fitVsDatCorrelation=0.827667388416553 cont.fitVsDatCorrelation=0.291866065368812 fstatistic=9766.22895075404,65,991 cont.fstatistic=3352.47472106679,65,991 residuals=-0.811058623333825,-0.0839783021248182,-0.00710909589230803,0.0699107163311364,1.45236868326743 cont.residuals=-0.525928681812702,-0.192564232467813,-0.0617469122396398,0.159955549626883,1.91028843878943 predictedValues: Include Exclude Both Lung 57.8223668630316 42.8680610410629 56.3030712161765 cerebhem 70.0314946750364 54.8159632898753 61.8844439566237 cortex 60.9119148663902 43.8021021650511 59.6424748712383 heart 64.1302953829705 44.6164289566874 61.5787708014579 kidney 59.326479752933 43.7068181613371 59.7103765686133 liver 61.5004487706463 47.8441136715626 59.5643186837471 stomach 61.2062604287175 47.5929441495735 56.2338513361721 testicle 61.8665212698632 47.2679271211892 63.7900398703644 diffExp=14.9543058219687,15.2155313851611,17.1098127013391,19.5138664262831,15.6196615915959,13.6563350990838,13.6133162791440,14.598594148674 diffExpScore=0.992017970642127 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=1,0,1,1,1,0,0,1 diffExp1.3Score=0.833333333333333 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 54.5124146515258 59.7468682353999 53.9412525408581 cerebhem 59.1864365607479 60.8653170765351 63.0396085336625 cortex 59.117607500863 62.3693289738848 58.9244221230184 heart 59.006782632469 74.7455267811392 59.0743341710043 kidney 60.434955403372 63.8962947429693 56.3244305652319 liver 60.881225337726 65.0359764200093 57.5376698979045 stomach 55.1614342515531 56.7902547529414 55.0974855287877 testicle 56.385968544527 64.7259332533227 56.6334138259784 cont.diffExp=-5.23445358387409,-1.67888051578719,-3.25172147302185,-15.7387441486702,-3.46133933959732,-4.1547510822833,-1.62882050138828,-8.3399647087956 cont.diffExpScore=0.977522369635508 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,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.866689105922063 cont.tran.correlation=0.473459889425052 tran.covariance=0.00393466889219509 cont.tran.covariance=0.00169665416058006 tran.mean=54.3318837853705 cont.tran.mean=60.8038953199366 weightedLogRatios: wLogRatio Lung 1.16938920198803 cerebhem 1.01083334605581 cortex 1.30070524361445 heart 1.44382465357150 kidney 1.20090345125981 liver 1.00275245143451 stomach 1.00335854402784 testicle 1.07400644888697 cont.weightedLogRatios: wLogRatio Lung -0.370812511579809 cerebhem -0.114532658267872 cortex -0.219871177677382 heart -0.992059001846332 kidney -0.229982394517343 liver -0.273432835223964 stomach -0.117124858000062 testicle -0.565724873988804 varWeightedLogRatios=0.0257622668741583 cont.varWeightedLogRatios=0.0864591054758391 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81901071616061 0.0752152229895954 50.7744385294038 5.48666535522527e-278 *** df.mm.trans1 0.286810967919170 0.0643608841866079 4.45629315917398 9.28917079972763e-06 *** df.mm.trans2 -0.0834378935585348 0.0562774102834419 -1.48261785924936 0.138493891473476 df.mm.exp2 0.342904041406021 0.0710635010886417 4.82531871006885 1.61765829854513e-06 *** df.mm.exp3 0.0159889899988558 0.0710635010886417 0.224995810140451 0.822028918799779 df.mm.exp4 0.053948214326401 0.0710635010886416 0.759155030359512 0.447940351963547 df.mm.exp5 -0.0136995970515732 0.0710635010886416 -0.192779652588252 0.847171038684093 df.mm.exp6 0.115182213957897 0.0710635010886417 1.62083505869242 0.105371133711836 df.mm.exp7 0.162661447474296 0.0710635010886417 2.28895909971279 0.0222913759043434 * df.mm.exp8 0.0404604732055515 0.0710635010886417 0.569356597771377 0.569243188337189 df.mm.trans1:exp2 -0.151334647386407 0.0649165280279232 -2.33121905905553 0.0199415664250255 * df.mm.trans2:exp2 -0.0970496397422665 0.0445553671566758 -2.17818067576457 0.0296277315266096 * df.mm.trans1:exp3 0.0360641412176667 0.0649165280279232 0.555546365667525 0.578646392893111 df.mm.trans2:exp3 0.00556577008723308 0.0445553671566758 0.124918061333923 0.900613753997129 df.mm.trans1:exp4 0.0495929940533184 0.0649165280279232 0.763950192037172 0.445078770297108 df.mm.trans2:exp4 -0.0139731116520144 0.0445553671566758 -0.313612310788034 0.753881505428929 df.mm.trans1:exp5 0.0393796714483370 0.0649165280279232 0.60662011115872 0.544241989032343 df.mm.trans2:exp5 0.0330766582010361 0.0445553671566758 0.742372026353735 0.458037882106337 df.mm.trans1:exp6 -0.0535134128617834 0.0649165280279232 -0.824341881604715 0.409943685559264 df.mm.trans2:exp6 -0.00536117067481667 0.0445553671566757 -0.120326035154519 0.904249270738918 df.mm.trans1:exp7 -0.105787639374278 0.0649165280279232 -1.62959484414777 0.103504946926572 df.mm.trans2:exp7 -0.0581039801566864 0.0445553671566758 -1.30408486933500 0.192507460463644 df.mm.trans1:exp8 0.0271430375384126 0.0649165280279232 0.418122138736953 0.675948404368386 df.mm.trans2:exp8 0.057244467968219 0.0445553671566758 1.28479399051798 0.199164503029629 df.mm.trans1:probe2 -0.293767800213182 0.0479440333449523 -6.12730677245183 1.28782364302302e-09 *** df.mm.trans1:probe3 -0.0272338315654350 0.0479440333449523 -0.568033802443996 0.570140689928088 df.mm.trans1:probe4 -0.212520304118374 0.0479440333449523 -4.43267471030884 1.03454152452119e-05 *** df.mm.trans1:probe5 0.207783266143615 0.0479440333449523 4.33387121706337 1.61449515399695e-05 *** df.mm.trans1:probe6 -0.270372273310775 0.0479440333449523 -5.63933099590256 2.22600638483935e-08 *** df.mm.trans1:probe7 -0.245559454920102 0.0479440333449523 -5.12179384561426 3.6358801073191e-07 *** df.mm.trans1:probe8 -0.267805156020145 0.0479440333449523 -5.58578695482951 3.00473577808645e-08 *** df.mm.trans1:probe9 -0.347880289695874 0.0479440333449523 -7.25596628871233 8.03990847641991e-13 *** df.mm.trans1:probe10 -0.329042678360317 0.0479440333449523 -6.8630579324207 1.18655689789609e-11 *** df.mm.trans1:probe11 -0.360687711077464 0.0479440333449523 -7.52309903679471 1.19781497146332e-13 *** df.mm.trans1:probe12 -0.293268412981012 0.0479440333449523 -6.11689072696442 1.37157102164853e-09 *** df.mm.trans1:probe13 -0.373307658778483 0.0479440333449523 -7.78632152394385 1.73206961702996e-14 *** df.mm.trans1:probe14 -0.210026739911504 0.0479440333449523 -4.38066481391718 1.30909589225096e-05 *** df.mm.trans1:probe15 -0.280636014212875 0.0479440333449523 -5.85340853978071 6.54186282843098e-09 *** df.mm.trans1:probe16 -0.231954851813764 0.0479440333449523 -4.8380337579209 1.51979499491476e-06 *** df.mm.trans1:probe17 0.382032189608162 0.0479440333449523 7.96829475858822 4.40116583493716e-15 *** df.mm.trans1:probe18 0.199988841338669 0.0479440333449523 4.17129781092404 3.29417795545229e-05 *** df.mm.trans1:probe19 0.145230874771331 0.0479440333449523 3.02917515775967 0.00251578470537259 ** df.mm.trans1:probe20 0.458557377033085 0.0479440333449523 9.56443054621235 8.61995712169567e-21 *** df.mm.trans1:probe21 0.174801609550465 0.0479440333449523 3.64595127599687 0.000280256544503670 *** df.mm.trans1:probe22 0.334720512892092 0.0479440333449523 6.98148423358155 5.34415658666347e-12 *** df.mm.trans2:probe2 0.0730290124456222 0.0479440333449523 1.52321378387559 0.128024255086054 df.mm.trans2:probe3 0.0618193527748326 0.0479440333449523 1.28940659476955 0.197557626235424 df.mm.trans2:probe4 -0.0142176753438185 0.0479440333449523 -0.296547335546925 0.766874260188285 df.mm.trans2:probe5 0.118580686046373 0.0479440333449523 2.47331477502524 0.0135533611199877 * df.mm.trans2:probe6 0.256981644001772 0.0479440333449523 5.36003389937631 1.03513680394955e-07 *** df.mm.trans3:probe2 -0.100225315761072 0.0479440333449523 -2.09046483511225 0.0368303927728281 * df.mm.trans3:probe3 0.0210992274580572 0.0479440333449523 0.440080360078396 0.659974868889797 df.mm.trans3:probe4 0.370269565510578 0.0479440333449523 7.72295402947281 2.77333188897819e-14 *** df.mm.trans3:probe5 -0.0672612974277202 0.0479440333449523 -1.40291278674412 0.160956059421728 df.mm.trans3:probe6 0.419086098643925 0.0479440333449523 8.74115232710283 9.72074041135527e-18 *** df.mm.trans3:probe7 0.0904747433147433 0.0479440333449523 1.88709078069813 0.0594401868915876 . df.mm.trans3:probe8 0.0597973350554659 0.0479440333449523 1.24723205128009 0.212607093595551 df.mm.trans3:probe9 -0.211748072755935 0.0479440333449523 -4.41656777669143 1.11306050344506e-05 *** df.mm.trans3:probe10 0.285478220260411 0.0479440333449523 5.95440559217089 3.62025118804977e-09 *** df.mm.trans3:probe11 -0.117470850607813 0.0479440333449523 -2.45016621281365 0.0144510756146011 * df.mm.trans3:probe12 -0.153439606524164 0.0479440333449523 -3.20039003435907 0.00141603693361321 ** df.mm.trans3:probe13 -0.075296723444495 0.0479440333449523 -1.57051291247741 0.116615042683751 df.mm.trans3:probe14 -0.0724718379513454 0.0479440333449523 -1.51159243174053 0.130956378024540 df.mm.trans3:probe15 -0.160118599097150 0.0479440333449523 -3.33969814231343 0.000869962017688273 *** df.mm.trans3:probe16 -0.154328181944749 0.0479440333449523 -3.21892363194339 0.00132851430463667 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07214000029171 0.128185788711863 31.7674840652196 2.67566082849596e-153 *** df.mm.trans1 -0.0835235808157033 0.109687246460659 -0.761470303164736 0.446557374411071 df.mm.trans2 0.04043622715617 0.0959109597380574 0.421601736304229 0.673407171136388 df.mm.exp2 -0.0550570299014256 0.121110203142440 -0.454602737613046 0.649494624481994 df.mm.exp3 0.0356970931754654 0.121110203142440 0.294748850627236 0.768247475295503 df.mm.exp4 0.212295509135234 0.121110203142440 1.75291184084258 0.0799263353632911 . df.mm.exp5 0.127050963357864 0.121110203142440 1.04905251631389 0.294409714593873 df.mm.exp6 0.130775874631250 0.121110203142440 1.079808895023 0.280490015729665 df.mm.exp7 -0.0601249904516219 0.121110203142440 -0.496448597158305 0.619688041800482 df.mm.exp8 0.0651334218334893 0.121110203142440 0.537802927775494 0.59083385973809 df.mm.trans1:exp2 0.137320965891951 0.110634204286627 1.24121619328671 0.214819615168966 df.mm.trans2:exp2 0.0736037612869954 0.0759336295674504 0.969317043137186 0.332623536158235 df.mm.trans1:exp3 0.0454032466219323 0.110634204286627 0.410390682652748 0.681608089111432 df.mm.trans2:exp3 0.00725976354091177 0.0759336295674504 0.0956066973522327 0.923852274718507 df.mm.trans1:exp4 -0.133071579464246 0.110634204286627 -1.20280685636324 0.229338358234333 df.mm.trans2:exp4 0.0116770839845088 0.0759336295674504 0.153780137351874 0.87781440443239 df.mm.trans1:exp5 -0.0239117614735851 0.110634204286627 -0.216133533275437 0.828928140722287 df.mm.trans2:exp5 -0.0599063638662698 0.0759336295674504 -0.788930599097152 0.43034126516961 df.mm.trans1:exp6 -0.0202795016820788 0.110634204286627 -0.183302278105056 0.854598342103621 df.mm.trans2:exp6 -0.0459520494339269 0.0759336295674503 -0.605160713318842 0.545210764355804 df.mm.trans1:exp7 0.0719605771987521 0.110634204286627 0.650436975280443 0.515560761314687 df.mm.trans2:exp7 0.0093729552400269 0.0759336295674504 0.123436154618437 0.901786758219755 df.mm.trans1:exp8 -0.0313415464527299 0.110634204286627 -0.283289843812963 0.777013828788188 df.mm.trans2:exp8 0.0149117474783356 0.0759336295674504 0.196378700231756 0.844354029199334 df.mm.trans1:probe2 -0.00559197153069585 0.0817087749537179 -0.068437833437881 0.945450911436097 df.mm.trans1:probe3 0.104692959433656 0.0817087749537179 1.28129395518360 0.200390165951551 df.mm.trans1:probe4 0.0203342223923186 0.0817087749537179 0.248862162036286 0.803518970082452 df.mm.trans1:probe5 0.202712253280808 0.0817087749537179 2.48091166947038 0.0132696968268512 * df.mm.trans1:probe6 -0.0448692125402417 0.0817087749537179 -0.549135788238862 0.583035973889032 df.mm.trans1:probe7 -0.0253815515858795 0.0817087749537179 -0.310634342520205 0.75614393683879 df.mm.trans1:probe8 -0.022199312817223 0.0817087749537179 -0.271688234584319 0.785918335993486 df.mm.trans1:probe9 0.0235121194857597 0.0817087749537179 0.287755133999715 0.773594347677015 df.mm.trans1:probe10 -0.0139063604699912 0.0817087749537179 -0.170194210816012 0.864892142525345 df.mm.trans1:probe11 0.0624523143863251 0.0817087749537179 0.764328120470535 0.444853680739644 df.mm.trans1:probe12 -0.00915706607034105 0.0817087749537179 -0.112069555265366 0.91079095179965 df.mm.trans1:probe13 0.0885430617166016 0.0817087749537179 1.08364201723444 0.278787126629877 df.mm.trans1:probe14 0.0123110717698226 0.0817087749537179 0.150670130310923 0.880266598524321 df.mm.trans1:probe15 0.009755075796731 0.0817087749537179 0.119388349687736 0.904991888041108 df.mm.trans1:probe16 -0.0193584546984867 0.0817087749537179 -0.236920143637595 0.812767706852388 df.mm.trans1:probe17 0.0693895906249494 0.0817087749537179 0.849230583425753 0.395958095667147 df.mm.trans1:probe18 -0.0161295415500513 0.0817087749537179 -0.197402807216085 0.84355281330018 df.mm.trans1:probe19 0.0380091490502227 0.0817087749537179 0.465178300271325 0.641905975529952 df.mm.trans1:probe20 0.0336347412182164 0.0817087749537179 0.411641726819036 0.680691054744797 df.mm.trans1:probe21 -0.0961992172932048 0.0817087749537179 -1.17734254794170 0.239341488357969 df.mm.trans1:probe22 -0.0396960456986416 0.0817087749537179 -0.485823532675977 0.62719968514592 df.mm.trans2:probe2 -0.195051728834022 0.0817087749537179 -2.38715766996268 0.0171658684638514 * df.mm.trans2:probe3 -0.0765473863566544 0.0817087749537179 -0.936831893514656 0.349073280581213 df.mm.trans2:probe4 0.0162829697805806 0.0817087749537179 0.199280552055807 0.842084170703306 df.mm.trans2:probe5 -0.0907647745328373 0.0817087749537179 -1.11083264415908 0.266909833305073 df.mm.trans2:probe6 -0.148027034622351 0.0817087749537179 -1.81164183046677 0.0703441720438082 . df.mm.trans3:probe2 -0.0991507015777787 0.0817087749537179 -1.21346454690993 0.225241397544757 df.mm.trans3:probe3 -0.0460669011546767 0.0817087749537179 -0.563793805264738 0.573022024462049 df.mm.trans3:probe4 -0.00696066849638631 0.0817087749537179 -0.085188751151012 0.93212854007038 df.mm.trans3:probe5 -0.068446838977619 0.0817087749537179 -0.837692634804392 0.402405330492258 df.mm.trans3:probe6 -0.0893395609122896 0.0817087749537179 -1.09339004241459 0.274488288908545 df.mm.trans3:probe7 -0.0683866513509616 0.0817087749537179 -0.83695602326308 0.402819067845071 df.mm.trans3:probe8 -0.0919482493993046 0.0817087749537179 -1.12531670498532 0.260727316377565 df.mm.trans3:probe9 -0.0591345437984109 0.0817087749537179 -0.72372329449201 0.469406425295455 df.mm.trans3:probe10 0.186063891578050 0.0817087749537179 2.27715923636649 0.0229891191930070 * df.mm.trans3:probe11 -0.0367608619306826 0.0817087749537179 -0.449901029008267 0.652880197266771 df.mm.trans3:probe12 -0.00200868593455010 0.0817087749537179 -0.0245834787718684 0.980392146446725 df.mm.trans3:probe13 0.0280619805498257 0.0817087749537179 0.343439007202357 0.731341064516359 df.mm.trans3:probe14 -0.155273081136693 0.0817087749537179 -1.90032320548979 0.0576807167285351 . df.mm.trans3:probe15 -0.0657743295047227 0.0817087749537179 -0.804984893507204 0.421021481672781 df.mm.trans3:probe16 -0.0833885289618635 0.0817087749537179 -1.02055781657597 0.307713054845104