chr12.5581_chr12_96107707_96108704_-_1.R fitVsDatCorrelation=0.796765395307276 cont.fitVsDatCorrelation=0.251300650898235 fstatistic=9325.38668557123,38,370 cont.fstatistic=3628.91057672333,38,370 residuals=-0.375111124337125,-0.0953962726361425,-0.00257113922865576,0.083787495957095,0.668256999085023 cont.residuals=-0.528911746628568,-0.138394856430187,-0.00508161346872339,0.142287106511191,0.892906145133971 predictedValues: Include Exclude Both chr12.5581_chr12_96107707_96108704_-_1.R.tl.Lung 78.3182114751714 83.9827157015581 63.2145872645561 chr12.5581_chr12_96107707_96108704_-_1.R.tl.cerebhem 76.5140563501292 83.443669750171 53.9286426512277 chr12.5581_chr12_96107707_96108704_-_1.R.tl.cortex 82.5022284609263 71.3533177592104 64.2300402056778 chr12.5581_chr12_96107707_96108704_-_1.R.tl.heart 88.7479890359301 75.0105344428228 66.2257022801036 chr12.5581_chr12_96107707_96108704_-_1.R.tl.kidney 72.3428435418701 76.967556325761 59.1449266182302 chr12.5581_chr12_96107707_96108704_-_1.R.tl.liver 74.6323501235308 73.9297608584451 58.6981971002691 chr12.5581_chr12_96107707_96108704_-_1.R.tl.stomach 94.9424333506867 83.537191686536 63.2617271053557 chr12.5581_chr12_96107707_96108704_-_1.R.tl.testicle 83.4038827028017 70.9273272563932 64.4613748721871 diffExp=-5.66450422638675,-6.92961340004179,11.1489107017159,13.7374545931073,-4.62471278389093,0.702589265085649,11.4052416641507,12.4765554464085 diffExpScore=2.00558582943335 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 71.8629981828795 75.6274465508011 76.6233901177384 cerebhem 83.364939268304 72.4986717690564 78.597916773801 cortex 79.0131461930545 73.544500448427 73.7985569212614 heart 78.2018554108718 78.8808144949882 73.9388800906217 kidney 78.0164801772234 73.126094263807 76.6222956458975 liver 74.7189437042691 80.0900031209718 75.3272303421374 stomach 73.7518091720937 81.7711523982184 77.8535065218505 testicle 76.6367864140666 79.1886424651957 85.9875041278889 cont.diffExp=-3.76444836792163,10.8662674992475,5.46864574462747,-0.678959084116414,4.89038591341647,-5.37105941670265,-8.01934322612476,-2.55185605112906 cont.diffExpScore=22.6191664575262 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=0.0799800187866695 cont.tran.correlation=-0.599956489768087 tran.covariance=0.000370110441979727 cont.tran.covariance=-0.00128242242357703 tran.mean=79.4097543013715 cont.tran.mean=76.8933927521393 weightedLogRatios: wLogRatio Lung -0.306955160811583 cerebhem -0.379805639287084 cortex 0.630121600834194 heart 0.740246067514647 kidney -0.267228459908203 liver 0.0407461637554484 stomach 0.574532843984394 testicle 0.703683073213552 cont.weightedLogRatios: wLogRatio Lung -0.219563209644828 cerebhem 0.607993772655265 cortex 0.310832218823204 heart -0.0377219839022363 kidney 0.279949134789799 liver -0.301857493240913 stomach -0.44924158342624 testicle -0.142665906729404 varWeightedLogRatios=0.243627799422594 cont.varWeightedLogRatios=0.129611480516311 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.50699183784236 0.077938086697449 57.8278480884222 2.16507723838394e-187 *** df.mm.trans1 -0.204274701165328 0.0641851172585633 -3.18258670997558 0.00158315600482399 ** df.mm.trans2 -0.138261265901133 0.0641851172585633 -2.15410163300257 0.0318774019963005 * df.mm.exp2 0.129128428819309 0.0877392743060828 1.47172893599323 0.141944105667136 df.mm.exp3 -0.126857958377429 0.0877392743060828 -1.44585146595672 0.149065357118453 df.mm.exp4 -0.0344953956864333 0.0877392743060828 -0.393157978103334 0.694429234843145 df.mm.exp5 -0.100046383264013 0.0877392743060828 -1.14026909904676 0.254911776135477 df.mm.exp6 -0.101575575114137 0.0877392743060828 -1.15769791712415 0.247734107994839 df.mm.exp7 0.186426077721645 0.0877392743060828 2.12477341756085 0.0342679092554479 * df.mm.exp8 -0.125571637074989 0.0877392743060828 -1.43119074175296 0.153219629039613 df.mm.trans1:exp2 -0.152434123569232 0.0727495630628367 -2.09532699787583 0.0368211899357046 * df.mm.trans2:exp2 -0.135567650600224 0.0727495630628367 -1.86348405258639 0.0631861963755661 . df.mm.trans1:exp3 0.178903101158194 0.0727495630628367 2.45916392657463 0.0143830622456274 * df.mm.trans2:exp3 -0.0361092112151603 0.0727495630628367 -0.496349526992641 0.619942564129137 df.mm.trans1:exp4 0.159516003192509 0.0727495630628367 2.19267300691179 0.0289530574093562 * df.mm.trans2:exp4 -0.07848705361615 0.0727495630628367 -1.07886632320194 0.281350133044514 df.mm.trans1:exp5 0.0206827551886702 0.0727495630628367 0.284300747906977 0.776338952399511 df.mm.trans2:exp5 0.0128193576946087 0.0727495630628367 0.176212160663235 0.86022367280821 df.mm.trans1:exp6 0.0533694742516883 0.0727495630628367 0.733605426682645 0.46365382417864 df.mm.trans2:exp6 -0.0259199722376290 0.0727495630628368 -0.356290418063966 0.721826159312714 df.mm.trans1:exp7 0.00606450363580099 0.0727495630628367 0.0833613753880945 0.933609280582392 df.mm.trans2:exp7 -0.191745147823628 0.0727495630628368 -2.63568796499863 0.00874997183453272 ** df.mm.trans1:exp8 0.188486338702846 0.0727495630628367 2.59089306887030 0.0099513187834777 ** df.mm.trans2:exp8 -0.0433835820608887 0.0727495630628368 -0.596341479376536 0.551311787742448 df.mm.trans1:probe2 0.0772955023429214 0.0424765935244729 1.8197198958148 0.0696096178716985 . df.mm.trans1:probe3 0.0887994650996509 0.0424765935244729 2.0905505298698 0.0372503264553259 * df.mm.trans1:probe4 0.222417747638820 0.0424765935244729 5.23624257935548 2.75480133311548e-07 *** df.mm.trans1:probe5 0.0715510499192314 0.0424765935244729 1.68448182828049 0.09293187368078 . df.mm.trans1:probe6 0.178629511821183 0.0424765935244729 4.20536340133457 3.27319351772779e-05 *** df.mm.trans2:probe2 0.313961598817409 0.0424765935244729 7.39140248232289 9.77365816012839e-13 *** df.mm.trans2:probe3 0.0323260364852032 0.0424765935244729 0.761031754266701 0.447122839105901 df.mm.trans2:probe4 0.220672909711970 0.0424765935244729 5.19516494619159 3.38639012144646e-07 *** df.mm.trans2:probe5 -0.0079105245204025 0.0424765935244729 -0.186232554544301 0.852364456462504 df.mm.trans2:probe6 0.121634822495911 0.0424765935244729 2.86357290929724 0.00442757186816902 ** df.mm.trans3:probe2 -0.100772109660866 0.0424765935244729 -2.37241504789705 0.0181829143952728 * df.mm.trans3:probe3 0.134211016440807 0.0424765935244729 3.15964641475973 0.00170963649178659 ** df.mm.trans3:probe4 0.00640155849168006 0.0424765935244729 0.150707906649619 0.880288262975807 df.mm.trans3:probe5 -0.129444390810847 0.0424765935244729 -3.04742871474070 0.00247385856358885 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27888192809179 0.124835983518593 34.2760300955572 7.08234103304346e-117 *** df.mm.trans1 0.0105931322081012 0.102807402385094 0.103038613585641 0.917988147570966 df.mm.trans2 0.0683888312689261 0.102807402385094 0.66521310413774 0.506328632149191 df.mm.exp2 0.0807724887100186 0.140534866396277 0.574750528329802 0.56580932414219 df.mm.exp3 0.104487353783084 0.140534866396277 0.743497727378576 0.45765224319054 df.mm.exp4 0.162314216407048 0.140534866396277 1.15497470890503 0.248846139345059 df.mm.exp5 0.0485388720277753 0.140534866396277 0.345386687819564 0.729999957214333 df.mm.exp6 0.113364618108618 0.140534866396277 0.806665427702152 0.420377463090919 df.mm.exp7 0.0881227218829997 0.140534866396277 0.627052375988411 0.531011632027025 df.mm.exp8 -0.00497028380814902 0.140534866396277 -0.0353669088362308 0.971806243255928 df.mm.trans1:exp2 0.067693836427532 0.116525355449794 0.580936536655305 0.561636910713957 df.mm.trans2:exp2 -0.123023514356712 0.116525355449794 -1.05576605093230 0.291763983073688 df.mm.trans1:exp3 -0.00963461102432207 0.116525355449794 -0.0826825285117726 0.934148688957501 df.mm.trans2:exp3 -0.132415949408931 0.116525355449794 -1.13637026806568 0.256537101341469 df.mm.trans1:exp4 -0.0777823460265982 0.116525355449794 -0.667514342490988 0.504859762204998 df.mm.trans2:exp4 -0.12019544733085 0.116525355449794 -1.03149607968917 0.302982015493247 df.mm.trans1:exp5 0.0336197132967192 0.116525355449794 0.288518435897022 0.773111484563012 df.mm.trans2:exp5 -0.0821728692090353 0.116525355449794 -0.705193036243858 0.481133808421607 df.mm.trans1:exp6 -0.0743924643792076 0.116525355449794 -0.638422977488877 0.5235933316611 df.mm.trans2:exp6 -0.0560328437174143 0.116525355449795 -0.480863958759227 0.630897406486996 df.mm.trans1:exp7 -0.0621786991880065 0.116525355449794 -0.533606603884564 0.593934140894591 df.mm.trans2:exp7 -0.0100174675546921 0.116525355449795 -0.085968135570358 0.931538248405345 df.mm.trans1:exp8 0.0692859822976128 0.116525355449794 0.594600051037519 0.552474263256397 df.mm.trans2:exp8 0.0509839022153843 0.116525355449795 0.437534835389118 0.661978799927107 df.mm.trans1:probe2 -0.0566925237088816 0.0680361496392834 -0.83327060701489 0.405229699925307 df.mm.trans1:probe3 -0.0825891373615523 0.0680361496392834 -1.21390081301524 0.225559865916695 df.mm.trans1:probe4 -0.00873081319678748 0.0680361496392834 -0.128326091983112 0.897960638141167 df.mm.trans1:probe5 0.00411910385811293 0.0680361496392834 0.0605428713992744 0.951755959895646 df.mm.trans1:probe6 -0.0179557558077721 0.0680361496392834 -0.263914932032024 0.791992397438944 df.mm.trans2:probe2 0.0380568270495079 0.0680361496392834 0.559361857648897 0.576253212637365 df.mm.trans2:probe3 -0.0159456602176590 0.0680361496392834 -0.234370408998750 0.81482706451048 df.mm.trans2:probe4 -0.0708492852335652 0.0680361496392834 -1.04134766016591 0.298394178433317 df.mm.trans2:probe5 -0.0679870646135975 0.0680361496392834 -0.999278544921397 0.318312837228972 df.mm.trans2:probe6 -0.119241233912947 0.0680361496392834 -1.75261584532847 0.0804962209796314 . df.mm.trans3:probe2 -0.0234194658171823 0.0680361496392834 -0.344220916988226 0.730875702088536 df.mm.trans3:probe3 -0.0132559007177609 0.0680361496392834 -0.194836139141347 0.845628168074051 df.mm.trans3:probe4 -0.0364743216277134 0.0680361496392834 -0.536102084275703 0.592210138967251 df.mm.trans3:probe5 -0.0216578489166008 0.0680361496392834 -0.318328550798762 0.750415330586113