chr6.20347_chr6_95302921_95306269_+_0.R fitVsDatCorrelation=0.827874850294384 cont.fitVsDatCorrelation=0.257960082591220 fstatistic=11786.4566478990,43,485 cont.fstatistic=3965.16907090327,43,485 residuals=-0.445168723489532,-0.0821808494542998,0.000288635777575364,0.0743947430014132,0.552315239552816 cont.residuals=-0.442182543602645,-0.160609507660608,-0.0359851914847739,0.128122873349105,0.941766882494598 predictedValues: Include Exclude Both chr6.20347_chr6_95302921_95306269_+_0.R.tl.Lung 53.0654860372706 51.1159102342921 60.1273929785442 chr6.20347_chr6_95302921_95306269_+_0.R.tl.cerebhem 54.0963867100983 57.6919076976549 65.1983635764764 chr6.20347_chr6_95302921_95306269_+_0.R.tl.cortex 53.7529756631956 56.4639541057447 66.5509784538218 chr6.20347_chr6_95302921_95306269_+_0.R.tl.heart 51.3528589270784 59.8001302914448 66.8652587925791 chr6.20347_chr6_95302921_95306269_+_0.R.tl.kidney 51.0464818619265 52.4560732450255 62.8696742839238 chr6.20347_chr6_95302921_95306269_+_0.R.tl.liver 54.9147691220456 57.3156573769436 58.4243814840013 chr6.20347_chr6_95302921_95306269_+_0.R.tl.stomach 52.1429446089065 51.910238256784 61.7698663925612 chr6.20347_chr6_95302921_95306269_+_0.R.tl.testicle 52.5745127735204 58.6169642009016 63.5344916078512 diffExp=1.94957580297848,-3.59552098755655,-2.71097844254911,-8.44727136436643,-1.40959138309901,-2.40088825489795,0.232706352122484,-6.04245142738115 diffExpScore=1.14363490547942 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 62.7672056230042 58.6807720985072 58.2377473937595 cerebhem 59.5724768661909 56.8662290714675 55.8278426541999 cortex 55.9675075705448 59.2172146490416 58.951893188657 heart 53.4418875204692 65.6728191390776 57.1596982313564 kidney 52.9731839242592 56.6088178835781 58.5592214227498 liver 59.8520980131502 54.1308893573589 62.0620407877815 stomach 59.0586665212854 58.6923875964405 57.0610464180194 testicle 57.9105307137257 58.4503523981341 57.8292818704696 cont.diffExp=4.08643352449702,2.70624779472343,-3.24970707849683,-12.2309316186084,-3.63563395931889,5.72120865579135,0.366278924844842,-0.539821684408366 cont.diffExpScore=4.18423035144301 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,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.225047566723066 cont.tran.correlation=-0.4362010948266 tran.covariance=0.000360256888821417 cont.tran.covariance=-0.00140746039761608 tran.mean=54.2698281945521 cont.tran.mean=58.1164399341397 weightedLogRatios: wLogRatio Lung 0.147957413122719 cerebhem -0.258874401800565 cortex -0.197256577180382 heart -0.611412550067579 kidney -0.107496745113466 liver -0.172329023615181 stomach 0.0176755656571616 testicle -0.436979750710618 cont.weightedLogRatios: wLogRatio Lung 0.276403155949778 cerebhem 0.188941130272790 cortex -0.228754858060797 heart -0.841186324761728 kidney -0.265713273889492 liver 0.406069688082769 stomach 0.0253542615950590 testicle -0.0377034251735975 varWeightedLogRatios=0.0580898817254092 cont.varWeightedLogRatios=0.154455569199905 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95732710373906 0.0657651055458129 60.1736600419858 4.24277844807209e-227 *** df.mm.trans1 0.0238968668793110 0.0526484062113851 0.453895352185293 0.650107187650604 df.mm.trans2 -0.0279226618324480 0.0526484062113851 -0.530361008846832 0.596104389501047 df.mm.exp2 0.0592929278485646 0.0704998026857471 0.84103679144838 0.400741781206792 df.mm.exp3 0.0108762211205818 0.0704998026857471 0.154273071785215 0.877458603053653 df.mm.exp4 0.0178919057945927 0.070499802685747 0.25378660808947 0.799767970233665 df.mm.exp5 -0.0575081880077274 0.0704998026857471 -0.815721261860409 0.415059997126498 df.mm.exp6 0.177465863737538 0.0704998026857471 2.51725333939716 0.0121486308268841 * df.mm.exp7 -0.0290677375199565 0.0704998026857471 -0.412309487581489 0.680294776600564 df.mm.exp8 0.0725156547046905 0.0704998026857471 1.02859372568643 0.304183320924372 df.mm.trans1:exp2 -0.0400522697723704 0.0553045953229331 -0.72421232880375 0.469284581270717 df.mm.trans2:exp2 0.061728184328269 0.0553045953229331 1.11614928140831 0.264910918505414 df.mm.trans1:exp3 0.00199606907725056 0.0553045953229331 0.0360922824874709 0.97122362587967 df.mm.trans2:exp3 0.0886304293791019 0.0553045953229332 1.60258707005400 0.109676887500180 df.mm.trans1:exp4 -0.0506980321148907 0.0553045953229331 -0.916705597769156 0.359752526685216 df.mm.trans2:exp4 0.139020130319020 0.0553045953229331 2.51371752215630 0.0122694798547479 * df.mm.trans1:exp5 0.0187180782550896 0.0553045953229331 0.338454302862022 0.73516732566782 df.mm.trans2:exp5 0.0833885036543561 0.0553045953229331 1.50780424605652 0.132255721923293 df.mm.trans1:exp6 -0.143210269237876 0.0553045953229331 -2.58948227360938 0.00990093507041602 ** df.mm.trans2:exp6 -0.0629878283191323 0.0553045953229331 -1.13892576107529 0.255296346439043 df.mm.trans1:exp7 0.0115298830235132 0.0553045953229331 0.208479656277896 0.83494198927664 df.mm.trans2:exp7 0.0444879735263837 0.0553045953229331 0.804417305046907 0.421550135087264 df.mm.trans1:exp8 -0.0818109368775894 0.0553045953229331 -1.47927918828230 0.139714913728333 df.mm.trans2:exp8 0.0644126878393621 0.0553045953229331 1.16468961508976 0.244717206615914 df.mm.trans1:probe2 -0.0501737213679602 0.0378644679900815 -1.32508718678163 0.185766154228623 df.mm.trans1:probe3 0.0167120715389205 0.0378644679900815 0.441365544692143 0.659145080092919 df.mm.trans1:probe4 -0.0441329190171179 0.0378644679900815 -1.16554969235745 0.244369446080713 df.mm.trans1:probe5 -0.0269195152393552 0.0378644679900815 -0.710943971176532 0.477460707949511 df.mm.trans1:probe6 -0.0506416625556706 0.0378644679900815 -1.33744550613879 0.181704119955176 df.mm.trans2:probe2 -0.0448135622679897 0.0378644679900815 -1.18352546983437 0.237180725629535 df.mm.trans2:probe3 0.113171878232778 0.0378644679900815 2.98886751194875 0.00294223876168899 ** df.mm.trans2:probe4 -0.0456296798404243 0.0378644679900815 -1.20507912199841 0.228760419781985 df.mm.trans2:probe5 0.0970047677538827 0.0378644679900815 2.56189438022193 0.0107113969591389 * df.mm.trans2:probe6 -0.0446716163472161 0.0378644679900815 -1.17977668031458 0.238667401122967 df.mm.trans3:probe2 -0.190368362712618 0.0378644679900815 -5.02762544458527 7.00019750975463e-07 *** df.mm.trans3:probe3 0.325109894738106 0.0378644679900815 8.58614717162454 1.23133043435479e-16 *** df.mm.trans3:probe4 0.0572751182104736 0.0378644679900815 1.51263496493538 0.131023726306003 df.mm.trans3:probe5 -0.104254453379826 0.0378644679900815 -2.75335846279778 0.00612007606863686 ** df.mm.trans3:probe6 0.373284737803885 0.0378644679900815 9.85844401409961 5.10028342733244e-21 *** df.mm.trans3:probe7 0.411167277522038 0.0378644679900815 10.8589212881518 9.76469395078308e-25 *** df.mm.trans3:probe8 0.369338782702059 0.0378644679900815 9.75423140234813 1.20708915007571e-20 *** df.mm.trans3:probe9 -0.149296109572613 0.0378644679900815 -3.94290788957395 9.23583497769207e-05 *** df.mm.trans3:probe10 0.339385303757955 0.0378644679900815 8.96316049777476 6.8376053195444e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20089858319576 0.113278422280056 37.0847200962065 1.10650923151197e-143 *** df.mm.trans1 -0.0621939327417916 0.0906853009918862 -0.685821539560818 0.4931533451735 df.mm.trans2 -0.122240446125998 0.0906853009918862 -1.34796317362320 0.178299470499352 df.mm.exp2 -0.0413885155680456 0.121433796129673 -0.340831933837009 0.733377699412955 df.mm.exp3 -0.117749273072955 0.121433796129673 -0.969658174460889 0.332700211613218 df.mm.exp4 -0.0295802159955677 0.121433796129673 -0.243591297796377 0.807650367448542 df.mm.exp5 -0.211099105743573 0.121433796129673 -1.73838842621827 0.0827770160674326 . df.mm.exp6 -0.191864185417865 0.121433796129673 -1.57999001540710 0.114761077266620 df.mm.exp7 -0.040291471049191 0.121433796129673 -0.331797838273669 0.740185248320343 df.mm.exp8 -0.0774294109885176 0.121433796129673 -0.637626537721299 0.524017715857272 df.mm.trans1:exp2 -0.0108505481922040 0.0952605070884387 -0.113903951635807 0.90936106816072 df.mm.trans2:exp2 0.00997805585107141 0.0952605070884387 0.104744937393708 0.916621505859558 df.mm.trans1:exp3 0.00308783983655197 0.0952605070884387 0.0324146903153188 0.974154682552622 df.mm.trans2:exp3 0.126849449715027 0.0952605070884387 1.33160586261903 0.183615227495133 df.mm.trans1:exp4 -0.131257668654826 0.0952605070884387 -1.37788127175271 0.168875343495538 df.mm.trans2:exp4 0.142153233182020 0.0952605070884387 1.49225778370093 0.136281747792720 df.mm.trans1:exp5 0.041452193936555 0.0952605070884387 0.435145635935691 0.663650288623094 df.mm.trans2:exp5 0.175151760846498 0.0952605070884387 1.83866080708440 0.0665763309699486 . df.mm.trans1:exp6 0.144307937706499 0.0952605070884387 1.51487685838713 0.130455009628645 df.mm.trans2:exp6 0.111157065150908 0.0952605070884387 1.16687458998840 0.243834423020242 df.mm.trans1:exp7 -0.0206099648861125 0.0952605070884387 -0.216353717989118 0.828803007749225 df.mm.trans2:exp7 0.0404893953106051 0.0952605070884387 0.425038628788898 0.670997023808172 df.mm.trans1:exp8 -0.00310407698151292 0.0952605070884387 -0.0325851402263809 0.974018825201404 df.mm.trans2:exp8 0.0734950169733619 0.0952605070884387 0.771516121629817 0.440776796382838 df.mm.trans1:probe2 -0.0298549381260521 0.0652204107146483 -0.457754524985638 0.647333811361047 df.mm.trans1:probe3 -0.0204683973725264 0.0652204107146483 -0.313834229932706 0.753781880900239 df.mm.trans1:probe4 0.0639974058547681 0.0652204107146483 0.981248126982348 0.32695961484999 df.mm.trans1:probe5 0.0151421327089312 0.0652204107146483 0.232168619348028 0.816504981130237 df.mm.trans1:probe6 -0.0171668723590031 0.0652204107146483 -0.263213190025918 0.792498047917659 df.mm.trans2:probe2 -0.000221977217283842 0.0652204107146483 -0.00340349309137342 0.99728581009166 df.mm.trans2:probe3 -0.0144026907843449 0.0652204107146483 -0.220831034740941 0.825316931150481 df.mm.trans2:probe4 -0.0315479943937478 0.0652204107146483 -0.483713519250534 0.62880751534354 df.mm.trans2:probe5 -0.0190119979549483 0.0652204107146483 -0.29150380604209 0.770790665644814 df.mm.trans2:probe6 -0.0395517572806134 0.0652204107146483 -0.606432201932304 0.544511529025153 df.mm.trans3:probe2 0.0582062329110504 0.0652204107146483 0.892454252790798 0.372592078092577 df.mm.trans3:probe3 0.0196447814312271 0.0652204107146483 0.30120603682146 0.763386477843758 df.mm.trans3:probe4 0.159402849792858 0.0652204107146483 2.44406387580532 0.0148780579810479 * df.mm.trans3:probe5 0.0661658895539961 0.0652204107146483 1.0144966710419 0.310851641455953 df.mm.trans3:probe6 -0.0191022912796181 0.0652204107146483 -0.292888239591043 0.769732845139352 df.mm.trans3:probe7 0.0371121597958128 0.0652204107146483 0.569026772281235 0.569601381284909 df.mm.trans3:probe8 0.055523543091471 0.0652204107146483 0.851321579902295 0.395010869186475 df.mm.trans3:probe9 0.0883307919923359 0.0652204107146483 1.35434277436246 0.176257678087163 df.mm.trans3:probe10 0.0154112509177224 0.0652204107146483 0.236294907512152 0.813303596224182