chr9.24842_chr9_108920965_108921318_-_0.R fitVsDatCorrelation=0.84619807239062 cont.fitVsDatCorrelation=0.275335829800701 fstatistic=8445.2596613292,40,416 cont.fstatistic=2587.52264867415,40,416 residuals=-0.551429765482419,-0.0924962254365556,0.000933381259723673,0.0861504097652065,0.64133865872832 cont.residuals=-0.605839382017175,-0.174512405271723,-0.0460443511882589,0.132198701387101,1.40466398490832 predictedValues: Include Exclude Both chr9.24842_chr9_108920965_108921318_-_0.R.tl.Lung 60.687664397143 69.1334995213549 75.7594253278396 chr9.24842_chr9_108920965_108921318_-_0.R.tl.cerebhem 56.4746302783969 76.715052575569 118.594669984150 chr9.24842_chr9_108920965_108921318_-_0.R.tl.cortex 57.1187467382388 77.7697854665455 82.5164741822542 chr9.24842_chr9_108920965_108921318_-_0.R.tl.heart 63.3143540452468 88.5380093561787 93.3465790908882 chr9.24842_chr9_108920965_108921318_-_0.R.tl.kidney 59.7066358412654 64.8548891725642 76.7036187296574 chr9.24842_chr9_108920965_108921318_-_0.R.tl.liver 55.6944843394817 72.888601900417 80.9482917372956 chr9.24842_chr9_108920965_108921318_-_0.R.tl.stomach 73.2121993683381 70.2224851042814 76.5721569140987 chr9.24842_chr9_108920965_108921318_-_0.R.tl.testicle 59.5446698644609 70.8670139867475 79.0070977908958 diffExp=-8.44583512421192,-20.2404222971721,-20.6510387283068,-25.2236553109319,-5.1482533312988,-17.1941175609353,2.98971426405667,-11.3223441222866 diffExpScore=1.04687140675521 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,-1,-1,-1,0,-1,0,0 diffExp1.3Score=0.8 diffExp1.2=0,-1,-1,-1,0,-1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 75.2871826773779 73.4108008769471 76.0022494802146 cerebhem 78.7077874742728 74.341901213322 74.666897736299 cortex 85.5240995832472 76.6201015001966 79.343249889903 heart 78.9836487099826 67.9109835458996 77.0990686481403 kidney 82.8588927624135 64.9205287219099 73.6398013367842 liver 76.2688600744006 68.2396260784472 79.595856062732 stomach 75.9314125803016 68.6373254518777 66.5765490637197 testicle 75.3159024750083 69.5292839581682 76.0152657570195 cont.diffExp=1.87638180043082,4.36588626095082,8.90399808305062,11.072665164083,17.9383640405036,8.02923399595343,7.29408712842397,5.78661851684008 cont.diffExpScore=0.984909586160531 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=-0.0558349863700469 cont.tran.correlation=0.209391678461191 tran.covariance=-0.000528347202032513 cont.tran.covariance=0.00047278489236276 tran.mean=67.2964201222644 cont.tran.mean=74.5305211052358 weightedLogRatios: wLogRatio Lung -0.54346260402798 cerebhem -1.28248807588907 cortex -1.29603459353348 heart -1.44716414408358 kidney -0.341654697496054 liver -1.11774792675185 stomach 0.178136137450212 testicle -0.726562050263889 cont.weightedLogRatios: wLogRatio Lung 0.108746297494420 cerebhem 0.247513046503986 cortex 0.483052000014681 heart 0.648536549948759 kidney 1.04791038555324 liver 0.475953511025507 stomach 0.432186907773624 testicle 0.342294734984460 varWeightedLogRatios=0.318964339122142 cont.varWeightedLogRatios=0.0805312710155216 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05050554744917 0.0814203598585382 49.7480673689802 3.18538090722371e-177 *** df.mm.trans1 -0.0113653271214995 0.0660406324261508 -0.172095976431005 0.863445764892225 df.mm.trans2 0.198735100771742 0.0660406324261508 3.00928524562474 0.00277789240891336 ** df.mm.exp2 -0.416039089560818 0.0893032343678774 -4.65872364540548 4.28891208886875e-06 *** df.mm.exp3 -0.0283295919327453 0.0893032343678774 -0.317229181375938 0.751228859859037 df.mm.exp4 0.0810077838689733 0.0893032343678774 0.907109181905644 0.364874202127347 df.mm.exp5 -0.092570422828182 0.0893032343678774 -1.03658533180160 0.300531314149512 df.mm.exp6 -0.0992141878918254 0.0893032343678774 -1.11098090224953 0.267218272626641 df.mm.exp7 0.192580099045248 0.0893032343678774 2.15647395537691 0.0316191270274251 * df.mm.exp8 -0.0362228332632264 0.0893032343678774 -0.405616140553313 0.685233200530832 df.mm.trans1:exp2 0.344090150290892 0.0719985693230541 4.77912482881397 2.44590452347665e-06 *** df.mm.trans2:exp2 0.520097620292676 0.0719985693230541 7.22372159867543 2.43096779239415e-12 *** df.mm.trans1:exp3 -0.0322784860944266 0.0719985693230541 -0.448321215239634 0.654154800107153 df.mm.trans2:exp3 0.146043175071357 0.0719985693230541 2.02841773724792 0.0431542240137112 * df.mm.trans1:exp4 -0.0386361732179321 0.0719985693230541 -0.53662417991354 0.591814093612454 df.mm.trans2:exp4 0.166384749192249 0.0719985693230541 2.31094521400403 0.0213240600166341 * df.mm.trans1:exp5 0.0762731352090792 0.0719985693230541 1.05937015035459 0.290045943499859 df.mm.trans2:exp5 0.0286833117685284 0.0719985693230541 0.398387246277461 0.690549145341753 df.mm.trans1:exp6 0.0133548505353691 0.0719985693230541 0.185487720949656 0.852936914403852 df.mm.trans2:exp6 0.152107051148798 0.0719985693230542 2.11263991186132 0.0352268320622016 * df.mm.trans1:exp7 -0.00495848888711569 0.0719985693230541 -0.068869269677668 0.945126770366004 df.mm.trans2:exp7 -0.176950949669296 0.0719985693230541 -2.45770091451852 0.0143899219673477 * df.mm.trans1:exp8 0.0172091632038982 0.0719985693230541 0.239020905077732 0.811207081429303 df.mm.trans2:exp8 0.0609885005255949 0.0719985693230541 0.847079339201067 0.397437952714681 df.mm.trans1:probe2 0.136106386992422 0.0457542922904296 2.97472390411973 0.00310336254072934 ** df.mm.trans1:probe3 0.117055843846158 0.0457542922904296 2.55835765316040 0.0108700013525413 * df.mm.trans1:probe4 0.329613766388763 0.0457542922904296 7.20399660640601 2.76515336641565e-12 *** df.mm.trans1:probe5 0.102519436531767 0.0457542922904296 2.24065178149877 0.0255756972601624 * df.mm.trans1:probe6 0.180507616469397 0.0457542922904296 3.94515153515233 9.35888792886955e-05 *** df.mm.trans2:probe2 0.141982497724371 0.0457542922904296 3.10315143381792 0.00204536334411165 ** df.mm.trans2:probe3 -0.0549906439264125 0.0457542922904296 -1.20186852803567 0.230098198593515 df.mm.trans2:probe4 -0.0421158335854494 0.0457542922904296 -0.920478308747851 0.35785632401816 df.mm.trans2:probe5 -0.106853136060477 0.0457542922904296 -2.33536856787593 0.0199987640901685 * df.mm.trans2:probe6 -0.109638968006875 0.0457542922904296 -2.39625535700414 0.0170043024018077 * df.mm.trans3:probe2 0.478984937033935 0.0457542922904296 10.4686339369765 6.38455937176825e-23 *** df.mm.trans3:probe3 0.450784895761027 0.0457542922904296 9.8522974172484 1.01019869513804e-20 *** df.mm.trans3:probe4 -0.164546146469951 0.0457542922904296 -3.59629967447598 0.00036154618461668 *** df.mm.trans3:probe5 0.108313998540833 0.0457542922904296 2.36729699266902 0.01837537250699 * df.mm.trans3:probe6 -0.350020505020889 0.0457542922904296 -7.65000369362292 1.41087413126720e-13 *** df.mm.trans3:probe7 0.104295616394054 0.0457542922904296 2.27947174293568 0.0231446873764474 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.18480976822833 0.146890482254095 28.4893187360454 8.43333308818818e-100 *** df.mm.trans1 0.192351759750199 0.119143913909212 1.61444889158814 0.107188288997485 df.mm.trans2 0.071350781331993 0.119143913909212 0.598862157460789 0.549590766560852 df.mm.exp2 0.0747619421768011 0.161111977224605 0.464037146490829 0.642863859369374 df.mm.exp3 0.127256249902044 0.161111977224605 0.789862132500785 0.430058276034834 df.mm.exp4 -0.0442706069005583 0.161111977224605 -0.274781600121765 0.78362044712054 df.mm.exp5 0.00449926104548538 0.161111977224605 0.0279262977401921 0.97773432583744 df.mm.exp6 -0.106289919163702 0.161111977224605 -0.659726986129182 0.509794114240035 df.mm.exp7 0.0736965159659426 0.161111977224605 0.457424191766965 0.647604974832675 df.mm.exp8 -0.0541129098248177 0.161111977224605 -0.335871427791984 0.737137156896385 df.mm.trans1:exp2 -0.0303297438097767 0.129892628672277 -0.233498575860679 0.81548913058566 df.mm.trans2:exp2 -0.0621582787744901 0.129892628672277 -0.478535844642248 0.632520269377752 df.mm.trans1:exp3 0.000232049245065967 0.129892628672277 0.00178646969761027 0.998575460518607 df.mm.trans2:exp3 -0.0844678616153816 0.129892628672277 -0.65028987771505 0.515863954657707 df.mm.trans1:exp4 0.0922015560405135 0.129892628672277 0.709829010183023 0.478207858657863 df.mm.trans2:exp4 -0.0336026867064731 0.129892628672277 -0.25869587096627 0.795997855459071 df.mm.trans1:exp5 0.0913299092135818 0.129892628672277 0.703118492151004 0.482375208809361 df.mm.trans2:exp5 -0.127406449932312 0.129892628672277 -0.980859739575848 0.327231998653229 df.mm.trans1:exp6 0.119244745755034 0.129892628672277 0.918025502862767 0.359137453588907 df.mm.trans2:exp6 0.0332442671182624 0.129892628672277 0.255936518169469 0.798126362459805 df.mm.trans1:exp7 -0.0651759527453677 0.129892628672277 -0.501767909477053 0.616096237838383 df.mm.trans2:exp7 -0.14093110211423 0.129892628672277 -1.08498152323796 0.278558198310442 df.mm.trans1:exp8 0.0544943070312497 0.129892628672277 0.419533483834101 0.675042856189092 df.mm.trans2:exp8 -0.000210150139059653 0.129892628672277 -0.00161787578870136 0.998709897990084 df.mm.trans1:probe2 -0.230117286385730 0.0825453249213467 -2.78776886038062 0.00555055578890164 ** df.mm.trans1:probe3 -0.164723867357658 0.0825453249213467 -1.99555659287325 0.0466338705671815 * df.mm.trans1:probe4 -0.0892685420628054 0.0825453249213467 -1.08144879371260 0.280124032294798 df.mm.trans1:probe5 -0.147232034521948 0.0825453249213467 -1.78365079624119 0.0752093028844133 . df.mm.trans1:probe6 -0.0947293872500473 0.0825453249213466 -1.14760451110115 0.251791409238370 df.mm.trans2:probe2 0.0909161234301502 0.0825453249213467 1.10140851122434 0.271355755974829 df.mm.trans2:probe3 0.0849037562396693 0.0825453249213467 1.02857134938375 0.30427881368963 df.mm.trans2:probe4 0.073212182013949 0.0825453249213467 0.886933113216397 0.375627371788145 df.mm.trans2:probe5 0.137214077728326 0.0825453249213467 1.66228769296226 0.097208368289721 . df.mm.trans2:probe6 0.132590700493661 0.0825453249213467 1.60627752837607 0.108971704892257 df.mm.trans3:probe2 -0.0995459387079869 0.0825453249213467 -1.20595489572352 0.228520316831263 df.mm.trans3:probe3 -0.166589652454781 0.0825453249213466 -2.01815975179110 0.0442159949276065 * df.mm.trans3:probe4 -0.128402584093757 0.0825453249213467 -1.55554035575129 0.120577846542407 df.mm.trans3:probe5 -0.185448739299567 0.0825453249213467 -2.24662922432339 0.0251875079431767 * df.mm.trans3:probe6 -0.155312764339077 0.0825453249213467 -1.88154525392040 0.0605957986831502 . df.mm.trans3:probe7 -0.0889459183732216 0.0825453249213467 -1.07754035080695 0.281863377090353