fitVsDatCorrelation=0.939502678530801 cont.fitVsDatCorrelation=0.282346387431471 fstatistic=10948.6794417491,66,1014 cont.fstatistic=1382.53935912901,66,1014 residuals=-0.682796932360418,-0.099490972587252,-0.00237334662418227,0.0959202903024825,1.17509622874158 cont.residuals=-1.00225157313792,-0.369353268312585,-0.0572939936974045,0.313309893750326,1.66649957756367 predictedValues: Include Exclude Both Lung 65.3751623116614 225.054039314860 71.4828840650784 cerebhem 62.2088145159931 167.978474085290 73.1333526290009 cortex 62.3772250284254 188.963782235341 79.652459915347 heart 64.910529733331 202.503272551665 75.9475631439835 kidney 68.2063732062463 277.161053052136 74.930100567199 liver 66.9189794330701 305.334559414661 61.1630384958301 stomach 68.2348723216316 213.063567566081 66.7046620502408 testicle 66.6352766497135 289.694319006928 70.1388678523496 diffExp=-159.678877003199,-105.769659569297,-126.586557206916,-137.592742818334,-208.95467984589,-238.415579981591,-144.828695244449,-223.059042357215 diffExpScore=0.999256994928754 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 94.2105194855529 124.942678451979 92.273150103823 cerebhem 94.579648688675 99.231990432392 81.6515851743902 cortex 95.1733756703799 80.4633249782461 94.0715713030316 heart 96.9935192792472 96.3153215320092 88.0216756391097 kidney 83.2908597014367 118.935277730949 100.320166383023 liver 83.4260651293037 74.6428646957774 87.4640047950531 stomach 89.7815600300135 77.8281784220026 91.9881356808033 testicle 85.4794791185526 86.506057881193 81.2518272911774 cont.diffExp=-30.7321589664263,-4.65234174371685,14.7100506921337,0.67819774723803,-35.6444180295128,8.7832004335263,11.9533816080109,-1.02657876264047 cont.diffExpScore=2.92928172460452 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,-1,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=-1,0,0,0,-1,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,0,0,0,-1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.719703444575509 cont.tran.correlation=0.128331106917814 tran.covariance=0.00592746130177781 cont.tran.covariance=0.00185302150501875 tran.mean=149.663768776690 cont.tran.mean=92.6125450767319 weightedLogRatios: wLogRatio Lung -5.93157720278341 cerebhem -4.59634535811018 cortex -5.19527928860427 heart -5.39505926688135 kidney -6.90314196426199 liver -7.53262218378434 stomach -5.45664899976286 testicle -7.25101219594608 cont.weightedLogRatios: wLogRatio Lung -1.32316235212442 cerebhem -0.219608554452902 cortex 0.750801358158702 heart 0.0320745466089241 kidney -1.63887053001294 liver 0.485959485382672 stomach 0.632361065129004 testicle -0.0531752623910218 varWeightedLogRatios=1.14353639444133 cont.varWeightedLogRatios=0.780880210674243 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.08472510845053 0.0787411650386827 64.5751825738493 0 *** df.mm.trans1 -1.22873412689077 0.0673132072139617 -18.2539828028862 1.37649549091257e-64 *** df.mm.trans2 0.382787949548290 0.0587937291151495 6.51069349247411 1.17470275217757e-10 *** df.mm.exp2 -0.364976896008156 0.0740890953291204 -4.92618912927532 9.78635031509471e-07 *** df.mm.exp3 -0.329942242315403 0.0740890953291204 -4.45331719667686 9.3936659694424e-06 *** df.mm.exp4 -0.173302156225678 0.0740890953291204 -2.33910476914088 0.0195234224731897 * df.mm.exp5 0.203556162998266 0.0740890953291204 2.74745105327611 0.00611253463716692 ** df.mm.exp6 0.484322740153213 0.0740890953291204 6.53703136746025 9.92436551096511e-11 *** df.mm.exp7 0.0572465567870734 0.0740890953291204 0.772671828867277 0.439896683523113 df.mm.exp8 0.290558392157405 0.0740890953291204 3.92174301584706 9.38244611459214e-05 *** df.mm.trans1:exp2 0.315331193028034 0.0675892707396537 4.66540309693021 3.49233176135755e-06 *** df.mm.trans2:exp2 0.0724721886575043 0.0459632183990647 1.57674312595523 0.115166551100560 df.mm.trans1:exp3 0.283000062388503 0.0675892707396537 4.18705601187188 3.07150063337869e-05 *** df.mm.trans2:exp3 0.155157062527741 0.0459632183990647 3.37567881301581 0.000764263464753749 *** df.mm.trans1:exp4 0.166169607120450 0.0675892707396537 2.45852049152176 0.0141173424175385 * df.mm.trans2:exp4 0.0677176552908536 0.0459632183990647 1.47330099260916 0.140980236915202 df.mm.trans1:exp5 -0.161160558903738 0.0675892707396537 -2.38441038259623 0.0172895602295563 * df.mm.trans2:exp5 0.00470204509153746 0.0459632183990647 0.102300170773793 0.918538645919782 df.mm.trans1:exp6 -0.460982519863495 0.0675892707396537 -6.82035055000295 1.55835304132085e-11 *** df.mm.trans2:exp6 -0.179255196704854 0.0459632183990647 -3.89997051878556 0.000102537806143078 *** df.mm.trans1:exp7 -0.0144332034160792 0.0675892707396537 -0.213542819121015 0.830946521886235 df.mm.trans2:exp7 -0.111996544424216 0.0459632183990647 -2.43665583754021 0.0149949605898166 * df.mm.trans1:exp8 -0.271466680577327 0.0675892707396537 -4.01641677157587 6.34512742187486e-05 *** df.mm.trans2:exp8 -0.0380726455915803 0.0459632183990647 -0.828328540030066 0.407679381978818 df.mm.trans1:probe2 0.405445194634822 0.0503232794123707 8.0568118645931 2.18771127654337e-15 *** df.mm.trans1:probe3 0.0445028878684114 0.0503232794123707 0.884339979192047 0.376722347775448 df.mm.trans1:probe4 -0.00850190025611427 0.0503232794123707 -0.168945671971138 0.865873068951927 df.mm.trans1:probe5 0.183017655124652 0.0503232794123707 3.63683880028815 0.000289891479697766 *** df.mm.trans1:probe6 0.250299538774555 0.0503232794123707 4.97383202560176 7.70586837981552e-07 *** df.mm.trans1:probe7 0.596399608390142 0.0503232794123707 11.8513661143382 1.94681061065813e-30 *** df.mm.trans1:probe8 0.0657423297567048 0.0503232794123707 1.30639995096472 0.191712821433635 df.mm.trans1:probe9 0.543067921578557 0.0503232794123707 10.7915844897234 8.91044433735911e-26 *** df.mm.trans1:probe10 0.0500261608154775 0.0503232794123707 0.99409580217421 0.320413387454251 df.mm.trans1:probe11 1.10172464243643 0.0503232794123707 21.8929420995882 2.58958590414254e-87 *** df.mm.trans1:probe12 0.838964080294773 0.0503232794123707 16.6714906121268 2.46903876605705e-55 *** df.mm.trans1:probe13 1.07088464152823 0.0503232794123706 21.2801044374103 2.27952735387417e-83 *** df.mm.trans1:probe14 1.24529322257012 0.0503232794123707 24.7458678590012 3.84479015102845e-106 *** df.mm.trans1:probe15 1.10381042669839 0.0503232794123706 21.934389801056 1.39633557447835e-87 *** df.mm.trans1:probe16 1.06599555419388 0.0503232794123707 21.1829508458432 9.53569820474476e-83 *** df.mm.trans1:probe17 0.95289697836464 0.0503232794123707 18.9355103540886 1.05436284009544e-68 *** df.mm.trans1:probe18 0.491894332712636 0.0503232794123707 9.77468754931176 1.25812508159025e-21 *** df.mm.trans1:probe19 0.677822137918217 0.0503232794123707 13.4693554520533 3.66066812334898e-38 *** df.mm.trans1:probe20 0.629611749591137 0.0503232794123707 12.5113418072743 1.67330697768701e-33 *** df.mm.trans1:probe21 0.739471708182397 0.0503232794123706 14.6944260552427 1.85006582736510e-44 *** df.mm.trans1:probe22 0.593536649260973 0.0503232794123707 11.7944747677765 3.53067104048516e-30 *** df.mm.trans2:probe2 0.0674948808201194 0.0503232794123707 1.34122580261587 0.180147479762817 df.mm.trans2:probe3 -0.237120358748222 0.0503232794123707 -4.71194170008587 2.7955429276833e-06 *** df.mm.trans2:probe4 -0.448809334803227 0.0503232794123707 -8.91852319729582 2.16123704041741e-18 *** df.mm.trans2:probe5 -0.176664486108243 0.0503232794123707 -3.51059168184525 0.000466774775813919 *** df.mm.trans2:probe6 -0.381868428837998 0.0503232794123707 -7.58830571650156 7.3238065055152e-14 *** df.mm.trans3:probe2 0.320433531659248 0.0503232794123707 6.36750099359537 2.90730206643848e-10 *** df.mm.trans3:probe3 -0.0319959410283463 0.0503232794123707 -0.635807948169628 0.525045074311162 df.mm.trans3:probe4 -0.264402562974839 0.0503232794123707 -5.25408053811857 1.81300228833223e-07 *** df.mm.trans3:probe5 0.0472071004866137 0.0503232794123707 0.938076791454276 0.348428328908204 df.mm.trans3:probe6 0.481014471246005 0.0503232794123707 9.55848817610563 8.69892408912899e-21 *** df.mm.trans3:probe7 0.247756973157065 0.0503232794123707 4.92330738477589 9.92820765343033e-07 *** df.mm.trans3:probe8 -0.299986564620981 0.0503232794123707 -5.96118870081502 3.45285123451841e-09 *** df.mm.trans3:probe9 -0.178970770022510 0.0503232794123707 -3.55642104633019 0.000393310764868228 *** df.mm.trans3:probe10 0.310995717255114 0.0503232794123707 6.17995728590501 9.27456067430155e-10 *** df.mm.trans3:probe11 0.0132507122778045 0.0503232794123707 0.263311780005879 0.792363775873424 df.mm.trans3:probe12 -0.281624614976126 0.0503232794123707 -5.59630887065948 2.81710692249787e-08 *** df.mm.trans3:probe13 -0.146414577421761 0.0503232794123707 -2.90948004842803 0.00369937376832834 ** df.mm.trans3:probe14 0.0569265821504871 0.0503232794123707 1.13121765543152 0.258230959363522 df.mm.trans3:probe15 0.205867409978631 0.0503232794123707 4.09089813665887 4.63785517734205e-05 *** df.mm.trans3:probe16 0.0428657728467561 0.0503232794123707 0.851808017031153 0.394521781092406 df.mm.trans3:probe17 -0.00137843410557769 0.0503232794123707 -0.0273915794374649 0.9781528041118 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.84233428241955 0.220520356090481 21.9586725156235 9.72197045434332e-88 *** df.mm.trans1 -0.271493901083950 0.188515529547002 -1.44016729940681 0.150128740877659 df.mm.trans2 0.007900665305301 0.164656111882402 0.047982824414946 0.961739379690988 df.mm.exp2 -0.104192170051449 0.207491896727371 -0.502150549948221 0.61567064601822 df.mm.exp3 -0.449187869598021 0.207491896727371 -2.16484535869958 0.0306322575259052 * df.mm.exp4 -0.183945231102358 0.207491896727371 -0.886517661670657 0.37554884134683 df.mm.exp5 -0.25608216502619 0.207491896727371 -1.23417911284826 0.217422124264907 df.mm.exp6 -0.583185312758252 0.207491896727370 -2.81064138868283 0.00503948258745736 ** df.mm.exp7 -0.51841014911102 0.207491896727370 -2.49845973403084 0.0126308044367656 * df.mm.exp8 -0.337696234911336 0.207491896727371 -1.62751529210340 0.103938366756659 df.mm.trans1:exp2 0.108102645655978 0.189288665516723 0.571099412428514 0.568058804323829 df.mm.trans2:exp2 -0.126202443188741 0.128723334020355 -0.98041620930036 0.327114566902994 df.mm.trans1:exp3 0.459356257784559 0.189288665516723 2.42674994052391 0.0154081714956716 * df.mm.trans2:exp3 0.00913430013572194 0.128723334020355 0.070960717458402 0.9434430120349 df.mm.trans1:exp4 0.213057548650958 0.189288665516723 1.12556950026220 0.260614119339051 df.mm.trans2:exp4 -0.0762824203929154 0.128723334020355 -0.592607556147145 0.55357594836904 df.mm.trans1:exp5 0.132889133536098 0.189288665516723 0.702044853944819 0.482812301299294 df.mm.trans2:exp5 0.206806565823299 0.128723334020355 1.60659733837065 0.108454060996249 df.mm.trans1:exp6 0.461614257652932 0.189288665516723 2.43867881044441 0.0149117873542073 * df.mm.trans2:exp6 0.0680451889876839 0.128723334020355 0.528615806182613 0.597187672043646 df.mm.trans1:exp7 0.470257911287033 0.189288665516723 2.48434268371703 0.0131395828467154 * df.mm.trans2:exp7 0.0450586454927232 0.128723334020355 0.350042560936139 0.72637945008822 df.mm.trans1:exp8 0.240440724569001 0.189288665516723 1.27023307979187 0.204293002750807 df.mm.trans2:exp8 -0.0299443800361212 0.128723334020355 -0.232625889191046 0.816098866228284 df.mm.trans1:probe2 0.041653384244477 0.140934001804584 0.295552412555721 0.767632429179912 df.mm.trans1:probe3 -0.0336775339770757 0.140934001804584 -0.238959609078385 0.811185189529548 df.mm.trans1:probe4 -0.0600840530190332 0.140934001804584 -0.426327587733899 0.669959644490109 df.mm.trans1:probe5 -0.0963914032932555 0.140934001804584 -0.683947110413497 0.494164831686799 df.mm.trans1:probe6 -0.172659561102625 0.140934001804584 -1.22510933409832 0.220818528715587 df.mm.trans1:probe7 -0.12132489595565 0.140934001804584 -0.860863201230011 0.389516896556838 df.mm.trans1:probe8 -0.131061703253053 0.140934001804584 -0.929950910176954 0.352617833737862 df.mm.trans1:probe9 0.110616158037587 0.140934001804584 0.784879139322 0.432707765269080 df.mm.trans1:probe10 -0.244902520835961 0.140934001804584 -1.73771068514423 0.082565405971301 . df.mm.trans1:probe11 -0.000405661965906509 0.140934001804584 -0.00287838251033979 0.997703952373378 df.mm.trans1:probe12 -0.0917901684147216 0.140934001804584 -0.651298957238127 0.51500109260536 df.mm.trans1:probe13 -0.165030014894986 0.140934001804584 -1.17097373793312 0.241884433657111 df.mm.trans1:probe14 -0.0696382817930174 0.140934001804584 -0.494119807153255 0.621328590236466 df.mm.trans1:probe15 0.179168340819606 0.140934001804584 1.27129250944025 0.203916142034105 df.mm.trans1:probe16 -0.0369902466310295 0.140934001804584 -0.262465027299228 0.7930162573194 df.mm.trans1:probe17 0.0437463304272085 0.140934001804584 0.310402953631206 0.756318336072629 df.mm.trans1:probe18 -0.197827676178836 0.140934001804584 -1.40369019289710 0.160717294020305 df.mm.trans1:probe19 0.178370702324385 0.140934001804584 1.26563284970585 0.205935284193865 df.mm.trans1:probe20 -0.0732734572743955 0.140934001804584 -0.51991326675017 0.603237507911166 df.mm.trans1:probe21 0.0894046424957434 0.140934001804584 0.634372410851642 0.525980898876075 df.mm.trans1:probe22 -0.134935212271045 0.140934001804584 -0.957435470101416 0.338575716803824 df.mm.trans2:probe2 -0.114590004253987 0.140934001804584 -0.813075643824227 0.416365552568005 df.mm.trans2:probe3 -0.00884139097496827 0.140934001804584 -0.0627342647037552 0.949990475418298 df.mm.trans2:probe4 -0.103130529914021 0.140934001804584 -0.731764716771611 0.464481269389363 df.mm.trans2:probe5 -0.131048417083467 0.140934001804584 -0.92985663789762 0.352666624783712 df.mm.trans2:probe6 -0.157127081386944 0.140934001804584 -1.11489831676541 0.265158165442792 df.mm.trans3:probe2 -0.0801056195087336 0.140934001804584 -0.568391009146299 0.569895342350224 df.mm.trans3:probe3 -0.227258586280499 0.140934001804584 -1.61251779819331 0.107160476748366 df.mm.trans3:probe4 -0.0603775747076178 0.140934001804584 -0.428410276686361 0.668443428358411 df.mm.trans3:probe5 -0.150211286813631 0.140934001804584 -1.06582715945234 0.286755492418276 df.mm.trans3:probe6 -0.0328685759403282 0.140934001804584 -0.233219631312983 0.81563793233211 df.mm.trans3:probe7 0.104392752912567 0.140934001804584 0.740720845047143 0.459034203582182 df.mm.trans3:probe8 -0.237441186401529 0.140934001804584 -1.68476863894606 0.092340936389597 . df.mm.trans3:probe9 0.0563428850551651 0.140934001804584 0.399782056379049 0.689401244009641 df.mm.trans3:probe10 -0.197639990473379 0.140934001804584 -1.40235846525825 0.161114295615635 df.mm.trans3:probe11 -0.116683849581400 0.140934001804584 -0.827932564798602 0.407903504194084 df.mm.trans3:probe12 -0.037639437506739 0.140934001804584 -0.267071374010433 0.789468513369784 df.mm.trans3:probe13 -0.084637750083097 0.140934001804584 -0.600548831363308 0.548274726878561 df.mm.trans3:probe14 0.178645776388016 0.140934001804584 1.26758464317024 0.205237324934793 df.mm.trans3:probe15 -0.0127588304342942 0.140934001804584 -0.09053053394443 0.927883510205203 df.mm.trans3:probe16 -0.105660022276313 0.140934001804584 -0.749712779906858 0.453601608962935 df.mm.trans3:probe17 0.0861079209971093 0.140934001804584 0.610980458189961 0.541349447862608