chr6.19981_chr6_84698840_84703851_-_1.R fitVsDatCorrelation=0.896394026664555 cont.fitVsDatCorrelation=0.271657487809178 fstatistic=10678.6640192589,40,416 cont.fstatistic=2257.09970564491,40,416 residuals=-0.438202238641197,-0.0796357146372102,-0.0031713513938691,0.0673983944266239,0.524497175061695 cont.residuals=-0.689237532172972,-0.220095270483284,0.0209873646360703,0.191668251506632,1.03489809031104 predictedValues: Include Exclude Both chr6.19981_chr6_84698840_84703851_-_1.R.tl.Lung 78.1468836654264 70.979411027235 56.0671499141073 chr6.19981_chr6_84698840_84703851_-_1.R.tl.cerebhem 90.6269863454597 78.3962670068563 58.3281635364943 chr6.19981_chr6_84698840_84703851_-_1.R.tl.cortex 76.5183069089989 70.4051340270858 60.4454638072728 chr6.19981_chr6_84698840_84703851_-_1.R.tl.heart 83.0648232323185 72.8777207482686 57.9095658599747 chr6.19981_chr6_84698840_84703851_-_1.R.tl.kidney 77.420930700088 70.468487265129 54.7757691081758 chr6.19981_chr6_84698840_84703851_-_1.R.tl.liver 74.3600729300415 68.2482972065236 57.0920698574897 chr6.19981_chr6_84698840_84703851_-_1.R.tl.stomach 87.2991572191725 75.0851235598889 54.6922976816707 chr6.19981_chr6_84698840_84703851_-_1.R.tl.testicle 75.9110935875734 67.492159713608 56.6958915845431 diffExp=7.16747263819133,12.2307193386034,6.11317288191309,10.1871024840499,6.95244343495901,6.11177572351788,12.2140336592836,8.41893387396547 diffExpScore=0.985794577609718 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 60.071124608444 62.0646386329874 72.9172299051067 cerebhem 66.7989611128864 66.5943343038453 66.7477304878441 cortex 69.2814421960856 69.4222894352385 73.1232026968225 heart 63.3729318918844 84.1980279195675 72.2354916558087 kidney 63.5068146718196 63.4752400455667 65.3787907271822 liver 64.5794268349256 65.7006190361878 70.0869034720446 stomach 67.318586265462 75.293796685931 65.3824651623858 testicle 76.2865596640841 68.4615530868944 62.6817508108088 cont.diffExp=-1.99351402454344,0.204626809041073,-0.140847239152933,-20.8250960276831,0.0315746262528975,-1.12119220126218,-7.97521042046893,7.82500657718978 cont.diffExpScore=1.60502607058065 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,-1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.971368473352021 cont.tran.correlation=0.083141359418155 tran.covariance=0.00345471031619547 cont.tran.covariance=0.000940787872818044 tran.mean=76.0813034464796 cont.tran.mean=67.9016466494881 weightedLogRatios: wLogRatio Lung 0.414670563137254 cerebhem 0.642860639651443 cortex 0.357692934370866 heart 0.569696525193424 kidney 0.404801720291225 liver 0.365883639896304 stomach 0.662253697303287 testicle 0.502036988011703 cont.weightedLogRatios: wLogRatio Lung -0.134240195029358 cerebhem 0.0128861751694644 cortex -0.00860941177310883 heart -1.21925147023091 kidney 0.00206427709412980 liver -0.0718878656020072 stomach -0.477561985362172 testicle 0.463242274706786 varWeightedLogRatios=0.0150539195211597 cont.varWeightedLogRatios=0.242264466849452 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.78306864934465 0.0709675300090787 67.3979867797676 5.62107060228434e-226 *** df.mm.trans1 -0.392689702996122 0.0575622678610639 -6.82199846510468 3.17966871658801e-11 *** df.mm.trans2 -0.543290814693532 0.0575622678610639 -9.43831497405305 2.7313149283551e-19 *** df.mm.exp2 0.208013335289852 0.0778383929513611 2.67237448516997 0.00782766733292668 ** df.mm.exp3 -0.104375279821116 0.0778383929513611 -1.34092285135353 0.180677206340924 df.mm.exp4 0.0550917485801962 0.0778383929513611 0.70777088903443 0.479483884581991 df.mm.exp5 0.00674490943771813 0.0778383929513611 0.0866527324367144 0.930989241363863 df.mm.exp6 -0.107023548554920 0.0778383929513612 -1.37494550564264 0.169888406657444 df.mm.exp7 0.191810419162577 0.0778383929513611 2.46421350556959 0.0141348468328283 * df.mm.exp8 -0.0905574274599933 0.0778383929513612 -1.16340309744807 0.245332864571282 df.mm.trans1:exp2 -0.059851483810772 0.0627553186687219 -0.953727669310727 0.340775349755338 df.mm.trans2:exp2 -0.108626873231052 0.0627553186687219 -1.73095883401502 0.084200586488522 . df.mm.trans1:exp3 0.0833151182741926 0.0627553186687219 1.32761844002424 0.185032247768435 df.mm.trans2:exp3 0.0962516173917279 0.0627553186687219 1.53376031599535 0.125848768167176 df.mm.trans1:exp4 0.00593937744115142 0.0627553186687219 0.0946434113816666 0.924643636663377 df.mm.trans2:exp4 -0.0286986196088476 0.0627553186687219 -0.457309758242873 0.647687144351612 df.mm.trans1:exp5 -0.0160779226681328 0.0627553186687219 -0.256200159750703 0.797922929503166 df.mm.trans2:exp5 -0.0139691381515102 0.0627553186687219 -0.222596880198341 0.823958512772566 df.mm.trans1:exp6 0.0573525124271861 0.0627553186687219 0.913906799357413 0.361295199411385 df.mm.trans2:exp6 0.0677861834983029 0.0627553186687219 1.08016634982189 0.28069394003349 df.mm.trans1:exp7 -0.0810597899607247 0.0627553186687219 -1.29167999908709 0.197185164038217 df.mm.trans2:exp7 -0.135577817886995 0.0627553186687219 -2.16041955906073 0.0313105720535857 * df.mm.trans1:exp8 0.0615300820049667 0.0627553186687219 0.980475970965536 0.327421082992895 df.mm.trans2:exp8 0.0401790167457925 0.0627553186687219 0.640248788439637 0.522363345833702 df.mm.trans1:probe2 -0.0675786544289556 0.0398803090137007 -1.69453688048754 0.0909117146425971 . df.mm.trans1:probe3 0.103308597720077 0.0398803090137007 2.59046632975149 0.00992125268472303 ** df.mm.trans1:probe4 -0.107838639846732 0.0398803090137007 -2.70405727823434 0.00713083020885513 ** df.mm.trans1:probe5 -0.0822847531296772 0.0398803090137007 -2.06329276689929 0.0397047253521653 * df.mm.trans1:probe6 -0.258860516091888 0.0398803090137007 -6.49093556428956 2.43174709820777e-10 *** df.mm.trans2:probe2 0.419313664859173 0.0398803090137007 10.5143033047994 4.35632311657002e-23 *** df.mm.trans2:probe3 0.0268709584012061 0.0398803090137007 0.673790125146088 0.500818948513964 df.mm.trans2:probe4 -0.195440544734213 0.0398803090137007 -4.90067779231777 1.37092312951422e-06 *** df.mm.trans2:probe5 -0.072325156403322 0.0398803090137007 -1.81355556644446 0.0704666090971425 . df.mm.trans2:probe6 0.115537270333854 0.0398803090137007 2.89710067928916 0.00396520853615925 ** df.mm.trans3:probe2 0.178627562941212 0.0398803090137007 4.47909174625114 9.69768724759175e-06 *** df.mm.trans3:probe3 0.44049372231135 0.0398803090137007 11.0453939100627 4.78020413445232e-25 *** df.mm.trans3:probe4 -0.0389805114654269 0.0398803090137007 -0.977437548240544 0.328920638301292 df.mm.trans3:probe5 -0.112933902099514 0.0398803090137007 -2.83182113911695 0.00485312886941921 ** df.mm.trans3:probe6 0.80286912289536 0.0398803090137007 20.1319684513863 1.99476956799089e-63 *** df.mm.trans3:probe7 -0.0138423702290431 0.0398803090137007 -0.347097867879801 0.728693319192142 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92242667948592 0.154083420035354 25.456513611821 7.30814544736864e-87 *** df.mm.trans1 0.165539699162542 0.124978156854117 1.32454905184569 0.18604795099213 df.mm.trans2 0.210730533325367 0.124978156854117 1.68613891122868 0.0925188339041136 . df.mm.exp2 0.265006025192748 0.169001313621415 1.56807080083647 0.117624914569798 df.mm.exp3 0.251858633655477 0.169001313621415 1.49027618932994 0.136909427915428 df.mm.exp4 0.367896118883251 0.169001313621415 2.17688319102291 0.0300509011558015 * df.mm.exp5 0.187218512144718 0.169001313621415 1.10779323623550 0.268591218041794 df.mm.exp6 0.168887577483166 0.169001313621415 0.999327010330202 0.318217224559962 df.mm.exp7 0.416199303317999 0.169001313621415 2.46269862878308 0.0141938191235427 * df.mm.exp8 0.488318030929657 0.169001313621415 2.88943334501856 0.00406121792803773 ** df.mm.trans1:exp2 -0.158847767292369 0.136253215021689 -1.16582766334786 0.24435210567384 df.mm.trans2:exp2 -0.194562922332822 0.136253215021689 -1.42795105643453 0.154056224965824 df.mm.trans1:exp3 -0.109210823018856 0.136253215021689 -0.80152841165232 0.423283387215955 df.mm.trans2:exp3 -0.139827044844320 0.136253215021689 -1.02622932473236 0.305379847024506 df.mm.trans1:exp4 -0.314388560693027 0.136253215021689 -2.30738453138872 0.0215235512845463 * df.mm.trans2:exp4 -0.0629010198469465 0.136253215021689 -0.461647967990582 0.644575094865961 df.mm.trans1:exp5 -0.131600564656736 0.136253215021689 -0.965852913164561 0.334678932147772 df.mm.trans2:exp5 -0.164745003381993 0.136253215021689 -1.20910910877053 0.227307670038092 df.mm.trans1:exp6 -0.096520957783541 0.136253215021689 -0.708393983717571 0.479097371974005 df.mm.trans2:exp6 -0.111955630427676 0.136253215021689 -0.821673311780968 0.411733512965429 df.mm.trans1:exp7 -0.30229220475084 0.136253215021689 -2.21860603217855 0.0270526470086155 * df.mm.trans2:exp7 -0.222977953789501 0.136253215021689 -1.6364968250768 0.102491914888048 df.mm.trans1:exp8 -0.249350529666232 0.136253215021689 -1.83005244776455 0.0679574897101759 . df.mm.trans2:exp8 -0.390222112591121 0.136253215021689 -2.86394792613888 0.00439575708255181 ** df.mm.trans1:probe2 0.0229187993586040 0.086587407003057 0.264689752838942 0.791379565488808 df.mm.trans1:probe3 0.0523812329835129 0.086587407003057 0.60495209172465 0.54554066696168 df.mm.trans1:probe4 -0.0462700617404800 0.086587407003057 -0.534374031305112 0.593368243971279 df.mm.trans1:probe5 0.0184508026843088 0.086587407003057 0.213088754160953 0.83136212092006 df.mm.trans1:probe6 0.0508368184308339 0.086587407003057 0.587115611731381 0.557444563325594 df.mm.trans2:probe2 0.0677587721716375 0.086587407003057 0.782547653485515 0.434338008882917 df.mm.trans2:probe3 -0.0528114804713626 0.086587407003057 -0.609921030081176 0.542247129709902 df.mm.trans2:probe4 -0.0555248842449248 0.086587407003057 -0.64125819408086 0.521708074031223 df.mm.trans2:probe5 -0.0249358036709201 0.086587407003057 -0.287984183081492 0.773502265931338 df.mm.trans2:probe6 0.000762836882318181 0.086587407003057 0.00881002109569176 0.992974934397122 df.mm.trans3:probe2 -0.00272435028684936 0.086587407003057 -0.0314635855390978 0.974914921922034 df.mm.trans3:probe3 -0.0283094114155853 0.086587407003057 -0.326946058271335 0.74387312698463 df.mm.trans3:probe4 -0.0535783728629934 0.086587407003057 -0.618777888349305 0.53640136594401 df.mm.trans3:probe5 -0.0151475715807647 0.086587407003057 -0.174939660454665 0.861212157588962 df.mm.trans3:probe6 0.0252421749788038 0.086587407003057 0.291522472522044 0.770797067005623 df.mm.trans3:probe7 0.00891387401928599 0.086587407003057 0.102946540701597 0.918055006849125