chr1.1605_chr1_54492797_54496812_-_0.R fitVsDatCorrelation=0.82253836832423 cont.fitVsDatCorrelation=0.294436079343016 fstatistic=7027.41849160132,36,324 cont.fstatistic=2482.81523792271,36,324 residuals=-0.417949916836833,-0.0887835298448558,-0.00281420279422450,0.0688849656072457,0.784937084991586 cont.residuals=-0.502875703830021,-0.166244099082995,-0.0226356080286424,0.120237330775983,1.11734727076955 predictedValues: Include Exclude Both chr1.1605_chr1_54492797_54496812_-_0.R.tl.Lung 62.1557448541812 45.2935736184649 80.567034968043 chr1.1605_chr1_54492797_54496812_-_0.R.tl.cerebhem 71.4862527426169 54.5147124155021 74.2048538912873 chr1.1605_chr1_54492797_54496812_-_0.R.tl.cortex 70.797166762023 44.3372581937197 101.381947519044 chr1.1605_chr1_54492797_54496812_-_0.R.tl.heart 52.0252095519412 45.5396715762867 64.1335661101582 chr1.1605_chr1_54492797_54496812_-_0.R.tl.kidney 53.0828279898173 43.4666871236001 54.1680050381159 chr1.1605_chr1_54492797_54496812_-_0.R.tl.liver 53.6103597451361 49.126954279418 56.2448474602159 chr1.1605_chr1_54492797_54496812_-_0.R.tl.stomach 50.4889989604588 46.5497509941502 56.5407305650701 chr1.1605_chr1_54492797_54496812_-_0.R.tl.testicle 50.2054748069234 50.5725670575979 53.8622921889376 diffExp=16.8621712357163,16.9715403271148,26.4599085683033,6.48553797565453,9.61614086621718,4.48340546571809,3.9392479663086,-0.36709225067456 diffExpScore=0.996889258947532 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=1,1,1,0,0,0,0,0 diffExp1.3Score=0.75 diffExp1.2=1,1,1,0,1,0,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 58.5815528045582 52.9097611580998 66.5363082298716 cerebhem 55.659906009216 58.5342813803865 51.4239040199134 cortex 52.863559963044 53.7145893198544 56.1974253107968 heart 53.9919238520177 52.7140052551139 47.955962171346 kidney 54.9100333214888 52.9245723270255 57.6516510757151 liver 55.3543925123116 58.7449998853665 57.500954850939 stomach 57.0882006928669 55.8379355255759 51.170708977652 testicle 57.8600268713174 52.2890997521873 50.4189845474582 cont.diffExp=5.67179164645835,-2.87437537117048,-0.851029356810429,1.27791859690384,1.98546099446327,-3.39060737305496,1.25026516729099,5.57092711913009 cont.diffExpScore=2.37256658198305 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=0.225425048621307 cont.tran.correlation=-0.0760416727119628 tran.covariance=0.00213940649120505 cont.tran.covariance=-0.00011411974236091 tran.mean=52.7033256669898 cont.tran.mean=55.2486775394019 weightedLogRatios: wLogRatio Lung 1.25686241439290 cerebhem 1.12045352288795 cortex 1.88405907712921 heart 0.517287369061237 kidney 0.773837552544631 liver 0.343929728583737 stomach 0.315279694628678 testicle -0.0285562670117583 cont.weightedLogRatios: wLogRatio Lung 0.409314138426902 cerebhem -0.203647567856125 cortex -0.0634934811979372 heart 0.0952588784064488 kidney 0.146845021294889 liver -0.240385053103735 stomach 0.0893182505644476 testicle 0.40570524067611 varWeightedLogRatios=0.384398606558306 cont.varWeightedLogRatios=0.0604303403884955 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.51461152455609 0.0856892216174496 41.0157947314155 2.53888755492347e-130 *** df.mm.trans1 0.529345587274823 0.0725778755883674 7.29348417797537 2.33636142539033e-12 *** df.mm.trans2 0.319295634271376 0.0725778755883674 4.39935216735045 1.47513555290304e-05 *** df.mm.exp2 0.407427377067494 0.101073491653338 4.03100130808668 6.92840515405717e-05 *** df.mm.exp3 -0.120969532487525 0.101073491653338 -1.19684726933573 0.232241082987275 df.mm.exp4 0.0556255316224546 0.101073491653338 0.550347383003637 0.58246034871175 df.mm.exp5 0.198039057920181 0.101073491653338 1.95935704486608 0.0509281665189926 . df.mm.exp6 0.292716904943304 0.101073491653338 2.89607987371471 0.00403540289698652 ** df.mm.exp7 0.173597013019243 0.101073491653338 1.71753256150135 0.0868374214655162 . df.mm.exp8 0.299383873885459 0.101073491653338 2.96204147089610 0.00328197933623249 ** df.mm.trans1:exp2 -0.267565464177125 0.0875322114209852 -3.05676573039234 0.00242352677686973 ** df.mm.trans2:exp2 -0.222121918949392 0.0875322114209852 -2.537602047789 0.0116297866883405 * df.mm.trans1:exp3 0.251145265911482 0.0875322114209853 2.86917537937665 0.00438565354273261 ** df.mm.trans2:exp3 0.09962973910949 0.0875322114209853 1.13820658123581 0.255875163111902 df.mm.trans1:exp4 -0.233540380431784 0.0875322114209853 -2.6680507283036 0.0080129209066919 ** df.mm.trans2:exp4 -0.0502068424749188 0.0875322114209852 -0.573581332630219 0.56664906165491 df.mm.trans1:exp5 -0.355828820985835 0.0875322114209852 -4.06511860273334 6.03123060996899e-05 *** df.mm.trans2:exp5 -0.239209386159352 0.0875322114209853 -2.73281552329208 0.00662419028713804 ** df.mm.trans1:exp6 -0.440617825704773 0.0875322114209852 -5.03377920598426 7.99307111359966e-07 *** df.mm.trans2:exp6 -0.211474213474305 0.0875322114209853 -2.4159587658219 0.0162464996220746 * df.mm.trans1:exp7 -0.381484791825621 0.0875322114209853 -4.35822179781194 1.76273775279398e-05 *** df.mm.trans2:exp7 -0.146240518221620 0.0875322114209852 -1.67070517067457 0.0957455563150707 . df.mm.trans1:exp8 -0.512903042224947 0.0875322114209852 -5.85959195933192 1.14105638633382e-08 *** df.mm.trans2:exp8 -0.189139757320497 0.0875322114209853 -2.16080176942899 0.0314431751688708 * df.mm.trans1:probe2 0.0414573589067090 0.0437661057104926 0.947248064082837 0.344218325049679 df.mm.trans1:probe3 0.154158932337710 0.0437661057104926 3.52233605972285 0.00048917485916227 *** df.mm.trans1:probe4 0.209146109605197 0.0437661057104926 4.77872331133761 2.67931298728793e-06 *** df.mm.trans1:probe5 0.110067639073295 0.0437661057104926 2.51490593660259 0.0123901420556481 * df.mm.trans1:probe6 0.256345194503915 0.0437661057104926 5.85716253119724 1.15624491013040e-08 *** df.mm.trans2:probe2 -0.076549181785719 0.0437661057104926 -1.74905170435045 0.0812289934497784 . df.mm.trans2:probe3 -0.0622376613480667 0.0437661057104926 -1.42205161591852 0.155973195872805 df.mm.trans2:probe4 -0.0198281632838104 0.0437661057104926 -0.453048379834644 0.650817145767879 df.mm.trans2:probe5 -0.0684482462905116 0.0437661057104926 -1.56395560398470 0.118804155489128 df.mm.trans2:probe6 0.0403852605472343 0.0437661057104926 0.922751976481018 0.35682310743306 df.mm.trans3:probe2 0.094986512125885 0.0437661057104926 2.17032131563656 0.0307085595720293 * df.mm.trans3:probe3 -0.0174760391804506 0.0437661057104926 -0.399305327644465 0.689931314668792 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.76884208933941 0.143993905066952 26.1736223320497 6.36271268057649e-82 *** df.mm.trans1 0.286888411906972 0.121961333411205 2.35228989289334 0.0192561571023100 * df.mm.trans2 0.189922832111952 0.121961333411205 1.55723807537925 0.120390192385560 df.mm.exp2 0.307509629261867 0.169845944299631 1.81052088426308 0.0711412665629133 . df.mm.exp3 0.0812680534227158 0.169845944299631 0.478480977322295 0.63263062108471 df.mm.exp4 0.242172612493253 0.169845944299631 1.42583688702055 0.154878160860121 df.mm.exp5 0.0788850342552168 0.169845944299631 0.464450502957273 0.642636948120686 df.mm.exp6 0.193900597982289 0.169845944299631 1.14162630601424 0.254452414531059 df.mm.exp7 0.290623756743674 0.169845944299631 1.71110212812014 0.088019352778668 . df.mm.exp8 0.253187009722651 0.169845944299631 1.49068622607788 0.137016775132121 df.mm.trans1:exp2 -0.358669410291452 0.147090902493238 -2.43842008045291 0.0152884374309144 * df.mm.trans2:exp2 -0.206484882945907 0.147090902493238 -1.40379098534255 0.161338839233243 df.mm.trans1:exp3 -0.183973647968948 0.147090902493238 -1.25074797183603 0.211928902324251 df.mm.trans2:exp3 -0.0661712496622011 0.147090902493238 -0.449866365224343 0.653107627044743 df.mm.trans1:exp4 -0.323757983876376 0.147090902493238 -2.20107415474768 0.0284356783159783 * df.mm.trans2:exp4 -0.245879280675310 0.147090902493238 -1.67161446770384 0.0955658338475655 . df.mm.trans1:exp5 -0.143608794592833 0.147090902493238 -0.976326830270386 0.329630915495625 df.mm.trans2:exp5 -0.0786051407927714 0.147090902493238 -0.534398385354833 0.593432434112834 df.mm.trans1:exp6 -0.25056443159351 0.147090902493238 -1.70346654583229 0.0894396832023131 . df.mm.trans2:exp6 -0.0892823997035509 0.147090902493238 -0.60698791149001 0.544284046966456 df.mm.trans1:exp7 -0.316446152758731 0.147090902493238 -2.15136454665019 0.0321863371289656 * df.mm.trans2:exp7 -0.236758113009283 0.147090902493238 -1.60960405433754 0.108458165454141 df.mm.trans1:exp8 -0.265580094221653 0.147090902493238 -1.80555078335904 0.0719166123447113 . df.mm.trans2:exp8 -0.264986920903397 0.147090902493238 -1.80151808447555 0.0725508199858234 . df.mm.trans1:probe2 -0.0331494834656017 0.0735454512466188 -0.450734653247854 0.652482287908501 df.mm.trans1:probe3 0.0541103392051314 0.0735454512466188 0.735740121080827 0.462421149625837 df.mm.trans1:probe4 -0.0235490689009094 0.0735454512466188 -0.320197490147184 0.749025019438408 df.mm.trans1:probe5 0.0778459282159085 0.0735454512466188 1.05847373150066 0.290628007305068 df.mm.trans1:probe6 0.0569464099262883 0.0735454512466188 0.774302271058625 0.439316708368807 df.mm.trans2:probe2 0.0644006002058701 0.0735454512466188 0.875657149616454 0.381865146373413 df.mm.trans2:probe3 -0.0821064775808042 0.0735454512466188 -1.11640456600746 0.265076249738527 df.mm.trans2:probe4 0.00481366275847134 0.0735454512466188 0.0654515361164861 0.947854857820133 df.mm.trans2:probe5 0.0932929816567515 0.0735454512466188 1.268507842095 0.205527297914031 df.mm.trans2:probe6 0.00800552500228397 0.0735454512466188 0.108851395519203 0.913387739963284 df.mm.trans3:probe2 -0.0491594210672014 0.0735454512466188 -0.668422318905297 0.504339963905702 df.mm.trans3:probe3 -0.0945572177573406 0.0735454512466188 -1.28569770331905 0.199466727380986