chr9.25056_chr9_108860718_108861501_+_2.R fitVsDatCorrelation=0.779858351286212 cont.fitVsDatCorrelation=0.254927253430549 fstatistic=11234.0381345878,59,853 cont.fstatistic=4699.27424707947,59,853 residuals=-0.461734362653377,-0.088784229548452,-0.0063731016079968,0.077202480803332,0.762884492084852 cont.residuals=-0.504554329622757,-0.155046402959752,-0.0452824887768289,0.12403949667474,1.03075701980092 predictedValues: Include Exclude Both chr9.25056_chr9_108860718_108861501_+_2.R.tl.Lung 66.2936303184018 51.7124279618186 57.8302691727344 chr9.25056_chr9_108860718_108861501_+_2.R.tl.cerebhem 64.2539620702097 48.7702918000105 56.7576266084224 chr9.25056_chr9_108860718_108861501_+_2.R.tl.cortex 52.0535889067106 51.9200191429472 50.8780200381549 chr9.25056_chr9_108860718_108861501_+_2.R.tl.heart 53.9061498060377 57.758841331502 54.0286382633882 chr9.25056_chr9_108860718_108861501_+_2.R.tl.kidney 54.8945267652998 51.4430507918736 53.4339113137962 chr9.25056_chr9_108860718_108861501_+_2.R.tl.liver 56.9710740581888 53.5705876487208 59.648013093887 chr9.25056_chr9_108860718_108861501_+_2.R.tl.stomach 54.2989002916168 52.2222252698047 55.1009225519069 chr9.25056_chr9_108860718_108861501_+_2.R.tl.testicle 55.0009273553157 49.8223675823621 55.6301925468939 diffExp=14.5812023565832,15.4836702701992,0.13356976376339,-3.85269152546425,3.4514759734262,3.40048640946801,2.07667502181208,5.17855977295365 diffExpScore=1.16175889454267 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=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,1,0,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 54.5903830163262 54.9350692323276 59.9637567590585 cerebhem 58.1973086043256 55.2875196141811 59.3990259409525 cortex 56.0409368131835 54.0819196724572 57.8975458663184 heart 55.1097800579427 56.3432208527766 53.6443915373776 kidney 53.8035433847714 57.229000481346 52.5955484505564 liver 55.3076501987004 57.1112617881101 53.3724060877791 stomach 58.7358373267524 56.1417209954061 54.7595092489958 testicle 55.9005526722908 57.3423712811303 58.664914578559 cont.diffExp=-0.344686216001435,2.90978899014451,1.95901714072624,-1.23344079483390,-3.42545709657462,-1.80361158940968,2.59411633134623,-1.44181860883952 cont.diffExpScore=8.79682465689772 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=-0.39655061049497 cont.tran.correlation=-0.255476245804334 tran.covariance=-0.00179051991521957 cont.tran.covariance=-0.000164828978007561 tran.mean=54.6807856938013 cont.tran.mean=56.0098797495018 weightedLogRatios: wLogRatio Lung 1.01094458005867 cerebhem 1.10977633185894 cortex 0.0101512870414242 heart -0.277629633366952 kidney 0.257996115878871 liver 0.246898432366987 stomach 0.155008536145936 testicle 0.391381653466682 cont.weightedLogRatios: wLogRatio Lung -0.025195698615231 cerebhem 0.207126647058031 cortex 0.142625061861789 heart -0.088990356295326 kidney -0.247885667061765 liver -0.129289559757766 stomach 0.182962855002039 testicle -0.102786768388501 varWeightedLogRatios=0.225855276036771 cont.varWeightedLogRatios=0.0276470462921194 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20601479573429 0.069454115519909 60.5581795153469 0 *** df.mm.trans1 -0.0297878519899736 0.0599788752581494 -0.49663905603042 0.61957156750051 df.mm.trans2 -0.254257900790251 0.0529910651908942 -4.79812775746847 1.89018296821143e-06 *** df.mm.exp2 -0.0711049266519732 0.0681634532065408 -1.04315323398478 0.297172872265683 df.mm.exp3 -0.109732449983420 0.0681634532065408 -1.60984288238628 0.107802142861263 df.mm.exp4 -0.0282728728402703 0.0681634532065408 -0.414780524023646 0.678406872608482 df.mm.exp5 -0.114836183843449 0.0681634532065408 -1.68471781345182 0.0924088273550657 . df.mm.exp6 -0.147196623622322 0.0681634532065408 -2.15946547156736 0.0310921690302114 * df.mm.exp7 -0.141433940214570 0.0681634532065408 -2.07492334324692 0.0382929332330376 * df.mm.exp8 -0.185191638728595 0.0681634532065408 -2.71687583326286 0.0067236210755511 ** df.mm.trans1:exp2 0.0398544960408947 0.0630048972566869 0.632561876555791 0.527189311258683 df.mm.trans2:exp2 0.0125281408076865 0.0465322004539516 0.269235941680523 0.787813233736527 df.mm.trans1:exp3 -0.132087625142565 0.0630048972566869 -2.09646600333986 0.0363349050042764 * df.mm.trans2:exp3 0.113738752345071 0.0465322004539516 2.44430203677187 0.0147147901277965 * df.mm.trans1:exp4 -0.178576378113842 0.0630048972566869 -2.8343253602384 0.0047007179415156 ** df.mm.trans2:exp4 0.138851168438513 0.0465322004539516 2.98398027782762 0.00292639699748236 ** df.mm.trans1:exp5 -0.0738439861795528 0.0630048972566869 -1.17203565746178 0.241509980842670 df.mm.trans2:exp5 0.109613431052820 0.0465322004539516 2.35564684204638 0.0187159707566792 * df.mm.trans1:exp6 -0.00435352896182875 0.0630048972566869 -0.0690982630142563 0.944927598084508 df.mm.trans2:exp6 0.18249866445539 0.0465322004539516 3.92198655286013 9.4876777223563e-05 *** df.mm.trans1:exp7 -0.0581559044545512 0.0630048972566869 -0.923037842877824 0.356248497371652 df.mm.trans2:exp7 0.151243977205467 0.0465322004539516 3.25030786702509 0.00119797254276087 ** df.mm.trans1:exp8 -0.00155213412285488 0.0630048972566869 -0.0246351345758309 0.980351756201927 df.mm.trans2:exp8 0.147957531427577 0.0465322004539516 3.17968052196449 0.0015275903069925 ** df.mm.trans1:probe2 -0.0562866878384848 0.0431365043259785 -1.30485046755601 0.192295673612255 df.mm.trans1:probe3 -0.219724716340064 0.0431365043259785 -5.09370705330281 4.32378299313183e-07 *** df.mm.trans1:probe4 -0.137742290570554 0.0431365043259785 -3.1931722962447 0.00145878902973557 ** df.mm.trans1:probe5 -0.203264408518914 0.0431365043259785 -4.71212055067939 2.86159850646396e-06 *** df.mm.trans1:probe6 -0.195108376282358 0.0431365043259785 -4.52304560443615 6.958377577618e-06 *** df.mm.trans1:probe7 0.188315460903386 0.0431365043259785 4.36557073517836 1.42348505331518e-05 *** df.mm.trans1:probe8 0.0163796686406339 0.0431365043259785 0.379717107275414 0.70424993615057 df.mm.trans1:probe9 -0.129327342488189 0.0431365043259785 -2.99809510550217 0.00279556864440238 ** df.mm.trans1:probe10 -0.0896619558157826 0.0431365043259785 -2.07856332395912 0.0379559169679781 * df.mm.trans1:probe11 0.0286660078969573 0.0431365043259785 0.664541745903446 0.506523242771729 df.mm.trans1:probe12 -0.0664585850228151 0.0431365043259785 -1.54065764162515 0.123770998298459 df.mm.trans1:probe13 0.406161267119644 0.0431365043259785 9.41572047772629 4.26010017112586e-20 *** df.mm.trans1:probe14 0.219727350571804 0.0431365043259785 5.09376812064661 4.32243102345105e-07 *** df.mm.trans1:probe15 -0.035096324513171 0.0431365043259785 -0.813610770310718 0.41609501351316 df.mm.trans1:probe16 0.256876896900364 0.0431365043259785 5.95497713396453 3.798897160088e-09 *** df.mm.trans1:probe17 0.0922736102936273 0.0431365043259785 2.13910727666582 0.0327103075562096 * df.mm.trans1:probe18 0.00599956053177199 0.0431365043259785 0.139083141425505 0.889417279204038 df.mm.trans1:probe19 -0.0131353304340420 0.0431365043259785 -0.304506140200411 0.760816589099334 df.mm.trans1:probe20 0.257524022317093 0.0431365043259785 5.96997893874312 3.47832567249098e-09 *** df.mm.trans1:probe21 0.0638316998908027 0.0431365043259785 1.47976060851924 0.139306359333749 df.mm.trans1:probe22 0.181790479260760 0.0431365043259785 4.21430716515615 2.77252986774105e-05 *** df.mm.trans2:probe2 -0.00638407229243165 0.0431365043259785 -0.147996978248117 0.88238012919588 df.mm.trans2:probe3 0.0874536982680997 0.0431365043259785 2.02737100825835 0.0429349791819342 * df.mm.trans2:probe4 -0.0407716641197435 0.0431365043259785 -0.945177750418436 0.344835784997287 df.mm.trans2:probe5 -0.0399498736143465 0.0431365043259785 -0.926126820858017 0.354642021712633 df.mm.trans2:probe6 -0.09728818758697 0.0431365043259785 -2.25535631843907 0.0243636798871749 * df.mm.trans3:probe2 0.444105156511169 0.0431365043259785 10.2953441279133 1.64388976948431e-23 *** df.mm.trans3:probe3 0.432814639436521 0.0431365043259785 10.0336048597212 1.80391154449482e-22 *** df.mm.trans3:probe4 -0.070127116028527 0.0431365043259785 -1.62570234014752 0.104382363357275 df.mm.trans3:probe5 0.0125381728423557 0.0431365043259785 0.290662700612130 0.771379954005266 df.mm.trans3:probe6 -0.122884932585733 0.0431365043259785 -2.84874573185401 0.00449479786663830 ** df.mm.trans3:probe7 0.219809003135828 0.0431365043259785 5.09566100847561 4.28072639780022e-07 *** df.mm.trans3:probe8 -0.0279229280002852 0.0431365043259785 -0.647315503112498 0.517601932505353 df.mm.trans3:probe9 0.258870070650673 0.0431365043259785 6.00118332942364 2.89376040123812e-09 *** df.mm.trans3:probe10 0.208230771608954 0.0431365043259785 4.82725188011003 1.64009981137982e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.8533570036899 0.107290871222199 35.9150499925535 8.4036851314259e-173 *** df.mm.trans1 0.161005221360593 0.0926537719644533 1.73770822220128 0.0826231441595565 . df.mm.trans2 0.143976068723258 0.0818591887430149 1.75882599050975 0.0789655977072207 . df.mm.exp2 0.0798391450600204 0.105297090392678 0.758227456829824 0.448524258969437 df.mm.exp3 0.0456380335259723 0.105297090392678 0.433421601259609 0.664818092059923 df.mm.exp4 0.146142865516515 0.105297090392678 1.38790981756014 0.165526914769619 df.mm.exp5 0.157499277826849 0.105297090392678 1.49576096774846 0.135085827012705 df.mm.exp6 0.168349358124513 0.105297090392678 1.5988035139119 0.110234586060100 df.mm.exp7 0.185708909818290 0.105297090392678 1.76366611010558 0.0781461612666857 . df.mm.exp8 0.0885029057515399 0.105297090392678 0.84050665997979 0.400859901368576 df.mm.trans1:exp2 -0.0158577673089485 0.0973282903012043 -0.162930708634386 0.870611582637833 df.mm.trans2:exp2 -0.0734438755096682 0.0718817062058674 -1.02173250172063 0.30719720376227 df.mm.trans1:exp3 -0.0194133273357689 0.0973282903012043 -0.199462327712631 0.841948646237102 df.mm.trans2:exp3 -0.0612900339303582 0.0718817062058674 -0.852651351302445 0.394092017209845 df.mm.trans1:exp4 -0.13667340059901 0.0973282903012043 -1.40425153032118 0.160607955783495 df.mm.trans2:exp4 -0.120832864977467 0.0718817062058674 -1.68099606082533 0.0931297638066653 . df.mm.trans1:exp5 -0.172017682662561 0.0973282903012043 -1.76739653116492 0.0775193410781766 . df.mm.trans2:exp5 -0.116590434911801 0.0718817062058674 -1.62197645361796 0.105177918194742 df.mm.trans1:exp6 -0.155295851229084 0.0973282903012043 -1.59558799141017 0.110951212474993 df.mm.trans2:exp6 -0.129499960106877 0.0718817062058674 -1.80157048214733 0.0719660207454596 . df.mm.trans1:exp7 -0.112516584639079 0.0973282903012043 -1.15605220528246 0.247983488435726 df.mm.trans2:exp7 -0.163981612343195 0.0718817062058674 -2.28127045111528 0.0227781521851506 * df.mm.trans1:exp8 -0.0647863708414124 0.0973282903012043 -0.665647887586604 0.505816143293226 df.mm.trans2:exp8 -0.0456150198165349 0.0718817062058674 -0.63458454486173 0.52586956761413 df.mm.trans1:probe2 -0.00290879405495691 0.0666361251017261 -0.0436519087884592 0.965192089932827 df.mm.trans1:probe3 -0.00945855414797792 0.0666361251017261 -0.141943339795623 0.887158278005434 df.mm.trans1:probe4 -0.059060049058414 0.0666361251017261 -0.886306773814556 0.37570203870957 df.mm.trans1:probe5 0.0780321930348238 0.0666361251017261 1.17101936698301 0.241918010291296 df.mm.trans1:probe6 -0.0197815308594130 0.0666361251017261 -0.296858960949705 0.766646458539087 df.mm.trans1:probe7 -0.0916035834910127 0.0666361251017261 -1.37468352715845 0.169590585532572 df.mm.trans1:probe8 0.00670291188984856 0.0666361251017261 0.100589760878442 0.919899762296198 df.mm.trans1:probe9 0.00754911886954933 0.0666361251017261 0.113288683248387 0.909828345537975 df.mm.trans1:probe10 0.0240317479565227 0.0666361251017261 0.360641437656167 0.718456796818426 df.mm.trans1:probe11 -0.0264974336664131 0.0666361251017261 -0.397643674897998 0.690992349726047 df.mm.trans1:probe12 -0.098627171342138 0.0666361251017261 -1.48008563210383 0.139219625121417 df.mm.trans1:probe13 -0.00369335505483498 0.0666361251017261 -0.0554257176448472 0.955812286675813 df.mm.trans1:probe14 0.0253006737328226 0.0666361251017261 0.379684048167550 0.704274469858301 df.mm.trans1:probe15 -0.123857581785382 0.0666361251017261 -1.85871524786746 0.0634117524818005 . df.mm.trans1:probe16 0.0229575378277694 0.0666361251017261 0.344520900528394 0.730539501435021 df.mm.trans1:probe17 -0.0295159436314495 0.0666361251017261 -0.442942076634719 0.6579199376305 df.mm.trans1:probe18 -0.00240966683738393 0.0666361251017261 -0.0361615690243896 0.971161989950214 df.mm.trans1:probe19 -0.0819079823359542 0.0666361251017261 -1.22918285255804 0.219342219930336 df.mm.trans1:probe20 -0.0221148120076110 0.0666361251017261 -0.331874219484563 0.740065764054212 df.mm.trans1:probe21 0.00206988200784902 0.0666361251017261 0.0310624605600815 0.975226993631187 df.mm.trans1:probe22 -0.0593513844985113 0.0666361251017261 -0.890678808347665 0.37335265237033 df.mm.trans2:probe2 0.0284300174308192 0.066636125101726 0.426645717880778 0.669745045881219 df.mm.trans2:probe3 -0.0672752698419044 0.066636125101726 -1.00959156522386 0.312977305802644 df.mm.trans2:probe4 0.0366821042807528 0.0666361251017261 0.550483753740997 0.582131744001571 df.mm.trans2:probe5 0.0630684410493137 0.066636125101726 0.946460211379849 0.344181936802024 df.mm.trans2:probe6 0.0801964030471898 0.0666361251017261 1.20349739611604 0.229117789797008 df.mm.trans3:probe2 -0.0264416300446189 0.066636125101726 -0.396806236921089 0.691609605649656 df.mm.trans3:probe3 -0.0173849316566827 0.066636125101726 -0.260893496285131 0.794237612041431 df.mm.trans3:probe4 -0.0935081284547565 0.0666361251017261 -1.40326479536450 0.160901794534081 df.mm.trans3:probe5 -0.140911042176353 0.0666361251017261 -2.11463439630140 0.0347506350775804 * df.mm.trans3:probe6 -0.054113096962387 0.0666361251017261 -0.812068482070025 0.416978896428394 df.mm.trans3:probe7 -0.097701430462275 0.0666361251017261 -1.46619315443575 0.142964207714668 df.mm.trans3:probe8 -0.00995301881513292 0.0666361251017261 -0.149363709248380 0.881301952350483 df.mm.trans3:probe9 -0.0866550369444244 0.0666361251017261 -1.30042130769365 0.193807842604687 df.mm.trans3:probe10 -0.119311365298061 0.066636125101726 -1.79049074531152 0.0737295844082986 .