fitVsDatCorrelation=0.885974166443344 cont.fitVsDatCorrelation=0.265069977162626 fstatistic=10262.5825781419,58,830 cont.fstatistic=2362.7504461638,58,830 residuals=-0.688403837006096,-0.104681811592139,-0.0109147053846522,0.0988906599197351,0.96073097079315 cont.residuals=-0.70159485717529,-0.240215989530493,-0.0438073273248305,0.197172215420153,1.32954089732048 predictedValues: Include Exclude Both Lung 94.4699035049897 119.194429278529 91.0062096062942 cerebhem 68.2220078643941 71.2681703462324 47.9670114730037 cortex 65.5869790989326 84.4605789568785 52.1101856205177 heart 74.289594657631 106.310748136199 62.4470647171483 kidney 73.1972388228735 100.245331256228 56.6693858495325 liver 75.4745055961268 105.710222710235 53.3900405422 stomach 88.0398109357356 101.998927519375 74.1748690033386 testicle 82.5339020231426 111.578184529883 63.4316306984575 diffExp=-24.7245257735389,-3.04616248183829,-18.8735998579459,-32.0211534785676,-27.0480924333541,-30.2357171141081,-13.9591165836389,-29.0442825067407 diffExpScore=0.99444298264725 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,-1,0,-1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,-1,-1,-1,0,-1 diffExp1.3Score=0.8 diffExp1.2=-1,0,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 73.895200743111 88.4121856587263 67.9850576167279 cerebhem 71.8000108719967 80.426836323734 64.0158461335951 cortex 73.1394836745554 82.1478412538654 70.9294690220813 heart 80.5156126057549 77.9453965106044 73.7350593736915 kidney 72.1290062338065 75.2481593813663 65.4284396271132 liver 75.0773753058332 82.1476164063528 78.214346778145 stomach 74.6518066881314 76.6765427002705 71.8980662548449 testicle 74.0111197895329 78.6874032097683 81.7791098561506 cont.diffExp=-14.5169849156153,-8.62682545173739,-9.00835757931,2.57021609515047,-3.11915314755981,-7.07024110051967,-2.02473601213906,-4.67628342023544 cont.diffExpScore=1.08721773486331 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=0.775817727310527 cont.tran.correlation=-0.110762370029337 tran.covariance=0.0158614284754014 cont.tran.covariance=-0.000184265288955871 tran.mean=88.9112834523365 cont.tran.mean=77.3069748348381 weightedLogRatios: wLogRatio Lung -1.08438265539936 cerebhem -0.185415536865536 cortex -1.08998953205648 heart -1.60818102584518 kidney -1.39948179344435 liver -1.51346843739924 stomach -0.669840479137553 testicle -1.37611104608207 cont.weightedLogRatios: wLogRatio Lung -0.78781655118029 cerebhem -0.491365917957153 cortex -0.505313750442854 heart 0.141846390589642 kidney -0.182025325344661 liver -0.392710456973329 stomach -0.115774296879364 testicle -0.26558638174957 varWeightedLogRatios=0.230709258139304 cont.varWeightedLogRatios=0.0806667170988776 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.88288396395384 0.0782502209016973 62.4008968624896 0 *** df.mm.trans1 -0.346577054835606 0.067744997283829 -5.11590624741725 3.88055005288941e-07 *** df.mm.trans2 -0.0261320698539854 0.0600180595391768 -0.435403444473703 0.663382809842989 df.mm.exp2 -0.199406075887335 0.0775714109786659 -2.57061297933818 0.0103251263649722 * df.mm.exp3 -0.151807919837258 0.0775714109786659 -1.95700861853614 0.0506813273035535 . df.mm.exp4 0.0219084751526221 0.0775714109786659 0.282429762153578 0.777684409399264 df.mm.exp5 0.0454344805148797 0.0775714109786659 0.585711668018715 0.558228512096651 df.mm.exp6 0.188762725694592 0.0775714109786659 2.43340585549626 0.0151676442782438 * df.mm.exp7 -0.0217835722621358 0.0775714109786659 -0.280819595612704 0.778918757339062 df.mm.exp8 0.159862470290835 0.0775714109786659 2.06084262583288 0.0396291219848153 * df.mm.trans1:exp2 -0.126108017704569 0.0719106225927323 -1.75367717811018 0.079854920279681 . df.mm.trans2:exp2 -0.314900134371967 0.0539874172026822 -5.83284310841828 7.80442217534652e-09 *** df.mm.trans1:exp3 -0.213096195604892 0.0719106225927322 -2.96334794390208 0.00313008574182815 ** df.mm.trans2:exp3 -0.192663195173508 0.0539874172026822 -3.56866850010998 0.000379386688817508 *** df.mm.trans1:exp4 -0.262218880565938 0.0719106225927322 -3.64645543470012 0.000282451724197368 *** df.mm.trans2:exp4 -0.136298102867648 0.0539874172026822 -2.52462721741163 0.0117674581671265 * df.mm.trans1:exp5 -0.300558083603099 0.0719106225927322 -4.17960619400165 3.23002921738035e-05 *** df.mm.trans2:exp5 -0.218570005721500 0.0539874172026822 -4.04853606722719 5.63614769363606e-05 *** df.mm.trans1:exp6 -0.413249103032913 0.0719106225927323 -5.74670456371043 1.27760921602583e-08 *** df.mm.trans2:exp6 -0.308817142411029 0.0539874172026822 -5.7201688543768 1.48518800759567e-08 *** df.mm.trans1:exp7 -0.0487086209083669 0.0719106225927322 -0.677349453421222 0.498373010665029 df.mm.trans2:exp7 -0.134010148323446 0.0539874172026822 -2.48224781378851 0.0132523920095522 * df.mm.trans1:exp8 -0.294934630091479 0.0719106225927322 -4.10140559847255 4.51087902227792e-05 *** df.mm.trans2:exp8 -0.225892937873707 0.0539874172026822 -4.18417752836089 3.16704007073532e-05 *** df.mm.trans1:probe2 -0.322151804857681 0.0482391121265044 -6.67822832254573 4.42615965004938e-11 *** df.mm.trans1:probe3 1.03466265893277 0.0482391121265044 21.4486256757676 1.51995665158646e-81 *** df.mm.trans1:probe4 -0.232793197407877 0.0482391121265044 -4.82581845199368 1.65925502673760e-06 *** df.mm.trans1:probe5 -0.383648599118765 0.0482391121265044 -7.95306095420388 5.94276743508242e-15 *** df.mm.trans1:probe6 -0.0320984853862552 0.0482391121265044 -0.66540373508697 0.505977156073345 df.mm.trans1:probe7 -0.311688878434427 0.0482391121265044 -6.46133116250233 1.76732631186050e-10 *** df.mm.trans1:probe8 -0.00514067677646349 0.0482391121265044 -0.106566571187760 0.915158590913602 df.mm.trans1:probe9 0.341715393134163 0.0482391121265044 7.08378280756979 2.99426156684384e-12 *** df.mm.trans1:probe10 -0.112173853502786 0.0482391121265044 -2.32537143736428 0.0202923186641766 * df.mm.trans1:probe11 0.0430858311550656 0.0482391121265044 0.893172143012818 0.372023884659854 df.mm.trans1:probe12 -0.029908599220417 0.0482391121265044 -0.620007249345372 0.535423190327615 df.mm.trans1:probe13 -0.0623606552290552 0.0482391121265044 -1.29274052693005 0.196460554110900 df.mm.trans1:probe14 -0.0975760909940311 0.0482391121265044 -2.02275885049756 0.0434182027873751 * df.mm.trans1:probe15 -0.0126413065361884 0.0482391121265044 -0.262055124543695 0.793343965149358 df.mm.trans1:probe16 -0.0283820448566077 0.0482391121265044 -0.58836167594 0.556449617676767 df.mm.trans1:probe17 0.243415474464989 0.0482391121265044 5.04601896126623 5.54445301296854e-07 *** df.mm.trans1:probe18 -0.136950181126149 0.0482391121265044 -2.83898635544958 0.00463624006147819 ** df.mm.trans1:probe19 0.397929465485648 0.0482391121265044 8.249104262991 6.23038355832197e-16 *** df.mm.trans1:probe20 0.153133072739969 0.0482391121265044 3.17445877400035 0.00155652075727927 ** df.mm.trans1:probe21 -0.18620208855407 0.0482391121265044 -3.85998166935091 0.000122200190263994 *** df.mm.trans1:probe22 0.110980756118819 0.0482391121265044 2.30063844931013 0.0216591991351390 * df.mm.trans2:probe2 -0.146767772968234 0.0482391121265044 -3.04250568674115 0.00242006825953767 ** df.mm.trans2:probe3 -0.373612609260949 0.0482391121265044 -7.74501421753307 2.78315785285124e-14 *** df.mm.trans2:probe4 -0.0154206646947744 0.0482391121265044 -0.319671403866941 0.749297884935002 df.mm.trans2:probe5 -0.197070248541558 0.0482391121265044 -4.08527934810973 4.82923406510512e-05 *** df.mm.trans2:probe6 -0.407066826552787 0.0482391121265044 -8.43852236511477 1.42042043484473e-16 *** df.mm.trans3:probe2 0.0938932889594612 0.0482391121265044 1.94641411958892 0.0519421121913416 . df.mm.trans3:probe3 0.065710197288432 0.0482391121265044 1.36217675640693 0.173511654751125 df.mm.trans3:probe4 0.0431045270396383 0.0482391121265044 0.893559709942402 0.371816524779001 df.mm.trans3:probe5 0.174542671901852 0.0482391121265044 3.61828118735112 0.000314505457547814 *** df.mm.trans3:probe6 0.00396882304314254 0.0482391121265044 0.0822739654232135 0.934448693004532 df.mm.trans3:probe7 -0.085530976784384 0.0482391121265044 -1.77306283250164 0.0765849784676468 . df.mm.trans3:probe8 -0.0279944434301587 0.0482391121265044 -0.580326672612564 0.561851856597091 df.mm.trans3:probe9 -0.260917966810988 0.0482391121265044 -5.40884679068604 8.30088498664067e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.48109817394491 0.162703293342098 27.5415333144058 4.03489898101288e-119 *** df.mm.trans1 -0.213294850472567 0.140860102866385 -1.51423182386066 0.130347746937192 df.mm.trans2 0.0201135417676796 0.124793717314788 0.161174313903510 0.871995332978159 df.mm.exp2 -0.0632678655883747 0.161291864610551 -0.392257016441212 0.694969022695384 df.mm.exp3 -0.126166820049218 0.161291864610551 -0.782226805758957 0.434304389265757 df.mm.exp4 -0.121388492217631 0.161291864610551 -0.752601456438802 0.451902831723544 df.mm.exp5 -0.147079058479116 0.161291864610551 -0.91188144445008 0.362095899743381 df.mm.exp6 -0.197785762087294 0.161291864610551 -1.22626000117774 0.220448625690087 df.mm.exp7 -0.188188563965564 0.161291864610551 -1.16675794169753 0.243643248327522 df.mm.exp8 -0.299693149300231 0.161291864610551 -1.85807976133116 0.0635115799806785 . df.mm.trans1:exp2 0.0345046098008842 0.149521560288201 0.230766785300909 0.817552855374132 df.mm.trans2:exp2 -0.0313940357612638 0.112254386974129 -0.279668675830898 0.779801391026529 df.mm.trans1:exp3 0.115887290070615 0.149521560288201 0.775054044695918 0.438528473034532 df.mm.trans2:exp3 0.0526775791227761 0.112254386974129 0.46926966992316 0.639000135018828 df.mm.trans1:exp4 0.207191719988049 0.149521560288201 1.38569795278145 0.166211366610743 df.mm.trans2:exp4 -0.00461277793611858 0.112254386974129 -0.0410921841048553 0.967232287905673 df.mm.trans1:exp5 0.12288744420524 0.149521560288201 0.821871066409258 0.411386304216445 df.mm.trans2:exp5 -0.0141393052011085 0.112254386974129 -0.125957707152836 0.89979590629872 df.mm.trans1:exp6 0.213657131299296 0.149521560288201 1.42893861519017 0.153398103755521 df.mm.trans2:exp6 0.124293784049119 0.112254386974129 1.10725101619204 0.268506199784484 df.mm.trans1:exp7 0.198375406513977 0.149521560288201 1.32673445977698 0.184961431944233 df.mm.trans2:exp7 0.0457745867921918 0.112254386974129 0.407775482331407 0.683543673497645 df.mm.trans1:exp8 0.301260615379279 0.149521560288201 2.01483060234659 0.0442444337151653 * df.mm.trans2:exp8 0.183166424083727 0.112254386974129 1.63170838148126 0.103120261370805 df.mm.trans1:probe2 0.0823392779805575 0.100302111871875 0.82091270506584 0.411931704925166 df.mm.trans1:probe3 0.103368663392963 0.100302111871875 1.03057315009483 0.30304124257213 df.mm.trans1:probe4 0.247547059931105 0.100302111871875 2.46801443470421 0.0137871052665838 * df.mm.trans1:probe5 0.0602444190060018 0.100302111871875 0.600629616682023 0.548250676490097 df.mm.trans1:probe6 0.0524534571112152 0.100302111871875 0.522954662990732 0.601145372818652 df.mm.trans1:probe7 0.0364500592511231 0.100302111871875 0.363402709782263 0.71639661379966 df.mm.trans1:probe8 0.0557285059634759 0.100302111871875 0.55560650641796 0.57862960625883 df.mm.trans1:probe9 -0.0308015151035835 0.100302111871875 -0.307087403532729 0.75885387416874 df.mm.trans1:probe10 -0.0274294713158867 0.100302111871875 -0.273468532257076 0.784561111776265 df.mm.trans1:probe11 -0.0689667896997464 0.100302111871875 -0.687590604152421 0.491902718610505 df.mm.trans1:probe12 0.0486891183271779 0.100302111871875 0.485424657751701 0.627503250072555 df.mm.trans1:probe13 0.0972519518221507 0.100302111871875 0.969590270904557 0.332533217333371 df.mm.trans1:probe14 0.0140530532783331 0.100302111871875 0.140107252141254 0.888609242693972 df.mm.trans1:probe15 0.0485828272078844 0.100302111871875 0.48436494806753 0.628254674931958 df.mm.trans1:probe16 0.105036233388568 0.100302111871875 1.04719862252492 0.295312831779557 df.mm.trans1:probe17 0.00431472249239612 0.100302111871875 0.043017264660466 0.965698117568606 df.mm.trans1:probe18 0.0110370000119428 0.100302111871875 0.110037563576341 0.912406183926982 df.mm.trans1:probe19 0.0617713866387406 0.100302111871875 0.615853300453401 0.538160187619973 df.mm.trans1:probe20 0.088956191030797 0.100302111871875 0.886882532886532 0.375399048178823 df.mm.trans1:probe21 0.100383380968384 0.100302111871875 1.00081024312442 0.317210235645411 df.mm.trans1:probe22 -0.0108281781973471 0.100302111871875 -0.107955635183224 0.914056974976156 df.mm.trans2:probe2 -0.127041225004451 0.100302111871875 -1.26658574414397 0.205658774318201 df.mm.trans2:probe3 -0.201815456879397 0.100302111871875 -2.01207584878366 0.0445346111861126 * df.mm.trans2:probe4 0.100019754293433 0.100302111871875 0.997184928879638 0.318965376731225 df.mm.trans2:probe5 -0.0124165688349027 0.100302111871875 -0.123791698930163 0.901510175775077 df.mm.trans2:probe6 -0.046775134837 0.100302111871875 -0.466342472397292 0.641092749015155 df.mm.trans3:probe2 -0.140691361416778 0.100302111871875 -1.40267596355793 0.161087415478415 df.mm.trans3:probe3 -0.164998170431082 0.100302111871875 -1.64501192798263 0.100345985854694 df.mm.trans3:probe4 -0.0951550205081086 0.100302111871875 -0.948684117734815 0.343057427335016 df.mm.trans3:probe5 -0.146945282291632 0.100302111871875 -1.46502680301825 0.143292284114006 df.mm.trans3:probe6 0.009786271840988 0.100302111871875 0.0975679540375868 0.922298927451731 df.mm.trans3:probe7 -0.0631385639851572 0.100302111871875 -0.629483894275424 0.529205535525353 df.mm.trans3:probe8 -0.0676777447600626 0.100302111871875 -0.67473898103475 0.500029512812183 df.mm.trans3:probe9 0.0511595452831442 0.100302111871875 0.510054517580795 0.610148871071353