chr3.14886_chr3_88636548_88637587_+_0.R fitVsDatCorrelation=0.875524211635336 cont.fitVsDatCorrelation=0.336934854002084 fstatistic=7527.2243229669,38,370 cont.fstatistic=1975.15749486651,38,370 residuals=-0.396964988971317,-0.0890145932147474,-0.00869257037671457,0.0753960736885349,0.721722498838278 cont.residuals=-0.640250864693043,-0.208793927565285,-0.0418710291655546,0.193928579211263,0.999190979453234 predictedValues: Include Exclude Both chr3.14886_chr3_88636548_88637587_+_0.R.tl.Lung 89.5975975837846 58.6941432604866 70.0451084684046 chr3.14886_chr3_88636548_88637587_+_0.R.tl.cerebhem 64.894766627313 46.818334830566 81.4155832794468 chr3.14886_chr3_88636548_88637587_+_0.R.tl.cortex 87.8661742998374 57.3830120873631 61.1574890643773 chr3.14886_chr3_88636548_88637587_+_0.R.tl.heart 81.6697652910544 64.8036849631246 70.5328095066678 chr3.14886_chr3_88636548_88637587_+_0.R.tl.kidney 94.8326044507869 55.3420207940293 69.3221347127011 chr3.14886_chr3_88636548_88637587_+_0.R.tl.liver 88.8601638414955 53.0360709594222 70.4000431524995 chr3.14886_chr3_88636548_88637587_+_0.R.tl.stomach 77.2387294331713 66.7428773288645 63.2577715480812 chr3.14886_chr3_88636548_88637587_+_0.R.tl.testicle 85.4808359232426 56.8439253666228 68.457989259237 diffExp=30.903454323298,18.0764317967471,30.4831622124744,16.8660803279297,39.4905836567576,35.8240928820732,10.4958521043068,28.6369105566197 diffExpScore=0.995278042277746 diffExp1.5=1,0,1,0,1,1,0,1 diffExp1.5Score=0.833333333333333 diffExp1.4=1,0,1,0,1,1,0,1 diffExp1.4Score=0.833333333333333 diffExp1.3=1,1,1,0,1,1,0,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,0,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 65.167886888157 74.6481512736136 73.1071466062746 cerebhem 83.6642664041409 63.5494435069717 65.9788615892001 cortex 87.0438617743203 67.010129091714 71.3135674973135 heart 69.6201156247375 73.2600082730769 66.8911943519975 kidney 70.0538208503101 75.4857976448941 68.8957137708619 liver 71.934017413424 62.4862450070876 70.2274228679537 stomach 66.8999518401719 84.5540946086549 74.5936754814083 testicle 72.1167453938342 77.2051385466209 74.0801988336141 cont.diffExp=-9.4802643854566,20.1148228971692,20.0337326826063,-3.63989264833937,-5.43197679458397,9.44777240633636,-17.6541427684830,-5.08839315278665 cont.diffExpScore=9.77148325870233 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=0,1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,1,1,0,0,0,-1,0 cont.diffExp1.2Score=1.5 tran.correlation=0.205960590416043 cont.tran.correlation=-0.650501593655215 tran.covariance=0.00408668647872441 cont.tran.covariance=-0.00704623151921652 tran.mean=70.6315441900728 cont.tran.mean=72.7937296338581 weightedLogRatios: wLogRatio Lung 1.81201286759900 cerebhem 1.30907685897379 cortex 1.81622889773439 heart 0.991680179322431 kidney 2.30664612933166 liver 2.18256103729262 stomach 0.62421397013739 testicle 1.73160192546878 cont.weightedLogRatios: wLogRatio Lung -0.576534611348279 cerebhem 1.17953471951465 cortex 1.13406310396194 heart -0.217530167940836 kidney -0.320126651557189 liver 0.592124844100021 stomach -1.01178392002129 testicle -0.294016712819831 varWeightedLogRatios=0.336358981379721 cont.varWeightedLogRatios=0.655838066454226 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36536149857659 0.0853423174301582 51.1511947416834 7.24172939428482e-170 *** df.mm.trans1 0.0652954807359387 0.0702827960434333 0.929039315618399 0.353474401805228 df.mm.trans2 -0.304826063801455 0.0702827960434333 -4.33713626892531 1.86391693905023e-05 *** df.mm.exp2 -0.699053829904118 0.096074632008724 -7.27615412402143 2.0713999242026e-12 *** df.mm.exp3 0.0935818747080916 0.096074632008724 0.974053949013242 0.330666124716874 df.mm.exp4 -0.000560666181256868 0.096074632008724 -0.00583573592252695 0.995346927932062 df.mm.exp5 0.0083524956831365 0.096074632008724 0.0869375766370675 0.93076815985508 df.mm.exp6 -0.114686682102897 0.096074632008724 -1.19372491681762 0.233350384154634 df.mm.exp7 0.0820015888472372 0.096074632008724 0.853519676659195 0.39392339647512 df.mm.exp8 -0.0561477047907576 0.096074632008724 -0.58441758887673 0.559295554788699 df.mm.trans1:exp2 0.376492305938723 0.0796608765611016 4.72618833976733 3.25604118721083e-06 *** df.mm.trans2:exp2 0.472988778230871 0.0796608765611016 5.9375291692664 6.66848174519495e-09 *** df.mm.trans1:exp3 -0.113095471271852 0.0796608765611016 -1.41971160943861 0.156533626265386 df.mm.trans2:exp3 -0.116173519611489 0.0796608765611016 -1.4583510077545 0.145592128381023 df.mm.trans1:exp4 -0.09208397726292 0.0796608765611016 -1.15594983683477 0.248447540432987 df.mm.trans2:exp4 0.0995831868785694 0.0796608765611016 1.25008901706206 0.212057101534663 df.mm.trans1:exp5 0.0484322763132105 0.0796608765611016 0.60798071028081 0.543573265240028 df.mm.trans2:exp5 -0.0671599537754939 0.0796608765611016 -0.843073245923685 0.399732153291181 df.mm.trans1:exp6 0.106422116484582 0.0796608765611016 1.33593956128456 0.182390130607229 df.mm.trans2:exp6 0.0133190005437495 0.0796608765611016 0.167196258925591 0.867306967388379 df.mm.trans1:exp7 -0.230429087959098 0.0796608765611016 -2.89262556359588 0.00404629390430263 ** df.mm.trans2:exp7 0.0465060482969293 0.0796608765611016 0.583800358526787 0.559710356817221 df.mm.trans1:exp8 0.00911140742099976 0.0796608765611016 0.114377443662839 0.909000634317564 df.mm.trans2:exp8 0.0241171178097253 0.0796608765611016 0.302747331574075 0.762252555122449 df.mm.trans1:probe2 0.522381171009032 0.0465119312203492 11.2311219358805 2.19991477155024e-25 *** df.mm.trans1:probe3 -0.0445128951101294 0.0465119312203493 -0.957021003906515 0.339181542247577 df.mm.trans1:probe4 0.329200399000208 0.0465119312203493 7.07776242273467 7.3989831080598e-12 *** df.mm.trans1:probe5 -0.254262490333566 0.0465119312203493 -5.46660789312323 8.4516231574983e-08 *** df.mm.trans1:probe6 0.158580619341553 0.0465119312203493 3.4094610819379 0.000722622189197816 *** df.mm.trans2:probe2 -0.0310948356917034 0.0465119312203493 -0.668534607698663 0.504209253784784 df.mm.trans2:probe3 -0.0456796393948561 0.0465119312203493 -0.982105842443948 0.326689449091965 df.mm.trans2:probe4 0.0280179905581517 0.0465119312203493 0.602382868718502 0.547288294771433 df.mm.trans2:probe5 0.0897420859663338 0.0465119312203493 1.9294422659249 0.0544403277029087 . df.mm.trans2:probe6 0.0888640413841258 0.0465119312203493 1.91056443051428 0.0568332592812965 . df.mm.trans3:probe2 0.352514414784504 0.0465119312203493 7.57901049333932 2.82684197455435e-13 *** df.mm.trans3:probe3 -0.193726040989624 0.0465119312203492 -4.16508271978326 3.87695002800605e-05 *** df.mm.trans3:probe4 0.307252562476301 0.0465119312203493 6.60588701468229 1.37759566318503e-10 *** df.mm.trans3:probe5 0.150501692173559 0.0465119312203493 3.23576528053760 0.00132249510603399 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.31681656019923 0.166300622230097 25.9579098521135 2.42137139680312e-85 *** df.mm.trans1 -0.097374529000883 0.136955183150014 -0.710995573597414 0.477534933025147 df.mm.trans2 0.014997933427023 0.136955183150014 0.109509790590364 0.912857525192781 df.mm.exp2 0.191469343841401 0.187213935181145 1.02273019183181 0.307103639433223 df.mm.exp3 0.206342894713092 0.187213935181145 1.10217700682077 0.271101139909858 df.mm.exp4 0.136174632160985 0.187213935181145 0.727374444798804 0.467456595150498 df.mm.exp5 0.142787930282294 0.187213935181145 0.762699262446112 0.446128734218033 df.mm.exp6 -0.0388695940954774 0.187213935181145 -0.207621265253934 0.835638925305746 df.mm.exp7 0.130707578271410 0.187213935181145 0.698172270909959 0.485508008391926 df.mm.exp8 0.121777564731262 0.187213935181145 0.650472757882217 0.51579059213843 df.mm.trans1:exp2 0.0583758026055728 0.155229594630444 0.376061038776455 0.70708704883762 df.mm.trans2:exp2 -0.352436861441105 0.155229594630444 -2.27042312569426 0.0237562905220019 * df.mm.trans1:exp3 0.0831024400134453 0.155229594630444 0.535351781413124 0.592728243052863 df.mm.trans2:exp3 -0.314284864697586 0.155229594630444 -2.0246452710632 0.0436216344115046 * df.mm.trans1:exp4 -0.0700879041898057 0.155229594630444 -0.451511223466533 0.651885671545069 df.mm.trans2:exp4 -0.154945520304899 0.155229594630444 -0.998169973153499 0.318849280386915 df.mm.trans1:exp5 -0.0704909295798199 0.155229594630444 -0.454107541462297 0.650017686634206 df.mm.trans2:exp5 -0.131629160808186 0.155229594630444 -0.84796434031511 0.397006038369867 df.mm.trans1:exp6 0.137652052915575 0.155229594630444 0.886764236183724 0.375781857093731 df.mm.trans2:exp6 -0.138969711692084 0.155229594630444 -0.895252686982337 0.371234053427758 df.mm.trans1:exp7 -0.104476146223793 0.155229594630444 -0.673042704727279 0.501340269888155 df.mm.trans2:exp7 -0.00610183427022982 0.155229594630444 -0.039308446850979 0.968665672989706 df.mm.trans1:exp8 -0.0204581102735489 0.155229594630444 -0.131792589694341 0.895219983321137 df.mm.trans2:exp8 -0.0880973068531106 0.155229594630444 -0.567529065980262 0.570698970041354 df.mm.trans1:probe2 -0.0817229883342834 0.0906345566418164 -0.901675821698438 0.367815662701305 df.mm.trans1:probe3 0.0669007741296716 0.0906345566418164 0.738137600143622 0.460898741474826 df.mm.trans1:probe4 -0.127287548132617 0.0906345566418164 -1.40440415718754 0.161037411331447 df.mm.trans1:probe5 -0.166204042304616 0.0906345566418164 -1.83378226211716 0.0674893670017807 . df.mm.trans1:probe6 -0.158913571246912 0.0906345566418164 -1.75334416733489 0.0803710079368962 . df.mm.trans2:probe2 0.0160865632703315 0.0906345566418164 0.177488188461106 0.859222066236853 df.mm.trans2:probe3 -0.0418958589929497 0.0906345566418164 -0.462250388210318 0.644173468558437 df.mm.trans2:probe4 -0.0353649323643383 0.0906345566418164 -0.390192589611255 0.696618672160237 df.mm.trans2:probe5 -0.0593464239491818 0.0906345566418164 -0.654788042751907 0.513011067590506 df.mm.trans2:probe6 -0.0887954352085746 0.0906345566418164 -0.979708386057318 0.327870232134706 df.mm.trans3:probe2 0.0589181885024701 0.0906345566418164 0.650063184347137 0.516054810223059 df.mm.trans3:probe3 -0.0132757698358248 0.0906345566418164 -0.146475807106224 0.883625559915663 df.mm.trans3:probe4 0.172987348637923 0.0906345566418164 1.9086246465745 0.0570840483060554 . df.mm.trans3:probe5 0.0688010434125236 0.0906345566418164 0.759103877833509 0.448273740738701