chr4.17231_chr4_58436937_58443835_-_1.R fitVsDatCorrelation=0.801204303093753 cont.fitVsDatCorrelation=0.232125152078257 fstatistic=9465.78978407972,52,692 cont.fstatistic=3574.19040264717,52,692 residuals=-0.530904244179048,-0.088681409749039,0.00172096025489526,0.0921050017875446,1.02236570709378 cont.residuals=-0.550804364869939,-0.175639247838962,-0.0341682097060804,0.135319553237828,0.941945468445616 predictedValues: Include Exclude Both chr4.17231_chr4_58436937_58443835_-_1.R.tl.Lung 66.948189801237 58.8714278586633 76.3956871000464 chr4.17231_chr4_58436937_58443835_-_1.R.tl.cerebhem 65.8561203833218 59.2605451157208 58.2488528958611 chr4.17231_chr4_58436937_58443835_-_1.R.tl.cortex 57.9418758648717 53.4415212817048 64.1104535873892 chr4.17231_chr4_58436937_58443835_-_1.R.tl.heart 61.8325957733264 55.9795219349945 71.5343669388511 chr4.17231_chr4_58436937_58443835_-_1.R.tl.kidney 60.0930462445971 54.2567722583364 63.8790550861102 chr4.17231_chr4_58436937_58443835_-_1.R.tl.liver 55.4840632855542 54.0843484480328 65.2401405282361 chr4.17231_chr4_58436937_58443835_-_1.R.tl.stomach 62.3133706115163 54.2683607695631 79.108044094428 chr4.17231_chr4_58436937_58443835_-_1.R.tl.testicle 66.139367141182 59.4140919241872 86.7605491094207 diffExp=8.07676194257368,6.59557526760099,4.50035458316686,5.85307383833191,5.83627398626071,1.39971483752135,8.04500984195321,6.72527521699476 diffExpScore=0.979180563429955 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 66.5779855422143 74.0231807459305 63.8922584681537 cerebhem 60.4398246996324 67.1087356701072 67.2299004334728 cortex 59.1647779268086 66.432557739238 64.8614849856342 heart 59.938868190683 64.8859690515734 62.4810397473747 kidney 65.7967868626056 63.8414470625787 66.0664184349992 liver 60.4851569739072 72.7504333978065 66.8259091876922 stomach 67.9291271704017 67.9365901565605 64.2471107430096 testicle 63.297741525938 64.6331840975933 63.175387359252 cont.diffExp=-7.44519520371617,-6.66891097047478,-7.26777981242935,-4.94710086089034,1.95533980002691,-12.2652764238992,-0.00746298615882779,-1.33544257165536 cont.diffExpScore=1.07466759955962 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,-1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.892959159755671 cont.tran.correlation=0.159010635518798 tran.covariance=0.00270790964871411 cont.tran.covariance=0.000448749654865489 tran.mean=59.1365761685506 cont.tran.mean=65.3276479258487 weightedLogRatios: wLogRatio Lung 0.532204858118779 cerebhem 0.436330065575697 cortex 0.324946829248492 heart 0.405208230461579 kidney 0.4132434236359 liver 0.102288819092201 stomach 0.561655443812955 testicle 0.443744772758888 cont.weightedLogRatios: wLogRatio Lung -0.450664244849544 cerebhem -0.434780546861305 cortex -0.479462021504611 heart -0.327770917379587 kidney 0.125847021680607 liver -0.774499302563804 stomach -0.000463439281848815 testicle -0.0868180635759164 varWeightedLogRatios=0.0201804850589046 cont.varWeightedLogRatios=0.087914998198718 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.65032567787252 0.079256303965341 46.0572281981356 6.20133957741442e-213 *** df.mm.trans1 0.549505762720223 0.0624064761488168 8.80526824507524 1.03947519520571e-17 *** df.mm.trans2 0.438872782608326 0.0624064761488169 7.03248780722329 4.88088382555853e-12 *** df.mm.exp2 0.261343041651528 0.0824828135911733 3.16845449704076 0.00159996324182944 ** df.mm.exp3 -0.0659277372637637 0.0824828135911733 -0.799290596348168 0.424396190025525 df.mm.exp4 -0.0641100493445905 0.0824828135911733 -0.777253424723755 0.437274888385401 df.mm.exp5 -0.0107182484515995 0.0824828135911733 -0.129945233254585 0.896647519157729 df.mm.exp6 -0.114782931940839 0.0824828135911733 -1.39159816382794 0.164491268318558 df.mm.exp7 -0.188045830881076 0.0824828135911733 -2.27981833661890 0.0229219313138409 * df.mm.exp8 -0.130205107828260 0.0824828135911733 -1.57857257966032 0.114891109071455 df.mm.trans1:exp2 -0.277789707165572 0.0621737600760792 -4.46795733160827 9.22305330186195e-06 *** df.mm.trans2:exp2 -0.25475517801792 0.0621737600760792 -4.09747098625188 4.67171839785674e-05 *** df.mm.trans1:exp3 -0.0785509286309291 0.0621737600760792 -1.26340965279903 0.206867476046619 df.mm.trans2:exp3 -0.0308401439975654 0.0621737600760792 -0.496031508466396 0.620029685102437 df.mm.trans1:exp4 -0.0153783192433509 0.0621737600760792 -0.247344204766338 0.804715217285548 df.mm.trans2:exp4 0.0137401162880523 0.0621737600760793 0.220995421078589 0.825161188052483 df.mm.trans1:exp5 -0.0973066534882609 0.0621737600760792 -1.56507589969130 0.118022272226721 df.mm.trans2:exp5 -0.0709098098462384 0.0621737600760793 -1.14051023710757 0.254468356791805 df.mm.trans1:exp6 -0.0730402702516861 0.0621737600760792 -1.17477646779461 0.240488240383667 df.mm.trans2:exp6 0.0299718909536279 0.0621737600760792 0.482066565009944 0.629911090469019 df.mm.trans1:exp7 0.116302816502891 0.0621737600760792 1.87060934324347 0.0618211218815415 . df.mm.trans2:exp7 0.106631336224164 0.0621737600760793 1.71505368331727 0.0867830684499044 . df.mm.trans1:exp8 0.118050213160922 0.0621737600760792 1.89871439360382 0.0580177516037958 . df.mm.trans2:exp8 0.139380666659049 0.0621737600760793 2.24179246177961 0.0252908921098944 * df.mm.trans1:probe2 0.0155172214823581 0.0469401594792645 0.330574536910399 0.741065944387164 df.mm.trans1:probe3 -0.0606450555782514 0.0469401594792645 -1.29196526494634 0.196800474017101 df.mm.trans1:probe4 0.10169946333686 0.0469401594792645 2.16657686009323 0.0306077460593263 * df.mm.trans1:probe5 0.0456021012174316 0.0469401594792645 0.971494381853902 0.331641593677182 df.mm.trans1:probe6 1.60972730858338e-05 0.0469401594792645 0.000342931793679667 0.999726478860232 df.mm.trans2:probe2 -0.0797575689522169 0.0469401594792645 -1.69913289253841 0.0897435973060555 . df.mm.trans2:probe3 -0.0568904304550922 0.0469401594792645 -1.21197778376154 0.225934537845624 df.mm.trans2:probe4 -0.080129959742467 0.0469401594792645 -1.70706620155102 0.0882583398550237 . df.mm.trans2:probe5 -0.0558870140560696 0.0469401594792645 -1.19060128205907 0.234218402162014 df.mm.trans2:probe6 -0.0733996092574667 0.0469401594792645 -1.56368470136729 0.118348801264370 df.mm.trans3:probe2 -0.505519692998032 0.0469401594792645 -10.7694498400957 4.12555464209249e-25 *** df.mm.trans3:probe3 -0.18610588254359 0.0469401594792645 -3.96474755535931 8.11143505839264e-05 *** df.mm.trans3:probe4 -0.283760897013779 0.0469401594792645 -6.04516261047491 2.44018837635143e-09 *** df.mm.trans3:probe5 -0.209036689259097 0.0469401594792645 -4.4532590340141 9.85886684992955e-06 *** df.mm.trans3:probe6 -0.192458772162601 0.0469401594792645 -4.10008773505805 4.62045052234978e-05 *** df.mm.trans3:probe7 -0.271538059578223 0.0469401594792645 -5.78477070786632 1.09992362474339e-08 *** df.mm.trans3:probe8 -0.204189422087873 0.0469401594792645 -4.34999421291001 1.56643801741642e-05 *** df.mm.trans3:probe9 -0.33359544448096 0.0469401594792645 -7.106823840859 2.96172918466778e-12 *** df.mm.trans3:probe10 -0.436029245300433 0.0469401594792645 -9.28904482084357 1.98115228677485e-19 *** df.mm.trans3:probe11 0.050690090688359 0.0469401594792645 1.07988748335529 0.280568515131793 df.mm.trans3:probe12 -0.355875693553236 0.0469401594792645 -7.58147602183675 1.10179256071061e-13 *** df.mm.trans3:probe13 -0.0513298273686994 0.0469401594792645 -1.09351625427208 0.274547703342743 df.mm.trans3:probe14 -0.589789228475366 0.0469401594792645 -12.5647044027599 9.22528374180005e-33 *** df.mm.trans3:probe15 -0.477276682620036 0.0469401594792645 -10.1677686636508 9.95197851109505e-23 *** df.mm.trans3:probe16 -0.180053195221368 0.0469401594792645 -3.83580280124327 0.000136586529084811 *** df.mm.trans3:probe17 -0.413445968983388 0.0469401594792645 -8.80793703238322 1.01746728689036e-17 *** df.mm.trans3:probe18 -0.438071620055744 0.0469401594792645 -9.33255500014353 1.37659666952724e-19 *** df.mm.trans3:probe19 -0.675395392636049 0.0469401594792645 -14.3884341282309 2.95033991688244e-41 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33584534808443 0.128831324182632 33.6552106065285 7.790317567843e-148 *** df.mm.trans1 -0.148909375577067 0.101441886103341 -1.46792790726869 0.142578172622044 df.mm.trans2 -0.0252716755962646 0.101441886103341 -0.249124662080117 0.803338276544183 df.mm.exp2 -0.245709703823286 0.134076023806346 -1.83261478710153 0.0672893985571717 . df.mm.exp3 -0.241294404431619 0.134076023806346 -1.79968347495250 0.0723461098780786 . df.mm.exp4 -0.214460630002457 0.134076023806346 -1.59954497391879 0.110155998052245 df.mm.exp5 -0.193241020413681 0.134076023806346 -1.44127946912261 0.149958091394845 df.mm.exp6 -0.158212078033045 0.134076023806346 -1.18001767610263 0.238398665459282 df.mm.exp7 -0.0712511368396303 0.134076023806346 -0.531423403057825 0.595295943863676 df.mm.exp8 -0.174891240198291 0.134076023806346 -1.30441845777659 0.192524610727514 df.mm.trans1:exp2 0.148983965331144 0.101063605533729 1.47416040170288 0.140893169254553 df.mm.trans2:exp2 0.147645630677368 0.101063605533729 1.46091790311293 0.144491859612486 df.mm.trans1:exp3 0.123246826316348 0.101063605533729 1.21949761900406 0.223070865166845 df.mm.trans2:exp3 0.133103370666996 0.101063605533729 1.31702574793430 0.188265944975869 df.mm.trans1:exp4 0.109411833852407 0.101063605533729 1.08260370560292 0.279361449083733 df.mm.trans2:exp4 0.0827137395151945 0.101063605533729 0.818432501773247 0.413392005644411 df.mm.trans1:exp5 0.181438050188037 0.101063605533729 1.79528574336767 0.0730444095979793 . df.mm.trans2:exp5 0.0452653428447085 0.101063605533729 0.447889649351582 0.654373020560301 df.mm.trans1:exp6 0.0622360982056719 0.101063605533729 0.615811180266087 0.5382216358722 df.mm.trans2:exp6 0.140868643951090 0.101063605533729 1.39386125408000 0.163806936270997 df.mm.trans1:exp7 0.091342075514733 0.101063605533729 0.903807805315718 0.366411841365435 df.mm.trans2:exp7 -0.0145523882023304 0.101063605533729 -0.143992371195125 0.885548415949764 df.mm.trans1:exp8 0.124366914327475 0.101063605533729 1.23058061970655 0.218897881431853 df.mm.trans2:exp8 0.0392409073427857 0.101063605533729 0.388279313166691 0.69792876446692 df.mm.trans1:probe2 0.0441073769659155 0.0763013489211167 0.578068115303118 0.563406191845882 df.mm.trans1:probe3 0.0856468855347378 0.0763013489211167 1.12248193178449 0.262046890916315 df.mm.trans1:probe4 -0.0403971535917331 0.0763013489211167 -0.529442194180568 0.59666853503951 df.mm.trans1:probe5 0.095328682022777 0.0763013489211167 1.24937086133735 0.211951879960277 df.mm.trans1:probe6 0.101264283616951 0.0763013489211167 1.32716242961369 0.184892665348339 df.mm.trans2:probe2 0.0697767966096676 0.0763013489211167 0.914489685913752 0.360778123993598 df.mm.trans2:probe3 0.00649193961337612 0.0763013489211167 0.0850828944071191 0.932220098497008 df.mm.trans2:probe4 -0.0300720958232028 0.0763013489211167 -0.39412272847617 0.693611852235248 df.mm.trans2:probe5 -0.137808778934413 0.0763013489211167 -1.80611196109895 0.0713352188077233 . df.mm.trans2:probe6 -0.0632722368200985 0.0763013489211167 -0.829241392383664 0.407253770173951 df.mm.trans3:probe2 -0.0196063210572811 0.0763013489211166 -0.256959035908407 0.79728677722684 df.mm.trans3:probe3 -0.0540672642759486 0.0763013489211166 -0.708601683200194 0.478810322240223 df.mm.trans3:probe4 -0.0976230312862993 0.0763013489211166 -1.27944043803506 0.201170815834734 df.mm.trans3:probe5 -0.065199056460374 0.0763013489211167 -0.85449415223025 0.393126966154421 df.mm.trans3:probe6 -0.0143022574824260 0.0763013489211166 -0.187444359564499 0.851367190661379 df.mm.trans3:probe7 -0.00604097760899046 0.0763013489211166 -0.0791726187598054 0.93691821205753 df.mm.trans3:probe8 0.0201686558086525 0.0763013489211166 0.264328954780388 0.791605154406193 df.mm.trans3:probe9 0.0473715258042047 0.0763013489211166 0.620847815589462 0.534904187516005 df.mm.trans3:probe10 0.0451548660473833 0.0763013489211166 0.591796431987935 0.554180196619751 df.mm.trans3:probe11 0.0220639278214737 0.0763013489211166 0.289168253686894 0.77253923277435 df.mm.trans3:probe12 0.00131312926243460 0.0763013489211167 0.0172097778217809 0.98627422237979 df.mm.trans3:probe13 0.0653347006595626 0.0763013489211166 0.856271895364631 0.392143805931952 df.mm.trans3:probe14 -0.0051252909758229 0.0763013489211167 -0.0671716954980917 0.946464420398795 df.mm.trans3:probe15 0.0157918867017993 0.0763013489211167 0.20696733314801 0.836096255289077 df.mm.trans3:probe16 0.0116206149647167 0.0763013489211166 0.152298945287724 0.878995549568602 df.mm.trans3:probe17 -8.48925521411338e-05 0.0763013489211166 -0.00111259569249423 0.9991125979032 df.mm.trans3:probe18 -0.0334030022990234 0.0763013489211167 -0.437777349566346 0.661684297174349 df.mm.trans3:probe19 -0.0182229914732324 0.0763013489211166 -0.238829217712416 0.811308723887942