chr10.2494_chr10_82288167_82300193_+_2.R fitVsDatCorrelation=0.531487182105014 cont.fitVsDatCorrelation=0.340450331437162 fstatistic=10829.3725380801,42,462 cont.fstatistic=8786.93639465421,42,462 residuals=-0.324593962081145,-0.0761209708094647,0.00237467375994738,0.0677682133256726,0.565155049501066 cont.residuals=-0.345527952362338,-0.0877197296016149,-0.00496911426049334,0.0755958713051996,0.63285871226383 predictedValues: Include Exclude Both chr10.2494_chr10_82288167_82300193_+_2.R.tl.Lung 45.8945857199459 42.3072247225091 45.2302930327593 chr10.2494_chr10_82288167_82300193_+_2.R.tl.cerebhem 53.4598999481514 51.9157915494455 56.5446637416745 chr10.2494_chr10_82288167_82300193_+_2.R.tl.cortex 45.4857522032643 42.9013336425531 44.6990161765363 chr10.2494_chr10_82288167_82300193_+_2.R.tl.heart 46.9308476149684 43.5316042619231 45.3388910249573 chr10.2494_chr10_82288167_82300193_+_2.R.tl.kidney 45.5530296270123 43.410140219905 47.1953575267094 chr10.2494_chr10_82288167_82300193_+_2.R.tl.liver 46.9602182869133 46.8412077023946 49.2113014413328 chr10.2494_chr10_82288167_82300193_+_2.R.tl.stomach 45.0898291881511 44.4083877106386 44.6023493866375 chr10.2494_chr10_82288167_82300193_+_2.R.tl.testicle 46.9837006300562 47.8387822620326 52.8363802861969 diffExp=3.5873609974368,1.54410839870591,2.58441856071124,3.39924335304523,2.14288940710736,0.119010584518669,0.681441477512486,-0.855081631976368 diffExpScore=1.04999955690861 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 47.6846887407302 47.0903694530873 45.8242695764512 cerebhem 47.1210744007579 43.985460173286 48.0177346029823 cortex 46.251911752962 48.5536875838013 53.714088423955 heart 49.6854506060745 46.4696576349571 50.6949460865618 kidney 46.2428452411933 46.9235492430639 45.6628971585977 liver 48.317687380709 46.5143677658139 42.9023193601083 stomach 48.2898372930556 49.2742698547236 49.6748295417895 testicle 47.4063114128602 46.8296766460757 49.0771706080533 cont.diffExp=0.594319287642911,3.13561422747186,-2.30177583083930,3.21579297111737,-0.680704001870623,1.80331961489514,-0.984432561667987,0.576634766784551 cont.diffExpScore=2.0904351711523 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=0.876576974446074 cont.tran.correlation=-0.0303236299663933 tran.covariance=0.00330459525994101 cont.tran.covariance=-2.25295055516442e-05 tran.mean=46.2195209556165 cont.tran.mean=47.290052823947 weightedLogRatios: wLogRatio Lung 0.308111544437768 cerebhem 0.116188319332986 cortex 0.221592537907325 heart 0.286547504097132 kidney 0.182848206578797 liver 0.0097643886997038 stomach 0.0578835687675075 testicle -0.0695973008954177 cont.weightedLogRatios: wLogRatio Lung 0.0483906978251277 cerebhem 0.262932057963988 cortex -0.187391598137773 heart 0.259102592396651 kidney -0.056131306720231 liver 0.146774245010221 stomach -0.0784494644273173 testicle 0.0471495397097596 varWeightedLogRatios=0.0180611248982221 cont.varWeightedLogRatios=0.0260476201732544 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.65760356682258 0.0696629791803178 52.5042656782611 7.26170697525424e-197 *** df.mm.trans1 0.113128689264790 0.0613853234141716 1.84292731507664 0.0659800577777815 . df.mm.trans2 0.080976142899274 0.058038821635215 1.39520652931626 0.163623871306021 df.mm.exp2 0.133986058847622 0.0799653038619264 1.67555242557412 0.0945025014363608 . df.mm.exp3 0.0168126008810833 0.0799653038619264 0.210248696235972 0.83356626549218 df.mm.exp4 0.0484592558305831 0.0799653038619263 0.606003522655978 0.544810026601846 df.mm.exp5 -0.0242632956546530 0.0799653038619263 -0.303422790671161 0.7617042687024 df.mm.exp6 0.0404028861877737 0.0799653038619263 0.505255207402652 0.613620539779364 df.mm.exp7 0.0447605752787202 0.0799653038619264 0.559749955505789 0.575921279237492 df.mm.exp8 -0.00910053106532203 0.0799653038619264 -0.113805996173486 0.909441023197442 df.mm.trans1:exp2 0.0185986283963418 0.074033485599791 0.251219137470873 0.801756386368333 df.mm.trans2:exp2 0.0706790851288231 0.0675830167895657 1.04581133672824 0.296194998199068 df.mm.trans1:exp3 -0.0257606142898109 0.074033485599791 -0.347958955074292 0.728029490697544 df.mm.trans2:exp3 -0.00286755688179536 0.0675830167895657 -0.0424301402632575 0.966174132465578 df.mm.trans1:exp4 -0.0261312168643548 0.074033485599791 -0.352964832773301 0.724275787578129 df.mm.trans2:exp4 -0.0199299154681876 0.0675830167895657 -0.294895321560499 0.768206313996427 df.mm.trans1:exp5 0.0167932773298348 0.074033485599791 0.226833536119259 0.820653517412401 df.mm.trans2:exp5 0.0499984863995704 0.0675830167895657 0.73980844263362 0.459791988251653 df.mm.trans1:exp6 -0.0174492142580509 0.074033485599791 -0.235693539439404 0.813774926527321 df.mm.trans2:exp6 0.0614025670883507 0.0675830167895657 0.908550254267885 0.364061264890477 df.mm.trans1:exp7 -0.0624510230330564 0.074033485599791 -0.843551030011651 0.39935687691378 df.mm.trans2:exp7 0.00370992002904402 0.0675830167895657 0.0548942649393064 0.9562464309963 df.mm.trans1:exp8 0.0325541255850901 0.074033485599791 0.439721638409282 0.660344346361923 df.mm.trans2:exp8 0.131979317304293 0.0675830167895657 1.95284738050743 0.0514410817339474 . df.mm.trans1:probe2 0.0385541613466991 0.0370167427998955 1.04153305857067 0.298172938319165 df.mm.trans1:probe3 0.0418100198285709 0.0370167427998955 1.12948943278415 0.259277521012015 df.mm.trans1:probe4 0.0878871113015772 0.0370167427998955 2.37425296376496 0.0179921875175883 * df.mm.trans1:probe5 0.00341826267299005 0.0370167427998955 0.0923436913795587 0.926465003841801 df.mm.trans1:probe6 0.0449811485758913 0.0370167427998955 1.21515684994354 0.224926905347078 df.mm.trans1:probe7 0.141219441416886 0.0370167427998955 3.81501533455516 0.000154682561917036 *** df.mm.trans1:probe8 0.0889629834015054 0.0370167427998955 2.403317436178 0.0166402510632406 * df.mm.trans1:probe9 0.0657767421050416 0.0370167427998955 1.77694570428891 0.076235061251779 . df.mm.trans1:probe10 0.0978551991994126 0.0370167427998955 2.64353889072295 0.0084833818103145 ** df.mm.trans1:probe11 0.130834258469653 0.0370167427998955 3.53446166716814 0.000449765436258377 *** df.mm.trans1:probe12 0.092924109437877 0.0370167427998955 2.51032647416345 0.0124026205158345 * df.mm.trans2:probe2 -0.0105072084771522 0.0370167427998955 -0.283850162991161 0.77665237295943 df.mm.trans2:probe3 -0.0328356275341274 0.0370167427998955 -0.887047996406105 0.375514586127422 df.mm.trans2:probe4 0.00132289607407876 0.0370167427998955 0.0357377763146275 0.971506884277319 df.mm.trans2:probe5 -0.00645979308404237 0.0370167427998955 -0.174510035066095 0.861541090737609 df.mm.trans2:probe6 0.10588316373895 0.0370167427998955 2.86041276811769 0.00442263282347733 ** df.mm.trans3:probe2 -0.0666326718413704 0.0370167427998955 -1.80006847716376 0.0725020438011921 . df.mm.trans3:probe3 -0.0531913385354337 0.0370167427998955 -1.43695351109022 0.151408065608275 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91198824872608 0.0773274587739237 50.5898979580236 1.62747239429520e-190 *** df.mm.trans1 -0.0299903427198950 0.0681390764719757 -0.440134270563961 0.660045698071456 df.mm.trans2 -0.0138213821168146 0.0644243849472377 -0.214536500862741 0.830223365725653 df.mm.exp2 -0.126855955731318 0.0887632686756303 -1.42914921480518 0.153637135196334 df.mm.exp3 -0.158767388184547 0.0887632686756303 -1.78866090167020 0.0743245200035865 . df.mm.exp4 -0.0731795127137144 0.0887632686756303 -0.824434631639535 0.410118073680752 df.mm.exp5 -0.0307246811095014 0.0887632686756303 -0.346141839613628 0.72939369763142 df.mm.exp6 0.0667680409269516 0.0887632686756303 0.752203495017106 0.452311746104917 df.mm.exp7 -0.0227402026124661 0.0887632686756303 -0.256189333175260 0.797918677878023 df.mm.exp8 -0.0799864905987956 0.0887632686756303 -0.901121508842718 0.367993221360592 df.mm.trans1:exp2 0.114965940583659 0.0821788182614098 1.39897291073174 0.162492080952086 df.mm.trans2:exp2 0.0586465745265829 0.0750186541848194 0.781759885775856 0.434756145466213 df.mm.trans1:exp3 0.128259830882521 0.0821788182614098 1.56074075529449 0.119269640874535 df.mm.trans2:exp3 0.189369024484707 0.0750186541848194 2.52429247821707 0.0119271099871309 * df.mm.trans1:exp4 0.114281303008368 0.0821788182614098 1.39064183966288 0.165003529720924 df.mm.trans2:exp4 0.0599105784361507 0.0750186541848194 0.798609080463537 0.424927455059133 df.mm.trans1:exp5 2.10800079717681e-05 0.0821788182614098 0.000256513885423770 0.999795442279768 df.mm.trans2:exp5 0.0271758366954226 0.0750186541848194 0.362254388468113 0.717327583250148 df.mm.trans1:exp6 -0.0535807045496752 0.0821788182614098 -0.652001399913487 0.514724499630614 df.mm.trans2:exp6 -0.0790753017534078 0.0750186541848194 -1.05407518453469 0.292399435629152 df.mm.trans1:exp7 0.0353509775154893 0.0821788182614098 0.430171402599612 0.667271546509028 df.mm.trans2:exp7 0.0680737278733448 0.0750186541848194 0.907424008242473 0.364655674338597 df.mm.trans1:exp8 0.0741315070005486 0.0821788182614098 0.902075602556576 0.367486750084143 df.mm.trans2:exp8 0.0744350990196755 0.0750186541848194 0.992221199227245 0.321609009948453 df.mm.trans1:probe2 -0.0231010695012864 0.0410894091307049 -0.562214692058534 0.574242419577539 df.mm.trans1:probe3 0.0173698682787204 0.0410894091307049 0.422733464564240 0.672686420296277 df.mm.trans1:probe4 0.0139065150378358 0.0410894091307049 0.338445242510042 0.735181437385057 df.mm.trans1:probe5 -0.0144214123923444 0.0410894091307049 -0.350976387771119 0.725766051000947 df.mm.trans1:probe6 -0.084282398708937 0.0410894091307049 -2.0511951982769 0.0408113244603569 * df.mm.trans1:probe7 -0.0490115986587559 0.0410894091307049 -1.19280368580747 0.233558497159732 df.mm.trans1:probe8 -0.0125408943957816 0.0410894091307049 -0.305209898635660 0.760343749683869 df.mm.trans1:probe9 -0.0469734054378999 0.0410894091307049 -1.14319982768499 0.253547669397324 df.mm.trans1:probe10 -0.00314757428522466 0.0410894091307049 -0.0766030554299835 0.938972489601606 df.mm.trans1:probe11 -0.0450843236728056 0.0410894091307049 -1.09722492064544 0.273114631965249 df.mm.trans1:probe12 -0.0135269622758245 0.0410894091307049 -0.329208001818557 0.74214766324564 df.mm.trans2:probe2 -0.0605332324983318 0.0410894091307049 -1.47320766540537 0.141376013420409 df.mm.trans2:probe3 -0.0684698961513148 0.0410894091307049 -1.66636361047473 0.0963187976747432 . df.mm.trans2:probe4 -0.0844613770410631 0.0410894091307049 -2.05555102465437 0.0403873011637788 * df.mm.trans2:probe5 -0.0993662707296874 0.0410894091307049 -2.41829397968719 0.0159791966040895 * df.mm.trans2:probe6 -0.102054433917708 0.0410894091307049 -2.48371626842027 0.0133554711535783 * df.mm.trans3:probe2 -0.0484888571247284 0.0410894091307049 -1.18008163540356 0.238574999207286 df.mm.trans3:probe3 -0.0815991519827666 0.0410894091307049 -1.98589256231942 0.0476358436012329 *