chr5.19031_chr5_123264301_123266298_-_1.R fitVsDatCorrelation=0.875185663662878 cont.fitVsDatCorrelation=0.265739669425062 fstatistic=10303.4726795759,46,554 cont.fstatistic=2585.75377077745,46,554 residuals=-0.548506021951981,-0.097269251554115,-0.00078921141958256,0.0890635797613802,0.969298103585097 cont.residuals=-0.81017186111235,-0.239086351714731,0.038325679029868,0.223244579070872,1.18858174765760 predictedValues: Include Exclude Both chr5.19031_chr5_123264301_123266298_-_1.R.tl.Lung 92.3506167810834 106.736975869204 72.7264770493713 chr5.19031_chr5_123264301_123266298_-_1.R.tl.cerebhem 71.1085857982842 69.5025121017649 69.365273209114 chr5.19031_chr5_123264301_123266298_-_1.R.tl.cortex 77.6990368710742 88.9822220865972 74.2729038893248 chr5.19031_chr5_123264301_123266298_-_1.R.tl.heart 90.8223624047104 107.409686496604 73.6336247535296 chr5.19031_chr5_123264301_123266298_-_1.R.tl.kidney 99.5409836663075 108.681009660043 69.6805602590939 chr5.19031_chr5_123264301_123266298_-_1.R.tl.liver 95.5869494534548 112.925830835912 66.9453281278126 chr5.19031_chr5_123264301_123266298_-_1.R.tl.stomach 87.9558272832818 90.2038614137447 68.028164964456 chr5.19031_chr5_123264301_123266298_-_1.R.tl.testicle 96.0969029786021 95.6726079552476 69.0587197148075 diffExp=-14.3863590881209,1.60607369651929,-11.2831852155231,-16.5873240918933,-9.14002599373504,-17.3388813824571,-2.24803413046293,0.424295023354460 diffExpScore=1.04375392243779 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 85.6351336674805 75.7433756686575 80.5813425549958 cerebhem 74.1002258356331 75.183245773494 81.9197125975985 cortex 82.1564984518802 86.107760625659 81.4930511909268 heart 83.9872293112997 81.209087130829 76.2618941195327 kidney 84.9593825662873 84.5984884094779 87.433323088928 liver 91.3932049399029 73.6376078060939 80.3191536003294 stomach 87.164103290658 76.5127430885504 75.6487396355122 testicle 87.2873430127038 72.7802467275079 85.2456155895444 cont.diffExp=9.89175799882294,-1.08301993786091,-3.95126217377883,2.77814218047071,0.360894156809465,17.755597133809,10.6513602021075,14.5070962851959 cont.diffExpScore=1.17469592318174 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=0,0,0,0,0,1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.871152053531598 cont.tran.correlation=-0.221062846870352 tran.covariance=0.0164598670707559 cont.tran.covariance=-0.000785932299710943 tran.mean=93.2047482284947 cont.tran.mean=81.4034797691322 weightedLogRatios: wLogRatio Lung -0.665673721370801 cerebhem 0.0971556879462966 cortex -0.599411057050147 heart -0.770414764204415 kidney -0.408007675836407 liver -0.774028579585639 stomach -0.113302626460312 testicle 0.0201921846388002 cont.weightedLogRatios: wLogRatio Lung 0.538692299912422 cerebhem -0.0625761616608369 cortex -0.208192274948687 heart 0.148471201821215 kidney 0.0189007876885494 liver 0.952014572374363 stomach 0.573817369764954 testicle 0.795808242209517 varWeightedLogRatios=0.127630539910025 cont.varWeightedLogRatios=0.182667424283954 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.59444824905193 0.0775881281985611 59.21586659874 8.64647528372883e-242 *** df.mm.trans1 -0.106709482549053 0.0616327373805659 -1.73137665280305 0.0839413218141162 . df.mm.trans2 0.0836474396777356 0.0616327373805659 1.35719170091756 0.175272962374078 df.mm.exp2 -0.643069882674797 0.0820346852609652 -7.83899981610329 2.34082360812305e-14 *** df.mm.exp3 -0.375721250122126 0.0820346852609652 -4.58002915384996 5.74669319776551e-06 *** df.mm.exp4 -0.0228003832194574 0.0820346852609652 -0.277935889519486 0.781165266716735 df.mm.exp5 0.135810647383264 0.0820346852609652 1.65552713405590 0.0983839223896778 . df.mm.exp6 0.173636739606997 0.0820346852609652 2.11662590103968 0.0347368315216342 * df.mm.exp7 -0.150269358571203 0.0820346852609652 -1.83177832758391 0.0675211925122299 . df.mm.exp8 -0.0179225453152581 0.0820346852609652 -0.218475212749871 0.827139324768905 df.mm.trans1:exp2 0.381685583493164 0.0632119703189808 6.03818519763105 2.85779934374374e-09 *** df.mm.trans2:exp2 0.214065141185137 0.0632119703189808 3.38646525499711 0.000758210793578336 *** df.mm.trans1:exp3 0.202971726580302 0.0632119703189808 3.21096978240141 0.00139957274818995 ** df.mm.trans2:exp3 0.193790209308910 0.0632119703189808 3.06572012122078 0.00227731129201177 ** df.mm.trans1:exp4 0.00611353513754674 0.0632119703189808 0.096714832755514 0.922987836754505 df.mm.trans2:exp4 0.0290831133145674 0.0632119703189808 0.460088701045197 0.645633070459708 df.mm.trans1:exp5 -0.0608335772377822 0.0632119703189808 -0.96237432452751 0.336281464516417 df.mm.trans2:exp5 -0.117761211414403 0.0632119703189808 -1.86295745600327 0.0629972532338107 . df.mm.trans1:exp6 -0.139192826218332 0.0632119703189808 -2.20200106903702 0.0280765563989187 * df.mm.trans2:exp6 -0.117273139436277 0.0632119703189808 -1.85523626054515 0.0640934763372838 . df.mm.trans1:exp7 0.101511698977054 0.0632119703189808 1.6058936063028 0.108867105498186 df.mm.trans2:exp7 -0.0180260445678133 0.0632119703189808 -0.285168212236545 0.775621849494424 df.mm.trans1:exp8 0.0576872483455747 0.0632119703189808 0.912600066956824 0.361849875666636 df.mm.trans2:exp8 -0.0915130642161002 0.0632119703189808 -1.44771731927206 0.148261774190955 df.mm.trans1:probe2 -0.0621528492768219 0.0452819201855821 -1.37257539040077 0.170439784172749 df.mm.trans1:probe3 0.210308691630587 0.0452819201855821 4.64442962596691 4.26400699649859e-06 *** df.mm.trans1:probe4 -0.0252037239434164 0.0452819201855821 -0.556595741526026 0.578028421715984 df.mm.trans1:probe5 0.272541381980758 0.0452819201855821 6.0187682161839 3.19877131739150e-09 *** df.mm.trans1:probe6 0.323725257835931 0.0452819201855821 7.14910623288908 2.77150162611249e-12 *** df.mm.trans2:probe2 -0.100213233681644 0.0452819201855821 -2.21309593919455 0.0272978333578769 * df.mm.trans2:probe3 -0.0275174262860465 0.0452819201855821 -0.607691241300498 0.543641286958747 df.mm.trans2:probe4 0.0155159598581647 0.0452819201855821 0.342652427162420 0.731989923099297 df.mm.trans2:probe5 -0.0142010142339197 0.0452819201855821 -0.313613340064174 0.753932770411281 df.mm.trans2:probe6 -0.0204172351502252 0.0452819201855821 -0.450891549354527 0.652244137088776 df.mm.trans3:probe2 -0.54667246431933 0.0452819201855821 -12.0726431670491 5.90171964018173e-30 *** df.mm.trans3:probe3 -0.434532361421833 0.0452819201855821 -9.59615580878544 2.81970161349441e-20 *** df.mm.trans3:probe4 -0.172314847099811 0.0452819201855821 -3.80537853504446 0.00015736119547439 *** df.mm.trans3:probe5 -0.135661490195814 0.0452819201855821 -2.99593059746192 0.00285851486743282 ** df.mm.trans3:probe6 -0.318525675596596 0.0452819201855821 -7.0342793391084 5.93098139439784e-12 *** df.mm.trans3:probe7 -0.346773218737635 0.0452819201855821 -7.6580943854949 8.4615151991887e-14 *** df.mm.trans3:probe8 -0.624476574533501 0.0452819201855821 -13.7908589559401 2.08994026124825e-37 *** df.mm.trans3:probe9 -0.543641880576161 0.0452819201855821 -12.0057161522328 1.12259886865922e-29 *** df.mm.trans3:probe10 0.241459596302734 0.0452819201855821 5.33236212848621 1.41404473417066e-07 *** df.mm.trans3:probe11 -0.458868676130465 0.0452819201855821 -10.1335957982756 2.96623463387697e-22 *** df.mm.trans3:probe12 -0.486841825669911 0.0452819201855821 -10.7513511722703 1.2900571897343e-24 *** df.mm.trans3:probe13 0.126000422731692 0.0452819201855821 2.78257684778595 0.00557682043812808 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43892775111174 0.154610195833421 28.7104464694832 9.90777870962654e-112 *** df.mm.trans1 0.0177451064938715 0.122815820118416 0.144485510716470 0.8851696425816 df.mm.trans2 -0.126701541640307 0.122815820118416 -1.03163860745419 0.302691601153012 df.mm.exp2 -0.168572151234393 0.163470869162765 -1.03120606195926 0.30289416998153 df.mm.exp3 0.0755282444509877 0.163470869162765 0.462028769026642 0.644242072752014 df.mm.exp4 0.105339026026652 0.163470869162766 0.644390199710551 0.519589148894378 df.mm.exp5 0.0210337191779055 0.163470869162766 0.128669525559092 0.897665846341771 df.mm.exp6 0.0401393954490607 0.163470869162766 0.245544638348344 0.806125684459161 df.mm.exp7 0.090969621444611 0.163470869162765 0.556488271644496 0.578101817193945 df.mm.exp8 -0.0770661217795915 0.163470869162766 -0.47143642273571 0.637514709575989 df.mm.trans1:exp2 0.0238950922652527 0.125962764368058 0.189699649615757 0.849613977930093 df.mm.trans2:exp2 0.161149571155529 0.125962764368058 1.27934292299792 0.201311878790714 df.mm.trans1:exp3 -0.116997937367771 0.125962764368058 -0.928829547007305 0.353381940720186 df.mm.trans2:exp3 0.0527203072990564 0.125962764368058 0.418538824259286 0.675715408881232 df.mm.trans1:exp4 -0.124769909730595 0.125962764368058 -0.990530101149759 0.322347504517727 df.mm.trans2:exp4 -0.0356628652891389 0.125962764368058 -0.283122281954162 0.777188861512233 df.mm.trans1:exp5 -0.0289560680375265 0.125962764368058 -0.229877997539956 0.818271424490127 df.mm.trans2:exp5 0.0895316891275856 0.125962764368058 0.710779011374964 0.477520309443786 df.mm.trans1:exp6 0.0249360970641173 0.125962764368058 0.197964034762329 0.843145820281934 df.mm.trans2:exp6 -0.0683345152383452 0.125962764368058 -0.54249774194122 0.587693820418033 df.mm.trans1:exp7 -0.0732726737764008 0.125962764368058 -0.581701061770138 0.561004669708352 df.mm.trans2:exp7 -0.0808633088102944 0.125962764368058 -0.641962005327347 0.521163386965743 df.mm.trans1:exp8 0.0961759524609102 0.125962764368058 0.763526848139727 0.445474282916871 df.mm.trans2:exp8 0.0371597133565098 0.125962764368058 0.295005540271653 0.768100166803873 df.mm.trans1:probe2 -0.00321983302751149 0.0902334765660195 -0.0356833533412147 0.97154769748289 df.mm.trans1:probe3 0.0111610646798681 0.0902334765660195 0.123690952677658 0.90160483759982 df.mm.trans1:probe4 0.0496399412436305 0.0902334765660195 0.550127770011293 0.582453465212139 df.mm.trans1:probe5 -0.07625569539906 0.0902334765660195 -0.845093177178731 0.398423635737938 df.mm.trans1:probe6 -0.106292631366797 0.0902334765660195 -1.17797335769312 0.239312985225469 df.mm.trans2:probe2 -0.0504002995492717 0.0902334765660195 -0.558554335567424 0.57669159017313 df.mm.trans2:probe3 0.102721138519519 0.0902334765660195 1.13839278313036 0.25544855019859 df.mm.trans2:probe4 -0.0161455607577522 0.0902334765660195 -0.178930939737640 0.858057345258811 df.mm.trans2:probe5 0.133238990833666 0.0902334765660195 1.47660265241117 0.140350360404559 df.mm.trans2:probe6 0.117956573285743 0.0902334765660195 1.30723737768698 0.191674394391288 df.mm.trans3:probe2 0.114916133937443 0.0902334765660195 1.27354212993627 0.203359804465951 df.mm.trans3:probe3 -0.00966447800396234 0.0902334765660195 -0.107105238230418 0.91474426593166 df.mm.trans3:probe4 0.089873320394121 0.0902334765660195 0.996008619133332 0.319680784996945 df.mm.trans3:probe5 0.0357763572317611 0.0902334765660195 0.396486521336516 0.691898821605526 df.mm.trans3:probe6 0.130692857565244 0.0902334765660195 1.44838548329258 0.148074989814830 df.mm.trans3:probe7 0.0876966400305813 0.0902334765660195 0.971885860636412 0.331531471604944 df.mm.trans3:probe8 -0.00536104049098709 0.0902334765660195 -0.0594129883388088 0.952644592950261 df.mm.trans3:probe9 -0.0040499751307182 0.0902334765660195 -0.0448832881636233 0.964216510629835 df.mm.trans3:probe10 0.0831133240211338 0.0902334765660195 0.921091896091622 0.357403370130001 df.mm.trans3:probe11 0.165997889159885 0.0902334765660195 1.83964860356935 0.066354694316522 . df.mm.trans3:probe12 0.113913576718938 0.0902334765660195 1.26243142849088 0.207324738890647 df.mm.trans3:probe13 -0.0320588829911851 0.0902334765660195 -0.355288128211808 0.722508974003419