chr15.8180_chr15_83408116_83410233_-_0.R fitVsDatCorrelation=0.823249110414185 cont.fitVsDatCorrelation=0.392541534900051 fstatistic=5448.58335357591,36,324 cont.fstatistic=2070.13768382740,36,324 residuals=-0.602712307789601,-0.0821155145758157,-0.00380872594794118,0.0952239561302787,0.696209277741889 cont.residuals=-0.462526287374302,-0.162838955889122,-0.0433467370208896,0.106388083396501,1.32233157964789 predictedValues: Include Exclude Both chr15.8180_chr15_83408116_83410233_-_0.R.tl.Lung 46.9721599150368 48.2827932269766 57.1795392140532 chr15.8180_chr15_83408116_83410233_-_0.R.tl.cerebhem 45.8203032313357 59.9341575844331 55.4642526765628 chr15.8180_chr15_83408116_83410233_-_0.R.tl.cortex 44.2371448091407 42.7235855527709 56.0636564315605 chr15.8180_chr15_83408116_83410233_-_0.R.tl.heart 48.7518886397861 43.7859617185979 54.0803777034099 chr15.8180_chr15_83408116_83410233_-_0.R.tl.kidney 58.0046962645032 45.9977532484692 59.5870837020217 chr15.8180_chr15_83408116_83410233_-_0.R.tl.liver 110.963637111954 47.2300607632919 69.2328918277227 chr15.8180_chr15_83408116_83410233_-_0.R.tl.stomach 47.9348214654365 43.7581382134409 59.5466510040257 chr15.8180_chr15_83408116_83410233_-_0.R.tl.testicle 47.1440461305632 51.3814191967768 53.5995786263632 diffExp=-1.31063331193987,-14.1138543530974,1.51355925636982,4.96592692118812,12.006943016034,63.733576348662,4.17668325199556,-4.23737306621366 diffExpScore=1.56579048856311 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,1,0,0 diffExp1.3Score=2 diffExp1.2=0,-1,0,0,1,1,0,0 diffExp1.2Score=1.5 cont.predictedValues: Include Exclude Both Lung 46.6480272676628 45.382652879069 49.2000694737751 cerebhem 61.8925322700008 48.1190412835299 54.5122700994854 cortex 47.3351790627677 47.8837107567242 50.3348850230079 heart 50.7685901887406 51.2236161874307 49.6027429945116 kidney 49.4144943911956 45.4802360349318 51.3767576690362 liver 48.0822527106528 52.8300503864905 59.7334022621111 stomach 54.4528200450167 53.0257015903341 49.4516025851128 testicle 55.7304671693833 49.5328838919906 64.1103551540777 cont.diffExp=1.26537438859387,13.7734909864709,-0.548531693956456,-0.455025998690054,3.93425835626378,-4.74779767583767,1.42711845468267,6.19758327739272 cont.diffExpScore=1.48075092641217 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.0826194681165255 cont.tran.correlation=0.176041564468164 tran.covariance=-0.00259052287263211 cont.tran.covariance=0.00124406062809991 tran.mean=52.0576604420321 cont.tran.mean=50.4876410072451 weightedLogRatios: wLogRatio Lung -0.106319072090951 cerebhem -1.06306436708404 cortex 0.131322752707205 heart 0.411784871472436 kidney 0.91486701734929 liver 3.65766399802113 stomach 0.348637087217917 testicle -0.335345226391016 cont.weightedLogRatios: wLogRatio Lung 0.105296894271817 cerebhem 1.00677014563773 cortex -0.044508172087617 heart -0.0350821993392469 kidney 0.320145684895395 liver -0.369134412742212 stomach 0.105808183658318 testicle 0.467031736117795 varWeightedLogRatios=1.97399460635324 cont.varWeightedLogRatios=0.170454748220701 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.61493650962603 0.095782713812214 37.7410115640831 1.23498852739777e-120 *** df.mm.trans1 0.253781099286151 0.0811269580392996 3.12819690790346 0.00191850207791012 ** df.mm.trans2 0.244846577353103 0.0811269580392996 3.0180667840953 0.00274565946608824 ** df.mm.exp2 0.221800958717759 0.112979125522380 1.96320300491105 0.0504776773941267 . df.mm.exp3 -0.162606033005943 0.112979125522380 -1.43925731637684 0.151043018871240 df.mm.exp4 -0.00484842547359102 0.112979125522380 -0.0429143476830204 0.965796245820307 df.mm.exp5 0.121243491116308 0.112979125522380 1.07314949160489 0.284002667327107 df.mm.exp6 0.646322854450991 0.112979125522380 5.72072806779656 2.41350338035494e-08 *** df.mm.exp7 -0.118674446937758 0.112979125522380 -1.0504103867776 0.294312307469754 df.mm.exp8 0.130508927395645 0.112979125522380 1.15515965265453 0.248876254410382 df.mm.trans1:exp2 -0.246628747805519 0.097842792799732 -2.52066341064412 0.0121931823294851 * df.mm.trans2:exp2 -0.00562962194218916 0.097842792799732 -0.0575374208063752 0.954152565702585 df.mm.trans1:exp3 0.102615765492259 0.0978427927997321 1.04878205697068 0.295060124387657 df.mm.trans2:exp3 0.0402819063160623 0.0978427927997321 0.411700291492217 0.680831328979893 df.mm.trans1:exp4 0.0420372795231374 0.0978427927997321 0.429641042740682 0.667742358049571 df.mm.trans2:exp4 -0.0929135665242023 0.097842792799732 -0.949620956899512 0.343012673924302 df.mm.trans1:exp5 0.0897254022570245 0.097842792799732 0.917036397772062 0.359805652918042 df.mm.trans2:exp5 -0.169726187543116 0.0978427927997321 -1.73468257279325 0.083748051002598 . df.mm.trans1:exp6 0.213324615632013 0.097842792799732 2.18027929832965 0.0299560020357101 * df.mm.trans2:exp6 -0.66836753330212 0.0978427927997321 -6.83103490995148 4.18002071155617e-11 *** df.mm.trans1:exp7 0.138961564973267 0.0978427927997321 1.42025345962577 0.156495444810575 df.mm.trans2:exp7 0.0202768094512235 0.097842792799732 0.207238661847344 0.835953662255865 df.mm.trans1:exp8 -0.126856285329964 0.097842792799732 -1.29653172911384 0.195715007520699 df.mm.trans2:exp8 -0.0683075638139363 0.0978427927997321 -0.698135875513595 0.485592962258518 df.mm.trans1:probe2 -0.23587537472476 0.0489213963998660 -4.82151761975064 2.19481935540191e-06 *** df.mm.trans1:probe3 -0.00465836227135448 0.048921396399866 -0.0952213676257048 0.92419786240582 df.mm.trans1:probe4 -0.139685225582789 0.048921396399866 -2.8552992322838 0.00457691901792874 ** df.mm.trans1:probe5 0.142165807323929 0.048921396399866 2.90600468886693 0.00391277691097384 ** df.mm.trans1:probe6 0.0655904308007636 0.048921396399866 1.34073096083871 0.180947060647517 df.mm.trans2:probe2 0.00735794987641239 0.048921396399866 0.15040351293882 0.880539858876995 df.mm.trans2:probe3 0.0100302363329680 0.048921396399866 0.205027596738745 0.837679308455108 df.mm.trans2:probe4 -0.045596513367298 0.048921396399866 -0.932036219788339 0.352011796425038 df.mm.trans2:probe5 0.08711165461184 0.048921396399866 1.78064530087859 0.075907319078213 . df.mm.trans2:probe6 0.096726133420257 0.0489213963998660 1.97717441729693 0.048869291778203 * df.mm.trans3:probe2 0.0604960362361171 0.0489213963998660 1.23659667728296 0.21713247890779 df.mm.trans3:probe3 -0.262600209995000 0.0489213963998660 -5.36779874083315 1.52213443144742e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.51122611205517 0.155183556734027 22.626276816642 1.16731256128479e-68 *** df.mm.trans1 0.293576331970972 0.131438851484550 2.23355825659722 0.0261944018626048 * df.mm.trans2 0.247007649909454 0.131438851484550 1.87925904037962 0.0611065345350023 . df.mm.exp2 0.238786142508346 0.183044537343516 1.30452482206678 0.192980568931084 df.mm.exp3 0.0454652256344867 0.183044537343516 0.248383405996777 0.803995127836274 df.mm.exp4 0.197566865085466 0.183044537343516 1.07933767351219 0.281240054333513 df.mm.exp5 0.0164702241001613 0.183044537343516 0.0899793260109806 0.928359240785761 df.mm.exp6 -0.0117635981282027 0.183044537343516 -0.0642663162688443 0.948797805434166 df.mm.exp7 0.305251365687398 0.183044537343516 1.66763439170293 0.0963545054133663 . df.mm.exp8 0.000692316839688036 0.183044537343516 0.00378223163463644 0.996984550621061 df.mm.trans1:exp2 0.0439827496021631 0.158521219363454 0.277456543538947 0.781606516852725 df.mm.trans2:exp2 -0.180238111907157 0.158521219363454 -1.13699675432038 0.256379828982029 df.mm.trans1:exp3 -0.030842101638329 0.158521219363454 -0.194561344923892 0.845858303881089 df.mm.trans2:exp3 0.00818021638129094 0.158521219363454 0.051603289541544 0.958876597683431 df.mm.trans1:exp4 -0.112919643561197 0.158521219363454 -0.712331409098594 0.476772213858151 df.mm.trans2:exp4 -0.0764961225796668 0.158521219363454 -0.482560775691979 0.629733496160462 df.mm.trans1:exp5 0.0411429273258868 0.158521219363454 0.259542082070131 0.795381852587271 df.mm.trans2:exp5 -0.0143223022247648 0.158521219363454 -0.0903494326013664 0.928065366880419 df.mm.trans1:exp6 0.0420461021799702 0.158521219363454 0.265239583374436 0.790993576593286 df.mm.trans2:exp6 0.163713826196014 0.158521219363454 1.03275654107009 0.302488107295943 df.mm.trans1:exp7 -0.150547364649758 0.158521219363454 -0.949698502536662 0.342973319300559 df.mm.trans2:exp7 -0.149604570867545 0.158521219363454 -0.943751073000108 0.346000069693413 df.mm.trans1:exp8 0.177204028973197 0.158521219363454 1.11785683761937 0.264456319304153 df.mm.trans2:exp8 0.0868145163660717 0.158521219363454 0.547652337741771 0.584307706786376 df.mm.trans1:probe2 0.116371949251657 0.079260609681727 1.46821920395202 0.143014823058540 df.mm.trans1:probe3 0.136391174029566 0.079260609681727 1.72079390478131 0.0862429157893635 . df.mm.trans1:probe4 0.0718437974141076 0.079260609681727 0.906424990958285 0.365384546559657 df.mm.trans1:probe5 -0.00449087956160462 0.079260609681727 -0.0566596646132027 0.954851230259736 df.mm.trans1:probe6 0.0203377087760211 0.079260609681727 0.256592888418190 0.797655919323276 df.mm.trans2:probe2 0.085612294207769 0.079260609681727 1.08013671042334 0.28088467764463 df.mm.trans2:probe3 0.132440776584098 0.079260609681727 1.67095329087068 0.0956964884361496 . df.mm.trans2:probe4 0.0610044098870495 0.079260609681727 0.769668693339784 0.442057207462995 df.mm.trans2:probe5 0.197519912750903 0.079260609681727 2.4920312062202 0.0132009271732873 * df.mm.trans2:probe6 0.0354881805893739 0.079260609681727 0.447740444236773 0.6546397438443 df.mm.trans3:probe2 -0.267150296007653 0.079260609681727 -3.3705304195918 0.000840916642150195 *** df.mm.trans3:probe3 -0.200594879101550 0.079260609681727 -2.53082684964251 0.0118522909508425 *