chr8.23553_chr8_123890887_123896210_-_2.R fitVsDatCorrelation=0.759212620180102 cont.fitVsDatCorrelation=0.238595551538097 fstatistic=9998.41950232433,59,853 cont.fstatistic=4482.98895653055,59,853 residuals=-0.406929665290088,-0.0899162200765363,-0.00384463044816904,0.0726414811106295,1.74380308176532 cont.residuals=-0.534392999223341,-0.156256051835974,-0.0301611768821384,0.128761054026156,1.76505209324829 predictedValues: Include Exclude Both chr8.23553_chr8_123890887_123896210_-_2.R.tl.Lung 54.8601331389367 50.5196353138617 55.9482151272723 chr8.23553_chr8_123890887_123896210_-_2.R.tl.cerebhem 63.6847232582428 52.2968725796361 58.7755735667296 chr8.23553_chr8_123890887_123896210_-_2.R.tl.cortex 59.3380937474034 52.1960745101606 56.8638001664492 chr8.23553_chr8_123890887_123896210_-_2.R.tl.heart 55.0037856484474 50.9781400771136 55.7108636601715 chr8.23553_chr8_123890887_123896210_-_2.R.tl.kidney 52.2306431509728 52.1756029294334 57.0148898798419 chr8.23553_chr8_123890887_123896210_-_2.R.tl.liver 50.734298262583 52.9070689893246 58.8073195276036 chr8.23553_chr8_123890887_123896210_-_2.R.tl.stomach 52.6040111095616 60.5564712270723 54.7190033328543 chr8.23553_chr8_123890887_123896210_-_2.R.tl.testicle 54.8345927166621 52.4020124821528 57.5796592506091 diffExp=4.34049782507497,11.3878506786067,7.1420192372428,4.02564557133377,0.0550402215393788,-2.17277072674157,-7.95246011751066,2.43258023450925 diffExpScore=1.95024577014641 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 55.0114350658548 56.1739962667148 53.5515806063875 cerebhem 57.2466226614353 57.6386048977509 57.8043273822402 cortex 53.7419757426079 56.3612324600085 54.013727085562 heart 53.6837950912918 53.8927510331461 55.7877661358103 kidney 55.3771905048444 61.0715965615831 54.2256784115042 liver 54.5962855127663 55.3920957769869 60.7268580136854 stomach 58.6171293168082 55.3675021632049 54.8945554957831 testicle 54.8992212046299 54.9490287008043 55.0514599268627 cont.diffExp=-1.16256120086000,-0.391982236315627,-2.61925671740057,-0.208955941854306,-5.69440605673869,-0.795810264220641,3.24962715360331,-0.049807496174374 cont.diffExpScore=1.63405481944162 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=-0.264304284352803 cont.tran.correlation=0.184035586106812 tran.covariance=-0.00112784815555774 cont.tran.covariance=0.000230214569412196 tran.mean=54.2076349463478 cont.tran.mean=55.8762789350274 weightedLogRatios: wLogRatio Lung 0.326696968199815 cerebhem 0.79895481345581 cortex 0.515430029364665 heart 0.301694595000506 kidney 0.00417009384559692 liver -0.165540473260540 stomach -0.56780768409576 testicle 0.180671588001877 cont.weightedLogRatios: wLogRatio Lung -0.0840278838562011 cerebhem -0.0276421997267275 cortex -0.190728872887838 heart -0.0154811007091106 kidney -0.397693308918803 liver -0.057988445708292 stomach 0.230561095050523 testicle -0.00363276578063584 varWeightedLogRatios=0.177316813386660 cont.varWeightedLogRatios=0.0316835952438642 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70721308687898 0.0735809334516218 50.3827950119226 6.93052860609967e-258 *** df.mm.trans1 0.233306195662176 0.0635426942786125 3.67164468411129 0.000255991249576041 *** df.mm.trans2 0.244860888222799 0.0561396831872988 4.36163644539763 1.44875791643752e-05 *** df.mm.exp2 0.134432470900192 0.0722135826895027 1.86159536604371 0.0630039065146504 . df.mm.exp3 0.0948773853192084 0.0722135826895026 1.31384404132327 0.189251926048648 df.mm.exp4 0.0159013028758703 0.0722135826895026 0.220198227032182 0.825769465537867 df.mm.exp5 -0.0357505528453814 0.0722135826895026 -0.495066876810397 0.620680410039429 df.mm.exp6 -0.0818496516466814 0.0722135826895026 -1.13343845573500 0.257348631766694 df.mm.exp7 0.161435194702879 0.0722135826895026 2.23552396502752 0.0256410072670429 * df.mm.exp8 0.00737442000364745 0.0722135826895026 0.102119569878638 0.91868576801221 df.mm.trans1:exp2 0.0147253272622575 0.0667485161601641 0.220609057839187 0.825449638494513 df.mm.trans2:exp2 -0.0998579781371888 0.0492970462488761 -2.02563816162648 0.043112768983501 * df.mm.trans1:exp3 -0.0164128076077885 0.0667485161601641 -0.24589022426215 0.805826363756232 df.mm.trans2:exp3 -0.0622321730205435 0.0492970462488761 -1.26239151746262 0.207152960432984 df.mm.trans1:exp4 -0.0132862024322805 0.0667485161601641 -0.199048656009072 0.842272118718293 df.mm.trans2:exp4 -0.00686646678426537 0.0492970462488761 -0.139287590367991 0.889255774170573 df.mm.trans1:exp5 -0.0133670033512522 0.0667485161601641 -0.200259183577622 0.84132561650132 df.mm.trans2:exp5 0.0680034828411847 0.0492970462488761 1.37946363962395 0.168113373242632 df.mm.trans1:exp6 0.00366491546372195 0.0667485161601641 0.0549063211372059 0.956225953013703 df.mm.trans2:exp6 0.128024532063719 0.0492970462488761 2.59700208846971 0.0095660931510382 ** df.mm.trans1:exp7 -0.203429733422826 0.0667485161601641 -3.04770420565894 0.00237706925875307 ** df.mm.trans2:exp7 0.0197790648834128 0.0492970462488761 0.401222109405059 0.6883570915372 df.mm.trans1:exp8 -0.00784008364493745 0.0667485161601641 -0.117457047676162 0.90652552971292 df.mm.trans2:exp8 0.0292084979263895 0.0492970462488761 0.592499959915051 0.553672875052294 df.mm.trans1:probe2 -0.0117988443565467 0.04569958497615 -0.258182746357638 0.796328152091953 df.mm.trans1:probe3 -0.133745252391714 0.04569958497615 -2.92661853409639 0.00351768600665295 ** df.mm.trans1:probe4 0.0676799427611411 0.04569958497615 1.48097499783559 0.138982506219326 df.mm.trans1:probe5 -0.103906848282837 0.04569958497615 -2.27369347745860 0.0232321783125461 * df.mm.trans1:probe6 -0.131891896443046 0.04569958497615 -2.88606333103198 0.00399936385010885 ** df.mm.trans1:probe7 -0.0183098103200637 0.04569958497615 -0.400655943147391 0.688773780742142 df.mm.trans1:probe8 0.0278487518687991 0.04569958497615 0.609387413109616 0.542429952460931 df.mm.trans1:probe9 0.255588604048866 0.04569958497615 5.5927992383794 3.00917494694347e-08 *** df.mm.trans1:probe10 -0.103558126166875 0.04569958497615 -2.26606272728561 0.0236973617631647 * df.mm.trans1:probe11 0.00736535663726334 0.04569958497615 0.161169004950641 0.87199845555441 df.mm.trans1:probe12 0.138165505641665 0.04569958497615 3.0233426783585 0.00257482024036812 ** df.mm.trans1:probe13 -0.0272179682116363 0.04569958497615 -0.595584581913403 0.551610762436495 df.mm.trans1:probe14 0.0273569962697094 0.04569958497615 0.598626798995804 0.549580708085062 df.mm.trans1:probe15 0.365817760033543 0.04569958497615 8.00483768560389 3.90186800760835e-15 *** df.mm.trans1:probe16 0.0607112078247471 0.04569958497615 1.32848488353738 0.184373273190043 df.mm.trans1:probe17 0.208163043112767 0.04569958497615 4.55503136891516 6.00062645929681e-06 *** df.mm.trans1:probe18 0.361226888581178 0.04569958497615 7.90438006755854 8.30416669102251e-15 *** df.mm.trans1:probe19 0.212775862117244 0.04569958497615 4.65596924410339 3.73812933660916e-06 *** df.mm.trans1:probe20 0.261205742628227 0.04569958497615 5.71571367145996 1.50985861553366e-08 *** df.mm.trans1:probe21 0.273541327808581 0.04569958497615 5.98564140027394 3.17181163336256e-09 *** df.mm.trans1:probe22 0.31954591206544 0.04569958497615 6.99231540575712 5.45882576272676e-12 *** df.mm.trans2:probe2 -0.0901367489285202 0.04569958497615 -1.97237565670588 0.048889365837206 * df.mm.trans2:probe3 -0.0516133311727094 0.04569958497615 -1.12940481187402 0.259044607868247 df.mm.trans2:probe4 -0.121583026964959 0.04569958497615 -2.66048426978958 0.00794937963653694 ** df.mm.trans2:probe5 -0.102982507640230 0.04569958497615 -2.25346702150524 0.0244829344627314 * df.mm.trans2:probe6 -0.109074726003306 0.04569958497615 -2.38677716789399 0.0172138171052954 * df.mm.trans3:probe2 0.0751008838731411 0.04569958497615 1.64336030430769 0.100676923825627 df.mm.trans3:probe3 -0.417903823691377 0.04569958497615 -9.14458684711196 4.29729629564598e-19 *** df.mm.trans3:probe4 0.104770989546440 0.04569958497615 2.2926026483855 0.0221135226114660 * df.mm.trans3:probe5 -0.0178175283893541 0.04569958497615 -0.389883811825706 0.696719793724045 df.mm.trans3:probe6 0.0542068631250935 0.04569958497615 1.18615657348712 0.235890756084988 df.mm.trans3:probe7 -0.314666571191003 0.04569958497615 -6.88554548920354 1.11618386690394e-11 *** df.mm.trans3:probe8 -0.399815102446574 0.04569958497615 -8.74876878324459 1.13834003347956e-17 *** df.mm.trans3:probe9 -0.40062671748868 0.04569958497615 -8.76652857345996 9.85168157706296e-18 *** df.mm.trans3:probe10 -0.292785370591436 0.04569958497615 -6.40674025254796 2.45534566011004e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02519804844229 0.109789733144776 36.6627910747757 1.942917768978e-177 *** df.mm.trans1 -0.0582525836104646 0.0948117279965706 -0.614402720437408 0.539113076126605 df.mm.trans2 0.0291667191201538 0.0837657331436033 0.348193921614129 0.727780464306202 df.mm.exp2 -0.0108521370120512 0.107749516090624 -0.100716341063878 0.919799306265775 df.mm.exp3 -0.0286120086996878 0.107749516090624 -0.265541876546557 0.790656216376053 df.mm.exp4 -0.106797143129332 0.107749516090624 -0.991161232125712 0.321887927506137 df.mm.exp5 0.0777103966512713 0.107749516090624 0.721213416735086 0.470975839353328 df.mm.exp6 -0.147333024929183 0.107749516090624 -1.36736600102470 0.171870798426587 df.mm.exp7 0.024255897877255 0.107749516090624 0.225113752314713 0.821944700473478 df.mm.exp8 -0.0517129264205588 0.107749516090624 -0.479936507344170 0.631395580670833 df.mm.trans1:exp2 0.0506797109825087 0.0995951183719679 0.508857379869062 0.610983786356601 df.mm.trans2:exp2 0.0365907541929337 0.073555869688012 0.49745525881393 0.618996249156846 df.mm.trans1:exp3 0.00526530214280982 0.0995951183719679 0.0528670704837658 0.957850199840472 df.mm.trans2:exp3 0.0319396131873425 0.073555869688012 0.434222494041805 0.664236690358096 df.mm.trans1:exp4 0.0823672578053315 0.0995951183719679 0.82702103428108 0.408456425467514 df.mm.trans2:exp4 0.0653391728783375 0.073555869688012 0.88829311862499 0.374633511371819 df.mm.trans1:exp5 -0.0710836853291834 0.0995951183719679 -0.713726601174367 0.475591487767883 df.mm.trans2:exp5 0.00588254344192396 0.073555869688012 0.0799738140120542 0.93627684099515 df.mm.trans1:exp6 0.139757800553728 0.0995951183719679 1.40325954563115 0.160903358933932 df.mm.trans2:exp6 0.133315983057148 0.0735558696880119 1.81244520148575 0.0702688962250176 . df.mm.trans1:exp7 0.0392299912418727 0.0995951183719678 0.393894719772876 0.69375721247231 df.mm.trans2:exp7 -0.038717029735957 0.073555869688012 -0.526362204677556 0.598773401217802 df.mm.trans1:exp8 0.0496710152170194 0.0995951183719678 0.498729415948964 0.618098599077841 df.mm.trans2:exp8 0.0296649811189455 0.0735558696880119 0.403298625177975 0.686829621304323 df.mm.trans1:probe2 0.058034970985026 0.068188116187155 0.851100957617573 0.394952128920152 df.mm.trans1:probe3 0.0305123495263178 0.068188116187155 0.447473126293311 0.654647094907336 df.mm.trans1:probe4 0.0828445847704496 0.068188116187155 1.21494168489810 0.224724525573046 df.mm.trans1:probe5 0.0401657497390905 0.068188116187155 0.589043252475961 0.555988216979669 df.mm.trans1:probe6 0.0148408876211497 0.068188116187155 0.217646247630835 0.827756798872567 df.mm.trans1:probe7 0.164952772508196 0.068188116187155 2.41908387754037 0.0157679446285494 * df.mm.trans1:probe8 0.0693737071249425 0.068188116187155 1.01738706103177 0.309257807855983 df.mm.trans1:probe9 0.0569827742124184 0.068188116187155 0.83567016363993 0.403574496203256 df.mm.trans1:probe10 0.056994102204722 0.068188116187155 0.835836292181632 0.40348107023657 df.mm.trans1:probe11 0.0338061235980728 0.068188116187155 0.495777350781852 0.620179212592802 df.mm.trans1:probe12 0.0517040353394527 0.068188116187155 0.75825581099119 0.448507296823989 df.mm.trans1:probe13 0.0287946712953137 0.068188116187155 0.422282839084180 0.672924997492099 df.mm.trans1:probe14 0.0255838905642033 0.068188116187155 0.375195737831846 0.707608171097596 df.mm.trans1:probe15 0.0370384453683825 0.068188116187155 0.543180358095296 0.587147537870258 df.mm.trans1:probe16 0.0915303574393474 0.068188116187155 1.34232125122984 0.179848994532152 df.mm.trans1:probe17 0.0635378952444643 0.068188116187155 0.931803058909454 0.351701965562241 df.mm.trans1:probe18 0.176198117761469 0.068188116187155 2.58400037446203 0.00993136980222807 ** df.mm.trans1:probe19 0.0781531461264646 0.068188116187155 1.14614027335729 0.252058537771027 df.mm.trans1:probe20 0.0783572213696654 0.068188116187155 1.14913310047457 0.250823210885360 df.mm.trans1:probe21 0.0769700525219687 0.068188116187155 1.12878983649746 0.259303858829843 df.mm.trans1:probe22 -0.0173163648070241 0.068188116187155 -0.25394989296223 0.799595468702838 df.mm.trans2:probe2 -0.0476318282534384 0.068188116187155 -0.69853562346119 0.485032682285268 df.mm.trans2:probe3 -0.133447145747864 0.068188116187155 -1.95704403068701 0.0506681234512071 . df.mm.trans2:probe4 -0.118430254287677 0.068188116187155 -1.73681663183983 0.0827805424438766 . df.mm.trans2:probe5 -0.0350854035676153 0.068188116187155 -0.514538390697243 0.607008912464894 df.mm.trans2:probe6 -0.0799784500944298 0.068188116187155 -1.17290892558043 0.241159761136656 df.mm.trans3:probe2 -0.0758265067914933 0.068188116187155 -1.11201938154991 0.266443126142774 df.mm.trans3:probe3 0.00539729201798315 0.068188116187155 0.0791529715114768 0.93692951509493 df.mm.trans3:probe4 -0.00981371942858206 0.068188116187155 -0.143921257505435 0.885596642988444 df.mm.trans3:probe5 0.0439654334431338 0.068188116187155 0.644766799576374 0.519251679738877 df.mm.trans3:probe6 -0.0619792212667721 0.068188116187155 -0.908944618687787 0.363636098165609 df.mm.trans3:probe7 -0.00863828126328884 0.068188116187155 -0.126683090050170 0.899221085024532 df.mm.trans3:probe8 -0.000878597061554502 0.068188116187155 -0.0128849000483168 0.989722634540986 df.mm.trans3:probe9 -0.0566323221966286 0.068188116187155 -0.83053067548003 0.406471204459638 df.mm.trans3:probe10 0.0097372122004739 0.068188116187155 0.142799255133377 0.886482448760993