chr6.19968_chr6_126071694_126072407_-_0.R fitVsDatCorrelation=0.819107831025914 cont.fitVsDatCorrelation=0.270318849719561 fstatistic=9881.7052422812,39,393 cont.fstatistic=3501.53798163015,39,393 residuals=-0.323534314576108,-0.0883963286436487,-0.00839965357220458,0.0756382892517463,0.424817184435039 cont.residuals=-0.514163256433648,-0.159786898370354,-0.0264870233121706,0.138558158253229,0.811626394361532 predictedValues: Include Exclude Both chr6.19968_chr6_126071694_126072407_-_0.R.tl.Lung 58.7881593660778 46.813445854392 59.9804422864108 chr6.19968_chr6_126071694_126072407_-_0.R.tl.cerebhem 72.3176687354355 57.1674129362695 59.3223024211872 chr6.19968_chr6_126071694_126072407_-_0.R.tl.cortex 62.2182224436465 48.2567573396298 66.2925018245275 chr6.19968_chr6_126071694_126072407_-_0.R.tl.heart 70.0907582411455 48.7197201608193 69.6961473153646 chr6.19968_chr6_126071694_126072407_-_0.R.tl.kidney 60.4239693629171 48.3487821107382 59.3867311483944 chr6.19968_chr6_126071694_126072407_-_0.R.tl.liver 66.859579336608 56.5753161870724 53.7916385121857 chr6.19968_chr6_126071694_126072407_-_0.R.tl.stomach 58.0627191345656 49.510302688124 59.6593654256032 chr6.19968_chr6_126071694_126072407_-_0.R.tl.testicle 67.1392162953097 53.3431556422145 64.9799896417241 diffExp=11.9747135116858,15.1502557991660,13.9614651040166,21.3710380803262,12.0751872521789,10.2842631495355,8.5524164464416,13.7960606530952 diffExpScore=0.990754899440737 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,1,1,1,1,0,0,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 62.3866307715873 62.9264520579862 57.7576922073773 cerebhem 61.5493861222647 57.3841433121978 55.9031574656698 cortex 61.5207679012161 55.6662700179929 58.0136807692575 heart 57.6772855569029 55.6140964037058 58.6194913254295 kidney 60.9018809934142 65.3083873723908 58.483873791926 liver 58.0176089483431 57.9967288144272 59.7192060369204 stomach 57.1980663983957 59.2260148957154 55.34689405897 testicle 57.7165073327355 58.7913455760487 61.5802739803697 cont.diffExp=-0.539821286398869,4.16524281006688,5.85449788322329,2.06318915319707,-4.40650637897658,0.0208801339158455,-2.02794849731971,-1.07483824331317 cont.diffExpScore=3.98697094410567 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=0.69641095584643 cont.tran.correlation=0.341009617511317 tran.covariance=0.00447146921259552 cont.tran.covariance=0.000679640220930109 tran.mean=57.7896991146853 cont.tran.mean=59.3675982797078 weightedLogRatios: wLogRatio Lung 0.901981821571801 cerebhem 0.97878027177119 cortex 1.01736096645851 heart 1.47953759374939 kidney 0.889530605700575 liver 0.68797680825304 stomach 0.634480319409806 testicle 0.941196750636154 cont.weightedLogRatios: wLogRatio Lung -0.0356484744790215 cerebhem 0.286229821340345 cortex 0.406939124492984 heart 0.147041961752293 kidney -0.289498397740418 liver 0.00146163270307011 stomach -0.141590907663777 testicle -0.0750008338719848 varWeightedLogRatios=0.0656323828318789 cont.varWeightedLogRatios=0.0526655196799336 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77147806465607 0.0710128140721632 53.1098241061606 2.02052576618613e-181 *** df.mm.trans1 0.272233862022471 0.0579817198919776 4.69516707213333 3.68641039274545e-06 *** df.mm.trans2 0.103647365681900 0.0579817198919776 1.78758694766211 0.0746130927302988 . df.mm.exp2 0.417974799849441 0.0787816438006693 5.30548462414654 1.88124200411657e-07 *** df.mm.exp3 -0.0129853898536840 0.0787816438006693 -0.164827607387061 0.869164419502689 df.mm.exp4 0.0656374047775903 0.0787816438006693 0.833156070513887 0.405262763108627 df.mm.exp5 0.0696637001097751 0.0787816438006693 0.884263094154723 0.377094852044823 df.mm.exp6 0.426956950133607 0.0787816438006693 5.4194978618862 1.04465062866393e-07 *** df.mm.exp7 0.0489610488466154 0.0787816438006693 0.621477878406485 0.534645564115515 df.mm.exp8 0.183342243505882 0.0787816438006693 2.32722033535843 0.0204608158643361 * df.mm.trans1:exp2 -0.210846782992573 0.0643249428031125 -3.27783863932749 0.00113914338271162 ** df.mm.trans2:exp2 -0.218161233958564 0.0643249428031125 -3.39154959882852 0.000765484381899275 *** df.mm.trans1:exp3 0.069692848723346 0.0643249428031125 1.08344983588503 0.279273065034531 df.mm.trans2:exp3 0.043350790081973 0.0643249428031125 0.673934374332244 0.500749220056059 df.mm.trans1:exp4 0.110213080482051 0.0643249428031125 1.71338015518170 0.087431232208502 . df.mm.trans2:exp4 -0.0257239914585622 0.0643249428031125 -0.399906946474852 0.689442321503731 df.mm.trans1:exp5 -0.0422182934352029 0.0643249428031125 -0.656328503305876 0.51199687580436 df.mm.trans2:exp5 -0.0373931337912835 0.0643249428031125 -0.581316238488348 0.561360586192501 df.mm.trans1:exp6 -0.298302824167829 0.0643249428031125 -4.63743629094056 4.80995302832324e-06 *** df.mm.trans2:exp6 -0.237554636057601 0.0643249428031125 -3.69304076623456 0.000252937775483197 *** df.mm.trans1:exp7 -0.0613777214658943 0.0643249428031125 -0.954182293698432 0.340577899283126 df.mm.trans2:exp7 0.00704926794396847 0.0643249428031125 0.109588405939902 0.912791741705893 df.mm.trans1:exp8 -0.05051438801779 0.0643249428031125 -0.785300162215547 0.432750761163867 df.mm.trans2:exp8 -0.0527670318461105 0.0643249428031125 -0.820319918629718 0.412531016923218 df.mm.trans1:probe2 0.132640782877149 0.0393908219003347 3.36730173370722 0.000833976188174942 *** df.mm.trans1:probe3 0.269622762451931 0.0393908219003347 6.84481179738066 2.95447885983474e-11 *** df.mm.trans1:probe4 0.0106466712723597 0.0393908219003347 0.27028304459596 0.787084387457163 df.mm.trans1:probe5 -0.253700318994334 0.0393908219003347 -6.44059470595048 3.47931461661964e-10 *** df.mm.trans1:probe6 0.20353254186772 0.0393908219003347 5.16700419155232 3.79177046332437e-07 *** df.mm.trans2:probe2 0.0521580176765405 0.0393908219003347 1.32411600368504 0.186234107662925 df.mm.trans2:probe3 -0.0577342576964459 0.0393908219003347 -1.46567791457927 0.143535653740382 df.mm.trans2:probe4 -0.0493388115495644 0.0393908219003347 -1.25254587666131 0.211115843101055 df.mm.trans2:probe5 -0.14264437808181 0.0393908219003347 -3.62125924771827 0.000331557902374264 *** df.mm.trans2:probe6 -0.149900139517556 0.0393908219003347 -3.8054585379515 0.000164103937125749 *** df.mm.trans3:probe2 -0.0861187310793 0.0393908219003347 -2.18626387885977 0.0293855579165213 * df.mm.trans3:probe3 0.0804665599850558 0.0393908219003347 2.04277433430177 0.0417414996260837 * df.mm.trans3:probe4 -0.138250629952661 0.0393908219003347 -3.50971681429897 0.000500547732442965 *** df.mm.trans3:probe5 -0.0274239048719565 0.0393908219003347 -0.696200372293412 0.486714883046418 df.mm.trans3:probe6 -0.148717765092863 0.0393908219003347 -3.7754420425434 0.000184392038314064 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23208382340974 0.119184712488019 35.5086129341896 1.06192105104158e-124 *** df.mm.trans1 -0.082718313278525 0.0973139102453217 -0.850015306855903 0.395834112745376 df.mm.trans2 -0.0917400596981906 0.0973139102453217 -0.9427229824279 0.346401764735462 df.mm.exp2 -0.0730740473663417 0.132223566808304 -0.552655242406853 0.580813696350101 df.mm.exp3 -0.140990732310612 0.132223566808303 -1.06630561944391 0.28694013109035 df.mm.exp4 -0.216828204510404 0.132223566808304 -1.639860501001 0.101834322477769 df.mm.exp5 0.000572398182848029 0.132223566808304 0.00432901786470400 0.996548150827253 df.mm.exp6 -0.187581581394891 0.132223566808303 -1.41866980238738 0.156787840001597 df.mm.exp7 -0.104800643060338 0.132223566808304 -0.792601845420468 0.428488031790713 df.mm.exp8 -0.209864789922417 0.132223566808303 -1.58719655647073 0.113272339654660 df.mm.trans1:exp2 0.0595629243387839 0.107960090217049 0.55171243576247 0.581458955197499 df.mm.trans2:exp2 -0.0191245531543495 0.107960090217049 -0.177144657029283 0.859486024925899 df.mm.trans1:exp3 0.127014537604360 0.107960090217049 1.17649528959270 0.240109128584121 df.mm.trans2:exp3 0.0183985138425560 0.107960090217049 0.170419585659540 0.864767868868286 df.mm.trans1:exp4 0.138340634028015 0.107960090217049 1.28140532070590 0.200806877332532 df.mm.trans2:exp4 0.0932982893077374 0.107960090217049 0.864192398507315 0.388009350678559 df.mm.trans1:exp5 -0.0246593393177545 0.107960090217049 -0.228411622000112 0.819445056020574 df.mm.trans2:exp5 0.0365814567666459 0.107960090217049 0.338842406421676 0.734909422444464 df.mm.trans1:exp6 0.114977146455877 0.107960090217049 1.06499676153216 0.287531270916476 df.mm.trans2:exp6 0.106001573833492 0.107960090217049 0.981858885263817 0.326773426723601 df.mm.trans1:exp7 0.0179697346857211 0.107960090217049 0.166447940619481 0.867890049584768 df.mm.trans2:exp7 0.0441949124644312 0.107960090217049 0.409363426573463 0.682495990577156 df.mm.trans1:exp8 0.132057009511241 0.107960090217049 1.22320210409001 0.221986315742988 df.mm.trans2:exp8 0.141892833147517 0.107960090217049 1.31430821206474 0.189509062485853 df.mm.trans1:probe2 -0.0672920534278117 0.0661117834041517 -1.01785264234130 0.309374179998039 df.mm.trans1:probe3 0.0289065029158474 0.0661117834041517 0.437236774254559 0.662179804805941 df.mm.trans1:probe4 -0.0218339386591526 0.0661117834041518 -0.330257898590911 0.741381082399966 df.mm.trans1:probe5 -0.0884269602819696 0.0661117834041517 -1.33753705812777 0.181820931707080 df.mm.trans1:probe6 -0.0435276485398053 0.0661117834041517 -0.65839471117749 0.51067004336205 df.mm.trans2:probe2 -0.00464066260986387 0.0661117834041518 -0.0701941828054277 0.944074808140924 df.mm.trans2:probe3 -0.0475493898984254 0.0661117834041518 -0.71922715513131 0.472428415072304 df.mm.trans2:probe4 -0.0263246139939296 0.0661117834041518 -0.398183389381633 0.690711230783165 df.mm.trans2:probe5 -0.0112391767364731 0.0661117834041518 -0.170002625216843 0.86509555021409 df.mm.trans2:probe6 0.109228079892519 0.0661117834041518 1.65217264258007 0.0992980461673936 . df.mm.trans3:probe2 -0.0832065193611298 0.0661117834041518 -1.25857320853796 0.208931668534966 df.mm.trans3:probe3 0.0708642154976281 0.0661117834041517 1.07188479645790 0.284429562957067 df.mm.trans3:probe4 0.0171592061926536 0.0661117834041518 0.259548378656747 0.795348067721783 df.mm.trans3:probe5 0.0079885119917133 0.0661117834041517 0.120833406397136 0.903884742645969 df.mm.trans3:probe6 -0.0210188833514607 0.0661117834041518 -0.317929456281174 0.750707312400158