chr8.23603_chr8_14029848_14061300_-_2.R fitVsDatCorrelation=0.791366146770779 cont.fitVsDatCorrelation=0.265860598852136 fstatistic=12001.4619071537,43,485 cont.fstatistic=4819.82996172241,43,485 residuals=-0.465519150754191,-0.0719455509744657,-0.00351532288005701,0.0708215278821867,0.390254837499751 cont.residuals=-0.392140795936609,-0.131328167206752,-0.0378969993395046,0.108449765629377,0.653332124127035 predictedValues: Include Exclude Both chr8.23603_chr8_14029848_14061300_-_2.R.tl.Lung 46.1385486733969 46.2549494863019 73.4797089723512 chr8.23603_chr8_14029848_14061300_-_2.R.tl.cerebhem 47.1870634986943 50.8256764666242 61.9010787476098 chr8.23603_chr8_14029848_14061300_-_2.R.tl.cortex 48.6364272408214 53.432030071988 65.5845761815336 chr8.23603_chr8_14029848_14061300_-_2.R.tl.heart 50.8974267738102 48.9821375424606 66.8886189044708 chr8.23603_chr8_14029848_14061300_-_2.R.tl.kidney 46.845204441472 45.3899396004136 72.6993392976948 chr8.23603_chr8_14029848_14061300_-_2.R.tl.liver 50.8919475941293 49.7490451633822 65.9751703438103 chr8.23603_chr8_14029848_14061300_-_2.R.tl.stomach 49.5493368322183 47.5012466828040 64.3293104704232 chr8.23603_chr8_14029848_14061300_-_2.R.tl.testicle 48.3256233110092 49.5520279963509 71.0124281800996 diffExp=-0.116400812904963,-3.63861296792989,-4.79560283116663,1.91528923134968,1.45526484105843,1.14290243074705,2.04809014941436,-1.22640468534173 diffExpScore=3.87585487441353 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 52.2643767919471 55.387477690708 58.1395615200424 cerebhem 51.1730272822987 50.0617504675591 54.0573973393004 cortex 53.7447105630905 54.2539965562973 55.4961680774958 heart 52.4770416895861 51.913117888719 55.0020683940958 kidney 53.3975137511953 53.1966812092635 54.3457257166031 liver 53.1630081947565 51.3689041729211 56.940762548755 stomach 52.9585292467323 55.1136832541013 53.0351206286676 testicle 51.2886365306588 50.9541582201561 52.8424164026328 cont.diffExp=-3.12310089876085,1.11127681473964,-0.509285993206824,0.563923800867038,0.200832541931845,1.79410402183540,-2.15515400736894,0.334478310502654 cont.diffExpScore=3.51865571241205 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=0.331254234275949 cont.tran.correlation=0.570893475812245 tran.covariance=0.000684441017483871 cont.tran.covariance=0.00039791124703181 tran.mean=48.7599144609923 cont.tran.mean=52.6697883443744 weightedLogRatios: wLogRatio Lung -0.00965769076667854 cerebhem -0.289050210862880 cortex -0.369698622571621 heart 0.149998922575263 kidney 0.120901497933536 liver 0.0889991612343214 stomach 0.163864748529331 testicle -0.0975006645126494 cont.weightedLogRatios: wLogRatio Lung -0.231302929974588 cerebhem 0.0861578708727624 cortex -0.0376215059540565 heart 0.0427305691113209 kidney 0.0149818111899130 liver 0.135815513273056 stomach -0.159134835234543 testicle 0.0257408559462368 varWeightedLogRatios=0.0420552795004454 cont.varWeightedLogRatios=0.0152774933225361 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.11186801738762 0.0660239414753915 47.1324181478544 3.43814717364229e-183 *** df.mm.trans1 0.649437212240434 0.0574222428570003 11.3098545080822 1.75370661732263e-26 *** df.mm.trans2 0.690401277880256 0.0539083224740294 12.8069516207420 1.49637276822744e-32 *** df.mm.exp2 0.288175867097511 0.0732469865839254 3.93430338280638 9.56231106003413e-05 *** df.mm.exp3 0.310634641106611 0.0732469865839254 4.24092041999148 2.66661512364597e-05 *** df.mm.exp4 0.249431255928084 0.0732469865839254 3.40534495084366 0.000715661654865935 *** df.mm.exp5 0.00699886110248144 0.0732469865839254 0.0955515227163952 0.923916234625897 df.mm.exp6 0.278609545071014 0.0732469865839254 3.80369975701031 0.000160753307429798 *** df.mm.exp7 0.230901494936808 0.0732469865839254 3.15236852334183 0.00171980628406871 ** df.mm.exp8 0.149322233176142 0.0732469865839254 2.03861264661107 0.0420304998697843 * df.mm.trans1:exp2 -0.265704887894554 0.066865044702157 -3.97374875135592 8.15031637270394e-05 *** df.mm.trans2:exp2 -0.193942672102284 0.0598059141090341 -3.24286778308748 0.00126486969675924 ** df.mm.trans1:exp3 -0.257910656659330 0.066865044702157 -3.85718214663827 0.000130181351161525 *** df.mm.trans2:exp3 -0.166392735027535 0.0598059141090341 -2.78221205220906 0.00560942312163358 ** df.mm.trans1:exp4 -0.151267685596275 0.066865044702157 -2.26228347367568 0.0241215650970029 * df.mm.trans2:exp4 -0.192144038380761 0.0598059141090341 -3.21279327041899 0.00140194949419332 ** df.mm.trans1:exp5 0.00820098509760546 0.066865044702157 0.122649811035584 0.902435214056744 df.mm.trans2:exp5 -0.0258768494681402 0.0598059141090341 -0.432680443960162 0.665439270371507 df.mm.trans1:exp6 -0.180553631942529 0.066865044702157 -2.70026936715267 0.00717055762558245 ** df.mm.trans2:exp6 -0.205786748499109 0.0598059141090341 -3.44090967531961 0.000629849144839088 *** df.mm.trans1:exp7 -0.159581415465003 0.066865044702157 -2.38661943883895 0.0173864765300668 * df.mm.trans2:exp7 -0.204314012439544 0.0598059141090341 -3.41628441740816 0.000688170505892224 *** df.mm.trans1:exp8 -0.103009107255942 0.066865044702157 -1.54055243236261 0.124077916260759 df.mm.trans2:exp8 -0.0804675191088106 0.0598059141090341 -1.34547762219816 0.179099733446870 df.mm.trans1:probe2 -0.0211993584448169 0.0366234932919627 -0.578845886595675 0.562961883041061 df.mm.trans1:probe3 0.0576799368653079 0.0366234932919627 1.57494361352925 0.115921508422315 df.mm.trans1:probe4 -0.00326862259005019 0.0366234932919627 -0.089249339597201 0.928920596745112 df.mm.trans1:probe5 0.151045790350973 0.0366234932919627 4.12428681084120 4.37560646220612e-05 *** df.mm.trans1:probe6 0.00771660926938999 0.0366234932919627 0.210701071246075 0.833209032615488 df.mm.trans1:probe7 0.152679802296031 0.0366234932919627 4.1689033069256 3.62541933122677e-05 *** df.mm.trans1:probe8 0.0876509419917922 0.0366234932919627 2.39329823872994 0.0170768780694574 * df.mm.trans1:probe9 0.0991781939977937 0.0366234932919627 2.70804844330781 0.00700711359280608 ** df.mm.trans1:probe10 0.319228589651549 0.0366234932919627 8.71649755272269 4.57711489490103e-17 *** df.mm.trans1:probe11 0.173797095246724 0.0366234932919627 4.74550840525268 2.74065267481736e-06 *** df.mm.trans1:probe12 0.100988105791746 0.0366234932919627 2.75746786322835 0.00604487095632499 ** df.mm.trans2:probe2 0.079966363134008 0.0366234932919627 2.18347175395083 0.0294796189160897 * df.mm.trans2:probe3 -0.00937307154576642 0.0366234932919627 -0.255930570878213 0.798112959228555 df.mm.trans2:probe4 0.092206620431642 0.0366234932919627 2.51769048071330 0.0121337637154322 * df.mm.trans2:probe5 0.109918844755611 0.0366234932919627 3.00132059711883 0.00282665624037631 ** df.mm.trans2:probe6 0.0462730320725419 0.0366234932919627 1.26347947487295 0.207023915075306 df.mm.trans3:probe2 -0.133249021282256 0.0366234932919627 -3.63834821053235 0.000303816169317046 *** df.mm.trans3:probe3 -0.382443087173573 0.0366234932919627 -10.4425616673083 3.66334643438083e-23 *** df.mm.trans3:probe4 -0.103096722771367 0.0366234932919627 -2.81504339166883 0.00507550237464038 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91828440561939 0.104111591173958 37.6354290760229 5.47167223003944e-146 *** df.mm.trans1 0.0572879077461063 0.0905477761403868 0.632681554291141 0.527239785760812 df.mm.trans2 0.119549997154524 0.085006758228482 1.40635873718648 0.160258008305658 df.mm.exp2 -0.0493987479827699 0.115501440107031 -0.427689455101114 0.669067060754044 df.mm.exp3 0.0537857509220325 0.115501440107031 0.465671690952004 0.641659560001705 df.mm.exp4 -0.00524569484063798 0.115501440107031 -0.0454167050711835 0.963793859415823 df.mm.exp5 0.0485720428847586 0.115501440107031 0.420531924448290 0.67428317542437 df.mm.exp6 -0.0374378292345775 0.115501440107031 -0.324133008210853 0.745977075205082 df.mm.exp7 0.100130625663456 0.115501440107031 0.866921014756775 0.386413977247779 df.mm.exp8 -0.00674080891125168 0.115501440107031 -0.0583612542406851 0.953484914182592 df.mm.trans1:exp2 0.0282963234121284 0.105437906951588 0.268369547824207 0.788528985691605 df.mm.trans2:exp2 -0.0516975329744776 0.094306530939619 -0.548186137899374 0.583816319507261 df.mm.trans1:exp3 -0.0258555037950941 0.105437906951588 -0.245220192079172 0.80638966673608 df.mm.trans2:exp3 -0.0744626257142488 0.094306530939619 -0.789580795437427 0.430158466533671 df.mm.trans1:exp4 0.0093064607335536 0.105437906951588 0.0882648470803456 0.929702610586488 df.mm.trans2:exp4 -0.0595363275095513 0.094306530939619 -0.631306516275847 0.528137536226708 df.mm.trans1:exp5 -0.0271228638787801 0.105437906951588 -0.257240158335405 0.797102483106111 df.mm.trans2:exp5 -0.0889295655074444 0.094306530939619 -0.942984166858843 0.346158483335553 df.mm.trans1:exp6 0.0544856421299768 0.105437906951588 0.516755725765631 0.605562241455311 df.mm.trans2:exp6 -0.0378826922972056 0.094306530939619 -0.401697442581792 0.688083651561311 df.mm.trans1:exp7 -0.0869364923511284 0.105437906951588 -0.824527865400872 0.410045004853754 df.mm.trans2:exp7 -0.105086139154275 0.094306530939619 -1.11430394170217 0.265700704583208 df.mm.trans1:exp8 -0.0121049804825145 0.105437906951588 -0.114806722103015 0.908645834335633 df.mm.trans2:exp8 -0.0766863547480558 0.094306530939619 -0.813160594329943 0.416524985471661 df.mm.trans1:probe2 -0.0969129933674032 0.0577507200535153 -1.67812614764972 0.0939666711385832 . df.mm.trans1:probe3 0.00804831717468914 0.0577507200535153 0.139363061919073 0.889221110517925 df.mm.trans1:probe4 -0.0155124237920554 0.0577507200535154 -0.268610049843200 0.788343994789179 df.mm.trans1:probe5 -0.0397851871989386 0.0577507200535153 -0.688912400781691 0.491207815614003 df.mm.trans1:probe6 -0.0776078599342811 0.0577507200535153 -1.34384229083836 0.179627713893139 df.mm.trans1:probe7 -0.0529787833116921 0.0577507200535153 -0.917370091015293 0.359404688775203 df.mm.trans1:probe8 0.0354101880605349 0.0577507200535153 0.613155784511806 0.540060760358001 df.mm.trans1:probe9 0.0168006175011913 0.0577507200535153 0.290916156294204 0.771239807572867 df.mm.trans1:probe10 0.0197275990671857 0.0577507200535154 0.341599187835318 0.732800502193809 df.mm.trans1:probe11 -0.019936875840476 0.0577507200535153 -0.345222982882313 0.730076411859388 df.mm.trans1:probe12 -0.0853695016484833 0.0577507200535154 -1.47824133741319 0.139992308823254 df.mm.trans2:probe2 -0.0379177617877925 0.0577507200535153 -0.656576433205604 0.511764706286752 df.mm.trans2:probe3 -0.00172725836578984 0.0577507200535153 -0.0299088628538182 0.976152040732378 df.mm.trans2:probe4 -0.0323243742814147 0.0577507200535153 -0.559722445909956 0.575927171000889 df.mm.trans2:probe5 -0.0623832852649636 0.0577507200535153 -1.08021657924188 0.280582612320586 df.mm.trans2:probe6 -0.100456010640737 0.0577507200535153 -1.73947633116346 0.082585428390982 . df.mm.trans3:probe2 -0.0676992595269452 0.0577507200535154 -1.17226693388777 0.241665392332113 df.mm.trans3:probe3 -0.0853802640483778 0.0577507200535154 -1.47842769699251 0.139942467652027 df.mm.trans3:probe4 0.0239756947246589 0.0577507200535154 0.415158368630582 0.678209565732343