chr4.16860_chr4_135819019_135820580_+_1.R fitVsDatCorrelation=0.908931480685286 cont.fitVsDatCorrelation=0.341252357787779 fstatistic=6885.30608910127,45,531 cont.fstatistic=1345.25031289759,45,531 residuals=-0.935397406281219,-0.0895129233567705,0.00887973343495834,0.101707088625149,0.752502815927655 cont.residuals=-0.900162582192152,-0.31835036525027,-0.0111041390890962,0.284949690895977,1.49041240930242 predictedValues: Include Exclude Both chr4.16860_chr4_135819019_135820580_+_1.R.tl.Lung 104.305789385474 51.7427135351595 101.260835647013 chr4.16860_chr4_135819019_135820580_+_1.R.tl.cerebhem 90.0284165992629 47.0973982504126 99.372383769088 chr4.16860_chr4_135819019_135820580_+_1.R.tl.cortex 183.339409105719 59.0472121734969 153.546309448556 chr4.16860_chr4_135819019_135820580_+_1.R.tl.heart 111.132385959806 63.0064052582322 108.541772258504 chr4.16860_chr4_135819019_135820580_+_1.R.tl.kidney 90.4555781531695 51.5642320949563 86.7747970869847 chr4.16860_chr4_135819019_135820580_+_1.R.tl.liver 86.1265918688688 54.6585358013689 85.893987358646 chr4.16860_chr4_135819019_135820580_+_1.R.tl.stomach 88.4653410193482 53.4139717094675 93.2328976227362 chr4.16860_chr4_135819019_135820580_+_1.R.tl.testicle 102.657124130802 52.2173763859469 106.686917146032 diffExp=52.5630758503144,42.9310183488503,124.292196932222,48.1259807015736,38.8913460582132,31.4680560674998,35.0513693098807,50.4397477448554 diffExpScore=0.997645744822389 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 100.693306636871 91.2383143133077 85.3633345483751 cerebhem 84.9246682810777 103.338682818655 100.109009255684 cortex 88.2048587107247 97.3913609557607 82.819561812655 heart 79.55097220521 70.5385860535314 95.5336363615882 kidney 83.986724596299 91.2476727419618 94.2123362408152 liver 94.126095272567 98.083216145129 89.6696957133499 stomach 108.245222092660 87.3980323817044 83.3545810967426 testicle 84.0125517106608 73.5052164373035 76.3261748944866 cont.diffExp=9.45499232356335,-18.4140145375777,-9.18650224503601,9.01238615167857,-7.26094814566277,-3.95712087256196,20.847189710956,10.5073352733572 cont.diffExpScore=7.38466578829759 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,-1,0,0,0,0,1,0 cont.diffExp1.2Score=2 tran.correlation=0.534963925973981 cont.tran.correlation=0.270122715193421 tran.covariance=0.0124045872523855 cont.tran.covariance=0.00477922698849695 tran.mean=80.5786550894682 cont.tran.mean=89.780342584589 weightedLogRatios: wLogRatio Lung 3.01224629817242 cerebhem 2.70577329230675 cortex 5.26261007215482 heart 2.5122477200644 kidney 2.37392944227374 liver 1.92273578902317 stomach 2.13437018133598 testicle 2.90225252879065 cont.weightedLogRatios: wLogRatio Lung 0.449909933744668 cerebhem -0.890940203813175 cortex -0.448732557220923 heart 0.518981160184642 kidney -0.370822408803201 liver -0.188000290706068 stomach 0.97923467263797 testicle 0.583094879908542 varWeightedLogRatios=1.08330314947748 cont.varWeightedLogRatios=0.412658093417626 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15569924696287 0.0962899456581602 43.1581845701321 9.25617411162385e-176 *** df.mm.trans1 0.480029249575326 0.0766580952762637 6.26195117221967 7.85278499786997e-10 *** df.mm.trans2 -0.209304937536673 0.0766580952762637 -2.73036966001270 0.0065364988986067 ** df.mm.exp2 -0.222441894137790 0.102210793701685 -2.17630532042452 0.0299724445858325 * df.mm.exp3 0.279763478580798 0.102210793701685 2.73712265064023 0.00640567269609946 ** df.mm.exp4 0.190912682781919 0.102210793701685 1.86783289580083 0.0623359638199476 . df.mm.exp5 0.00846014387459525 0.102210793701685 0.0827715309528585 0.934064398286942 df.mm.exp6 0.0278990049381006 0.102210793701685 0.272955564942851 0.784993493379682 df.mm.exp7 -0.050328205043178 0.102210793701685 -0.492396186552148 0.622642814314163 df.mm.exp8 -0.0589994431906103 0.102210793701685 -0.577233001074305 0.564026714681759 df.mm.trans1:exp2 0.0752403871066933 0.0791721403618536 0.950339181974994 0.342372273715243 df.mm.trans2:exp2 0.128376033810603 0.0791721403618535 1.62147989461779 0.105508353417518 df.mm.trans1:exp3 0.284248783487331 0.0791721403618535 3.59026271347702 0.000360981552006209 *** df.mm.trans2:exp3 -0.147709769318105 0.0791721403618535 -1.86567861678367 0.0626375280492636 . df.mm.trans1:exp4 -0.127517393344277 0.0791721403618535 -1.61063465963485 0.107853618821418 df.mm.trans2:exp4 0.006040088227989 0.0791721403618535 0.0762905764626672 0.939216657814655 df.mm.trans1:exp5 -0.150928130328627 0.0791721403618535 -1.90632878735897 0.0571471176450501 . df.mm.trans2:exp5 -0.0119155091783863 0.0791721403618535 -0.150501289013116 0.88042628570371 df.mm.trans1:exp6 -0.219407660054927 0.0791721403618535 -2.77127356987107 0.00577955675574094 ** df.mm.trans2:exp6 0.0269227668358852 0.0791721403618535 0.340053542986657 0.733950678846206 df.mm.trans1:exp7 -0.11438781406659 0.0791721403618535 -1.44479880856807 0.149104233753397 df.mm.trans2:exp7 0.082116938307195 0.0791721403618535 1.03719487602435 0.300117125248603 df.mm.trans1:exp8 0.0430671188805659 0.0791721403618535 0.54396810145247 0.586691744164065 df.mm.trans2:exp8 0.0681311426643727 0.0791721403618535 0.860544408083219 0.389877392454335 df.mm.trans1:probe2 0.284423133697032 0.0559831573309198 5.08051255515636 5.22140982226944e-07 *** df.mm.trans1:probe3 -0.0137815948703971 0.0559831573309198 -0.246173948156107 0.805642710535947 df.mm.trans1:probe4 0.247229456876655 0.0559831573309198 4.41613993678969 1.21808783824825e-05 *** df.mm.trans1:probe5 -0.0510641021136531 0.0559831573309198 -0.912133301303642 0.362112464961931 df.mm.trans1:probe6 -0.258036215956906 0.0559831573309198 -4.60917583536132 5.06913427064933e-06 *** df.mm.trans2:probe2 -0.0743641775751453 0.0559831573309198 -1.32833125390864 0.184639313942112 df.mm.trans2:probe3 0.130657260382450 0.0559831573309198 2.33386730244826 0.0199748272041700 * df.mm.trans2:probe4 0.0147543096780407 0.0559831573309198 0.263549081214322 0.792229638707372 df.mm.trans2:probe5 -0.0725514029339593 0.0559831573309198 -1.29595053928637 0.195555533199969 df.mm.trans2:probe6 -0.00048838159918596 0.0559831573309198 -0.0087237237496111 0.993042840227274 df.mm.trans3:probe2 -0.0079255792814737 0.0559831573309198 -0.141570780558609 0.887472741612317 df.mm.trans3:probe3 0.533994126216136 0.0559831573309198 9.53847820800219 5.20530353033882e-20 *** df.mm.trans3:probe4 0.0872989548025231 0.0559831573309198 1.55937890902604 0.119502496135976 df.mm.trans3:probe5 0.380350626886908 0.0559831573309198 6.79401886247023 2.93428208022456e-11 *** df.mm.trans3:probe6 -0.493305509544268 0.0559831573309198 -8.81167717333822 1.75679528469461e-17 *** df.mm.trans3:probe7 0.207075754503088 0.0559831573309198 3.6988938169216 0.00023909352118016 *** df.mm.trans3:probe8 0.473257102014576 0.0559831573309198 8.45356218866194 2.73772916259327e-16 *** df.mm.trans3:probe9 0.121937031544322 0.0559831573309198 2.17810208208775 0.0298376914717786 * df.mm.trans3:probe10 0.00253393160599811 0.0559831573309198 0.0452623918836855 0.96391517989833 df.mm.trans3:probe11 0.644343769597867 0.0559831573309198 11.5096003926522 1.60220498221381e-27 *** df.mm.trans3:probe12 0.345753711593936 0.0559831573309198 6.17603093641477 1.30811306717778e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.6103201039823 0.217078304060742 21.2380510522703 6.42313467679223e-73 *** df.mm.trans1 0.0147832582953104 0.1728198017078 0.0855414608119134 0.931863178368681 df.mm.trans2 -0.0993292654825474 0.1728198017078 -0.574756275038963 0.565699566414215 df.mm.exp2 -0.205120880467583 0.230426402277067 -0.890179590709155 0.373772590136229 df.mm.exp3 -0.0369023085260660 0.230426402277067 -0.16014791777938 0.872825457296404 df.mm.exp4 -0.605558123006831 0.230426402277067 -2.62798931469104 0.00883762659839932 ** df.mm.exp5 -0.279952477548459 0.230426402277067 -1.21493229413807 0.224931868991880 df.mm.exp6 -0.0443188667920926 0.230426402277067 -0.192334152484850 0.847554000485668 df.mm.exp7 0.0531308476113911 0.230426402277067 0.230576214732138 0.817732854246163 df.mm.exp8 -0.285330923712856 0.230426402277067 -1.23827356975253 0.216161803099677 df.mm.trans1:exp2 0.0348061593219772 0.178487523709114 0.195006119187925 0.845462747021811 df.mm.trans2:exp2 0.329657735209473 0.178487523709114 1.84695113898675 0.0653103770843816 . df.mm.trans1:exp3 -0.0955149717178396 0.178487523709114 -0.535135284152986 0.592780339381175 df.mm.trans2:exp3 0.102164896746014 0.178487523709114 0.57239237019454 0.567298441047644 df.mm.trans1:exp4 0.369876769863364 0.178487523709114 2.07228360939200 0.0387210333986501 * df.mm.trans2:exp4 0.348243081066384 0.178487523709114 1.95107800158584 0.05157328572314 . df.mm.trans1:exp5 0.0985318942192258 0.178487523709114 0.552037992189335 0.581154582898995 df.mm.trans2:exp5 0.280055043546137 0.178487523709114 1.56904548691342 0.117233019531161 df.mm.trans1:exp6 -0.0231251399561951 0.178487523709114 -0.129561660533107 0.896962299706453 df.mm.trans2:exp6 0.116660207539617 0.178487523709114 0.653604269448778 0.513649718376237 df.mm.trans1:exp7 0.0191890514223888 0.178487523709114 0.107509203016686 0.914425632439793 df.mm.trans2:exp7 -0.0961329999404681 0.178487523709114 -0.538597869154926 0.590390065832159 df.mm.trans1:exp8 0.104217807357684 0.178487523709114 0.583894073893536 0.559539526135009 df.mm.trans2:exp8 0.069212377407355 0.178487523709114 0.387771514608227 0.698340519928243 df.mm.trans1:probe2 -0.0582427793971602 0.126209738371909 -0.461476112291216 0.644646035446454 df.mm.trans1:probe3 -0.166477205957374 0.126209738371909 -1.31905198525019 0.187720265889207 df.mm.trans1:probe4 0.0877853283675775 0.126209738371909 0.695551147637241 0.487014253151728 df.mm.trans1:probe5 -0.0715665513443145 0.126209738371909 -0.567044605808668 0.570923498371207 df.mm.trans1:probe6 -0.0259313877392628 0.126209738371909 -0.205462653466956 0.837289415252367 df.mm.trans2:probe2 -0.0114487968943368 0.126209738371909 -0.0907124683247503 0.927755270719027 df.mm.trans2:probe3 0.11849362278699 0.126209738371909 0.938862755881944 0.348228068527144 df.mm.trans2:probe4 0.0113636744529434 0.126209738371909 0.09003801607969 0.928290956195036 df.mm.trans2:probe5 0.00643829808586984 0.126209738371909 0.0510126886318214 0.959334593009028 df.mm.trans2:probe6 -0.0801332964708506 0.126209738371909 -0.634921659014278 0.525752986606913 df.mm.trans3:probe2 -0.180792755422921 0.126209738371909 -1.43247864828123 0.152595300499174 df.mm.trans3:probe3 0.172584056167117 0.126209738371909 1.36743850667493 0.172066412099208 df.mm.trans3:probe4 0.115427482769106 0.126209738371909 0.91456875085954 0.360833173325171 df.mm.trans3:probe5 -0.125761384923199 0.126209738371909 -0.996447552665156 0.319486591745791 df.mm.trans3:probe6 -0.180530055411354 0.126209738371909 -1.43039719232581 0.153191216429603 df.mm.trans3:probe7 0.0463900340652984 0.126209738371909 0.367563031694102 0.713345538330553 df.mm.trans3:probe8 0.00311409456908256 0.126209738371909 0.0246739642222067 0.980324291749417 df.mm.trans3:probe9 -0.00626810595805661 0.126209738371909 -0.0496642021361777 0.960408658796052 df.mm.trans3:probe10 -0.269294833541897 0.126209738371909 -2.13370883273960 0.0333242386181181 * df.mm.trans3:probe11 -0.259606040280877 0.126209738371909 -2.05694143439139 0.0401797748261962 * df.mm.trans3:probe12 -0.261551604433752 0.126209738371909 -2.07235675953169 0.0387141878035188 *