chr14.7524_chr14_56507942_56511637_-_1.R fitVsDatCorrelation=0.72748715092937 cont.fitVsDatCorrelation=0.347961229155392 fstatistic=11155.1021210825,38,370 cont.fstatistic=5970.29439222566,38,370 residuals=-0.308888106374750,-0.070164333253892,-0.00409705830982900,0.0681842419210194,0.481160208666866 cont.residuals=-0.341303535486112,-0.110261042022172,-0.0215852637890829,0.094318278558866,0.620215625164144 predictedValues: Include Exclude Both chr14.7524_chr14_56507942_56511637_-_1.R.tl.Lung 51.2419068997416 51.4311589836346 60.6125804976257 chr14.7524_chr14_56507942_56511637_-_1.R.tl.cerebhem 60.7020823377283 54.7153572478386 60.4859419956024 chr14.7524_chr14_56507942_56511637_-_1.R.tl.cortex 59.9565751685021 51.8396085196229 64.7872294921862 chr14.7524_chr14_56507942_56511637_-_1.R.tl.heart 47.7718766638895 50.1878611298396 57.4320024915288 chr14.7524_chr14_56507942_56511637_-_1.R.tl.kidney 47.9860754504885 52.8556385023426 64.2693046315724 chr14.7524_chr14_56507942_56511637_-_1.R.tl.liver 48.7979473977364 50.9778225778855 64.5763585905006 chr14.7524_chr14_56507942_56511637_-_1.R.tl.stomach 52.5934230353646 54.3083201437894 58.8647240643977 chr14.7524_chr14_56507942_56511637_-_1.R.tl.testicle 53.9868645234303 48.6858293645529 63.8465429422698 diffExp=-0.189252083893052,5.98672508988965,8.1169666488792,-2.41598446595010,-4.86956305185415,-2.17987518014910,-1.71489710842481,5.30103515887745 diffExpScore=3.406062072294 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 51.992153827833 55.0337700092101 56.0772868824458 cerebhem 60.659408047884 59.0091737209562 52.2643680383951 cortex 55.0991432840538 57.3621972322626 54.8083220801437 heart 55.5646098825235 53.609368222121 50.4593746345786 kidney 59.9537044383278 54.8610184680582 53.3312470630808 liver 54.997963137494 61.1508520201362 56.1866620517908 stomach 54.3402844722068 53.9431720029714 55.6887675354313 testicle 57.2321642431869 57.3984087358953 49.8875850604102 cont.diffExp=-3.04161618137709,1.65023432692782,-2.26305394820874,1.9552416604025,5.09268597026961,-6.15288888264217,0.397112469235381,-0.166244492708422 cont.diffExpScore=5.87187393760172 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=0.357385610625759 cont.tran.correlation=0.225506456832491 tran.covariance=0.00127080766850263 cont.tran.covariance=0.00055613497930475 tran.mean=52.3773967466492 cont.tran.mean=56.38796198407 weightedLogRatios: wLogRatio Lung -0.0145189280829177 cerebhem 0.42094768438332 cortex 0.584902636806238 heart -0.191971856497494 kidney -0.378808118484775 liver -0.170856399519460 stomach -0.127659915411533 testicle 0.406906462198603 cont.weightedLogRatios: wLogRatio Lung -0.226252332051180 cerebhem 0.112850591320748 cortex -0.162183082993908 heart 0.143277540937306 kidney 0.359444873808718 liver -0.430587369597889 stomach 0.029277244867245 testicle -0.0117429810887052 varWeightedLogRatios=0.125136723701288 cont.varWeightedLogRatios=0.0602911089103773 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66681897141590 0.065888205653066 55.6521297715028 7.31261108614565e-182 *** df.mm.trans1 0.285137413016251 0.0542615604898702 5.25486938528947 2.50757298291006e-07 *** df.mm.trans2 0.250801226527082 0.0542615604898702 4.62207913415802 5.2559033341143e-06 *** df.mm.exp2 0.233412037049881 0.0741740475586909 3.14681542577532 0.00178438604564273 ** df.mm.exp3 0.0983671295144419 0.0741740475586909 1.32616639851840 0.185602635110183 df.mm.exp4 -0.0406907989218358 0.0741740475586909 -0.548585391536559 0.583620871421092 df.mm.exp5 -0.0969062859823797 0.0741740475586909 -1.30647159177476 0.192203747850677 df.mm.exp6 -0.121068856124490 0.0741740475586909 -1.63222663599007 0.103482483668825 df.mm.exp7 0.109727100745976 0.0741740475586909 1.47931930853785 0.139905864015276 df.mm.exp8 -0.0546530761701718 0.0741740475586909 -0.736822082237417 0.461697489581716 df.mm.trans1:exp2 -0.0639917255710776 0.0615018712335387 -1.04048420458760 0.298794413511160 df.mm.trans2:exp2 -0.171511807651993 0.0615018712335386 -2.78872502920632 0.0055650601613581 ** df.mm.trans1:exp3 0.0586957320872117 0.0615018712335387 0.95437310946733 0.340517901955301 df.mm.trans2:exp3 -0.0904568239035718 0.0615018712335387 -1.47079791377540 0.142195681663617 df.mm.trans1:exp4 -0.0294297804383872 0.0615018712335387 -0.47851845558706 0.632563873503617 df.mm.trans2:exp4 0.0162197915163394 0.0615018712335387 0.263728423071041 0.792136010203668 df.mm.trans1:exp5 0.0312594686257682 0.0615018712335387 0.508268577830223 0.611567895300535 df.mm.trans2:exp5 0.124226486693999 0.0615018712335387 2.01988141502685 0.0441160100604269 * df.mm.trans1:exp6 0.0721994151798044 0.0615018712335387 1.17393851165348 0.241174724251651 df.mm.trans2:exp6 0.112215348159681 0.0615018712335387 1.82458429164813 0.0688700607712314 . df.mm.trans1:exp7 -0.0836937175679982 0.0615018712335387 -1.36083205094998 0.174395073291274 df.mm.trans2:exp7 -0.0552938547595917 0.0615018712335387 -0.89905971396588 0.369205572112786 df.mm.trans1:exp8 0.106836152180985 0.0615018712335387 1.73712035159548 0.0831981115193067 . df.mm.trans2:exp8 -0.000203108924066365 0.0615018712335387 -0.00330248364793825 0.997366783886498 df.mm.trans1:probe2 -0.133548405779938 0.0359093563644509 -3.71904203529937 0.000230965984308521 *** df.mm.trans1:probe3 -0.0108463197196793 0.0359093563644508 -0.302047177053187 0.762785807091696 df.mm.trans1:probe4 0.0214298955787726 0.0359093563644508 0.596777490559187 0.551020921335702 df.mm.trans1:probe5 -0.0486927448170843 0.0359093563644508 -1.35599046451550 0.175929169384548 df.mm.trans1:probe6 0.00227195135551032 0.0359093563644508 0.0632690637072928 0.94958641548484 df.mm.trans2:probe2 0.196786475467488 0.0359093563644508 5.48008918539961 7.87710605474008e-08 *** df.mm.trans2:probe3 -0.00224931787145184 0.0359093563644508 -0.0626387688106433 0.95008798078703 df.mm.trans2:probe4 -0.00772158135182834 0.0359093563644508 -0.21502978982582 0.829862564582324 df.mm.trans2:probe5 0.0357803996725138 0.0359093563644508 0.996408827531514 0.319702728415534 df.mm.trans2:probe6 0.0262679884086665 0.0359093563644509 0.731508193632537 0.464931831085258 df.mm.trans3:probe2 -0.0843678599572942 0.0359093563644508 -2.34946733940394 0.0193253198811262 * df.mm.trans3:probe3 -0.0511281189101273 0.0359093563644508 -1.42381050752395 0.155344074857043 df.mm.trans3:probe4 -0.0437603329590539 0.0359093563644508 -1.21863317501216 0.223759869321228 df.mm.trans3:probe5 -0.312019390468144 0.0359093563644508 -8.68908334923634 1.19650071220160e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95717953357962 0.0900289501974327 43.9545226829988 6.07021673756797e-149 *** df.mm.trans1 0.0102080269133793 0.0741424247110329 0.137681320150571 0.890567176524095 df.mm.trans2 0.06388092249485 0.0741424247110329 0.861597428784172 0.38946711489612 df.mm.exp2 0.294343868771246 0.101350637301695 2.90421329956771 0.00390275475714279 ** df.mm.exp3 0.122368669905728 0.101350637301695 1.20737938274100 0.228057324691113 df.mm.exp4 0.145792814806250 0.101350637301695 1.43849924073257 0.151137768314203 df.mm.exp5 0.189544359549877 0.101350637301695 1.87018419021532 0.0622476296158947 . df.mm.exp6 0.159651593199363 0.101350637301695 1.57524015092397 0.116055641843879 df.mm.exp7 0.0311095404867847 0.101350637301695 0.306949628685408 0.75905438893614 df.mm.exp8 0.255051503335939 0.101350637301695 2.51652589590252 0.0122740350939752 * df.mm.trans1:exp2 -0.140161944353663 0.08403550904828 -1.66788951409978 0.0961837054161395 . df.mm.trans2:exp2 -0.224597947135426 0.08403550904828 -2.67265528202358 0.00785836521016035 ** df.mm.trans1:exp3 -0.0643273215219264 0.08403550904828 -0.765477858710525 0.444475051479506 df.mm.trans2:exp3 -0.0809301653251787 0.08403550904828 -0.963047243263353 0.336152785167072 df.mm.trans1:exp4 -0.0793391483847196 0.08403550904828 -0.944114568749 0.345727187717824 df.mm.trans2:exp4 -0.172015978717282 0.08403550904828 -2.04694397243974 0.0413695724497022 * df.mm.trans1:exp5 -0.0470645071235796 0.08403550904828 -0.56005500123216 0.575780836072467 df.mm.trans2:exp5 -0.192688306334634 0.08403550904828 -2.29293912200771 0.0224118626230122 * df.mm.trans1:exp6 -0.103448261786819 0.08403550904828 -1.23100654661812 0.219102343197499 df.mm.trans2:exp6 -0.0542547948611465 0.0840355090482801 -0.645617495218314 0.518927276312687 df.mm.trans1:exp7 0.0130634792697532 0.08403550904828 0.155451896676773 0.876549844737528 df.mm.trans2:exp7 -0.0511254153472451 0.08403550904828 -0.60837871902308 0.543309605754149 df.mm.trans1:exp8 -0.159028270314891 0.08403550904828 -1.89239372874539 0.0592189324174182 . df.mm.trans2:exp8 -0.212981919744800 0.08403550904828 -2.53442767416852 0.0116746702526677 * df.mm.trans1:probe2 -0.0425707856450667 0.0490661662996216 -0.86761996821005 0.386164761394846 df.mm.trans1:probe3 -0.0331768243141883 0.0490661662996216 -0.676164999555797 0.499358320221277 df.mm.trans1:probe4 -0.00663433177722567 0.0490661662996216 -0.135211944962507 0.892517829380798 df.mm.trans1:probe5 -0.055949381154062 0.0490661662996216 -1.14028434201295 0.254905435865971 df.mm.trans1:probe6 -0.0409108305579443 0.0490661662996216 -0.83378901681707 0.404937832173583 df.mm.trans2:probe2 -0.0302888801583473 0.0490661662996216 -0.617306841814149 0.537412056447667 df.mm.trans2:probe3 -0.0187425079748018 0.0490661662996216 -0.381984356804057 0.702692261107732 df.mm.trans2:probe4 0.0391790252127239 0.0490661662996216 0.798493710991765 0.425096262217483 df.mm.trans2:probe5 -0.101966351644626 0.0490661662996216 -2.07813977195549 0.0383853891591400 * df.mm.trans2:probe6 -0.0324293354856925 0.0490661662996216 -0.660930696881093 0.509068069047361 df.mm.trans3:probe2 0.048501987377475 0.0490661662996216 0.988501671014982 0.323552995653843 df.mm.trans3:probe3 0.0263005586033652 0.0490661662996216 0.536022285555415 0.592265232189273 df.mm.trans3:probe4 -0.047147547716599 0.0490661662996216 -0.960897320338691 0.33723131440892 df.mm.trans3:probe5 -0.0503431024949629 0.0490661662996216 -1.02602478024355 0.305550208172784