chr11.3000_chr11_102886518_102886889_-_1.R fitVsDatCorrelation=0.701743240050496 cont.fitVsDatCorrelation=0.25580631001616 fstatistic=12001.0896178265,40,416 cont.fstatistic=6512.97816752498,40,416 residuals=-0.353851361752684,-0.0755276491229116,-0.00658323831875317,0.0751315504359124,0.497680228000479 cont.residuals=-0.428718404910309,-0.112887985068764,-0.00889813527874754,0.104012391029690,0.517627782473951 predictedValues: Include Exclude Both chr11.3000_chr11_102886518_102886889_-_1.R.tl.Lung 57.6706588294484 52.4115045524269 57.1923477150582 chr11.3000_chr11_102886518_102886889_-_1.R.tl.cerebhem 57.8457056138078 54.7252951861949 53.7185003275711 chr11.3000_chr11_102886518_102886889_-_1.R.tl.cortex 52.1120534415743 52.5487639116574 52.5309643665657 chr11.3000_chr11_102886518_102886889_-_1.R.tl.heart 57.7332320661708 53.8396082526496 54.6611478796522 chr11.3000_chr11_102886518_102886889_-_1.R.tl.kidney 66.0011414197676 58.2449105612098 56.6963211522918 chr11.3000_chr11_102886518_102886889_-_1.R.tl.liver 62.7514364737762 59.3479564856136 54.6352093667852 chr11.3000_chr11_102886518_102886889_-_1.R.tl.stomach 54.3997321196108 53.5829057441419 56.0933819865626 chr11.3000_chr11_102886518_102886889_-_1.R.tl.testicle 55.4875234064212 58.3068314504143 54.775219154746 diffExp=5.25915427702154,3.12041042761289,-0.436710470083099,3.89362381352124,7.75623085855788,3.40347998816265,0.816826375468892,-2.81930804399305 diffExpScore=1.25061882344095 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 60.8622832752463 52.7089896155921 56.0505177428158 cerebhem 57.8414610459887 55.7557799691502 54.5546446006417 cortex 58.8649777349179 55.2395574902282 56.512339840804 heart 54.3220516006924 53.4734977506559 59.113199361837 kidney 56.9430072577203 56.0640616068224 59.1641438526025 liver 56.4474362580992 55.5744392862379 57.7757612912049 stomach 58.2008523789407 59.5422256406298 55.6981506036253 testicle 56.9931293606399 56.3854785100916 56.6303559071416 cont.diffExp=8.15329365965417,2.08568107683851,3.62542024468972,0.848553850036474,0.878945650897876,0.872996971861276,-1.34137326168908,0.607650850548325 cont.diffExpScore=1.10057554968632 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=0.668899258700283 cont.tran.correlation=-0.046165302455625 tran.covariance=0.00255266732989457 cont.tran.covariance=-4.52733197255864e-05 tran.mean=56.6880787196803 cont.tran.mean=56.5762017988533 weightedLogRatios: wLogRatio Lung 0.383153011552948 cerebhem 0.223479235276554 cortex -0.0330271033543032 heart 0.280754967979367 kidney 0.515958774469153 liver 0.229261608878507 stomach 0.060347013135806 testicle -0.200273255393722 cont.weightedLogRatios: wLogRatio Lung 0.580589006190284 cerebhem 0.148343903438064 cortex 0.257030762054674 heart 0.0627726004823272 kidney 0.0627567496351989 liver 0.0627437266118133 stomach -0.0928585440185478 testicle 0.0432790118635367 varWeightedLogRatios=0.053266429202758 cont.varWeightedLogRatios=0.0413369935670083 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07128735284438 0.0640521900323031 63.5620320053245 2.52027240113088e-216 *** df.mm.trans1 -0.0186389999518696 0.0519531864678894 -0.358765288889253 0.719952687816626 df.mm.trans2 -0.160714735435381 0.0519531864678894 -3.09345290177175 0.00211183437239879 ** df.mm.exp2 0.108893257130226 0.0702535305440651 1.55000405370268 0.121900944741765 df.mm.exp3 -0.0137194874531488 0.0702535305440651 -0.195285380633554 0.8452648856993 df.mm.exp4 0.0732346387251198 0.070253530544065 1.04243357106708 0.297816124221959 df.mm.exp5 0.249164883236598 0.0702535305440651 3.54665283448373 0.000434545047676342 *** df.mm.exp6 0.254466080312871 0.0702535305440651 3.62211092228685 0.000328305106813192 *** df.mm.exp7 -0.0168830565267637 0.0702535305440651 -0.240316129253805 0.810203571425374 df.mm.exp8 0.111185026333251 0.0702535305440651 1.58262546340660 0.114266785726767 df.mm.trans1:exp2 -0.105862571059251 0.0566402070974217 -1.86903573422969 0.0623203005976548 . df.mm.trans2:exp2 -0.0656933395002624 0.0566402070974218 -1.15983579274823 0.246780904665094 df.mm.trans1:exp3 -0.087632770189551 0.0566402070974217 -1.54718308213142 0.122579484186818 df.mm.trans2:exp3 0.0163349426055383 0.0566402070974218 0.288398355914236 0.77318546669066 df.mm.trans1:exp4 -0.0721502170064278 0.0566402070974217 -1.27383391946870 0.203433508191129 df.mm.trans2:exp4 -0.0463513493107117 0.0566402070974218 -0.818347101573745 0.413627523649208 df.mm.trans1:exp5 -0.114241378810647 0.0566402070974217 -2.01696612115401 0.044340969096643 * df.mm.trans2:exp5 -0.143634286703818 0.0566402070974218 -2.53590680656879 0.0115806377869190 * df.mm.trans1:exp6 -0.170033142240461 0.0566402070974218 -3.00198659139791 0.00284389874836462 ** df.mm.trans2:exp6 -0.130174511187985 0.0566402070974218 -2.29827039587061 0.0220416270126611 * df.mm.trans1:exp7 -0.04150624558516 0.0566402070974217 -0.732805328797065 0.464089872417246 df.mm.trans2:exp7 0.0389870312968481 0.0566402070974218 0.68832783802838 0.491630009440937 df.mm.trans1:exp8 -0.149775366030696 0.0566402070974217 -2.64432942084905 0.00849494474730696 ** df.mm.trans2:exp8 -0.00459188206053076 0.0566402070974218 -0.081071067636329 0.935424423333753 df.mm.trans1:probe2 0.0318047226263727 0.0359942234309937 0.88360630108737 0.377419131946954 df.mm.trans1:probe3 0.0216367980326633 0.0359942234309937 0.601118623218648 0.548088374680065 df.mm.trans1:probe4 -0.0374660042291018 0.0359942234309937 -1.04088936106455 0.298531458517809 df.mm.trans1:probe5 0.00968385713335056 0.0359942234309937 0.269039201579552 0.788032897578387 df.mm.trans1:probe6 0.00164295080197720 0.0359942234309937 0.0456448464606266 0.963615226856213 df.mm.trans2:probe2 0.0674029908555052 0.0359942234309937 1.87260578033380 0.0618240459664833 . df.mm.trans2:probe3 0.195415370024154 0.0359942234309937 5.4290758737661 9.64729640563468e-08 *** df.mm.trans2:probe4 0.0198633046848900 0.0359942234309937 0.551847012978929 0.581349494944825 df.mm.trans2:probe5 0.186053449648044 0.0359942234309937 5.16898079506387 3.66251544658844e-07 *** df.mm.trans2:probe6 0.162460415299734 0.0359942234309937 4.51351355339545 8.31120353991732e-06 *** df.mm.trans3:probe2 0.131640957758331 0.0359942234309937 3.65728011914762 0.000287612647545032 *** df.mm.trans3:probe3 0.27950882580541 0.0359942234309937 7.76538008498142 6.39813686839514e-14 *** df.mm.trans3:probe4 0.00871046860192044 0.0359942234309937 0.241996291950004 0.808902286492808 df.mm.trans3:probe5 0.00561744183373576 0.0359942234309937 0.156065093181 0.876057417244044 df.mm.trans3:probe6 0.244642146248813 0.0359942234309937 6.79670577468711 3.72448928215762e-11 *** df.mm.trans3:probe7 0.411285595168704 0.0359942234309937 11.4264333541520 1.74572031672563e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95562584971531 0.0869152814182181 45.5112816201062 1.23727041812399e-163 *** df.mm.trans1 0.160128797141154 0.0704976023482175 2.27140770476434 0.0236324207544195 * df.mm.trans2 -0.0156689071135622 0.0704976023482175 -0.222261560558710 0.82421934999335 df.mm.exp2 0.0323377707179028 0.0953301577164082 0.339218684753490 0.734616168593449 df.mm.exp3 0.00532031163848329 0.0953301577164082 0.0558093237851379 0.95552050115471 df.mm.exp4 -0.152484121500869 0.0953301577164081 -1.59953707361404 0.110460481071366 df.mm.exp5 -0.0589160870078663 0.0953301577164082 -0.61802149937832 0.53689935731208 df.mm.exp6 -0.0526823849246293 0.0953301577164082 -0.552630837781166 0.580813031333406 df.mm.exp7 0.0834924963092303 0.0953301577164082 0.875824590132401 0.381630821572576 df.mm.exp8 -0.00854908564006543 0.0953301577164082 -0.089678710754865 0.92858569366553 df.mm.trans1:exp2 -0.083245593666288 0.0768576302695634 -1.08311423829124 0.27938510054536 df.mm.trans2:exp2 0.0238572888838966 0.0768576302695635 0.310408853359409 0.756405518453777 df.mm.trans1:exp3 -0.0386876639591956 0.0768576302695634 -0.503367900148704 0.614972058833943 df.mm.trans2:exp3 0.041572984252919 0.0768576302695635 0.540909004182274 0.588859817012523 df.mm.trans1:exp4 0.0388007122173836 0.0768576302695634 0.504838778938376 0.613939395710811 df.mm.trans2:exp4 0.166884261579312 0.0768576302695635 2.17134279308375 0.0304698409401681 * df.mm.trans1:exp5 -0.00764667864635839 0.0768576302695634 -0.0994914703919329 0.920795985711596 df.mm.trans2:exp5 0.120625059246102 0.0768576302695635 1.56946107787909 0.117300822024496 df.mm.trans1:exp6 -0.0226214022762427 0.0768576302695635 -0.294328646315303 0.768653592927949 df.mm.trans2:exp6 0.105619733466466 0.0768576302695635 1.37422573524613 0.170111517865159 df.mm.trans1:exp7 -0.128206156603884 0.0768576302695634 -1.66809926554104 0.0960485014090267 . df.mm.trans2:exp7 0.0384072172337521 0.0768576302695635 0.49971898819995 0.617537163154099 df.mm.trans1:exp8 -0.0571338519343149 0.0768576302695634 -0.743372541332966 0.457675777871822 df.mm.trans2:exp8 0.0759747158257622 0.0768576302695635 0.98851233845352 0.323476392446693 df.mm.trans1:probe2 -0.0506851544377902 0.0488421716315599 -1.03773343290574 0.299996977806791 df.mm.trans1:probe3 0.0196831302961180 0.0488421716315599 0.402994576993777 0.687159241482535 df.mm.trans1:probe4 -0.0264982814691143 0.0488421716315599 -0.542528732526548 0.587744838060246 df.mm.trans1:probe5 -0.0178655447574087 0.0488421716315599 -0.365781130539754 0.71471432882437 df.mm.trans1:probe6 -0.0174669708219382 0.0488421716315599 -0.357620683897923 0.72080856518687 df.mm.trans2:probe2 0.0695721239370717 0.0488421716315599 1.42442732607976 0.155072516314024 df.mm.trans2:probe3 0.0670309169454334 0.0488421716315599 1.37239837432045 0.170678945007848 df.mm.trans2:probe4 0.0596266423162826 0.0488421716315599 1.22080244027795 0.222852319677284 df.mm.trans2:probe5 0.0347182695571512 0.0488421716315599 0.710825673744564 0.47759060179061 df.mm.trans2:probe6 0.0918300786552965 0.0488421716315599 1.88013914180588 0.0607876369019003 . df.mm.trans3:probe2 -0.0815444653927677 0.0488421716315599 -1.66955036331916 0.0957606341144921 . df.mm.trans3:probe3 -0.0936806191754302 0.0488421716315599 -1.91802731217826 0.0557918326805532 . df.mm.trans3:probe4 -0.0805640514000862 0.0488421716315599 -1.64947725927954 0.0998047524853542 . df.mm.trans3:probe5 -0.095416533233026 0.0488421716315599 -1.95356860773512 0.0514221336213939 . df.mm.trans3:probe6 -0.0492613764357344 0.0488421716315599 -1.00858284531934 0.313761014231553 df.mm.trans3:probe7 -0.116359426114957 0.0488421716315599 -2.38235570262339 0.0176507336830736 *