chr15.8450_chr15_80645017_80647703_-_0.R fitVsDatCorrelation=0.901843351868101 cont.fitVsDatCorrelation=0.292037579927511 fstatistic=9216.1441093214,41,439 cont.fstatistic=1872.34609640606,41,439 residuals=-0.470067943756907,-0.0872318903835736,-0.00409708736550283,0.0833792843896668,0.813273344591335 cont.residuals=-0.619033920773538,-0.229010958270479,-0.0712585914893425,0.20931065509237,1.01471647105094 predictedValues: Include Exclude Both chr15.8450_chr15_80645017_80647703_-_0.R.tl.Lung 57.4967415265343 74.4943874097842 110.052468645431 chr15.8450_chr15_80645017_80647703_-_0.R.tl.cerebhem 55.5680785127684 63.9043825761968 115.323066022957 chr15.8450_chr15_80645017_80647703_-_0.R.tl.cortex 57.4149367759935 63.6833402403853 98.0677825665552 chr15.8450_chr15_80645017_80647703_-_0.R.tl.heart 56.2049285557335 62.2682381198644 92.8404398400687 chr15.8450_chr15_80645017_80647703_-_0.R.tl.kidney 58.1429448587202 74.6220938239594 89.305505519128 chr15.8450_chr15_80645017_80647703_-_0.R.tl.liver 56.1432713848335 74.319639765372 89.1024550906507 chr15.8450_chr15_80645017_80647703_-_0.R.tl.stomach 54.1428504730682 63.4776720624723 94.3319494508994 chr15.8450_chr15_80645017_80647703_-_0.R.tl.testicle 57.7901132566325 68.6972084948473 91.5292241387876 diffExp=-16.9976458832500,-8.33630406342844,-6.26840346439182,-6.06330956413099,-16.4791489652392,-18.1763683805385,-9.33482158940403,-10.9070952382148 diffExpScore=0.98931202546222 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,-1,0,0 diffExp1.3Score=0.5 diffExp1.2=-1,0,0,0,-1,-1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 62.8801448750329 70.7125474451881 63.6315905732089 cerebhem 66.2279823968969 69.6916465252276 52.6351805181033 cortex 68.4729911612492 78.9580195077754 60.218665741803 heart 60.4916466056931 63.2448202857471 57.5855090787716 kidney 59.8815030541438 68.1537701370035 62.1220013546658 liver 59.3320752620641 62.6814331290935 65.6737312603352 stomach 60.0100084129009 61.5884536053158 56.2069910497237 testicle 65.0368881467423 68.1907915944432 57.5478069474686 cont.diffExp=-7.83240257015526,-3.46366412833075,-10.4850283465262,-2.75317368005403,-8.27226708285973,-3.34935786702946,-1.57844519241493,-3.15390344770083 cont.diffExpScore=0.976126952463694 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=0.536285943202634 cont.tran.correlation=0.852097038474637 tran.covariance=0.00104315728451963 cont.tran.covariance=0.00372287415022076 tran.mean=62.3981767398229 cont.tran.mean=65.3471701340323 weightedLogRatios: wLogRatio Lung -1.08291876288422 cerebhem -0.571346585752025 cortex -0.425054879561434 heart -0.418007214005617 kidney -1.04495816095975 liver -1.16903187582341 stomach -0.647567568429917 testicle -0.71633230642079 cont.weightedLogRatios: wLogRatio Lung -0.493040234012553 cerebhem -0.215052810909950 cortex -0.612319965676256 heart -0.183584573287763 kidney -0.537919562715729 liver -0.225734938870033 stomach -0.106643069002353 testicle -0.198825870376396 varWeightedLogRatios=0.0902953180519038 cont.varWeightedLogRatios=0.0373678047371788 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06469009974574 0.0807338860537832 50.34676637562 1.75430675774126e-184 *** df.mm.trans1 -0.0338164735022382 0.0708759211507906 -0.477122172850391 0.633512556365783 df.mm.trans2 0.457192546220177 0.0673724476563256 6.7860462566592 3.74582411763664e-11 *** df.mm.exp2 -0.234235429746027 0.0929260974199703 -2.52066358374465 0.0120667692580384 * df.mm.exp3 -0.0429262357955638 0.0929260974199703 -0.461939508785814 0.644353531764557 df.mm.exp4 -0.0319212180581161 0.0929260974199703 -0.343511876043297 0.731377860194354 df.mm.exp5 0.221783211198469 0.0929260974199703 2.38666227632634 0.0174247072458252 * df.mm.exp6 0.185000377031503 0.0929260974199703 1.99083338446263 0.0471186924903782 * df.mm.exp7 -0.066000605999525 0.0929260974199703 -0.710248335311466 0.477927267077325 df.mm.exp8 0.108373141377419 0.0929260974199703 1.16622934123271 0.244154905885907 df.mm.trans1:exp2 0.200116061566288 0.0854795771374497 2.34109793552797 0.0196737352014722 * df.mm.trans2:exp2 0.0808995879285509 0.0787380057268411 1.02745284417298 0.304773053600811 df.mm.trans1:exp3 0.0415024507535476 0.0854795771374498 0.485524755074681 0.627546377898053 df.mm.trans2:exp3 -0.113874556286444 0.0787380057268411 -1.44624638680206 0.148821669293312 df.mm.trans1:exp4 0.00919739074006603 0.0854795771374497 0.107597522684006 0.914364118348984 df.mm.trans2:exp4 -0.147351093428790 0.0787380057268411 -1.87141002707108 0.0619531185359526 . df.mm.trans1:exp5 -0.210606943279486 0.0854795771374498 -2.46382762213286 0.0141285952514985 * df.mm.trans2:exp5 -0.220070369742704 0.0787380057268411 -2.79497007463174 0.00541795952159688 ** df.mm.trans1:exp6 -0.208821812982761 0.0854795771374497 -2.44294391684904 0.0149611829369982 * df.mm.trans2:exp6 -0.187348915378992 0.0787380057268411 -2.3793962477148 0.0177674506218044 * df.mm.trans1:exp7 0.00589826174411976 0.0854795771374497 0.0690019995610818 0.945019446111106 df.mm.trans2:exp7 -0.0940349567645752 0.0787380057268411 -1.19427658722781 0.233014677011382 df.mm.trans1:exp8 -0.103283708347075 0.0854795771374498 -1.20828520455824 0.227588037730704 df.mm.trans2:exp8 -0.189388362003580 0.0787380057268411 -2.40529792766925 0.0165721098048953 * df.mm.trans1:probe2 -0.133636809304722 0.0427397885687249 -3.12675410384486 0.00188494737943952 ** df.mm.trans1:probe3 -0.179679576383037 0.0427397885687249 -4.20403521870764 3.17887496023688e-05 *** df.mm.trans1:probe4 0.00507238317342394 0.0427397885687249 0.118680586481321 0.905582703326512 df.mm.trans1:probe5 -0.168226722962552 0.0427397885687249 -3.93606820707701 9.6282139457772e-05 *** df.mm.trans1:probe6 -0.115034334188115 0.0427397885687249 -2.69150452167402 0.00738478642445203 ** df.mm.trans1:probe7 0.017148429815535 0.0427397885687249 0.401228700230012 0.688446977044985 df.mm.trans1:probe8 0.0744051100335088 0.0427397885687249 1.74088624500017 0.0824041189489704 . df.mm.trans1:probe9 0.736964708314607 0.0427397885687249 17.2430592895793 2.98800870508561e-51 *** df.mm.trans1:probe10 -0.0361273987344553 0.0427397885687249 -0.845287259116012 0.39841092899699 df.mm.trans1:probe11 0.0910793221398785 0.0427397885687249 2.13101948301463 0.033642559046854 * df.mm.trans2:probe2 -0.0896306002747905 0.0427397885687249 -2.09712315564374 0.0365541364880108 * df.mm.trans2:probe3 -0.255274557315767 0.0427397885687249 -5.97276135106027 4.82275545172848e-09 *** df.mm.trans2:probe4 -0.488688934318906 0.0427397885687249 -11.4340512829890 1.11477931213946e-26 *** df.mm.trans2:probe5 -0.533954709267305 0.0427397885687249 -12.4931528009015 7.41654824549543e-31 *** df.mm.trans2:probe6 -0.532880940621296 0.0427397885687249 -12.4680294046011 9.3615551118688e-31 *** df.mm.trans3:probe2 0.394749267539140 0.0427397885687249 9.23610716754928 1.10150541631040e-18 *** df.mm.trans3:probe3 0.243923934336884 0.0427397885687249 5.70718626613369 2.11642137431766e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.40006496900515 0.178711006829899 24.6211190181097 1.01106974690371e-84 *** df.mm.trans1 -0.290748146170952 0.156889601727041 -1.85320214322936 0.0645241704408807 . df.mm.trans2 -0.0450069265194335 0.149134378905478 -0.301787735663277 0.76295679062434 df.mm.exp2 0.227055373138244 0.205699455859625 1.10382097118037 0.270275478202595 df.mm.exp3 0.250629894491381 0.205699455859625 1.21842760081202 0.223715902108467 df.mm.exp4 -0.0504958727323662 0.205699455859625 -0.245483744822476 0.806196612147524 df.mm.exp5 -0.061709506791012 0.205699455859625 -0.299998395878715 0.764320378008249 df.mm.exp6 -0.210227107118914 0.205699455859625 -1.02201100260751 0.307338548463646 df.mm.exp7 -0.0607988079593778 0.205699455859625 -0.29557106850525 0.767697419890385 df.mm.exp8 0.0979046968776551 0.205699455859625 0.475959921568621 0.634339700020306 df.mm.trans1:exp2 -0.175182756798945 0.189215979067959 -0.92583489862675 0.355040455928552 df.mm.trans2:exp2 -0.241597943174707 0.174292963797730 -1.38616004863561 0.166401673660905 df.mm.trans1:exp3 -0.165420968703142 0.189215979067959 -0.874244181268269 0.382463294025674 df.mm.trans2:exp3 -0.140336613530456 0.174292963797730 -0.805176585862174 0.421153656264182 df.mm.trans1:exp4 0.0117707034925368 0.189215979067959 0.0622077667568934 0.950425689645909 df.mm.trans2:exp4 -0.0611139273569028 0.174292963797730 -0.350639096526160 0.72602731202599 df.mm.trans1:exp5 0.0128467150214679 0.189215979067959 0.0678944510117394 0.94590056763512 df.mm.trans2:exp5 0.0248529529346066 0.174292963797730 0.142592979045608 0.886677063329094 df.mm.trans1:exp6 0.152146712959184 0.189215979067959 0.804090192110777 0.421780089582969 df.mm.trans2:exp6 0.0896693569338583 0.174292963797730 0.514474910403847 0.607178896782982 df.mm.trans1:exp7 0.0140797110418889 0.189215979067959 0.0744107929533372 0.940717415068273 df.mm.trans2:exp7 -0.077349812170689 0.174292963797730 -0.443791937926161 0.657411671974171 df.mm.trans1:exp8 -0.0641805301155582 0.189215979067959 -0.339191914085157 0.734627355059727 df.mm.trans2:exp8 -0.134218193381726 0.174292963797730 -0.770072356664314 0.441671156443433 df.mm.trans1:probe2 0.00245847469909718 0.0946079895339796 0.0259859099766007 0.979280384855702 df.mm.trans1:probe3 0.107377251763010 0.0946079895339797 1.13497023128733 0.257007092964092 df.mm.trans1:probe4 0.149326215494002 0.0946079895339796 1.57836791828632 0.115201369976530 df.mm.trans1:probe5 -0.00696331020822659 0.0946079895339796 -0.0736017142159609 0.941360830565077 df.mm.trans1:probe6 0.00629326676796046 0.0946079895339797 0.0665194007288375 0.946994595172413 df.mm.trans1:probe7 -0.0337228442200977 0.0946079895339797 -0.356448164538849 0.721676214611764 df.mm.trans1:probe8 -0.0068424509925755 0.0946079895339797 -0.0723242405454345 0.942376812624922 df.mm.trans1:probe9 0.0664209354040236 0.0946079895339797 0.702064759342208 0.483010829771393 df.mm.trans1:probe10 0.0437172237280446 0.0946079895339797 0.462088074626541 0.644247076812315 df.mm.trans1:probe11 0.118726047433700 0.0946079895339797 1.25492622788542 0.210173189331703 df.mm.trans2:probe2 -0.0879666621185 0.0946079895339797 -0.929801621953985 0.352984793641315 df.mm.trans2:probe3 -0.146422708731593 0.0946079895339797 -1.54767804973811 0.122420391595121 df.mm.trans2:probe4 -0.196217122839085 0.0946079895339797 -2.07400161239671 0.0386615598773685 * df.mm.trans2:probe5 -0.179795579231895 0.0946079895339797 -1.90042701591623 0.0580319254206348 . df.mm.trans2:probe6 -0.257513024797974 0.0946079895339797 -2.72189511759454 0.00674916217926678 ** df.mm.trans3:probe2 0.099014419406702 0.0946079895339797 1.04657566337079 0.295871216773797 df.mm.trans3:probe3 0.167386054110558 0.0946079895339797 1.76925918133414 0.0775448582522611 .