chr7.22313_chr7_79937450_79939573_+_1.R fitVsDatCorrelation=0.886950738093606 cont.fitVsDatCorrelation=0.300525441256594 fstatistic=5964.40941177073,45,531 cont.fstatistic=1389.60391143606,45,531 residuals=-0.666451134037645,-0.111616645837170,0.00354420445466777,0.088952950793857,1.31058381753617 cont.residuals=-0.987166709633287,-0.315648652770166,-0.0692635671185511,0.289015387979465,1.90975457069521 predictedValues: Include Exclude Both chr7.22313_chr7_79937450_79939573_+_1.R.tl.Lung 72.5950473054327 122.176704417005 88.5272296280855 chr7.22313_chr7_79937450_79939573_+_1.R.tl.cerebhem 109.719996145220 208.577632595837 85.0098233043439 chr7.22313_chr7_79937450_79939573_+_1.R.tl.cortex 78.3793793591211 119.583330970793 86.0923802940265 chr7.22313_chr7_79937450_79939573_+_1.R.tl.heart 71.958194016113 135.970624631502 84.4210184228168 chr7.22313_chr7_79937450_79939573_+_1.R.tl.kidney 74.2183203750876 137.391104437273 92.8644113606268 chr7.22313_chr7_79937450_79939573_+_1.R.tl.liver 67.647444474959 107.715385398172 66.3093442865645 chr7.22313_chr7_79937450_79939573_+_1.R.tl.stomach 72.6121062781729 115.478292672547 69.915854060358 chr7.22313_chr7_79937450_79939573_+_1.R.tl.testicle 68.3996342507657 112.895496991321 69.260533492419 diffExp=-49.5816571115724,-98.8576364506174,-41.2039516116721,-64.0124306153887,-63.1727840621859,-40.0679409232134,-42.8661863943746,-44.4958627405551 diffExpScore=0.997754113368981 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 71.0520595988493 108.164840772742 90.4293139419675 cerebhem 81.1033021667684 95.7889623391706 81.0740733232407 cortex 102.540701093144 84.9044723570455 87.184603892962 heart 98.01398761704 83.2130940484881 92.7123653154483 kidney 92.7816987910635 105.297454803770 84.4530815909288 liver 97.5232867836585 113.855124741635 91.223234122126 stomach 97.7409400035293 103.602030930086 92.540110824009 testicle 87.360225875594 84.8932425267481 93.2662132597774 cont.diffExp=-37.1127811738929,-14.6856601724022,17.6362287360987,14.8008935685519,-12.5157560127067,-16.3318379579763,-5.8610909265569,2.46698334884587 cont.diffExpScore=2.30806578282357 cont.diffExp1.5=-1,0,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=-1,0,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=-1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,0,1,0,0,0,0,0 cont.diffExp1.2Score=2 tran.correlation=0.95346852274502 cont.tran.correlation=-0.263733742511818 tran.covariance=0.0303259750091285 cont.tran.covariance=-0.00429443878794916 tran.mean=104.707418394958 cont.tran.mean=94.2397140280833 weightedLogRatios: wLogRatio Lung -2.36609337404462 cerebhem -3.22418164219541 cortex -1.93178575579706 heart -2.92357501471469 kidney -2.84196367142966 liver -2.0686220821037 stomach -2.09571595230854 testicle -2.24285912618402 cont.weightedLogRatios: wLogRatio Lung -1.87997395059801 cerebhem -0.745401411051456 cortex 0.856072900671333 heart 0.737207939129896 kidney -0.581263195826043 liver -0.721148275912329 stomach -0.268553700716505 testicle 0.127636698707838 varWeightedLogRatios=0.223657528373631 cont.varWeightedLogRatios=0.792375822159018 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99162964009644 0.104083028139823 38.3504372560545 3.924331535992e-155 *** df.mm.trans1 0.26640701619063 0.0828623033614561 3.21505685195026 0.00138350792139762 ** df.mm.trans2 0.800394237307042 0.082862303361456 9.65932884843448 1.91689115014888e-20 *** df.mm.exp2 0.988421213218908 0.110483071148608 8.94635895746785 6.12260218041538e-18 *** df.mm.exp3 0.0830985136958453 0.110483071148608 0.752137977628007 0.452301351167846 df.mm.exp4 0.145652864574624 0.110483071148608 1.31832744202693 0.187962424720155 df.mm.exp5 0.0916472635195495 0.110483071148608 0.829514083621707 0.407186314987912 df.mm.exp6 0.092416269514489 0.110483071148608 0.83647448024125 0.403264292404219 df.mm.exp7 0.179866886881943 0.110483071148608 1.62800404633945 0.104117178129286 df.mm.exp8 0.106899910993111 0.110483071148608 0.967568242643461 0.3337005218384 df.mm.trans1:exp2 -0.575386282852691 0.0855798189192817 -6.72338747754762 4.59622016178612e-11 *** df.mm.trans2:exp2 -0.453578296370678 0.0855798189192816 -5.3000614174995 1.70011458940423e-07 *** df.mm.trans1:exp3 -0.00643433988374074 0.0855798189192816 -0.0751852477020262 0.940095638018486 df.mm.trans2:exp3 -0.104553448725522 0.0855798189192816 -1.22170682347594 0.222360606473866 df.mm.trans1:exp4 -0.154464253429875 0.0855798189192816 -1.80491446909423 0.0716544299668837 . df.mm.trans2:exp4 -0.0386823912044972 0.0855798189192816 -0.45200365802342 0.651450924574198 df.mm.trans1:exp5 -0.0695329390679868 0.0855798189192817 -0.812492243452511 0.416873341404333 df.mm.trans2:exp5 0.0257159784070751 0.0855798189192816 0.300491152374724 0.763920154134567 df.mm.trans1:exp6 -0.163003391865783 0.0855798189192816 -1.90469428335115 0.0573598675526991 . df.mm.trans2:exp6 -0.218392235026071 0.0855798189192816 -2.55191279654445 0.0109922911623527 * df.mm.trans1:exp7 -0.179631926380375 0.0855798189192816 -2.09899867338821 0.036288591906052 * df.mm.trans2:exp7 -0.236252710491439 0.0855798189192816 -2.76061241394156 0.00596880362168896 ** df.mm.trans1:exp8 -0.166429134160229 0.0855798189192816 -1.94472407469341 0.0523357819406471 . df.mm.trans2:exp8 -0.185905719242611 0.0855798189192816 -2.17230792948927 0.0302741229969373 * df.mm.trans1:probe2 0.0712770132549376 0.0605140702905409 1.17785851972478 0.239380627340406 df.mm.trans1:probe3 0.157192607522590 0.0605140702905409 2.59762079740919 0.00964768992052739 ** df.mm.trans1:probe4 0.309102691190607 0.0605140702905409 5.10794745265918 4.54822213115891e-07 *** df.mm.trans1:probe5 -0.0544157993581022 0.0605140702905409 -0.899225570133366 0.368940112008259 df.mm.trans1:probe6 0.000324284488025952 0.0605140702905409 0.00535882789686751 0.995726307028438 df.mm.trans2:probe2 -0.0771389554565473 0.0605140702905409 -1.27472759783281 0.202963187031013 df.mm.trans2:probe3 0.142596187184990 0.0605140702905408 2.35641374808132 0.0188146671093411 * df.mm.trans2:probe4 0.236145114282731 0.0605140702905409 3.90231748003312 0.000107527418720400 *** df.mm.trans2:probe5 -0.126468212637228 0.0605140702905408 -2.08989763917758 0.0371021626486969 * df.mm.trans2:probe6 0.0668671595846644 0.0605140702905409 1.10498532429931 0.269666371987034 df.mm.trans3:probe2 -0.99718931962947 0.0605140702905408 -16.4786357097077 1.40874302194224e-49 *** df.mm.trans3:probe3 -0.530168563488554 0.0605140702905408 -8.76107921584357 2.60262569485955e-17 *** df.mm.trans3:probe4 -0.289642913506112 0.0605140702905409 -4.78637302226532 2.20465027807974e-06 *** df.mm.trans3:probe5 -0.0283963437590808 0.0605140702905409 -0.469251921458001 0.63908218858041 df.mm.trans3:probe6 -0.810194237726148 0.0605140702905409 -13.3885265664040 1.98729384875801e-35 *** df.mm.trans3:probe7 -0.747068457310355 0.0605140702905408 -12.3453678412892 5.85129575046839e-31 *** df.mm.trans3:probe8 -1.01468136978437 0.0605140702905408 -16.7676932804664 5.90736980239271e-51 *** df.mm.trans3:probe9 -0.810396356486512 0.0605140702905408 -13.3918665955806 1.92159547412668e-35 *** df.mm.trans3:probe10 -0.620204281709901 0.0605140702905409 -10.2489268815032 1.29901664145897e-22 *** df.mm.trans3:probe11 -1.00665840238061 0.0605140702905408 -16.6351130827497 2.53565931569975e-50 *** df.mm.trans3:probe12 -0.0468482681041721 0.0605140702905409 -0.774171492336306 0.439173933047183 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.44772107814076 0.214936988576098 20.6931394526636 3.37520569643678e-70 *** df.mm.trans1 -0.184863109760011 0.171115063322952 -1.08034387020102 0.280479513331813 df.mm.trans2 0.246900773136 0.171115063322952 1.44289326925014 0.149640158788715 df.mm.exp2 0.120007192932590 0.228153417763936 0.525993404388788 0.599112391694618 df.mm.exp3 0.161258094002652 0.228153417763936 0.706796749236083 0.480002829267837 df.mm.exp4 0.0345123357586583 0.228153417763936 0.151268107648368 0.879821675572718 df.mm.exp5 0.308341799274252 0.228153417763936 1.3514669308758 0.177121366354972 df.mm.exp6 0.35920764314819 0.228153417763936 1.57441272047851 0.115987653408248 df.mm.exp7 0.252734536333976 0.228153417763936 1.10773942731585 0.268475883247732 df.mm.exp8 -0.066524042755368 0.228153417763936 -0.291575920305513 0.770724756967174 df.mm.trans1:exp2 0.0123036424392814 0.176726877476006 0.0696195316468026 0.944522700303865 df.mm.trans2:exp2 -0.241516097218183 0.176726877476006 -1.36660648718231 0.172327042220807 df.mm.trans1:exp3 0.205588867451078 0.176726877476006 1.16331409453545 0.24522457559612 df.mm.trans2:exp3 -0.403387691091792 0.176726877476006 -2.28254862448164 0.0228514956149637 * df.mm.trans1:exp4 0.287185021369603 0.176726877476006 1.62502175940156 0.104751281119424 df.mm.trans2:exp4 -0.296763986882302 0.176726877476006 -1.67922384597438 0.0936966925360023 . df.mm.trans1:exp5 -0.0415052323773617 0.176726877476006 -0.234855235208899 0.81441163047053 df.mm.trans2:exp5 -0.335208918289104 0.176726877476006 -1.89676252461720 0.058401680269928 . df.mm.trans1:exp6 -0.0425292969984457 0.176726877476006 -0.240649852505994 0.809919392126459 df.mm.trans2:exp6 -0.307937205508040 0.176726877476006 -1.7424469322718 0.08200920966705 . df.mm.trans1:exp7 0.0661731307066898 0.176726877476006 0.374437276614442 0.708228495406173 df.mm.trans2:exp7 -0.295833970086774 0.176726877476006 -1.67396139348945 0.0947270675643313 . df.mm.trans1:exp8 0.273151298177533 0.176726877476006 1.54561265427563 0.122793886555136 df.mm.trans2:exp8 -0.175737827377863 0.176726877476006 -0.994403510590646 0.320479378510516 df.mm.trans1:probe2 -0.0261788723222426 0.124964773481208 -0.209490015409658 0.834146075428319 df.mm.trans1:probe3 0.156785433906739 0.124964773481208 1.25463704321695 0.210162497710081 df.mm.trans1:probe4 0.0176459450210865 0.124964773481208 0.141207354116799 0.887759689515839 df.mm.trans1:probe5 -0.128192158734827 0.124964773481208 -1.02582636021106 0.305440515153541 df.mm.trans1:probe6 -0.0100726201436338 0.124964773481208 -0.0806036762443982 0.935787512104881 df.mm.trans2:probe2 -0.256379734401817 0.124964773481208 -2.051616045544 0.0406969256263227 * df.mm.trans2:probe3 -0.0713391494909599 0.124964773481208 -0.570874075178377 0.568326514036003 df.mm.trans2:probe4 0.157266590666663 0.124964773481208 1.25848738236870 0.208768588728056 df.mm.trans2:probe5 -0.00377149863700954 0.124964773481208 -0.0301804943260806 0.975934444558889 df.mm.trans2:probe6 -0.0231549258692006 0.124964773481208 -0.185291624384712 0.853071059492139 df.mm.trans3:probe2 -0.00313951509252065 0.124964773481208 -0.0251232007633957 0.979966132813745 df.mm.trans3:probe3 -0.0302402569484638 0.124964773481208 -0.241990251380813 0.808881144741334 df.mm.trans3:probe4 -0.00218364845070477 0.124964773481208 -0.0174741120227225 0.986064948942004 df.mm.trans3:probe5 -0.000639716306624844 0.124964773481208 -0.0051191730981775 0.995917431260941 df.mm.trans3:probe6 0.132119978355010 0.124964773481208 1.05725777492710 0.29087470116847 df.mm.trans3:probe7 -0.155683616986090 0.124964773481208 -1.24582002310837 0.213379874311468 df.mm.trans3:probe8 -0.0865814796619366 0.124964773481208 -0.692847090023787 0.488708419244537 df.mm.trans3:probe9 -0.0525538408648239 0.124964773481208 -0.420549242805029 0.674254403343517 df.mm.trans3:probe10 0.0391518716419595 0.124964773481208 0.31330326580272 0.754173293289642 df.mm.trans3:probe11 0.144079026951881 0.124964773481208 1.15295713294393 0.249446721203668 df.mm.trans3:probe12 -0.0466118915934235 0.124964773481208 -0.373000248749565 0.709297106318719