chr5.18125_chr5_101457478_101457999_-_0.R fitVsDatCorrelation=0.908432052971184 cont.fitVsDatCorrelation=0.284561644828495 fstatistic=10518.6926324374,37,347 cont.fstatistic=1992.51902041150,37,347 residuals=-0.308437354716634,-0.0868020907309343,0.000373838451702229,0.0755582136753921,0.476074084736869 cont.residuals=-0.608019267776076,-0.208884807325064,-0.0378272566251738,0.191604861911421,0.779247255031418 predictedValues: Include Exclude Both chr5.18125_chr5_101457478_101457999_-_0.R.tl.Lung 42.8942295018858 92.3599190140164 57.0132438126134 chr5.18125_chr5_101457478_101457999_-_0.R.tl.cerebhem 46.6934318590088 69.9346798075608 71.6830248955634 chr5.18125_chr5_101457478_101457999_-_0.R.tl.cortex 48.4935034659739 73.6257693975405 72.8675870100303 chr5.18125_chr5_101457478_101457999_-_0.R.tl.heart 50.5281852959853 68.8149991048332 58.4574264767785 chr5.18125_chr5_101457478_101457999_-_0.R.tl.kidney 46.5419291445487 97.3157660491313 57.1948877929132 chr5.18125_chr5_101457478_101457999_-_0.R.tl.liver 56.2278905146689 90.223651637113 54.5802947622393 chr5.18125_chr5_101457478_101457999_-_0.R.tl.stomach 49.0744726107415 74.1056508145714 59.7506209269949 chr5.18125_chr5_101457478_101457999_-_0.R.tl.testicle 48.6944096545811 76.920522130746 58.573777570188 diffExp=-49.4656895121306,-23.2412479485520,-25.1322659315666,-18.2868138088479,-50.7738369045826,-33.9957611224442,-25.0311782038298,-28.2261124761649 diffExpScore=0.99608078145753 diffExp1.5=-1,0,-1,0,-1,-1,-1,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,-1,-1,0,-1,-1,-1,-1 diffExp1.4Score=0.875 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 61.2286655466437 67.6524442240022 59.4180186656007 cerebhem 61.4593198787148 71.4181435798586 68.1146869921294 cortex 70.6720072842608 63.3779026157644 59.8996028062881 heart 67.1973744496987 64.7632024805067 65.4627577597573 kidney 64.315546779117 57.4023374694874 60.0503809234084 liver 56.0287587939113 62.8557332822317 59.7045868959874 stomach 60.9972214486418 61.6713257139255 62.5463655035885 testicle 64.363220023888 58.8753644348348 63.1757604838606 cont.diffExp=-6.42377867735843,-9.9588237011438,7.29410466849638,2.43417196919204,6.91320930962956,-6.82697448832044,-0.674104265283702,5.48785558905317 cont.diffExpScore=16.7056461518846 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=-0.111012396075455 cont.tran.correlation=-0.151881829692440 tran.covariance=-0.00143496892184298 cont.tran.covariance=-0.000741648342743423 tran.mean=64.5280631251817 cont.tran.mean=63.392410500343 weightedLogRatios: wLogRatio Lung -3.17689598282986 cerebhem -1.63424606250844 cortex -1.70793060040699 heart -1.25933901420531 kidney -3.10470559293576 liver -2.01723066785776 stomach -1.68958551210007 testicle -1.88103269903181 cont.weightedLogRatios: wLogRatio Lung -0.41548383911062 cerebhem -0.62975952929217 cortex 0.457914292244994 heart 0.154566702953606 kidney 0.467026640793914 liver -0.469491929022087 stomach -0.0452415775650075 testicle 0.367171237928324 varWeightedLogRatios=0.493742700864721 cont.varWeightedLogRatios=0.196656252614331 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23021464782875 0.0707081594296868 59.8264002619857 6.98907159966344e-185 *** df.mm.trans1 -0.458894547751735 0.0589096002149362 -7.78980923444435 7.84850720879152e-14 *** df.mm.trans2 0.244858447160494 0.0589096002149362 4.15651177850655 4.0750968737754e-05 *** df.mm.exp2 -0.422235623252493 0.0811650538182833 -5.20218497233818 3.37553980694328e-07 *** df.mm.exp3 -0.349365811192625 0.0811650538182833 -4.30438710697838 2.18118464452326e-05 *** df.mm.exp4 -0.155492525719833 0.0811650538182833 -1.9157570703761 0.056217551665305 . df.mm.exp5 0.130703274069766 0.0811650538182833 1.61033927683202 0.108233066812012 df.mm.exp6 0.290884770809005 0.0811650538182832 3.58386715864507 0.000386987831088502 *** df.mm.exp7 -0.132495626273229 0.0811650538182832 -1.63242208364536 0.103497680897564 df.mm.exp8 -0.0830969996544926 0.0811650538182833 -1.02380268040645 0.306641678136481 df.mm.trans1:exp2 0.507101826177403 0.0685969905697912 7.39247920302675 1.08851178057568e-12 *** df.mm.trans2:exp2 0.144104176409542 0.0685969905697912 2.10073612869255 0.0363852377596745 * df.mm.trans1:exp3 0.472058344567818 0.0685969905697912 6.88161886763155 2.77599537668762e-11 *** df.mm.trans2:exp3 0.122667795696399 0.0685969905697911 1.78823873580279 0.0746098584232049 . df.mm.trans1:exp4 0.319286524539398 0.0685969905697912 4.65452670572412 4.62868895493385e-06 *** df.mm.trans2:exp4 -0.138778850491618 0.0685969905697911 -2.02310406533685 0.0438287630738877 * df.mm.trans1:exp5 -0.0490869721732892 0.0685969905697911 -0.715584922393174 0.474728909967215 df.mm.trans2:exp5 -0.0784353701903474 0.0685969905697911 -1.14342290439909 0.253651266339621 df.mm.trans1:exp6 -0.0202091709499049 0.0685969905697911 -0.294607252913580 0.768470088651888 df.mm.trans2:exp6 -0.314286272475193 0.0685969905697911 -4.58163353617438 6.44345726300605e-06 *** df.mm.trans1:exp7 0.267097313339714 0.0685969905697911 3.89371765614071 0.000118407380450153 *** df.mm.trans2:exp7 -0.087705692661003 0.0685969905697911 -1.27856472904260 0.201904853693277 df.mm.trans1:exp8 0.209923925225871 0.0685969905697911 3.06024977892132 0.00238380367319258 ** df.mm.trans2:exp8 -0.099823399311386 0.0685969905697911 -1.45521543266282 0.146513668096897 df.mm.trans1:probe2 -0.0777579025694545 0.0375721191120438 -2.06956393216930 0.0392329152799115 * df.mm.trans1:probe3 -0.0728199547759839 0.0375721191120438 -1.93813807943139 0.0534172292788093 . df.mm.trans1:probe4 -0.0112348025946160 0.0375721191120438 -0.299019668310769 0.765104041029375 df.mm.trans1:probe5 -0.0334391632299073 0.0375721191120438 -0.889999393704369 0.374082824956969 df.mm.trans1:probe6 0.0694238868106392 0.0375721191120438 1.84775009904579 0.0654893856679354 . df.mm.trans2:probe2 0.127090344903447 0.0375721191120438 3.38257058443926 0.000800088357354109 *** df.mm.trans2:probe3 -0.0135075277974351 0.0375721191120438 -0.359509341412293 0.719432771356903 df.mm.trans2:probe4 0.182351040323074 0.0375721191120438 4.85336054054564 1.83793052664499e-06 *** df.mm.trans2:probe5 0.0671572413901212 0.0375721191120438 1.78742224227097 0.074741893744537 . df.mm.trans2:probe6 0.143109027634768 0.0375721191120438 3.80891552078825 0.000165039427769827 *** df.mm.trans3:probe2 0.0368468329491664 0.0375721191120438 0.980696160343939 0.32742584598313 df.mm.trans3:probe3 0.214812627807229 0.0375721191120438 5.71734128614455 2.33480190271273e-08 *** df.mm.trans3:probe4 -0.143239295963459 0.0375721191120438 -3.8123826749379 0.000162833009807179 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25100915132846 0.16215226806007 26.2161559760216 2.67016981147392e-84 *** df.mm.trans1 -0.109807804628120 0.135095091746277 -0.812818609534318 0.416879375802495 df.mm.trans2 -0.0301810158296523 0.135095091746277 -0.223405716962209 0.823351216089697 df.mm.exp2 -0.0786668491685556 0.186132656683560 -0.422638620058465 0.672820718102233 df.mm.exp3 0.0700935146437789 0.186132656683559 0.376578274294679 0.706717195975168 df.mm.exp4 -0.0475110725658399 0.186132656683559 -0.255253824946004 0.798678285844733 df.mm.exp5 -0.125698910542180 0.186132656683559 -0.675318951450193 0.499922899686438 df.mm.exp6 -0.167103019155627 0.186132656683559 -0.897763037035007 0.36993436626895 df.mm.exp7 -0.147662161613776 0.186132656683559 -0.793316789459538 0.428135388254824 df.mm.exp8 -0.150357026958523 0.186132656683559 -0.807794986852534 0.419762013097191 df.mm.trans1:exp2 0.0824268685871746 0.157310806740033 0.523974609852398 0.600630703696631 df.mm.trans2:exp2 0.132835313595685 0.157310806740033 0.84441314839358 0.399020362848147 df.mm.trans1:exp3 0.0733405721394163 0.157310806740033 0.466214455696085 0.641354530983696 df.mm.trans2:exp3 -0.135361737538233 0.157310806740033 -0.860473227131357 0.390122366266241 df.mm.trans1:exp4 0.140529777500305 0.157310806740033 0.893325642481386 0.37230193598596 df.mm.trans2:exp4 0.00386516727348317 0.157310806740033 0.0245702590532807 0.980411865209577 df.mm.trans1:exp5 0.174884826474357 0.157310806740033 1.11171527308589 0.267030239337761 df.mm.trans2:exp5 -0.0385995488967076 0.157310806740033 -0.245371247510644 0.806314042878516 df.mm.trans1:exp6 0.0783526568180807 0.157310806740033 0.498075487894256 0.618746354982218 df.mm.trans2:exp6 0.0935616873055616 0.157310806740033 0.594756897154428 0.552393596283705 df.mm.trans1:exp7 0.143875003585035 0.157310806740033 0.9145907173612 0.361041617008012 df.mm.trans2:exp7 0.0550977628875761 0.157310806740033 0.350247793075203 0.726365246349803 df.mm.trans1:exp8 0.200283908135187 0.157310806740033 1.27317323129726 0.203808848895973 df.mm.trans2:exp8 0.0113962848517020 0.157310806740033 0.0724443862940398 0.942290000390326 df.mm.trans1:probe2 -0.168082275484974 0.0861626773908515 -1.95075502032647 0.0518908217243662 . df.mm.trans1:probe3 -0.0150999003963239 0.0861626773908516 -0.175248737081691 0.860986404886871 df.mm.trans1:probe4 0.00516801250024644 0.0861626773908515 0.059979711131808 0.952206322123613 df.mm.trans1:probe5 -0.038813393559619 0.0861626773908515 -0.450466428562259 0.65265547552831 df.mm.trans1:probe6 -0.0490311986391707 0.0861626773908516 -0.569053795958024 0.56968776941209 df.mm.trans2:probe2 0.0498327082539738 0.0861626773908516 0.578356079023896 0.563398902464555 df.mm.trans2:probe3 -0.121770782060781 0.0861626773908516 -1.41326599576756 0.158473921669827 df.mm.trans2:probe4 0.0740066785056225 0.0861626773908516 0.858918046034167 0.3909786709998 df.mm.trans2:probe5 0.00313249688949704 0.0861626773908515 0.0363556122482986 0.9710197121207 df.mm.trans2:probe6 -0.0696476125742506 0.0861626773908515 -0.808326930909015 0.419456218978624 df.mm.trans3:probe2 -0.0809443966639063 0.0861626773908516 -0.939436878182486 0.348159950441079 df.mm.trans3:probe3 -0.0102393492366063 0.0861626773908515 -0.118837407873928 0.905472936702278 df.mm.trans3:probe4 -0.0145074779515031 0.0861626773908516 -0.168373109921993 0.866387856483809