chr15.8673_chr15_100651906_100655136_-_2.R fitVsDatCorrelation=0.924877921317106 cont.fitVsDatCorrelation=0.224892312760389 fstatistic=7306.49107721615,60,876 cont.fstatistic=1100.43023620473,60,876 residuals=-0.576218736577837,-0.115236637618884,-0.00811142290327243,0.115865918579679,0.987071503002782 cont.residuals=-1.0962658769818,-0.415799853459300,-0.0791655355730364,0.413978631014917,1.42357803913513 predictedValues: Include Exclude Both chr15.8673_chr15_100651906_100655136_-_2.R.tl.Lung 83.0981229715658 213.176036521480 83.355253525015 chr15.8673_chr15_100651906_100655136_-_2.R.tl.cerebhem 87.064374618386 213.677426535698 84.0434121473273 chr15.8673_chr15_100651906_100655136_-_2.R.tl.cortex 78.3951413808281 164.170759810997 82.8885201896057 chr15.8673_chr15_100651906_100655136_-_2.R.tl.heart 77.9025305669939 167.477907255676 81.2247382458282 chr15.8673_chr15_100651906_100655136_-_2.R.tl.kidney 86.0136525019365 224.865372399227 79.5897022535957 chr15.8673_chr15_100651906_100655136_-_2.R.tl.liver 84.0588870168701 188.149789778541 61.7306109162349 chr15.8673_chr15_100651906_100655136_-_2.R.tl.stomach 78.6374632439759 173.114209614480 68.7407747289365 chr15.8673_chr15_100651906_100655136_-_2.R.tl.testicle 80.0917369536067 173.691576907368 88.5041223583173 diffExp=-130.077913549915,-126.613051917313,-85.7756184301688,-89.5753766886825,-138.85171989729,-104.090902761671,-94.4767463705038,-93.5998399537616 diffExpScore=0.998842674529052 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 88.2910244184314 94.6188912731297 117.566220627389 cerebhem 88.585987346036 96.7334179428722 104.278451046711 cortex 93.8385031031057 78.9754236428872 96.9883964370006 heart 86.1555000564407 83.739598229561 82.9301091685583 kidney 92.9275443231102 100.622066222537 114.829315017475 liver 95.706206909907 92.4682159276046 106.087846080194 stomach 93.1667873785658 109.458336209263 89.1415507821978 testicle 88.038798159984 78.302701151286 91.7567807821992 cont.diffExp=-6.32786685469827,-8.1474305968362,14.8630794602185,2.41590182687968,-7.69452189942632,3.23799098230238,-16.2915488306968,9.73609700869787 cont.diffExpScore=7.46222925422158 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=0.910188048842975 cont.tran.correlation=0.308511781655394 tran.covariance=0.00504260690055101 cont.tran.covariance=0.00138121420040310 tran.mean=135.849061754852 cont.tran.mean=91.35181264342 weightedLogRatios: wLogRatio Lung -4.60785851724587 cerebhem -4.41328470490466 cortex -3.49714297590752 heart -3.62655066494794 kidney -4.74251829453856 liver -3.89515895634667 stomach -3.75565829801605 testicle -3.69267317067744 cont.weightedLogRatios: wLogRatio Lung -0.312539577357757 cerebhem -0.398394191924424 cortex 0.768275088216927 heart 0.126336748706156 kidney -0.363677231142731 liver 0.156398761214734 stomach -0.743714431546618 testicle 0.517908017319499 varWeightedLogRatios=0.234634616565747 cont.varWeightedLogRatios=0.261841800837522 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.38394001684476 0.103574006225353 51.9815754266622 6.44518553890428e-270 *** df.mm.trans1 -0.976704940201374 0.0917726648967557 -10.6426564086394 5.84881251327645e-25 *** df.mm.trans2 0.00064380015727553 0.0820465112633454 0.00784677065925594 0.993741033646902 df.mm.exp2 0.0407530677090885 0.108966345332420 0.373996829798819 0.708497176731673 df.mm.exp3 -0.313856281032761 0.108966345332420 -2.88030474065446 0.00406979460916824 ** df.mm.exp4 -0.279938740255382 0.108966345332420 -2.56903853571836 0.010362627219144 * df.mm.exp5 0.134094432066296 0.108966345332420 1.23060410677460 0.218801298759882 df.mm.exp6 0.186947291901489 0.108966345332420 1.71564248879941 0.086581094364926 . df.mm.exp7 -0.0705715404688509 0.108966345332420 -0.647645291337986 0.517384088445065 df.mm.exp8 -0.301624025775829 0.108966345332420 -2.76804755501044 0.00575831031860921 ** df.mm.trans1:exp2 0.00587260144875497 0.104018214151667 0.05645743388934 0.954990278580285 df.mm.trans2:exp2 -0.0384038294425348 0.0834001112870613 -0.460476956803447 0.645288190995889 df.mm.trans1:exp3 0.255596120265607 0.104018214151667 2.45722465387578 0.0141943643886297 * df.mm.trans2:exp3 0.0526450986053689 0.0834001112870613 0.631235352002897 0.528051316607705 df.mm.trans1:exp4 0.215375063398404 0.104018214151667 2.07055144288835 0.0386931898496882 * df.mm.trans2:exp4 0.0386718989208229 0.0834001112870613 0.463691214844008 0.64298418401913 df.mm.trans1:exp5 -0.099610512409568 0.104018214151667 -0.95762567375294 0.338515758707359 df.mm.trans2:exp5 -0.0807108407534167 0.0834001112870613 -0.967754592983837 0.333434049409991 df.mm.trans1:exp6 -0.175451816773796 0.104018214151667 -1.68674129050104 0.092009150454337 . df.mm.trans2:exp6 -0.311827179089921 0.0834001112870613 -3.73893001193511 0.000196807564916636 *** df.mm.trans1:exp7 0.0153976439341509 0.104018214151667 0.148028343494726 0.882354465161863 df.mm.trans2:exp7 -0.137595198588014 0.0834001112870613 -1.64982032355345 0.099338182352462 . df.mm.trans1:exp8 0.264774601378024 0.104018214151667 2.54546382609454 0.0110836269237047 * df.mm.trans2:exp8 0.0967869187420727 0.0834001112870613 1.16051306465209 0.246156143942791 df.mm.trans1:probe2 -0.16721609938669 0.0607335524179801 -2.75327381207469 0.00602235419784904 ** df.mm.trans1:probe3 0.0933790178850046 0.0607335524179801 1.53751944629143 0.124527260048849 df.mm.trans1:probe4 -0.0355200423641651 0.0607335524179801 -0.584850398997069 0.55879891473801 df.mm.trans1:probe5 -0.195288000609467 0.0607335524179801 -3.21548786188986 0.00134993960145008 ** df.mm.trans1:probe6 -0.339633179191187 0.0607335524179801 -5.5921836558113 2.99652038161797e-08 *** df.mm.trans1:probe7 -0.441446361909633 0.0607335524179801 -7.26857468951451 8.04485610303933e-13 *** df.mm.trans1:probe8 -0.00542422575216431 0.0607335524179801 -0.089311847178537 0.92885449361575 df.mm.trans1:probe9 0.0773134634114768 0.0607335524179801 1.27299425660779 0.203357807786188 df.mm.trans1:probe10 0.331282881303711 0.0607335524179801 5.45469296812671 6.38502038937576e-08 *** df.mm.trans1:probe11 -0.604749946120989 0.0607335524179801 -9.95742751813664 3.35838626709332e-22 *** df.mm.trans1:probe12 -0.484447686923765 0.0607335524179801 -7.97660712466977 4.68927281933464e-15 *** df.mm.trans1:probe13 -0.599362387851836 0.0607335524179801 -9.86871941438412 7.46335449277492e-22 *** df.mm.trans1:probe14 -0.180057177731846 0.0607335524179801 -2.96470683112125 0.00311185424049856 ** df.mm.trans1:probe15 -0.42717412848765 0.0607335524179801 -7.03357718230863 4.05560483336321e-12 *** df.mm.trans1:probe16 -0.554071641506429 0.0607335524179801 -9.12299082545345 4.91097344682796e-19 *** df.mm.trans1:probe17 0.585944242696541 0.0607335524179801 9.64778478070835 5.32241159196481e-21 *** df.mm.trans1:probe18 0.815002719629922 0.0607335524179801 13.4193158012710 1.77931855019833e-37 *** df.mm.trans1:probe19 0.814448710043138 0.0607335524179801 13.4101938321991 1.97052916239745e-37 *** df.mm.trans1:probe20 0.62541589639124 0.0607335524179801 10.2976998955537 1.49143784057307e-23 *** df.mm.trans1:probe21 0.66208851543432 0.0607335524179801 10.9015278882042 4.88234316441808e-26 *** df.mm.trans1:probe22 0.880374564351708 0.0607335524179801 14.4956869687582 7.70333694118634e-43 *** df.mm.trans1:probe23 -0.201780379963153 0.0607335524179801 -3.32238724608864 0.00092947311098623 *** df.mm.trans1:probe24 0.134595165157044 0.0607335524179801 2.21615828151684 0.0269369527699706 * df.mm.trans1:probe25 -0.213859026863837 0.0607335524179801 -3.52126655447417 0.00045165762396799 *** df.mm.trans1:probe26 -0.453880446616081 0.0607335524179801 -7.47330641047286 1.89438344138359e-13 *** df.mm.trans1:probe27 -0.0275748928117159 0.0607335524179801 -0.454030625805324 0.649919250404456 df.mm.trans1:probe28 0.333612681209734 0.0607335524179801 5.49305396980152 5.1786474908805e-08 *** df.mm.trans2:probe2 0.148903390874979 0.0607335524179801 2.45174841494858 0.0144106792767815 * df.mm.trans2:probe3 -0.0526501003333693 0.0607335524179801 -0.86690302538243 0.386232534280798 df.mm.trans2:probe4 -0.129719440632330 0.0607335524179801 -2.13587770627306 0.032966064972388 * df.mm.trans2:probe5 0.0340310897830705 0.0607335524179801 0.560334253936965 0.575394735530593 df.mm.trans2:probe6 -0.247685770600534 0.0607335524179801 -4.0782361765357 4.95120075474502e-05 *** df.mm.trans3:probe2 0.253088444562404 0.0607335524179801 4.16719316565907 3.38956913563072e-05 *** df.mm.trans3:probe3 0.192609735131341 0.0607335524179801 3.17138924800189 0.00156990662017927 ** df.mm.trans3:probe4 -0.177319756561434 0.0607335524179801 -2.91963419727345 0.00359423622412533 ** df.mm.trans3:probe5 -0.192214622611824 0.0607335524179801 -3.16488357685658 0.00160502276816168 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.24733888085236 0.265396601771507 16.0037425215757 9.19497840275298e-51 *** df.mm.trans1 0.271627405334851 0.235157007889807 1.1550895623835 0.248368728429815 df.mm.trans2 0.227440337810177 0.210234846271395 1.08183938982490 0.279621615053001 df.mm.exp2 0.145374028480761 0.279213856957153 0.520654777184176 0.602738852762327 df.mm.exp3 0.072626765041726 0.279213856957153 0.260111607042737 0.794838808754167 df.mm.exp4 0.202373720076284 0.279213856957153 0.724798268544888 0.468769345222747 df.mm.exp5 0.136251015839207 0.279213856957153 0.487980852111204 0.625685516139081 df.mm.exp6 0.160386789178215 0.279213856957153 0.574422741500352 0.565829240913288 df.mm.exp7 0.476215869131496 0.279213856957153 1.70555958189631 0.0884446118704823 . df.mm.exp8 0.0557244646320548 0.279213856957153 0.199576286217794 0.84185829188458 df.mm.trans1:exp2 -0.142038793470991 0.26653483402128 -0.532908930994179 0.594231781315078 df.mm.trans2:exp2 -0.123272254485762 0.213703292260347 -0.576838349947291 0.564196857327777 df.mm.trans1:exp3 -0.0116899661383635 0.26653483402128 -0.0438590557263903 0.96502674783617 df.mm.trans2:exp3 -0.253347206556688 0.213703292260347 -1.18550914156270 0.236137683120407 df.mm.trans1:exp4 -0.226858370077204 0.26653483402128 -0.851139667766997 0.394924369814293 df.mm.trans2:exp4 -0.324518909783114 0.213703292260347 -1.51854894864120 0.129236875223622 df.mm.trans1:exp5 -0.0850693733646994 0.266534834021280 -0.319167937943554 0.749675259998541 df.mm.trans2:exp5 -0.0747365885324121 0.213703292260347 -0.349721278235448 0.72663194178782 df.mm.trans1:exp6 -0.0797420885576832 0.266534834021280 -0.299180738797229 0.764873108710453 df.mm.trans2:exp6 -0.183378967755758 0.213703292260347 -0.858100807976105 0.391071518115641 df.mm.trans1:exp7 -0.422463023277792 0.26653483402128 -1.58501992742931 0.113322711501032 df.mm.trans2:exp7 -0.33052903597418 0.213703292260347 -1.54667264354312 0.122303372729558 df.mm.trans1:exp8 -0.0585853128627975 0.26653483402128 -0.219803588067292 0.826075334123797 df.mm.trans2:exp8 -0.244999517222645 0.213703292260347 -1.14644708853699 0.251923262389763 df.mm.trans1:probe2 0.071366460786627 0.155622815150875 0.458586105883241 0.646645165627203 df.mm.trans1:probe3 0.00171548036973441 0.155622815150875 0.0110233217929599 0.991207349732386 df.mm.trans1:probe4 -0.0310323366115568 0.155622815150875 -0.199407372122598 0.841990369899903 df.mm.trans1:probe5 -0.154339416490855 0.155622815150875 -0.9917531458432 0.321591816077303 df.mm.trans1:probe6 -0.17248405294815 0.155622815150875 -1.10834682421680 0.268016267640274 df.mm.trans1:probe7 0.0463083627429419 0.155622815150875 0.297567954274869 0.766103488860814 df.mm.trans1:probe8 -0.114487405020633 0.155622815150875 -0.735672368538239 0.462127035076779 df.mm.trans1:probe9 0.146201691836247 0.155622815150875 0.93946181152491 0.347752654806090 df.mm.trans1:probe10 -0.0653142689994826 0.155622815150875 -0.419695974116399 0.674810474791424 df.mm.trans1:probe11 -0.0619096096962901 0.155622815150875 -0.397818338116229 0.690861022489956 df.mm.trans1:probe12 0.00916018667451954 0.155622815150875 0.0588614636333293 0.953075876761288 df.mm.trans1:probe13 -0.156119693476941 0.155622815150875 -1.00319283728150 0.316044851801447 df.mm.trans1:probe14 -0.068151228075652 0.155622815150875 -0.437925685957937 0.661548113044296 df.mm.trans1:probe15 0.168015978245178 0.155622815150875 1.07963590095892 0.280601510971541 df.mm.trans1:probe16 -0.174579971829358 0.155622815150875 -1.12181476514291 0.262248608813188 df.mm.trans1:probe17 0.0731831147090275 0.155622815150875 0.470259547985153 0.638286648280483 df.mm.trans1:probe18 -0.0193573961044264 0.155622815150875 -0.124386620854144 0.901037702226173 df.mm.trans1:probe19 0.00597752422993102 0.155622815150875 0.0384103334985675 0.969369273608925 df.mm.trans1:probe20 -0.172979261742229 0.155622815150875 -1.11152893343130 0.266645711473536 df.mm.trans1:probe21 -0.0622561296301245 0.155622815150875 -0.400045003489802 0.689220899286196 df.mm.trans1:probe22 -0.119536777601381 0.155622815150875 -0.768118591644108 0.442623738930647 df.mm.trans1:probe23 -0.0780218472433112 0.155622815150875 -0.501352241749835 0.616249207234418 df.mm.trans1:probe24 -0.0842862400390463 0.155622815150875 -0.541605933277403 0.588227722602052 df.mm.trans1:probe25 -0.0131945722861608 0.155622815150875 -0.0847855905534723 0.932451221850698 df.mm.trans1:probe26 -0.0590824470229655 0.155622815150875 -0.379651575931753 0.704296087620811 df.mm.trans1:probe27 -0.100997508846626 0.155622815150875 -0.648989087806372 0.516515534017197 df.mm.trans1:probe28 -0.0786171070847833 0.155622815150875 -0.505177258286741 0.613561410899636 df.mm.trans2:probe2 0.117582575101407 0.155622815150875 0.755561290851934 0.450115378106554 df.mm.trans2:probe3 0.277560666706556 0.155622815150875 1.78354739591019 0.0748433578017414 . df.mm.trans2:probe4 0.189354959561844 0.155622815150875 1.21675577824670 0.224024866850373 df.mm.trans2:probe5 0.111439967793706 0.155622815150875 0.716090167663822 0.474126517212018 df.mm.trans2:probe6 0.129919102547723 0.155622815150875 0.834833262859098 0.404039207022367 df.mm.trans3:probe2 -0.187971321540938 0.155622815150875 -1.20786480670397 0.227425258468229 df.mm.trans3:probe3 0.0771629783971068 0.155622815150875 0.495833328309207 0.620136378396038 df.mm.trans3:probe4 0.107153023217254 0.155622815150875 0.688543149109403 0.49129310956137 df.mm.trans3:probe5 0.106630973141260 0.155622815150875 0.685188563372806 0.493406203582911