chr13.6312_chr13_112426866_112429545_+_2.R fitVsDatCorrelation=0.858058364986619 cont.fitVsDatCorrelation=0.214738456011356 fstatistic=6156.613473542,55,761 cont.fstatistic=1692.20230790225,55,761 residuals=-0.73956682468308,-0.130459769712455,-0.00461375967515583,0.110856190833023,1.60505068968241 cont.residuals=-0.876401989875721,-0.267855438352859,-0.0760062717223007,0.195963453701198,1.72550310972040 predictedValues: Include Exclude Both chr13.6312_chr13_112426866_112429545_+_2.R.tl.Lung 66.736082727873 79.9999672466754 70.0849667915608 chr13.6312_chr13_112426866_112429545_+_2.R.tl.cerebhem 95.912937377702 116.641004919470 81.6804038663832 chr13.6312_chr13_112426866_112429545_+_2.R.tl.cortex 90.9375742754502 73.9377912147242 96.5857071673588 chr13.6312_chr13_112426866_112429545_+_2.R.tl.heart 72.7295457845745 75.2625694191293 75.5123148395841 chr13.6312_chr13_112426866_112429545_+_2.R.tl.kidney 91.6807291731532 77.4815355569314 109.036471872650 chr13.6312_chr13_112426866_112429545_+_2.R.tl.liver 104.661533964863 85.3728412272338 127.4829656108 chr13.6312_chr13_112426866_112429545_+_2.R.tl.stomach 70.8071040405229 86.9387712018788 72.882764082822 chr13.6312_chr13_112426866_112429545_+_2.R.tl.testicle 168.369756722661 100.616324670970 214.376244346916 diffExp=-13.2638845188024,-20.7280675417675,16.999783060726,-2.53302363455477,14.1991936162217,19.2886927376295,-16.1316671613559,67.7534320516909 diffExpScore=2.56663113121156 diffExp1.5=0,0,0,0,0,0,0,1 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,0,1 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,0,1 diffExp1.3Score=0.5 diffExp1.2=0,-1,1,0,0,1,-1,1 diffExp1.2Score=2.5 cont.predictedValues: Include Exclude Both Lung 90.0996855075297 101.883671424244 87.5070685122159 cerebhem 95.1243749454615 94.4804928422861 94.7094296274859 cortex 86.564833770784 75.8062664750387 83.5978016753304 heart 92.8592788385783 94.6989195305708 100.951178943779 kidney 95.887999544689 87.5073432491925 102.354496562758 liver 94.0178667797186 89.135952467282 89.949629378342 stomach 96.5682143331818 102.809511411556 91.7565008734276 testicle 89.45207402857 90.5778580554588 85.6709779874765 cont.diffExp=-11.7839859167145,0.643882103175386,10.7585672957454,-1.83964069199254,8.3806562954964,4.88191431243663,-6.24129707837434,-1.12578402688875 cont.diffExpScore=9.76736787362952 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=0.465168596362166 cont.tran.correlation=0.508366740400281 tran.covariance=0.0229266323004497 cont.tran.covariance=0.00200043971460301 tran.mean=91.1303793452383 cont.tran.mean=92.3421464502589 weightedLogRatios: wLogRatio Lung -0.777944377582033 cerebhem -0.912024289052196 cortex 0.911962738353442 heart -0.147343759758762 kidney 0.746150088076692 liver 0.926623317163525 stomach -0.895397020936998 testicle 2.50666016628929 cont.weightedLogRatios: wLogRatio Lung -0.560784277504284 cerebhem 0.0309151023289063 cortex 0.583210894501174 heart -0.0890804153091527 kidney 0.4131576749654 liver 0.240846423270197 stomach -0.288187790137942 testicle -0.0562800308335548 varWeightedLogRatios=1.44219975055463 cont.varWeightedLogRatios=0.138437183729314 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15704413494173 0.108035576895778 38.4784739841027 1.07371873105553e-180 *** df.mm.trans1 -0.0423017243887058 0.0946009278518146 -0.447159719775353 0.654886980859113 df.mm.trans2 0.293315963112627 0.084835240908431 3.45747781195347 0.000575545165295632 *** df.mm.exp2 0.586663958059969 0.111877402621694 5.24381103164985 2.04011376898852e-07 *** df.mm.exp3 -0.0900971018656598 0.111877402621694 -0.805319928371214 0.420886628703886 df.mm.exp4 -0.0496288043986089 0.111877402621694 -0.443599898063647 0.657457971261012 df.mm.exp5 -0.156394252164880 0.111877402621694 -1.39790742813110 0.162548182922608 df.mm.exp6 -0.0832867483912594 0.111877402621694 -0.744446567756746 0.456836170848327 df.mm.exp7 0.103247563036719 0.111877402621694 0.922863425653922 0.356370881498604 df.mm.exp8 0.0366807038505238 0.111877402621694 0.327865171973623 0.74310378866655 df.mm.trans1:exp2 -0.223968857475146 0.104950306484981 -2.13404672150432 0.0331580178145517 * df.mm.trans2:exp2 -0.20958929952925 0.0837213819865084 -2.5034142360792 0.0125085530161042 * df.mm.trans1:exp3 0.399524598923147 0.104950306484981 3.80679782941198 0.000152099266419465 *** df.mm.trans2:exp3 0.0112949569999508 0.0837213819865085 0.134911258413901 0.892717712063268 df.mm.trans1:exp4 0.135630736234952 0.104950306484981 1.29233292190873 0.196634147964285 df.mm.trans2:exp4 -0.0114144957083599 0.0837213819865085 -0.136339074171032 0.891589293878179 df.mm.trans1:exp5 0.473960681476328 0.104950306484981 4.51604856955951 7.29986623783657e-06 *** df.mm.trans2:exp5 0.124407684002387 0.0837213819865085 1.48597265179444 0.137700499822943 df.mm.trans1:exp6 0.533272628794011 0.104950306484981 5.08119172448846 4.72346407945073e-07 *** df.mm.trans2:exp6 0.148288554933498 0.0837213819865085 1.77121484876341 0.0769251717407932 . df.mm.trans1:exp7 -0.0440340048980154 0.104950306484981 -0.419570045794169 0.674917991099888 df.mm.trans2:exp7 -0.0200696966970258 0.0837213819865085 -0.239720083696899 0.810611816810135 df.mm.trans1:exp8 0.888736012838255 0.104950306484981 8.46816024272822 1.27864306937889e-16 *** df.mm.trans2:exp8 0.192607588464298 0.0837213819865085 2.30057822618524 0.0216850736920411 * df.mm.trans1:probe2 0.463705096149844 0.0642686748092277 7.21510280904789 1.30510905285213e-12 *** df.mm.trans1:probe3 0.166870869064924 0.0642686748092277 2.59645728747724 0.00960082841917387 ** df.mm.trans1:probe4 -0.140225611648262 0.0642686748092277 -2.18186561438371 0.0294246749880082 * df.mm.trans1:probe5 -0.234926764822915 0.0642686748092277 -3.65538523270102 0.000274447035868973 *** df.mm.trans1:probe6 -0.350956840237200 0.0642686748092277 -5.46077605114722 6.42266292009813e-08 *** df.mm.trans1:probe7 0.165035547479215 0.0642686748092277 2.5679002713079 0.0104214507258243 * df.mm.trans1:probe8 -0.487947741110857 0.0642686748092277 -7.59231060169297 9.2132248252095e-14 *** df.mm.trans1:probe9 0.139187539562392 0.0642686748092277 2.16571354513766 0.0306430256292709 * df.mm.trans1:probe10 -0.137551531513239 0.0642686748092277 -2.14025778377322 0.0326512275870142 * df.mm.trans1:probe11 0.452361383521170 0.0642686748092277 7.03859827301464 4.33976794609256e-12 *** df.mm.trans1:probe12 0.337072338114919 0.0642686748092277 5.24473764420177 2.03024422840406e-07 *** df.mm.trans1:probe13 0.317607341328401 0.0642686748092277 4.94186852725955 9.52101910172504e-07 *** df.mm.trans1:probe14 0.246528648861309 0.0642686748092277 3.83590683319819 0.000135465315759732 *** df.mm.trans1:probe15 0.43852755775525 0.0642686748092277 6.82334837394666 1.81587111175726e-11 *** df.mm.trans1:probe16 0.177681318441656 0.0642686748092277 2.76466441807112 0.00583602421567426 ** df.mm.trans1:probe17 0.0246730396027437 0.0642686748092277 0.383904595449992 0.701156396736585 df.mm.trans1:probe18 0.262328783845572 0.0642686748092277 4.08175187405461 4.94224994364032e-05 *** df.mm.trans1:probe19 0.0893119634722674 0.0642686748092277 1.38966555226752 0.165036865040628 df.mm.trans1:probe20 0.140702957532404 0.0642686748092277 2.18929296348587 0.0288785921950659 * df.mm.trans1:probe21 0.178294487668072 0.0642686748092277 2.77420513488591 0.00566926906974012 ** df.mm.trans1:probe22 0.159813879763238 0.0642686748092277 2.48665279372296 0.0131084568298359 * df.mm.trans2:probe2 -0.106300854538398 0.0642686748092277 -1.65400725709588 0.0985384224717437 . df.mm.trans2:probe3 -0.220848004249692 0.0642686748092277 -3.43632422646409 0.000621664374455217 *** df.mm.trans2:probe4 -0.185697503003899 0.0642686748092277 -2.88939368292742 0.0039695646951476 ** df.mm.trans2:probe5 -0.170247317821966 0.0642686748092277 -2.64899374893478 0.00824058776898574 ** df.mm.trans2:probe6 -0.136912793951478 0.0642686748092277 -2.13031923184792 0.0334653886750563 * df.mm.trans3:probe2 -0.288268535858253 0.0642686748092277 -4.48536610897823 8.40192383260185e-06 *** df.mm.trans3:probe3 -0.155911847230056 0.0642686748092277 -2.42593841701043 0.0155003326583095 * df.mm.trans3:probe4 -0.211971186070174 0.0642686748092277 -3.29820377811399 0.00101827065049124 ** df.mm.trans3:probe5 0.222142478469741 0.0642686748092277 3.45646583081321 0.000577676738330995 *** df.mm.trans3:probe6 -0.516091247601181 0.0642686748092277 -8.0302145506053 3.69320882418219e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.58033138118952 0.205461650593436 22.2928773713251 4.01710459647014e-85 *** df.mm.trans1 -0.042061480205005 0.179911685970401 -0.233789595034560 0.81521120430094 df.mm.trans2 0.0266970240289748 0.161339339561755 0.165471261389143 0.868617015260192 df.mm.exp2 -0.100263722629965 0.212768020195187 -0.471234927777146 0.637608036695067 df.mm.exp3 -0.289971394283876 0.212768020195187 -1.36285234039338 0.173332181387408 df.mm.exp4 -0.185878003242729 0.212768020195187 -0.873618145585086 0.382601830797653 df.mm.exp5 -0.246567479923725 0.212768020195187 -1.15885592062910 0.246878429464091 df.mm.exp6 -0.118631030971257 0.212768020195187 -0.55756044006251 0.577308540864287 df.mm.exp7 0.0309603892287745 0.212768020195187 0.145512418644364 0.884344843733583 df.mm.exp8 -0.103630121905229 0.212768020195187 -0.487056850978646 0.626358300028154 df.mm.trans1:exp2 0.154532293788925 0.199594095021987 0.774232793670083 0.439033603160673 df.mm.trans2:exp2 0.0248254246582857 0.159221006886518 0.155918023279300 0.876138988169982 df.mm.trans1:exp3 0.249948376705286 0.199594095021987 1.25228342390466 0.210851325411065 df.mm.trans2:exp3 -0.00567933152367901 0.159221006886518 -0.035669486299172 0.971555255452973 df.mm.trans1:exp4 0.216046545563801 0.199594095021987 1.08242954552339 0.279404564198607 df.mm.trans2:exp4 0.112748907764142 0.159221006886518 0.708128342917099 0.479082390538816 df.mm.trans1:exp5 0.308831644765459 0.199594095021987 1.54729850465485 0.122207021659968 df.mm.trans2:exp5 0.0944585064004226 0.159221006886518 0.593254045100635 0.553187417123318 df.mm.trans1:exp6 0.161199193178776 0.199594095021987 0.80763508139366 0.419553051984190 df.mm.trans2:exp6 -0.0150378952244991 0.159221006886518 -0.0944466783532974 0.92477919918311 df.mm.trans1:exp7 0.0383725795470931 0.199594095021987 0.192253080146815 0.847595259259879 df.mm.trans2:exp7 -0.0219142032424849 0.159221006886518 -0.137633869242542 0.890566193462746 df.mm.trans1:exp8 0.0964164439223257 0.199594095021987 0.483062607196393 0.629190223186249 df.mm.trans2:exp8 -0.0139917734518232 0.159221006886518 -0.0878764286536366 0.929998009839275 df.mm.trans1:probe2 -0.0404646535903169 0.122225922119112 -0.331064416522734 0.740686966587103 df.mm.trans1:probe3 -0.077964772711675 0.122225922119112 -0.637874285257563 0.523747399875995 df.mm.trans1:probe4 -0.0333681743693485 0.122225922119112 -0.273004071401731 0.784924152720844 df.mm.trans1:probe5 0.0183263847414967 0.122225922119112 0.149938608960849 0.880852779689875 df.mm.trans1:probe6 -0.0281888510350654 0.122225922119112 -0.23062907234682 0.817664965672112 df.mm.trans1:probe7 -0.124716198813453 0.122225922119112 -1.02037437436483 0.307875209108166 df.mm.trans1:probe8 -0.0480708105405951 0.122225922119112 -0.393294725923597 0.694211968579044 df.mm.trans1:probe9 0.0240575577742028 0.122225922119112 0.196828605234478 0.844014219145828 df.mm.trans1:probe10 0.0160112586654470 0.122225922119112 0.130997241729489 0.89581211107989 df.mm.trans1:probe11 -0.0421817551797561 0.122225922119112 -0.345113004250023 0.730104781604072 df.mm.trans1:probe12 -0.139342117985484 0.122225922119112 -1.14003736334828 0.254629415511716 df.mm.trans1:probe13 0.0146342664745235 0.122225922119112 0.119731283027361 0.904727619251132 df.mm.trans1:probe14 -0.0244099599284357 0.122225922119112 -0.199711808307305 0.841759330037663 df.mm.trans1:probe15 -0.0296668161466708 0.122225922119112 -0.242721148119135 0.808286826196778 df.mm.trans1:probe16 -0.0137504091498794 0.122225922119112 -0.112499942004768 0.910456683802938 df.mm.trans1:probe17 0.0270376951713400 0.122225922119112 0.221210809479442 0.82498763333418 df.mm.trans1:probe18 0.0243441066963888 0.122225922119112 0.199173025445984 0.84218060195206 df.mm.trans1:probe19 -0.118740580080594 0.122225922119112 -0.971484428359469 0.331615777086876 df.mm.trans1:probe20 -0.140434532182910 0.122225922119112 -1.14897502713094 0.250927274447505 df.mm.trans1:probe21 -0.0893499896125564 0.122225922119112 -0.731023240106814 0.464989971784942 df.mm.trans1:probe22 -0.219652000242513 0.122225922119112 -1.79709832770545 0.0727164000934484 . df.mm.trans2:probe2 0.0561015455461175 0.122225922119112 0.458998750620554 0.646366133003213 df.mm.trans2:probe3 -0.0437079162817846 0.122225922119112 -0.357599398916297 0.720742281399357 df.mm.trans2:probe4 0.127020896836754 0.122225922119112 1.03923042374734 0.299027653737964 df.mm.trans2:probe5 -0.00443406677642528 0.122225922119112 -0.0362776299785587 0.971070500559846 df.mm.trans2:probe6 0.0666589126149494 0.122225922119112 0.54537459369698 0.585655717582466 df.mm.trans3:probe2 -0.214815264759175 0.122225922119112 -1.75752623530901 0.0792302000961683 . df.mm.trans3:probe3 -0.0736946350865016 0.122225922119112 -0.602937853188659 0.546729670013103 df.mm.trans3:probe4 -0.0395909008467211 0.122225922119112 -0.323915746842465 0.746090823549749 df.mm.trans3:probe5 -0.231471307205533 0.122225922119112 -1.89379882100590 0.0586307333248731 . df.mm.trans3:probe6 8.7996402309487e-05 0.122225922119112 0.000719948770144949 0.999425752744212