chr9.25310_chr9_6099732_6220164_+_1.R fitVsDatCorrelation=0.781365170008212 cont.fitVsDatCorrelation=0.248621187467602 fstatistic=6824.85533372943,37,347 cont.fstatistic=2827.70753503705,37,347 residuals=-0.408089743794709,-0.0845887448081761,-0.00232564905599,0.073508887853894,1.68859000687624 cont.residuals=-0.54151488493747,-0.146336496674871,-0.0279640866012763,0.0951341069267071,1.86840421011360 predictedValues: Include Exclude Both chr9.25310_chr9_6099732_6220164_+_1.R.tl.Lung 55.2631862003788 54.3781357847968 71.6192186406236 chr9.25310_chr9_6099732_6220164_+_1.R.tl.cerebhem 62.7315005418546 60.9203255971421 55.3421350860906 chr9.25310_chr9_6099732_6220164_+_1.R.tl.cortex 60.7780875792767 49.064856099149 57.7442632629012 chr9.25310_chr9_6099732_6220164_+_1.R.tl.heart 59.351400764312 49.86198202678 64.0165181790523 chr9.25310_chr9_6099732_6220164_+_1.R.tl.kidney 57.1766353688257 52.5468260654156 63.1326101654313 chr9.25310_chr9_6099732_6220164_+_1.R.tl.liver 61.8207410407904 48.7239388765343 60.2030072100697 chr9.25310_chr9_6099732_6220164_+_1.R.tl.stomach 71.9208997294172 50.3640188815241 59.8375651201966 chr9.25310_chr9_6099732_6220164_+_1.R.tl.testicle 59.4748674620209 50.3379093544961 57.1594966724862 diffExp=0.885050415582015,1.81117494471255,11.7132314801277,9.48941873753196,4.62980930341006,13.0968021642561,21.5568808478931,9.13695810752483 diffExpScore=0.986361031196797 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,1,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,1,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,1,1,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 56.2199750809608 53.988327384667 59.9530789242638 cerebhem 62.243277154834 60.1080357896245 55.6813047274062 cortex 58.7777193283621 57.7774270946907 52.053490916732 heart 61.3533347507248 57.9770456756225 65.4161218180102 kidney 58.3746752120263 61.6106026318827 56.7160818085517 liver 55.5059030239703 57.536412984382 62.5466254125138 stomach 57.7513628597286 58.5238733060591 57.2287584872018 testicle 55.7446287221184 60.0732851819474 62.5612789184362 cont.diffExp=2.23164769629381,2.13524136520941,1.00029223367142,3.37628907510224,-3.2359274198564,-2.03050996041175,-0.772510446330458,-4.32865645982906 cont.diffExpScore=7.28281226010661 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=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=-0.0996122362828107 cont.tran.correlation=0.314287505784622 tran.covariance=-0.000662857890503652 cont.tran.covariance=0.000542578263085995 tran.mean=56.5447069607946 cont.tran.mean=58.3478678863501 weightedLogRatios: wLogRatio Lung 0.0646444126729112 cerebhem 0.120826516050364 cortex 0.856385078606599 heart 0.69623469163698 kidney 0.338094012163057 liver 0.953512899234 stomach 1.45987002236206 testicle 0.667541112189608 cont.weightedLogRatios: wLogRatio Lung 0.162382382452774 cerebhem 0.143593300525432 cortex 0.0697775852505118 heart 0.231410156135711 kidney -0.220871110672856 liver -0.144952437588150 stomach -0.053985606225755 testicle -0.303487137749104 varWeightedLogRatios=0.216110114089979 cont.varWeightedLogRatios=0.0383621336613258 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.55618772037187 0.0862241104386742 41.2435420009483 7.97707127546178e-136 *** df.mm.trans1 0.367188620766093 0.0718365166877503 5.11144801692071 5.29298377970667e-07 *** df.mm.trans2 0.463563574211447 0.0718365166877502 6.45303524705139 3.68983677700951e-10 *** df.mm.exp2 0.498190361468954 0.0989756291301556 5.03346496351965 7.75195368324633e-07 *** df.mm.exp3 0.207642653303096 0.0989756291301555 2.09791698348329 0.0366352870769537 * df.mm.exp4 0.0968877262585767 0.0989756291301555 0.97890487900983 0.328308957516364 df.mm.exp5 0.125906923268147 0.0989756291301555 1.27210025715094 0.204189328062739 df.mm.exp6 0.175981407518465 0.0989756291301555 1.77802767272179 0.0762749266314484 . df.mm.exp7 0.366504587449622 0.0989756291301555 3.70297810350525 0.000247796460430805 *** df.mm.exp8 0.221761059814099 0.0989756291301555 2.24056226530752 0.0256867232800468 * df.mm.trans1:exp2 -0.371433615187013 0.0836496740737827 -4.4403474287227 1.20875572398584e-05 *** df.mm.trans2:exp2 -0.384585646140287 0.0836496740737827 -4.59757495051404 5.99594181124971e-06 *** df.mm.trans1:exp3 -0.112520307130223 0.0836496740737827 -1.34513742433682 0.17945928610464 df.mm.trans2:exp3 -0.310461793861951 0.0836496740737827 -3.71145252267343 0.000239952216427973 *** df.mm.trans1:exp4 -0.0255189799061725 0.0836496740737827 -0.305069687225125 0.76049597957638 df.mm.trans2:exp4 -0.183591054463974 0.0836496740737827 -2.19476114517839 0.0288428557727062 * df.mm.trans1:exp5 -0.0918685570853478 0.0836496740737826 -1.09825361667657 0.272855217599106 df.mm.trans2:exp5 -0.160164383495306 0.0836496740737827 -1.91470421455598 0.0563522535404272 . df.mm.trans1:exp6 -0.0638494599966806 0.0836496740737827 -0.763295980572053 0.445805521885271 df.mm.trans2:exp6 -0.28577309751676 0.0836496740737827 -3.41630855925028 0.000710071602255311 *** df.mm.trans1:exp7 -0.103044663404775 0.0836496740737827 -1.23185971189660 0.218835606589372 df.mm.trans2:exp7 -0.443189735760517 0.0836496740737827 -5.29816452565738 2.08324083916204e-07 *** df.mm.trans1:exp8 -0.148314208275281 0.0836496740737827 -1.77303988231277 0.0770992589201385 . df.mm.trans2:exp8 -0.298964758823485 0.0836496740737827 -3.574009846826 0.000401317749758043 *** df.mm.trans1:probe2 0.490683791576606 0.0458168134181658 10.7096883211449 2.57824064173292e-23 *** df.mm.trans1:probe3 0.129921657886092 0.0458168134181658 2.83567642953055 0.00484104288905462 ** df.mm.trans1:probe4 0.0593841106836301 0.0458168134181658 1.29612049056395 0.195795362147199 df.mm.trans1:probe5 0.0539084429939409 0.0458168134181658 1.17660830101656 0.240158386372013 df.mm.trans1:probe6 0.153408347303444 0.0458168134181658 3.34829805607169 0.000902335322476633 *** df.mm.trans2:probe2 -0.141191926369461 0.0458168134181658 -3.08166185807851 0.00222311000828961 ** df.mm.trans2:probe3 0.0392337351282748 0.0458168134181658 0.856317412784519 0.392413175528194 df.mm.trans2:probe4 -0.109328730027822 0.0458168134181658 -2.38621418364452 0.0175583665107206 * df.mm.trans2:probe5 -0.04737622800445 0.0458168134181658 -1.03403585867161 0.30183950307601 df.mm.trans2:probe6 0.0207717769551274 0.0458168134181658 0.453365814980307 0.65056872350738 df.mm.trans3:probe2 -0.348696868410524 0.0458168134181658 -7.61067482428338 2.59851794278242e-13 *** df.mm.trans3:probe3 0.105043114604302 0.0458168134181658 2.29267613278958 0.0224644756739229 * df.mm.trans3:probe4 -0.218652088720399 0.0458168134181658 -4.77231113226452 2.68823767309444e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92190956085899 0.133825043123404 29.3062454479651 7.03154549781611e-96 *** df.mm.trans1 0.102666830954913 0.11149462597716 0.920823134345007 0.357782496804082 df.mm.trans2 0.0814437787103385 0.11149462597716 0.73047268419038 0.465594194933053 df.mm.exp2 0.283071875694997 0.153616172658919 1.84272183582853 0.0662224905277823 . df.mm.exp3 0.253611415942514 0.153616172658919 1.65094216027383 0.0996550955842417 . df.mm.exp4 0.0714502072449939 0.153616172658919 0.465121647078383 0.642136091558658 df.mm.exp5 0.225180580696586 0.153616172658919 1.46586506354747 0.143590609342703 df.mm.exp6 0.0085173598029468 0.153616172658919 0.0554457232954128 0.955815279915122 df.mm.exp7 0.154047419790912 0.153616172658919 1.00280730293255 0.316652616399353 df.mm.exp8 0.055722009870026 0.153616172658919 0.362735309085900 0.717023461170067 df.mm.trans1:exp2 -0.181293465931790 0.129829361917796 -1.39639803549662 0.163487245364188 df.mm.trans2:exp2 -0.175696199764940 0.129829361917796 -1.35328555243294 0.176845353524691 df.mm.trans1:exp3 -0.209120678071178 0.129829361917796 -1.61073485213296 0.108146750884316 df.mm.trans2:exp3 -0.185781114891877 0.129829361917796 -1.43096378313488 0.153340535452769 df.mm.trans1:exp4 0.0159271963629663 0.129829361917796 0.122677922218018 0.902433166819466 df.mm.trans2:exp4 -0.000170902902532886 0.129829361917796 -0.00131636557407635 0.998950448963066 df.mm.trans1:exp5 -0.187570550779431 0.129829361917796 -1.4447467661298 0.149431406520944 df.mm.trans2:exp5 -0.0931144679759529 0.129829361917796 -0.717206544039784 0.473729155947953 df.mm.trans1:exp6 -0.0213001061509593 0.129829361917796 -0.164062318695257 0.869777604123182 df.mm.trans2:exp6 0.0551327932083437 0.129829361917796 0.424655812783337 0.671350778923761 df.mm.trans1:exp7 -0.127172593601559 0.129829361917796 -0.979536460189036 0.327997407604746 df.mm.trans2:exp7 -0.0733805217528284 0.129829361917796 -0.565207443592696 0.57229791423643 df.mm.trans1:exp8 -0.0642130721997007 0.129829361917796 -0.494595916140745 0.621198372906577 df.mm.trans2:exp8 0.0510753630697719 0.129829361917796 0.393403790292917 0.694262871330124 df.mm.trans1:probe2 0.045017580461489 0.0711104701488791 0.633065431394825 0.527107898694638 df.mm.trans1:probe3 -0.0181359271252678 0.0711104701488791 -0.255038773999073 0.798844241827471 df.mm.trans1:probe4 -0.0214677724345246 0.0711104701488791 -0.301893270984975 0.762914278360324 df.mm.trans1:probe5 0.0568606475217796 0.0711104701488791 0.799610063085426 0.424483777677694 df.mm.trans1:probe6 -0.0153172241950177 0.0711104701488791 -0.215400406760763 0.829581701196647 df.mm.trans2:probe2 -0.0931290673229982 0.0711104701488791 -1.30963931370472 0.191184301993662 df.mm.trans2:probe3 -0.00340941005339569 0.0711104701488791 -0.047945261031992 0.961787450474047 df.mm.trans2:probe4 -0.0483008205403356 0.0711104701488791 -0.679236411167181 0.497440887334239 df.mm.trans2:probe5 0.0101374181034616 0.0711104701488791 0.142558727037490 0.886721434410486 df.mm.trans2:probe6 -0.0111528793277505 0.0711104701488791 -0.156838779217750 0.875463122592759 df.mm.trans3:probe2 -0.0783693122270449 0.0711104701488791 -1.10207838681095 0.271191397734824 df.mm.trans3:probe3 0.0146385426782354 0.0711104701488791 0.205856361905465 0.83702383250986 df.mm.trans3:probe4 0.0138990408823826 0.0711104701488791 0.195457024166527 0.845149578826542