chr10.2817_chr10_125594325_125601812_-_2.R fitVsDatCorrelation=0.782019094359384 cont.fitVsDatCorrelation=0.261963805681206 fstatistic=11368.3671319677,49,623 cont.fstatistic=4733.96376479994,49,623 residuals=-0.368124450198327,-0.0806481535699498,-0.00336199086694803,0.077981965512521,0.654288361368083 cont.residuals=-0.484655329549551,-0.140627037401989,-0.0354176172582268,0.102145303009821,0.826087342965736 predictedValues: Include Exclude Both chr10.2817_chr10_125594325_125601812_-_2.R.tl.Lung 72.0004610201865 60.7763077916071 48.5486692058719 chr10.2817_chr10_125594325_125601812_-_2.R.tl.cerebhem 67.4219633733717 63.6487772327831 53.1190829659925 chr10.2817_chr10_125594325_125601812_-_2.R.tl.cortex 68.1715614550955 65.3520105301173 54.5998324582236 chr10.2817_chr10_125594325_125601812_-_2.R.tl.heart 71.6716841111208 59.6129216763076 48.8995625039529 chr10.2817_chr10_125594325_125601812_-_2.R.tl.kidney 77.0794042151612 72.519193017571 53.710465594224 chr10.2817_chr10_125594325_125601812_-_2.R.tl.liver 75.546630458906 61.8594500028951 49.6222660939605 chr10.2817_chr10_125594325_125601812_-_2.R.tl.stomach 70.0548644798312 74.0332297369117 58.4785618081964 chr10.2817_chr10_125594325_125601812_-_2.R.tl.testicle 68.8710477321739 56.3459712502383 48.0617137286173 diffExp=11.2241532285794,3.77318614058865,2.81955092497827,12.0587624348132,4.56021119759018,13.6871804560108,-3.97836525708055,12.5250764819356 diffExpScore=1.12063048370655 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=0,0,0,1,0,1,0,1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 58.5768531028919 51.4603346942082 55.8854805506238 cerebhem 57.631795436342 54.310241596381 52.3435689968137 cortex 59.6729289634796 55.1524489307054 54.2276693933849 heart 57.0749353964296 57.5083827289798 51.7590284240892 kidney 59.2607927267311 60.153021416059 57.2181790197868 liver 61.8246818697394 54.9358339320223 55.8695741378343 stomach 54.4464720539203 53.5867827673657 54.6148094132439 testicle 59.0062483140776 50.0048210717017 56.7052439505194 cont.diffExp=7.11651840868366,3.32155383996109,4.52048003277412,-0.433447332550195,-0.892228689327922,6.88884793771711,0.859689286554598,9.00142724237588 cont.diffExpScore=1.05261958463748 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=0.262222065831792 cont.tran.correlation=0.075732427168704 tran.covariance=0.00115955216881050 cont.tran.covariance=0.000155620412700971 tran.mean=67.8103423802674 cont.tran.mean=56.5379109375647 weightedLogRatios: wLogRatio Lung 0.710417852115534 cerebhem 0.240854483607077 cortex 0.177443344914065 heart 0.770050834981026 kidney 0.263109628340682 liver 0.844476536463552 stomach -0.236235780987199 testicle 0.829370848386941 cont.weightedLogRatios: wLogRatio Lung 0.518835418650695 cerebhem 0.238894440290523 cortex 0.319007941232658 heart -0.0306269237779019 kidney -0.0611112808256558 liver 0.480253845194314 stomach 0.0634914998722226 testicle 0.66124842514808 varWeightedLogRatios=0.156237210811901 cont.varWeightedLogRatios=0.0721592781842748 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.79046100361801 0.0666119560972665 71.9159334793141 6.65756842384204e-304 *** df.mm.trans1 -0.297825899675825 0.0561076789476581 -5.30811299383212 1.54239922926879e-07 *** df.mm.trans2 -0.595994016144131 0.0517024857864693 -11.5273764322586 5.29307674009891e-28 *** df.mm.exp2 -0.109491073683304 0.06752241230715 -1.62155156994754 0.105405349147359 df.mm.exp3 -0.0995208956594719 0.06752241230715 -1.47389425612884 0.141015156540035 df.mm.exp4 -0.0311061216448542 0.06752241230715 -0.46067847077733 0.645190065863552 df.mm.exp5 0.143773722597915 0.06752241230715 2.12927408375027 0.0336230783540889 * df.mm.exp6 0.0438695405761704 0.06752241230715 0.649703395912664 0.516123302254111 df.mm.exp7 -0.0161732430440097 0.06752241230715 -0.239524070473666 0.810778014721569 df.mm.exp8 -0.110045039374312 0.06752241230715 -1.62975574500704 0.103658596470060 df.mm.trans1:exp2 0.0437893825388108 0.0589383934292443 0.742968716841257 0.457780680208686 df.mm.trans2:exp2 0.155671149156235 0.0488691341877878 3.18546976007479 0.00151736992360306 ** df.mm.trans1:exp3 0.0448758640297959 0.0589383934292443 0.76140290596264 0.446704564592789 df.mm.trans2:exp3 0.172109062279109 0.0488691341877878 3.52183571777129 0.000459894964798862 *** df.mm.trans1:exp4 0.0265293474529082 0.0589383934292443 0.450119962715929 0.652780526533947 df.mm.trans2:exp4 0.0117784403173044 0.0488691341877878 0.241020032645632 0.80961888840514 df.mm.trans1:exp5 -0.0756101305701806 0.0589383934292443 -1.28286717996395 0.200015667270523 df.mm.trans2:exp5 0.0328774969552693 0.0488691341877878 0.672766102811072 0.501345562036536 df.mm.trans1:exp6 0.00420802494741581 0.0589383934292443 0.0713970080040875 0.943104703999519 df.mm.trans2:exp6 -0.0262047029054106 0.0488691341877878 -0.536221959748963 0.591996560344817 df.mm.trans1:exp7 -0.0112205656708299 0.0589383934292443 -0.190377867769678 0.849075056443199 df.mm.trans2:exp7 0.213487247404839 0.0488691341877878 4.36854982092538 1.46488452375527e-05 *** df.mm.trans1:exp8 0.065608399948282 0.0589383934292443 1.11316912679415 0.266065112131861 df.mm.trans2:exp8 0.0343557436394951 0.0488691341877878 0.703015189658924 0.482308899206366 df.mm.trans1:probe2 -0.202850561190133 0.0403523594803892 -5.0269814157637 6.52124102272809e-07 *** df.mm.trans1:probe3 -0.180922315090853 0.0403523594803892 -4.48356223577904 8.73890088896251e-06 *** df.mm.trans1:probe4 -0.322208602178982 0.0403523594803892 -7.9848763821499 6.80342987673756e-15 *** df.mm.trans1:probe5 -0.300405981504102 0.0403523594803892 -7.44457041353669 3.24951017055497e-13 *** df.mm.trans1:probe6 -0.474035528682882 0.0403523594803892 -11.7474054748461 6.3624319104104e-29 *** df.mm.trans1:probe7 -0.535386458401937 0.0403523594803892 -13.2677857080979 1.46051297563123e-35 *** df.mm.trans1:probe8 -0.642026448972609 0.0403523594803892 -15.9105057855322 4.39706554052625e-48 *** df.mm.trans1:probe9 -0.469578429329922 0.0403523594803892 -11.6369509832041 1.84872589587311e-28 *** df.mm.trans1:probe10 -0.571403164865121 0.0403523594803892 -14.1603408629133 1.14936851422174e-39 *** df.mm.trans1:probe11 -0.520848188962227 0.0403523594803892 -12.9075027004395 6.02427258737394e-34 *** df.mm.trans1:probe12 -0.531511122367846 0.0403523594803892 -13.1717482995302 3.95842648865375e-35 *** df.mm.trans2:probe2 -0.270988232635068 0.0403523594803892 -6.71554863518614 4.22603439300954e-11 *** df.mm.trans2:probe3 -0.321084786101163 0.0403523594803892 -7.95702631111835 8.3473378117015e-15 *** df.mm.trans2:probe4 -0.293815524182874 0.0403523594803892 -7.28124768827124 1.00183337458958e-12 *** df.mm.trans2:probe5 -0.224357236857193 0.0403523594803892 -5.55995336446753 4.00611971492289e-08 *** df.mm.trans2:probe6 -0.286025403019441 0.0403523594803892 -7.08819525555739 3.69350971906903e-12 *** df.mm.trans3:probe2 -0.198122588278572 0.0403523594803892 -4.90981421730389 1.16591110171856e-06 *** df.mm.trans3:probe3 0.121024416579729 0.0403523594803892 2.99919058360257 0.0028149851030965 ** df.mm.trans3:probe4 -0.0474654675660061 0.0403523594803892 -1.17627489884634 0.239934306416537 df.mm.trans3:probe5 -0.0951103173757829 0.0403523594803892 -2.35699519434558 0.0187319356952313 * df.mm.trans3:probe6 -0.0428489310649039 0.0403523594803892 -1.06186928389474 0.288706481541759 df.mm.trans3:probe7 0.0224266391965622 0.0403523594803892 0.555770207376877 0.578567481290404 df.mm.trans3:probe8 0.160879859152464 0.0403523594803892 3.98687613869643 7.48853580010323e-05 *** df.mm.trans3:probe9 0.00651940938311739 0.0403523594803892 0.161562036695419 0.871703134744498 df.mm.trans3:probe10 -0.068046062346994 0.0403523594803892 -1.68629699039194 0.092239275747839 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02216884429635 0.103145209943742 38.995207305217 2.56387430846791e-169 *** df.mm.trans1 0.0874330516362323 0.086879873577977 1.00636716002768 0.314629688335587 df.mm.trans2 -0.0955645080343612 0.0800586570866 -1.19368112721387 0.233057058874515 df.mm.exp2 0.103111832144105 0.104555004857674 0.986197000176755 0.324419297458691 df.mm.exp3 0.117942008000655 0.104555004857674 1.12803789891458 0.259738138986307 df.mm.exp4 0.161850580660139 0.104555004857674 1.54799457836053 0.122131409888878 df.mm.exp5 0.144121604355947 0.104555004857674 1.37842855587958 0.16856579835902 df.mm.exp6 0.119602250662784 0.104555004857674 1.14391703032861 0.25309737825333 df.mm.exp7 -0.00963090264611526 0.104555004857674 -0.0921132628631727 0.926637662027041 df.mm.exp8 -0.0359502526032166 0.104555004857674 -0.343840571306499 0.731082200922565 df.mm.trans1:exp2 -0.119377032943766 0.0912630902946225 -1.30805380968783 0.191337478956501 df.mm.trans2:exp2 -0.0492103224384386 0.0756713569288971 -0.650316373798847 0.515727619638959 df.mm.trans1:exp3 -0.0994031619466253 0.0912630902946225 -1.08919346940504 0.27648971305021 df.mm.trans2:exp3 -0.0486521691792207 0.0756713569288971 -0.6429403562161 0.520499356963763 df.mm.trans1:exp4 -0.187825140457988 0.0912630902946226 -2.05806246371491 0.039999566567465 * df.mm.trans2:exp4 -0.050731167692451 0.0756713569288971 -0.670414404490188 0.502842090397839 df.mm.trans1:exp5 -0.132513305510072 0.0912630902946225 -1.4519923123607 0.147007144204838 df.mm.trans2:exp5 0.0119587573583777 0.0756713569288971 0.158035455471144 0.87448006950704 df.mm.trans1:exp6 -0.0656392032322578 0.0912630902946226 -0.719230556628712 0.472268565615142 df.mm.trans2:exp6 -0.0542477130850911 0.0756713569288971 -0.716885692112853 0.47371329803272 df.mm.trans1:exp7 -0.063490662778636 0.0912630902946225 -0.695688285085137 0.486883486539716 df.mm.trans2:exp7 0.0501220393526932 0.0756713569288971 0.662364749184943 0.507982436572939 df.mm.trans1:exp8 0.0432539742235299 0.0912630902946225 0.473948165505836 0.635702922982413 df.mm.trans2:exp8 0.0072583640164113 0.0756713569288971 0.0959195699798467 0.923615297089991 df.mm.trans1:probe2 -0.140357154734810 0.0624835665274945 -2.24630510924900 0.0250339944226396 * df.mm.trans1:probe3 -0.0541930142818808 0.0624835665274945 -0.867316276801107 0.386102673921043 df.mm.trans1:probe4 -0.0768347416559205 0.0624835665274945 -1.22967919288204 0.219281439551947 df.mm.trans1:probe5 -0.117999884485036 0.0624835665274945 -1.88849470417333 0.0594238821861775 . df.mm.trans1:probe6 -0.131869005840308 0.0624835665274945 -2.11045900816629 0.0352167987192953 * df.mm.trans1:probe7 -0.091645603826661 0.0624835665274945 -1.46671531283885 0.142958097942770 df.mm.trans1:probe8 -0.0307116536754694 0.0624835665274945 -0.491515695762268 0.62323487054107 df.mm.trans1:probe9 -0.0669725810371028 0.0624835665274945 -1.07184312226533 0.284205529441414 df.mm.trans1:probe10 -0.0469980786692734 0.0624835665274945 -0.752167030167732 0.452234696443967 df.mm.trans1:probe11 -0.105910130225588 0.0624835665274945 -1.69500776142452 0.0905736505823647 . df.mm.trans1:probe12 -0.000278215342926873 0.0624835665274945 -0.00445261623797436 0.996448763349035 df.mm.trans2:probe2 0.0611016589446569 0.0624835665274945 0.977883663503275 0.328511429659704 df.mm.trans2:probe3 0.0520607453268751 0.0624835665274945 0.83319100077245 0.405056322564493 df.mm.trans2:probe4 0.0564082348446978 0.0624835665274945 0.902769127621366 0.366997345342178 df.mm.trans2:probe5 0.0388459075476607 0.0624835665274945 0.62169798727106 0.534367902868713 df.mm.trans2:probe6 0.0188950459561154 0.0624835665274945 0.302400247076183 0.76244789762732 df.mm.trans3:probe2 -0.0275547850046869 0.0624835665274945 -0.440992512688309 0.65937140913925 df.mm.trans3:probe3 0.0313128882051251 0.0624835665274945 0.501137978277002 0.616451036225981 df.mm.trans3:probe4 -0.0426159080286502 0.0624835665274945 -0.682033859413227 0.495471022248356 df.mm.trans3:probe5 0.0299576546174096 0.0624835665274945 0.479448537948413 0.631787826747631 df.mm.trans3:probe6 0.0442719585398423 0.0624835665274945 0.708537636377742 0.478876472647362 df.mm.trans3:probe7 -0.0127872039314469 0.0624835665274945 -0.20464907242163 0.837913152344397 df.mm.trans3:probe8 0.0127125137356653 0.0624835665274945 0.203453714987147 0.838846853690243 df.mm.trans3:probe9 -0.0215638285000441 0.0624835665274945 -0.34511199821725 0.730126656864881 df.mm.trans3:probe10 0.0789388985869829 0.0624835665274945 1.26335455823008 0.206934356808597