chr4.17157_chr4_129939278_129948015_-_2.R fitVsDatCorrelation=0.840956338504646 cont.fitVsDatCorrelation=0.299544789311093 fstatistic=8006.5298054188,53,715 cont.fstatistic=2566.17675972037,53,715 residuals=-0.579466057407228,-0.0851777775760912,-0.00702344108738273,0.0746778287316061,2.26755432533656 cont.residuals=-0.588830709605439,-0.198725664039807,-0.064012059972137,0.143980102993657,2.46147859445788 predictedValues: Include Exclude Both chr4.17157_chr4_129939278_129948015_-_2.R.tl.Lung 58.4570215552363 54.4987323113199 67.58465357772 chr4.17157_chr4_129939278_129948015_-_2.R.tl.cerebhem 55.7770939254807 75.3566540922356 74.1063334094479 chr4.17157_chr4_129939278_129948015_-_2.R.tl.cortex 50.1120286817665 50.1542098740951 56.0333995692148 chr4.17157_chr4_129939278_129948015_-_2.R.tl.heart 54.617558731019 51.1318897675502 63.8186751894235 chr4.17157_chr4_129939278_129948015_-_2.R.tl.kidney 57.4125755410766 51.555416664415 65.8325593768584 chr4.17157_chr4_129939278_129948015_-_2.R.tl.liver 55.1680206333987 49.0998832836214 57.6895108061122 chr4.17157_chr4_129939278_129948015_-_2.R.tl.stomach 55.5797272421629 53.021816120536 64.6384854226204 chr4.17157_chr4_129939278_129948015_-_2.R.tl.testicle 59.1045171625264 54.7409132040645 68.857104777689 diffExp=3.95828924391638,-19.5795601667549,-0.0421811923285489,3.48566896346881,5.85715887666161,6.06813734977736,2.55791112162684,4.3636039584619 diffExpScore=5.98674433657981 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=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 54.7497714107421 61.0436288205947 63.8076446117661 cerebhem 58.0707020213777 50.8441331979485 65.100962456018 cortex 64.097646316918 64.6458967031747 52.7414303131073 heart 54.9191888588553 57.8445768673957 73.8934827417012 kidney 57.5625592449225 46.6744181934482 63.7953064685558 liver 59.562926910933 63.3733987069562 55.9405341250214 stomach 60.5083185348734 55.2023178474773 61.3813419125226 testicle 59.1959621901236 48.1463488807668 65.6429540000698 cont.diffExp=-6.29385740985256,7.2265688234292,-0.548250386256768,-2.92538800854044,10.8881410514743,-3.81047179602324,5.30600068739609,11.0496133093567 cont.diffExpScore=2.19475194344493 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=0,0,0,0,1,0,0,1 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.169062493677039 cont.tran.correlation=0.235256860947939 tran.covariance=0.00144658317721975 cont.tran.covariance=0.00128343636691300 tran.mean=55.3617536744065 cont.tran.mean=57.2776121691567 weightedLogRatios: wLogRatio Lung 0.282787700421592 cerebhem -1.25516440346709 cortex -0.00329374985116555 heart 0.261637136377175 kidney 0.430043447368745 liver 0.460527868925625 stomach 0.188189858148199 testicle 0.309925290430687 cont.weightedLogRatios: wLogRatio Lung -0.441484754941625 cerebhem 0.530949934450331 cortex -0.0354704176699068 heart -0.209238478370203 kidney 0.827808506101968 liver -0.255363067302188 stomach 0.372325035942155 testicle 0.821793613265327 varWeightedLogRatios=0.313638455339680 cont.varWeightedLogRatios=0.251679891395308 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.59852428280481 0.0859698991064345 41.8579563336427 1.80747183646162e-194 *** df.mm.trans1 0.336254416444181 0.0756345221553314 4.44577961044841 1.01493359465050e-05 *** df.mm.trans2 0.351645544003351 0.0683862508840379 5.14205033113505 3.51206457340612e-07 *** df.mm.exp2 0.185006200688494 0.091123497716436 2.03027984356141 0.0426977733243145 * df.mm.exp3 -0.0496727163981578 0.0911234977164359 -0.545114242132503 0.585844939891574 df.mm.exp4 -0.0743703859142252 0.0911234977164359 -0.816149377251254 0.414686520677938 df.mm.exp5 -0.0472822048382421 0.0911234977164359 -0.51888048662682 0.604004641318198 df.mm.exp6 -0.0039235736230322 0.091123497716436 -0.0430577592098345 0.965667512608442 df.mm.exp7 -0.0333763242057774 0.0911234977164359 -0.366275714192184 0.714267702123047 df.mm.exp8 -0.00320298372953424 0.0911234977164359 -0.0351499208195618 0.97197000540509 df.mm.trans1:exp2 -0.231934728671242 0.0857267022992422 -2.70551324675524 0.00698236102332314 ** df.mm.trans2:exp2 0.13904858854965 0.0704486097426123 1.97375915660609 0.0487937174207029 * df.mm.trans1:exp3 -0.104358020843362 0.0857267022992422 -1.21733389999168 0.223878820016285 df.mm.trans2:exp3 -0.0334022681280889 0.0704486097426123 -0.474136654365869 0.635547217833333 df.mm.trans1:exp4 0.00643399548384086 0.0857267022992422 0.0750524085410636 0.940194006650964 df.mm.trans2:exp4 0.0106013133209956 0.0704486097426123 0.15048293159692 0.880426057595554 df.mm.trans1:exp5 0.0292537602864323 0.0857267022992422 0.341244437285334 0.733019828161772 df.mm.trans2:exp5 -0.00823795534046267 0.0704486097426123 -0.116935669427125 0.906943869668687 df.mm.trans1:exp6 -0.0539847872884897 0.0857267022992422 -0.629731295390875 0.52907157661011 df.mm.trans2:exp6 -0.100397209750892 0.0704486097426123 -1.42511271858590 0.154560981700839 df.mm.trans1:exp7 -0.0170969689366364 0.0857267022992422 -0.199435747300262 0.84197860213774 df.mm.trans2:exp7 0.0059023369234592 0.0704486097426123 0.0837821632680007 0.933253080878342 df.mm.trans1:exp8 0.0142185277878737 0.0857267022992422 0.165858797860225 0.868314959284074 df.mm.trans2:exp8 0.00763692872033261 0.0704486097426123 0.108404250250424 0.913705443245354 df.mm.trans1:probe2 0.124430639568165 0.0500536104198069 2.4859473377554 0.0131480391126154 * df.mm.trans1:probe3 0.0771774151172535 0.0500536104198069 1.54189506950558 0.123541535688837 df.mm.trans1:probe4 -0.0746886079650975 0.0500536104198069 -1.49217223969806 0.136095076853391 df.mm.trans1:probe5 -0.0604884021985769 0.0500536104198069 -1.20847230981445 0.227265153557373 df.mm.trans1:probe6 0.0504318464335111 0.0500536104198069 1.00755661800481 0.314008212500963 df.mm.trans1:probe7 -0.0249293648854629 0.0500536104198069 -0.49805328079986 0.61859964156153 df.mm.trans1:probe8 -0.0624017963079063 0.0500536104198069 -1.24669920480328 0.212916068672434 df.mm.trans1:probe9 0.0163518298733487 0.0500536104198069 0.326686321649998 0.744000753221303 df.mm.trans1:probe10 0.438197973449658 0.0500536104198069 8.75457274259393 1.46701340112013e-17 *** df.mm.trans1:probe11 0.920974434737232 0.0500536104198069 18.3997603172455 3.42689125125053e-62 *** df.mm.trans1:probe12 0.576809941309982 0.0500536104198069 11.5238428651239 2.65270251167987e-28 *** df.mm.trans1:probe13 0.643567926423876 0.0500536104198069 12.8575725312555 3.47632079195414e-34 *** df.mm.trans1:probe14 0.550420067556819 0.0500536104198069 10.9966106928225 4.27033302941312e-26 *** df.mm.trans1:probe15 0.514983869608291 0.0500536104198069 10.288645819733 3.01531571665315e-23 *** df.mm.trans1:probe16 -0.0527981123075417 0.0500536104198069 -1.05483124723104 0.291858813098338 df.mm.trans1:probe17 -0.0892109137795621 0.0500536104198069 -1.78230727077102 0.0751233927147737 . df.mm.trans1:probe18 -0.056351935487302 0.0500536104198069 -1.12583158366939 0.260614558105802 df.mm.trans1:probe19 -0.0437410863743112 0.0500536104198069 -0.873884740929741 0.382474438457469 df.mm.trans1:probe20 0.0382880615911971 0.0500536104198069 0.764941055601574 0.444559042187548 df.mm.trans1:probe21 -0.0156829078004575 0.0500536104198069 -0.313322209305635 0.75412725513613 df.mm.trans2:probe2 0.022878996053584 0.0500536104198069 0.457089825522965 0.647745324056269 df.mm.trans2:probe3 0.0921157322372367 0.0500536104198069 1.84034141522756 0.0661323882880655 . df.mm.trans2:probe4 0.230596524016668 0.0500536104198069 4.60699082608869 4.8367714221356e-06 *** df.mm.trans2:probe5 0.0420716402689962 0.0500536104198069 0.84053158036224 0.400891389710447 df.mm.trans2:probe6 0.140420864148893 0.0500536104198069 2.80540929957225 0.00516182230963363 ** df.mm.trans3:probe2 -0.0643236696160864 0.0500536104198069 -1.2850955021345 0.199175037579897 df.mm.trans3:probe3 -0.152529215458462 0.0500536104198069 -3.04731695034938 0.00239396987856269 ** df.mm.trans3:probe4 -0.203721559661299 0.0500536104198069 -4.07006723296595 5.22400496277669e-05 *** df.mm.trans3:probe5 0.0553579244508982 0.0500536104198069 1.10597265585046 0.269110337361838 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97408094918279 0.151583629419255 26.2170853436367 1.10582760609660e-106 *** df.mm.trans1 0.065594694832896 0.133360112049242 0.491861425616347 0.622968277602308 df.mm.trans2 0.116861166654081 0.120579833396621 0.969160127047882 0.332793019034182 df.mm.exp2 -0.144002347627465 0.16067054460694 -0.896258539359211 0.370416345022544 df.mm.exp3 0.405441953534696 0.16067054460694 2.52343672903181 0.0118370342408068 * df.mm.exp4 -0.197491141983967 0.160670544606940 -1.22916831126143 0.219413065144869 df.mm.exp5 -0.218100077741218 0.160670544606940 -1.35743659968772 0.175070815964825 df.mm.exp6 0.253299283613409 0.16067054460694 1.57651350615057 0.115349827293281 df.mm.exp7 0.0381908591388409 0.16067054460694 0.237696705592615 0.812184434526856 df.mm.exp8 -0.187620651549469 0.16067054460694 -1.16773520627853 0.243302839656900 df.mm.trans1:exp2 0.20289042262313 0.151154820556146 1.34226895230091 0.179934842746634 df.mm.trans2:exp2 -0.0388217462935555 0.124216220599549 -0.3125336297158 0.754726137535671 df.mm.trans1:exp3 -0.247807502748696 0.151154820556146 -1.63942838102638 0.101563953142556 df.mm.trans2:exp3 -0.348106154820689 0.124216220599549 -2.80242107786326 0.0052093499763181 ** df.mm.trans1:exp4 0.200580759729308 0.151154820556146 1.32698883827396 0.184935979219023 df.mm.trans2:exp4 0.143662011411985 0.124216220599549 1.15654791877082 0.247843379148100 df.mm.trans1:exp5 0.268199227442337 0.151154820556146 1.77433459585045 0.0764332943380011 . df.mm.trans2:exp5 -0.0502925330538416 0.124216220599549 -0.40487895068049 0.685687590891581 df.mm.trans1:exp6 -0.16903912832147 0.151154820556146 -1.11831781281948 0.263806869367482 df.mm.trans2:exp6 -0.215843924060465 0.124216220599549 -1.73764684691468 0.082703630307514 . df.mm.trans1:exp7 0.0618167993487418 0.151154820556146 0.408963466208344 0.682688938723124 df.mm.trans2:exp7 -0.138774751834623 0.124216220599549 -1.11720314114216 0.264282742919769 df.mm.trans1:exp8 0.265700791323733 0.151154820556146 1.75780560848894 0.0792084646334929 . df.mm.trans2:exp8 -0.0497228761907518 0.124216220599549 -0.400292940412746 0.689060335446421 df.mm.trans1:probe2 -0.0530234025537561 0.0882554011559131 -0.600794986587668 0.548166940617053 df.mm.trans1:probe3 -0.117443627525192 0.0882554011559131 -1.33072453342221 0.183703881128006 df.mm.trans1:probe4 -0.0601129340813201 0.0882554011559131 -0.681124705049199 0.496013115678112 df.mm.trans1:probe5 -0.111020112394473 0.088255401155913 -1.25794128110464 0.208823651535453 df.mm.trans1:probe6 0.00663921269670028 0.0882554011559131 0.075227267790346 0.940054931641507 df.mm.trans1:probe7 -0.0660979021204536 0.0882554011559131 -0.748938889345529 0.454140346331162 df.mm.trans1:probe8 -0.0278244406672391 0.0882554011559131 -0.315271816827211 0.752647273074791 df.mm.trans1:probe9 -0.00138058075699618 0.0882554011559131 -0.0156430171855117 0.98752355099056 df.mm.trans1:probe10 -0.0530526419683485 0.0882554011559131 -0.601126291122115 0.547946393083259 df.mm.trans1:probe11 -0.119258231715292 0.0882554011559131 -1.35128536217981 0.177031434732772 df.mm.trans1:probe12 -0.0332241776052883 0.088255401155913 -0.376454893073276 0.706690426313177 df.mm.trans1:probe13 -0.0481124141399921 0.088255401155913 -0.545149798310884 0.585820500125201 df.mm.trans1:probe14 -0.0179892949517723 0.088255401155913 -0.203832226879715 0.83854260777298 df.mm.trans1:probe15 -0.00263288087903984 0.088255401155913 -0.0298325184017753 0.976208949304053 df.mm.trans1:probe16 -0.0277963898063857 0.088255401155913 -0.314953979499569 0.752888487107486 df.mm.trans1:probe17 -0.0583815130797068 0.0882554011559131 -0.661506404311383 0.508500722450584 df.mm.trans1:probe18 -0.094658492854878 0.0882554011559131 -1.07255183949199 0.283834108827547 df.mm.trans1:probe19 0.0133195643011203 0.0882554011559131 0.150920670312174 0.880080862426878 df.mm.trans1:probe20 -0.0348095959777846 0.088255401155913 -0.394418874333702 0.693389425295031 df.mm.trans1:probe21 -0.052603856245315 0.0882554011559131 -0.596041211714446 0.551336332578371 df.mm.trans2:probe2 -0.00132346517973513 0.088255401155913 -0.0149958547851035 0.988039670704908 df.mm.trans2:probe3 0.0261051924133617 0.0882554011559131 0.295791442466438 0.767475241383591 df.mm.trans2:probe4 0.149035843311949 0.088255401155913 1.68868807302412 0.0917152458714357 . df.mm.trans2:probe5 0.0674502495747941 0.088255401155913 0.764262002000712 0.444963263320618 df.mm.trans2:probe6 -0.0141539083600725 0.088255401155913 -0.160374415329755 0.872631439980867 df.mm.trans3:probe2 0.0887813746568608 0.088255401155913 1.00595967492141 0.314775280229591 df.mm.trans3:probe3 -0.0643091854738806 0.0882554011559131 -0.728671385904996 0.466441291306766 df.mm.trans3:probe4 -0.0546101156104901 0.088255401155913 -0.618773637593185 0.536262571782031 df.mm.trans3:probe5 0.0268188964474504 0.0882554011559131 0.303878245367350 0.761309061690479