chr1.1201_chr1_133284745_133300711_+_0.R fitVsDatCorrelation=0.729081049690613 cont.fitVsDatCorrelation=0.267138200162514 fstatistic=8388.8267780136,42,462 cont.fstatistic=4226.20032431372,42,462 residuals=-0.449430933745989,-0.0825106958189441,0.000146380861782453,0.080340258251276,0.837190765805191 cont.residuals=-0.393623149349822,-0.150275075449491,-0.0301883820878019,0.116587002553509,1.12625319046034 predictedValues: Include Exclude Both chr1.1201_chr1_133284745_133300711_+_0.R.tl.Lung 54.0917649115135 56.3995489597021 67.4281844508586 chr1.1201_chr1_133284745_133300711_+_0.R.tl.cerebhem 68.0564270867821 70.8726040657704 66.1351632929544 chr1.1201_chr1_133284745_133300711_+_0.R.tl.cortex 56.9209079732325 51.7922657847827 73.7385697944478 chr1.1201_chr1_133284745_133300711_+_0.R.tl.heart 52.1622751360785 55.239464651038 64.0035486293472 chr1.1201_chr1_133284745_133300711_+_0.R.tl.kidney 51.6407126085155 52.9197787731023 63.1043414438054 chr1.1201_chr1_133284745_133300711_+_0.R.tl.liver 51.8410066197806 57.1753169777106 58.8437664098784 chr1.1201_chr1_133284745_133300711_+_0.R.tl.stomach 51.4765930044887 54.3781432363716 59.4355913620984 chr1.1201_chr1_133284745_133300711_+_0.R.tl.testicle 55.0621732511764 57.0308229117247 62.5985794419755 diffExp=-2.30778404818859,-2.81617697898831,5.12864218844981,-3.07718951495953,-1.27906616458684,-5.33431035793002,-2.90155023188289,-1.96864966054828 diffExpScore=1.59509089302244 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 62.672978969291 58.4557567710245 62.6742993791222 cerebhem 55.7126577639589 59.9402215054652 60.4767968668542 cortex 60.3138422484075 58.2998420138008 59.5953351229484 heart 61.7589936297352 63.3941853756602 59.6786647364562 kidney 62.0993794922052 60.9714984281827 55.0124212951599 liver 62.2120843486953 54.3976103765234 64.1605755147705 stomach 60.1176432853372 54.6296977162512 59.7920965369412 testicle 63.3410377353411 60.1080844174652 58.1056513674122 cont.diffExp=4.21722219826647,-4.22756374150631,2.01400023460668,-1.63519174592496,1.12788106402256,7.81447397217188,5.48794556908596,3.23295331787597 cont.diffExpScore=1.5635597037659 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=0.86161163578609 cont.tran.correlation=0.0197639233092094 tran.covariance=0.00750299265899836 cont.tran.covariance=2.00883866543373e-05 tran.mean=56.0662378719856 cont.tran.mean=59.901594629834 weightedLogRatios: wLogRatio Lung -0.167600277662813 cerebhem -0.171943125792027 cortex 0.37716370983181 heart -0.228299065955603 kidney -0.09680393883428 liver -0.391484309331197 stomach -0.217615527419466 testicle -0.141429934768357 cont.weightedLogRatios: wLogRatio Lung 0.285822883879816 cerebhem -0.296714282256835 cortex 0.138653621013513 heart -0.108092150627957 kidney 0.0755096018975154 liver 0.545430932720543 stomach 0.387539861469248 testicle 0.215965525037789 varWeightedLogRatios=0.0496190563417563 cont.varWeightedLogRatios=0.0724556578464734 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77769194742424 0.0780745541448864 48.3856999095226 5.41216534438158e-183 *** df.mm.trans1 0.230926410618816 0.0627252418633344 3.68155472595797 0.000259192936975775 *** df.mm.trans2 0.224516943502336 0.0627252418633344 3.57937150711213 0.000380859454256188 *** df.mm.exp2 0.47744053263984 0.0842205658661781 5.66893047712915 2.53429292899514e-08 *** df.mm.exp3 -0.123702461970693 0.0842205658661782 -1.46879162706232 0.142569860468169 df.mm.exp4 -0.00498137529817856 0.0842205658661781 -0.0591467802067929 0.952860773173737 df.mm.exp5 -0.0437820638940521 0.0842205658661781 -0.519850032397305 0.603417088781082 df.mm.exp6 0.107337830449065 0.0842205658661781 1.27448479293786 0.203132312565674 df.mm.exp7 0.0401162474748361 0.0842205658661781 0.47632365161947 0.634068999973267 df.mm.exp8 0.103232240442028 0.0842205658661781 1.22573672333261 0.220922213431430 df.mm.trans1:exp2 -0.247785315981673 0.0665822034913386 -3.72149467858789 0.000222449321968885 *** df.mm.trans2:exp2 -0.249017737535457 0.0665822034913387 -3.74000445280924 0.000207138193154676 *** df.mm.trans1:exp3 0.174683232292863 0.0665822034913387 2.62357241324383 0.00898915482204978 ** df.mm.trans2:exp3 0.0384821296106937 0.0665822034913387 0.57796419452684 0.563569881961479 df.mm.trans1:exp4 -0.0313410441438870 0.0665822034913387 -0.470712029648643 0.638068547850861 df.mm.trans2:exp4 -0.0158021488393832 0.0665822034913387 -0.237332920972476 0.812503730289925 df.mm.trans1:exp5 -0.00258952519311412 0.0665822034913387 -0.0388921522167853 0.968993173491439 df.mm.trans2:exp5 -0.0199019385740151 0.0665822034913387 -0.298907779112538 0.765144815911946 df.mm.trans1:exp6 -0.149838315289350 0.0665822034913387 -2.25042590110196 0.024891528367998 * df.mm.trans2:exp6 -0.0936767078677819 0.0665822034913387 -1.40693312860948 0.160119560978496 df.mm.trans1:exp7 -0.0896710024065763 0.0665822034913387 -1.34677132483669 0.178714167027508 df.mm.trans2:exp7 -0.0766151143944476 0.0665822034913387 -1.15068457300927 0.250457240027942 df.mm.trans1:exp8 -0.0854512254232143 0.0665822034913387 -1.28339437480963 0.199997306902193 df.mm.trans2:exp8 -0.092101527227965 0.0665822034913386 -1.38327544596727 0.167248494993562 df.mm.trans1:probe2 -0.00966576913025122 0.0446646999295071 -0.216407345073546 0.828765761700932 df.mm.trans1:probe3 -0.107185784325386 0.0446646999295071 -2.39978740469664 0.0167995293884687 * df.mm.trans1:probe4 -0.0304016965071923 0.0446646999295071 -0.680664967080812 0.49642448623162 df.mm.trans1:probe5 -0.0538515612668316 0.0446646999295071 -1.20568505669631 0.228556075751459 df.mm.trans1:probe6 -0.0679412416818719 0.0446646999295071 -1.52113955291542 0.12890891520368 df.mm.trans2:probe2 0.0636261741106241 0.0446646999295071 1.42452930862725 0.154968421330209 df.mm.trans2:probe3 0.0226172762058338 0.0446646999295071 0.506379226582287 0.612832012786469 df.mm.trans2:probe4 0.064149395222506 0.0446646999295071 1.43624372991985 0.151609766498646 df.mm.trans2:probe5 0.0223457768070904 0.0446646999295071 0.500300614184313 0.61710164583939 df.mm.trans2:probe6 0.281045433309787 0.0446646999295071 6.29233900044894 7.26840597985436e-10 *** df.mm.trans3:probe2 -0.267058665789635 0.0446646999295071 -5.97918862571841 4.49207477010476e-09 *** df.mm.trans3:probe3 0.202582379823975 0.0446646999295071 4.53562612406899 7.32910548108193e-06 *** df.mm.trans3:probe4 0.0839666967078593 0.0446646999295071 1.8799341950216 0.0607460504820284 . df.mm.trans3:probe5 0.0948875284998853 0.0446646999295071 2.12444119516404 0.0341636759414173 * df.mm.trans3:probe6 -0.0685193558303043 0.0446646999295071 -1.53408297690226 0.125693732049839 df.mm.trans3:probe7 -0.188122052382572 0.0446646999295071 -4.21187319470363 3.04584781364174e-05 *** df.mm.trans3:probe8 0.108078391418640 0.0446646999295071 2.41977202554181 0.0159152297489933 * df.mm.trans3:probe9 -0.164464759418671 0.0446646999295071 -3.68220898558012 0.000258547530447819 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1172964182331 0.109927203395234 37.4547545199499 4.40536705680495e-142 *** df.mm.trans1 0.0306145585477957 0.0883157194536171 0.346649030741059 0.729012835111049 df.mm.trans2 -0.0494709707251449 0.0883157194536171 -0.560160422529613 0.5756415276125 df.mm.exp2 -0.0569537468209466 0.118580648656057 -0.480295456859414 0.631244649295954 df.mm.exp3 0.0093346103931018 0.118580648656057 0.07871950861204 0.937289811131044 df.mm.exp4 0.115388067156826 0.118580648656057 0.973076707410404 0.331024371122681 df.mm.exp5 0.163334405887346 0.118580648656057 1.37741197858596 0.169051828392084 df.mm.exp6 -0.102768550073116 0.118580648656057 -0.866655320559053 0.386580760398363 df.mm.exp7 -0.0622415798135658 0.118580648656057 -0.524888171206563 0.599912708154524 df.mm.exp8 0.114165738783602 0.118580648656057 0.962768715448164 0.336167239649157 df.mm.trans1:exp2 -0.0607692804812849 0.0937462340433313 -0.648231698066892 0.517157138758743 df.mm.trans2:exp2 0.082031330565405 0.0937462340433313 0.875036009739746 0.382008984668533 df.mm.trans1:exp3 -0.0477033739681165 0.0937462340433313 -0.508856429860073 0.611095781669178 df.mm.trans2:exp3 -0.0120054005141119 0.0937462340433314 -0.128062749790703 0.898155039000465 df.mm.trans1:exp4 -0.13007885375085 0.0937462340433313 -1.38756351205239 0.165938889987797 df.mm.trans2:exp4 -0.0342860968276695 0.0937462340433313 -0.365733057733517 0.714731672746616 df.mm.trans1:exp5 -0.172528806394282 0.0937462340433314 -1.84038119669464 0.066353470970192 . df.mm.trans2:exp5 -0.121198063376822 0.0937462340433313 -1.29283127598280 0.196715566080636 df.mm.trans1:exp6 0.0953874157588263 0.0937462340433313 1.01750664154398 0.309445015768194 df.mm.trans2:exp6 0.0308186024776185 0.0937462340433313 0.328744965513746 0.742497424026503 df.mm.trans1:exp7 0.0206145464301755 0.0937462340433313 0.219897328575856 0.82604824482678 df.mm.trans2:exp7 -0.00545094467667714 0.0937462340433313 -0.0581457456110462 0.953657676530288 df.mm.trans1:exp8 -0.103562711559502 0.0937462340433313 -1.10471329986048 0.269858833746944 df.mm.trans2:exp8 -0.086291563781766 0.0937462340433313 -0.920480322888282 0.357802185032818 df.mm.trans1:probe2 -0.0389589700600634 0.0628868855866481 -0.619508657435168 0.535886709073588 df.mm.trans1:probe3 -0.0709280814636677 0.0628868855866481 -1.12786761185589 0.259961217807137 df.mm.trans1:probe4 -0.0197508864639267 0.0628868855866481 -0.314070036696492 0.753609612618718 df.mm.trans1:probe5 -0.0308587758600489 0.0628868855866481 -0.49070287981634 0.623869591992443 df.mm.trans1:probe6 0.0107880219711872 0.0628868855866482 0.171546449956136 0.863869221240455 df.mm.trans2:probe2 0.0226007027925987 0.0628868855866482 0.359386580870801 0.719470113815447 df.mm.trans2:probe3 -0.0624139491039271 0.0628868855866482 -0.9924795690181 0.321483153856576 df.mm.trans2:probe4 0.0257114616671825 0.0628868855866482 0.408852520320094 0.68283734753579 df.mm.trans2:probe5 -0.0212298094118178 0.0628868855866482 -0.337587228462228 0.735827595287484 df.mm.trans2:probe6 0.0420024852932021 0.0628868855866482 0.667905317640978 0.504527543121174 df.mm.trans3:probe2 -0.0188445201138149 0.0628868855866481 -0.29965739180787 0.764573269010187 df.mm.trans3:probe3 0.0751847287131135 0.0628868855866481 1.19555497162474 0.232483561166194 df.mm.trans3:probe4 0.0324646014808594 0.0628868855866481 0.516238022888387 0.605935164949175 df.mm.trans3:probe5 0.039702762154066 0.0628868855866481 0.631336116961332 0.528133003201873 df.mm.trans3:probe6 0.108333071297984 0.0628868855866481 1.722665549222 0.0856182167946812 . df.mm.trans3:probe7 0.100737220466526 0.0628868855866482 1.60187962127216 0.109865708337563 df.mm.trans3:probe8 0.00702460597669643 0.0628868855866481 0.111702239841686 0.911108016188058 df.mm.trans3:probe9 0.0110006636548714 0.0628868855866482 0.174927785853128 0.861213010998524