chr3.15166_chr3_98924442_98928675_-_0.R fitVsDatCorrelation=0.90744232971416 cont.fitVsDatCorrelation=0.373788157871058 fstatistic=8646.6557884483,37,347 cont.fstatistic=1767.02564575837,37,347 residuals=-0.429580081764986,-0.0870428623614649,0.00115767901330460,0.0875173141921317,0.607339928095853 cont.residuals=-0.726814701992355,-0.210076481178295,-0.0367044643889567,0.178211296492156,0.936029978459918 predictedValues: Include Exclude Both chr3.15166_chr3_98924442_98928675_-_0.R.tl.Lung 54.3203375831173 49.3938326692619 56.6412348565319 chr3.15166_chr3_98924442_98928675_-_0.R.tl.cerebhem 41.4596545068401 41.9077321244965 60.6878997012494 chr3.15166_chr3_98924442_98928675_-_0.R.tl.cortex 56.2928479180901 49.8367617205105 61.4036906688233 chr3.15166_chr3_98924442_98928675_-_0.R.tl.heart 61.4721056981373 55.9476227232838 68.0549078167921 chr3.15166_chr3_98924442_98928675_-_0.R.tl.kidney 105.731687679244 83.0546765462467 108.320766222628 chr3.15166_chr3_98924442_98928675_-_0.R.tl.liver 79.3245224181228 71.6612957480029 86.8920858065937 chr3.15166_chr3_98924442_98928675_-_0.R.tl.stomach 58.9599160726659 50.0809862733398 68.8053536407008 chr3.15166_chr3_98924442_98928675_-_0.R.tl.testicle 48.526017616259 46.5624740838665 56.7114076947884 diffExp=4.92650491385535,-0.448077617656409,6.4560861975796,5.52448297485349,22.6770111329972,7.66322667011991,8.8789297993261,1.96354353239246 diffExpScore=0.99822916540239 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=0,0,0,0,1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 63.3427424535986 54.7637017797284 54.7246453071964 cerebhem 58.8683906892396 55.6223506744496 56.5539713529959 cortex 61.1925591680483 66.2604429235377 55.470613941094 heart 52.312877927999 57.2661749956623 60.9777577828448 kidney 67.6381365510482 57.3786830560919 59.6904715381742 liver 58.2431029736297 51.9237017992405 50.5472702500769 stomach 61.0889276405346 59.4862041086316 67.3557541749052 testicle 73.8834644073047 60.0877680206205 74.9455971342377 cont.diffExp=8.57904067387025,3.24604001478996,-5.06788375548939,-4.95329706766329,10.2594534949563,6.31940117438914,1.60272353190309,13.7956963866842 cont.diffExpScore=1.54749047280725 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=0.985688423115233 cont.tran.correlation=0.238664809849386 tran.covariance=0.0661226240321695 cont.tran.covariance=0.00186051805648037 tran.mean=59.6582794613428 cont.tran.mean=59.9599518230853 weightedLogRatios: wLogRatio Lung 0.375288058769872 cerebhem -0.0400969370478354 cortex 0.48356254108681 heart 0.383403200729775 kidney 1.09602967252507 liver 0.439176275020071 stomach 0.65209027591893 testicle 0.159497708606608 cont.weightedLogRatios: wLogRatio Lung 0.593161461009568 cerebhem 0.229539364375225 cortex -0.330508148175677 heart -0.362094279908580 kidney 0.679697581272861 liver 0.460228236762472 stomach 0.108977875765488 testicle 0.86789140018575 varWeightedLogRatios=0.113388036967967 cont.varWeightedLogRatios=0.207531562693383 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90708556801944 0.0774786493408093 50.4278998312573 8.73290654334291e-162 *** df.mm.trans1 0.0655211015271921 0.0645503474376114 1.01503871207694 0.310794587733971 df.mm.trans2 -0.00750634483453352 0.0645503474376114 -0.116286668197851 0.90749262080057 df.mm.exp2 -0.503540229562468 0.0889368185261294 -5.66177470599006 3.14403787234527e-08 *** df.mm.exp3 -0.0361365698201562 0.0889368185261294 -0.406317320756637 0.684759925608693 df.mm.exp4 0.064697584744136 0.0889368185261294 0.727455578199349 0.467437444120666 df.mm.exp5 0.537319879829407 0.0889368185261294 6.04159097136518 3.93355912785722e-09 *** df.mm.exp6 0.322844136178997 0.0889368185261294 3.63003918432439 0.000326032538544830 *** df.mm.exp7 -0.0987693300821692 0.0889368185261294 -1.11055614220281 0.267528399901851 df.mm.exp8 -0.173067363650473 0.0889368185261294 -1.94595856382727 0.0524667270290406 . df.mm.trans1:exp2 0.233362305424608 0.0751653305793762 3.10465348353882 0.00206170690579478 ** df.mm.trans2:exp2 0.339185005394739 0.0751653305793762 4.51251930617869 8.78289183860163e-06 *** df.mm.trans1:exp3 0.0718053637510634 0.0751653305793762 0.955298981559529 0.340091553816521 df.mm.trans2:exp3 0.0450638970351397 0.0751653305793762 0.599530351131114 0.549210528404467 df.mm.trans1:exp4 0.0589872233498496 0.0751653305793762 0.784766366291143 0.433125993284496 df.mm.trans2:exp4 0.0598927881559495 0.0751653305793762 0.796814005796215 0.426103900627652 df.mm.trans1:exp5 0.128686059005848 0.0751653305793762 1.71204008568755 0.0877827277549379 . df.mm.trans2:exp5 -0.0176463069947994 0.0751653305793762 -0.234766572019057 0.814528384164724 df.mm.trans1:exp6 0.0558044827576766 0.0751653305793762 0.742423166738366 0.458333248459168 df.mm.trans2:exp6 0.0492810857727246 0.0751653305793762 0.655635854893005 0.512492897260407 df.mm.trans1:exp7 0.180728456557570 0.0751653305793762 2.40441244872484 0.0167219474783285 * df.mm.trans2:exp7 0.112585178988216 0.0751653305793762 1.49783388325983 0.135085504686466 df.mm.trans1:exp8 0.060268765511998 0.0751653305793762 0.801816010751831 0.423208138413698 df.mm.trans2:exp8 0.114036731553656 0.0751653305793762 1.51714534712557 0.130140464458850 df.mm.trans1:probe2 -0.132581047790330 0.0411697471006572 -3.22035128042392 0.00140133869332168 ** df.mm.trans1:probe3 -0.0951319023194167 0.0411697471006572 -2.31072350497626 0.0214339428530924 * df.mm.trans1:probe4 0.270165929812906 0.0411697471006572 6.56224409521799 1.93082756296790e-10 *** df.mm.trans1:probe5 0.185413606755157 0.0411697471006572 4.50363725338981 9.13702614076335e-06 *** df.mm.trans1:probe6 -0.00494630250046127 0.0411697471006572 -0.120144106991182 0.904438524991632 df.mm.trans2:probe2 -0.0615675476486914 0.0411697471006572 -1.49545605655927 0.135704340325070 df.mm.trans2:probe3 0.0863679971237738 0.0411697471006572 2.09785104855296 0.0366411528507222 * df.mm.trans2:probe4 0.023699033874155 0.0411697471006572 0.575641959038818 0.565230323397107 df.mm.trans2:probe5 -0.0380440666185688 0.0411697471006572 -0.924078219998624 0.356087729768394 df.mm.trans2:probe6 -0.00799193209636676 0.0411697471006572 -0.194121476549930 0.846194356870147 df.mm.trans3:probe2 0.444303249303365 0.0411697471006572 10.7919839346371 1.32023456583388e-23 *** df.mm.trans3:probe3 -0.103465218098967 0.0411697471006572 -2.51313708209093 0.0124188481184171 * df.mm.trans3:probe4 -0.0542903260793665 0.0411697471006572 -1.31869467030318 0.188140859886912 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.074202455593 0.171029351125971 23.8216565096606 5.22963417679616e-75 *** df.mm.trans1 0.0212039617181658 0.142490920158512 0.148809213208657 0.881790622452367 df.mm.trans2 -0.0858571193392577 0.142490920158512 -0.602544493668417 0.547205307878487 df.mm.exp2 -0.090579804071646 0.196322554576598 -0.461382566394352 0.644813234834055 df.mm.exp3 0.142491505696528 0.196322554576598 0.725803033705601 0.468448757341338 df.mm.exp4 -0.254830237008674 0.196322554576598 -1.29801813937404 0.195143209189448 df.mm.exp5 0.0253986575271771 0.196322554576598 0.129372081480671 0.897138193788698 df.mm.exp6 -0.0577816671778612 0.196322554576598 -0.29432006578397 0.768689324542107 df.mm.exp7 -0.161187055109489 0.196322554576598 -0.821031773232145 0.412191864310464 df.mm.exp8 -0.0677409121768335 0.196322554576598 -0.345049056247908 0.730266545305148 df.mm.trans1:exp2 0.0173237525367157 0.165922842299583 0.104408484670463 0.916905510856275 df.mm.trans2:exp2 0.106137316895733 0.165922842299583 0.639678753236978 0.522803560756559 df.mm.trans1:exp3 -0.1770262433059 0.165922842299583 -1.06691906221248 0.286750116933858 df.mm.trans2:exp3 0.048073977578631 0.165922842299583 0.289736945874100 0.772190537267852 df.mm.trans1:exp4 0.0635124721478575 0.165922842299583 0.382783173598137 0.702114903385059 df.mm.trans2:exp4 0.299512774009955 0.165922842299583 1.80513285487942 0.0719205836386654 . df.mm.trans1:exp5 0.0402129790977960 0.165922842299583 0.242359512050723 0.808644783335242 df.mm.trans2:exp5 0.0212466032884871 0.165922842299583 0.128051104923366 0.898182708498032 df.mm.trans1:exp6 -0.0261529887873108 0.165922842299583 -0.157621388501109 0.874846827494288 df.mm.trans2:exp6 0.00452943696492577 0.165922842299583 0.0272984533181249 0.978237383022795 df.mm.trans1:exp7 0.124957350338223 0.165922842299583 0.753105169887372 0.451897354042399 df.mm.trans2:exp7 0.243903878852494 0.165922842299583 1.46998373142688 0.142472259878603 df.mm.trans1:exp8 0.221669621250546 0.165922842299583 1.33598013497328 0.182431265681907 df.mm.trans2:exp8 0.160519607688849 0.165922842299583 0.967435257642359 0.334000276907609 df.mm.trans1:probe2 0.224645793577312 0.090879683532854 2.4719033434585 0.0139187261053927 * df.mm.trans1:probe3 0.0972391784455895 0.090879683532854 1.06997708030571 0.285373320666404 df.mm.trans1:probe4 0.158584059278787 0.090879683532854 1.74498912313507 0.0818720160821853 . df.mm.trans1:probe5 0.0475639683776906 0.090879683532854 0.523372953433489 0.601048802079434 df.mm.trans1:probe6 0.00350620235402066 0.090879683532854 0.0385807060249404 0.969246871573328 df.mm.trans2:probe2 0.00802679261431336 0.090879683532854 0.0883232896757567 0.929670679344624 df.mm.trans2:probe3 0.00295815917512154 0.090879683532854 0.0325502803280795 0.974051933838448 df.mm.trans2:probe4 0.133594757340885 0.090879683532854 1.47001785379885 0.142463022699802 df.mm.trans2:probe5 0.0729551640829565 0.090879683532854 0.802766484729037 0.422659200776859 df.mm.trans2:probe6 -0.0707122537151435 0.090879683532854 -0.778086487169383 0.437048233442005 df.mm.trans3:probe2 0.00912562172851036 0.090879683532854 0.100414321152553 0.920073390943446 df.mm.trans3:probe3 -0.00729540783512554 0.090879683532854 -0.0802754537815723 0.936064457327296 df.mm.trans3:probe4 -0.0307707576969166 0.090879683532854 -0.338587861453023 0.735125009979001