chr15.8288_chr15_10516263_10518871_+_1.R fitVsDatCorrelation=0.668050848217503 cont.fitVsDatCorrelation=0.277019342552037 fstatistic=5354.57478561934,36,324 cont.fstatistic=3207.70352733492,36,324 residuals=-0.508706062962921,-0.0753573320711,-0.0103011737769831,0.0562410443587775,1.64557701408016 cont.residuals=-0.397188627273517,-0.125647677262498,-0.039249259707616,0.082045192883486,1.56014637463645 predictedValues: Include Exclude Both chr15.8288_chr15_10516263_10518871_+_1.R.tl.Lung 47.1192675672835 46.6131972774422 66.6942376827541 chr15.8288_chr15_10516263_10518871_+_1.R.tl.cerebhem 62.0446997264565 49.2477521509631 89.260968199897 chr15.8288_chr15_10516263_10518871_+_1.R.tl.cortex 48.6906849365881 47.7306923039519 66.1340035641768 chr15.8288_chr15_10516263_10518871_+_1.R.tl.heart 49.6131351885166 50.6714335560033 65.0883889887226 chr15.8288_chr15_10516263_10518871_+_1.R.tl.kidney 49.5366495810333 48.0176032857606 63.1218752573523 chr15.8288_chr15_10516263_10518871_+_1.R.tl.liver 50.8074238614241 48.4416521797296 59.5737882642089 chr15.8288_chr15_10516263_10518871_+_1.R.tl.stomach 49.8199309985397 47.6638042025748 60.0448089668701 chr15.8288_chr15_10516263_10518871_+_1.R.tl.testicle 54.4083964567549 52.0707105430539 66.5910553488238 diffExp=0.506070289841261,12.7969475754934,0.959992632636158,-1.05829836748669,1.51904629527270,2.36577168169447,2.15612679596487,2.33768591370094 diffExpScore=1.04944337709504 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 51.0199537949889 54.5693167883255 52.7021823615392 cerebhem 51.7524945912439 50.3038793976101 56.354458742563 cortex 51.4665243642119 52.7848056672424 61.7206842991552 heart 57.1958599883505 50.8134901729877 54.3034246236775 kidney 52.3787731953551 52.2265759193232 56.6855589021193 liver 51.7304481388355 55.7649635370459 56.1127808150085 stomach 49.8136041684304 51.8077408356662 54.9879564677286 testicle 51.3534935292674 56.6225397214028 53.4836469603113 cont.diffExp=-3.54936299333662,1.44861519363384,-1.31828130303050,6.38236981536284,0.152197276031849,-4.03451539821037,-1.99413666723581,-5.26904619213536 cont.diffExpScore=2.62993937501999 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=0.454734002380187 cont.tran.correlation=-0.366320519683913 tran.covariance=0.00152527243898901 cont.tran.covariance=-0.000643893002599096 tran.mean=50.1560646135047 cont.tran.mean=52.600278988143 weightedLogRatios: wLogRatio Lung 0.0415440790745893 cerebhem 0.926820370764422 cortex 0.0771738473960255 heart -0.0826286595455713 kidney 0.121065419519769 liver 0.186161908633634 stomach 0.171940657914016 testicle 0.174546306639781 cont.weightedLogRatios: wLogRatio Lung -0.266722675371699 cerebhem 0.111639071585074 cortex -0.0999930666948488 heart 0.471778483667015 kidney 0.0115147479723169 liver -0.299165010689737 stomach -0.154176323021662 testicle -0.389482581066874 varWeightedLogRatios=0.0937377409662818 cont.varWeightedLogRatios=0.0762941937980453 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.51992915406486 0.09724809585347 36.1953529595949 7.09561788120656e-116 *** df.mm.trans1 0.285027664327269 0.0823681213206573 3.46041235076447 0.000611577178140845 *** df.mm.trans2 0.324926321592541 0.0823681213206573 3.94480675755138 9.79402908236393e-05 *** df.mm.exp2 0.0387073654489765 0.114707595879796 0.337443786107578 0.736000886069742 df.mm.exp3 0.0649321901967416 0.114707595879797 0.566067048121075 0.57174004972387 df.mm.exp4 0.159424613333016 0.114707595879796 1.38983484145269 0.165533217492261 df.mm.exp5 0.134765942470355 0.114707595879796 1.17486502473279 0.240911432062678 df.mm.exp6 0.226739683639184 0.114707595879796 1.97667540584485 0.0489259845494145 * df.mm.exp7 0.183049171280533 0.114707595879796 1.59578944948295 0.111510703238206 df.mm.exp8 0.256103695106411 0.114707595879796 2.23266552787651 0.0262538843653934 * df.mm.trans1:exp2 0.236465727922366 0.099339692038943 2.38037508541568 0.0178729382735734 * df.mm.trans2:exp2 0.0162726551070199 0.099339692038943 0.163808189586905 0.869984278895448 df.mm.trans1:exp3 -0.0321264480406683 0.099339692038943 -0.323399915796741 0.746601025694404 df.mm.trans2:exp3 -0.0412412591125064 0.099339692038943 -0.415153885280207 0.678304033238342 df.mm.trans1:exp4 -0.107850987546738 0.099339692038943 -1.08567869834405 0.278428274580519 df.mm.trans2:exp4 -0.0759460066539202 0.099339692038943 -0.764508174880872 0.445120900032113 df.mm.trans1:exp5 -0.08473514651135 0.099339692038943 -0.852983784952065 0.394298418328066 df.mm.trans2:exp5 -0.105081968089021 0.099339692038943 -1.05780444787193 0.290932624535989 df.mm.trans1:exp6 -0.151379195962081 0.099339692038943 -1.52385408948859 0.128520605742637 df.mm.trans2:exp6 -0.188263362195032 0.099339692038943 -1.89514743131304 0.0589628319736129 . df.mm.trans1:exp7 -0.127316041697199 0.099339692038943 -1.28162307617471 0.200891306320255 df.mm.trans2:exp7 -0.160760587601826 0.099339692038943 -1.61829158418173 0.106572801725444 df.mm.trans1:exp8 -0.112267201757688 0.099339692038943 -1.13013438489096 0.259255530251899 df.mm.trans2:exp8 -0.145384786520399 0.099339692038943 -1.46351154846952 0.144296910270468 df.mm.trans1:probe2 0.0669228423871884 0.0496698460194715 1.34735353036837 0.178807873697064 df.mm.trans1:probe3 0.0428860878074876 0.0496698460194715 0.86342300700259 0.388543631390979 df.mm.trans1:probe4 0.104374856024031 0.0496698460194715 2.10137265138921 0.0363802598226763 * df.mm.trans1:probe5 0.173088241548234 0.0496698460194715 3.48477507823118 0.000560349783237402 *** df.mm.trans1:probe6 0.042254563814184 0.0496698460194715 0.85070857271471 0.39555950840909 df.mm.trans2:probe2 0.0335283834099845 0.0496698460194715 0.675024911428973 0.500141355079012 df.mm.trans2:probe3 -0.0401609693533181 0.0496698460194715 -0.808558362302438 0.419362520121976 df.mm.trans2:probe4 0.0590622571858266 0.0496698460194715 1.18909684484774 0.235272291512638 df.mm.trans2:probe5 -0.021196566571623 0.0496698460194715 -0.426749190309822 0.669845435168432 df.mm.trans2:probe6 -0.0579790458463937 0.0496698460194715 -1.16728861659174 0.243952175203358 df.mm.trans3:probe2 0.0994374840711419 0.0496698460194715 2.00196884105823 0.046121345063007 * df.mm.trans3:probe3 0.111268768854951 0.0496698460194715 2.24016738065449 0.0257576647691481 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91839282688082 0.125574876309796 31.2036367626122 1.21370454986452e-99 *** df.mm.trans1 -0.0242148667673375 0.106360608461649 -0.227667621665298 0.820048245103132 df.mm.trans2 0.0933291619368395 0.106360608461649 0.877478638818543 0.380876900484596 df.mm.exp2 -0.134138230813013 0.148120043256205 -0.905604858493004 0.365817974375896 df.mm.exp3 -0.182495855338019 0.148120043256205 -1.23208075913368 0.218812304249281 df.mm.exp4 0.0130243957612298 0.148120043256205 0.0879313526711667 0.92998555928346 df.mm.exp5 -0.0904582876546504 0.148120043256205 -0.610709298121011 0.541820212325983 df.mm.exp6 -0.0272029855731457 0.148120043256205 -0.183654993444016 0.854398999238422 df.mm.exp7 -0.118318192629105 0.148120043256205 -0.798799338887908 0.424991925685742 df.mm.exp8 0.0287324565138939 0.148120043256205 0.193980881197794 0.846312412568517 df.mm.trans1:exp2 0.14839405937061 0.128275720269523 1.15683668786904 0.248191287794285 df.mm.trans2:exp2 0.052748668939345 0.128275720269523 0.411213196297113 0.681188069684917 df.mm.trans1:exp3 0.191210632224900 0.128275720269523 1.49062216780497 0.137033595328831 df.mm.trans2:exp3 0.149247471975980 0.128275720269523 1.16348964295342 0.245487021220770 df.mm.trans1:exp4 0.101240315091291 0.128275720269523 0.789239887942724 0.430548971737922 df.mm.trans2:exp4 -0.0843342830501612 0.128275720269523 -0.657445406449206 0.511361337953604 df.mm.trans1:exp5 0.116742898130353 0.128275720269523 0.910093491465589 0.363449743984884 df.mm.trans2:exp5 0.0465780090090917 0.128275720269523 0.363108536137825 0.716760586803728 df.mm.trans1:exp6 0.0410327254989299 0.128275720269523 0.319879127653426 0.749266132386219 df.mm.trans2:exp6 0.04887700294498 0.128275720269523 0.381030820503548 0.703430100115937 df.mm.trans1:exp7 0.0943895083390296 0.128275720269523 0.735833002073234 0.462364693925467 df.mm.trans2:exp7 0.0663860065055603 0.128275720269523 0.517525891619046 0.605142249154228 df.mm.trans1:exp8 -0.0222162958652105 0.128275720269523 -0.173191745238548 0.862608858399405 df.mm.trans2:exp8 0.00820291651037561 0.128275720269523 0.0639475381088508 0.949051434112002 df.mm.trans1:probe2 0.0803046874180625 0.0641378601347615 1.25206371477521 0.211449729710877 df.mm.trans1:probe3 0.0336262548164969 0.0641378601347615 0.524280896585013 0.600441662074994 df.mm.trans1:probe4 0.0482287201674548 0.0641378601347615 0.751953995130495 0.452624621197037 df.mm.trans1:probe5 0.0885653504597798 0.0641378601347615 1.38085914113276 0.168274006534462 df.mm.trans1:probe6 0.0916246099069524 0.0641378601347615 1.42855732502515 0.154094799404444 df.mm.trans2:probe2 0.0604643149497563 0.0641378601347615 0.942724232188498 0.346524372371665 df.mm.trans2:probe3 -0.0875658543291304 0.0641378601347615 -1.36527558208434 0.173113627996292 df.mm.trans2:probe4 -0.0263507509431067 0.0641378601347615 -0.410845495745267 0.681457415128444 df.mm.trans2:probe5 -0.074729389679644 0.0641378601347615 -1.16513693351522 0.244820655998250 df.mm.trans2:probe6 0.0179296315004020 0.0641378601347615 0.279548327036942 0.780002426109378 df.mm.trans3:probe2 -0.0152509566040422 0.0641378601347615 -0.237783994851062 0.812198930184081 df.mm.trans3:probe3 -0.0532998123725267 0.0641378601347615 -0.831019498632122 0.406574589402186