chr18.11412_chr18_24727713_24732926_+_2.R fitVsDatCorrelation=0.840756303010856 cont.fitVsDatCorrelation=0.216453711197348 fstatistic=9581.05192176751,50,646 cont.fstatistic=2937.58765174737,50,646 residuals=-0.516798627788104,-0.0892666878442428,-0.00162280053501298,0.0743588444774313,1.04112857282556 cont.residuals=-0.559533751240044,-0.209527083227836,-0.044039649229936,0.164127096255484,1.39703717966731 predictedValues: Include Exclude Both chr18.11412_chr18_24727713_24732926_+_2.R.tl.Lung 60.7426490724511 56.7332439445674 58.4738113713544 chr18.11412_chr18_24727713_24732926_+_2.R.tl.cerebhem 71.1277336709576 75.6541511676878 68.7935743787502 chr18.11412_chr18_24727713_24732926_+_2.R.tl.cortex 70.952832100869 61.2033008448435 70.0126081779089 chr18.11412_chr18_24727713_24732926_+_2.R.tl.heart 64.8641160663183 59.0165702888419 57.7501470472502 chr18.11412_chr18_24727713_24732926_+_2.R.tl.kidney 63.8825759273976 58.6467984368904 56.3751742734013 chr18.11412_chr18_24727713_24732926_+_2.R.tl.liver 67.8817981728713 61.2409662267723 59.0828260684153 chr18.11412_chr18_24727713_24732926_+_2.R.tl.stomach 64.4699096667972 69.6989322837674 60.2005357238237 chr18.11412_chr18_24727713_24732926_+_2.R.tl.testicle 68.7356380202523 60.5890570121993 60.1924670076473 diffExp=4.00940512788365,-4.52641749673018,9.74953125602548,5.84754577747643,5.23577749050725,6.64083194609906,-5.22902261697025,8.14658100805305 diffExpScore=1.59955758356069 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 58.6263082587294 59.9315615585431 60.6287926809402 cerebhem 60.1398012441877 60.6141674645582 62.8279956571294 cortex 56.6750617202514 57.2122082621473 61.6311798858415 heart 58.5286141547345 65.90211666917 63.9139647563738 kidney 63.350821363377 65.7091414550874 60.9509548304333 liver 57.1591819988725 65.0711641303975 61.8846984634559 stomach 63.2111055380506 59.9587890468807 58.9809159398471 testicle 60.7450302616517 60.5133855163766 60.094499245673 cont.diffExp=-1.3052532998137,-0.474366220370435,-0.537146541895972,-7.37350251443553,-2.35832009171038,-7.91198213152494,3.25231649116987,0.231644745275126 cont.diffExpScore=1.34148056299320 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=0.500824520480502 cont.tran.correlation=0.150237564820512 tran.covariance=0.00277385991689948 cont.tran.covariance=0.000351049362991133 tran.mean=64.7150170564678 cont.tran.mean=60.8342786651885 weightedLogRatios: wLogRatio Lung 0.278093814443415 cerebhem -0.265000002929445 cortex 0.619061850481936 heart 0.389720078375276 kidney 0.351827984643140 liver 0.428926367285589 stomach -0.327947772844755 testicle 0.525705794376269 cont.weightedLogRatios: wLogRatio Lung -0.0898887895704277 cerebhem -0.0322175125960973 cortex -0.0381287514784507 heart -0.489907038678355 kidney -0.152303258842207 liver -0.532912070531901 stomach 0.21763245157615 testicle 0.0156830467195858 varWeightedLogRatios=0.124880498990853 cont.varWeightedLogRatios=0.0647957171761942 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.851577620966 0.0744313022821233 51.7467450235794 5.80228181780353e-232 *** df.mm.trans1 0.330125514266981 0.062542131141229 5.27845003428986 1.78138790938379e-07 *** df.mm.trans2 0.211205322509015 0.0575460186761152 3.67019869259308 0.000262374308022649 *** df.mm.exp2 0.283111854878594 0.0749009050623037 3.7798188772632 0.000171472045881396 *** df.mm.exp3 0.051113661731129 0.0749009050623037 0.682417144206895 0.495219837761834 df.mm.exp4 0.117559482090629 0.0749009050623036 1.56953353224292 0.1170133294847 df.mm.exp5 0.120123305155846 0.0749009050623037 1.6037630660928 0.109255057521860 df.mm.exp6 0.177216549017453 0.0749009050623036 2.36601345297552 0.0182749955454197 * df.mm.exp7 0.236274907008513 0.0749009050623036 3.15450002656144 0.00168235159308954 ** df.mm.exp8 0.160407462178298 0.0749009050623037 2.14159577971547 0.0325994184622724 * df.mm.trans1:exp2 -0.125280600406237 0.06510588805787 -1.92425914373336 0.0547610681221092 . df.mm.trans2:exp2 0.0047001053091421 0.0535481814859812 0.087773387979059 0.930083990490662 df.mm.trans1:exp3 0.104255585956763 0.06510588805787 1.60132346039262 0.109794158634104 df.mm.trans2:exp3 0.0247271098311968 0.0535481814859812 0.461773101252192 0.644399501779825 df.mm.trans1:exp4 -0.0519109944196389 0.06510588805787 -0.797331792379474 0.425551322288179 df.mm.trans2:exp4 -0.0781015771380508 0.0535481814859812 -1.45852902882421 0.145180791685693 df.mm.trans1:exp5 -0.0697227301246648 0.06510588805787 -1.07091281917069 0.284608569503231 df.mm.trans2:exp5 -0.086950671027376 0.0535481814859812 -1.62378382634972 0.104909770535499 df.mm.trans1:exp6 -0.0660946905123136 0.06510588805787 -1.01518760413136 0.310396472110111 df.mm.trans2:exp6 -0.100760552520263 0.0535481814859812 -1.88168019387628 0.0603288283536464 . df.mm.trans1:exp7 -0.176722380830041 0.06510588805787 -2.71438399969230 0.00681717741762439 ** df.mm.trans2:exp7 -0.0304502599749046 0.0535481814859812 -0.568651616729064 0.569790214605079 df.mm.trans1:exp8 -0.03678572095478 0.06510588805787 -0.565013734580851 0.572260576451342 df.mm.trans2:exp8 -0.0946535146402849 0.0535481814859812 -1.76763266302638 0.0775943551725573 . df.mm.trans1:probe2 0.198740405870491 0.0453547503468212 4.38190937775541 1.37269790180077e-05 *** df.mm.trans1:probe3 0.0953255371638554 0.0453547503468212 2.10177625132792 0.0359595041242125 * df.mm.trans1:probe4 0.00350671645644168 0.0453547503468212 0.0773175120494838 0.938394900398947 df.mm.trans1:probe5 0.336326922352200 0.0453547503468213 7.4154729059328 3.82088170270547e-13 *** df.mm.trans1:probe6 -0.0217756744552908 0.0453547503468213 -0.480118935475895 0.63130535146088 df.mm.trans1:probe7 -0.291074632524616 0.0453547503468213 -6.41773199717361 2.67738234940018e-10 *** df.mm.trans1:probe8 -0.405238479725039 0.0453547503468213 -8.93486297744424 4.22478188993536e-18 *** df.mm.trans1:probe9 -0.403607902086372 0.0453547503468212 -8.89891133784313 5.64012362403725e-18 *** df.mm.trans1:probe10 -0.444065475297678 0.0453547503468213 -9.79093638267157 3.36697211209818e-21 *** df.mm.trans1:probe11 -0.387602702627638 0.0453547503468212 -8.54602218430697 9.17211714919353e-17 *** df.mm.trans1:probe12 -0.406847170023189 0.0453547503468213 -8.97033203605106 3.17416477669987e-18 *** df.mm.trans2:probe2 0.0114624029487159 0.0453547503468213 0.252727726667319 0.800559015973197 df.mm.trans2:probe3 -0.270857281246493 0.0453547503468213 -5.97197160551621 3.87005771662955e-09 *** df.mm.trans2:probe4 -0.234671536413341 0.0453547503468213 -5.17413357187155 3.05985536457634e-07 *** df.mm.trans2:probe5 -0.0249526760553418 0.0453547503468213 -0.550166760141602 0.582395171282443 df.mm.trans2:probe6 0.103835033562012 0.0453547503468213 2.28939709221195 0.0223779856132558 * df.mm.trans3:probe2 -0.385076121759699 0.0453547503468213 -8.49031510073537 1.41326021324504e-16 *** df.mm.trans3:probe3 -0.594631762695606 0.0453547503468213 -13.1106831841988 5.46441040285884e-35 *** df.mm.trans3:probe4 -0.354455755546362 0.0453547503468212 -7.81518480061933 2.23337660290432e-14 *** df.mm.trans3:probe5 -0.366446373630744 0.0453547503468213 -8.07955882963926 3.19908091070608e-15 *** df.mm.trans3:probe6 -0.504740490527838 0.0453547503468213 -11.128723819846 1.93280272654406e-26 *** df.mm.trans3:probe7 -0.274856160784469 0.0453547503468213 -6.06014052955166 2.31132073252848e-09 *** df.mm.trans3:probe8 -0.548057859162656 0.0453547503468213 -12.0838027984221 1.88162751919308e-30 *** df.mm.trans3:probe9 0.155096116364741 0.0453547503468213 3.41962231472433 0.000666547687280258 *** df.mm.trans3:probe10 -0.326455629312914 0.0453547503468213 -7.19782661830469 1.7044487921654e-12 *** df.mm.trans3:probe11 -0.36800016158347 0.0453547503468213 -8.11381737898292 2.47766538450964e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99387038379246 0.134216672190355 29.7568872675377 3.38829104529885e-123 *** df.mm.trans1 0.0535395439984285 0.112777775695114 0.474734881659384 0.635136397146878 df.mm.trans2 0.101890357658084 0.103768641521771 0.981899311428367 0.326517042323443 df.mm.exp2 0.00118285435679581 0.135063473475226 0.00875776645128832 0.993015106475725 df.mm.exp3 -0.0966832230257452 0.135063473475226 -0.715835455271918 0.474351619049236 df.mm.exp4 0.0405315418602794 0.135063473475226 0.300092547728782 0.764203103128826 df.mm.exp5 0.164239499971305 0.135063473475226 1.21601714916232 0.224422544577541 df.mm.exp6 0.0364316888113492 0.135063473475226 0.269737538017868 0.787448318962133 df.mm.exp7 0.103306644583610 0.135063473475226 0.764874780171851 0.44462543245473 df.mm.exp8 0.0540146448429547 0.135063473475226 0.399920448165153 0.689347310291358 df.mm.trans1:exp2 0.0243054760812679 0.117400816151296 0.207029873199051 0.836051768304488 df.mm.trans2:exp2 0.0101425274330663 0.0965596261321864 0.105039008945432 0.91637746015447 df.mm.trans1:exp3 0.0628339662081146 0.117400816151296 0.535208938642638 0.592689570960507 df.mm.trans2:exp3 0.050247259289275 0.0965596261321864 0.520375454027633 0.602980206375623 df.mm.trans1:exp4 -0.0421993185891937 0.117400816151296 -0.359446552184194 0.719378518657061 df.mm.trans2:exp4 0.0544357480376839 0.0965596261321864 0.563752680267874 0.573118104238302 df.mm.trans1:exp5 -0.0867351700361374 0.117400816151296 -0.738795290182315 0.460299645317354 df.mm.trans2:exp5 -0.0722047153183353 0.0965596261321864 -0.747773352182307 0.454868994212813 df.mm.trans1:exp6 -0.061775188809982 0.117400816151296 -0.526190454505627 0.598936482228604 df.mm.trans2:exp6 0.0458465444618596 0.0965596261321864 0.474800351847857 0.635089752431749 df.mm.trans1:exp7 -0.0280101805604553 0.117400816151296 -0.238585910036249 0.8115023554597 df.mm.trans2:exp7 -0.102852438074609 0.0965596261321864 -1.06517021859435 0.287196862354051 df.mm.trans1:exp8 -0.0185129145925731 0.117400816151296 -0.157689828737777 0.874750491435338 df.mm.trans2:exp8 -0.0443533265788175 0.0965596261321864 -0.459336146539129 0.646147320611182 df.mm.trans1:probe2 -0.0162302113695637 0.0817849946585812 -0.198449745424796 0.842755636060335 df.mm.trans1:probe3 0.0610020120935827 0.0817849946585812 0.745882693374758 0.456009600202584 df.mm.trans1:probe4 0.125941601852656 0.0817849946585812 1.53991086480363 0.124071746619751 df.mm.trans1:probe5 0.0795616489679276 0.0817849946585812 0.972814747987266 0.331009494751759 df.mm.trans1:probe6 -0.000261891839077321 0.0817849946585812 -0.0032021991340907 0.997446007698974 df.mm.trans1:probe7 0.0427667652427657 0.0817849946585812 0.52291701456116 0.601211301907368 df.mm.trans1:probe8 0.084051676535463 0.0817849946585812 1.02771513144122 0.304468496086608 df.mm.trans1:probe9 0.069047429320024 0.0817849946585812 0.844255472636131 0.398839195720872 df.mm.trans1:probe10 0.0776719747047011 0.0817849946585812 0.949709357186483 0.342615109960817 df.mm.trans1:probe11 -0.0327390505090884 0.0817849946585812 -0.400306323253556 0.689063252979693 df.mm.trans1:probe12 0.0559811771320651 0.0817849946585812 0.684492031402136 0.493910009217938 df.mm.trans2:probe2 -0.0251531043035321 0.0817849946585812 -0.307551579706473 0.758522694783876 df.mm.trans2:probe3 -0.0813913878031711 0.0817849946585812 -0.995187297412523 0.32001779551996 df.mm.trans2:probe4 0.0889061719479274 0.0817849946585812 1.08707192950338 0.277410457134688 df.mm.trans2:probe5 -0.0095392120423199 0.0817849946585812 -0.116637680079851 0.90718342776097 df.mm.trans2:probe6 -0.0162994738653951 0.0817849946585812 -0.19929663055477 0.842093426932283 df.mm.trans3:probe2 0.0691654377650994 0.0817849946585812 0.845698383350598 0.398034156398065 df.mm.trans3:probe3 -0.0745231768953989 0.0817849946585812 -0.911208433851497 0.362525446979268 df.mm.trans3:probe4 -0.0442996243362484 0.0817849946585812 -0.541659561404646 0.588239813660596 df.mm.trans3:probe5 -0.0821965551901012 0.0817849946585812 -1.00503222544964 0.315257634470253 df.mm.trans3:probe6 -0.0698340681807 0.0817849946585812 -0.853873849013851 0.393491402638063 df.mm.trans3:probe7 -0.115922931081614 0.0817849946585812 -1.41741075567156 0.156844989313234 df.mm.trans3:probe8 -0.0328646101617505 0.0817849946585812 -0.401841563956160 0.68793353941821 df.mm.trans3:probe9 -0.00253704607599569 0.0817849946585812 -0.0310209236619360 0.975262434384326 df.mm.trans3:probe10 -0.0551200683598046 0.0817849946585812 -0.673963097875206 0.500575852997087 df.mm.trans3:probe11 -0.0817015205708621 0.0817849946585812 -0.998979347152034 0.31817873658906