chr7.21708_chr7_34641319_34642942_+_0.R fitVsDatCorrelation=0.885636131135128 cont.fitVsDatCorrelation=0.294190775036567 fstatistic=11062.7834296747,40,416 cont.fstatistic=2603.76860145114,40,416 residuals=-0.445881333099538,-0.0803945897413226,-0.00683493350183157,0.0858073183055844,0.539275376907724 cont.residuals=-0.75807910467978,-0.164094440851010,0.0306037748172128,0.186525928633566,0.78057455072195 predictedValues: Include Exclude Both chr7.21708_chr7_34641319_34642942_+_0.R.tl.Lung 81.8742301464741 117.241085995173 71.5986649161107 chr7.21708_chr7_34641319_34642942_+_0.R.tl.cerebhem 73.466535651801 95.415241374782 65.7051530891522 chr7.21708_chr7_34641319_34642942_+_0.R.tl.cortex 101.112900299288 96.147177643651 73.9444276130283 chr7.21708_chr7_34641319_34642942_+_0.R.tl.heart 88.4054216884862 107.535031272805 69.646494500941 chr7.21708_chr7_34641319_34642942_+_0.R.tl.kidney 85.0172896184104 130.535602871193 66.1441649185281 chr7.21708_chr7_34641319_34642942_+_0.R.tl.liver 79.2643429403083 136.573831380201 66.9677896198628 chr7.21708_chr7_34641319_34642942_+_0.R.tl.stomach 81.039325556384 103.806129727148 74.3878396563463 chr7.21708_chr7_34641319_34642942_+_0.R.tl.testicle 90.6956984822504 128.500017088773 71.0230054689304 diffExp=-35.3668558486991,-21.9487057229810,4.96572265563698,-19.1296095843183,-45.518313252783,-57.3094884398925,-22.7668041707643,-37.8043186065229 diffExpScore=1.03786462149456 diffExp1.5=0,0,0,0,-1,-1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,0,0,0,-1,-1,0,-1 diffExp1.4Score=0.8 diffExp1.3=-1,0,0,0,-1,-1,0,-1 diffExp1.3Score=0.8 diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 81.8674145649338 79.5294660000139 84.8918419312111 cerebhem 83.7368936270289 88.170060851058 77.3782695479164 cortex 80.6887544161379 75.4607929997893 87.6225645313174 heart 82.579995480276 89.229960831663 88.2962994858573 kidney 80.1224145275615 96.6450666797182 68.8041974428361 liver 77.1333201577724 80.7793753637538 76.9493725929307 stomach 90.4669259507182 81.8742814307445 78.7647612018704 testicle 83.360468942991 82.3725143249638 82.6107660559761 cont.diffExp=2.33794856491993,-4.43316722402916,5.22796141634861,-6.64996535138711,-16.5226521521566,-3.64605520598141,8.59264451997376,0.987954618027146 cont.diffExpScore=3.20405753755847 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,-1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.118376995712611 cont.tran.correlation=-0.0400201883552569 tran.covariance=-0.00106025971901642 cont.tran.covariance=-6.48119634293082e-05 tran.mean=99.7893663585705 cont.tran.mean=83.3761066343203 weightedLogRatios: wLogRatio Lung -1.64613076027951 cerebhem -1.15739428268941 cortex 0.231194705620153 heart -0.897121335336055 kidney -1.99698872624763 liver -2.52714336502718 stomach -1.11879449636472 testicle -1.63120052222801 cont.weightedLogRatios: wLogRatio Lung 0.127211238415917 cerebhem -0.2297448802635 cortex 0.291865081154935 heart -0.344842852247337 kidney -0.839446878875688 liver -0.201770884531181 stomach 0.444614804674694 testicle 0.0526637196662811 varWeightedLogRatios=0.68092269780325 cont.varWeightedLogRatios=0.165393095701423 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.26560571313417 0.0729154353406659 72.2152406898882 1.81000842464888e-237 *** df.mm.trans1 -0.824194459939326 0.0591422277166552 -13.9358034311451 1.70120181220219e-36 *** df.mm.trans2 -0.411165324849169 0.0591422277166552 -6.95214469801548 1.39927245998469e-11 *** df.mm.exp2 -0.228449264781741 0.0799748886221663 -2.85651244681349 0.00449797913455118 ** df.mm.exp3 -0.019536240225987 0.0799748886221663 -0.244279680316706 0.807134654207729 df.mm.exp4 0.0179773545593699 0.0799748886221663 0.224787491037371 0.82225496978586 df.mm.exp5 0.224323732191102 0.0799748886221663 2.80492709719045 0.00526877700827443 ** df.mm.exp6 0.187101743075716 0.0799748886221663 2.33950614122965 0.0197815401986992 * df.mm.exp7 -0.170173165772794 0.0799748886221663 -2.12783248222777 0.0339383944862270 * df.mm.exp8 0.202094879737535 0.0799748886221663 2.52697919583624 0.0118745296932163 * df.mm.trans1:exp2 0.120094978946804 0.0644778165463374 1.86257825372386 0.063226323433261 . df.mm.trans2:exp2 0.0224552145048708 0.0644778165463374 0.348262638340633 0.72781911875259 df.mm.trans1:exp3 0.230589666391734 0.0644778165463374 3.57626357006086 0.0003895001985748 *** df.mm.trans2:exp3 -0.178816020659002 0.0644778165463374 -2.77329522364478 0.00579878320349471 ** df.mm.trans1:exp4 0.0587716534012033 0.0644778165463374 0.911501917236398 0.362558856730451 df.mm.trans2:exp4 -0.104393066580114 0.0644778165463374 -1.61905399673532 0.106193486419994 df.mm.trans1:exp5 -0.186653380229878 0.0644778165463374 -2.89484647942038 0.00399321843186594 ** df.mm.trans2:exp5 -0.116910102509099 0.0644778165463374 -1.81318333608708 0.0705240923472873 . df.mm.trans1:exp6 -0.219497654339991 0.0644778165463374 -3.40423522533905 0.000727893607660167 *** df.mm.trans2:exp6 -0.0344687642550891 0.0644778165463374 -0.534583304791624 0.59322362222417 df.mm.trans1:exp7 0.159923412203947 0.0644778165463374 2.48028579083501 0.0135224112269715 * df.mm.trans2:exp7 0.0484658092002909 0.0644778165463374 0.751666414843012 0.452676731511166 df.mm.trans1:exp8 -0.0997692406356628 0.0644778165463374 -1.54734210895561 0.1225411542287 df.mm.trans2:exp8 -0.110398221224481 0.0644778165463374 -1.71218920146191 0.0876070426054338 . df.mm.trans1:probe2 -0.128965487352283 0.0409749373112220 -3.14742366468399 0.00176571147404979 ** df.mm.trans1:probe3 -0.0134785330690589 0.0409749373112221 -0.328945788658168 0.742362200030159 df.mm.trans1:probe4 -0.124386029712278 0.0409749373112220 -3.03566125720983 0.00255089408823389 ** df.mm.trans1:probe5 -0.045646935107249 0.0409749373112220 -1.11402086501173 0.265913462796371 df.mm.trans1:probe6 -0.158473521731384 0.0409749373112221 -3.86757203623548 0.000127535162088307 *** df.mm.trans2:probe2 -0.290674235439140 0.0409749373112220 -7.09395192557212 5.64563818058412e-12 *** df.mm.trans2:probe3 -0.156005713997786 0.0409749373112220 -3.80734478768951 0.000161602067272130 *** df.mm.trans2:probe4 -0.0993904841779848 0.0409749373112220 -2.42564090880901 0.0157059128211337 * df.mm.trans2:probe5 -0.286992928395474 0.0409749373112221 -7.00410902927419 1.00495978615388e-11 *** df.mm.trans2:probe6 -0.339640761180549 0.0409749373112220 -8.2889879391598 1.59203342550738e-15 *** df.mm.trans3:probe2 0.302889718318612 0.0409749373112220 7.39207276921588 8.01199945238596e-13 *** df.mm.trans3:probe3 0.425590111331374 0.0409749373112220 10.3865957890023 1.26565426620372e-22 *** df.mm.trans3:probe4 -0.144210076919560 0.0409749373112220 -3.51947034901415 0.000480150760397159 *** df.mm.trans3:probe5 0.00357650421875996 0.040974937311222 0.0872851663346032 0.930486843421121 df.mm.trans3:probe6 0.420067283713309 0.040974937311222 10.2518102839968 3.86962444539499e-22 *** df.mm.trans3:probe7 0.67789995250582 0.040974937311222 16.5442584416147 1.31735955107392e-47 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26233595158618 0.150068286290276 28.4026429364402 1.94995325174927e-99 *** df.mm.trans1 0.106712561833921 0.121721453343333 0.876694772390881 0.3811584212846 df.mm.trans2 0.135689477294096 0.121721453343333 1.11475400241372 0.265599446235239 df.mm.exp2 0.218390498471557 0.164597446695771 1.32681583375478 0.185297442943562 df.mm.exp3 -0.0986767515601083 0.164597446695771 -0.599503537515347 0.549163518018062 df.mm.exp4 0.084435495120284 0.164597446695771 0.512981803881491 0.608236342811868 df.mm.exp5 0.383485416260335 0.164597446695771 2.3298381837547 0.0202923725212377 * df.mm.exp6 0.054258686752782 0.164597446695771 0.329644765711765 0.74183431176427 df.mm.exp7 0.203852899071656 0.164597446695771 1.23849368968914 0.216231468421936 df.mm.exp8 0.0804353698093385 0.164597446695771 0.488679329017838 0.625326252752177 df.mm.trans1:exp2 -0.195811877146772 0.132702703997313 -1.47556810259673 0.140816337762842 df.mm.trans2:exp2 -0.115250633716117 0.132702703997314 -0.868487455375819 0.38562829043746 df.mm.trans1:exp3 0.0841749235159677 0.132702703997313 0.634312044746819 0.52622584952558 df.mm.trans2:exp3 0.0461623804178411 0.132702703997314 0.347863148431215 0.72811890974141 df.mm.trans1:exp4 -0.075769072522104 0.132702703997313 -0.570968565370288 0.568329255833972 df.mm.trans2:exp4 0.0306537773330964 0.132702703997314 0.230995875816645 0.817431571025128 df.mm.trans1:exp5 -0.405030812655176 0.132702703997313 -3.05216699023236 0.00241763725851743 ** df.mm.trans2:exp5 -0.188567849556325 0.132702703997314 -1.42097970784486 0.156071792987097 df.mm.trans1:exp6 -0.113824374605027 0.132702703997314 -0.857739678065126 0.39153011448803 df.mm.trans2:exp6 -0.0386646037073202 0.132702703997314 -0.291362591286029 0.770919244130031 df.mm.trans1:exp7 -0.103969617398617 0.132702703997313 -0.783477760940891 0.433792432587016 df.mm.trans2:exp7 -0.174795576072439 0.132702703997314 -1.31719679258365 0.188497725523841 df.mm.trans1:exp8 -0.0623622094326792 0.132702703997313 -0.469939251832741 0.638644669170954 df.mm.trans2:exp8 -0.0453111470554909 0.132702703997314 -0.341448559001542 0.732938327214118 df.mm.trans1:probe2 0.061036773439986 0.0843310966247111 0.723775402940755 0.469610428813071 df.mm.trans1:probe3 0.127455656194317 0.0843310966247111 1.51137197659741 0.131452936871284 df.mm.trans1:probe4 0.0746689777539156 0.0843310966247111 0.885426381755787 0.376438215676514 df.mm.trans1:probe5 0.116159242334495 0.0843310966247111 1.37741885240061 0.169123407633344 df.mm.trans1:probe6 0.0893622364838885 0.0843310966247111 1.05965936719129 0.289914456609136 df.mm.trans2:probe2 -0.0784817922305774 0.0843310966247111 -0.930638819744463 0.352580165197456 df.mm.trans2:probe3 -0.0735720191478183 0.0843310966247111 -0.872418622459368 0.383483301553918 df.mm.trans2:probe4 -0.00618281110188083 0.0843310966247111 -0.0733159101368678 0.94158996958159 df.mm.trans2:probe5 0.00192302272650890 0.0843310966247111 0.0228032458188788 0.981818152563256 df.mm.trans2:probe6 -0.128358247198958 0.0843310966247111 -1.52207492059750 0.128750053728486 df.mm.trans3:probe2 -0.0975309015689743 0.0843310966247111 -1.15652357757192 0.248130769285147 df.mm.trans3:probe3 -0.107147137626423 0.0843310966247111 -1.27055311640554 0.204597769292721 df.mm.trans3:probe4 -0.0401482705817341 0.0843310966247111 -0.476079076267694 0.634267969196477 df.mm.trans3:probe5 -0.0772854660114539 0.084331096624711 -0.916452757105584 0.359960437571099 df.mm.trans3:probe6 -9.97360742178081e-05 0.084331096624711 -0.00118267256338017 0.99905693095688 df.mm.trans3:probe7 -0.121308434525456 0.084331096624711 -1.43847808674065 0.151050341961152