fitVsDatCorrelation=0.943712821601348 cont.fitVsDatCorrelation=0.203771407686790 fstatistic=9739.63271915584,69,1083 cont.fstatistic=1097.83372266764,69,1083 residuals=-0.771326602189644,-0.09781814034971,-0.0089846645628663,0.093914008945872,1.56393093511088 cont.residuals=-0.98076975841889,-0.380967119655184,-0.116399533168576,0.225301640734922,1.74954648023058 predictedValues: Include Exclude Both Lung 57.7953520627652 226.838187064344 65.674894199219 cerebhem 61.3061977024633 237.69424386711 66.3077872583316 cortex 55.6270275348217 306.189888723192 85.3877566931444 heart 57.7341071848283 280.274316367243 84.0513074754356 kidney 59.1060272204678 256.97351034911 78.1257186589942 liver 60.0461112144748 230.528851424377 77.6394133094447 stomach 59.7709111459525 266.455285262368 72.3056446841523 testicle 56.6313770273876 284.966292027929 76.0137162570976 diffExp=-169.042835001578,-176.388046164647,-250.562861188370,-222.540209182415,-197.867483128642,-170.482740209902,-206.684374116416,-228.334915000542 diffExpScore=0.999383820404487 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 85.414710601852 80.1073348758594 77.5430247104251 cerebhem 77.5720187428905 92.2909080869863 92.3427573339025 cortex 77.7585795573885 71.531062822003 85.9061476058805 heart 85.2279637843486 97.7547999056371 84.6301935605703 kidney 77.8476233120037 108.892046054468 77.1529579076295 liver 86.9227174891701 109.182992049641 88.3553201203062 stomach 81.7465053506333 66.6543510063081 82.6349757274504 testicle 79.1514767307881 80.0116120270648 87.6956552866875 cont.diffExp=5.30737572599261,-14.7188893440958,6.22751673538548,-12.5268361212885,-31.0444227424643,-22.2602745604708,15.0921543443253,-0.860135296276738 cont.diffExpScore=1.93673009160169 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,-1,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,-1,-1,1,0 cont.diffExp1.2Score=1.5 tran.correlation=-0.724724473289481 cont.tran.correlation=0.230145972358575 tran.covariance=-0.00249760161079342 cont.tran.covariance=0.00194369202588538 tran.mean=159.871105386177 cont.tran.mean=84.8791688998152 weightedLogRatios: wLogRatio Lung -6.48192042785729 cerebhem -6.49560133466824 cortex -8.30841305027315 heart -7.65599317295799 kidney -7.07507225099785 liver -6.41387126802442 stomach -7.23109223121771 testicle -7.82772717597516 cont.weightedLogRatios: wLogRatio Lung 0.283254722537841 cerebhem -0.771066272949674 cortex 0.359942400026960 heart -0.619002775529786 kidney -1.51778588748157 liver -1.04404402987574 stomach 0.877962547204056 testicle -0.0473055528378787 varWeightedLogRatios=0.496488209085054 cont.varWeightedLogRatios=0.65523844351463 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.25058178150047 0.0830327795932683 63.2350477392203 0 *** df.mm.trans1 -1.39675014970411 0.0708147529991798 -19.7239994570099 3.13210760210702e-74 *** df.mm.trans2 0.189630814728825 0.0616830517886623 3.07427744299254 0.00216295370792680 ** df.mm.exp2 0.096130190530619 0.0773337762164509 1.24305568968414 0.214116355296865 df.mm.exp3 -0.000756640199268165 0.0773337762164508 -0.00978408447494394 0.992195356555879 df.mm.exp4 -0.0362390441971178 0.0773337762164508 -0.468605646460193 0.639445844381373 df.mm.exp5 -0.0264419618906823 0.0773337762164509 -0.341919962846162 0.732477462392097 df.mm.exp6 -0.113014955636727 0.0773337762164509 -1.4613919191067 0.144197989709222 df.mm.exp7 0.0983947874243056 0.0773337762164509 1.27233910250169 0.203525766102115 df.mm.exp8 0.0615917299750298 0.0773337762164509 0.796440222996995 0.425950755803344 df.mm.trans1:exp2 -0.0371576065603420 0.0703095283331808 -0.528486073527055 0.597270289507546 df.mm.trans2:exp2 -0.049381963394287 0.0466677046346794 -1.05816139407016 0.290217826290924 df.mm.trans1:exp3 -0.0374825282203363 0.0703095283331808 -0.533107376893714 0.594068628860425 df.mm.trans2:exp3 0.300725170092259 0.0466677046346794 6.44396745986058 1.74812533683069e-10 *** df.mm.trans1:exp4 0.0351787971558352 0.0703095283331807 0.500341816960156 0.616936068124412 df.mm.trans2:exp4 0.247770938103855 0.0466677046346794 5.30925915562886 1.33499640457953e-07 *** df.mm.trans1:exp5 0.0488665062227011 0.0703095283331808 0.695019684830396 0.487192051092385 df.mm.trans2:exp5 0.151178037922023 0.0466677046346794 3.23945733147717 0.00123398731346358 ** df.mm.trans1:exp6 0.151219384633680 0.0703095283331808 2.15076659193453 0.0317153810768271 * df.mm.trans2:exp6 0.129154048129796 0.0466677046346794 2.76752518986802 0.00574461140017664 ** df.mm.trans1:exp7 -0.0647840388082804 0.0703095283331808 -0.921411938667597 0.357040603924158 df.mm.trans2:exp7 0.0625747259922883 0.0466677046346794 1.34085716197381 0.180247993724843 df.mm.trans1:exp8 -0.0819368922335475 0.0703095283331808 -1.16537394256533 0.24412426448211 df.mm.trans2:exp8 0.166542238732557 0.0466677046346794 3.56868288329736 0.000374490527587608 *** df.mm.trans1:probe2 0.496721163039875 0.0534039204180725 9.3012115805599 7.44355162630966e-20 *** df.mm.trans1:probe3 0.0333062115904989 0.0534039204180725 0.623666040428517 0.532978259140269 df.mm.trans1:probe4 0.315890723036766 0.0534039204180725 5.91512234614642 4.44247048477991e-09 *** df.mm.trans1:probe5 0.00591719523012657 0.0534039204180725 0.110800764883998 0.911794868241548 df.mm.trans1:probe6 0.0686290067180815 0.0534039204180725 1.28509304524498 0.199034532512457 df.mm.trans1:probe7 -0.0498110120920789 0.0534039204180725 -0.932722011832342 0.351171405765423 df.mm.trans1:probe8 0.329804107443416 0.0534039204180725 6.1756534887616 9.30555880874903e-10 *** df.mm.trans1:probe9 0.0261619965932212 0.0534039204180725 0.489889064106382 0.624311620535856 df.mm.trans1:probe10 0.943994370141506 0.0534039204180725 17.6764994545615 1.24503934737597e-61 *** df.mm.trans1:probe11 0.316085511143618 0.0534039204180725 5.9187697957218 4.34809818335247e-09 *** df.mm.trans1:probe12 0.595610284816302 0.0534039204180725 11.1529318475791 2.02255773160154e-27 *** df.mm.trans1:probe13 0.448445893614509 0.0534039204180725 8.39724668346165 1.41720137213413e-16 *** df.mm.trans1:probe14 0.539731440396362 0.0534039204180725 10.1065883585152 5.21291707042581e-23 *** df.mm.trans1:probe15 0.452512602758861 0.0534039204180725 8.47339669478134 7.692821323836e-17 *** df.mm.trans1:probe16 0.529341054084196 0.0534039204180725 9.91202611981013 3.15345815580932e-22 *** df.mm.trans1:probe17 0.525751411610132 0.0534039204180725 9.84480928542865 5.8341046720202e-22 *** df.mm.trans1:probe18 0.596707196283184 0.0534039204180725 11.1734717528575 1.64396535242734e-27 *** df.mm.trans1:probe19 0.659148847295736 0.0534039204180725 12.3427052196841 7.66786066284847e-33 *** df.mm.trans1:probe20 0.727208388469738 0.0534039204180725 13.6171348990259 4.29473457515941e-39 *** df.mm.trans1:probe21 0.670198213911617 0.0534039204180725 12.5496070075937 7.94396641785253e-34 *** df.mm.trans1:probe22 0.297867907316102 0.0534039204180725 5.57764121031273 3.07833921350971e-08 *** df.mm.trans2:probe2 -0.0959169944108774 0.0534039204180725 -1.79606653706304 0.0727625716786256 . df.mm.trans2:probe3 -0.0439031975159498 0.0534039204180725 -0.822096901730318 0.411202701055684 df.mm.trans2:probe4 -0.120885290336142 0.0534039204180725 -2.26360329709490 0.0237957486799046 * df.mm.trans2:probe5 0.188197721533272 0.0534039204180725 3.52404317997568 0.000442756583735915 *** df.mm.trans2:probe6 -0.342859535752592 0.0534039204180725 -6.42011921725066 2.03338845797699e-10 *** df.mm.trans3:probe2 0.30026257937135 0.0534039204180725 5.62248196425928 2.39410109440530e-08 *** df.mm.trans3:probe3 0.0155597879454015 0.0534039204180725 0.291360406194747 0.770831485954932 df.mm.trans3:probe4 0.111377936345210 0.0534039204180725 2.08557603024811 0.0372506971830743 * df.mm.trans3:probe5 0.767518080524755 0.0534039204180725 14.3719426311073 5.35412558914527e-43 *** df.mm.trans3:probe6 0.222144394588488 0.0534039204180725 4.1597020003294 3.43878724981321e-05 *** df.mm.trans3:probe7 0.207595154326615 0.0534039204180725 3.88726431882635 0.000107542114969901 *** df.mm.trans3:probe8 1.20209153878873 0.0534039204180725 22.5094249519166 2.37094377626276e-92 *** df.mm.trans3:probe9 0.619711273360428 0.0534039204180725 11.6042280886688 1.98991307036775e-29 *** df.mm.trans3:probe10 0.178127532214669 0.0534039204180725 3.33547669946697 0.000880416892894292 *** df.mm.trans3:probe11 -0.0486955576310575 0.0534039204180725 -0.911834884964332 0.362058561943938 df.mm.trans3:probe12 0.073542326784148 0.0534039204180725 1.37709602981246 0.168767122445893 df.mm.trans3:probe13 -0.320390493392222 0.0534039204180725 -5.99938152263065 2.69713984239167e-09 *** df.mm.trans3:probe14 -0.235332696109116 0.0534039204180725 -4.40665580854016 1.15411155457695e-05 *** df.mm.trans3:probe15 0.279977539238879 0.0534039204180725 5.24264018534735 1.90243349395047e-07 *** df.mm.trans3:probe16 -0.101124050988547 0.0534039204180725 -1.89356980155947 0.0585484676891458 . df.mm.trans3:probe17 -0.107174834600194 0.0534039204180725 -2.00687203787992 0.045011462963189 * df.mm.trans3:probe18 -0.301902373070688 0.0534039204180725 -5.65318745716132 2.01342712043246e-08 *** df.mm.trans3:probe19 0.179862520939749 0.0534039204180725 3.36796473988605 0.000783891659509675 *** df.mm.trans3:probe20 -0.218065097233599 0.0534039204180725 -4.0833162720354 4.7664752594202e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53608471111268 0.245764801805303 18.4570153162380 2.37343586813964e-66 *** df.mm.trans1 -0.143239176333688 0.209601242075556 -0.683388967141969 0.49450726329577 df.mm.trans2 -0.137071984675196 0.182572751048983 -0.75078008020168 0.45294811047093 df.mm.exp2 -0.129407688368678 0.228896590934221 -0.565354371773351 0.571949851640439 df.mm.exp3 -0.309567522787570 0.228896590934221 -1.35243395947532 0.176518845438406 df.mm.exp4 0.109447971405036 0.228896590934221 0.47815465909009 0.632636632540753 df.mm.exp5 0.219267616512997 0.228896590934221 0.957933080689737 0.338310297052747 df.mm.exp6 0.196625483760147 0.228896590934221 0.859014470061078 0.390522620775990 df.mm.exp7 -0.29134246183942 0.228896590934221 -1.27281258602556 0.203357721743903 df.mm.exp8 -0.200389962265836 0.228896590934221 -0.87546066740427 0.381517420474511 df.mm.trans1:exp2 0.0330961260706271 0.208105851453748 0.159035057589348 0.873670905005951 df.mm.trans2:exp2 0.270985899726888 0.13812953434092 1.96182446440500 0.0500387536841727 . df.mm.trans1:exp3 0.215658074512724 0.208105851453748 1.03629029653044 0.300298057411665 df.mm.trans2:exp3 0.196331901840789 0.13812953434092 1.42136077398349 0.155499784368649 df.mm.trans1:exp4 -0.111636719239983 0.208105851453748 -0.53644200035766 0.591763279752395 df.mm.trans2:exp4 0.089646908783079 0.13812953434092 0.649006088457664 0.516472041948369 df.mm.trans1:exp5 -0.312032589018977 0.208105851453748 -1.49939363472596 0.134062978587270 df.mm.trans2:exp5 0.0877219504070518 0.13812953434092 0.635070195708788 0.525516780671598 df.mm.trans1:exp6 -0.179124405855484 0.208105851453748 -0.860736998043014 0.389573422430803 df.mm.trans2:exp6 0.113032395593342 0.13812953434092 0.81830722251162 0.413361791871321 df.mm.trans1:exp7 0.247447181447283 0.208105851453748 1.18904480445269 0.234682675471454 df.mm.trans2:exp7 0.107495366382585 0.13812953434092 0.778221449131027 0.436608457537911 df.mm.trans1:exp8 0.124235064536918 0.208105851453748 0.596980160187998 0.550645404746162 df.mm.trans2:exp8 0.199194315378948 0.13812953434092 1.44208344963585 0.149567945296515 df.mm.trans1:probe2 0.0293528312471661 0.158067741215757 0.185697796535856 0.852716529407655 df.mm.trans1:probe3 0.133341695800229 0.158067741215757 0.843573108432176 0.399094276985781 df.mm.trans1:probe4 0.161217304380771 0.158067741215757 1.01992540122854 0.307991620988865 df.mm.trans1:probe5 0.079098391386807 0.158067741215757 0.500408184354582 0.616889361079257 df.mm.trans1:probe6 0.0194892432983367 0.158067741215757 0.123296778637044 0.901894968907896 df.mm.trans1:probe7 0.08269807894945 0.158067741215757 0.523181253261346 0.600955148781799 df.mm.trans1:probe8 0.130025747958576 0.158067741215757 0.822595090930637 0.410919367550237 df.mm.trans1:probe9 0.220202749381213 0.158067741215757 1.39309101077521 0.163878139749251 df.mm.trans1:probe10 0.321731740837872 0.158067741215757 2.03540417774882 0.0420532849107651 * df.mm.trans1:probe11 0.130008676038640 0.158067741215757 0.822487087110216 0.410980782350144 df.mm.trans1:probe12 0.088153791870391 0.158067741215757 0.55769628383608 0.57716704986024 df.mm.trans1:probe13 0.00386532418673028 0.158067741215757 0.0244535928520307 0.980495304956032 df.mm.trans1:probe14 0.195225281403022 0.158067741215757 1.23507351912207 0.217071017558533 df.mm.trans1:probe15 0.0761264638898643 0.158067741215757 0.481606577688449 0.630182751557539 df.mm.trans1:probe16 -0.0394148813039813 0.158067741215757 -0.249354365418440 0.803133965215095 df.mm.trans1:probe17 0.0405963529221469 0.158067741215757 0.256828829272218 0.79735969594566 df.mm.trans1:probe18 0.0624767088185625 0.158067741215757 0.395252746310102 0.692734206432198 df.mm.trans1:probe19 0.277228844270748 0.158067741215757 1.75386098478082 0.0797371721410596 . df.mm.trans1:probe20 0.121233526605395 0.158067741215757 0.7669719683026 0.443265341777554 df.mm.trans1:probe21 0.0501679126431996 0.158067741215757 0.317382359343784 0.751014668000994 df.mm.trans1:probe22 0.113432087251552 0.158067741215757 0.717616930431876 0.47314827135479 df.mm.trans2:probe2 -0.0435109906141414 0.158067741215757 -0.275267997628627 0.78316285669925 df.mm.trans2:probe3 -0.0734004735067883 0.158067741215757 -0.464360867955967 0.642482552339842 df.mm.trans2:probe4 -0.131521440030063 0.158067741215757 -0.832057439541323 0.405559951213517 df.mm.trans2:probe5 -0.0497400971704629 0.158067741215757 -0.314675826882155 0.753068412710012 df.mm.trans2:probe6 -0.108604930542563 0.158067741215757 -0.687078398838643 0.492180422449892 df.mm.trans3:probe2 0.0573696715512014 0.158067741215757 0.362943577923934 0.716717855381025 df.mm.trans3:probe3 0.0738806612029112 0.158067741215757 0.467398728131799 0.640308659340767 df.mm.trans3:probe4 0.122455948127986 0.158067741215757 0.774705497694419 0.43868280289998 df.mm.trans3:probe5 0.169681340573667 0.158067741215757 1.07347229275616 0.283298386693433 df.mm.trans3:probe6 0.158567439739909 0.158067741215757 1.00316129350814 0.316007124777636 df.mm.trans3:probe7 0.177430246632301 0.158067741215757 1.12249498390766 0.261900828876027 df.mm.trans3:probe8 0.0435376422256142 0.158067741215757 0.275436606424247 0.783033365114407 df.mm.trans3:probe9 0.099953339322589 0.158067741215757 0.632344958900602 0.527294963839156 df.mm.trans3:probe10 0.0207975260042821 0.158067741215757 0.131573500350676 0.895346084836212 df.mm.trans3:probe11 0.0829888905928262 0.158067741215757 0.52502104448718 0.599676019735891 df.mm.trans3:probe12 -0.0191357264933258 0.158067741215757 -0.121060289380654 0.9036657385967 df.mm.trans3:probe13 0.216728275373763 0.158067741215757 1.37111009309569 0.170624681737346 df.mm.trans3:probe14 0.0255031555156825 0.158067741215757 0.161343202095053 0.871853191175023 df.mm.trans3:probe15 0.0954265700554956 0.158067741215757 0.603706798879611 0.546165016816486 df.mm.trans3:probe16 0.0773911899772952 0.158067741215757 0.489607742744035 0.624510649679473 df.mm.trans3:probe17 0.145614330394125 0.158067741215757 0.921214722714143 0.357143492078831 df.mm.trans3:probe18 0.174101815349741 0.158067741215757 1.10143799114645 0.270950806676958 df.mm.trans3:probe19 -0.0241249357510332 0.158067741215757 -0.152624030466175 0.878723202718464 df.mm.trans3:probe20 0.203020414933670 0.158067741215757 1.28438866382327 0.199280668724471