chr17.10733_chr17_24096021_24097082_-_1.R fitVsDatCorrelation=0.885718363627856 cont.fitVsDatCorrelation=0.279473113141635 fstatistic=4067.56384739641,38,370 cont.fstatistic=943.376756414706,38,370 residuals=-0.855927281287445,-0.113371046431188,-0.00273165458096437,0.0996579807416468,0.937172723654874 cont.residuals=-0.794053153949737,-0.304761876622788,-0.0630470658972725,0.215408065624218,1.97692008293287 predictedValues: Include Exclude Both chr17.10733_chr17_24096021_24097082_-_1.R.tl.Lung 53.7603502004004 67.2869445917293 93.3958946936837 chr17.10733_chr17_24096021_24097082_-_1.R.tl.cerebhem 65.393679385765 66.0270189738289 145.3169687899 chr17.10733_chr17_24096021_24097082_-_1.R.tl.cortex 108.875418793781 77.3011645304323 198.968126184962 chr17.10733_chr17_24096021_24097082_-_1.R.tl.heart 55.5554867184035 74.7545342549319 95.0613018839015 chr17.10733_chr17_24096021_24097082_-_1.R.tl.kidney 47.8444660739722 69.4600778170784 80.0481381312958 chr17.10733_chr17_24096021_24097082_-_1.R.tl.liver 48.5258247175492 68.5494628981508 70.3928157387846 chr17.10733_chr17_24096021_24097082_-_1.R.tl.stomach 47.1803178696584 77.8619094689937 73.7569464378444 chr17.10733_chr17_24096021_24097082_-_1.R.tl.testicle 48.8547939511946 60.7294021093615 76.5270592989638 diffExp=-13.5265943913289,-0.633339588063905,31.5742542633489,-19.1990475365284,-21.6156117431062,-20.0236381806016,-30.6815915993353,-11.8746081581669 diffExpScore=1.71451347557054 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,-1,-1,-1,0 diffExp1.4Score=1.33333333333333 diffExp1.3=0,0,1,-1,-1,-1,-1,0 diffExp1.3Score=1.25 diffExp1.2=-1,0,1,-1,-1,-1,-1,-1 diffExp1.2Score=1.16666666666667 cont.predictedValues: Include Exclude Both Lung 78.6796915443037 70.036340704338 66.67726391198 cerebhem 63.6339909136958 60.4369529463308 78.7255372882568 cortex 68.7359447742488 70.3684249962704 78.4675721778505 heart 65.2803493864158 72.3233367443032 61.7716461573479 kidney 68.5315941029453 63.0003900858243 68.6432144899269 liver 59.1175963612198 72.1561752073961 79.0732452547285 stomach 72.8897345417755 82.6612909659109 62.2135129770732 testicle 69.4934255205806 68.6195689832692 86.9562524695988 cont.diffExp=8.64335083996566,3.19703796736501,-1.63248022202166,-7.04298735788748,5.53120401712101,-13.0385788461763,-9.77155642413537,0.87385653731134 cont.diffExpScore=3.49231152966805 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,-1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.411472790516565 cont.tran.correlation=0.263994756052234 tran.covariance=0.00904372955393278 cont.tran.covariance=0.00209024208700661 tran.mean=64.872553272202 cont.tran.mean=69.1228004861768 weightedLogRatios: wLogRatio Lung -0.9194339551953 cerebhem -0.040339182265911 cortex 1.54772123842332 heart -1.23652319851157 kidney -1.51144975461881 liver -1.40077789161994 stomach -2.05616892343833 testicle -0.86978839343762 cont.weightedLogRatios: wLogRatio Lung 0.501232326429876 cerebhem 0.212753984499227 cortex -0.099570026082366 heart -0.433379692169879 kidney 0.352202611003384 liver -0.832928488701032 stomach -0.547478406568464 testicle 0.0535901603722454 varWeightedLogRatios=1.24895446325688 cont.varWeightedLogRatios=0.219418663690885 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.36563171745947 0.116369490835224 28.9219424550471 5.40432684969044e-97 *** df.mm.trans1 0.674851774442685 0.09583490859319 7.04181580959571 9.2932949179024e-12 *** df.mm.trans2 0.845002349517057 0.09583490859319 8.81727088720887 4.66766605594016e-17 *** df.mm.exp2 -0.265082753006218 0.131003660853084 -2.02347591876458 0.0437425483989894 * df.mm.exp3 0.0881136106117426 0.131003660853084 0.672604185546835 0.501618964836281 df.mm.exp4 0.120415135197976 0.131003660853084 0.91917381860815 0.358603494346527 df.mm.exp5 0.0694243972592297 0.131003660853084 0.529942421510545 0.596469709046588 df.mm.exp6 0.198905436942681 0.131003660853084 1.51831968394949 0.129787794890494 df.mm.exp7 0.25148344814258 0.131003660853084 1.91966733223287 0.0556686351138383 . df.mm.exp8 0.000981030316886296 0.131003660853084 0.00748857177347497 0.994029075962167 df.mm.trans1:exp2 0.460972150983267 0.108622497303163 4.24379997171953 2.78149992040466e-05 *** df.mm.trans2:exp2 0.246180560054808 0.108622497303163 2.26638648684098 0.0240046044502033 * df.mm.trans1:exp3 0.617554460771466 0.108622497303163 5.6853274054995 2.65209050057064e-08 *** df.mm.trans2:exp3 0.05062918034819 0.108622497303163 0.466102157519774 0.641416654840799 df.mm.trans1:exp4 -0.0875690635195394 0.108622497303163 -0.806177962150294 0.420658083248179 df.mm.trans2:exp4 -0.0151714954347153 0.108622497303163 -0.139671760559620 0.888995335739874 df.mm.trans1:exp5 -0.186005147923082 0.108622497303163 -1.71239984847657 0.0876608421797872 . df.mm.trans2:exp5 -0.0376384592256622 0.108622497303163 -0.346507032706256 0.729158669984013 df.mm.trans1:exp6 -0.301345522645195 0.108622497303163 -2.77424594468812 0.00581351487586193 ** df.mm.trans2:exp6 -0.180316095745560 0.108622497303163 -1.66002531908562 0.0977565832484103 . df.mm.trans1:exp7 -0.382042847142293 0.108622497303163 -3.5171613305485 0.000490421026350262 *** df.mm.trans2:exp7 -0.105512811467849 0.108622497303163 -0.9713716227069 0.331997828555224 df.mm.trans1:exp8 -0.0966647308256928 0.108622497303163 -0.889914458106261 0.374090066833474 df.mm.trans2:exp8 -0.103519295114959 0.108622497303163 -0.953018920436332 0.341202651691096 df.mm.trans1:probe2 -0.0590138497431011 0.063421874597025 -0.930496774465719 0.352720633036667 df.mm.trans1:probe3 -0.234193176357095 0.063421874597025 -3.69262463219718 0.000255381058695336 *** df.mm.trans1:probe4 -0.271423835296023 0.063421874597025 -4.2796564595515 2.38702683653712e-05 *** df.mm.trans1:probe5 -0.0299095709684545 0.063421874597025 -0.471597081582598 0.637492376598498 df.mm.trans1:probe6 -0.0208796648407626 0.063421874597025 -0.329218664276790 0.742176692843476 df.mm.trans2:probe2 0.0884026690456428 0.063421874597025 1.39388294034736 0.164189520277050 df.mm.trans2:probe3 0.140023010251403 0.063421874597025 2.20780308278637 0.0278709304891333 * df.mm.trans2:probe4 -0.0304795129242094 0.063421874597025 -0.480583601760001 0.63109649966726 df.mm.trans2:probe5 -0.179368634410876 0.063421874597025 -2.82818247726929 0.00493605659581018 ** df.mm.trans2:probe6 -0.0369237431399852 0.063421874597025 -0.582192553824596 0.560791564986881 df.mm.trans3:probe2 -0.111698246048537 0.063421874597025 -1.76119433173892 0.0790314670356026 . df.mm.trans3:probe3 -0.381924903580578 0.063421874597025 -6.02197437409857 4.15681683313854e-09 *** df.mm.trans3:probe4 -0.255374943162227 0.063421874597025 -4.02660666820160 6.8674801699098e-05 *** df.mm.trans3:probe5 -0.99419416310621 0.063421874597025 -15.6758873720337 7.8511057231999e-43 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32002852042622 0.240687298003183 17.9487183422911 2.89606325047743e-52 *** df.mm.trans1 0.0344989853592965 0.198215572123952 0.174047805576662 0.861923079446086 df.mm.trans2 -0.0975696801617503 0.198215572123952 -0.492240236810133 0.622841458139641 df.mm.exp2 -0.525754403776953 0.270955187076492 -1.94037401331804 0.0530936288704214 . df.mm.exp3 -0.29320382564789 0.270955187076492 -1.08211187544130 0.279907551797586 df.mm.exp4 -0.0781418522068075 0.270955187076492 -0.288394007326192 0.773206643978891 df.mm.exp5 -0.273021809762551 0.270955187076492 -1.00762717521062 0.314291973893004 df.mm.exp6 -0.4265483986235 0.270955187076492 -1.57423964909402 0.116286703315088 df.mm.exp7 0.158591704186124 0.270955187076492 0.585306027529021 0.558698753708727 df.mm.exp8 -0.41013054899825 0.270955187076492 -1.51364715849661 0.130969063503463 df.mm.trans1:exp2 0.313517106554279 0.224664172633320 1.39549222681793 0.163704384697753 df.mm.trans2:exp2 0.378340865134087 0.224664172633320 1.68402847992852 0.093019530272707 . df.mm.trans1:exp3 0.158091028515989 0.224664172633320 0.703677077938071 0.482076479779025 df.mm.trans2:exp3 0.297934219370907 0.224664172633320 1.32613142486752 0.185614206141950 df.mm.trans1:exp4 -0.108552158161982 0.224664172633320 -0.483175207197604 0.629257118592099 df.mm.trans2:exp4 0.110274445581544 0.224664172633320 0.490841260041608 0.623829708587728 df.mm.trans1:exp5 0.134931603347457 0.224664172633320 0.600592438776086 0.548479177343685 df.mm.trans2:exp5 0.167148467718175 0.224664172633320 0.743992536767234 0.457353200374596 df.mm.trans1:exp6 0.140691944358027 0.224664172633320 0.626232223451373 0.531548770948123 df.mm.trans2:exp6 0.456367008385776 0.224664172633320 2.03132970885671 0.0429358748019360 * df.mm.trans1:exp7 -0.235028963831124 0.224664172633320 -1.04613459759212 0.296181825894948 df.mm.trans2:exp7 0.00714546230290055 0.224664172633320 0.0318050814206271 0.974644643622478 df.mm.trans1:exp8 0.285977627137597 0.224664172633320 1.27291158080799 0.203848648746854 df.mm.trans2:exp8 0.389694044942141 0.224664172633320 1.73456248219057 0.0836511317540766 . df.mm.trans1:probe2 0.0343227584115964 0.131175615889471 0.261655020095479 0.7937330160265 df.mm.trans1:probe3 0.00469737098274901 0.131175615889471 0.0358097879007256 0.971453339727356 df.mm.trans1:probe4 -0.0515891991120829 0.131175615889471 -0.393283452585823 0.694336649452434 df.mm.trans1:probe5 0.22675236180784 0.131175615889471 1.7286167118049 0.0847119294435328 . df.mm.trans1:probe6 -0.094750051293536 0.131175615889471 -0.722314514409237 0.470557401778576 df.mm.trans2:probe2 0.0725469662578405 0.131175615889471 0.553052225186187 0.580561639599079 df.mm.trans2:probe3 0.0283997989260372 0.131175615889471 0.216502119951676 0.828715690291195 df.mm.trans2:probe4 0.0146521036030146 0.131175615889471 0.111698378571826 0.911123134282092 df.mm.trans2:probe5 0.138074074228090 0.131175615889471 1.05258948693964 0.293216097938450 df.mm.trans2:probe6 0.0384366768832762 0.131175615889471 0.293016934760672 0.769673471790629 df.mm.trans3:probe2 -0.137922628552536 0.131175615889471 -1.05143496081429 0.293745075483801 df.mm.trans3:probe3 0.068481544180204 0.131175615889471 0.522060016382212 0.601940939221085 df.mm.trans3:probe4 -0.110434698912286 0.131175615889471 -0.84188435604784 0.400396500346496 df.mm.trans3:probe5 -0.105593225199038 0.131175615889471 -0.804976019994532 0.421350479094104