chr11.4752_chr11_109253171_109284936_+_2.R fitVsDatCorrelation=0.70410775566195 cont.fitVsDatCorrelation=0.257671227677815 fstatistic=19522.6742322356,53,715 cont.fstatistic=10537.8229030242,53,715 residuals=-0.357150065778793,-0.0665555498240851,-0.00314351122545208,0.065270882942681,0.389791835784321 cont.residuals=-0.388862636883053,-0.092009977313916,-0.0154835589368375,0.0855058735030644,0.572188846690746 predictedValues: Include Exclude Both chr11.4752_chr11_109253171_109284936_+_2.R.tl.Lung 48.8468728406813 45.0865414433236 60.3426580257176 chr11.4752_chr11_109253171_109284936_+_2.R.tl.cerebhem 51.0331450331605 51.6508132728553 63.7815920759287 chr11.4752_chr11_109253171_109284936_+_2.R.tl.cortex 48.2121783542052 45.6534967091531 60.8579751461223 chr11.4752_chr11_109253171_109284936_+_2.R.tl.heart 48.2820780688316 46.6868267122118 59.2465040845482 chr11.4752_chr11_109253171_109284936_+_2.R.tl.kidney 47.6985109949422 45.5372540869638 57.8775674335956 chr11.4752_chr11_109253171_109284936_+_2.R.tl.liver 48.7501181482047 49.5315755304597 52.2147064052524 chr11.4752_chr11_109253171_109284936_+_2.R.tl.stomach 48.0720660505635 48.6607352975098 59.0383166124603 chr11.4752_chr11_109253171_109284936_+_2.R.tl.testicle 49.4621491723878 51.3161344622067 55.0692694517696 diffExp=3.76033139735770,-0.61766823969483,2.55868164505217,1.59525135661976,2.16125690797846,-0.781457382254942,-0.58866924694621,-1.85398528981887 diffExpScore=1.92394242210431 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 50.0661561870412 49.5710831251428 47.7418660583672 cerebhem 49.684049028104 49.2010312602372 48.6475981715641 cortex 50.2187327546154 49.5552151361551 48.0053555777061 heart 50.4950640656488 48.3641119070548 48.1476833811618 kidney 51.5340610639595 47.4763744559021 52.0497191932999 liver 49.1701357577834 47.6694407100721 50.8610166210874 stomach 50.5543957165952 49.9112401141236 50.885083095277 testicle 49.1910882744911 51.9054238247489 48.321237557559 cont.diffExp=0.495073061898431,0.483017767866848,0.663517618460268,2.13095215859398,4.05768660805736,1.50069504771128,0.643155602471616,-2.71433555025784 cont.diffExpScore=1.53617415752742 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=0.739439669121449 cont.tran.correlation=-0.447888696994973 tran.covariance=0.000855147581944981 cont.tran.covariance=-0.000198189459628290 tran.mean=48.4050310111038 cont.tran.mean=49.6604752113547 weightedLogRatios: wLogRatio Lung 0.308301133655195 cerebhem -0.0473824725414765 cortex 0.209855882575530 heart 0.129698584604812 kidney 0.178138115460701 liver -0.0619357287167015 stomach -0.0472094406874487 testicle -0.144231721908867 cont.weightedLogRatios: wLogRatio Lung 0.0388398521968606 cerebhem 0.0381082835020043 cortex 0.0520019147423661 heart 0.168172070660929 kidney 0.319943465425361 liver 0.120257558524903 stomach 0.0501474665947913 testicle -0.210684319988809 varWeightedLogRatios=0.026038964994362 cont.varWeightedLogRatios=0.02229855273465 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82249861940421 0.0542053918731613 70.5187895025043 0 *** df.mm.trans1 0.124461260592032 0.0481354370358178 2.58564725400581 0.00991635941242989 ** df.mm.trans2 -0.0357516553404486 0.0437854395376674 -0.816519274853743 0.414475158376678 df.mm.exp2 0.124281778851739 0.0590292791399431 2.10542599642965 0.0356024374048594 * df.mm.exp3 -0.00908588221175867 0.0590292791399431 -0.153921618968417 0.87771497087698 df.mm.exp4 0.041580840064123 0.0590292791399431 0.704410432753983 0.481406574548763 df.mm.exp5 0.027866206920146 0.0590292791399431 0.47207432186462 0.637017777563312 df.mm.exp6 0.23671892618603 0.0590292791399431 4.01019510376928 6.70480604922805e-05 *** df.mm.exp7 0.08215217855244 0.0590292791399431 1.39171915614417 0.164440247617468 df.mm.exp8 0.233386227762898 0.0590292791399431 3.9537367076701 8.45909636359484e-05 *** df.mm.trans1:exp2 -0.0804968155074954 0.0560510699168109 -1.43613343379468 0.151401738605719 df.mm.trans2:exp2 0.0116403763512687 0.0471992247844563 0.246622193572598 0.80527141537362 df.mm.trans1:exp3 -0.0039928265911052 0.0560510699168109 -0.0712355107053481 0.943230239794407 df.mm.trans2:exp3 0.0215822984653023 0.0471992247844563 0.457259596187475 0.64762336660003 df.mm.trans1:exp4 -0.0532107636347046 0.0560510699168109 -0.94932645734824 0.342775359790089 df.mm.trans2:exp4 -0.00670258451543879 0.0471992247844563 -0.142006241544164 0.887115078942385 df.mm.trans1:exp5 -0.051656386447854 0.0560510699168109 -0.921595011915395 0.357050714853773 df.mm.trans2:exp5 -0.0179192309143413 0.0471992247844563 -0.379650958170873 0.704317261981616 df.mm.trans1:exp6 -0.23870166608802 0.0560510699168109 -4.25864602481795 2.33142178422408e-05 *** df.mm.trans2:exp6 -0.142692356555104 0.0471992247844563 -3.02319280044818 0.00259066587952442 ** df.mm.trans1:exp7 -0.0981412784251653 0.0560510699168109 -1.75092604959768 0.0803874720460598 . df.mm.trans2:exp7 -0.00586351647122448 0.0471992247844563 -0.124229084227575 0.901168810394662 df.mm.trans1:exp8 -0.220868874772178 0.0560510699168109 -3.94049346604773 8.92940745831067e-05 *** df.mm.trans2:exp8 -0.103964799183753 0.0471992247844563 -2.20268022745134 0.0279356472998785 * df.mm.trans1:probe2 -0.181949124603132 0.0307004353657365 -5.92659753633974 4.80908040871471e-09 *** df.mm.trans1:probe3 -0.0310417004437903 0.0307004353657365 -1.01111596868215 0.312302969025122 df.mm.trans1:probe4 -0.0560565792526757 0.0307004353657365 -1.82592131299995 0.0682790390428444 . df.mm.trans1:probe5 -0.0555826082343408 0.0307004353657365 -1.81048273655344 0.070640604844203 . df.mm.trans1:probe6 -0.05508636503442 0.0307004353657365 -1.79431869216746 0.0731846638711793 . df.mm.trans1:probe7 0.00546191707807386 0.0307004353657365 0.177910085411026 0.858843981648694 df.mm.trans1:probe8 -0.183697345268819 0.0307004353657365 -5.98354202734973 3.45142275729844e-09 *** df.mm.trans1:probe9 0.0149073034496861 0.0307004353657365 0.485573030873806 0.627418678825209 df.mm.trans1:probe10 0.0748519186982954 0.0307004353657365 2.43813867153931 0.0150056076695285 * df.mm.trans1:probe11 -0.0393325807728062 0.0307004353657365 -1.28117338742055 0.200548135879669 df.mm.trans1:probe12 -0.0647777638036014 0.0307004353657365 -2.10999495713657 0.0352056279126063 * df.mm.trans1:probe13 -0.0818567699118726 0.0307004353657365 -2.66630648512658 0.00784229481655004 ** df.mm.trans1:probe14 -0.0696357078278923 0.0307004353657366 -2.26823193216373 0.0236124984907400 * df.mm.trans1:probe15 -0.0983809640220801 0.0307004353657365 -3.20454621734384 0.00141284643189818 ** df.mm.trans1:probe16 -0.0153410113976016 0.0307004353657365 -0.499700125253697 0.617439979569317 df.mm.trans1:probe17 -0.119544162019307 0.0307004353657365 -3.89389142515957 0.000107888441471137 *** df.mm.trans1:probe18 -0.131369611275262 0.0307004353657365 -4.27907974952949 2.13223242974347e-05 *** df.mm.trans1:probe19 -0.112060256850519 0.0307004353657365 -3.65011946949732 0.000281215010903445 *** df.mm.trans1:probe20 -0.121877799528761 0.0307004353657365 -3.96990459831665 7.9166994542499e-05 *** df.mm.trans1:probe21 -0.124170860257516 0.0307004353657365 -4.04459607097619 5.81140436169712e-05 *** df.mm.trans1:probe22 -0.0684674253326115 0.0307004353657365 -2.23017766741592 0.0260452792019235 * df.mm.trans2:probe2 0.0373882239151858 0.0307004353657365 1.21784018597056 0.223686447563111 df.mm.trans2:probe3 -0.0373037037085132 0.0307004353657365 -1.215087123818 0.224733953343465 df.mm.trans2:probe4 0.0170701920384289 0.0307004353657365 0.556024428809249 0.578368106612139 df.mm.trans2:probe5 0.121819580286714 0.0307004353657365 3.96800823296048 7.97856925607113e-05 *** df.mm.trans2:probe6 0.0793939276597376 0.0307004353657366 2.58608474811226 0.00990390028755629 ** df.mm.trans3:probe2 0.153436746258051 0.0307004353657365 4.99786874127833 7.29660514189339e-07 *** df.mm.trans3:probe3 0.257373223906334 0.0307004353657365 8.3833737482947 2.72721862086564e-16 *** df.mm.trans3:probe4 0.344514265484794 0.0307004353657365 11.2218039054030 4.9722116626997e-27 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00824858357502 0.0737579573170374 54.3432699247102 4.40971475114183e-256 *** df.mm.trans1 -0.0507748244212801 0.0654984935563704 -0.775205988174086 0.438474250729927 df.mm.trans2 -0.0972445452895675 0.0595793973426845 -1.63218410435143 0.103081011195966 df.mm.exp2 -0.0339481262673917 0.080321881288993 -0.422651035092773 0.672676959456211 df.mm.exp3 -0.00278116320975639 0.0803218812889931 -0.0346252249713865 0.972388250796815 df.mm.exp4 -0.0245835938894666 0.0803218812889931 -0.306063472306088 0.75964541353015 df.mm.exp5 -0.100668432470654 0.080321881288993 -1.25331268211280 0.210501614806757 df.mm.exp6 -0.120463978123583 0.080321881288993 -1.49976539630791 0.134116588716575 df.mm.exp7 -0.0472178967152336 0.080321881288993 -0.587858450991039 0.556812921320769 df.mm.exp8 0.0163203907285991 0.080321881288993 0.203187356504753 0.839046403976588 df.mm.trans1:exp2 0.0262868081354008 0.0762693946051037 0.344657359239637 0.730453330206386 df.mm.trans2:exp2 0.0264550479097085 0.0642245777909944 0.411914703367939 0.680525400033796 df.mm.trans1:exp3 0.00582402813008942 0.0762693946051037 0.0763612738798335 0.939153039977632 df.mm.trans2:exp3 0.00246100621001904 0.0642245777909944 0.0383187604288793 0.969444229267033 df.mm.trans1:exp4 0.0331139295074108 0.0762693946051037 0.434170609047878 0.664295536677785 df.mm.trans2:exp4 -6.60191611344847e-05 0.0642245777909944 -0.00102794231437894 0.999180107562262 df.mm.trans1:exp5 0.129566146795666 0.0762693946051037 1.69879605661634 0.0897926444555516 . df.mm.trans2:exp5 0.0574929776909019 0.0642245777909944 0.89518654179403 0.370988633263843 df.mm.trans1:exp6 0.102405165919591 0.0762693946051037 1.34267705217550 0.179802667440915 df.mm.trans2:exp6 0.0813468525266157 0.0642245777909944 1.26660003575800 0.205710792768206 df.mm.trans1:exp7 0.0569225415851506 0.0762693946051037 0.746335301071624 0.455710207701985 df.mm.trans2:exp7 0.0540564646849515 0.0642245777909944 0.841678786287504 0.40024919274682 df.mm.trans1:exp8 -0.0339531708812283 0.0762693946051037 -0.445174254457191 0.656328572446408 df.mm.trans2:exp8 0.0296952370665571 0.0642245777909944 0.46236562524699 0.643959811080713 df.mm.trans1:probe2 -1.53240325451423e-05 0.0417744678724781 -0.000366827713806454 0.999707416114247 df.mm.trans1:probe3 -0.0576530741076038 0.0417744678724781 -1.38010313581006 0.167986249757569 df.mm.trans1:probe4 -0.0655343051466839 0.0417744678724781 -1.56876457042459 0.117145228798104 df.mm.trans1:probe5 -0.100670668160984 0.0417744678724781 -2.40986117329594 0.0162101646331618 * df.mm.trans1:probe6 -0.0965909095178852 0.0417744678724781 -2.31219963861039 0.0210498873417929 * df.mm.trans1:probe7 -0.062647120118349 0.0417744678724781 -1.49965094252277 0.134146244658302 df.mm.trans1:probe8 0.0249905678293221 0.0417744678724781 0.598225880593117 0.549878670171853 df.mm.trans1:probe9 -0.0340555573076101 0.0417744678724781 -0.815224203730591 0.415215451395074 df.mm.trans1:probe10 -0.0832695953353325 0.0417744678724781 -1.99331313063098 0.0466067297953814 * df.mm.trans1:probe11 -0.0403837071865024 0.0417744678724781 -0.966707877878393 0.33401695075665 df.mm.trans1:probe12 -0.055266112742681 0.0417744678724781 -1.32296389534842 0.186270321841877 df.mm.trans1:probe13 -0.0586506987192396 0.0417744678724781 -1.40398434034584 0.160757706889363 df.mm.trans1:probe14 -0.0404333659680329 0.0417744678724781 -0.967896613104944 0.333423283303187 df.mm.trans1:probe15 -0.0423414171466236 0.0417744678724781 -1.01357166956324 0.311130042487678 df.mm.trans1:probe16 -0.0843803471712136 0.0417744678724781 -2.01990238221096 0.0437660113016708 * df.mm.trans1:probe17 -0.0571006681556888 0.0417744678724781 -1.36687960526501 0.172092657959329 df.mm.trans1:probe18 -0.0212290632113608 0.0417744678724781 -0.508182732001882 0.611481942561774 df.mm.trans1:probe19 -0.072710866643001 0.0417744678724781 -1.74055757849412 0.0821913560964781 . df.mm.trans1:probe20 -0.0897828564404386 0.0417744678724781 -2.14922800966639 0.0319513154957948 * df.mm.trans1:probe21 -0.0663347980550712 0.0417744678724781 -1.58792682309124 0.112744974244851 df.mm.trans1:probe22 -0.0432812307449611 0.0417744678724781 -1.03606899020432 0.300520228968474 df.mm.trans2:probe2 -0.0153210749458094 0.0417744678724781 -0.366756914596231 0.71390885800389 df.mm.trans2:probe3 0.0488333096586543 0.0417744678724781 1.16897502579145 0.242803268370444 df.mm.trans2:probe4 -0.0619948471719529 0.0417744678724781 -1.48403678919861 0.138239843703171 df.mm.trans2:probe5 -0.0320734144940222 0.0417744678724781 -0.767775536768783 0.442874025858141 df.mm.trans2:probe6 -0.0154077337993189 0.0417744678724781 -0.368831360015237 0.712362614817182 df.mm.trans3:probe2 -0.0144224604024476 0.0417744678724781 -0.345245819682827 0.730011115891882 df.mm.trans3:probe3 0.0388749306626385 0.0417744678724781 0.930590684752926 0.35237942235998 df.mm.trans3:probe4 -0.00213449336042810 0.0417744678724781 -0.0510956445200908 0.959263577392423