chr12.5326_chr12_22740182_22742804_+_0.R fitVsDatCorrelation=0.760747184639037 cont.fitVsDatCorrelation=0.247659286131363 fstatistic=8799.26782591544,43,485 cont.fstatistic=3942.80918569018,43,485 residuals=-0.612238980234474,-0.0748327349701793,-0.00350289104765985,0.0719635812544511,0.764429813815141 cont.residuals=-0.64295820528966,-0.148349084521079,-0.0399779790715267,0.0934457615480958,0.94663729079878 predictedValues: Include Exclude Both chr12.5326_chr12_22740182_22742804_+_0.R.tl.Lung 54.4843353579843 58.8302254209628 62.7415823791035 chr12.5326_chr12_22740182_22742804_+_0.R.tl.cerebhem 73.1200299766915 70.619880207787 63.721449326497 chr12.5326_chr12_22740182_22742804_+_0.R.tl.cortex 53.001640895949 55.2154404572623 64.1417858047433 chr12.5326_chr12_22740182_22742804_+_0.R.tl.heart 53.1722039124762 54.8849803974787 62.2027555241773 chr12.5326_chr12_22740182_22742804_+_0.R.tl.kidney 53.7490614783997 57.9392806135285 60.5748518594752 chr12.5326_chr12_22740182_22742804_+_0.R.tl.liver 59.094872239666 55.7265140131063 60.7730012225941 chr12.5326_chr12_22740182_22742804_+_0.R.tl.stomach 53.7592970235542 53.7667131928499 62.0760177969653 chr12.5326_chr12_22740182_22742804_+_0.R.tl.testicle 59.0646152380172 59.958762479341 67.7319345653247 diffExp=-4.34589006297845,2.50014976890452,-2.21379956131325,-1.71277648500245,-4.19021913512885,3.36835822655968,-0.0074161692957233,-0.894147241323765 diffExpScore=2.26381164646584 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 64.6790543363295 56.3574829838685 55.7445090803766 cerebhem 61.9411489748761 52.0134291707433 64.7976867178748 cortex 56.1917968536738 59.7076285475089 62.1868176765007 heart 59.2681560340092 57.8596530410374 59.2940484480167 kidney 62.7517700124703 58.6387691946355 61.7041265934917 liver 56.7370993109771 57.2266042036277 61.2318602640786 stomach 59.2787146768806 59.3433262589857 58.3533587600563 testicle 61.1884071531876 57.2101002079473 58.4698739434342 cont.diffExp=8.32157135246096,9.92771980413285,-3.51583169383518,1.40850299297178,4.11300081783488,-0.489504892650615,-0.0646115821050373,3.97830694524031 cont.diffExpScore=1.28930879928989 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=0.919430896341534 cont.tran.correlation=-0.440654227706399 tran.covariance=0.00857064092417164 cont.tran.covariance=-0.000924283901399701 tran.mean=57.8992408065659 cont.tran.mean=58.7745713100474 weightedLogRatios: wLogRatio Lung -0.309754663495981 cerebhem 0.148719805308362 cortex -0.163301869367557 heart -0.126479361935628 kidney -0.301918564193209 liver 0.23767510676523 stomach -0.000549641176168182 testicle -0.0613943858658131 cont.weightedLogRatios: wLogRatio Lung 0.564741147322898 cerebhem 0.705516403940199 cortex -0.246343691613766 heart 0.0978921863699898 kidney 0.278300648494991 liver -0.0347293935174232 stomach -0.00444767038689209 testicle 0.274310460448728 varWeightedLogRatios=0.038646752791318 cont.varWeightedLogRatios=0.100946596720436 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95173174894245 0.0764209614765935 51.7100501300654 1.77346911078832e-199 *** df.mm.trans1 0.0470189430871914 0.0611789761377562 0.768547400684169 0.442536103327590 df.mm.trans2 0.161114766817433 0.0611789761377562 2.63349890744578 0.00872089358950784 ** df.mm.exp2 0.461348228623266 0.0819228169777937 5.63149859395485 3.02953175896215e-08 *** df.mm.exp3 -0.113075102873835 0.0819228169777937 -1.38026385133321 0.168141265990231 df.mm.exp4 -0.0851683655289384 0.0819228169777937 -1.03961715027481 0.299035793898711 df.mm.exp5 0.00629738839174773 0.0819228169777937 0.0768697735754707 0.938758843390957 df.mm.exp6 0.0589100006019685 0.0819228169777937 0.719091490932701 0.472430868486622 df.mm.exp7 -0.092733118451247 0.0819228169777937 -1.13195715030629 0.258211795679592 df.mm.exp8 0.0231867036896857 0.0819228169777938 0.283031083952726 0.777273769740428 df.mm.trans1:exp2 -0.167159127242445 0.0642655449812709 -2.60106916219509 0.00957720809731709 ** df.mm.trans2:exp2 -0.278692295021383 0.0642655449812709 -4.33657405539163 1.76123853086616e-05 *** df.mm.trans1:exp3 0.085484740516938 0.0642655449812709 1.33017996722585 0.184084132914363 df.mm.trans2:exp3 0.0496619748677435 0.0642655449812709 0.77276205908184 0.440039635559232 df.mm.trans1:exp4 0.0607909067559369 0.0642655449812709 0.945933108847881 0.344653729326612 df.mm.trans2:exp4 0.0157523349079944 0.0642655449812709 0.245113223774655 0.806472440594899 df.mm.trans1:exp5 -0.0198844181340840 0.0642655449812709 -0.309410246810775 0.757142359635094 df.mm.trans2:exp5 -0.021557572790748 0.0642655449812709 -0.335445265375570 0.73743427526212 df.mm.trans1:exp6 0.0223209201809272 0.0642655449812709 0.347323284777750 0.728499126273602 df.mm.trans2:exp6 -0.113109712933679 0.0642655449812709 -1.76003662563888 0.0790319307359157 . df.mm.trans1:exp7 0.0793365026511948 0.0642655449812709 1.23451069580622 0.217610454014354 df.mm.trans2:exp7 0.00273191964511422 0.0642655449812709 0.0425098650592692 0.966109742416057 df.mm.trans1:exp8 0.0575320787460342 0.0642655449812709 0.895224319077991 0.371111258482366 df.mm.trans2:exp8 -0.00418543037589684 0.0642655449812709 -0.0651271280297494 0.948099616435413 df.mm.trans1:probe2 -0.0246046012952203 0.0439996108207562 -0.559200430100473 0.576283060541303 df.mm.trans1:probe3 0.0690245642531598 0.0439996108207562 1.56875397226465 0.117357439742391 df.mm.trans1:probe4 -0.0584827295536736 0.0439996108207562 -1.32916470084061 0.184418544746967 df.mm.trans1:probe5 0.0161586294702574 0.0439996108207562 0.36724482714367 0.713596591918112 df.mm.trans1:probe6 -0.0154951636578010 0.0439996108207562 -0.352165925306125 0.724866799456211 df.mm.trans2:probe2 -0.155959819636101 0.0439996108207562 -3.5445727070506 0.00043134744372501 *** df.mm.trans2:probe3 -0.102876608380923 0.0439996108207562 -2.33812541660920 0.0197862764121038 * df.mm.trans2:probe4 -0.067312400992396 0.0439996108207562 -1.52984082669755 0.126708072513636 df.mm.trans2:probe5 -0.106469785679550 0.0439996108207562 -2.41978925934783 0.0158961539617569 * df.mm.trans2:probe6 -0.178433467806722 0.0439996108207562 -4.05534195594631 5.83181576285579e-05 *** df.mm.trans3:probe2 0.296974647591193 0.0439996108207562 6.74948350795637 4.24674704788669e-11 *** df.mm.trans3:probe3 -0.294916149502453 0.0439996108207562 -6.70269904667725 5.69772108275912e-11 *** df.mm.trans3:probe4 -0.215397651528208 0.0439996108207562 -4.89544447121785 1.33780295565238e-06 *** df.mm.trans3:probe5 0.332358578568258 0.0439996108207562 7.55367087045853 2.12187666589778e-13 *** df.mm.trans3:probe6 -0.244811270178558 0.0439996108207562 -5.56394171702701 4.36793750338592e-08 *** df.mm.trans3:probe7 0.0494119239665804 0.0439996108207562 1.12300820495601 0.261989603543999 df.mm.trans3:probe8 -0.019125028288131 0.0439996108207562 -0.434663578413039 0.663999965352655 df.mm.trans3:probe9 -0.155505507665232 0.0439996108207562 -3.53424734365773 0.000448110319707216 *** df.mm.trans3:probe10 0.0426001051725265 0.0439996108207562 0.968192772114941 0.333430658726564 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25377543521878 0.114075072031301 37.2892636355434 1.53319804159715e-144 *** df.mm.trans1 -0.062806927405968 0.0913230607789896 -0.687744441220237 0.491942496409832 df.mm.trans2 -0.228040498258610 0.0913230607789896 -2.49707463058525 0.0128527749419247 * df.mm.exp2 -0.273956767930125 0.122287799933153 -2.24026246346634 0.0255259614817224 * df.mm.exp3 -0.192285993141676 0.122287799933153 -1.57240536870224 0.11650866995307 df.mm.exp4 -0.122789997201844 0.122287799933153 -1.00410668332381 0.315827888699663 df.mm.exp5 -0.092141483798084 0.122287799933153 -0.753480591264638 0.451526782589226 df.mm.exp6 -0.209593969001271 0.122287799933153 -1.71394014051968 0.08717865163518 . df.mm.exp7 -0.0813004800292223 0.122287799933153 -0.664829035060438 0.506475837385491 df.mm.exp8 -0.0881969671411467 0.122287799933153 -0.721224579961027 0.471118865931406 df.mm.trans1:exp2 0.230704078155818 0.095930442789766 2.40490996858434 0.0165501502636086 * df.mm.trans2:exp2 0.193743680039311 0.095930442789766 2.01962666287182 0.0439715705790482 * df.mm.trans1:exp3 0.0516193619782741 0.095930442789766 0.538091563815662 0.590760674904119 df.mm.trans2:exp3 0.250030760328071 0.095930442789766 2.60637554729128 0.00943213867395587 ** df.mm.trans1:exp4 0.0354247472788681 0.095930442789766 0.369275344183518 0.712083666048365 df.mm.trans2:exp4 0.149095273792276 0.095930442789766 1.55420187227762 0.120788541216418 df.mm.trans1:exp5 0.0618908548306196 0.095930442789766 0.645163860717863 0.519126072908808 df.mm.trans2:exp5 0.131822525564647 0.095930442789766 1.37414695201125 0.170030743879512 df.mm.trans1:exp6 0.0785848606522716 0.095930442789766 0.819185843064358 0.413082738879134 df.mm.trans2:exp6 0.224897841298515 0.095930442789766 2.34438448065318 0.0194610564194604 * df.mm.trans1:exp7 -0.00588663540844817 0.095930442789766 -0.0613635800821735 0.951094935048431 df.mm.trans2:exp7 0.132925121153262 0.095930442789766 1.38564065053438 0.166493497727846 df.mm.trans1:exp8 0.0327172991630395 0.095930442789766 0.341052310523993 0.733211896824593 df.mm.trans2:exp8 0.103212400586893 0.095930442789766 1.07590872704596 0.282502989556053 df.mm.trans1:probe2 -0.101520931669290 0.0656790843342671 -1.54571173910722 0.122826428483495 df.mm.trans1:probe3 -0.0568362470402077 0.0656790843342671 -0.865362963206754 0.387267438804076 df.mm.trans1:probe4 -0.0298896117591291 0.0656790843342671 -0.455085695272622 0.649251232265353 df.mm.trans1:probe5 -0.0624564428656196 0.0656790843342671 -0.95093352014096 0.342111756562267 df.mm.trans1:probe6 -0.0937942684454166 0.0656790843342671 -1.42806906332707 0.153915585999861 df.mm.trans2:probe2 0.00664936242884843 0.0656790843342671 0.101240181653678 0.919401639210204 df.mm.trans2:probe3 0.0288165626370862 0.0656790843342671 0.438747935193908 0.661039569211985 df.mm.trans2:probe4 0.0345891990887165 0.0656790843342671 0.526639484081085 0.598684732437196 df.mm.trans2:probe5 0.0383095444123847 0.0656790843342671 0.583283777487093 0.559973404263754 df.mm.trans2:probe6 -0.0126832373037375 0.0656790843342671 -0.193109228490266 0.84695426592318 df.mm.trans3:probe2 0.0709574637174117 0.0656790843342671 1.08036621455136 0.280516067299026 df.mm.trans3:probe3 0.106055998361345 0.0656790843342671 1.61476061118002 0.107012881423545 df.mm.trans3:probe4 0.0852285437728465 0.0656790843342671 1.29765121783800 0.195024054050054 df.mm.trans3:probe5 0.0268927196520297 0.0656790843342671 0.409456372978069 0.682385546610153 df.mm.trans3:probe6 0.064864493512749 0.0656790843342671 0.98759740898073 0.323842287043527 df.mm.trans3:probe7 0.077228473385436 0.0656790843342671 1.17584576837870 0.240233373918547 df.mm.trans3:probe8 0.0263908982340954 0.0656790843342671 0.401815867282521 0.687996546642635 df.mm.trans3:probe9 0.0443357648511857 0.0656790843342671 0.675036281345569 0.499974388269966 df.mm.trans3:probe10 0.0765546855486647 0.0656790843342671 1.16558697985263 0.244354377265063