fitVsDatCorrelation=0.834955175052804 cont.fitVsDatCorrelation=0.253602959827786 fstatistic=5733.8305151488,62,922 cont.fstatistic=1845.78984419919,62,922 residuals=-0.583605760623742,-0.112988629321710,-0.0123370477610126,0.0796559282196932,1.55603653458657 cont.residuals=-0.643447940753102,-0.241466836466640,-0.0985890188702992,0.150857694844097,2.41204482004906 predictedValues: Include Exclude Both Lung 60.3429800211832 42.8707485239953 68.116126492374 cerebhem 68.9273811126695 52.8420338026281 79.0348270144645 cortex 59.070465771296 44.6993098637844 64.1851991854988 heart 60.6610394604045 47.2055185894635 66.7992055913422 kidney 60.2962376018202 44.1457742661528 65.5419505795425 liver 97.9067891114545 54.2520997733209 86.0238322444397 stomach 64.1799813687126 48.464829288247 72.2435739628198 testicle 61.8023582509932 49.972710182208 70.309773472048 diffExp=17.4722314971879,16.0853473100413,14.3711559075116,13.455520870941,16.1504633356674,43.6546893381337,15.7151520804656,11.8296480687852 diffExpScore=0.993321499404663 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=1,0,0,0,0,1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=1,1,1,0,1,1,1,0 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 66.3714567276772 57.0215701705592 66.7927384253536 cerebhem 62.9842983855492 60.6829250916333 77.5628644044793 cortex 57.4707404581744 57.9117553049102 52.6669760879 heart 57.5414632242702 52.8116153855195 71.2422428870904 kidney 60.7413585754522 65.5758325547238 63.041176413654 liver 61.0595385440724 60.0674712434484 69.1912600701825 stomach 59.8450810831579 57.9233549936 58.9044949027808 testicle 58.1776453533106 54.4974086456227 54.988709481531 cont.diffExp=9.34988655711805,2.30137329391594,-0.441014846735868,4.72984783875074,-4.83447397927169,0.992067300624036,1.92172608955795,3.68023670768788 cont.diffExpScore=1.51075705600700 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=0.750856390163397 cont.tran.correlation=0.330205122551162 tran.covariance=0.0109257688401672 cont.tran.covariance=0.00118613532559259 tran.mean=57.3525160617709 cont.tran.mean=59.4177197338551 weightedLogRatios: wLogRatio Lung 1.34318787779555 cerebhem 1.08960868642105 cortex 1.09818291856904 heart 0.99812399628771 kidney 1.22943851042190 liver 2.53201385801821 stomach 1.12938421729675 testicle 0.853620447394005 cont.weightedLogRatios: wLogRatio Lung 0.625471454166491 cerebhem 0.153518280145027 cortex -0.030998934402838 heart 0.343923551264224 kidney -0.317427598121733 liver 0.067221949753958 stomach 0.133016533092638 testicle 0.263406605504877 varWeightedLogRatios=0.275313158005125 cont.varWeightedLogRatios=0.0763944209649847 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33221536636078 0.114611531879680 37.7991227873016 1.38052488024582e-189 *** df.mm.trans1 0.382858380408744 0.103696815905144 3.69209388993161 0.000235476315446629 *** df.mm.trans2 -0.6383017767624 0.0943385460180487 -6.76607604955328 2.34950014177614e-11 *** df.mm.exp2 0.193451498607117 0.128912057300073 1.50064705085587 0.133789227736447 df.mm.exp3 0.0798961174649655 0.128912057300073 0.619772262876766 0.535560862991398 df.mm.exp4 0.121100885584906 0.128912057300073 0.939406973414561 0.347767866627619 df.mm.exp5 0.0670561427156197 0.128912057300073 0.520169673186822 0.603070192836477 df.mm.exp6 0.486012845455194 0.128912057300073 3.7701116220951 0.000173559899976417 *** df.mm.exp7 0.125465960331782 0.128912057300073 0.973267845999315 0.330675465862435 df.mm.exp8 0.145487399574529 0.128912057300073 1.12857868085895 0.259369176193510 df.mm.trans1:exp2 -0.0604426155347966 0.125063065567971 -0.483297089035023 0.628999552513726 df.mm.trans2:exp2 0.0156657293572499 0.106792922959581 0.146692579649487 0.883406743762747 df.mm.trans1:exp3 -0.101209670773896 0.125063065567971 -0.809269070082795 0.418569180777292 df.mm.trans2:exp3 -0.0381277960328190 0.106792922959581 -0.357025493601757 0.72115441798331 df.mm.trans1:exp4 -0.115843867296078 0.125063065567971 -0.926283605555129 0.354541003799465 df.mm.trans2:exp4 -0.0247798213388775 0.106792922959581 -0.232036174796490 0.816561407521764 df.mm.trans1:exp5 -0.0678310552626831 0.125063065567971 -0.542374800702589 0.587691400331825 df.mm.trans2:exp5 -0.0377486741366145 0.106792922959581 -0.353475427869893 0.723812852963233 df.mm.trans1:exp6 -0.00204157058355229 0.125063065567971 -0.0163243286439569 0.986979180371294 df.mm.trans2:exp6 -0.250560889063912 0.106792922959581 -2.34623121195721 0.0191752877216719 * df.mm.trans1:exp7 -0.063819234618078 0.125063065567971 -0.5102964198762 0.609965951582091 df.mm.trans2:exp7 -0.00281733554454196 0.106792922959581 -0.0263812944384738 0.97895892253363 df.mm.trans1:exp8 -0.121590496108312 0.125063065567971 -0.972233453227068 0.331189410295818 df.mm.trans2:exp8 0.00779991971747735 0.106792922959581 0.0730377959635906 0.941791882248282 df.mm.trans1:probe2 -0.66999862446256 0.0625315327839855 -10.7145722267366 2.50496200037541e-25 *** df.mm.trans1:probe3 -0.130933442235082 0.0625315327839855 -2.09387866258437 0.0365433908548528 * df.mm.trans1:probe4 -0.560508892734767 0.0625315327839855 -8.96361991750688 1.71381030059108e-18 *** df.mm.trans1:probe5 -0.405054085687409 0.0625315327839855 -6.47759726419411 1.51424854044475e-10 *** df.mm.trans1:probe6 -0.932811146821505 0.0625315327839855 -14.9174521284148 3.06837145142926e-45 *** df.mm.trans1:probe7 -0.691085361045109 0.0625315327839855 -11.0517898774760 9.43428876195565e-27 *** df.mm.trans1:probe8 -0.901701211006412 0.0625315327839855 -14.4199441603539 1.17032504453791e-42 *** df.mm.trans1:probe9 -0.898069847757781 0.0625315327839855 -14.3618716473840 2.32325263327589e-42 *** df.mm.trans1:probe10 -0.528397077643849 0.0625315327839855 -8.45008996451744 1.12511326299655e-16 *** df.mm.trans1:probe11 -0.888856322918884 0.0625315327839855 -14.2145295876471 1.31301616459177e-41 *** df.mm.trans1:probe12 -0.776389785977024 0.0625315327839855 -12.4159724128154 7.67450849747398e-33 *** df.mm.trans1:probe13 -0.870784789325017 0.0625315327839855 -13.9255308570979 3.79572578057991e-40 *** df.mm.trans1:probe14 -0.669628881885848 0.0625315327839855 -10.7086593287114 2.65138545748178e-25 *** df.mm.trans1:probe15 -0.77276633372407 0.0625315327839855 -12.3580264119478 1.42566562903096e-32 *** df.mm.trans1:probe16 -0.763756748629679 0.0625315327839855 -12.2139457426714 6.59096386007333e-32 *** df.mm.trans1:probe17 -0.955500173427272 0.0625315327839855 -15.2802934917338 3.72449475314323e-47 *** df.mm.trans1:probe18 -0.717914205691038 0.0625315327839855 -11.4808349280525 1.30390315935756e-28 *** df.mm.trans1:probe19 -1.00718615599729 0.0625315327839855 -16.1068521937021 1.28158716207178e-51 *** df.mm.trans1:probe20 -0.746271946585041 0.0625315327839855 -11.9343299829708 1.24030052924753e-30 *** df.mm.trans1:probe21 -0.942495456919922 0.0625315327839855 -15.0723229538569 4.7046481890696e-46 *** df.mm.trans1:probe22 -0.936614050834798 0.0625315327839855 -14.9782679095733 1.47133772173714e-45 *** df.mm.trans1:probe23 -0.0401432343553974 0.0625315327839855 -0.6419678611441 0.521053623760327 df.mm.trans1:probe24 -0.115759239089216 0.0625315327839855 -1.85121384260810 0.0644584148143851 . df.mm.trans1:probe25 -0.778697201905053 0.0625315327839855 -12.4528724506891 5.16788252018685e-33 *** df.mm.trans1:probe26 -0.563463362008646 0.0625315327839855 -9.01086758827941 1.15440416332395e-18 *** df.mm.trans1:probe27 -0.408082300674404 0.0625315327839855 -6.52602427137233 1.11299456734587e-10 *** df.mm.trans1:probe28 -0.689818959082429 0.0625315327839855 -11.0315376638120 1.15126199995669e-26 *** df.mm.trans1:probe29 -0.491980273268514 0.0625315327839855 -7.86771491701722 1.00859174085765e-14 *** df.mm.trans1:probe30 -0.935925865302955 0.0625315327839855 -14.9672624935663 1.68086496410935e-45 *** df.mm.trans1:probe31 -0.884842107225165 0.0625315327839855 -14.1503345245325 2.78261458634653e-41 *** df.mm.trans1:probe32 -0.850582364241485 0.0625315327839855 -13.6024550554328 1.54762096760676e-38 *** df.mm.trans2:probe2 0.00075614671602035 0.0625315327839855 0.0120922466211159 0.99035463418705 df.mm.trans2:probe3 0.00897673927896008 0.0625315327839855 0.143555401240045 0.885882928860853 df.mm.trans2:probe4 0.0880980517567276 0.0625315327839855 1.40885802465552 0.159214300943645 df.mm.trans2:probe5 0.333912753660367 0.0625315327839855 5.33990994293815 1.17162816315623e-07 *** df.mm.trans2:probe6 0.146741669215771 0.0625315327839855 2.34668274840932 0.0191522098619395 * df.mm.trans3:probe2 0.647632038140765 0.0625315327839855 10.3568873064091 7.45472652154287e-24 *** df.mm.trans3:probe3 -0.21430597463186 0.0625315327839855 -3.42716650449267 0.000636881350911863 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91141421154322 0.201455714208546 19.4157521265152 1.14419392604689e-70 *** df.mm.trans1 0.307800637104952 0.182270629898339 1.68870123111237 0.0916148350007595 . df.mm.trans2 0.0445022679411557 0.165821351950983 0.268374774524276 0.78847088801105 df.mm.exp2 -0.139643286657513 0.226592125133993 -0.61627599182865 0.537864501226205 df.mm.exp3 0.109105381582205 0.226592125133993 0.481505619481201 0.630271445871276 df.mm.exp4 -0.283951346464853 0.226592125133993 -1.25313863532080 0.210472957518009 df.mm.exp5 0.10894159419334 0.226592125133993 0.480782790350363 0.630784945584725 df.mm.exp6 -0.0666590214485742 0.226592125133993 -0.294180662320705 0.768686071146245 df.mm.exp7 0.0378601629669082 0.226592125133993 0.167085078285575 0.867339744007824 df.mm.exp8 0.0174241038680488 0.226592125133993 0.0768963345824363 0.938722696412883 df.mm.trans1:exp2 0.0872616545068524 0.219826650790736 0.396956666505014 0.691491282453241 df.mm.trans2:exp2 0.201876024882486 0.187712738974872 1.07545191650264 0.282453790642289 df.mm.trans1:exp3 -0.253096520393203 0.219826650790736 -1.15134593318322 0.249888426183821 df.mm.trans2:exp3 -0.0936146101870648 0.187712738974872 -0.49871207835072 0.61810118906061 df.mm.trans1:exp4 0.141190038449987 0.219826650790736 0.642278986383649 0.520851719361454 df.mm.trans2:exp4 0.207252881050162 0.187712738974872 1.10409598294714 0.269839565520348 df.mm.trans1:exp5 -0.197583863265461 0.219826650790736 -0.898816692856547 0.368984974061601 df.mm.trans2:exp5 0.0308360074053568 0.187712738974872 0.164272321493773 0.869552774515873 df.mm.trans1:exp6 -0.0167586441943320 0.219826650790736 -0.0762357254411583 0.939248099357634 df.mm.trans2:exp6 0.118697852299860 0.187712738974872 0.632337756873013 0.527322969343136 df.mm.trans1:exp7 -0.141368017259739 0.219826650790736 -0.643088618924161 0.520326498406512 df.mm.trans2:exp7 -0.0221691122488817 0.187712738974872 -0.118101266701187 0.906013143355416 df.mm.trans1:exp8 -0.149190018878047 0.219826650790736 -0.678671209070408 0.497516520086812 df.mm.trans2:exp8 -0.0627005714111489 0.187712738974872 -0.334024061198862 0.73843736003347 df.mm.trans1:probe2 -0.0364155287949058 0.109913325395368 -0.331311318840694 0.740484629016333 df.mm.trans1:probe3 -0.00425934398697196 0.109913325395368 -0.0387518435244382 0.969096628228726 df.mm.trans1:probe4 -0.0928422163035502 0.109913325395368 -0.844685719129945 0.398505459037848 df.mm.trans1:probe5 -0.0659871427956153 0.109913325395368 -0.600356167537044 0.54841642564278 df.mm.trans1:probe6 0.0100251562280172 0.109913325395368 0.0912096526236088 0.927345816962465 df.mm.trans1:probe7 0.0528200726711807 0.109913325395368 0.480561137434266 0.630942444195401 df.mm.trans1:probe8 0.00593392820557418 0.109913325395368 0.0539873412457435 0.9569569451022 df.mm.trans1:probe9 -0.00131512065092915 0.109913325395368 -0.0119650701695953 0.990456071457696 df.mm.trans1:probe10 0.0746322283257277 0.109913325395368 0.679009829402111 0.497302040942622 df.mm.trans1:probe11 -0.00799974522358785 0.109913325395368 -0.0727823054648929 0.941995137361616 df.mm.trans1:probe12 0.0613731581013364 0.109913325395368 0.558377775220353 0.57672201459738 df.mm.trans1:probe13 -0.0557685756227738 0.109913325395368 -0.507386847064896 0.612004751551728 df.mm.trans1:probe14 -0.086637165147943 0.109913325395368 -0.788231680156171 0.430763809026607 df.mm.trans1:probe15 0.00389781119518272 0.109913325395368 0.0354625900104645 0.971718552417857 df.mm.trans1:probe16 -0.0775119705015069 0.109913325395368 -0.705209948135855 0.48085770642018 df.mm.trans1:probe17 0.119478072432817 0.109913325395368 1.08702081392810 0.277311702012489 df.mm.trans1:probe18 0.145249149077758 0.109913325395368 1.32148807758553 0.186666492716637 df.mm.trans1:probe19 -0.0735883844516441 0.109913325395368 -0.669512856488874 0.503336003408778 df.mm.trans1:probe20 -0.104309202856623 0.109913325395368 -0.94901325641284 0.342862547186553 df.mm.trans1:probe21 -0.0800644216281476 0.109913325395368 -0.728432347398722 0.466533933365883 df.mm.trans1:probe22 -0.0157264307609358 0.109913325395368 -0.143080292624815 0.886258031578809 df.mm.trans1:probe23 0.0625669832786467 0.109913325395368 0.5692392897184 0.569332404935232 df.mm.trans1:probe24 -0.094194532622275 0.109913325395368 -0.856989198383805 0.391673542353157 df.mm.trans1:probe25 -0.127460257002194 0.109913325395368 -1.15964335119248 0.246494259479098 df.mm.trans1:probe26 -0.0030169891097373 0.109913325395368 -0.0274488020345569 0.978107714371582 df.mm.trans1:probe27 -0.112664940650519 0.109913325395368 -1.02503441002493 0.305615803857089 df.mm.trans1:probe28 -0.0866149658932222 0.109913325395368 -0.788029709606732 0.430881876022737 df.mm.trans1:probe29 -0.192631210032418 0.109913325395368 -1.75257376063827 0.0800075689757529 . df.mm.trans1:probe30 -0.0259136984874298 0.109913325395368 -0.235764848294926 0.813667505105017 df.mm.trans1:probe31 0.0283425956779805 0.109913325395368 0.25786314421868 0.796570073218137 df.mm.trans1:probe32 -0.057568672740459 0.109913325395368 -0.523764270923287 0.600568376927978 df.mm.trans2:probe2 0.152416313187498 0.109913325395368 1.38669549519353 0.165869812271413 df.mm.trans2:probe3 0.287320722987808 0.109913325395368 2.6140663286666 0.00909306544648019 ** df.mm.trans2:probe4 0.087699865079732 0.109913325395368 0.797900206951866 0.425133853958908 df.mm.trans2:probe5 0.0716849593661205 0.109913325395368 0.652195346726734 0.514437716535062 df.mm.trans2:probe6 0.188496406675650 0.109913325395368 1.71495499747289 0.0866894947304926 . df.mm.trans3:probe2 -0.157795810760538 0.109913325395368 -1.43563858333767 0.151444370172433 df.mm.trans3:probe3 -0.0285724337283203 0.109913325395368 -0.259954228711966 0.794957117785465