chr6.20168_chr6_114176291_114177534_+_2.R fitVsDatCorrelation=0.88014237953839 cont.fitVsDatCorrelation=0.244803564611886 fstatistic=11174.2667110261,60,876 cont.fstatistic=2667.54180800859,60,876 residuals=-0.509108072526773,-0.092396338059447,0.00250716005522922,0.087886445518591,0.813001487505689 cont.residuals=-0.82303561285725,-0.219147500936257,-0.0308253092491321,0.144926951366814,1.09116859782545 predictedValues: Include Exclude Both chr6.20168_chr6_114176291_114177534_+_2.R.tl.Lung 62.8585094102867 61.0495650516229 108.564693954701 chr6.20168_chr6_114176291_114177534_+_2.R.tl.cerebhem 68.1008123478359 55.8544008007578 85.5778809301991 chr6.20168_chr6_114176291_114177534_+_2.R.tl.cortex 61.3394557785137 69.219059533281 95.9709114503896 chr6.20168_chr6_114176291_114177534_+_2.R.tl.heart 64.3595756103648 69.0542659530874 105.551493981287 chr6.20168_chr6_114176291_114177534_+_2.R.tl.kidney 62.5863781524081 61.8273859691426 105.936023297036 chr6.20168_chr6_114176291_114177534_+_2.R.tl.liver 63.3150002197973 62.1161128475878 101.481456032281 chr6.20168_chr6_114176291_114177534_+_2.R.tl.stomach 68.760060083461 78.5795678232455 114.897085159651 chr6.20168_chr6_114176291_114177534_+_2.R.tl.testicle 63.6877905107479 63.419663567057 107.184185035655 diffExp=1.80894435866372,12.2464115470782,-7.87960375476725,-4.69469034272258,0.758992183265534,1.19888737220951,-9.81950773978448,0.268126943690902 diffExpScore=5.4376792393034 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 76.7199901346215 86.5217019004392 75.5419323165746 cerebhem 77.6947690427694 71.812885056038 79.770052557309 cortex 76.5921697517564 71.9562725181173 73.0838031668829 heart 79.0715293312386 76.0837218447811 72.203204067524 kidney 78.7801427225713 67.8657407865008 84.1203431430253 liver 76.6816662443789 69.7555919040717 77.006135821291 stomach 74.6904383254308 75.609863964256 76.4817768126836 testicle 75.5967928150724 67.5372496655396 74.9827737428686 cont.diffExp=-9.80171176581766,5.88188398673132,4.63589723363913,2.98780748645751,10.9144019360706,6.92607434030711,-0.919425638825217,8.05954314953274 cont.diffExpScore=1.68865215743725 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=0.219618464026698 cont.tran.correlation=-0.0650638131397584 tran.covariance=0.000689376722726107 cont.tran.covariance=-0.000100079781622434 tran.mean=64.7579752286998 cont.tran.mean=75.185657875474 weightedLogRatios: wLogRatio Lung 0.120488556215808 cerebhem 0.817122647919724 cortex -0.504784679136327 heart -0.295687225072717 kidney 0.050396608808696 liver 0.079116304041174 stomach -0.573649813466773 testicle 0.0175164150808012 cont.weightedLogRatios: wLogRatio Lung -0.529058585188706 cerebhem 0.339570207495073 cortex 0.268930166067139 heart 0.167597773997825 kidney 0.640078652021534 liver 0.406333686564608 stomach -0.0528472718485536 testicle 0.481269115701208 varWeightedLogRatios=0.193164377530548 cont.varWeightedLogRatios=0.133462536113898 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.73442959554673 0.0747430329792908 49.9635811752707 1.19528869356353e-258 *** df.mm.trans1 0.305272241473148 0.064409396152779 4.739560680697 2.49852337746512e-06 *** df.mm.trans2 0.395570225335329 0.0567714089350204 6.96777185480973 6.32763160294628e-12 *** df.mm.exp2 0.229084470254776 0.0727256072134276 3.14998360319609 0.00168819543484985 ** df.mm.exp3 0.224428256717551 0.0727256072134276 3.08595920084823 0.0020927506290774 ** df.mm.exp4 0.174953316872925 0.0727256072134276 2.40566319865148 0.0163494781627837 * df.mm.exp5 0.0328325585234789 0.0727256072134276 0.451458018454563 0.651771219138395 df.mm.exp6 0.0920254688296527 0.0727256072134276 1.26537917462258 0.206071904149494 df.mm.exp7 0.285471811204529 0.0727256072134276 3.92532729725798 9.3404571546386e-05 *** df.mm.exp8 0.0639919852591743 0.0727256072134276 0.879909947968907 0.379149413247989 df.mm.trans1:exp2 -0.148981646659896 0.0670496968887816 -2.22195854079727 0.0265408918660193 * df.mm.trans2:exp2 -0.318022227123014 0.048785820430252 -6.51874303472428 1.19576446100318e-10 *** df.mm.trans1:exp3 -0.248891288521705 0.0670496968887816 -3.71204196395627 0.000218580253328873 *** df.mm.trans2:exp3 -0.0988380813051958 0.048785820430252 -2.02595919128802 0.0430716187144271 * df.mm.trans1:exp4 -0.151353907006169 0.0670496968887816 -2.25733916824749 0.0242324323198487 * df.mm.trans2:exp4 -0.0517467343572786 0.048785820430252 -1.06069210071520 0.289122201798715 df.mm.trans1:exp5 -0.0371712237341433 0.0670496968887816 -0.554383173361707 0.579458139107582 df.mm.trans2:exp5 -0.0201722296273006 0.0487858204302519 -0.413485505612034 0.679352140733709 df.mm.trans1:exp6 -0.0847895156944583 0.0670496968887816 -1.2645771663234 0.206359275332197 df.mm.trans2:exp6 -0.074706123470801 0.048785820430252 -1.53130813035330 0.126054286731774 df.mm.trans1:exp7 -0.195735075114978 0.0670496968887816 -2.91925369088025 0.00359858374941982 ** df.mm.trans2:exp7 -0.0330461729905727 0.048785820430252 -0.677372496744584 0.498348491495845 df.mm.trans1:exp8 -0.0508854310220997 0.0670496968887816 -0.758921119457194 0.448103894601853 df.mm.trans2:exp8 -0.0259040970726838 0.048785820430252 -0.530975944326247 0.595570040208028 df.mm.trans1:probe2 0.255308101409355 0.0467088669540562 5.46594507763336 6.00542527037399e-08 *** df.mm.trans1:probe3 -0.153407534508229 0.0467088669540562 -3.2843343140634 0.00106278070703984 ** df.mm.trans1:probe4 0.00109572129816449 0.0467088669540562 0.0234585287466352 0.981289861011221 df.mm.trans1:probe5 0.218051810641419 0.0467088669540562 4.66831727808553 3.51208098027808e-06 *** df.mm.trans1:probe6 0.362646593053249 0.0467088669540562 7.76397752079826 2.29341931277387e-14 *** df.mm.trans1:probe7 0.366762465104283 0.0467088669540562 7.85209509502849 1.19312823303967e-14 *** df.mm.trans1:probe8 0.0134683868154198 0.0467088669540562 0.288347538566233 0.77314893267358 df.mm.trans1:probe9 0.23596993261028 0.0467088669540562 5.05193013657097 5.32337273476511e-07 *** df.mm.trans1:probe10 0.393192203030497 0.0467088669540562 8.4179349376479 1.55200119642794e-16 *** df.mm.trans1:probe11 0.371885470172303 0.0467088669540562 7.96177459277907 5.24445671175529e-15 *** df.mm.trans1:probe12 0.412292672149506 0.0467088669540562 8.82686091604504 5.7674273964873e-18 *** df.mm.trans1:probe13 0.231821700726213 0.0467088669540562 4.96311976383923 8.33531175201712e-07 *** df.mm.trans1:probe14 0.219146928259902 0.0467088669540562 4.69176288252633 3.14133831534245e-06 *** df.mm.trans1:probe15 0.291931248551157 0.0467088669540562 6.2500177715359 6.40128188097558e-10 *** df.mm.trans1:probe16 0.158104017772809 0.0467088669540562 3.38488231642020 0.000743727439882383 *** df.mm.trans1:probe17 -0.0648122365732586 0.0467088669540562 -1.38757886456568 0.165618182321864 df.mm.trans1:probe18 0.0361934300128176 0.0467088669540562 0.774872789109147 0.438623935188337 df.mm.trans1:probe19 0.102267167997796 0.0467088669540562 2.18945940389409 0.0288266335403443 * df.mm.trans1:probe20 0.0240339517746865 0.0467088669540562 0.514547950784737 0.606998734289647 df.mm.trans1:probe21 -0.0317999049182692 0.0467088669540562 -0.680810882215324 0.496171073488668 df.mm.trans1:probe22 -0.105064245153421 0.0467088669540562 -2.24934261960935 0.0247383461692117 * df.mm.trans2:probe2 -0.0716968412903831 0.0467088669540562 -1.53497282134687 0.125151580146303 df.mm.trans2:probe3 -0.148215894285015 0.0467088669540562 -3.17318539177590 0.00156033703810194 ** df.mm.trans2:probe4 -0.0474615619180466 0.0467088669540562 -1.01611460549302 0.309855370624545 df.mm.trans2:probe5 -0.107873669151394 0.0467088669540562 -2.30949017148073 0.0211481787692613 * df.mm.trans2:probe6 0.0639143048363687 0.0467088669540562 1.36835485431998 0.171551868676123 df.mm.trans3:probe2 -0.131113474715070 0.0467088669540562 -2.8070360782683 0.00511095934055236 ** df.mm.trans3:probe3 0.493024024676453 0.0467088669540562 10.5552554970216 1.33895051540809e-24 *** df.mm.trans3:probe4 0.560678578189169 0.0467088669540562 12.0036861253917 7.69191559192231e-31 *** df.mm.trans3:probe5 0.124916295646619 0.0467088669540562 2.67435936242019 0.00762671239266671 ** df.mm.trans3:probe6 0.546441460474874 0.0467088669540562 11.6988806646148 1.76816186535763e-29 *** df.mm.trans3:probe7 -0.0442666661399902 0.0467088669540562 -0.947714406849812 0.343536221521218 df.mm.trans3:probe8 0.587247936826907 0.0467088669540562 12.5725151373193 1.90539552361655e-33 *** df.mm.trans3:probe9 0.610345139517442 0.0467088669540562 13.0670080290706 8.87124741544354e-36 *** df.mm.trans3:probe10 0.0684664810410437 0.0467088669540562 1.46581335634600 0.143057982170936 df.mm.trans3:probe11 -0.0429236791224582 0.0467088669540562 -0.918962114081654 0.358368350513424 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.60902983359744 0.152659170855916 30.1916341334487 8.0474586159377e-138 *** df.mm.trans1 -0.212843378988624 0.131553197938031 -1.61792630148668 0.106038465650124 df.mm.trans2 -0.157228892875633 0.115952964054103 -1.35597131266322 0.175457721369082 df.mm.exp2 -0.228165979081193 0.148538672497689 -1.53607121461748 0.124882003468337 df.mm.exp3 -0.152922990436509 0.148538672497689 -1.02951633985343 0.303521273179078 df.mm.exp4 -0.0531668844888922 0.148538672497689 -0.357932944969057 0.7204797222125 df.mm.exp5 -0.323925784030360 0.148538672497689 -2.18075049805901 0.0294672682815196 * df.mm.exp6 -0.235094545206279 0.148538672497689 -1.58271607826532 0.113847137325688 df.mm.exp7 -0.173983343993876 0.148538672497689 -1.17129997911206 0.241796714014929 df.mm.exp8 -0.255034928435022 0.148538672497689 -1.71695979334263 0.086339993878552 . df.mm.trans1:exp2 0.240791609715876 0.136945889471971 1.75829746072925 0.0790460853461067 . df.mm.trans2:exp2 0.0418346251469048 0.0996427706977234 0.419846064636384 0.67470086066272 df.mm.trans1:exp3 0.151255537410268 0.136945889471971 1.10449125558621 0.269683373986384 df.mm.trans2:exp3 -0.0314136726747811 0.0996427706977234 -0.315262938342789 0.752637106848217 df.mm.trans1:exp4 0.0833574598841544 0.136945889471971 0.608689024588906 0.542888385162906 df.mm.trans2:exp4 -0.0753940498164233 0.0996427706977234 -0.756643450282398 0.449466945115405 df.mm.trans1:exp5 0.350424451245657 0.136945889471971 2.55885337337838 0.0106688333396185 * df.mm.trans2:exp5 0.0810618658176358 0.0996427706977234 0.813524807168854 0.416138290549278 df.mm.trans1:exp6 0.234594891005085 0.136945889471971 1.71304806525865 0.0870575327671086 . df.mm.trans2:exp6 0.0196968618690900 0.0996427706977234 0.197674770895747 0.843345389181064 df.mm.trans1:exp7 0.147173124955794 0.136945889471971 1.07468085039468 0.282813556700922 df.mm.trans2:exp7 0.0391748228929555 0.0996427706977234 0.393152685524938 0.694302371431424 df.mm.trans1:exp8 0.240286485653793 0.136945889471971 1.75460896694510 0.0796758121624312 . df.mm.trans2:exp8 0.0073189495060216 0.0996427706977234 0.0734518867226644 0.9414632974558 df.mm.trans1:probe2 0.0369231002806588 0.0954006897579493 0.387031795832295 0.698826672824174 df.mm.trans1:probe3 -0.0541344544376804 0.0954006897579493 -0.567443008798263 0.570558623378539 df.mm.trans1:probe4 -0.065470003862705 0.0954006897579493 -0.686263422505807 0.492728606513561 df.mm.trans1:probe5 -0.0277716433275256 0.0954006897579494 -0.291105267666175 0.77103972170941 df.mm.trans1:probe6 -0.0925407650494732 0.0954006897579494 -0.97002197032608 0.332303288708655 df.mm.trans1:probe7 -0.141625159148697 0.0954006897579493 -1.48452971889437 0.138028174563159 df.mm.trans1:probe8 -0.0772560637403464 0.0954006897579493 -0.809806133858786 0.418271521115262 df.mm.trans1:probe9 -0.218450068331736 0.0954006897579493 -2.28981644562516 0.0222689458444617 * df.mm.trans1:probe10 -0.108217506208682 0.0954006897579493 -1.13434720947251 0.256959242632872 df.mm.trans1:probe11 -0.108649588732242 0.0954006897579494 -1.13887634363974 0.255066077436521 df.mm.trans1:probe12 -0.221385671022065 0.0954006897579493 -2.32058773981368 0.0205379473012165 * df.mm.trans1:probe13 -0.11150219443136 0.0954006897579493 -1.16877765469268 0.242811179976964 df.mm.trans1:probe14 -0.0827790099007774 0.0954006897579493 -0.86769823269417 0.385797183047212 df.mm.trans1:probe15 -0.000760562595920029 0.0954006897579493 -0.00797229661388957 0.993640910144339 df.mm.trans1:probe16 -0.115090720191883 0.0954006897579493 -1.20639295673743 0.227991710769501 df.mm.trans1:probe17 -0.106270265843557 0.0954006897579493 -1.11393603246671 0.265612171560274 df.mm.trans1:probe18 0.0107910740108459 0.0954006897579493 0.113113165515103 0.909966753505946 df.mm.trans1:probe19 -0.132496047916408 0.0954006897579493 -1.38883742090940 0.165235179423833 df.mm.trans1:probe20 -0.0697223531682548 0.0954006897579493 -0.730836992323163 0.465074148554718 df.mm.trans1:probe21 -0.151265392520608 0.0954006897579493 -1.58557965256224 0.113195589396862 df.mm.trans1:probe22 -0.0111237404216990 0.0954006897579493 -0.116600209599345 0.907203597078323 df.mm.trans2:probe2 0.150537456806462 0.0954006897579493 1.57794935433282 0.114938264643554 df.mm.trans2:probe3 -0.0596656656523096 0.0954006897579493 -0.625421742795501 0.531857187282772 df.mm.trans2:probe4 0.117770981271153 0.0954006897579493 1.23448773347406 0.217352260854329 df.mm.trans2:probe5 0.0398659117966353 0.0954006897579493 0.417878653684613 0.67613824838185 df.mm.trans2:probe6 -0.102405061113931 0.0954006897579493 -1.07342055255316 0.283378065297921 df.mm.trans3:probe2 0.0733751529910481 0.0954006897579493 0.769126021805666 0.442025820965922 df.mm.trans3:probe3 0.121734532121152 0.0954006897579493 1.27603408769912 0.202281691997161 df.mm.trans3:probe4 0.114010361359489 0.0954006897579493 1.19506852255216 0.232383696771538 df.mm.trans3:probe5 0.154801011489406 0.0954006897579493 1.62264038008705 0.105026103848727 df.mm.trans3:probe6 0.137511768715540 0.0954006897579493 1.44141273049948 0.149825482365448 df.mm.trans3:probe7 -0.0100713118912661 0.0954006897579493 -0.105568543758111 0.915948851720785 df.mm.trans3:probe8 0.0503326976621176 0.0954006897579493 0.527592596969915 0.597915731324438 df.mm.trans3:probe9 0.121563272384844 0.0954006897579493 1.27423892524545 0.202916684984805 df.mm.trans3:probe10 0.0225096041638443 0.0954006897579493 0.235948023237104 0.81352815570962 df.mm.trans3:probe11 0.157266598958873 0.0954006897579493 1.64848492561102 0.0996117673819753 .