chr2.13748_chr2_151922392_151924408_+_0.R fitVsDatCorrelation=0.670654692052783 cont.fitVsDatCorrelation=0.291499186830147 fstatistic=7599.17121219837,36,324 cont.fstatistic=4565.92482908898,36,324 residuals=-0.525155771390491,-0.0799784711616194,-0.00286217803859535,0.0747159666503628,0.514341610288001 cont.residuals=-0.388141742315411,-0.11612558042444,-0.0292698296152624,0.0692060953877988,0.768252591456251 predictedValues: Include Exclude Both chr2.13748_chr2_151922392_151924408_+_0.R.tl.Lung 50.895916105873 51.2709571010805 53.5127149135051 chr2.13748_chr2_151922392_151924408_+_0.R.tl.cerebhem 68.0490879079788 61.9096595956326 47.7066370188587 chr2.13748_chr2_151922392_151924408_+_0.R.tl.cortex 47.6976434814866 46.8318176613619 46.4249131724571 chr2.13748_chr2_151922392_151924408_+_0.R.tl.heart 47.6799858107892 48.1868594149886 52.6195924781023 chr2.13748_chr2_151922392_151924408_+_0.R.tl.kidney 46.8359089955729 52.9906122533173 56.4482058973925 chr2.13748_chr2_151922392_151924408_+_0.R.tl.liver 50.5696123136538 50.983862650391 51.7207465660149 chr2.13748_chr2_151922392_151924408_+_0.R.tl.stomach 48.2102866568458 48.2008198417391 55.6319271407713 chr2.13748_chr2_151922392_151924408_+_0.R.tl.testicle 51.3857844461218 52.843995267911 52.1573193508246 diffExp=-0.375040995207478,6.13942831234622,0.865825820124705,-0.506873604199392,-6.15470325774447,-0.414250336737148,0.0094668151066557,-1.45821082178916 diffExpScore=5.50166896720835 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 46.494380907664 54.2038995111 47.582688052518 cerebhem 53.5377991419574 55.0477591435305 52.3598154384932 cortex 47.9316844532895 50.5586958712065 52.6547539339801 heart 50.8872848319252 51.7890360327374 52.1473729023018 kidney 51.9160267334315 51.6733452887538 49.538915847534 liver 51.1545231952205 50.5240061525773 51.2541458802769 stomach 49.9415134212136 47.942848786855 52.8221012762452 testicle 54.0663014349138 52.2341672041579 51.6594868548613 cont.diffExp=-7.70951860343598,-1.5099600015731,-2.62701141791691,-0.901751200812214,0.2426814446777,0.630517042643248,1.99866463435863,1.83213423075596 cont.diffExpScore=1.92965148049022 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=0.903351795017459 cont.tran.correlation=0.154103635513731 tran.covariance=0.00935968335130155 cont.tran.covariance=0.000308873214388132 tran.mean=51.5339255940465 cont.tran.mean=51.2439545069083 weightedLogRatios: wLogRatio Lung -0.0288784989015343 cerebhem 0.394565648344372 cortex 0.070633585391893 heart -0.0409217026252901 kidney -0.482546784621708 liver -0.0320412123180349 stomach 0.000761082397306408 testicle -0.110624776352285 cont.weightedLogRatios: wLogRatio Lung -0.600804623281552 cerebhem -0.111094217058893 cortex -0.207907867892494 heart -0.069179425301017 kidney 0.0184948419864077 liver 0.0487243942817368 stomach 0.158896636785699 testicle 0.136965388449629 varWeightedLogRatios=0.057534003807415 cont.varWeightedLogRatios=0.0598676808323213 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74106333178069 0.0806986414894805 46.3584425057311 4.78207644907566e-145 *** df.mm.trans1 0.236539842946162 0.0683509063522767 3.46066871047838 0.00061101604501903 *** df.mm.trans2 0.175972871104315 0.0683509063522767 2.57455066063588 0.0104806321197433 * df.mm.exp2 0.593847163273751 0.095186924482014 6.23874724921868 1.37792129058996e-09 *** df.mm.exp3 -0.0133792592530609 0.095186924482014 -0.140557742839868 0.88830667557401 df.mm.exp4 -0.110478300696618 0.095186924482014 -1.16064576408805 0.246640438375123 df.mm.exp5 -0.103546394969915 0.095186924482014 -1.08782162606252 0.277482403033838 df.mm.exp6 0.0220131716603926 0.095186924482014 0.231262558173650 0.817256807766597 df.mm.exp7 -0.154796673661400 0.095186924482014 -1.62623884009037 0.10487103609351 df.mm.exp8 0.0654532784562314 0.095186924482014 0.687628881933244 0.492178461199866 df.mm.trans1:exp2 -0.303400524189937 0.082434294709535 -3.68051337442745 0.00027254191053326 *** df.mm.trans2:exp2 -0.405295397611643 0.082434294709535 -4.91658719274228 1.4020610594766e-06 *** df.mm.trans1:exp3 -0.0515214336380693 0.082434294709535 -0.624999993262633 0.53241097552395 df.mm.trans2:exp3 -0.0771823577469198 0.082434294709535 -0.936289417151915 0.349821537973008 df.mm.trans1:exp4 0.0452073391999423 0.082434294709535 0.54840451245728 0.583791842163439 df.mm.trans2:exp4 0.0484402048594501 0.082434294709535 0.587621996768865 0.557195346123484 df.mm.trans1:exp5 0.0204139032488962 0.082434294709535 0.247638477660621 0.804570995759762 df.mm.trans2:exp5 0.136536712057863 0.082434294709535 1.6563095801203 0.0986272227913275 . df.mm.trans1:exp6 -0.0284450095898577 0.082434294709535 -0.345062812632611 0.730271047335366 df.mm.trans2:exp6 -0.0276284611061678 0.082434294709535 -0.335157366282071 0.737723271786518 df.mm.trans1:exp7 0.100586401403888 0.082434294709535 1.22020090980719 0.223276088678472 df.mm.trans2:exp7 0.0930482502586294 0.082434294709535 1.12875655194836 0.259835611598889 df.mm.trans1:exp8 -0.0558743981041626 0.082434294709535 -0.677805254488333 0.498378916672555 df.mm.trans2:exp8 -0.0352336444677846 0.082434294709535 -0.427414883476999 0.669361083800446 df.mm.trans1:probe2 -0.184092549501007 0.0412171473547675 -4.46640685529425 1.10019190892494e-05 *** df.mm.trans1:probe3 -0.00144881238181442 0.0412171473547675 -0.0351507194164625 0.971981203693739 df.mm.trans1:probe4 -0.0732105790721417 0.0412171473547675 -1.77621654507037 0.0766356212867854 . df.mm.trans1:probe5 -0.0148473191263162 0.0412171473547675 -0.360221899844771 0.718915906734816 df.mm.trans1:probe6 -0.156785132509436 0.0412171473547675 -3.80388121380508 0.000170241090627472 *** df.mm.trans2:probe2 0.0872270063457174 0.0412171473547675 2.11627955702344 0.0350832973815272 * df.mm.trans2:probe3 0.0178731559778235 0.0412171473547675 0.433633987912465 0.664842825695829 df.mm.trans2:probe4 -0.0184760553102036 0.0412171473547675 -0.448261378963834 0.654264179124865 df.mm.trans2:probe5 0.090659268862319 0.0412171473547675 2.19955224174029 0.0285446363877465 * df.mm.trans2:probe6 0.00351087947373156 0.0412171473547675 0.0851800694383927 0.932170845253873 df.mm.trans3:probe2 -0.264564662706652 0.0412171473547675 -6.41880090413511 4.87916636675608e-10 *** df.mm.trans3:probe3 -0.256405611988321 0.0412171473547675 -6.22084808008099 1.52590938878688e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91201245355778 0.104067322533561 37.5911703916104 3.52925208881498e-120 *** df.mm.trans1 -0.0625591353636052 0.0881439350840975 -0.70973839894847 0.478376836210939 df.mm.trans2 0.0787659150852069 0.0881439350840975 0.893605612343684 0.372196214040411 df.mm.exp2 0.0608343523250702 0.122751116849207 0.495591029121159 0.620518952969814 df.mm.exp3 -0.140460133689665 0.122751116849207 -1.14426766366788 0.253357290528005 df.mm.exp4 -0.0468975700540004 0.122751116849207 -0.382054121035912 0.702671685728674 df.mm.exp5 0.0221957684314074 0.122751116849207 0.180819278888303 0.85662248268964 df.mm.exp6 -0.0491123172272857 0.122751116849207 -0.400096703703457 0.689348946376876 df.mm.exp7 -0.155682765922872 0.122751116849207 -1.26827983255027 0.205608580112527 df.mm.exp8 0.031658749748087 0.122751116849207 0.257910074960687 0.796640047553365 df.mm.trans1:exp2 0.0802221135266899 0.106305585534325 0.754636862432658 0.45101504765932 df.mm.trans2:exp2 -0.0453860483812995 0.106305585534325 -0.426939451517763 0.669706989020231 df.mm.trans1:exp3 0.170905425676155 0.106305585534325 1.60768058251248 0.108879160618133 df.mm.trans2:exp3 0.0708422369522007 0.106305585534325 0.666401832002761 0.505628519708601 df.mm.trans1:exp4 0.137179190969059 0.106305585534325 1.29042317277642 0.197823901876524 df.mm.trans2:exp4 0.00132318475989688 0.106305585534325 0.0124469918795531 0.990076654418718 df.mm.trans1:exp5 0.0881003097341567 0.106305585534325 0.828745820751908 0.407858341186578 df.mm.trans2:exp5 -0.0700065374376203 0.106305585534325 -0.658540537505582 0.510658542578527 df.mm.trans1:exp6 0.144631771133217 0.106305585534325 1.36052842761039 0.174608478363776 df.mm.trans2:exp6 -0.0211919426424753 0.106305585534325 -0.199349286643387 0.842114574251653 df.mm.trans1:exp7 0.227203890509251 0.106305585534325 2.1372714271527 0.0333242755176887 * df.mm.trans2:exp7 0.0329395646129048 0.106305585534325 0.309857327320481 0.756868651306134 df.mm.trans1:exp8 0.119220883435001 0.106305585534325 1.12149218534246 0.262908905369384 df.mm.trans2:exp8 -0.0686747773959478 0.106305585534325 -0.646012879292905 0.518728297478419 df.mm.trans1:probe2 -0.0164606064207411 0.0531527927671627 -0.309684695079847 0.756999819523282 df.mm.trans1:probe3 -0.0280461904736994 0.0531527927671627 -0.527652245791798 0.598101871116294 df.mm.trans1:probe4 -0.00594171532821821 0.0531527927671627 -0.111785571724256 0.911062647814798 df.mm.trans1:probe5 -0.0396067322989877 0.0531527927671627 -0.745148659873548 0.456722037400254 df.mm.trans1:probe6 -0.00104143786116737 0.0531527927671627 -0.0195932858265683 0.984379880220972 df.mm.trans2:probe2 0.0225301111061595 0.0531527927671627 0.423874455757259 0.671938643955407 df.mm.trans2:probe3 -0.0493484004518706 0.0531527927671627 -0.928425354205614 0.353878105968289 df.mm.trans2:probe4 0.0348813949093688 0.0531527927671627 0.656247641815694 0.51213057852586 df.mm.trans2:probe5 0.0293276447221502 0.0531527927671627 0.551761124775152 0.581492375406427 df.mm.trans2:probe6 -0.0196203950613161 0.0531527927671627 -0.369131969175426 0.712270472643543 df.mm.trans3:probe2 -0.0889241816494962 0.0531527927671627 -1.67299170974950 0.0952941389856711 . df.mm.trans3:probe3 -0.108326522064252 0.0531527927671627 -2.03802126708147 0.0423596557702246 *