chr8.22996_chr8_106263643_106264183_+_1.R fitVsDatCorrelation=0.857626258372323 cont.fitVsDatCorrelation=0.271065524320146 fstatistic=11086.8625341482,47,577 cont.fstatistic=3155.98552449879,47,577 residuals=-0.420661161991290,-0.0837134945114215,-0.00468278546880551,0.073965721494698,0.685658177128565 cont.residuals=-0.546886806913242,-0.189278014863061,-0.0305847758694599,0.165022142596541,1.24368528229339 predictedValues: Include Exclude Both chr8.22996_chr8_106263643_106264183_+_1.R.tl.Lung 56.4802425019055 73.926967977533 52.1339551812484 chr8.22996_chr8_106263643_106264183_+_1.R.tl.cerebhem 62.7225829904205 86.13147259776 61.0426152093243 chr8.22996_chr8_106263643_106264183_+_1.R.tl.cortex 55.2589362942083 70.4580442866215 51.2397087863679 chr8.22996_chr8_106263643_106264183_+_1.R.tl.heart 54.91049740842 74.4911585015389 50.8175182407353 chr8.22996_chr8_106263643_106264183_+_1.R.tl.kidney 56.5058891122152 76.3463405892498 50.8406303203601 chr8.22996_chr8_106263643_106264183_+_1.R.tl.liver 58.4188477095763 75.5520133589989 51.5755288633733 chr8.22996_chr8_106263643_106264183_+_1.R.tl.stomach 55.5847099263621 71.6530587252049 53.9388485383278 chr8.22996_chr8_106263643_106264183_+_1.R.tl.testicle 55.6073379323518 75.4267511926746 55.0491929781521 diffExp=-17.4467254756275,-23.4088896073396,-15.1991079924132,-19.5806610931189,-19.8404514770346,-17.1331656494226,-16.0683487988428,-19.8194132603227 diffExpScore=0.993310891971412 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=-1,-1,0,-1,-1,0,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 61.3259162816992 58.251229966474 61.4993632887401 cerebhem 61.8857845456998 65.344741125825 60.4690598837162 cortex 61.4752943584084 54.1955052578912 69.5900680036943 heart 58.3675748600844 64.9724204780496 57.2532425238885 kidney 60.6628203647406 64.089016948135 58.3010508041744 liver 56.8653898684402 62.2425501480663 70.094678286555 stomach 60.0354023583664 70.4698751223616 59.5976592303467 testicle 60.7212786362599 66.1050785276938 66.9959522274302 cont.diffExp=3.07468631522512,-3.45895658012518,7.27978910051722,-6.60484561796511,-3.42619658339441,-5.37716027962607,-10.4344727639952,-5.38379989143395 cont.diffExpScore=1.77805790659645 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=0.907750857521906 cont.tran.correlation=-0.211180033098745 tran.covariance=0.00238221875008283 cont.tran.covariance=-0.000517106980979647 tran.mean=66.2171781940651 cont.tran.mean=61.6881174280122 weightedLogRatios: wLogRatio Lung -1.12210094553742 cerebhem -1.36290240563865 cortex -1.00439396383423 heart -1.26814849976518 kidney -1.25935753345618 liver -1.07919802328199 stomach -1.05249773594337 testicle -1.27143644797283 cont.weightedLogRatios: wLogRatio Lung 0.210403543748876 cerebhem -0.225838902895062 cortex 0.511159381951732 heart -0.44171253433812 kidney -0.227064452873477 liver -0.369165711483964 stomach -0.669057050783177 testicle -0.352443743900049 varWeightedLogRatios=0.0166342162925514 cont.varWeightedLogRatios=0.143672847615934 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09965099977821 0.0734878406715693 55.7867936016831 1.25424772208773e-234 *** df.mm.trans1 -0.0793606187275228 0.0646430096754731 -1.22767518291516 0.220069525990616 df.mm.trans2 0.0476599722169975 0.0596573002153588 0.798895894466365 0.424679496116099 df.mm.exp2 0.0998721018491468 0.0807215469513692 1.23724216917343 0.216500467616045 df.mm.exp3 -0.0526194133413844 0.0807215469513693 -0.651863292127998 0.514748931996368 df.mm.exp4 0.00499173195665534 0.0807215469513693 0.0618389035539992 0.950712536245605 df.mm.exp5 0.0577770155141846 0.0807215469513693 0.715757040050192 0.474430929986438 df.mm.exp6 0.0662604910582285 0.0807215469513693 0.820852592160394 0.412069235711669 df.mm.exp7 -0.081259071130626 0.0807215469513693 -1.00665899254360 0.314520634176232 df.mm.exp8 -0.0499021370903915 0.0807215469513692 -0.618200950985926 0.536686820154111 df.mm.trans1:exp2 0.00495856946324532 0.075273230660748 0.065874274555762 0.94750073928829 df.mm.trans2:exp2 0.0529250915344549 0.0651885299771335 0.811877358685948 0.417196808649441 df.mm.trans1:exp3 0.0307585967771259 0.0752732306607481 0.408625968450233 0.682965779532868 df.mm.trans2:exp3 0.00455914284946448 0.0651885299771335 0.069937807326898 0.944267392299987 df.mm.trans1:exp4 -0.0331780788590112 0.0752732306607481 -0.440768631394909 0.659545595662865 df.mm.trans2:exp4 0.00261102189818844 0.0651885299771335 0.0400533943487347 0.968064412577259 df.mm.trans1:exp5 -0.0573230374837179 0.0752732306607481 -0.761532844817958 0.446650003287266 df.mm.trans2:exp5 -0.0255746010682862 0.0651885299771335 -0.392317499370781 0.694968507952269 df.mm.trans1:exp6 -0.0325128053034343 0.0752732306607481 -0.431930515244757 0.665953272318224 df.mm.trans2:exp6 -0.0445168399927478 0.0651885299771335 -0.682893754597062 0.494948067193218 df.mm.trans1:exp7 0.0652763464781934 0.0752732306607481 0.867192040320283 0.386197284453986 df.mm.trans2:exp7 0.0500172276518592 0.0651885299771335 0.767270372094661 0.44323461914007 df.mm.trans1:exp8 0.0343264200745273 0.0752732306607481 0.456024270158330 0.648544108750144 df.mm.trans2:exp8 0.0699864528786545 0.0651885299771335 1.07360072244617 0.283450449944843 df.mm.trans1:probe2 0.0833595115970538 0.0412288464091812 2.02187349046202 0.0436504286896298 * df.mm.trans1:probe3 0.153567678570291 0.0412288464091812 3.72476292560283 0.00021470166879097 *** df.mm.trans1:probe4 0.221379731124469 0.0412288464091812 5.36953493501507 1.14587190335628e-07 *** df.mm.trans1:probe5 0.122955853388119 0.0412288464091812 2.98227731544627 0.00298192923768077 ** df.mm.trans1:probe6 0.0965694119298699 0.0412288464091812 2.34227780645264 0.0195054680652468 * df.mm.trans1:probe7 0.128790204912856 0.0412288464091812 3.12378870935802 0.00187495928651544 ** df.mm.trans1:probe8 -0.160761593747168 0.0412288464091812 -3.89925034893452 0.000107835494040496 *** df.mm.trans1:probe9 -0.146267406373017 0.0412288464091812 -3.54769582736724 0.000420219291088309 *** df.mm.trans1:probe10 -0.228823123556450 0.0412288464091812 -5.55007339486203 4.35771754984327e-08 *** df.mm.trans1:probe11 0.0614109091859459 0.0412288464091812 1.48951315727986 0.136898671027511 df.mm.trans1:probe12 0.0612569330710953 0.0412288464091812 1.48577848778845 0.137883874528525 df.mm.trans1:probe13 -0.155670186602401 0.0412288464091812 -3.7757589687917 0.000176045643682105 *** df.mm.trans1:probe14 -0.162118719718434 0.0412288464091812 -3.93216725274012 9.44157838195149e-05 *** df.mm.trans1:probe15 -0.115658162459386 0.0412288464091812 -2.80527282552417 0.00519691041198236 ** df.mm.trans1:probe16 0.284818061530277 0.0412288464091812 6.9082229151784 1.30098621665016e-11 *** df.mm.trans2:probe2 0.183702241156898 0.0412288464091812 4.45567259713553 1.00481142135487e-05 *** df.mm.trans2:probe3 0.144156114548924 0.0412288464091812 3.49648673451174 0.000507679504049998 *** df.mm.trans2:probe4 0.479375339639711 0.0412288464091812 11.6271829408489 3.19089931804955e-28 *** df.mm.trans2:probe5 0.315797546673575 0.0412288464091812 7.65962606713271 7.91373729884154e-14 *** df.mm.trans2:probe6 0.434635904590903 0.0412288464091812 10.5420340961593 6.97805169606902e-24 *** df.mm.trans3:probe2 -0.217081095788347 0.0412288464091812 -5.26527212607156 1.97818056338899e-07 *** df.mm.trans3:probe3 -0.124302108466694 0.0412288464091812 -3.01493054724455 0.00268337651549759 ** df.mm.trans3:probe4 -0.115152339211594 0.0412288464091812 -2.79300415220813 0.00539538282705601 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03606346744633 0.137546511995306 29.3432629362791 1.64233564812395e-116 *** df.mm.trans1 0.0377708320150786 0.120991723589724 0.312176989420842 0.755018797594875 df.mm.trans2 0.00692324230346348 0.111660017285744 0.0620028768735234 0.950582012041077 df.mm.exp2 0.140894721199557 0.151085773164126 0.932547904735553 0.351443486496483 df.mm.exp3 -0.193329420758254 0.151085773164126 -1.27960043298212 0.201199977063324 df.mm.exp4 0.131298194120458 0.151085773164126 0.869030825144783 0.385191603600734 df.mm.exp5 0.13804296808584 0.151085773164126 0.913672844205404 0.361270398815266 df.mm.exp6 -0.140062046873069 0.151085773164127 -0.927036635811615 0.354295109133075 df.mm.exp7 0.200562526915866 0.151085773164126 1.32747460409785 0.184876705697764 df.mm.exp8 0.0309666453746883 0.151085773164126 0.204960697001225 0.837675113811865 df.mm.trans1:exp2 -0.131806751248897 0.140888209932253 -0.935541386410384 0.349900736109888 df.mm.trans2:exp2 -0.0259829643379502 0.122012768895046 -0.212952829226426 0.831438970477598 df.mm.trans1:exp3 0.195762265672535 0.140888209932253 1.38948649973386 0.165220785732065 df.mm.trans2:exp3 0.121162189416713 0.122012768895046 0.99302876669355 0.321112182653756 df.mm.trans1:exp4 -0.180740214917852 0.140888209932253 -1.28286259726603 0.200055286565266 df.mm.trans2:exp4 -0.0221005221305395 0.122012768895046 -0.181132862819875 0.856326896790116 df.mm.trans1:exp5 -0.148914503706799 0.140888209932253 -1.05696923666221 0.290967948120269 df.mm.trans2:exp5 -0.0425351688299663 0.122012768895046 -0.348612437986343 0.727507452788015 df.mm.trans1:exp6 0.0645464090826056 0.140888209932253 0.458139180799039 0.64702493920866 df.mm.trans2:exp6 0.206335691192657 0.122012768895046 1.69109916167990 0.0913578761310725 . df.mm.trans1:exp7 -0.221830630830950 0.140888209932253 -1.5745152198159 0.115916503060569 df.mm.trans2:exp7 -0.0101424191737573 0.122012768895046 -0.0831258831809782 0.933780274685432 df.mm.trans1:exp8 -0.0408749860381532 0.140888209932253 -0.290123538781621 0.771825961972583 df.mm.trans2:exp8 0.0955137219831706 0.122012768895046 0.782817428439232 0.434055491809255 df.mm.trans1:probe2 0.0455573481803508 0.0771676506664185 0.590368474184692 0.555174783576951 df.mm.trans1:probe3 0.0283800481385319 0.0771676506664185 0.367771312116441 0.713178627063765 df.mm.trans1:probe4 0.123107480075107 0.0771676506664185 1.59532497117578 0.111187130748232 df.mm.trans1:probe5 0.0164279469448002 0.0771676506664185 0.212886446625351 0.831490724625534 df.mm.trans1:probe6 0.0690338509635902 0.0771676506664185 0.894595732375096 0.371376243287034 df.mm.trans1:probe7 0.0311132498617373 0.0771676506664185 0.403190321242695 0.686957575445009 df.mm.trans1:probe8 0.0136719755596278 0.0771676506664185 0.177172370048289 0.859435172063338 df.mm.trans1:probe9 -0.00135786051657923 0.0771676506664185 -0.0175962401971911 0.985967038811228 df.mm.trans1:probe10 0.120432550603986 0.0771676506664185 1.56066110039548 0.119152015613541 df.mm.trans1:probe11 0.0747352733513917 0.0771676506664185 0.968479313624027 0.333210815214715 df.mm.trans1:probe12 0.00992319367856621 0.0771676506664185 0.128592662765676 0.897724780527337 df.mm.trans1:probe13 0.0707954149877151 0.0771676506664185 0.917423484793526 0.359304074435344 df.mm.trans1:probe14 0.0443497102581841 0.0771676506664185 0.574718938249134 0.56570540366097 df.mm.trans1:probe15 0.0937946953928123 0.0771676506664185 1.2154665145667 0.224685296055259 df.mm.trans1:probe16 0.0226632981085656 0.0771676506664185 0.293689103048308 0.769101088839188 df.mm.trans2:probe2 0.0143428268820933 0.0771676506664185 0.185865796849184 0.852615326326259 df.mm.trans2:probe3 0.00410658172400126 0.0771676506664185 0.0532163631850509 0.957577937243366 df.mm.trans2:probe4 0.0255792935548309 0.0771676506664185 0.331476899114183 0.740404644109268 df.mm.trans2:probe5 0.0682433701567755 0.0771676506664185 0.884352051247212 0.376874546030445 df.mm.trans2:probe6 0.105512905455950 0.0771676506664185 1.36732043213370 0.172057282084625 df.mm.trans3:probe2 0.14991369575798 0.0771676506664185 1.94270130635477 0.0525383595130683 . df.mm.trans3:probe3 0.0313447243894347 0.0771676506664185 0.406189952897907 0.684753634216781 df.mm.trans3:probe4 -0.0281813002858582 0.0771676506664185 -0.365195778833293 0.715099093500977