chr8.23314_chr8_21371134_21376055_-_2.R fitVsDatCorrelation=0.871436127369064 cont.fitVsDatCorrelation=0.309279031822035 fstatistic=6341.9785390543,42,462 cont.fstatistic=1679.19374632549,42,462 residuals=-0.598761079371372,-0.104419398676992,-0.00301565889629885,0.0863584721049552,0.713961501885947 cont.residuals=-0.592878005074951,-0.23362572302878,-0.0752539773150013,0.158298958805897,1.35188529871978 predictedValues: Include Exclude Both chr8.23314_chr8_21371134_21376055_-_2.R.tl.Lung 52.6255744482539 46.0243223151358 53.1382104035924 chr8.23314_chr8_21371134_21376055_-_2.R.tl.cerebhem 60.1856171415295 62.2978678353932 57.5886350445102 chr8.23314_chr8_21371134_21376055_-_2.R.tl.cortex 67.331810668398 43.8937210389802 67.7731854907808 chr8.23314_chr8_21371134_21376055_-_2.R.tl.heart 60.7725148900786 47.9291923744462 57.9571583542003 chr8.23314_chr8_21371134_21376055_-_2.R.tl.kidney 52.8914741658553 44.5328802394462 56.0566739957619 chr8.23314_chr8_21371134_21376055_-_2.R.tl.liver 56.2496511221797 49.9370046087403 54.761141632664 chr8.23314_chr8_21371134_21376055_-_2.R.tl.stomach 55.4779367665073 47.7483592721953 52.9144270434256 chr8.23314_chr8_21371134_21376055_-_2.R.tl.testicle 81.2829643182216 49.2274005617638 73.2355672751666 diffExp=6.60125213311802,-2.11225069386371,23.4380896294178,12.8433225156323,8.35859392640915,6.3126465134394,7.72957749431205,32.0555637564578 diffExpScore=1.03350939183327 diffExp1.5=0,0,1,0,0,0,0,1 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,1,0,0,0,0,1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,1,0,0,0,0,1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,1,1,0,0,0,1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 52.5922307555564 57.5402703030516 66.0021331991353 cerebhem 61.4166489021843 65.6895583347291 58.5906084645488 cortex 57.5325988628941 51.4468670028909 53.4785968412943 heart 57.6257573568644 76.227208158577 50.721884738339 kidney 53.2370537319593 59.1917255401891 69.2719218263626 liver 60.8016538695035 56.1924818862818 59.3973096814466 stomach 53.9489995404957 61.1885997722925 58.6364828740656 testicle 53.0080133524804 56.4878095437519 54.8228159142211 cont.diffExp=-4.94803954749516,-4.27290943254487,6.08573186000328,-18.6014508017126,-5.95467180822975,4.6091719832217,-7.23960023179675,-3.47979619127149 cont.diffExpScore=1.5858876798454 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,-1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.0649757806948954 cont.tran.correlation=0.229347634336928 tran.covariance=0.00149719042566172 cont.tran.covariance=0.00164556686179432 tran.mean=54.9005182354453 cont.tran.mean=58.3829673071064 weightedLogRatios: wLogRatio Lung 0.522214415134928 cerebhem -0.141930939709945 cortex 1.70960712148414 heart 0.94690488152042 kidney 0.667800160642571 liver 0.472612474100072 stomach 0.591305157339671 testicle 2.07975970235583 cont.weightedLogRatios: wLogRatio Lung -0.360343383170095 cerebhem -0.279213116353930 cortex 0.446811804435324 heart -1.17322291380849 kidney -0.427052615518154 liver 0.320712545394955 stomach -0.51010899808321 testicle -0.254469068649248 varWeightedLogRatios=0.513463446643334 cont.varWeightedLogRatios=0.252662721754546 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.54986128532078 0.0964177396481885 36.8175119876654 1.67820955100012e-139 *** df.mm.trans1 0.163911026673921 0.0849609678025322 1.92925093620504 0.0543118229215848 . df.mm.trans2 0.286076400079293 0.080329208709651 3.56129986432846 0.000407302855900723 *** df.mm.exp2 0.356559419127415 0.110676774656601 3.22162820730654 0.00136466771817816 ** df.mm.exp3 -0.044238494518075 0.110676774656601 -0.399708924074945 0.689555643344419 df.mm.exp4 0.0976823556081985 0.110676774656601 0.882591274558549 0.377916132030959 df.mm.exp5 -0.0813692073093766 0.110676774656601 -0.735196770612829 0.462592701783235 df.mm.exp6 0.118105346106588 0.110676774656601 1.06711951511992 0.286475172259242 df.mm.exp7 0.0937781390950494 0.110676774656601 0.847315431679473 0.397258045277636 df.mm.exp8 0.18122970157528 0.110676774656601 1.63746822346048 0.102213567352000 df.mm.trans1:exp2 -0.222328221097142 0.102466782555078 -2.16975897508673 0.0305341119677198 * df.mm.trans2:exp2 -0.053802220804067 0.0935389470006553 -0.575185230636498 0.565446040557748 df.mm.trans1:exp3 0.290669081335523 0.102466782555078 2.83671521723913 0.00475836360817204 ** df.mm.trans2:exp3 -0.00316022708861526 0.0935389470006553 -0.0337851471493807 0.973063071327893 df.mm.trans1:exp4 0.0462530656017754 0.102466782555078 0.451395705500105 0.651916189447901 df.mm.trans2:exp4 -0.0571275954174711 0.0935389470006553 -0.610735926042345 0.54167475139882 df.mm.trans1:exp5 0.0864091564728305 0.102466782555078 0.843289447742576 0.39950296967629 df.mm.trans2:exp5 0.0484270026947139 0.0935389470006553 0.51772020369627 0.60490130662717 df.mm.trans1:exp6 -0.05150771513793 0.102466782555078 -0.502677197951868 0.615430769973514 df.mm.trans2:exp6 -0.0365130455881838 0.0935389470006553 -0.390351257513387 0.696456739993885 df.mm.trans1:exp7 -0.0409949409438231 0.102466782555078 -0.400080298430248 0.689282291147827 df.mm.trans2:exp7 -0.0570034362837844 0.0935389470006553 -0.60940857377179 0.542553226130292 df.mm.trans1:exp8 0.253504544216145 0.102466782555078 2.47401682667142 0.0137186035471657 * df.mm.trans2:exp8 -0.11394931384685 0.0935389470006553 -1.21820180257163 0.223769034888718 df.mm.trans1:probe2 -0.0148556099335940 0.0512333912775391 -0.289959527627576 0.771977292994745 df.mm.trans1:probe3 -0.0071495847363688 0.0512333912775391 -0.139549316531447 0.889076900820959 df.mm.trans1:probe4 -0.00124666736669507 0.0512333912775391 -0.0243331026037625 0.980597415096126 df.mm.trans1:probe5 0.0876941059843445 0.0512333912775391 1.71165920891889 0.0876306194611483 . df.mm.trans1:probe6 -0.0305704053801121 0.0512333912775391 -0.596689085337091 0.55100725180047 df.mm.trans1:probe7 0.704839791085001 0.0512333912775391 13.7574299399151 2.45154527886980e-36 *** df.mm.trans1:probe8 0.777806966870902 0.0512333912775391 15.1816412592601 1.60071901902186e-42 *** df.mm.trans1:probe9 0.575659624698899 0.0512333912775391 11.2360242089083 4.61296965480172e-26 *** df.mm.trans1:probe10 0.63961377750966 0.0512333912775391 12.4843146541828 5.04343452586242e-31 *** df.mm.trans1:probe11 0.511778788990948 0.0512333912775391 9.98916480501094 2.10032215589104e-21 *** df.mm.trans1:probe12 0.497877649720613 0.0512333912775391 9.7178351326293 1.95605247534523e-20 *** df.mm.trans2:probe2 0.056999673620767 0.0512333912775391 1.11254930035748 0.266480601427138 df.mm.trans2:probe3 -0.0319943626982962 0.0512333912775391 -0.624482625500582 0.53261893856433 df.mm.trans2:probe4 0.0389701219046829 0.0512333912775391 0.760639124854644 0.447260665498063 df.mm.trans2:probe5 -0.0958273063651296 0.0512333912775391 -1.87040724760964 0.0620593391452787 . df.mm.trans2:probe6 -0.0290572704620318 0.0512333912775391 -0.567154930358292 0.570884384686516 df.mm.trans3:probe2 -0.225146830650532 0.0512333912775391 -4.39453303863641 1.37838835881866e-05 *** df.mm.trans3:probe3 0.144289482686863 0.0512333912775391 2.81631723157314 0.00506570308891423 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77163306998747 0.186929306533643 20.1767884336995 2.18712538854447e-65 *** df.mm.trans1 0.088538363173198 0.164717559773793 0.537516238674177 0.591169872740123 df.mm.trans2 0.277662767185609 0.155737764993056 1.78288655418927 0.0752612504974163 . df.mm.exp2 0.406680120299429 0.214573923962833 1.89529143517864 0.0586777403617186 . df.mm.exp3 0.188253377864547 0.214573923962833 0.877335765631777 0.380760278422731 df.mm.exp4 0.635964238754843 0.214573923962833 2.96384680398072 0.00319518312675960 ** df.mm.exp5 -0.00786963980702855 0.214573923962833 -0.0366756577951741 0.97075945910852 df.mm.exp6 0.226784632672786 0.214573923962833 1.05690676893278 0.291106468519854 df.mm.exp7 0.205276566131808 0.214573923962833 0.956670607223295 0.33923386498418 df.mm.exp8 0.174995098302513 0.214573923962833 0.815546899038976 0.415179575489586 df.mm.trans1:exp2 -0.251567571710765 0.198656851691858 -1.26634228604901 0.206028672247600 df.mm.trans2:exp2 -0.274225192662736 0.181348064790981 -1.51214843664752 0.131179811926901 df.mm.trans1:exp3 -0.098470058530506 0.198656851691858 -0.495679145682051 0.62035649962063 df.mm.trans2:exp3 -0.300188867221401 0.181348064790981 -1.65531883434981 0.0985389188662955 . df.mm.trans1:exp4 -0.544562999239414 0.198656851691858 -2.74122435043973 0.00635844848202584 ** df.mm.trans2:exp4 -0.354730833112421 0.181348064790981 -1.9560773009697 0.0510582269986612 . df.mm.trans1:exp5 0.0200558878656749 0.198656851691858 0.100957443425028 0.919628040166715 df.mm.trans2:exp5 0.0361663448804128 0.181348064790981 0.199430553185652 0.842013705072166 df.mm.trans1:exp6 -0.0817360468369108 0.198656851691858 -0.411443381593976 0.680938247208751 df.mm.trans2:exp6 -0.250486714754235 0.181348064790981 -1.38124834716567 0.167870290359349 df.mm.trans1:exp7 -0.179805823365348 0.198656851691858 -0.905107585437072 0.365880145744735 df.mm.trans2:exp7 -0.143800727963058 0.181348064790981 -0.792954301049752 0.428211466948899 df.mm.trans1:exp8 -0.167120405416760 0.198656851691858 -0.84125165577468 0.400642173206116 df.mm.trans2:exp8 -0.193455299484365 0.181348064790981 -1.06676241462702 0.286636263810729 df.mm.trans1:probe2 0.157030383000827 0.0993284258459288 1.58092088607547 0.114580469867952 df.mm.trans1:probe3 0.0616393612077933 0.0993284258459288 0.62056114030644 0.535194410596328 df.mm.trans1:probe4 0.158247308787488 0.0993284258459288 1.59317242209144 0.111805411607742 df.mm.trans1:probe5 0.0425139315193691 0.0993284258459288 0.428013744880179 0.668840563816045 df.mm.trans1:probe6 0.14628368262405 0.0993284258459288 1.47272728202654 0.141505507108767 df.mm.trans1:probe7 0.200214210375989 0.0993284258459288 2.01567888216155 0.0444120362123131 * df.mm.trans1:probe8 0.191464548097280 0.0993284258459288 1.92759068178798 0.0545187622346929 . df.mm.trans1:probe9 0.100930109516977 0.0993284258459288 1.01612512890854 0.310101635088259 df.mm.trans1:probe10 0.103859683155363 0.0993284258459288 1.04561893809193 0.296283758623948 df.mm.trans1:probe11 0.096896260491406 0.0993284258459288 0.975513904163795 0.329815906071294 df.mm.trans1:probe12 0.276875092323301 0.0993284258459288 2.78747085706131 0.00553109782952344 ** df.mm.trans2:probe2 -0.00325054075841375 0.0993284258459288 -0.0327251814445923 0.973907875760106 df.mm.trans2:probe3 -0.0591203359869934 0.0993284258459288 -0.595200573083647 0.552000826015344 df.mm.trans2:probe4 0.0929451234645499 0.0993284258459288 0.935735391686563 0.349898290724695 df.mm.trans2:probe5 0.0459010039202902 0.0993284258459288 0.462113473855798 0.644217484498914 df.mm.trans2:probe6 -0.047772283293532 0.0993284258459288 -0.480952787549789 0.630777740524205 df.mm.trans3:probe2 0.0300569984186578 0.0993284258459288 0.302602182232104 0.762329242971632 df.mm.trans3:probe3 0.125501588651882 0.0993284258459288 1.26350123424438 0.207046307984450