chr9.24604_chr9_43440834_43443012_-_1.R fitVsDatCorrelation=0.874092157102212 cont.fitVsDatCorrelation=0.298983319303293 fstatistic=6792.7434728271,40,416 cont.fstatistic=1752.4746404857,40,416 residuals=-0.66469645693068,-0.0951466894482902,-0.0133128931298710,0.0944455934089293,1.07627327849055 cont.residuals=-0.82845084422409,-0.218776653162845,-0.0405728561301612,0.153941872019051,1.43578584127725 predictedValues: Include Exclude Both chr9.24604_chr9_43440834_43443012_-_1.R.tl.Lung 86.16506492175 99.7091880018552 65.580087085668 chr9.24604_chr9_43440834_43443012_-_1.R.tl.cerebhem 105.025434739631 124.750310292405 56.8815578163986 chr9.24604_chr9_43440834_43443012_-_1.R.tl.cortex 75.6143704470671 104.179327778867 68.5716072466182 chr9.24604_chr9_43440834_43443012_-_1.R.tl.heart 77.040878886459 116.384349572567 71.3727485030557 chr9.24604_chr9_43440834_43443012_-_1.R.tl.kidney 80.7744201914399 116.154686757386 82.5151930629815 chr9.24604_chr9_43440834_43443012_-_1.R.tl.liver 83.404946108412 241.973627892205 159.063246169085 chr9.24604_chr9_43440834_43443012_-_1.R.tl.stomach 86.4366573281321 130.739507844492 68.6262081338298 chr9.24604_chr9_43440834_43443012_-_1.R.tl.testicle 82.1994789125583 102.334473754070 64.795052245867 diffExp=-13.5441230801052,-19.7248755527733,-28.5649573317994,-39.3434706861077,-35.3802665659462,-158.568681783793,-44.3028505163602,-20.1349948415113 diffExpScore=0.997226568961818 diffExp1.5=0,0,0,-1,0,-1,-1,0 diffExp1.5Score=0.75 diffExp1.4=0,0,0,-1,-1,-1,-1,0 diffExp1.4Score=0.8 diffExp1.3=0,0,-1,-1,-1,-1,-1,0 diffExp1.3Score=0.833333333333333 diffExp1.2=0,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 84.4290510130821 88.6759733115098 84.3624837973265 cerebhem 94.7252402496851 98.2759007855895 92.8598697811821 cortex 96.5177610604974 107.696170012037 86.8096921746335 heart 83.2491122481472 94.4839991626454 91.9537366072158 kidney 94.9880383980265 100.099037264440 85.7132984810926 liver 92.8580127266145 90.296799105968 104.530775789889 stomach 92.5949851744853 74.4111283292545 93.008436614958 testicle 94.1388946421148 95.6383747976664 94.795283014646 cont.diffExp=-4.24692229842771,-3.55066053590433,-11.1784089515398,-11.2348869144981,-5.11099886641321,2.56121362064643,18.1838568452309,-1.49948015555168 cont.diffExpScore=3.37113257253522 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=0,0,0,0,0,0,1,0 cont.diffExp1.2Score=0.5 tran.correlation=0.0559987964408334 cont.tran.correlation=0.337222429380721 tran.covariance=0.00329234919084121 cont.tran.covariance=0.00178917293586286 tran.mean=107.055420214331 cont.tran.mean=92.6924048926102 weightedLogRatios: wLogRatio Lung -0.661240507246617 cerebhem -0.815853766281237 cortex -1.43757817428805 heart -1.87741123231691 kidney -1.66130521830099 liver -5.27902596888622 stomach -1.93089544931688 testicle -0.990036021259373 cont.weightedLogRatios: wLogRatio Lung -0.218907426940876 cerebhem -0.168145605003593 cortex -0.506787230111235 heart -0.567786718762332 kidney -0.240031330663451 liver 0.126340971537072 stomach 0.966106232741752 testicle -0.0719452280983891 varWeightedLogRatios=2.17009560351696 cont.varWeightedLogRatios=0.229932309442301 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.97837416065 0.0952824981758077 52.2485687923957 7.2924315385388e-185 *** df.mm.trans1 -0.612166459905953 0.077284311314844 -7.9209667459167 2.17328747978702e-14 *** df.mm.trans2 -0.286632329114509 0.077284311314844 -3.70880356230147 0.000236475915690533 *** df.mm.exp2 0.564295059434985 0.104507463250406 5.39956709199707 1.12537560786791e-07 *** df.mm.exp3 -0.131369036379072 0.104507463250406 -1.25703018993298 0.209448004562378 df.mm.exp4 -0.0419324208964369 0.104507463250406 -0.401238529692032 0.688450536426966 df.mm.exp5 -0.141649837859522 0.104507463250406 -1.35540403961506 0.176024297077296 df.mm.exp6 -0.0320160858038919 0.104507463250406 -0.306352147570356 0.759489804511355 df.mm.exp7 0.228693663661292 0.104507463250406 2.18829982614092 0.0292032721578151 * df.mm.exp8 -0.00908422645581618 0.104507463250406 -0.086924188696934 0.930773596356996 df.mm.trans1:exp2 -0.366357319187974 0.084256610534561 -4.34811365973117 1.72877433184764e-05 *** df.mm.trans2:exp2 -0.340238666702247 0.0842566105345611 -4.03812430316888 6.41567659038509e-05 *** df.mm.trans1:exp3 0.000750570587568058 0.084256610534561 0.00890815074100545 0.9928966883833 df.mm.trans2:exp3 0.175224926968677 0.084256610534561 2.07965791475557 0.0381687713522647 * df.mm.trans1:exp4 -0.0699962196869746 0.084256610534561 -0.830750480501029 0.406591133893404 df.mm.trans2:exp4 0.196572664100457 0.084256610534561 2.33302363877818 0.0201227990017195 * df.mm.trans1:exp5 0.0770453552831304 0.084256610534561 0.914413181284184 0.361029472181523 df.mm.trans2:exp5 0.294314817955227 0.084256610534561 3.49307687655561 0.00052868798687631 *** df.mm.trans1:exp6 -0.000541116925409206 0.084256610534561 -0.00642224891288793 0.99487890056254 df.mm.trans2:exp6 0.918587001160596 0.0842566105345611 10.9022543790057 1.63223793894685e-24 *** df.mm.trans1:exp7 -0.225546619390893 0.084256610534561 -2.67690117083901 0.00772449553562905 ** df.mm.trans2:exp7 0.0422553609229774 0.0842566105345611 0.501507960679771 0.616278967487171 df.mm.trans1:exp8 -0.0380316269571616 0.084256610534561 -0.451378553158883 0.651951952418637 df.mm.trans2:exp8 0.0350730002797237 0.0842566105345611 0.416264077764405 0.67743144903689 df.mm.trans1:probe2 0.368976250769587 0.0535441415301121 6.89106670170596 2.05984363623380e-11 *** df.mm.trans1:probe3 0.106351500347997 0.0535441415301121 1.98623971379180 0.0476625221392904 * df.mm.trans1:probe4 0.030518235201126 0.053544141530112 0.569964039557217 0.569009760895078 df.mm.trans1:probe5 0.344258349296326 0.053544141530112 6.4294307361847 3.51823543816793e-10 *** df.mm.trans1:probe6 0.320638165204948 0.0535441415301121 5.98829593756074 4.58858579970322e-09 *** df.mm.trans2:probe2 -0.396783764564458 0.0535441415301121 -7.41040482162396 7.09181911288978e-13 *** df.mm.trans2:probe3 -0.187600327257453 0.053544141530112 -3.50365739176061 0.000508704040604674 *** df.mm.trans2:probe4 -0.29228129543329 0.053544141530112 -5.45869794679437 8.25962501536673e-08 *** df.mm.trans2:probe5 -0.0440837549412841 0.0535441415301121 -0.823316121643156 0.410799972275835 df.mm.trans2:probe6 -0.242542888039332 0.053544141530112 -4.52977452076491 7.7243771184005e-06 *** df.mm.trans3:probe2 0.0787605205054316 0.0535441415301121 1.47094562084142 0.142061812692977 df.mm.trans3:probe3 0.0783641333471085 0.0535441415301121 1.46354262310916 0.144074131061613 df.mm.trans3:probe4 0.0796757138361514 0.0535441415301121 1.48803793579067 0.137498501630316 df.mm.trans3:probe5 0.286678909954976 0.0535441415301121 5.35406678980471 1.42514941136746e-07 *** df.mm.trans3:probe6 0.21403622520571 0.053544141530112 3.99737896788093 7.57699597178024e-05 *** df.mm.trans3:probe7 -0.0116384088348154 0.053544141530112 -0.217361012843397 0.828033578972222 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.55022682866706 0.18717904840316 24.3094879874934 7.12512540489017e-82 *** df.mm.trans1 -0.129401727929165 0.151822256189322 -0.852323836946548 0.394524798746802 df.mm.trans2 -0.0330875054191156 0.151822256189322 -0.2179358036799 0.827585991097734 df.mm.exp2 0.121890022696106 0.205301161249421 0.593713264719537 0.55302659443537 df.mm.exp3 0.299545061866574 0.205301161249421 1.45905196075659 0.145305448801406 df.mm.exp4 -0.0367953508037808 0.205301161249421 -0.179226218594438 0.857847336565502 df.mm.exp5 0.223125324787819 0.205301161249421 1.08681959434581 0.277745866855902 df.mm.exp6 -0.101085666582979 0.205301161249421 -0.492377471066369 0.622712271606642 df.mm.exp7 -0.180627443402842 0.205301161249421 -0.879816959161752 0.379466431141553 df.mm.exp8 0.0678480655543284 0.205301161249421 0.330480671134146 0.741203171172279 df.mm.trans1:exp2 -0.00682107820136964 0.165519087801784 -0.0412102210805933 0.96714807635851 df.mm.trans2:exp2 -0.0191001622266644 0.165519087801784 -0.115395526161536 0.908187304596081 df.mm.trans1:exp3 -0.165729566709464 0.165519087801784 -1.00127162921495 0.317277554199854 df.mm.trans2:exp3 -0.105220016735716 0.165519087801784 -0.63569717627807 0.525323361135228 df.mm.trans1:exp4 0.0227212672289110 0.165519087801784 0.137272791498952 0.890881589244054 df.mm.trans2:exp4 0.100236873171514 0.165519087801784 0.605591019759314 0.545116611967805 df.mm.trans1:exp5 -0.105285901407177 0.165519087801784 -0.63609522506106 0.525064157838583 df.mm.trans2:exp5 -0.101954233016093 0.165519087801784 -0.615966619742296 0.538253425744833 df.mm.trans1:exp6 0.196245699505904 0.165519087801784 1.18563787483482 0.236442128517631 df.mm.trans2:exp6 0.119198702280844 0.165519087801784 0.720150792660174 0.471836583828343 df.mm.trans1:exp7 0.272950879112758 0.165519087801784 1.64905983193689 0.099890280440838 . df.mm.trans2:exp7 0.00524397160937024 0.165519087801784 0.0316819750459846 0.974740863976439 df.mm.trans1:exp8 0.0410116800181968 0.165519087801784 0.247776136050907 0.804429871432776 df.mm.trans2:exp8 0.00773710721167845 0.165519087801784 0.0467445012803838 0.962739293763726 df.mm.trans1:probe2 -0.0389843808997468 0.105185544575858 -0.370624890111512 0.711105586145141 df.mm.trans1:probe3 0.0670095199849932 0.105185544575858 0.637060161215092 0.52443607892144 df.mm.trans1:probe4 -0.0240443742852273 0.105185544575858 -0.228590101255663 0.819299845334251 df.mm.trans1:probe5 0.104445942671410 0.105185544575858 0.99296859746812 0.321302468752368 df.mm.trans1:probe6 0.0876971159402948 0.105185544575858 0.833737338090686 0.404907472773487 df.mm.trans2:probe2 -0.184374785064713 0.105185544575858 -1.75285288304749 0.080363868410199 . df.mm.trans2:probe3 -0.149971442410880 0.105185544575858 -1.425779968299 0.154681793512834 df.mm.trans2:probe4 0.0801206114441626 0.105185544575858 0.761707435819579 0.446666241526616 df.mm.trans2:probe5 -0.0128762574171758 0.105185544575858 -0.122414705072803 0.902629707606158 df.mm.trans2:probe6 -0.150852630825381 0.105185544575858 -1.43415743516533 0.152278586810888 df.mm.trans3:probe2 0.125201913294669 0.105185544575858 1.19029581298004 0.234608953952821 df.mm.trans3:probe3 0.0276651323064299 0.105185544575858 0.263012683139919 0.792671016153378 df.mm.trans3:probe4 -0.000874218049657386 0.105185544575858 -0.00831119953965649 0.993372682688116 df.mm.trans3:probe5 -0.0626753468729011 0.105185544575858 -0.595855135091313 0.551596052939433 df.mm.trans3:probe6 0.0770898203559332 0.105185544575858 0.732893675331382 0.464036040391929 df.mm.trans3:probe7 0.165289117799625 0.105185544575858 1.57140525788143 0.116848787413188