chr3.15710_chr3_142820589_142822918_-_0.R fitVsDatCorrelation=0.955156676948454 cont.fitVsDatCorrelation=0.24704939646238 fstatistic=10058.1098653571,55,761 cont.fstatistic=926.628575639981,55,761 residuals=-0.635606005079386,-0.0937016687877677,-0.00650410507958647,0.0948951016702488,1.01437579289857 cont.residuals=-1.14315781772535,-0.358033949824659,-0.143243317071884,0.221931053540800,2.30693045416935 predictedValues: Include Exclude Both chr3.15710_chr3_142820589_142822918_-_0.R.tl.Lung 63.4618037339133 166.160959049595 125.418394674035 chr3.15710_chr3_142820589_142822918_-_0.R.tl.cerebhem 56.3139975425107 81.5588271232357 77.8135053620883 chr3.15710_chr3_142820589_142822918_-_0.R.tl.cortex 57.6878797550808 107.764596026945 80.3166049480259 chr3.15710_chr3_142820589_142822918_-_0.R.tl.heart 60.1745862162045 661.25408767976 372.833560273789 chr3.15710_chr3_142820589_142822918_-_0.R.tl.kidney 63.6933885579432 115.792822875527 86.6854717598302 chr3.15710_chr3_142820589_142822918_-_0.R.tl.liver 61.1727592207484 110.950234917671 84.9669042075906 chr3.15710_chr3_142820589_142822918_-_0.R.tl.stomach 73.6483607633395 134.261683570907 109.001488060177 chr3.15710_chr3_142820589_142822918_-_0.R.tl.testicle 75.6151498561717 108.296795560102 115.446260149410 diffExp=-102.699155315682,-25.2448295807250,-50.0767162718644,-601.079501463555,-52.0994343175835,-49.7774756969224,-60.6133228075672,-32.6816457039306 diffExpScore=0.998974645107432 diffExp1.5=-1,0,-1,-1,-1,-1,-1,0 diffExp1.5Score=0.857142857142857 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 104.150887553152 75.3127563123542 94.1947656954752 cerebhem 113.188555800877 89.0848200686026 95.6969462042015 cortex 92.2836569177722 97.9706161143209 88.6068034057128 heart 112.238600539464 72.7134536600047 108.503345470355 kidney 89.6907936733892 75.143677120455 98.8272329873965 liver 91.243014758709 84.3481807400467 102.240156629488 stomach 90.3113213473082 78.1456249571016 94.8297036285399 testicle 95.4810074536978 73.9122054050455 80.0640151182076 cont.diffExp=28.8381312407977,24.103735732274,-5.6869591965487,39.5251468794594,14.5471165529342,6.89483401866225,12.1656963902066,21.5688020486524 cont.diffExpScore=1.07256695657095 cont.diffExp1.5=0,0,0,1,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=1,0,0,1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=1,1,0,1,0,0,0,1 cont.diffExp1.2Score=0.8 tran.correlation=-0.175150924420301 cont.tran.correlation=-0.098205245150891 tran.covariance=-0.00528420550726757 cont.tran.covariance=-0.00109051046622008 tran.mean=124.862995778103 cont.tran.mean=89.7011982763938 weightedLogRatios: wLogRatio Lung -4.45809573040369 cerebhem -1.56157771113942 cortex -2.72925869981517 heart -12.6931860314986 kidney -2.66162039255287 liver -2.62645355405053 stomach -2.76197638488025 testicle -1.61837676827153 cont.weightedLogRatios: wLogRatio Lung 1.45359053921771 cerebhem 1.10377642516912 cortex -0.272377532786643 heart 1.95500508740132 kidney 0.780046461896806 liver 0.351555333612974 stomach 0.641104072845996 testicle 1.13452986876171 varWeightedLogRatios=13.4465502965099 cont.varWeightedLogRatios=0.467479401926876 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13860632936768 0.0811379138063752 51.0070586636466 9.49948562463334e-248 *** df.mm.trans1 -0.0534498936622713 0.0689859129509443 -0.774794322143412 0.438701869918659 df.mm.trans2 1.00808344500835 0.0619167517685783 16.2812714849156 2.32181190582828e-51 *** df.mm.exp2 -0.353787168260302 0.0796580708250395 -4.44132232422949 1.02660272116324e-05 *** df.mm.exp3 -0.0827199638657957 0.0796580708250395 -1.03843795122131 0.299396036835616 df.mm.exp4 0.238516316374077 0.0796580708250395 2.99425172997163 0.00284033949348758 ** df.mm.exp5 0.0118571959564273 0.0796580708250395 0.148851156368955 0.881710518243172 df.mm.exp6 -0.051217926922221 0.0796580708250395 -0.642972223551782 0.520435802834506 df.mm.exp7 0.0759912546659675 0.0796580708250395 0.953968052187382 0.340402729418669 df.mm.exp8 -0.170012773419194 0.0796580708250395 -2.13428183306885 0.0331387118128082 * df.mm.trans1:exp2 0.234292087791020 0.0712483445285125 3.28838640871523 0.00105393442885905 ** df.mm.trans2:exp2 -0.357845218071062 0.0544168219903668 -6.57600361400027 8.9777760320989e-11 *** df.mm.trans1:exp3 -0.0126711498367716 0.0712483445285125 -0.177844831632555 0.85889216606512 df.mm.trans2:exp3 -0.350287805752656 0.0544168219903669 -6.43712353901641 2.15435061571404e-10 *** df.mm.trans1:exp4 -0.291704417983134 0.0712483445285125 -4.09419222177714 4.6896887126913e-05 *** df.mm.trans2:exp4 1.14266489713048 0.0544168219903668 20.9983761516385 1.41406861777107e-77 *** df.mm.trans1:exp5 -0.00821463797801114 0.0712483445285125 -0.115295843466563 0.908241047663147 df.mm.trans2:exp5 -0.373011562755856 0.0544168219903668 -6.85471053090694 1.47750568265180e-11 *** df.mm.trans1:exp6 0.0144816976632599 0.0712483445285125 0.203256619632258 0.838988794904883 df.mm.trans2:exp6 -0.352657257767427 0.0544168219903668 -6.48066617763633 1.64011510418009e-10 *** df.mm.trans1:exp7 0.0728724219641162 0.0712483445285125 1.02279459889701 0.306729990797606 df.mm.trans2:exp7 -0.289157447997795 0.0544168219903668 -5.31375110529209 1.41182148579927e-07 *** df.mm.trans1:exp8 0.345231222537255 0.0712483445285125 4.84546307457149 1.53115332418584e-06 *** df.mm.trans2:exp8 -0.258068612956165 0.0544168219903668 -4.74244183171611 2.52117901039596e-06 *** df.mm.trans1:probe2 0.139403175755908 0.0503801875644266 2.76702375467813 0.00579437982614148 ** df.mm.trans1:probe3 0.200686024736421 0.0503801875644266 3.98343147253635 7.44496396009558e-05 *** df.mm.trans1:probe4 0.0811645095322397 0.0503801875644266 1.61104024133387 0.107585609074289 df.mm.trans1:probe5 0.0435334590187522 0.0503801875644266 0.864098788101598 0.387805897574870 df.mm.trans1:probe6 -0.00425707147141965 0.0503801875644266 -0.0844989206516088 0.932681984558238 df.mm.trans1:probe7 0.236211137495109 0.0503801875644266 4.68857201440626 3.26019897481578e-06 *** df.mm.trans1:probe8 0.268686044478955 0.0503801875644266 5.3331688004408 1.27370064410040e-07 *** df.mm.trans1:probe9 0.352624017372394 0.0503801875644266 6.99925971735329 5.65310007093556e-12 *** df.mm.trans1:probe10 0.0298031150807143 0.0503801875644266 0.591564194607291 0.554318138426553 df.mm.trans1:probe11 0.0420674353305955 0.0503801875644266 0.834999577498581 0.403980003531647 df.mm.trans1:probe12 0.0316452850330642 0.0503801875644266 0.628129559712257 0.530107523057388 df.mm.trans1:probe13 0.0865510287087054 0.0503801875644266 1.71795765146811 0.0862111228370216 . df.mm.trans1:probe14 0.164744096942847 0.0503801875644266 3.27001753878291 0.00112379214010017 ** df.mm.trans1:probe15 0.0612265421613391 0.0503801875644266 1.21529007971719 0.224632372562110 df.mm.trans1:probe16 0.093800849663498 0.0503801875644266 1.86185987385507 0.0630081226590597 . df.mm.trans2:probe2 -0.384787216006131 0.0503801875644266 -7.63766938171998 6.64818771587318e-14 *** df.mm.trans2:probe3 -0.408318247696084 0.0503801875644266 -8.1047385378215 2.10517325055500e-15 *** df.mm.trans2:probe4 -0.144032073519311 0.0503801875644266 -2.85890308238971 0.00436731496280566 ** df.mm.trans2:probe5 0.274580574873962 0.0503801875644266 5.45016976212773 6.80240126410099e-08 *** df.mm.trans2:probe6 0.0553661486920832 0.0503801875644266 1.09896670434743 0.272130141563312 df.mm.trans3:probe2 0.191461664591920 0.0503801875644266 3.80033647844359 0.000156044140718413 *** df.mm.trans3:probe3 -0.709612592178845 0.0503801875644266 -14.0851518520328 3.30079915369521e-40 *** df.mm.trans3:probe4 -0.032435328095431 0.0503801875644266 -0.643811181805396 0.519891857372115 df.mm.trans3:probe5 -0.117609947788297 0.0503801875644266 -2.33444839080634 0.0198315227019965 * df.mm.trans3:probe6 -0.124130441569845 0.0503801875644266 -2.46387414519063 0.0139645343547724 * df.mm.trans3:probe7 -0.876233402344018 0.0503801875644266 -17.3924204077939 2.67002203955813e-57 *** df.mm.trans3:probe8 -0.708477332966738 0.0503801875644266 -14.0626180095247 4.25060127744053e-40 *** df.mm.trans3:probe9 -0.183531347142491 0.0503801875644266 -3.64292703173820 0.000287841360929394 *** df.mm.trans3:probe10 -0.487969155602818 0.0503801875644266 -9.6857351906203 5.32376167341129e-21 *** df.mm.trans3:probe11 0.170366202993098 0.0503801875644266 3.38161112987584 0.000757417733322 *** df.mm.trans3:probe12 -0.260843223278939 0.0503801875644266 -5.17749607314126 2.88099487512742e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.52521527766002 0.265527331879832 17.0423709138461 2.07239425521515e-55 *** df.mm.trans1 0.239234876116366 0.225759383546282 1.05968962334326 0.289622042833312 df.mm.trans2 -0.241359120494442 0.202625247858969 -1.19116014931383 0.233962102735691 df.mm.exp2 0.235332023628837 0.260684481725059 0.902746577285865 0.366946057010963 df.mm.exp3 0.203200414254856 0.260684481725059 0.779487957665116 0.435934669444014 df.mm.exp4 -0.101753216810182 0.260684481725059 -0.390330932385533 0.696401085237737 df.mm.exp5 -0.199728701628806 0.260684481725059 -0.766170277214497 0.443812575321144 df.mm.exp6 -0.100970445262372 0.260684481725059 -0.387328177704356 0.698621566326272 df.mm.exp7 -0.11237139190501 0.260684481725059 -0.431062835660186 0.666544755625733 df.mm.exp8 0.056853222252223 0.260684481725059 0.218092085405319 0.827415831290923 df.mm.trans1:exp2 -0.152117649680247 0.233163288726614 -0.652408235065711 0.514334866302379 df.mm.trans2:exp2 -0.0673926002637912 0.178081403312425 -0.378437046262253 0.705211497036388 df.mm.trans1:exp3 -0.324174042781172 0.233163288726614 -1.39033054710971 0.164835006212798 df.mm.trans2:exp3 0.0598176568911413 0.178081403312425 0.335900637452848 0.737038384645017 df.mm.trans1:exp4 0.176539494517011 0.233163288726614 0.757149615967225 0.449194590683895 df.mm.trans2:exp4 0.0666301144534535 0.178081403312425 0.374155376216111 0.708392946019597 df.mm.trans1:exp5 0.0502561412484916 0.233163288726614 0.215540540378196 0.829403662353096 df.mm.trans2:exp5 0.197481150504316 0.178081403312425 1.10893752425039 0.267807459526698 df.mm.trans1:exp6 -0.0313438053390933 0.233163288726614 -0.134428560817926 0.893099242521338 df.mm.trans2:exp6 0.214274159035045 0.178081403312425 1.20323714351646 0.229258748045351 df.mm.trans1:exp7 -0.0302064702434834 0.233163288726614 -0.129550712757791 0.896956133178948 df.mm.trans2:exp7 0.149295937574004 0.178081403312425 0.83835782286643 0.402093011295253 df.mm.trans1:exp8 -0.143766558918578 0.233163288726614 -0.616591744368236 0.53768844730294 df.mm.trans2:exp8 -0.0756247738357178 0.178081403312425 -0.424664071761845 0.671201612562048 df.mm.trans1:probe2 -0.293416048610624 0.164871342582345 -1.77966676327680 0.0755295040829235 . df.mm.trans1:probe3 -0.0794016846833167 0.164871342582345 -0.481597853451453 0.630230109008693 df.mm.trans1:probe4 -0.23496565775829 0.164871342582345 -1.42514553516743 0.154525131695397 df.mm.trans1:probe5 -0.182224786672257 0.164871342582345 -1.10525445973878 0.269398650824389 df.mm.trans1:probe6 -0.387139697642415 0.164871342582345 -2.34813213490426 0.0191229206727455 * df.mm.trans1:probe7 -0.233881881384042 0.164871342582346 -1.4185720678973 0.156433208779682 df.mm.trans1:probe8 -0.263591551613043 0.164871342582345 -1.59877118415162 0.110286534202538 df.mm.trans1:probe9 -0.305909324929139 0.164871342582345 -1.85544267510500 0.0639195146688501 . df.mm.trans1:probe10 -0.0677545521253474 0.164871342582346 -0.41095408737578 0.681221893522774 df.mm.trans1:probe11 -0.129357645834066 0.164871342582345 -0.784597515905217 0.432933751023979 df.mm.trans1:probe12 -0.263682111326284 0.164871342582345 -1.59932045919131 0.110164479230732 df.mm.trans1:probe13 -0.0953662369001866 0.164871342582345 -0.578428218066798 0.563146183176452 df.mm.trans1:probe14 -0.209168849154076 0.164871342582345 -1.26867923726408 0.204943419142866 df.mm.trans1:probe15 -0.280339544208813 0.164871342582345 -1.70035337747551 0.0894730588315286 . df.mm.trans1:probe16 -0.294865425653003 0.164871342582345 -1.78845772124244 0.0740998911355458 . df.mm.trans2:probe2 0.180490981747052 0.164871342582345 1.09473835125049 0.273977645099677 df.mm.trans2:probe3 0.0481293359430389 0.164871342582345 0.291920567814874 0.770426917462972 df.mm.trans2:probe4 0.281144714093803 0.164871342582345 1.70523700292781 0.0885583343849616 . df.mm.trans2:probe5 0.151825539298045 0.164871342582345 0.920872826775313 0.357408651572659 df.mm.trans2:probe6 0.0186900858790989 0.164871342582346 0.113361640575978 0.909773748166568 df.mm.trans3:probe2 0.00106935977322515 0.164871342582345 0.00648602574878077 0.994826636331322 df.mm.trans3:probe3 0.0769705490958484 0.164871342582345 0.466852200572123 0.640739251751175 df.mm.trans3:probe4 0.0602344181418824 0.164871342582345 0.365341952084839 0.714957684360818 df.mm.trans3:probe5 0.0760400670732961 0.164871342582345 0.46120851496868 0.644780801042559 df.mm.trans3:probe6 -0.172184978286025 0.164871342582345 -1.04435965395276 0.296650648515106 df.mm.trans3:probe7 -0.0259426180287331 0.164871342582345 -0.157350680975840 0.875010218541333 df.mm.trans3:probe8 0.150656349646452 0.164871342582346 0.9137812993256 0.361121190399717 df.mm.trans3:probe9 0.196235993166893 0.164871342582346 1.19023712728537 0.234324369366895 df.mm.trans3:probe10 0.0627856218133723 0.164871342582345 0.380815858171434 0.703446171494283 df.mm.trans3:probe11 -0.135673540746681 0.164871342582345 -0.82290553726108 0.410819496501217 df.mm.trans3:probe12 -0.0229082509564991 0.164871342582345 -0.138946226783211 0.889529402098113