chr9.24762_chr9_7827861_7831548_-_0.R fitVsDatCorrelation=0.835362128726525 cont.fitVsDatCorrelation=0.290482785167128 fstatistic=9456.51796312676,46,554 cont.fstatistic=3112.74307681202,46,554 residuals=-0.381682818258381,-0.0880696057712027,-0.00938078462074456,0.0829502844478614,0.806688171704074 cont.residuals=-0.626160270848205,-0.187500139138763,-0.0423171338779981,0.153044639872606,1.12346071938479 predictedValues: Include Exclude Both chr9.24762_chr9_7827861_7831548_-_0.R.tl.Lung 69.339779593608 74.7542859517291 70.5502449374727 chr9.24762_chr9_7827861_7831548_-_0.R.tl.cerebhem 60.5352392210933 66.9965543629023 52.0149305460073 chr9.24762_chr9_7827861_7831548_-_0.R.tl.cortex 55.3849883530153 81.2768258063332 48.462535707694 chr9.24762_chr9_7827861_7831548_-_0.R.tl.heart 63.3078386727104 85.8099148509137 57.5748538507945 chr9.24762_chr9_7827861_7831548_-_0.R.tl.kidney 55.9866297431635 76.1183678606228 55.6236128303552 chr9.24762_chr9_7827861_7831548_-_0.R.tl.liver 57.2963910695699 73.704927748906 55.9308382700958 chr9.24762_chr9_7827861_7831548_-_0.R.tl.stomach 64.7967476480814 104.284554256767 57.6946875172858 chr9.24762_chr9_7827861_7831548_-_0.R.tl.testicle 69.2538446669922 75.3325500675018 71.6873987669165 diffExp=-5.41450635812116,-6.46131514180907,-25.8918374533178,-22.5020761782034,-20.1317381174593,-16.4085366793361,-39.4878066086854,-6.07870540050959 diffExpScore=0.99302535738427 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,-1,0,0,0,-1,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,-1,0,-1,0 diffExp1.3Score=0.8 diffExp1.2=0,0,-1,-1,-1,-1,-1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 63.1307442894952 74.723247324765 67.0842481436173 cerebhem 64.3835114863444 58.8483958245006 63.9909274051913 cortex 66.5982465825611 63.6008688791773 59.7488645283246 heart 65.2773523359913 62.6891604046006 69.217066451056 kidney 64.3791322834642 62.9642410883314 67.060356589099 liver 69.3014023356458 65.8149408808285 64.0613640911534 stomach 66.0703366999793 66.2768650360745 66.4425075365079 testicle 62.66099679489 66.1624736014927 69.8419499955458 cont.diffExp=-11.5925030352699,5.53511566184388,2.99737770338378,2.58819193139070,1.41489119513283,3.4864614548173,-0.206528336095175,-3.50147680660270 cont.diffExpScore=18.1946003466414 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=0.149977714013961 cont.tran.correlation=-0.206436063461094 tran.covariance=0.00179880714965351 cont.tran.covariance=-0.000432942496081275 tran.mean=70.8862149921193 cont.tran.mean=65.1801197405089 weightedLogRatios: wLogRatio Lung -0.321549037748430 cerebhem -0.421273354677418 cortex -1.61325365939136 heart -1.30776108342477 kidney -1.28359963181815 liver -1.05118554756862 stomach -2.09819144968987 testicle -0.36007902673645 cont.weightedLogRatios: wLogRatio Lung -0.713023293840045 cerebhem 0.370351136617966 cortex 0.192293473684170 heart 0.168235402511689 kidney 0.092305465035314 liver 0.217449990471988 stomach -0.0130841511464888 testicle -0.226464723059266 varWeightedLogRatios=0.419785045605874 cont.varWeightedLogRatios=0.116733568936969 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21433904058758 0.0835632469532859 50.4329258883814 3.27684657158663e-209 *** df.mm.trans1 -0.0922936002180076 0.0744038062840822 -1.24044191859782 0.215337062144511 df.mm.trans2 0.0746118296656824 0.0692964411393528 1.07670507227984 0.282080682632023 df.mm.exp2 0.0594358900569459 0.0951776687961023 0.62447305979173 0.532574038985156 df.mm.exp3 0.234478320085826 0.0951776687961023 2.4635854507862 0.0140582941979187 * df.mm.exp4 0.250157646134707 0.0951776687961023 2.6283228965254 0.00881870607238401 ** df.mm.exp5 0.0418945466112034 0.0951776687961023 0.440172018721676 0.659984203664302 df.mm.exp6 0.0272912420458795 0.0951776687961023 0.286739971582463 0.774418634380526 df.mm.exp7 0.466313415152766 0.0951776687961023 4.89939941848904 1.26382735429616e-06 *** df.mm.exp8 -0.00952416661727814 0.0951776687961023 -0.100067239907731 0.920327144147708 df.mm.trans1:exp2 -0.195228989742665 0.0897343666976683 -2.17563233493838 0.0300047408605242 * df.mm.trans2:exp2 -0.169001247181746 0.0798417379272897 -2.11670301234741 0.0347302527016915 * df.mm.trans1:exp3 -0.459188493027387 0.0897343666976683 -5.1171976793961 4.28238287250031e-07 *** df.mm.trans2:exp3 -0.150823937371751 0.0798417379272898 -1.88903625205532 0.0594089899729173 . df.mm.trans1:exp4 -0.341167252578345 0.0897343666976683 -3.80196868974180 0.000159487052877605 *** df.mm.trans2:exp4 -0.112229636388073 0.0798417379272897 -1.40565122079730 0.160388276444463 df.mm.trans1:exp5 -0.25580040058795 0.0897343666976683 -2.85064028422677 0.00452566793403997 ** df.mm.trans2:exp5 -0.0238114938499580 0.0798417379272897 -0.298233661592420 0.765636650258107 df.mm.trans1:exp6 -0.218072365006877 0.0897343666976683 -2.43019896425640 0.0154070497068 * df.mm.trans2:exp6 -0.0414281305950642 0.0798417379272897 -0.51887811651585 0.604052939358631 df.mm.trans1:exp7 -0.534076765289274 0.0897343666976683 -5.95175276701598 4.70900084795793e-09 *** df.mm.trans2:exp7 -0.133396701466723 0.0798417379272897 -1.67076400050566 0.0953330831441665 . df.mm.trans1:exp8 0.00828406718143219 0.0897343666976683 0.0923176647509281 0.926479026556202 df.mm.trans2:exp8 0.017229932059392 0.0798417379272897 0.215801064789985 0.829222247102491 df.mm.trans1:probe2 0.260505319560348 0.0448671833488341 5.80614382532033 1.07756071294337e-08 *** df.mm.trans1:probe3 0.272956143237914 0.0448671833488342 6.08364784380892 2.19233615838159e-09 *** df.mm.trans1:probe4 0.290172531295358 0.0448671833488342 6.46736678430022 2.19268719060243e-10 *** df.mm.trans1:probe5 0.621082590184754 0.0448671833488341 13.8426917811165 1.22355317249013e-37 *** df.mm.trans1:probe6 0.243018764278544 0.0448671833488341 5.41640339642268 9.0776843065858e-08 *** df.mm.trans1:probe7 0.0583081961968741 0.0448671833488342 1.29957335952067 0.194287740840937 df.mm.trans1:probe8 -0.00207082218355800 0.0448671833488341 -0.0461544948667210 0.9632037437473 df.mm.trans1:probe9 0.0470159834631427 0.0448671833488342 1.04789246736534 0.295145096349943 df.mm.trans1:probe10 0.150565727465533 0.0448671833488342 3.35580966371149 0.000845548293759622 *** df.mm.trans1:probe11 0.0242962684242964 0.0448671833488341 0.541515348431779 0.588370120998939 df.mm.trans1:probe12 -0.0420143365109124 0.0448671833488341 -0.936415735845475 0.349467194257793 df.mm.trans1:probe13 0.00777739299747538 0.0448671833488342 0.173342572833413 0.862445477049341 df.mm.trans1:probe14 0.023071752040918 0.0448671833488341 0.514223321342447 0.607300920666368 df.mm.trans1:probe15 0.0603342561238016 0.0448671833488341 1.34473019299459 0.179262657784172 df.mm.trans1:probe16 -0.143446626581715 0.0448671833488341 -3.19713910869874 0.00146715121301431 ** df.mm.trans2:probe2 -0.0622836502255997 0.0448671833488341 -1.38817829818635 0.165640716498395 df.mm.trans2:probe3 -0.176781490209806 0.0448671833488341 -3.94010671085283 9.18562552514695e-05 *** df.mm.trans2:probe4 0.34912080722145 0.0448671833488341 7.78120624392888 3.53795546936077e-14 *** df.mm.trans2:probe5 0.144555733882088 0.0448671833488341 3.22185889758655 0.00134840479143059 ** df.mm.trans2:probe6 -0.0273103029817981 0.0448671833488341 -0.608692165261756 0.542977994781463 df.mm.trans3:probe2 0.152636549649091 0.0448671833488341 3.40196415857822 0.000717323362795435 *** df.mm.trans3:probe3 0.0263670906078544 0.0448671833488341 0.5876698432985 0.556993371417116 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30744407095327 0.145461187551802 29.6123257581635 3.05573284539151e-116 *** df.mm.trans1 -0.127719969106085 0.129517059413777 -0.986124682603005 0.324502408944295 df.mm.trans2 -0.00110946750537595 0.120626507331372 -0.0091975431430518 0.99266483681755 df.mm.exp2 -0.171969042421633 0.165678779083736 -1.03796662054540 0.299738408473932 df.mm.exp3 0.00810532403472966 0.165678779083736 0.0489219203542847 0.960999151547445 df.mm.exp4 -0.173463624123127 0.165678779083736 -1.04698758092282 0.295561874806046 df.mm.exp5 -0.151286417810303 0.165678779083736 -0.913130931112432 0.361570889176628 df.mm.exp6 0.0124207108834195 0.165678779083736 0.0749686287653165 0.940266734558289 df.mm.exp7 -0.0648261211445399 0.165678779083736 -0.391275946763079 0.69574371626513 df.mm.exp8 -0.169432070989588 0.165678779083736 -1.02265402924025 0.306917799062138 df.mm.trans1:exp2 0.191618727828337 0.156203450918424 1.22672531689719 0.220446940899474 df.mm.trans2:exp2 -0.0668576358507866 0.138983039057778 -0.481048884123137 0.630671685552457 df.mm.trans1:exp3 0.0453650434749985 0.156203450918424 0.290422799293277 0.771601483389285 df.mm.trans2:exp3 -0.169269445049798 0.138983039057778 -1.21791440306201 0.223774956242944 df.mm.trans1:exp4 0.206900893277026 0.156203450918424 1.32456032219851 0.185863157374638 df.mm.trans2:exp4 -0.00213907638303361 0.138983039057778 -0.0153909167444838 0.987725850956172 df.mm.trans1:exp5 0.170868083444289 0.156203450918424 1.09388161682500 0.274482289356367 df.mm.trans2:exp5 -0.0199378716076612 0.138983039057778 -0.143455429833943 0.885982672317548 df.mm.trans1:exp6 0.0808365487304377 0.156203450918424 0.517508084841571 0.605008095338586 df.mm.trans2:exp6 -0.139365086159444 0.138983039057778 -1.00274887572078 0.316419812541511 df.mm.trans1:exp7 0.110338121182356 0.156203450918424 0.706374414480633 0.480252287747171 df.mm.trans2:exp7 -0.0551242390977082 0.138983039057778 -0.396625656421226 0.691796262060145 df.mm.trans1:exp8 0.161963382280013 0.156203450918424 1.03687454616222 0.300246683428839 df.mm.trans2:exp8 0.0477542562884938 0.138983039057778 0.343597726832274 0.731279193578273 df.mm.trans1:probe2 0.0172251194448844 0.0781017254592118 0.220547233029828 0.82552624331193 df.mm.trans1:probe3 -0.0317917542914940 0.0781017254592118 -0.407055722579356 0.684124285575921 df.mm.trans1:probe4 -0.0799073811865047 0.0781017254592118 -1.02311928086961 0.306697994850987 df.mm.trans1:probe5 -0.0584371455203045 0.0781017254592118 -0.748218367477976 0.454645961356997 df.mm.trans1:probe6 -0.104131638274937 0.0781017254592118 -1.33328217350742 0.182987191448234 df.mm.trans1:probe7 -0.00821817747094021 0.0781017254592118 -0.105224019349382 0.916236137722045 df.mm.trans1:probe8 0.0672501404209052 0.0781017254592118 0.861058318820706 0.389578430651921 df.mm.trans1:probe9 -0.0668279641972588 0.0781017254592118 -0.855652852793365 0.392559734710857 df.mm.trans1:probe10 0.0174733720943892 0.0781017254592118 0.223725813887615 0.823053134326689 df.mm.trans1:probe11 -0.0761188185741031 0.0781017254592118 -0.974611228196947 0.330178498364822 df.mm.trans1:probe12 0.0538585537628793 0.0781017254592118 0.689594928232497 0.490737697172962 df.mm.trans1:probe13 -0.143585741510422 0.0781017254592118 -1.83844519011823 0.066531973511519 . df.mm.trans1:probe14 0.0194872331778270 0.0781017254592118 0.249510917502126 0.803058081133586 df.mm.trans1:probe15 -0.0513748528018446 0.0781017254592118 -0.657794082010068 0.510943678036005 df.mm.trans1:probe16 -0.107160461014239 0.0781017254592118 -1.37206265782442 0.170599242419231 df.mm.trans2:probe2 -0.0171233749553549 0.0781017254592118 -0.21924451546589 0.826540331783333 df.mm.trans2:probe3 -0.0354952809402715 0.0781017254592118 -0.454474990553298 0.649665003597882 df.mm.trans2:probe4 0.140321432721593 0.0781017254592118 1.79664958612054 0.0729358827525064 . df.mm.trans2:probe5 -0.043165650388295 0.0781017254592118 -0.552684977630078 0.580702064360631 df.mm.trans2:probe6 0.0225727185422706 0.0781017254592118 0.289016899556964 0.772676561608904 df.mm.trans3:probe2 -0.0803516579181053 0.0781017254592118 -1.02880771769977 0.304018997367747 df.mm.trans3:probe3 0.162355548275877 0.0781017254592118 2.07877031296404 0.0380980532187635 *