chr2.13555_chr2_91750266_91752912_+_2.R fitVsDatCorrelation=0.802014403321485 cont.fitVsDatCorrelation=0.268671197879703 fstatistic=13475.5507758925,53,715 cont.fstatistic=5173.44893282725,53,715 residuals=-0.473222168028208,-0.0739278633534103,-0.00617049524771366,0.065654495133138,0.63710036822377 cont.residuals=-0.461736668969428,-0.145623253222017,-0.0428057042016077,0.118399872035772,0.941794684129407 predictedValues: Include Exclude Both chr2.13555_chr2_91750266_91752912_+_2.R.tl.Lung 52.1966534864123 47.615160365622 64.2177598924161 chr2.13555_chr2_91750266_91752912_+_2.R.tl.cerebhem 57.3706516992488 56.4232255484588 68.998162338176 chr2.13555_chr2_91750266_91752912_+_2.R.tl.cortex 48.6739606959053 46.3387155932603 57.9315492080174 chr2.13555_chr2_91750266_91752912_+_2.R.tl.heart 48.3158271056153 48.3050016711061 57.6892578544266 chr2.13555_chr2_91750266_91752912_+_2.R.tl.kidney 51.0295248387538 46.2444130573377 63.8827624497662 chr2.13555_chr2_91750266_91752912_+_2.R.tl.liver 51.9528790958296 49.075371880184 60.0074411338544 chr2.13555_chr2_91750266_91752912_+_2.R.tl.stomach 49.6818271274898 46.2774907067891 64.1838762899364 chr2.13555_chr2_91750266_91752912_+_2.R.tl.testicle 51.1032082149877 52.8878189583618 62.2909336720155 diffExp=4.58149312079038,0.947426150790001,2.33524510264498,0.0108254345091296,4.78511178141611,2.87750721564561,3.40433642070074,-1.78461074337410 diffExpScore=1.14149772309015 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 51.5144147398015 56.3259965529499 54.8097210162394 cerebhem 52.2733718885129 49.9796177841057 50.0068246138512 cortex 53.48494910721 49.2441742747611 52.051766793004 heart 54.2064970739477 49.6075311735207 55.389944996675 kidney 51.2713081315033 57.0566426791343 50.7414948164544 liver 54.0269243695907 54.6379905791666 50.8433761286445 stomach 53.2631011465849 57.6520383725228 54.0032517412791 testicle 51.784616503576 52.0582336074455 45.7082722961469 cont.diffExp=-4.81158181314847,2.29375410440717,4.24077483244895,4.59896590042693,-5.78533454763098,-0.611066209575924,-4.38893722593788,-0.273617103869462 cont.diffExpScore=4.70696073716624 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=0.783626768162532 cont.tran.correlation=-0.382365412968871 tran.covariance=0.00299170578040342 cont.tran.covariance=-0.000561638320228257 tran.mean=50.2182331278351 cont.tran.mean=53.0242129990209 weightedLogRatios: wLogRatio Lung 0.35911658774571 cerebhem 0.0672941926214642 cortex 0.189809876807361 heart 0.000868906135009869 kidney 0.382351634012560 liver 0.223466143581782 stomach 0.274716742340466 testicle -0.135621460580622 cont.weightedLogRatios: wLogRatio Lung -0.35597337163747 cerebhem 0.176528043514524 cortex 0.325323002368977 heart 0.350064052028228 kidney -0.426646544892175 liver -0.0449326261943151 stomach -0.317901832820635 testicle -0.020814445085147 varWeightedLogRatios=0.0325189631028431 cont.varWeightedLogRatios=0.0942068337973625 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.6035134625189 0.0662820454274577 54.3663587820741 3.45281455719434e-256 *** df.mm.trans1 0.238963645618347 0.058859739114963 4.05988285390819 5.45176507237919e-05 *** df.mm.trans2 0.295091576541321 0.053540586871651 5.51154915893475 4.9689336361946e-08 *** df.mm.exp2 0.192444098206895 0.0721806673893087 2.66614462247822 0.00784603387678522 ** df.mm.exp3 0.00597009050812902 0.0721806673893088 0.0827103811042526 0.934104979273236 df.mm.exp4 0.0443335333732463 0.0721806673893088 0.61420232004966 0.539277017564557 df.mm.exp5 -0.0465943079528987 0.0721806673893087 -0.645523374030208 0.51879517114515 df.mm.exp6 0.0933360983183408 0.0721806673893087 1.29308998786240 0.19639757975166 df.mm.exp7 -0.0773469178112243 0.0721806673893087 -1.07157387994283 0.284273032802540 df.mm.exp8 0.114314654248976 0.0721806673893087 1.58372952735968 0.113697465534969 df.mm.trans1:exp2 -0.0979296034529133 0.0685389300602615 -1.42881721916013 0.153493482623152 df.mm.trans2:exp2 -0.0227144295695255 0.0577149440894111 -0.393562359418328 0.694021505494765 df.mm.trans1:exp3 -0.0758442747630538 0.0685389300602616 -1.10658679229993 0.268844783755789 df.mm.trans2:exp3 -0.0331434945134078 0.0577149440894111 -0.574261918404746 0.56597118888414 df.mm.trans1:exp4 -0.121592726422901 0.0685389300602616 -1.77406805615426 0.0764774069179507 . df.mm.trans2:exp4 -0.0299496294135602 0.0577149440894111 -0.518923302899899 0.603974797290653 df.mm.trans1:exp5 0.0239803081977541 0.0685389300602615 0.349878648188262 0.726532805415416 df.mm.trans2:exp5 0.0173837602496324 0.0577149440894111 0.301200330761852 0.763349308938444 df.mm.trans1:exp6 -0.0980173451701054 0.0685389300602616 -1.43009739259025 0.153125893409764 df.mm.trans2:exp6 -0.0631299859963554 0.0577149440894111 -1.09382391324083 0.274400578017946 df.mm.trans1:exp7 0.0279677493109035 0.0685389300602616 0.408056403657213 0.683354428481046 df.mm.trans2:exp7 0.0488513932433446 0.0577149440894111 0.846425375855252 0.397598681507698 df.mm.trans1:exp8 -0.135485759315718 0.0685389300602615 -1.97677085412035 0.0484513564135173 * df.mm.trans2:exp8 -0.00929281294194938 0.0577149440894111 -0.161012248882250 0.872129235513258 df.mm.trans1:probe2 0.00949821274836003 0.0375403180612742 0.253013646097959 0.800330393440344 df.mm.trans1:probe3 0.0773128126138782 0.0375403180612742 2.05946077728181 0.0398116515936182 * df.mm.trans1:probe4 0.0793923204871766 0.0375403180612742 2.11485476381928 0.0347877164196612 * df.mm.trans1:probe5 0.0554856461008496 0.0375403180612742 1.47802812992380 0.139840607914526 df.mm.trans1:probe6 0.087024141411881 0.0375403180612742 2.31815141442963 0.0207223661851131 * df.mm.trans1:probe7 0.0132078871549266 0.0375403180612742 0.351832052498021 0.725067883426476 df.mm.trans1:probe8 0.0381521168756670 0.0375403180612742 1.01629711323687 0.309831691280327 df.mm.trans1:probe9 0.0078467927440664 0.0375403180612742 0.209023075703799 0.834489759934391 df.mm.trans1:probe10 0.0880240326798595 0.0375403180612742 2.34478654486051 0.0193106045269658 * df.mm.trans1:probe11 0.368374070227576 0.0375403180612742 9.81275836891704 2.07466363863254e-21 *** df.mm.trans1:probe12 0.314253533618799 0.0375403180612742 8.37109406227904 2.99892401408348e-16 *** df.mm.trans1:probe13 0.321814195964539 0.0375403180612742 8.57249518875324 6.22944583779823e-17 *** df.mm.trans1:probe14 0.556547802843495 0.0375403180612742 14.8253353084299 1.48278872985099e-43 *** df.mm.trans1:probe15 0.279272878774595 0.0375403180612742 7.43927843975001 2.90938929740022e-13 *** df.mm.trans1:probe16 0.414008796057259 0.0375403180612742 11.0283774202846 3.15873655493935e-26 *** df.mm.trans1:probe17 -0.0176256054966771 0.0375403180612742 -0.469511352245556 0.638847317675794 df.mm.trans1:probe18 -0.000673052258723555 0.0375403180612742 -0.0179287841308373 0.985700667996384 df.mm.trans1:probe19 0.188109449385792 0.0375403180612742 5.01086456110348 6.83625338205702e-07 *** df.mm.trans1:probe20 0.0276287521128289 0.0375403180612742 0.735975440264854 0.461987015961759 df.mm.trans1:probe21 -0.00312705049760173 0.0375403180612742 -0.083298455076957 0.933637543451788 df.mm.trans1:probe22 0.0215454227056328 0.0375403180612742 0.573927548255343 0.566197321758785 df.mm.trans2:probe2 -0.144770210030566 0.0375403180612742 -3.85639274004735 0.000125450058858064 *** df.mm.trans2:probe3 -0.132963957068851 0.0375403180612742 -3.54189745680428 0.000423041382335445 *** df.mm.trans2:probe4 -0.00495680266156219 0.0375403180612742 -0.13203944232629 0.894990241178396 df.mm.trans2:probe5 -0.00575262951900496 0.0375403180612742 -0.153238699512758 0.87825327633849 df.mm.trans2:probe6 -0.066094735275463 0.0375403180612742 -1.76063333207731 0.0787279584015084 . df.mm.trans3:probe2 0.0312573283543143 0.0375403180612742 0.832633551567022 0.405329453551712 df.mm.trans3:probe3 -0.113873643843465 0.0375403180612742 -3.03336918077246 0.00250595373740427 ** df.mm.trans3:probe4 0.181460801887231 0.0375403180612742 4.83375771060454 1.64076526040008e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95091504719224 0.106888468300513 36.9629681294 4.76852345963214e-168 *** df.mm.trans1 -0.00513357768172851 0.0949190284939505 -0.0540837571052014 0.956883540944988 df.mm.trans2 0.0918707638151242 0.0863411997278316 1.06404316948019 0.287668354718842 df.mm.exp2 -0.0132073870916119 0.116400767785538 -0.113464776417504 0.909693937697092 df.mm.exp3 -0.045197654031567 0.116400767785538 -0.388293435614111 0.697914478530457 df.mm.exp4 -0.0866049215750437 0.116400767785538 -0.744023628217026 0.457106616387609 df.mm.exp5 0.085281515195608 0.116400767785538 0.73265423259694 0.464009452106699 df.mm.exp6 0.0923118332331765 0.116400767785538 0.793051755494052 0.428010745128484 df.mm.exp7 0.0714748704731073 0.116400767785538 0.614041228703884 0.539383400356757 df.mm.exp8 0.108026524547568 0.116400767785538 0.92805680411491 0.353691283219093 df.mm.trans1:exp2 0.0278328204289877 0.110527989983582 0.251816941872571 0.801254923574899 df.mm.trans2:exp2 -0.106333513975798 0.0930728967698895 -1.1424756042427 0.253638804947673 df.mm.trans1:exp3 0.0827362768895062 0.110527989983582 0.748554976000163 0.454371638756567 df.mm.trans2:exp3 -0.0891674532524872 0.0930728967698895 -0.95803887433462 0.338366985298542 df.mm.trans1:exp4 0.137544028683787 0.110527989983582 1.24442712388254 0.213750168708472 df.mm.trans2:exp4 -0.0404085971126394 0.0930728967698895 -0.434160733307188 0.664302704278855 df.mm.trans1:exp5 -0.0900118815310687 0.110527989983582 -0.8143808780422 0.415697936761354 df.mm.trans2:exp5 -0.0723931886403138 0.0930728967698895 -0.777811706229543 0.436937316386738 df.mm.trans1:exp6 -0.0446909778572493 0.110527989983582 -0.40434081777736 0.686083033185468 df.mm.trans2:exp6 -0.122738573352526 0.0930728967698895 -1.31873593293202 0.187679633601531 df.mm.trans1:exp7 -0.0380927315882484 0.110527989983582 -0.344643303419405 0.730463893926633 df.mm.trans2:exp7 -0.0482054457318551 0.0930728967698895 -0.517932152160652 0.604665824556114 df.mm.trans1:exp8 -0.102795064450295 0.110527989983582 -0.930036495421338 0.352666077680695 df.mm.trans2:exp8 -0.186819734562579 0.0930728967698895 -2.00724100190484 0.0450999008144936 * df.mm.trans1:probe2 -0.0600888016778937 0.0605386733497127 -0.99256885480096 0.321255888606564 df.mm.trans1:probe3 0.0336816253180465 0.0605386733497127 0.556365434760661 0.578135138941743 df.mm.trans1:probe4 0.030516145150965 0.0605386733497127 0.504076872888887 0.614362643440179 df.mm.trans1:probe5 -0.00226451462680809 0.0605386733497127 -0.0374060827816081 0.970171663743037 df.mm.trans1:probe6 0.0703244120435185 0.0605386733497127 1.16164441921739 0.245767565443617 df.mm.trans1:probe7 0.0112393269085616 0.0605386733497127 0.185655322237334 0.852767721334027 df.mm.trans1:probe8 -0.0284728697013474 0.0605386733497127 -0.470325299942876 0.638266053050961 df.mm.trans1:probe9 0.0852079525929889 0.0605386733497127 1.40749619835191 0.159714880429269 df.mm.trans1:probe10 -0.0834413625927819 0.0605386733497127 -1.37831501709275 0.168537177752686 df.mm.trans1:probe11 -0.0318359754787019 0.0605386733497127 -0.525878314095116 0.599135817583303 df.mm.trans1:probe12 -0.0169929775773044 0.0605386733497127 -0.280696233284488 0.77902459839212 df.mm.trans1:probe13 0.0384726622610498 0.0605386733497127 0.635505539389763 0.52530209055424 df.mm.trans1:probe14 -0.078922765136778 0.0605386733497127 -1.30367516778682 0.19276394637326 df.mm.trans1:probe15 -0.0558917519156689 0.0605386733497127 -0.92324044818095 0.356193360888347 df.mm.trans1:probe16 0.0301465522998495 0.0605386733497127 0.497971802680617 0.61865704093306 df.mm.trans1:probe17 -0.000244311594134971 0.0605386733497127 -0.00403562847708374 0.996781168756687 df.mm.trans1:probe18 0.0124185584961443 0.0605386733497127 0.205134301909892 0.837525581685994 df.mm.trans1:probe19 -0.0120760620970166 0.0605386733497127 -0.199476820829175 0.841946487749133 df.mm.trans1:probe20 -0.0454966553939971 0.0605386733497127 -0.75153043297096 0.452580786209204 df.mm.trans1:probe21 -0.0198724610740905 0.0605386733497127 -0.32826059730932 0.742810740766575 df.mm.trans1:probe22 0.0216783920332092 0.0605386733497127 0.358091626950264 0.720380435571519 df.mm.trans2:probe2 0.0262414502545688 0.0605386733497127 0.43346589547776 0.664807079894091 df.mm.trans2:probe3 -0.0354984411263385 0.0605386733497127 -0.586376264330658 0.557807759239792 df.mm.trans2:probe4 -0.029319566064432 0.0605386733497127 -0.484311340869037 0.628313243108719 df.mm.trans2:probe5 0.0124384277106622 0.0605386733497127 0.205462508879396 0.837269268305745 df.mm.trans2:probe6 -0.0901581901667695 0.0605386733497127 -1.48926603736349 0.136858270386081 df.mm.trans3:probe2 -0.0727983882851005 0.0605386733497127 -1.20251046574092 0.229563883958054 df.mm.trans3:probe3 -0.00369626468222527 0.0605386733497127 -0.0610562550796764 0.951331469681854 df.mm.trans3:probe4 -0.0586440042177543 0.0605386733497127 -0.968703160688483 0.333020873319888