chr17.10155_chr17_55357100_55359497_-_0.R fitVsDatCorrelation=0.870195651787828 cont.fitVsDatCorrelation=0.277579010384915 fstatistic=5689.01749779752,36,324 cont.fstatistic=1489.72495416792,36,324 residuals=-0.450416008397297,-0.0936079958979678,-0.0126439391816889,0.0818001444887686,1.6377911740384 cont.residuals=-0.618379287585867,-0.264805565845041,-0.0393448266296519,0.212042637854596,1.38827899644967 predictedValues: Include Exclude Both chr17.10155_chr17_55357100_55359497_-_0.R.tl.Lung 44.3284995486101 86.7306887989146 80.7603569787827 chr17.10155_chr17_55357100_55359497_-_0.R.tl.cerebhem 56.3944305018706 53.6760368990215 82.3439770182244 chr17.10155_chr17_55357100_55359497_-_0.R.tl.cortex 46.4274965401897 57.73016352839 72.1577268564609 chr17.10155_chr17_55357100_55359497_-_0.R.tl.heart 46.2967778449507 71.6810440799618 77.0925447925654 chr17.10155_chr17_55357100_55359497_-_0.R.tl.kidney 44.33256180549 86.72639839193 93.7392811817283 chr17.10155_chr17_55357100_55359497_-_0.R.tl.liver 49.6857303010143 82.4192011595223 92.2132997034725 chr17.10155_chr17_55357100_55359497_-_0.R.tl.stomach 48.0707223337447 64.7959367785423 79.4012108089422 chr17.10155_chr17_55357100_55359497_-_0.R.tl.testicle 46.0431516141711 67.6085870589787 82.4001854324185 diffExp=-42.4021892503044,2.71839360284918,-11.3026669882003,-25.3842662350111,-42.3938365864401,-32.733470858508,-16.7252144447976,-21.5654354448076 diffExpScore=1.02325498064873 diffExp1.5=-1,0,0,-1,-1,-1,0,0 diffExp1.5Score=0.8 diffExp1.4=-1,0,0,-1,-1,-1,0,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 63.7290260202144 66.0809615515932 64.2322720212323 cerebhem 59.3095947896435 52.0399841754314 59.0188693913613 cortex 64.7845179760865 60.9849931213704 61.7630632250832 heart 57.750180244142 63.887755678396 59.8555822574811 kidney 68.1674789101398 68.5028918698051 58.5673930366917 liver 57.0529889093535 61.9890676029407 65.2765216755097 stomach 59.8514859144472 75.3773195084907 70.0727312337738 testicle 63.1445569031746 65.4966732307959 59.7362348806542 cont.diffExp=-2.3519355313788,7.26961061421204,3.79952485471607,-6.13757543425396,-0.335412959665248,-4.93607869358713,-15.5258335940435,-2.35211632762132 cont.diffExpScore=1.97999305546576 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,-1,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.607052092983243 cont.tran.correlation=0.241709280237861 tran.covariance=-0.00913322152152812 cont.tran.covariance=0.00168633203310541 tran.mean=59.5592141990814 cont.tran.mean=63.0093422753766 weightedLogRatios: wLogRatio Lung -2.77010597963616 cerebhem 0.197993822140421 cortex -0.859967852691677 heart -1.77206890117062 kidney -2.76953775607351 liver -2.10475527556219 stomach -1.20083437719816 testicle -1.54494354162052 cont.weightedLogRatios: wLogRatio Lung -0.151223149277735 cerebhem 0.525309096454898 cortex 0.250267983697871 heart -0.414774493161813 kidney -0.0207349793427703 liver -0.33900232944173 stomach -0.970346167051436 testicle -0.152278222494261 varWeightedLogRatios=0.994867515392482 cont.varWeightedLogRatios=0.201586579408944 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.5247201774874 0.0983745198929524 35.8296048745435 9.89222571998745e-115 *** df.mm.trans1 0.244653684685966 0.0833221907153156 2.93623682461575 0.00355990115476217 ** df.mm.trans2 0.89630909154695 0.0833221907153157 10.7571474519836 2.79844383545814e-23 *** df.mm.exp2 -0.258517623524702 0.116036253190526 -2.22790392154621 0.0265731349732762 * df.mm.exp3 -0.248132064283322 0.116036253190526 -2.1384012104898 0.0332317938957918 * df.mm.exp4 -0.100657254234003 0.116036253190526 -0.867463843982696 0.386329920515311 df.mm.exp5 -0.148988944304072 0.116036253190526 -1.28398616990364 0.200064211244761 df.mm.exp6 -0.0695175335580033 0.116036253190526 -0.599101846591503 0.549523631336183 df.mm.exp7 -0.193546785498215 0.116036253190526 -1.66798548019661 0.0962847257745227 . df.mm.exp8 -0.231223023222334 0.116036253190526 -1.99267915728610 0.0471352018982901 * df.mm.trans1:exp2 0.499260226314757 0.100490343022959 4.96824084082082 1.09586699687635e-06 *** df.mm.trans2:exp2 -0.221323501337903 0.100490343022959 -2.20243552444973 0.0283385191719252 * df.mm.trans1:exp3 0.294396144976653 0.100490343022959 2.92959637832456 0.00363481862099759 ** df.mm.trans2:exp3 -0.158895920547346 0.100490343022959 -1.58120587279758 0.114806647684846 df.mm.trans1:exp4 0.144101819051836 0.100490343022959 1.43398673660526 0.152540426204364 df.mm.trans2:exp4 -0.0899241978986538 0.100490343022959 -0.894854124222754 0.371529353322793 df.mm.trans1:exp5 0.149080579951574 0.100490343022959 1.48353140676925 0.138905373355640 df.mm.trans2:exp5 0.148939474927469 0.100490343022959 1.48212724175338 0.139278372368540 df.mm.trans1:exp6 0.183607507883883 0.100490343022959 1.82711594328955 0.0686021253225635 . df.mm.trans2:exp6 0.0185281805672839 0.100490343022959 0.184377722375282 0.853832492557688 df.mm.trans1:exp7 0.274592293125894 0.100490343022959 2.73252418954485 0.00662991703929434 ** df.mm.trans2:exp7 -0.0980181036853103 0.100490343022959 -0.975398239638972 0.330090435337668 df.mm.trans1:exp8 0.269174257688613 0.100490343022959 2.67860820842373 0.00777002326581214 ** df.mm.trans2:exp8 -0.0178497608373044 0.100490343022959 -0.177626628592823 0.859127207350492 df.mm.trans1:probe2 0.0060217248953785 0.0502451715114793 0.11984683730262 0.904678757547531 df.mm.trans1:probe3 -0.00883085749864749 0.0502451715114794 -0.175755345896868 0.860595945682329 df.mm.trans1:probe4 0.138138221579683 0.0502451715114793 2.74928351171262 0.00630765370975909 ** df.mm.trans1:probe5 0.0966968301746325 0.0502451715114793 1.92449995224995 0.0551674398086448 . df.mm.trans1:probe6 -0.0317404708721208 0.0502451715114794 -0.631711862399935 0.528020625085583 df.mm.trans2:probe2 0.09346181495072 0.0502451715114793 1.86011535316119 0.0637752006641166 . df.mm.trans2:probe3 0.094957044250667 0.0502451715114793 1.88987401961545 0.0596672720305698 . df.mm.trans2:probe4 -0.116078136953164 0.0502451715114793 -2.31023466457157 0.0215030985820949 * df.mm.trans2:probe5 0.110170124422021 0.0502451715114793 2.19265097735512 0.0290432626398557 * df.mm.trans2:probe6 0.193495810888983 0.0502451715114793 3.85103294641508 0.000141747779203026 *** df.mm.trans3:probe2 -0.635648616433198 0.0502451715114793 -12.650939330319 4.3713401133719e-30 *** df.mm.trans3:probe3 -0.186941603695712 0.0502451715114794 -3.72058842814384 0.000234230642889983 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23615028606631 0.191815432720759 22.084512314675 1.3805614593725e-66 *** df.mm.trans1 -0.0955526229954635 0.162465667783603 -0.588140400978349 0.55684778199494 df.mm.trans2 -0.0476841467153209 0.162465667783603 -0.293502912743596 0.769325683187378 df.mm.exp2 -0.226088812534217 0.226253140968374 -0.99927369656194 0.318407828599981 df.mm.exp3 -0.0246260720246309 0.226253140968374 -0.108843006197528 0.913394388884993 df.mm.exp4 -0.0616955447319368 0.226253140968374 -0.272683704932789 0.785270069212994 df.mm.exp5 0.195650411975083 0.226253140968374 0.864741197128538 0.387820629645605 df.mm.exp6 -0.190708962461434 0.226253140968374 -0.842900839498585 0.399905711924168 df.mm.exp7 -0.0181760766550111 0.226253140968374 -0.0803351351376455 0.936020312692807 df.mm.exp8 0.0544721743394267 0.226253140968374 0.240757649181281 0.809895228562322 df.mm.trans1:exp2 0.154219779986215 0.195940967764633 0.787072666556723 0.431814674292413 df.mm.trans2:exp2 -0.0127795183386578 0.195940967764633 -0.0652212678361818 0.948038050979205 df.mm.trans1:exp3 0.0410526004142821 0.195940967764633 0.209515145722843 0.83417778786922 df.mm.trans2:exp3 -0.055626788904745 0.195940967764634 -0.283895652549622 0.7766716814412 df.mm.trans1:exp4 -0.0368181110608417 0.195940967764634 -0.187904099284985 0.851069454679922 df.mm.trans2:exp4 0.0279425903956316 0.195940967764633 0.142607187840353 0.88668906116663 df.mm.trans1:exp5 -0.128322935979649 0.195940967764633 -0.654906104851906 0.512992873407243 df.mm.trans2:exp5 -0.159655130952989 0.195940967764634 -0.814812403829547 0.41577820793087 df.mm.trans1:exp6 0.0800493021458014 0.195940967764633 0.40853785228803 0.683148724245633 df.mm.trans2:exp6 0.126786322541498 0.195940967764634 0.647063878411561 0.518048756419456 df.mm.trans1:exp7 -0.0445977906406356 0.195940967764633 -0.227608300343841 0.820094326919187 df.mm.trans2:exp7 0.149801823711768 0.195940967764633 0.76452528238868 0.445110723608241 df.mm.trans1:exp8 -0.0636856486988096 0.195940967764633 -0.325024671590423 0.745372169270188 df.mm.trans2:exp8 -0.0633535037900394 0.195940967764634 -0.323329544162201 0.746654264737912 df.mm.trans1:probe2 -0.0109089920091363 0.0979704838823167 -0.111349781861242 0.911407927384296 df.mm.trans1:probe3 0.0795297221279045 0.0979704838823167 0.811772270344572 0.417518292202897 df.mm.trans1:probe4 0.00267650778785435 0.0979704838823167 0.0273195321875658 0.978221697318278 df.mm.trans1:probe5 0.0623465643893221 0.0979704838823167 0.636381100905998 0.524977364135994 df.mm.trans1:probe6 -0.00726163322911309 0.0979704838823167 -0.0741206222665578 0.94096014573038 df.mm.trans2:probe2 0.0151400371192811 0.0979704838823167 0.154536718808774 0.877282780250005 df.mm.trans2:probe3 0.102947888277765 0.0979704838823167 1.05080514251034 0.294131206747592 df.mm.trans2:probe4 -0.0817902657749384 0.0979704838823167 -0.83484599170895 0.404419572677489 df.mm.trans2:probe5 -0.0508003762083046 0.0979704838823167 -0.518527358396296 0.604444315682343 df.mm.trans2:probe6 0.0362335863190384 0.0979704838823167 0.369841863418401 0.711741944274812 df.mm.trans3:probe2 0.117823966746234 0.0979704838823167 1.20264759422609 0.229990853473851 df.mm.trans3:probe3 0.0909527757472884 0.0979704838823167 0.92836915919025 0.353907200441344