chr19.11983_chr19_8308132_8308536_+_1.R fitVsDatCorrelation=0.920056231834555 cont.fitVsDatCorrelation=0.309434456098144 fstatistic=9747.60687663688,43,485 cont.fstatistic=1645.29223931546,43,485 residuals=-0.496697696895911,-0.0975453811217576,-0.00632032912120114,0.0958930003056838,0.539893578941023 cont.residuals=-0.821229543673026,-0.292773476992496,0.0110241803264648,0.253519572370777,0.911618280439304 predictedValues: Include Exclude Both chr19.11983_chr19_8308132_8308536_+_1.R.tl.Lung 56.6201833088283 116.483059488909 85.3795512347033 chr19.11983_chr19_8308132_8308536_+_1.R.tl.cerebhem 61.5195508747208 91.1513875999012 66.748592098811 chr19.11983_chr19_8308132_8308536_+_1.R.tl.cortex 56.834238913648 77.5841793138125 79.2915000844884 chr19.11983_chr19_8308132_8308536_+_1.R.tl.heart 55.2726303529603 86.7607311082899 87.9128938065132 chr19.11983_chr19_8308132_8308536_+_1.R.tl.kidney 54.2254788439491 122.100982279942 72.9126140018458 chr19.11983_chr19_8308132_8308536_+_1.R.tl.liver 54.5563231047388 111.234098171038 76.2580158054639 chr19.11983_chr19_8308132_8308536_+_1.R.tl.stomach 58.0133722892815 88.7337633848296 86.8657804021549 chr19.11983_chr19_8308132_8308536_+_1.R.tl.testicle 63.0098296363347 93.5509259706605 110.442904941328 diffExp=-59.8628761800802,-29.6318367251804,-20.7499404001645,-31.4881007553296,-67.8755034359932,-56.6777750662991,-30.7203910955481,-30.5410963343258 diffExpScore=0.996956300263592 diffExp1.5=-1,0,0,-1,-1,-1,-1,0 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 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 69.8342117178256 78.6158571537238 83.240637699954 cerebhem 70.2011728542531 72.5630203294072 72.35664065607 cortex 71.9274716991814 78.3593109518743 69.9851605534094 heart 69.8670429806006 98.1639848692678 65.8992929987433 kidney 65.9312436987278 71.04359142106 86.9644847603077 liver 84.7409354545565 83.21467386266 85.4003559550955 stomach 76.3726338817214 79.4586999900476 80.8451054605505 testicle 76.0704742520986 90.6712386839067 91.3064634999073 cont.diffExp=-8.78164543589821,-2.36184747515404,-6.43183925269288,-28.2969418886672,-5.11234772233219,1.52626159189644,-3.08606610832625,-14.6007644318081 cont.diffExpScore=1.03011985382999 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,-1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,-1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.423119076324746 cont.tran.correlation=0.272060454193491 tran.covariance=-0.0035394721045237 cont.tran.covariance=0.00269342180077933 tran.mean=77.9781709151152 cont.tran.mean=77.314722737557 weightedLogRatios: wLogRatio Lung -3.17194949387227 cerebhem -1.69688324624747 cortex -1.30582095709019 heart -1.91068552130907 kidney -3.57065719363769 liver -3.10282671123971 stomach -1.81595038342242 testicle -1.71558905386764 cont.weightedLogRatios: wLogRatio Lung -0.509965952485997 cerebhem -0.141226907656795 cortex -0.369862940667416 heart -1.50184995846403 kidney -0.315599309784126 liver 0.0805249114895039 stomach -0.172531479862476 testicle -0.775967064958586 varWeightedLogRatios=0.728205934639934 cont.varWeightedLogRatios=0.241870477357234 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33498910054326 0.0777649850543893 55.7447429265414 5.00662936694538e-213 *** df.mm.trans1 -0.256422334744224 0.0622549372982257 -4.11890760592786 4.47544446079159e-05 *** df.mm.trans2 0.385833295289267 0.0622549372982257 6.19763366624198 1.22596442356000e-09 *** df.mm.exp2 0.0839388017513226 0.083363602273479 1.00689988750674 0.314484965000189 df.mm.exp3 -0.328633204896664 0.083363602273479 -3.94216655631752 9.26354185213962e-05 *** df.mm.exp4 -0.347919255059809 0.083363602273479 -4.1735151261631 3.55527549098069e-05 *** df.mm.exp5 0.161732915398972 0.083363602273479 1.94009029106490 0.0529480112706434 . df.mm.exp6 0.0297433149462112 0.083363602273479 0.356790183426054 0.721404061395026 df.mm.exp7 -0.265054911164301 0.083363602273479 -3.17950405135773 0.00156962570034073 ** df.mm.exp8 -0.369706741362646 0.083363602273479 -4.43487003056564 1.14074953018181e-05 *** df.mm.trans1:exp2 -0.00094929426290154 0.0653957899562857 -0.0145161372549533 0.98842417424492 df.mm.trans2:exp2 -0.329162927805258 0.0653957899562857 -5.03339630923166 6.80279587870683e-07 *** df.mm.trans1:exp3 0.332406629583273 0.0653957899562857 5.08299738875351 5.31400721015864e-07 *** df.mm.trans2:exp3 -0.0777491138414798 0.0653957899562857 -1.18890090468288 0.235060436662818 df.mm.trans1:exp4 0.323831593537053 0.0653957899562857 4.95187218861521 1.01643451040153e-06 *** df.mm.trans2:exp4 0.0533275174528401 0.0653957899562857 0.815457959732382 0.415210494360621 df.mm.trans1:exp5 -0.204947545245400 0.0653957899562857 -3.13395625899463 0.00182914029016620 ** df.mm.trans2:exp5 -0.114630339753916 0.0653957899562857 -1.75287032744068 0.0802560694000999 . df.mm.trans1:exp6 -0.0668752126636375 0.0653957899562857 -1.02262259861592 0.306996117436755 df.mm.trans2:exp6 -0.0758521922248808 0.0653957899562857 -1.15989411972215 0.24666258704317 df.mm.trans1:exp7 0.289362934641968 0.0653957899562857 4.42479454465486 1.19313114467550e-05 *** df.mm.trans2:exp7 -0.00705047531374386 0.0653957899562857 -0.107812373219389 0.914189112687414 df.mm.trans1:exp8 0.47663196435057 0.0653957899562857 7.28841970819801 1.28183688819995e-12 *** df.mm.trans2:exp8 0.150466841850145 0.0653957899562857 2.30086435152363 0.0218226248731791 * df.mm.trans1:probe2 -0.0844886096736744 0.0447734366561593 -1.88702534322997 0.0597538710657776 . df.mm.trans1:probe3 -0.187394504321173 0.0447734366561593 -4.18539469641971 3.38049681677603e-05 *** df.mm.trans1:probe4 -0.100304332029367 0.0447734366561593 -2.24026430670624 0.0255258410201775 * df.mm.trans1:probe5 -0.0935431169138065 0.0447734366561593 -2.08925478810522 0.0372053698391027 * df.mm.trans1:probe6 -0.209489414544387 0.0447734366561593 -4.67887725825415 3.74678469353948e-06 *** df.mm.trans2:probe2 0.287144610856758 0.0447734366561593 6.41328055878096 3.38559194145355e-10 *** df.mm.trans2:probe3 -0.126178338688861 0.0447734366561593 -2.81815174604210 0.00502742012651386 ** df.mm.trans2:probe4 0.15839804518701 0.0447734366561593 3.53776830676275 0.000442326802433736 *** df.mm.trans2:probe5 0.450328793287484 0.0447734366561593 10.0579456686744 9.64294782441592e-22 *** df.mm.trans2:probe6 -0.178917838842640 0.0447734366561593 -3.99607115747339 7.441321288968e-05 *** df.mm.trans3:probe2 0.184364033494297 0.0447734366561593 4.11771012598683 4.49796273258522e-05 *** df.mm.trans3:probe3 0.0236915597511003 0.0447734366561593 0.52914320455321 0.596948202240029 df.mm.trans3:probe4 -0.00520908336772049 0.0447734366561593 -0.116343165875874 0.907428734150783 df.mm.trans3:probe5 0.374916018877652 0.0447734366561593 8.37362612472314 6.04181184229814e-16 *** df.mm.trans3:probe6 -0.576516271690056 0.0447734366561593 -12.8763015472199 7.6681034460332e-33 *** df.mm.trans3:probe7 -0.351687035204628 0.0447734366561593 -7.85481440492122 2.59668923652660e-14 *** df.mm.trans3:probe8 0.0638872069065386 0.0447734366561593 1.42689977982179 0.154252228603203 df.mm.trans3:probe9 0.0512710723855506 0.0447734366561593 1.14512255959466 0.252723148769790 df.mm.trans3:probe10 0.0623481401852714 0.0447734366561593 1.39252523017338 0.164401473358663 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16802796552276 0.188746603998665 22.0826646796371 2.63892888222729e-75 *** df.mm.trans1 0.0633323705481129 0.151101527107240 0.419137858899099 0.675300952705566 df.mm.trans2 0.208999130929659 0.151101527107240 1.38317020966524 0.167249067877317 df.mm.exp2 0.0652514239207624 0.202335238863734 0.322491644496524 0.74721923664856 df.mm.exp3 0.199718055506615 0.202335238863734 0.987065113463103 0.324102881582608 df.mm.exp4 0.456143941629775 0.202335238863734 2.25439693150521 0.0246166110510549 * df.mm.exp5 -0.202555515452594 0.202335238863734 -1.00108867140542 0.317283135268454 df.mm.exp6 0.224710425708203 0.202335238863734 1.11058472547898 0.267297428507699 df.mm.exp7 0.129364990281926 0.202335238863734 0.639359663736325 0.522890840503835 df.mm.exp8 0.135717036761363 0.202335238863734 0.67075333749829 0.502696964790197 df.mm.trans1:exp2 -0.0600104346162153 0.158724820192867 -0.378078453913487 0.705537730404502 df.mm.trans2:exp2 -0.145369417758426 0.158724820192867 -0.915858134737765 0.360196450207066 df.mm.trans1:exp3 -0.170183810620537 0.158724820192867 -1.07219406778188 0.284166090750741 df.mm.trans2:exp3 -0.202986679880204 0.158724820192867 -1.27885909483819 0.201558245105816 df.mm.trans1:exp4 -0.455673920605981 0.158724820192867 -2.87084225423781 0.00427280828812692 ** df.mm.trans2:exp4 -0.234077970444114 0.158724820192867 -1.47474081343854 0.140931058446121 df.mm.trans1:exp5 0.145043923414000 0.158724820192867 0.913807451397686 0.361272078860215 df.mm.trans2:exp5 0.101275743726520 0.158724820192867 0.638058645166269 0.523736643569396 df.mm.trans1:exp6 -0.0312356703694891 0.158724820192867 -0.196791341968663 0.844073263492806 df.mm.trans2:exp6 -0.167860148936258 0.158724820192867 -1.05755450680171 0.290785045297561 df.mm.trans1:exp7 -0.0398645824553211 0.158724820192867 -0.251155316521269 0.801800402672783 df.mm.trans2:exp7 -0.118701024633182 0.158724820192867 -0.747841607184988 0.454918150827248 df.mm.trans1:exp8 -0.0501808622678175 0.158724820192867 -0.316150065294404 0.752024620121019 df.mm.trans2:exp8 0.00694974221017624 0.158724820192867 0.0437848485305044 0.965093925059312 df.mm.trans1:probe2 0.111546906902149 0.108671455569481 1.02646004249781 0.305186449429493 df.mm.trans1:probe3 0.0510306464322009 0.108671455569481 0.469586481240914 0.638861531064567 df.mm.trans1:probe4 0.0277669502409093 0.108671455569481 0.255512821608946 0.798435365542555 df.mm.trans1:probe5 -0.0427704000836289 0.108671455569481 -0.393575294077872 0.694067585173822 df.mm.trans1:probe6 0.0886449819854835 0.108671455569481 0.815715419665165 0.415063336035518 df.mm.trans2:probe2 0.0152099873300441 0.108671455569481 0.139963040435391 0.888747296811013 df.mm.trans2:probe3 -0.0330764379128242 0.108671455569481 -0.304370984445646 0.760975822361531 df.mm.trans2:probe4 -0.0698810441281163 0.108671455569481 -0.643048754264975 0.520496363599787 df.mm.trans2:probe5 -0.0704152517686175 0.108671455569481 -0.647964558858755 0.51731449485139 df.mm.trans2:probe6 -0.0410960120280094 0.108671455569481 -0.378167494054904 0.70547163130356 df.mm.trans3:probe2 -0.0414838164537851 0.108671455569481 -0.381736089172576 0.702824318799997 df.mm.trans3:probe3 -0.0713442468604546 0.108671455569481 -0.656513216709787 0.511805331319254 df.mm.trans3:probe4 0.00125671441941178 0.108671455569481 0.0115643469835396 0.990777947024354 df.mm.trans3:probe5 0.0934997185073036 0.108671455569481 0.860388940382996 0.389999791062934 df.mm.trans3:probe6 -0.141857669882041 0.108671455569481 -1.30538115219541 0.192382003151975 df.mm.trans3:probe7 0.117919438446559 0.108671455569481 1.08510038656071 0.278416261941249 df.mm.trans3:probe8 -0.0156508426575752 0.108671455569481 -0.144019812521684 0.885544646280833 df.mm.trans3:probe9 -0.0687039967019242 0.108671455569481 -0.632217506813435 0.527542671027545 df.mm.trans3:probe10 -0.0598733440645089 0.108671455569481 -0.550957413340505 0.581916570486197