chr8.23506_chr8_95532369_95535280_-_1.R fitVsDatCorrelation=0.794457273961014 cont.fitVsDatCorrelation=0.300125146587873 fstatistic=6437.39694757326,36,324 cont.fstatistic=2604.04404915893,36,324 residuals=-0.424122637565959,-0.0914497542033061,-0.00268283152190804,0.0830848750815837,1.14402757351376 cont.residuals=-0.450638792575929,-0.167193423800471,-0.0330798029983713,0.14369795299272,1.25660309566641 predictedValues: Include Exclude Both chr8.23506_chr8_95532369_95535280_-_1.R.tl.Lung 54.2909070434262 57.7268734568475 80.3960583906075 chr8.23506_chr8_95532369_95535280_-_1.R.tl.cerebhem 46.334422678362 59.5522956831509 75.5008964684786 chr8.23506_chr8_95532369_95535280_-_1.R.tl.cortex 57.3628728955963 59.7660045473963 86.4637218870257 chr8.23506_chr8_95532369_95535280_-_1.R.tl.heart 64.6767344871661 58.4927145664339 88.6695035559172 chr8.23506_chr8_95532369_95535280_-_1.R.tl.kidney 51.0627121753048 55.1856722107915 72.0003962998252 chr8.23506_chr8_95532369_95535280_-_1.R.tl.liver 55.3387907301784 55.7458759097235 68.1980084230072 chr8.23506_chr8_95532369_95535280_-_1.R.tl.stomach 57.6033593875157 61.4363011796622 70.8594412716582 chr8.23506_chr8_95532369_95535280_-_1.R.tl.testicle 56.9866343087249 66.2268745647898 91.2855988608398 diffExp=-3.43596641342124,-13.2178730047889,-2.40313165180004,6.18401992073225,-4.12296003548676,-0.407085179545049,-3.83294179214653,-9.24024025606492 diffExpScore=1.36116328013131 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,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 62.3750002453653 64.3317553364416 60.9210481124162 cerebhem 61.9604017129671 64.1513426241837 55.2906548633796 cortex 62.8225532615033 66.796777107486 76.962092882121 heart 57.9216070683048 65.2917097328475 64.8513965120147 kidney 62.7783622796622 59.269900559098 62.183914152248 liver 55.5186540858826 64.836066639033 64.5009912613676 stomach 57.4066021369956 56.866445522144 58.1734931459631 testicle 54.366413233338 63.317996969174 60.8334980427095 cont.diffExp=-1.95675509107635,-2.19094091121661,-3.97422384598276,-7.37010266454276,3.50846172056416,-9.3174125531503,0.540156614851604,-8.95158373583602 cont.diffExpScore=1.23108700599937 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=0.204121001298718 cont.tran.correlation=0.0912618667377428 tran.covariance=0.00118790379356496 cont.tran.covariance=0.000271912021018308 tran.mean=57.3618153640669 cont.tran.mean=61.2507242821517 weightedLogRatios: wLogRatio Lung -0.247000810332710 cerebhem -0.994183802334727 cortex -0.16702831341668 heart 0.41397203298404 kidney -0.308411754307322 liver -0.0294428570681687 stomach -0.263206633287202 testicle -0.618802875179324 cont.weightedLogRatios: wLogRatio Lung -0.128145608519556 cerebhem -0.143997508017718 cortex -0.255851087614669 heart -0.493348823945995 kidney 0.236410670203748 liver -0.63520034947495 stomach 0.0382449472304517 testicle -0.620660236097265 varWeightedLogRatios=0.169154132975348 cont.varWeightedLogRatios=0.0986837006267633 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.6084530724326 0.091882000713245 39.2726871903262 3.12677687108159e-125 *** df.mm.trans1 0.317096439160864 0.0778230947918683 4.07458017454732 5.80270859046465e-05 *** df.mm.trans2 0.46778340064852 0.0778230947918683 6.01085579929158 4.96834951629976e-09 *** df.mm.exp2 -0.0645189438738359 0.108378095364692 -0.595313505526461 0.552049498287453 df.mm.exp3 0.0169949533108839 0.108378095364692 0.156811699390877 0.875490920269426 df.mm.exp4 0.0902733260485272 0.108378095364692 0.83294807631337 0.405487587479907 df.mm.exp5 0.00397184810920746 0.108378095364692 0.0366480707733624 0.970788183148732 df.mm.exp6 0.148747778217402 0.108378095364692 1.37248931822308 0.170860494552181 df.mm.exp7 0.247769144391546 0.108378095364692 2.28615518253761 0.0228902568336234 * df.mm.exp8 0.058795639659432 0.108378095364692 0.542504824997937 0.587843735807283 df.mm.trans1:exp2 -0.0939526560496618 0.0938581837995958 -1.0010065424904 0.317570642419097 df.mm.trans2:exp2 0.0956509798719542 0.0938581837995958 1.01910111617103 0.308915318328715 df.mm.trans1:exp3 0.0380455719213780 0.0938581837995958 0.405351673995867 0.685486552725983 df.mm.trans2:exp3 0.0177192505920268 0.0938581837995959 0.188787486340676 0.850377576693809 df.mm.trans1:exp4 0.0847714650209645 0.0938581837995959 0.90318671840025 0.367097800865565 df.mm.trans2:exp4 -0.0770939264668968 0.0938581837995958 -0.82138736704629 0.412029635417005 df.mm.trans1:exp5 -0.0652740754431748 0.0938581837995958 -0.695454277940715 0.487269148191879 df.mm.trans2:exp5 -0.0489912994915986 0.0938581837995959 -0.521971526704628 0.602046811238604 df.mm.trans1:exp6 -0.129630410892012 0.0938581837995958 -1.38113061263572 0.168190612079661 df.mm.trans2:exp6 -0.183667154901765 0.0938581837995959 -1.95685818184941 0.0512226752355377 . df.mm.trans1:exp7 -0.188545010843961 0.0938581837995958 -2.00882867333699 0.045384573218607 * df.mm.trans2:exp7 -0.185491069093134 0.0938581837995958 -1.97629084203452 0.048969712737282 * df.mm.trans1:exp8 -0.0103356402863057 0.0938581837995958 -0.110119755868856 0.912382576631824 df.mm.trans2:exp8 0.0785678915223378 0.0938581837995959 0.837091538976446 0.403158120785916 df.mm.trans1:probe2 0.0286258122001543 0.0469290918997979 0.60998010064366 0.542302554484474 df.mm.trans1:probe3 0.151738084185105 0.0469290918997979 3.23334797334441 0.00134948067985614 ** df.mm.trans1:probe4 -0.0290633036288789 0.0469290918997979 -0.619302493449784 0.536152324062736 df.mm.trans1:probe5 0.36918748378677 0.0469290918997979 7.86692153717894 5.47677709556567e-14 *** df.mm.trans1:probe6 0.0987771154970514 0.0469290918997979 2.10481625572381 0.0360770609322127 * df.mm.trans2:probe2 -0.0266946637993472 0.0469290918997979 -0.568829753968928 0.569865756109694 df.mm.trans2:probe3 -0.132000408456835 0.0469290918997979 -2.81276289638589 0.0052114708048407 ** df.mm.trans2:probe4 0.255978286561112 0.0469290918997979 5.45457574818776 9.75993321706097e-08 *** df.mm.trans2:probe5 -0.235944321395690 0.0469290918997979 -5.02767711549743 8.23252940899743e-07 *** df.mm.trans2:probe6 -0.0459618644656703 0.0469290918997979 -0.979389598328628 0.328118234682604 df.mm.trans3:probe2 0.129023876577345 0.0469290918997979 2.74933674090338 0.006306653138342 ** df.mm.trans3:probe3 -0.148127163331151 0.0469290918997979 -3.15640378568222 0.00174731845292031 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19065367589188 0.144316371188805 29.0379645869099 3.52355813247437e-92 *** df.mm.trans1 -0.0980276957911476 0.122234458847889 -0.801964492787876 0.423161293095483 df.mm.trans2 0.00606605451162672 0.122234458847889 0.0496263866081777 0.960450697315241 df.mm.exp2 0.0874974179294679 0.170226304586030 0.514006446549206 0.607597863186192 df.mm.exp3 -0.188983227425246 0.170226304586030 -1.11018815737574 0.267741208698463 df.mm.exp4 -0.121782076262413 0.170226304586030 -0.715412794506541 0.474869228634551 df.mm.exp5 -0.0960234858082695 0.170226304586030 -0.56409311147174 0.573081022265244 df.mm.exp6 -0.165738682561605 0.170226304586030 -0.97363731747959 0.33096298417613 df.mm.exp7 -0.160204229566367 0.170226304586030 -0.94112499214481 0.347341952440192 df.mm.exp8 -0.151863628340081 0.170226304586030 -0.892127857145204 0.372986481792230 df.mm.trans1:exp2 -0.0941664769829535 0.147420304163849 -0.638761923040756 0.523429099790722 df.mm.trans2:exp2 -0.0903057691242216 0.147420304163849 -0.612573482577081 0.540588089023272 df.mm.trans1:exp3 0.196132806790734 0.147420304163849 1.33043279148810 0.184311354786565 df.mm.trans2:exp3 0.226584688423152 0.147420304163849 1.53699783559879 0.125269802518515 df.mm.trans1:exp4 0.047708012294597 0.147420304163849 0.323619005978799 0.746435282717326 df.mm.trans2:exp4 0.136593776380315 0.147420304163849 0.926560131286251 0.354844619886977 df.mm.trans1:exp5 0.102469392219239 0.147420304163849 0.695083304843478 0.487501279207728 df.mm.trans2:exp5 0.0140717123586286 0.147420304163849 0.0954530140094454 0.924014017869368 df.mm.trans1:exp6 0.0492931984919463 0.147420304163849 0.334371840917922 0.738315322841926 df.mm.trans2:exp6 0.173547343533285 0.147420304163849 1.17722822861902 0.239968485283101 df.mm.trans1:exp7 0.0771989880805072 0.147420304163849 0.523665912361062 0.600868922706594 df.mm.trans2:exp7 0.0368563156362846 0.147420304163849 0.250008408579330 0.802739288430416 df.mm.trans1:exp8 0.0144456297254397 0.147420304163849 0.0979894174508298 0.922001287845766 df.mm.trans2:exp8 0.135979857903497 0.147420304163849 0.922395722046289 0.357008552222549 df.mm.trans1:probe2 0.035904085961387 0.0737101520819246 0.487098248304816 0.626518080228489 df.mm.trans1:probe3 0.0598608389971368 0.0737101520819246 0.81211118558818 0.417324093673963 df.mm.trans1:probe4 0.0379131590967306 0.0737101520819246 0.514354644860756 0.607354716605208 df.mm.trans1:probe5 0.159117889895666 0.0737101520819246 2.15869707769447 0.0316076216482096 * df.mm.trans1:probe6 0.0720512271910565 0.0737101520819246 0.977493942910005 0.329053951982778 df.mm.trans2:probe2 -0.0849701720808515 0.0737101520819245 -1.15276077556335 0.249858356035619 df.mm.trans2:probe3 -0.067586079622833 0.0737101520819246 -0.916916838642729 0.359868209641349 df.mm.trans2:probe4 -0.103825801315348 0.0737101520819245 -1.40856854019174 0.159921673979955 df.mm.trans2:probe5 -0.0449826385750383 0.0737101520819246 -0.610263814474874 0.542114860772217 df.mm.trans2:probe6 0.00736746134422051 0.0737101520819246 0.0999517859633773 0.92044441936294 df.mm.trans3:probe2 0.0141786535940834 0.0737101520819246 0.192356862570635 0.847583187677762 df.mm.trans3:probe3 0.0184814404087611 0.0737101520819246 0.250731274956807 0.80218080486749