chr7.21291_chr7_12057002_12057345_-_0.R fitVsDatCorrelation=0.979996138400875 cont.fitVsDatCorrelation=0.279441003065005 fstatistic=5995.19435223913,39,393 cont.fstatistic=247.923848642116,39,393 residuals=-1.03620446303026,-0.101706132552525,0.00751164498174324,0.102458507900613,0.789406471509728 cont.residuals=-1.85684744661024,-0.805399741532683,-0.199467291828071,0.525982586408356,2.83218912336972 predictedValues: Include Exclude Both chr7.21291_chr7_12057002_12057345_-_0.R.tl.Lung 298.120386728592 52.6525615585685 140.396570602734 chr7.21291_chr7_12057002_12057345_-_0.R.tl.cerebhem 1194.91857005776 67.9186028483537 445.408066886664 chr7.21291_chr7_12057002_12057345_-_0.R.tl.cortex 2176.88244489582 59.5774449688549 931.807100448405 chr7.21291_chr7_12057002_12057345_-_0.R.tl.heart 294.502705456234 55.3578883947392 162.533985330558 chr7.21291_chr7_12057002_12057345_-_0.R.tl.kidney 216.512550474735 50.1604684067484 145.836767221615 chr7.21291_chr7_12057002_12057345_-_0.R.tl.liver 140.374609279256 52.933596173788 74.9001886744273 chr7.21291_chr7_12057002_12057345_-_0.R.tl.stomach 345.686324796296 64.411994898267 140.503735004672 chr7.21291_chr7_12057002_12057345_-_0.R.tl.testicle 237.552198743488 57.1511178619041 104.932027473039 diffExp=245.467825170023,1126.99996720941,2117.30499992696,239.144817061495,166.352082067986,87.4410131054675,281.274329898029,180.401080881584 diffExpScore=0.99977504766199 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 162.646454816252 176.317912933969 141.634609510625 cerebhem 202.198258478349 157.504088042863 119.338054945831 cortex 153.742938481283 149.221061883167 154.752582620511 heart 207.646353443292 214.842698309760 193.307697427831 kidney 144.477004373918 270.620438119170 228.144421351082 liver 111.479637907779 176.641436998347 177.852324994730 stomach 178.736838034072 267.219857574702 135.808540094421 testicle 142.182311734637 137.991115297840 114.773314698654 cont.diffExp=-13.6714581177172,44.6941704354863,4.52187659811602,-7.196344866468,-126.143433745252,-65.1617990905678,-88.4830195406304,4.19119643679733 cont.diffExpScore=1.42624367921481 cont.diffExp1.5=0,0,0,0,-1,-1,0,0 cont.diffExp1.5Score=0.666666666666667 cont.diffExp1.4=0,0,0,0,-1,-1,-1,0 cont.diffExp1.4Score=0.75 cont.diffExp1.3=0,0,0,0,-1,-1,-1,0 cont.diffExp1.3Score=0.75 cont.diffExp1.2=0,1,0,0,-1,-1,-1,0 cont.diffExp1.2Score=1.33333333333333 tran.correlation=0.484288323987396 cont.tran.correlation=0.146537201070137 tran.covariance=0.0623332812578858 cont.tran.covariance=0.00890238806820673 tran.mean=335.294591596462 cont.tran.mean=178.341775401837 weightedLogRatios: wLogRatio Lung 8.37522037425946 cerebhem 16.2074474114814 cortex 21.181683853142 heart 8.10587874534519 kidney 6.79504962977188 liver 4.34649136020928 stomach 8.41025943141746 testicle 6.77872906505067 cont.weightedLogRatios: wLogRatio Lung -0.414197516734434 cerebhem 1.29503654160345 cortex 0.149873341839583 heart -0.182370802412318 kidney -3.31805377550745 liver -2.27561572261079 stomach -2.1664181395139 testicle 0.147873163967179 varWeightedLogRatios=32.1318216541900 cont.varWeightedLogRatios=2.44125643790738 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.00299728868623 0.115056819236548 43.4828402339237 2.94214501724839e-152 *** df.mm.trans1 0.88486115244366 0.0939434995190605 9.41907803066382 3.94700911376695e-19 *** df.mm.trans2 -1.08733991344902 0.0939434995190605 -11.5744029019103 7.35222577260294e-27 *** df.mm.exp2 0.488411213030295 0.127644080415127 3.82635224008722 0.000151246987986655 *** df.mm.exp3 0.219059092104707 0.127644080415127 1.71617117998953 0.08691875550508 . df.mm.exp4 -0.108520975148501 0.127644080415127 -0.850184158917253 0.395740358783501 df.mm.exp5 -0.406353425189833 0.127644080415127 -3.18348821087731 0.00157099586273147 ** df.mm.exp6 -0.119544766088697 0.127644080415127 -0.936547669895151 0.349566431191140 df.mm.exp7 0.348856376172426 0.127644080415127 2.73303998930359 0.00655868689557866 ** df.mm.exp8 0.146032338733835 0.127644080415127 1.14405884126318 0.253295576929221 df.mm.trans1:exp2 0.899924719565218 0.104220955234615 8.63477711885646 1.4884386894094e-16 *** df.mm.trans2:exp2 -0.233816131768049 0.104220955234615 -2.24346563742097 0.0254231657315816 * df.mm.trans1:exp3 1.76909258176481 0.104220955234615 16.9744422106126 7.21154653696414e-49 *** df.mm.trans2:exp3 -0.0954969197607049 0.104220955234615 -0.916292885108649 0.360075211376269 df.mm.trans1:exp4 0.0963117775693182 0.104220955234615 0.924111445270365 0.3559953384277 df.mm.trans2:exp4 0.158625252590949 0.104220955234615 1.52200919895488 0.128810959593651 df.mm.trans1:exp5 0.0865045535751637 0.104220955234615 0.830011137207777 0.407036385923776 df.mm.trans2:exp5 0.357865769742572 0.104220955234615 3.43372183585315 0.000658700289959631 *** df.mm.trans1:exp6 -0.633637991500707 0.104220955234615 -6.07975613037037 2.85196422187655e-09 *** df.mm.trans2:exp6 0.124868101740185 0.104220955234615 1.19810935774951 0.231596485952416 df.mm.trans1:exp7 -0.200821975273498 0.104220955234615 -1.92688672658412 0.0547144068381416 . df.mm.trans2:exp7 -0.147271394202262 0.104220955234615 -1.41306893484842 0.158426954407206 df.mm.trans1:exp8 -0.373142342462268 0.104220955234615 -3.58030054150123 0.000386171773366662 *** df.mm.trans2:exp8 -0.0640482785268913 0.104220955234615 -0.614543192227611 0.539212057588456 df.mm.trans1:probe2 -0.250575509348079 0.0638220402075636 -3.92615949808485 0.000101912670957035 *** df.mm.trans1:probe3 -0.191821868171987 0.0638220402075636 -3.00557405479578 0.00282082453969089 ** df.mm.trans1:probe4 -1.09927604507088 0.0638220402075636 -17.2240818609963 6.18650407208883e-50 *** df.mm.trans1:probe5 -0.254111241117477 0.0638220402075636 -3.98155935302365 8.1562204806495e-05 *** df.mm.trans1:probe6 -0.488547983003462 0.0638220402075636 -7.65484746984888 1.51507496532719e-13 *** df.mm.trans2:probe2 -0.137337692216269 0.0638220402075636 -2.15188501918170 0.0320150002924231 * df.mm.trans2:probe3 0.541109366627979 0.0638220402075636 8.47840910237544 4.66698937253781e-16 *** df.mm.trans2:probe4 0.243968972928504 0.0638220402075636 3.82264453055812 0.000153456772417084 *** df.mm.trans2:probe5 -0.0129427445934335 0.0638220402075636 -0.202794278455229 0.839400777938861 df.mm.trans2:probe6 -0.0581077258226365 0.0638220402075636 -0.910464874417319 0.36313546979404 df.mm.trans3:probe2 0.625166056197478 0.0638220402075636 9.79545708918576 2.04730899049915e-20 *** df.mm.trans3:probe3 -0.194304084707188 0.0638220402075636 -3.04446683426708 0.00248780453674418 ** df.mm.trans3:probe4 -0.653621985396024 0.0638220402075636 -10.2413207611398 5.63622416668158e-22 *** df.mm.trans3:probe5 0.557808869161252 0.0638220402075636 8.74006639943086 6.84223945055747e-17 *** df.mm.trans3:probe6 0.528666370980946 0.0638220402075636 8.28344517445077 1.90265126203507e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.61936221561606 0.555096100508175 10.123224087634 1.47265746180323e-21 *** df.mm.trans1 -0.390755304473739 0.453234068151238 -0.862149012909923 0.389131285081003 df.mm.trans2 -0.346002341716798 0.453234068151238 -0.763407620985239 0.445678364197877 df.mm.exp2 0.276122867374103 0.615823831751488 0.448379639009375 0.654126333559713 df.mm.exp3 -0.311733756642561 0.615823831751488 -0.506206061814702 0.612995840236232 df.mm.exp4 0.130842350334078 0.615823831751488 0.212467175818682 0.831852739321405 df.mm.exp5 -0.166758234544809 0.615823831751488 -0.270788861922615 0.786695588051667 df.mm.exp6 -0.603606694825027 0.615823831751488 -0.980161311893835 0.327609488487411 df.mm.exp7 0.552123224688437 0.615823831751488 0.896560341158156 0.370502423573842 df.mm.exp8 -0.169276562269345 0.615823831751488 -0.274878225787235 0.78355424305739 df.mm.trans1:exp2 -0.0584530299777336 0.502818053078901 -0.116250857780083 0.907513111116738 df.mm.trans2:exp2 -0.388960142703802 0.502818053078901 -0.773560416779162 0.439655837490444 df.mm.trans1:exp3 0.255436877411225 0.502818053078901 0.508010553414124 0.611730948076496 df.mm.trans2:exp3 0.144873910799852 0.502818053078901 0.288123924574201 0.773403768977914 df.mm.trans1:exp4 0.113415202229783 0.502818053078901 0.225559129262183 0.82166158048547 df.mm.trans2:exp4 0.0667750851627217 0.502818053078901 0.132801685925631 0.894418190442713 df.mm.trans1:exp5 0.0482997338662617 0.502818053078901 0.0960580742288555 0.923523412097944 df.mm.trans2:exp5 0.595186787238767 0.502818053078901 1.18370210376152 0.237246483477265 df.mm.trans1:exp6 0.225869793100838 0.502818053078901 0.449207803335165 0.653529376134663 df.mm.trans2:exp6 0.605439903975287 0.502818053078901 1.20409340966976 0.229278178272195 df.mm.trans1:exp7 -0.457787535487402 0.502818053078901 -0.910443713554507 0.363146610943014 df.mm.trans2:exp7 -0.136340157602986 0.502818053078901 -0.271152073335744 0.786416436423193 df.mm.trans1:exp8 0.0348078255634333 0.502818053078901 0.0692254889224738 0.944845343257636 df.mm.trans2:exp8 -0.0758228256320055 0.502818053078901 -0.150795750406574 0.880214206908454 df.mm.trans1:probe2 -0.412722490209512 0.307911915875744 -1.34039142017506 0.180892472423758 df.mm.trans1:probe3 -0.39238507253649 0.307911915875744 -1.27434195399841 0.20329526127534 df.mm.trans1:probe4 -0.266631886930211 0.307911915875744 -0.865935591261135 0.387053804717256 df.mm.trans1:probe5 -0.475999534356832 0.307911915875744 -1.54589514018327 0.122934741302907 df.mm.trans1:probe6 -0.0965976742405006 0.307911915875744 -0.313718532021612 0.753901270900848 df.mm.trans2:probe2 -0.0791639906152613 0.307911915875744 -0.257099470769450 0.79723653731445 df.mm.trans2:probe3 0.104185113396676 0.307911915875744 0.338360121921099 0.735272509927196 df.mm.trans2:probe4 -0.246571810587258 0.307911915875744 -0.80078684154192 0.423738897125518 df.mm.trans2:probe5 -0.378629699688083 0.307911915875744 -1.22966887660456 0.219556770538021 df.mm.trans2:probe6 -0.612673830262284 0.307911915875744 -1.98976979672824 0.0473088768784449 * df.mm.trans3:probe2 0.0566403026901267 0.307911915875744 0.18394969395398 0.854147794211637 df.mm.trans3:probe3 0.166585667632198 0.307911915875744 0.541017281381933 0.588802191704445 df.mm.trans3:probe4 0.237518384282991 0.307911915875744 0.77138419150638 0.440942787687835 df.mm.trans3:probe5 -0.061641671996684 0.307911915875744 -0.200192551241048 0.841433543846554 df.mm.trans3:probe6 0.0247733138930002 0.307911915875744 0.0804558466746618 0.935915666174666