chr16.9735_chr16_50265406_50271399_-_0.R fitVsDatCorrelation=0.914000414455263 cont.fitVsDatCorrelation=0.260278497890333 fstatistic=10533.6574690484,43,485 cont.fstatistic=1850.58335217119,43,485 residuals=-0.406013708606256,-0.078515285420819,-0.000917230469739828,0.0690507217530543,1.02730466337795 cont.residuals=-0.536537725806401,-0.246479951209526,-0.0589600119628294,0.170325664067367,1.22184631626960 predictedValues: Include Exclude Both chr16.9735_chr16_50265406_50271399_-_0.R.tl.Lung 49.1030055630612 95.4324548683047 60.5225422179226 chr16.9735_chr16_50265406_50271399_-_0.R.tl.cerebhem 53.2442568316844 64.4923025488923 68.0714080029493 chr16.9735_chr16_50265406_50271399_-_0.R.tl.cortex 52.565724197032 66.0596303341049 60.1384937455917 chr16.9735_chr16_50265406_50271399_-_0.R.tl.heart 52.2317875862317 96.081627595429 60.2724139291069 chr16.9735_chr16_50265406_50271399_-_0.R.tl.kidney 50.1413003646052 104.979327338454 59.806702082788 chr16.9735_chr16_50265406_50271399_-_0.R.tl.liver 59.706821827611 91.3355187464984 57.259253160711 chr16.9735_chr16_50265406_50271399_-_0.R.tl.stomach 51.5409655810514 71.6858747919514 59.4558688222763 chr16.9735_chr16_50265406_50271399_-_0.R.tl.testicle 52.8466809843437 83.9856617085871 58.8251539898033 diffExp=-46.3294493052435,-11.2480457172079,-13.4939061370729,-43.8498400091973,-54.838026973849,-31.6286969188874,-20.1449092109,-31.1389807242435 diffExpScore=0.996057899288775 diffExp1.5=-1,0,0,-1,-1,-1,0,-1 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,0,-1,-1,-1,0,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 63.5188062148516 61.5330692535011 58.2212058879757 cerebhem 71.5048517841905 61.5786409176908 63.1850924340273 cortex 61.0943293868071 68.4756813372767 60.745140900774 heart 60.9636469959852 65.7745145630345 63.5903769640714 kidney 58.7350863931691 63.722741486918 67.2468637630905 liver 63.9512824389938 59.4622317764138 63.1350270829384 stomach 55.4946946401594 57.5239585915525 59.5775879796277 testicle 67.329464732884 65.4756500362532 61.2310153374943 cont.diffExp=1.98573696135050,9.92621086649964,-7.38135195046961,-4.81086756704931,-4.9876550937489,4.48905066258004,-2.02926395139308,1.85381469663083 cont.diffExpScore=19.1697617077823 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=-0.126928581641016 cont.tran.correlation=0.129880372136209 tran.covariance=-0.00137644602215119 cont.tran.covariance=0.00075260581806977 tran.mean=68.4645588042402 cont.tran.mean=62.8836656593551 weightedLogRatios: wLogRatio Lung -2.80828316484565 cerebhem -0.780177229079193 cortex -0.931410774301478 heart -2.59677022614608 kidney -3.16575132330318 liver -1.82874910794408 stomach -1.35507886452408 testicle -1.94520120423377 cont.weightedLogRatios: wLogRatio Lung 0.131347361201091 cerebhem 0.626949731305112 cortex -0.475565719620952 heart -0.315079516581101 kidney -0.335291260924248 liver 0.299980566810091 stomach -0.144886292142471 testicle 0.117140682267798 varWeightedLogRatios=0.771890439233571 cont.varWeightedLogRatios=0.138820106636385 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.34537923769217 0.0707020028247157 61.4604829295322 4.85749361253403e-231 *** df.mm.trans1 -0.38679364020175 0.056600650660875 -6.83373133851834 2.49135308752511e-11 *** df.mm.trans2 0.217918427194091 0.056600650660875 3.85010463041772 0.000133885474373791 *** df.mm.exp2 -0.428444519801489 0.0757921272574759 -5.65288949267787 2.69608212618211e-08 *** df.mm.exp3 -0.293351101428964 0.0757921272574759 -3.87046929600499 0.000123487486493438 *** df.mm.exp4 0.0726917960368288 0.0757921272574759 0.959094284158105 0.337989116139417 df.mm.exp5 0.128167675589347 0.0757921272574759 1.6910420676536 0.0914712119981058 . df.mm.exp6 0.207073700896712 0.0757921272574759 2.73212678400298 0.00652226424301042 ** df.mm.exp7 -0.219886743466784 0.0757921272574759 -2.9011818433305 0.00388655857256022 ** df.mm.exp8 -0.0258516224961873 0.0757921272574759 -0.341085854581782 0.733186660714194 df.mm.trans1:exp2 0.509414219624364 0.0594562362865503 8.56788541355416 1.41325037487800e-16 *** df.mm.trans2:exp2 0.0365716777732735 0.0594562362865503 0.615102469604966 0.53877554231976 df.mm.trans1:exp3 0.361495131509584 0.0594562362865503 6.08002043330412 2.43647584970801e-09 *** df.mm.trans2:exp3 -0.0745097929163368 0.0594562362865503 -1.25318717715726 0.210741410055156 df.mm.trans1:exp4 -0.0109207749210257 0.0594562362865503 -0.183677534992172 0.854343198603713 df.mm.trans2:exp4 -0.065912396787266 0.0594562362865503 -1.1085867674099 0.268157913611523 df.mm.trans1:exp5 -0.107242894544483 0.0594562362865503 -1.80372827549366 0.0718942984199265 . df.mm.trans2:exp5 -0.0328229456877054 0.0594562362865503 -0.552052193978688 0.581166881926165 df.mm.trans1:exp6 -0.0115476645811046 0.0594562362865503 -0.194221250828096 0.846083966214947 df.mm.trans2:exp6 -0.250952673962718 0.0594562362865503 -4.22079649901228 2.90689393709429e-05 *** df.mm.trans1:exp7 0.268343437086999 0.0594562362865503 4.51329337083689 8.01964878807865e-06 *** df.mm.trans2:exp7 -0.066238251012959 0.0594562362865503 -1.11406734011421 0.265802085095385 df.mm.trans1:exp8 0.0993262861417184 0.0594562362865503 1.67057809820006 0.0954501841751215 . df.mm.trans2:exp8 -0.101921005556828 0.0594562362865503 -1.71421892676855 0.0871274118571348 . df.mm.trans1:probe2 -0.246148221596562 0.040706902248126 -6.04684237813487 2.95167731784939e-09 *** df.mm.trans1:probe3 -0.208265226332933 0.040706902248126 -5.11621407749151 4.49882621769047e-07 *** df.mm.trans1:probe4 -0.218148329011173 0.040706902248126 -5.35900097928026 1.29602532854648e-07 *** df.mm.trans1:probe5 -0.231018737353749 0.040706902248126 -5.67517360927124 2.3867573783792e-08 *** df.mm.trans1:probe6 -0.13106510916837 0.040706902248126 -3.21972692418283 0.00136917920938468 ** df.mm.trans2:probe2 -0.0454058085264874 0.040706902248126 -1.11543266666964 0.2652174291752 df.mm.trans2:probe3 -0.204746845784212 0.040706902248126 -5.02978203883441 6.92578837279639e-07 *** df.mm.trans2:probe4 0.430049513208896 0.040706902248126 10.5645354831365 1.2781147783647e-23 *** df.mm.trans2:probe5 -0.472505510204779 0.040706902248126 -11.6075034971872 1.17385240465375e-27 *** df.mm.trans2:probe6 0.214545507223434 0.040706902248126 5.27049456909511 2.05141527139384e-07 *** df.mm.trans3:probe2 0.0252565261070776 0.040706902248126 0.620448246175263 0.535254145197861 df.mm.trans3:probe3 -0.348796059442479 0.040706902248126 -8.56847463647362 1.40698548761200e-16 *** df.mm.trans3:probe4 0.200162022765895 0.040706902248126 4.91715192538655 1.20400532755597e-06 *** df.mm.trans3:probe5 -0.366900511901552 0.040706902248126 -9.01322605353598 4.62764544624704e-18 *** df.mm.trans3:probe6 -0.0379976655488238 0.040706902248126 -0.933445274641922 0.351054583190316 df.mm.trans3:probe7 -0.165384490722429 0.040706902248126 -4.06281199474084 5.65419784863078e-05 *** df.mm.trans3:probe8 -0.212619647677755 0.040706902248126 -5.22318417603353 2.61535745661788e-07 *** df.mm.trans3:probe9 0.161855113354984 0.040706902248126 3.97610980979116 8.072400886689e-05 *** df.mm.trans3:probe10 0.00954340904503368 0.040706902248126 0.234442036067066 0.814740764829076 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19744310579109 0.168259621807374 24.9462292896176 5.49483460828019e-89 *** df.mm.trans1 -0.0228318861504937 0.134700626485236 -0.169500964815455 0.86547328739793 df.mm.trans2 -0.0675734954609642 0.134700626485236 -0.501656875874819 0.616136542225223 df.mm.exp2 0.0373508685193754 0.1803733155896 0.207075355893325 0.83603791897962 df.mm.exp3 0.0255495106133425 0.1803733155896 0.141647951249479 0.887416906494002 df.mm.exp4 -0.0626130849718628 0.180373315589600 -0.347130531848321 0.728643832019478 df.mm.exp5 -0.187452715591877 0.1803733155896 -1.03924859937922 0.299206945525126 df.mm.exp6 -0.108473901255417 0.1803733155896 -0.60138552590687 0.547864215811635 df.mm.exp7 -0.225451620390727 0.1803733155896 -1.24991670554914 0.211932759058054 df.mm.exp8 0.0699613093240235 0.1803733155896 0.387869508831368 0.698282777848097 df.mm.trans1:exp2 0.0810784130844623 0.141496470141969 0.573006612837147 0.566905774824345 df.mm.trans2:exp2 -0.036610538193238 0.141496470141969 -0.258738173160823 0.795947034878524 df.mm.trans1:exp3 -0.064466480384829 0.141496470141969 -0.455604866468734 0.648878050485652 df.mm.trans2:exp3 0.0813544129108086 0.141496470141969 0.57495719030434 0.565586862597898 df.mm.trans1:exp4 0.0215547976948940 0.141496470141969 0.152334525895008 0.87898644494408 df.mm.trans2:exp4 0.129270789868931 0.141496470141969 0.913597277297646 0.361382433848528 df.mm.trans1:exp5 0.109153964848742 0.141496470141969 0.771425355976887 0.440830525754901 df.mm.trans2:exp5 0.222419481934631 0.141496470141969 1.57190834309484 0.116623918712287 df.mm.trans1:exp6 0.115259459940007 0.141496470141969 0.814574807586102 0.415715519250928 df.mm.trans2:exp6 0.074240510464747 0.141496470141969 0.524681007167589 0.60004469254355 df.mm.trans1:exp7 0.0904030215892247 0.141496470141969 0.638906550096407 0.523185333607688 df.mm.trans2:exp7 0.158078410831625 0.141496470141969 1.11718978341310 0.264466310683055 df.mm.trans1:exp8 -0.0116993796190963 0.141496470141969 -0.0826831906644585 0.934137563796506 df.mm.trans2:exp8 -0.0078577327091296 0.141496470141969 -0.0555330652506427 0.955736657215133 df.mm.trans1:probe2 -0.171619847125247 0.0968760106301409 -1.77154123099131 0.0770986222731047 . df.mm.trans1:probe3 -0.110944453420207 0.0968760106301409 -1.14522112026038 0.252682368804136 df.mm.trans1:probe4 0.070064473432951 0.096876010630141 0.723238632321962 0.469881932426047 df.mm.trans1:probe5 -0.109406148738174 0.0968760106301409 -1.12934201177907 0.259311840398343 df.mm.trans1:probe6 -0.0504971717511278 0.096876010630141 -0.521255689852041 0.60242658581475 df.mm.trans2:probe2 0.0298638305854024 0.0968760106301409 0.308268583637474 0.758010323437365 df.mm.trans2:probe3 -0.0355846667553837 0.0968760106301409 -0.367321760298749 0.713539248851963 df.mm.trans2:probe4 -0.059756158473478 0.096876010630141 -0.616831329911165 0.537635425109261 df.mm.trans2:probe5 -0.0939722758033884 0.0968760106301409 -0.970026275773901 0.332516890171236 df.mm.trans2:probe6 -0.005268627050741 0.0968760106301409 -0.0543852602565963 0.956650616899758 df.mm.trans3:probe2 0.0304087806335645 0.0968760106301409 0.31389381577304 0.753736650958585 df.mm.trans3:probe3 -0.021555942487783 0.0968760106301409 -0.222510633412441 0.824010068213462 df.mm.trans3:probe4 -0.142987284081135 0.0968760106301409 -1.47598237325277 0.140597550359179 df.mm.trans3:probe5 -0.0471028502295312 0.0968760106301409 -0.486217897734903 0.627032296326333 df.mm.trans3:probe6 0.0337448870530146 0.0968760106301409 0.348330684072529 0.727742997642957 df.mm.trans3:probe7 -0.0412111034935343 0.0968760106301409 -0.42540050137771 0.670733429817779 df.mm.trans3:probe8 -0.190058315705243 0.0968760106301409 -1.96187182429362 0.0503489048185115 . df.mm.trans3:probe9 -0.0392859228978695 0.0968760106301409 -0.405527876739864 0.685268362340597 df.mm.trans3:probe10 -0.00983298348867714 0.0968760106301409 -0.101500706157462 0.91919494476767