chrX.25579_chrX_57508332_57511483_+_2.R fitVsDatCorrelation=0.912533066426388 cont.fitVsDatCorrelation=0.260621419026958 fstatistic=7843.72411294165,57,807 cont.fstatistic=1396.12685551223,57,807 residuals=-0.74418727991712,-0.0983683182232036,-0.00549855085487797,0.0992856249178775,1.01169899908999 cont.residuals=-0.804531576082663,-0.351843889072105,-0.0470580873430152,0.273762287784708,1.36381585563747 predictedValues: Include Exclude Both chrX.25579_chrX_57508332_57511483_+_2.R.tl.Lung 103.578721273228 46.6384777864930 79.6480897835489 chrX.25579_chrX_57508332_57511483_+_2.R.tl.cerebhem 124.220245283790 51.7915534973502 87.989939197444 chrX.25579_chrX_57508332_57511483_+_2.R.tl.cortex 154.208266671049 43.7160161180796 106.039897991610 chrX.25579_chrX_57508332_57511483_+_2.R.tl.heart 106.818012186401 46.6160993614627 80.0591115087544 chrX.25579_chrX_57508332_57511483_+_2.R.tl.kidney 99.4296965144505 45.9974538090091 77.3790170587207 chrX.25579_chrX_57508332_57511483_+_2.R.tl.liver 77.2741288170576 47.1854255003184 68.7403454987383 chrX.25579_chrX_57508332_57511483_+_2.R.tl.stomach 98.0099739721093 46.5336867996523 82.2205012689369 chrX.25579_chrX_57508332_57511483_+_2.R.tl.testicle 100.841168101071 48.9279541049662 80.3581186014453 diffExp=56.940243486735,72.42869178644,110.492250552969,60.2019128249387,53.4322427054414,30.0887033167392,51.4762871724569,51.9132139961049 diffExpScore=0.997950708581395 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 74.2601029798924 69.680285071862 87.0283437290988 cerebhem 86.9001855183773 75.2210535192097 86.9773161196694 cortex 89.2575231129216 85.3436257621654 90.0359897833107 heart 82.0145902484077 73.3153059651224 76.7887273829707 kidney 92.406951447045 81.4200602497337 75.6826432243978 liver 83.9565598946106 83.0056785260973 87.8543108489416 stomach 92.5842949730543 68.9576489051726 75.6528958929787 testicle 74.6132591016797 76.5044404046601 83.958532525337 cont.diffExp=4.57981790803046,11.6791319991676,3.91389735075616,8.6992842832853,10.9868911973113,0.95088136851325,23.6266460678817,-1.89118130298036 cont.diffExpScore=1.04378545054269 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=0,0,0,0,0,0,1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,0,0,1,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.20997525616106 cont.tran.correlation=0.295788755558762 tran.covariance=-0.00182198549799898 cont.tran.covariance=0.00205693288985624 tran.mean=77.6116799872805 cont.tran.mean=80.5900978550007 weightedLogRatios: wLogRatio Lung 3.38422172967156 cerebhem 3.83581201835369 cortex 5.55669038993934 heart 3.5294373116863 kidney 3.24843840952496 liver 2.02278023150211 stomach 3.13795224135986 testicle 3.07499926205697 cont.weightedLogRatios: wLogRatio Lung 0.272178624559356 cerebhem 0.633978976050047 cortex 0.200394463239049 heart 0.487849196750463 kidney 0.564917736497127 liver 0.0503984528847749 stomach 1.29070371336364 testicle -0.108252988501620 varWeightedLogRatios=0.986955259273799 cont.varWeightedLogRatios=0.187874506839685 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33882002709167 0.0919627040800596 47.1802136582939 3.19806840439964e-234 *** df.mm.trans1 0.644920416898024 0.079857502289714 8.07589015942829 2.43191493517919e-15 *** df.mm.trans2 -0.543423471358447 0.0709818477162045 -7.65580903910999 5.49021484660422e-14 *** df.mm.exp2 0.186920990979730 0.092254701405684 2.02614054494369 0.0430788868481624 * df.mm.exp3 0.0470634206513115 0.092254701405684 0.510146582604535 0.610088268152886 df.mm.exp4 0.0251675052039823 0.092254701405684 0.272804581452276 0.785073228925725 df.mm.exp5 -0.0258185428702347 0.092254701405684 -0.279861540678554 0.779655451234727 df.mm.exp6 -0.134031786460320 0.092254701405684 -1.45284505199278 0.146655491660321 df.mm.exp7 -0.0892986979739048 0.092254701405684 -0.967958235333932 0.33335522553619 df.mm.exp8 0.0122626839757640 0.092254701405684 0.132922049379788 0.894288165125713 df.mm.trans1:exp2 -0.00519674487251759 0.0858101906366537 -0.0605609291153096 0.951723884689256 df.mm.trans2:exp2 -0.0821198189054696 0.0655993839820467 -1.25183826311455 0.210991516666008 df.mm.trans1:exp3 0.350908733485863 0.0858101906366537 4.08935967724063 4.75914039119187e-05 *** df.mm.trans2:exp3 -0.111774788135925 0.0655993839820467 -1.70389996598924 0.0887847443026756 . df.mm.trans1:exp4 0.00562714491867632 0.0858101906366537 0.0655766509423496 0.947731120134946 df.mm.trans2:exp4 -0.0256474478758460 0.0655993839820467 -0.390970864648138 0.695921966658865 df.mm.trans1:exp5 -0.0150625460095479 0.0858101906366537 -0.175533300856157 0.860704640441159 df.mm.trans2:exp5 0.0119786818505376 0.0655993839820467 0.182603572219763 0.855154943057878 df.mm.trans1:exp6 -0.158940914999432 0.0858101906366537 -1.85223822275884 0.0643566214564986 . df.mm.trans2:exp6 0.145690945597255 0.0655993839820467 2.22091941651660 0.0266333120757709 * df.mm.trans1:exp7 0.0340360310775661 0.0858101906366537 0.396643228794175 0.691735433052306 df.mm.trans2:exp7 0.0870492915875332 0.0655993839820467 1.32698336940720 0.184889511076429 df.mm.trans1:exp8 -0.0390479136293357 0.0858101906366537 -0.455049841279067 0.649195774256026 df.mm.trans2:exp8 0.0356603037600511 0.0655993839820467 0.543607296218066 0.586861870963298 df.mm.trans1:probe2 -0.886436048557667 0.0561759562844367 -15.7796343344715 4.32495596668511e-49 *** df.mm.trans1:probe3 -0.271579662639696 0.0561759562844367 -4.83444663166216 1.59859164136624e-06 *** df.mm.trans1:probe4 -0.528008591436053 0.0561759562844367 -9.39919186711444 5.51922312581651e-20 *** df.mm.trans1:probe5 -0.601694717744283 0.0561759562844367 -10.7108940824739 4.02066238125701e-25 *** df.mm.trans1:probe6 -0.265054110911819 0.0561759562844366 -4.71828391438085 2.80270384749292e-06 *** df.mm.trans1:probe7 -0.129344335704474 0.0561759562844367 -2.30248569422766 0.0215614458715165 * df.mm.trans1:probe8 -0.203705900383945 0.0561759562844367 -3.62621152993849 0.000305664099180517 *** df.mm.trans1:probe9 -0.367672741422286 0.0561759562844367 -6.54501971556376 1.05687996380372e-10 *** df.mm.trans1:probe10 -0.571757580229503 0.0561759562844367 -10.1779768079873 5.65900731162893e-23 *** df.mm.trans1:probe11 -0.840176749035906 0.0561759562844366 -14.9561628249250 8.03595147265442e-45 *** df.mm.trans1:probe12 -1.07304511809832 0.0561759562844366 -19.1015015866424 2.14572538330853e-67 *** df.mm.trans1:probe13 -1.08440584463162 0.0561759562844366 -19.3037362664719 1.48202169721817e-68 *** df.mm.trans1:probe14 -0.810684731661786 0.0561759562844367 -14.431169227579 3.63139870330858e-42 *** df.mm.trans1:probe15 -1.09022185650321 0.0561759562844367 -19.4072683157020 3.75764319187175e-69 *** df.mm.trans1:probe16 -0.746231145740376 0.0561759562844367 -13.2838174033384 1.46278361256937e-36 *** df.mm.trans1:probe17 -0.363005108035193 0.0561759562844367 -6.46193019300255 1.78671420060646e-10 *** df.mm.trans1:probe18 -0.0450292136516128 0.0561759562844367 -0.8015744925394 0.423035090856263 df.mm.trans1:probe19 -0.39155877807938 0.0561759562844367 -6.97022007238816 6.5803574628185e-12 *** df.mm.trans1:probe20 -0.0336126425189496 0.0561759562844367 -0.598345711264053 0.549777173693154 df.mm.trans1:probe21 0.00422232868006934 0.0561759562844367 0.0751625599160316 0.940104000662254 df.mm.trans1:probe22 -0.00325330297174619 0.0561759562844367 -0.0579127296965572 0.95383247966972 df.mm.trans2:probe2 0.0691569710323979 0.0561759562844366 1.23107777075007 0.218652438495682 df.mm.trans2:probe3 0.0698583801430467 0.0561759562844366 1.24356370168995 0.214021307865909 df.mm.trans2:probe4 0.221138847157603 0.0561759562844367 3.93653907799819 8.9797715285959e-05 *** df.mm.trans2:probe5 0.146015836397368 0.0561759562844366 2.59925858062911 0.00951316916386297 ** df.mm.trans2:probe6 0.152240840987024 0.0561759562844367 2.71007119516009 0.00686963963926333 ** df.mm.trans3:probe2 -0.316012930710114 0.0561759562844367 -5.62541257170666 2.55199165350164e-08 *** df.mm.trans3:probe3 0.150177987479232 0.0561759562844367 2.6733499064766 0.00766151225045333 ** df.mm.trans3:probe4 -0.340502952131006 0.0561759562844367 -6.06136458820446 2.07050280544804e-09 *** df.mm.trans3:probe5 -0.305351910530380 0.0561759562844367 -5.43563351168044 7.2372080935736e-08 *** df.mm.trans3:probe6 0.506678283225372 0.0561759562844367 9.01948656930554 1.35477578543978e-18 *** df.mm.trans3:probe7 -0.189323799167478 0.0561759562844366 -3.37019272460395 0.0007868354471727 *** df.mm.trans3:probe8 -0.00725603170498203 0.0561759562844367 -0.129166144822572 0.897258369026974 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09972145115431 0.217075634714718 18.8861428715488 3.6525844660096e-66 *** df.mm.trans1 0.270291559670337 0.18850161236212 1.43389521332632 0.151989510479494 df.mm.trans2 0.112351786851843 0.167550854450784 0.670553350623734 0.502696959403372 df.mm.exp2 0.234286571897267 0.217764887009211 1.07586937047090 0.282307217333228 df.mm.exp3 0.352744592146299 0.217764887009211 1.61984145833106 0.105657017870119 df.mm.exp4 0.275351306894508 0.217764887009211 1.26444309124483 0.206436090445439 df.mm.exp5 0.514017636926784 0.217764887009211 2.360424786504 0.0184910091601782 * df.mm.exp6 0.288271254804749 0.217764887009211 1.32377289453720 0.185953337036868 df.mm.exp7 0.350198935467199 0.217764887009211 1.60815152652405 0.108193124027570 df.mm.exp8 0.134086590874018 0.217764887009211 0.615740180685539 0.538239629455494 df.mm.trans1:exp2 -0.0771002402749924 0.202552782497851 -0.380642711120546 0.703568556698653 df.mm.trans2:exp2 -0.157772836807641 0.154845685076859 -1.01890366999461 0.308554058374599 df.mm.trans1:exp3 -0.168792717952999 0.202552782497851 -0.833327075893363 0.404906857152956 df.mm.trans2:exp3 -0.149976252901286 0.154845685076859 -0.968553000536266 0.333058443601455 df.mm.trans1:exp4 -0.176027981095047 0.202552782497851 -0.869047459749975 0.385079557481713 df.mm.trans2:exp4 -0.224499330872665 0.154845685076859 -1.44982619800631 0.147495501008380 df.mm.trans1:exp5 -0.295389264493846 0.202552782497851 -1.45833229665448 0.14513803673392 df.mm.trans2:exp5 -0.358313377558806 0.154845685076859 -2.31400298549458 0.0209178977908775 * df.mm.trans1:exp6 -0.165545569334132 0.202552782497851 -0.817295952653173 0.414000537960544 df.mm.trans2:exp6 -0.113279657064825 0.154845685076859 -0.731564828613709 0.464646595765069 df.mm.trans1:exp7 -0.129653244417634 0.202552782497851 -0.640096091590393 0.522291814977887 df.mm.trans2:exp7 -0.360623826988932 0.154845685076859 -2.32892396588211 0.0201091428112051 * df.mm.trans1:exp8 -0.129342199005798 0.202552782497851 -0.638560465132934 0.523290081926906 df.mm.trans2:exp8 -0.0406552309141677 0.154845685076859 -0.262553205108609 0.792961981078977 df.mm.trans1:probe2 -0.00172569120086795 0.132601922574333 -0.0130140737582487 0.989619781117995 df.mm.trans1:probe3 0.109078207745123 0.132601922574333 0.8225989912324 0.410979063053617 df.mm.trans1:probe4 -0.24033743154485 0.132601922574333 -1.81247320460322 0.0702846051076913 . df.mm.trans1:probe5 -0.142277480270346 0.132601922574333 -1.07296694880566 0.283606685241055 df.mm.trans1:probe6 -0.139450865769080 0.132601922574333 -1.05165040643289 0.293274770988462 df.mm.trans1:probe7 0.0856892598753806 0.132601922574333 0.646214309806448 0.518324294566348 df.mm.trans1:probe8 -0.0204716041474345 0.132601922574333 -0.154383916537550 0.87734563498204 df.mm.trans1:probe9 -0.0150549745171229 0.132601922574333 -0.113535114912708 0.909634563254828 df.mm.trans1:probe10 -0.0271731866533043 0.132601922574333 -0.204923021670909 0.837683942437146 df.mm.trans1:probe11 -0.125250732788418 0.132601922574333 -0.944561966801092 0.345165258468735 df.mm.trans1:probe12 -0.300597414780317 0.132601922574333 -2.26691596128111 0.0236591981179634 * df.mm.trans1:probe13 -0.176936283613113 0.132601922574333 -1.33434176653002 0.182468257827421 df.mm.trans1:probe14 -0.122034083919948 0.132601922574333 -0.920304031425627 0.357688916186586 df.mm.trans1:probe15 -0.0338823637716996 0.132601922574333 -0.255519400578118 0.798387071666356 df.mm.trans1:probe16 -0.152173594404572 0.132601922574333 -1.14759719505023 0.251474979753007 df.mm.trans1:probe17 -0.0410015952349287 0.132601922574333 -0.309208150522436 0.757243008348465 df.mm.trans1:probe18 -0.144548213368047 0.132601922574333 -1.09009138451230 0.275998277065746 df.mm.trans1:probe19 -0.0845383897544248 0.132601922574333 -0.637535173798366 0.523957140299877 df.mm.trans1:probe20 -0.0318659071184896 0.132601922574333 -0.240312557313236 0.810148992585168 df.mm.trans1:probe21 -0.0562839913162016 0.132601922574333 -0.424458335320518 0.671344716480742 df.mm.trans1:probe22 -0.21233892331326 0.132601922574333 -1.60132612854258 0.109696053606547 df.mm.trans2:probe2 0.0060341758392912 0.132601922574333 0.0455059453297791 0.963715293984163 df.mm.trans2:probe3 0.0635983908761304 0.132601922574333 0.479618919857507 0.631628343764532 df.mm.trans2:probe4 0.181821809235364 0.132601922574333 1.37118531696582 0.170698360415723 df.mm.trans2:probe5 0.00741771379817842 0.132601922574333 0.055939715308579 0.95540367849917 df.mm.trans2:probe6 0.186946509873698 0.132601922574333 1.40983257440254 0.158974395259081 df.mm.trans3:probe2 -0.0241344171085379 0.132601922574333 -0.182006539875083 0.855623300757153 df.mm.trans3:probe3 0.0279067517182399 0.132601922574333 0.210455106354858 0.83336561207861 df.mm.trans3:probe4 0.00911202502633146 0.132601922574333 0.0687171411200581 0.945231796363812 df.mm.trans3:probe5 -0.195781021386333 0.132601922574333 -1.47645688377243 0.140211372757638 df.mm.trans3:probe6 0.0387469291482188 0.132601922574333 0.292204882070984 0.770205083354335 df.mm.trans3:probe7 -0.0254605166229886 0.132601922574333 -0.192007145361834 0.847784911163145 df.mm.trans3:probe8 0.0405627125026736 0.132601922574333 0.305898373984248 0.759760917324256