chr6.19572_chr6_84687158_84687857_+_1.R fitVsDatCorrelation=0.843881373126802 cont.fitVsDatCorrelation=0.273725504180154 fstatistic=7817.12091021228,43,485 cont.fstatistic=2424.75898642292,43,485 residuals=-0.525791303341869,-0.0882381992042691,-0.00828821977615763,0.0857296927487906,0.901914244894604 cont.residuals=-0.871980515346361,-0.205201669359097,-0.0139019199984132,0.203662451377967,1.38799957561131 predictedValues: Include Exclude Both chr6.19572_chr6_84687158_84687857_+_1.R.tl.Lung 100.786252862913 134.334613767565 81.4650849722406 chr6.19572_chr6_84687158_84687857_+_1.R.tl.cerebhem 106.458100117047 163.083119851794 79.2101285373808 chr6.19572_chr6_84687158_84687857_+_1.R.tl.cortex 84.8592037173911 100.528629711721 90.7401823190947 chr6.19572_chr6_84687158_84687857_+_1.R.tl.heart 87.7672029059538 102.713046596002 78.2885372738362 chr6.19572_chr6_84687158_84687857_+_1.R.tl.kidney 107.184818965596 130.273164993307 84.8428729475928 chr6.19572_chr6_84687158_84687857_+_1.R.tl.liver 99.8854488087694 128.055448232452 76.3809383185205 chr6.19572_chr6_84687158_84687857_+_1.R.tl.stomach 91.1790501668711 105.617846623834 87.3055464042458 chr6.19572_chr6_84687158_84687857_+_1.R.tl.testicle 97.5126898813011 121.463783105096 101.658704396013 diffExp=-33.5483609046523,-56.6250197347471,-15.6694259943297,-14.9458436900484,-23.0883460277113,-28.1699994236829,-14.4387964569624,-23.9510932237949 diffExpScore=0.995270456250603 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,-1,0,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,-1,0,0,-1,-1,0,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 92.6110183270041 93.0953728678213 89.9528862208757 cerebhem 103.293609608216 101.189513363429 97.2619213350429 cortex 108.143050933644 110.060568357777 93.5844750970647 heart 113.207053475904 91.711903963529 98.7639748443347 kidney 95.8582227371678 93.345321913316 96.8585278648769 liver 108.137252133012 96.5461524728704 105.165134993617 stomach 99.1564256967183 103.002979078045 96.285000442097 testicle 96.6826092411307 100.028709513040 92.985955250202 cont.diffExp=-0.484354540817208,2.10409624478747,-1.91751742413288,21.4951495123746,2.51290082385184,11.5910996601413,-3.84655338132706,-3.34610027190971 cont.diffExpScore=1.62486604863086 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=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.88531172244207 cont.tran.correlation=0.152476514249448 tran.covariance=0.0129441747373255 cont.tran.covariance=0.000701602131726029 tran.mean=110.106401269226 cont.tran.mean=100.379360230164 weightedLogRatios: wLogRatio Lung -1.36674213102225 cerebhem -2.08179057045902 cortex -0.76687885887055 heart -0.716014274903335 kidney -0.930934816849849 liver -1.17468175058986 stomach -0.674200056789668 testicle -1.03003757916289 cont.weightedLogRatios: wLogRatio Lung -0.0236354094543559 cerebhem 0.0952313048897552 cortex -0.0824705758931169 heart 0.973644901185543 kidney 0.120857521641641 liver 0.524577051447003 stomach -0.17567115768239 testicle -0.156115871202015 varWeightedLogRatios=0.216201221172763 cont.varWeightedLogRatios=0.157968325571341 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.18248870618894 0.0904800231770623 57.2777119657365 5.46289587962361e-218 *** df.mm.trans1 -0.545262904441662 0.0757515364613388 -7.19804415742705 2.33924622896648e-12 *** df.mm.trans2 -0.262164524995505 0.0721298901321074 -3.63461700156961 0.000308127600348295 *** df.mm.exp2 0.276746066159642 0.0962741560956498 2.87456236837528 0.0042236388299218 ** df.mm.exp3 -0.569725515356208 0.0962741560956498 -5.91774094379157 6.17589755279135e-09 *** df.mm.exp4 -0.366935412614797 0.0962741560956498 -3.81135942911035 0.000155993031768732 *** df.mm.exp5 -0.00977410051435951 0.0962741560956498 -0.101523616625097 0.919176768365608 df.mm.exp6 0.0075929625144035 0.0962741560956498 0.0788681285023136 0.93717004292969 df.mm.exp7 -0.409922724603443 0.0962741560956498 -4.25786879083291 2.47906539450249e-05 *** df.mm.exp8 -0.355183756190201 0.0962741560956498 -3.6892949322487 0.000250338308800650 *** df.mm.trans1:exp2 -0.221996550561924 0.0825855545120414 -2.68807967535721 0.0074335832455318 ** df.mm.trans2:exp2 -0.0828198625875012 0.0748077813808648 -1.10710224335948 0.268798506694352 df.mm.trans1:exp3 0.397717005592138 0.0825855545120414 4.81581806820887 1.96249964682542e-06 *** df.mm.trans2:exp3 0.279834270038621 0.0748077813808648 3.74071072384724 0.000205438138035164 *** df.mm.trans1:exp4 0.228621334250473 0.0825855545120414 2.76829689648858 0.00585067495804549 ** df.mm.trans2:exp4 0.0985407524676384 0.0748077813808648 1.31725270618498 0.188375906094988 df.mm.trans1:exp5 0.071326758987549 0.0825855545120414 0.86367112758381 0.3881954886137 df.mm.trans2:exp5 -0.0209261894307417 0.0748077813808648 -0.279732790419239 0.77980177790346 df.mm.trans1:exp6 -0.0165709110472423 0.0825855545120414 -0.200651447400843 0.841055240191371 df.mm.trans2:exp6 -0.0554634080797755 0.0748077813808648 -0.741412284337075 0.458802339235151 df.mm.trans1:exp7 0.309745916204706 0.0825855545120414 3.75060648360172 0.000197717352713045 *** df.mm.trans2:exp7 0.169416278714370 0.0748077813808649 2.26468791865154 0.0239723682593395 * df.mm.trans1:exp8 0.322164312345235 0.0825855545120414 3.90097655998983 0.000109329051598186 *** df.mm.trans2:exp8 0.254466088094543 0.0748077813808649 3.40159918390032 0.00072530773291595 *** df.mm.trans1:probe2 0.167498234430972 0.0524822039609781 3.19152439854681 0.00150705182142387 ** df.mm.trans1:probe3 0.00524196505911267 0.0524822039609781 0.0998808103221087 0.920480222989547 df.mm.trans1:probe4 0.277300780807172 0.0524822039609781 5.28371066530194 1.91622948418027e-07 *** df.mm.trans1:probe5 -0.133033746929861 0.0524822039609781 -2.53483537064821 0.0115632785943408 * df.mm.trans1:probe6 0.427023922291494 0.0524822039609781 8.13654705905639 3.44346329892917e-15 *** df.mm.trans1:probe7 -0.181783533843361 0.0524822039609781 -3.46371760565775 0.000579977424822266 *** df.mm.trans1:probe8 -0.495498167748997 0.0524822039609781 -9.44126066270795 1.54749554174568e-19 *** df.mm.trans1:probe9 -0.430106990149977 0.0524822039609781 -8.19529207404806 2.24483002871699e-15 *** df.mm.trans2:probe2 -0.234053315829952 0.0524822039609781 -4.45967010082078 1.02103140469588e-05 *** df.mm.trans2:probe3 -0.0362678202264976 0.0524822039609781 -0.69104987003716 0.489864817069698 df.mm.trans2:probe4 0.0665615770525946 0.0524822039609781 1.26826947096362 0.205310179221649 df.mm.trans2:probe5 0.100874740355873 0.0524822039609781 1.92207515581617 0.0551818100789134 . df.mm.trans2:probe6 -0.156990071845556 0.0524822039609781 -2.99130105058626 0.00291931748880948 ** df.mm.trans3:probe2 0.253982720141358 0.0524822039609781 4.83940652206986 1.75286423400155e-06 *** df.mm.trans3:probe3 -0.297203997282007 0.0524822039609781 -5.66294810147429 2.55190837379367e-08 *** df.mm.trans3:probe4 -0.0557528018452615 0.0524822039609781 -1.06231822670243 0.288619911112780 df.mm.trans3:probe5 0.093117348048868 0.0524822039609781 1.77426519888729 0.0766465750875831 . df.mm.trans3:probe6 0.0924269411755918 0.0524822039609781 1.76111013257587 0.0788498744092589 . df.mm.trans3:probe7 0.0892225149444934 0.0524822039609781 1.70005274570467 0.0897621275834014 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.68565760260695 0.162198626554798 28.8883925969862 1.7692081978771e-107 *** df.mm.trans1 -0.125355030660428 0.135795667839304 -0.923115093839143 0.356406233868093 df.mm.trans2 -0.124024161512566 0.129303339037409 -0.959172148498693 0.337949935560136 df.mm.exp2 0.114416522403163 0.172585454149125 0.6629557685916 0.507673828544967 df.mm.exp3 0.282874668143278 0.172585454149125 1.63904118998845 0.101853153379009 df.mm.exp4 0.0923911890210787 0.172585454149125 0.535335897666363 0.592662981652012 df.mm.exp5 -0.0368219881193734 0.172585454149125 -0.213355107479434 0.831139648087866 df.mm.exp6 0.0351440438380945 0.172585454149125 0.203632710597545 0.838725939809094 df.mm.exp7 0.101397465037944 0.172585454149125 0.587520341953789 0.557127716113856 df.mm.exp8 0.0816957623415405 0.172585454149125 0.473364124133835 0.6361664053952 df.mm.trans1:exp2 -0.00524913363404347 0.148046641068007 -0.0354559454789130 0.971730762413986 df.mm.trans2:exp2 -0.031045876190076 0.134103849330842 -0.231506227039643 0.81701918660582 df.mm.trans1:exp3 -0.127827894760114 0.148046641068007 -0.863429888297124 0.388327930100286 df.mm.trans2:exp3 -0.11546831483292 0.134103849330842 -0.861036543015651 0.389643382860601 df.mm.trans1:exp4 0.108419161678484 0.148046641068007 0.732331114683516 0.464320237677644 df.mm.trans2:exp4 -0.107363486348682 0.134103849330842 -0.800599586696499 0.423755484870588 df.mm.trans1:exp5 0.0712841185005602 0.148046641068007 0.481497709007899 0.63037998116042 df.mm.trans2:exp5 0.0395032610628488 0.134103849330842 0.294572163737015 0.76844676861526 df.mm.trans1:exp6 0.119849106533160 0.148046641068007 0.809536141236098 0.418603801057952 df.mm.trans2:exp6 0.00125263176262237 0.134103849330842 0.00934075918680051 0.992551101682533 df.mm.trans1:exp7 -0.0331069273244514 0.148046641068007 -0.223624981192537 0.823143285736196 df.mm.trans2:exp7 -0.000264036518785964 0.134103849330842 -0.00196889589749636 0.998429858940249 df.mm.trans1:exp8 -0.0386703414294985 0.148046641068007 -0.261203774368206 0.794046246278095 df.mm.trans2:exp8 -0.0098630048069986 0.134103849330842 -0.0735475145285798 0.941400757585446 df.mm.trans1:probe2 -0.167008964429324 0.0940819984581684 -1.77514261140595 0.0765014293499279 . df.mm.trans1:probe3 -0.0866167961439059 0.0940819984581684 -0.920652171120899 0.357689750473767 df.mm.trans1:probe4 -0.0406500756312819 0.0940819984581684 -0.432070707440978 0.665882049514587 df.mm.trans1:probe5 0.0397566244515768 0.0940819984581684 0.422574191695701 0.672793239311603 df.mm.trans1:probe6 -0.0844719202000782 0.0940819984581684 -0.897854229123724 0.369708758793981 df.mm.trans1:probe7 -0.049792504585866 0.0940819984581684 -0.529245821749898 0.596877078080147 df.mm.trans1:probe8 -0.0793866866349804 0.0940819984581684 -0.84380314976279 0.399195400113168 df.mm.trans1:probe9 -0.0102464112318049 0.0940819984581684 -0.108909370546170 0.913319418114641 df.mm.trans2:probe2 -0.141501367647445 0.0940819984581684 -1.50402170411336 0.133226665889891 df.mm.trans2:probe3 -0.095401476576954 0.0940819984581684 -1.01402476712240 0.31107652698206 df.mm.trans2:probe4 -0.0403487397491999 0.0940819984581684 -0.428867800540399 0.668209856105472 df.mm.trans2:probe5 -0.0487131654553634 0.0940819984581684 -0.517773498157808 0.604852397951736 df.mm.trans2:probe6 -0.0381517138575974 0.0940819984581684 -0.405515555396719 0.685277411271897 df.mm.trans3:probe2 0.0445134774862526 0.0940819984581684 0.473134905887916 0.636329801971354 df.mm.trans3:probe3 0.071968168250045 0.0940819984581684 0.764951525578446 0.44467245655226 df.mm.trans3:probe4 -0.0938834189411532 0.0940819984581684 -0.997889293166923 0.318830642584566 df.mm.trans3:probe5 0.0920129518541676 0.0940819984581684 0.978008050021165 0.328557914557953 df.mm.trans3:probe6 0.144742550817203 0.0940819984581684 1.53847232402870 0.124585300307509 df.mm.trans3:probe7 0.181699725296615 0.0940819984581684 1.93129108941498 0.0540294209841056 .