.287378578557306 df.mm.exp5 -0.0326470354353495 0.0826674777988308 -0.394919940763105 0.693053973450446 df.mm.exp6 0.155353066828413 0.0826674777988308 1.87925253031744 0.0607347110610893 . df.mm.exp7 0.0751079228446666 0.0826674777988308 0.90855466798491 0.363980306090947 df.mm.exp8 0.209587015511517 0.0826674777988308 2.53530192395057 0.0115089198350128 * df.mm.trans1:exp2 -0.213616184483668 0.0636995697166549 -3.35349493621173 0.000852509091075797 *** df.mm.trans2:exp2 -0.501557257902187 0.0636995697166549 -7.87379349865608 1.82339487666208e-14 *** df.mm.trans1:exp3 -0.0364411427618181 0.0636995697166549 -0.57207831895747 0.56750097100357 df.mm.trans2:exp3 -0.104813969487361 0.0636995697166549 -1.64544234684142 0.100445822662729 df.mm.trans1:exp4 0.00871106817291474 0.0636995697166549 0.136752386423689 0.891276201232161 df.mm.trans2:exp4 -0.104481046439836 0.0636995697166549 -1.64021589006932 0.101527913990702 df.mm.trans1:exp5 0.0349960557270805 0.0636995697166549 0.549392340996149 0.582957609159226 df.mm.trans2:exp5 -0.119864387730305 0.0636995697166549 -1.88171424490746 0.060398849116094 . df.mm.trans1:exp6 -0.0263457282635861 0.0636995697166549 -0.413593504332537 0.67933188455909 df.mm.trans2:exp6 -0.363015186664435 0.063699569716655 -5.69886403125138 1.96139290353858e-08 *** df.mm.trans1:exp7 0.0461601987973136 0.0636995697166549 0.724654797554222 0.468969764417337 df.mm.trans2:exp7 -0.112921569315002 0.0636995697166549 -1.77272106887526 0.076824382804731 . df.mm.trans1:exp8 -0.031004076381307 0.0636995697166549 -0.486723482108556 0.626646799446757 df.mm.trans2:exp8 -0.243137812284626 0.0636995697166549 -3.81694591291807 0.000150347059322426 *** df.mm.trans1:probe2 0.0331621808138691 0.0456312122088587 0.726743367282956 0.46769013923535 df.mm.trans1:probe3 0.0888233891847916 0.0456312122088587 1.94654897131021 0.052093932999088 . df.mm.trans1:probe4 0.0497465496839417 0.0456312122088587 1.09018689786821 0.276104773310326 df.mm.trans1:probe5 -0.00363770207963044 0.0456312122088587 -0.0797196020780756 0.936489056639763 df.mm.trans1:probe6 -0.0424250983645755 0.0456312122088587 -0.929738578286973 0.352911387134666 df.mm.trans2:probe2 0.0325823596631985 0.0456312122088587 0.714036688617119 0.475505212198985 df.mm.trans2:probe3 0.0151045550119236 0.0456312122088587 0.331013669827321 0.74075932164987 df.mm.trans2:probe4 0.061883967445024 0.0456312122088587 1.35617627604927 0.175595555244033 df.mm.trans2:probe5 0.123353669860476 0.0456312122088587 2.70327400674506 0.00707680450129864 ** df.mm.trans2:probe6 0.158770496502529 0.0456312122088587 3.47942754130267 0.00054201977590916 *** df.mm.trans3:probe2 -0.13385577369431 0.0456312122088587 -2.93342576746895 0.00349119175276844 ** df.mm.trans3:probe3 -0.452021852868728 0.0456312122088587 -9.90597950367345 2.08392218853255e-21 *** df.mm.trans3:probe4 0.21497159317644 0.0456312122088587 4.71106470265337 3.11964919995435e-06 *** df.mm.trans3:probe5 0.0676051673252785 0.0456312122088587 1.48155536644179 0.139027177069170 df.mm.trans3:probe6 -0.103630647727739 0.0456312122088587 -2.27104744124288 0.0235269994970297 * df.mm.trans3:probe7 -0.393771613243588 0.0456312122088587 -8.62943573449803 6.53802348503416e-17 *** df.mm.trans3:probe8 -0.00172646789304432 0.0456312122088587 -0.0378352406055333 0.96983267600566 df.mm.trans3:probe9 -0.244269979588020 0.0456312122088587 -5.35313369432247 1.26801294434953e-07 *** df.mm.trans3:probe10 -0.291750519633818 0.0456312122088587 -6.39366138901693 3.44299023501887e-10 *** df.mm.trans3:probe11 -0.145224566686304 0.0456312122088587 -3.18257086885171 0.00154158459839889 ** df.mm.trans3:probe12 -0.209086989008560 0.0456312122088587 -4.58210463600106 5.69200479296347e-06 *** df.mm.trans3:probe13 -0.0410991082787036 0.0456312122088587 -0.900679738477882 0.368149984409764 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.47245917970042 0.121938243630103 36.678067901876 2.44099750343656e-150 *** df.mm.trans1 -0.0813715730759356 0.096862598967054 -0.84007216349432 0.401230317911698 df.mm.trans2 -0.196961574711968 0.096862598967054 -2.03341203738463 0.0424870117454983 * df.mm.exp2 -0.276000906390242 0.128926495196154 -2.14076172605423 0.0327290841290711 * df.mm.exp3 -0.0478104348909378 0.128926495196154 -0.370834829708176 0.710902312543987 df.mm.exp4 -0.219130995827417 0.128926495196154 -1.69965836342655 0.089756535150942 . df.mm.exp5 -0.187559094486177 0.128926495196154 -1.45477540672161 0.146297789934028 df.mm.exp6 -0.359753586758102 0.128926495196154 -2.79037746438975 0.00544604267175273 ** df.mm.exp7 -0.14200356207045 0.128926495196154 -1.10143040694932 0.27118767630287 df.mm.exp8 -0.0669030081235088 0.128926495196154 -0.518923655077412 0.604021202537858 df.mm.trans1:exp2 0.0928075808760671 0.0993445365426107 0.934199142760711 0.350608171124769 df.mm.trans2:exp2 0.223402953138706 0.0993445365426107 2.24876939299913 0.0249193042145109 * df.mm.trans1:exp3 -0.0112494809237302 0.0993445365426107 -0.113237036632660 0.909883654737289 df.mm.trans2:exp3 0.00912980816030231 0.0993445365426107 0.0919004555060395 0.926810350174051 df.mm.trans1:exp4 0.00117570006116030 0.0993445365426107 0.0118345719057838 0.99056185865152 df.mm.trans2:exp4 0.227469451750560 0.0993445365426107 2.28970268186811 0.0224136812846789 * df.mm.trans1:exp5 0.00177872280316233 0.0993445365426107 0.0179045860503804 0.985721416694557 df.mm.trans2:exp5 0.175765866172180 0.0993445365426107 1.76925548489313 0.0774013554397791 . df.mm.trans1:exp6 0.0785249471585141 0.0993445365426107 0.79043045436961 0.429614639767007 df.mm.trans2:exp6 0.283561709261833 0.0993445365426108 2.85432615753568 0.004474249828735 ** df.mm.trans1:exp7 -0.0251055662882556 0.0993445365426107 -0.252712098339574 0.800584435248901 df.mm.trans2:exp7 0.136289897845641 0.0993445365426107 1.37189122410555 0.170652582767215 df.mm.trans1:exp8 -0.155829414572482 0.0993445365426107 -1.56857558548924 0.117317827025789 df.mm.trans2:exp8 0.254626601344094 0.0993445365426107 2.56306597429120 0.0106381858828885 * df.mm.trans1:probe2 -0.0632556107444811 0.0711654984316375 -0.888852212638478 0.374468280707119 df.mm.trans1:probe3 -0.0295383418485392 0.0711654984316375 -0.415065481160287 0.678254660402163 df.mm.trans1:probe4 -0.0379822116706057 0.0711654984316375 -0.533716653542333 0.593751654623682 df.mm.trans1:probe5 -0.00305848198887804 0.0711654984316375 -0.0429770332012226 0.965735326696235 df.mm.trans1:probe6 -0.0362896070068267 0.0711654984316375 -0.509932591024947 0.610301772728225 df.mm.trans2:probe2 0.0191351553317515 0.0711654984316375 0.268882474702724 0.788120226228567 df.mm.trans2:probe3 -0.152028120269926 0.0711654984316375 -2.13626158209187 0.0330956890560563 * df.mm.trans2:probe4 -0.0396567554489883 0.0711654984316375 -0.557246928960711 0.57758379381779 df.mm.trans2:probe5 -0.0845877368979312 0.0711654984316375 -1.18860597848812 0.235104007134810 df.mm.trans2:probe6 -0.147903464456262 0.0711654984316375 -2.07830293774083 0.0381412267605596 * df.mm.trans3:probe2 -0.029627380725216 0.0711654984316375 -0.416316633455135 0.677339558356609 df.mm.trans3:probe3 0.0271418005749832 0.0711654984316375 0.381389875334829 0.70306019790626 df.mm.trans3:probe4 -0.0126260792983762 0.0711654984316375 -0.177418546579912 0.859244487860645 df.mm.trans3:probe5 -0.0227455968271622 0.0711654984316375 -0.319615506508564 0.749380308197471 df.mm.trans3:probe6 0.088040810646229 0.0711654984316375 1.23712771759481 0.216563799322776 df.mm.trans3:probe7 0.00516458051831104 0.0711654984316375 0.0725714093504482 0.94217337112153 df.mm.trans3:probe8 -0.103049942509613 0.0711654984316375 -1.44803233000052 0.148173690882568 df.mm.trans3:probe9 -0.131965645858993 0.0711654984316375 -1.85434864881556 0.0642205018403822 . df.mm.trans3:probe10 0.000709367124737235 0.0711654984316375 0.00996785156249081 0.992050525186723 df.mm.trans3:probe11 -0.00770512842912952 0.0711654984316375 -0.108270560860768 0.913820275381802 df.mm.trans3:probe12 -0.0567920426867553 0.0711654984316375 -0.798027751345134 0.425196319797845 df.mm.trans3:probe13 -0.0409247809237974 0.0711654984316375 -0.575064909622045 0.565480866146474