fitVsDatCorrelation=0.863463636690988 cont.fitVsDatCorrelation=0.272620544534726 fstatistic=11741.7291483915,59,853 cont.fstatistic=3216.83113631895,59,853 residuals=-0.466737594369145,-0.0849982157218043,0.00163250617550548,0.0745269368611042,0.912559042957613 cont.residuals=-0.703771263895393,-0.193283234677249,-0.0531007340378612,0.148373583534820,1.26056611268254 predictedValues: Include Exclude Both Lung 46.7958700740667 63.9504547312497 58.03345276606 cerebhem 51.5038913007312 76.3238211304249 66.1503380180887 cortex 45.8400491014948 79.3497859641096 67.2353294180625 heart 46.4560710991374 59.5640081648514 59.0088462885875 kidney 46.1573037351421 54.695885537703 56.3718290138781 liver 47.7115866276469 57.1148561500912 57.6600061961907 stomach 47.8266208792273 63.5955940475258 59.853474179319 testicle 50.1891150090216 54.950159395835 56.577320340388 diffExp=-17.1545846571830,-24.8199298296937,-33.5097368626148,-13.1079370657139,-8.53858180256087,-9.4032695224443,-15.7689731682985,-4.76104438681335 diffExpScore=0.992191407791384 diffExp1.5=0,0,-1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,-1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=-1,-1,-1,0,0,0,-1,0 diffExp1.3Score=0.8 diffExp1.2=-1,-1,-1,-1,0,0,-1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 61.4678564280492 57.1675705804189 59.0867647351966 cerebhem 56.0288205855711 60.2280006079203 59.2711928295141 cortex 60.8077592633309 50.3070674608407 57.8341754928742 heart 59.8819966578375 49.3138628600088 61.9263432826583 kidney 58.1904218862659 54.8553144118302 58.09827685821 liver 56.5773240263279 58.3854173177445 53.6462441173242 stomach 60.9010961269261 57.9509661933297 59.2544243117263 testicle 58.0481265598056 54.0652476080349 60.6066448325666 cont.diffExp=4.30028584763024,-4.19918002234918,10.5006918024902,10.5681337978287,3.33510747443568,-1.80809329141655,2.95012993359637,3.98287895177067 cont.diffExpScore=1.35960048943899 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=0,0,1,1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.118463917679661 cont.tran.correlation=-0.445842918051388 tran.covariance=0.00062998487334871 cont.tran.covariance=-0.00113334946938226 tran.mean=55.7515670592662 cont.tran.mean=57.1360530358901 weightedLogRatios: wLogRatio Lung -1.24986431644804 cerebhem -1.62771649094417 cortex -2.24943338617547 heart -0.984926386158023 kidney -0.664831987766247 liver -0.711486349457847 stomach -1.14271479705729 testicle -0.358989320014596 cont.weightedLogRatios: wLogRatio Lung 0.296075791564154 cerebhem -0.293565576873332 cortex 0.760738753094176 heart 0.775768238225294 kidney 0.238105914950668 liver -0.127446503497841 stomach 0.202807717094675 testicle 0.286152367509040 varWeightedLogRatios=0.36030110671409 cont.varWeightedLogRatios=0.139801678539694 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11524661777075 0.0682024529984965 60.3386892530312 0 *** df.mm.trans1 -0.304335064216235 0.0566344268018581 -5.37367607305332 9.95980240128763e-08 *** df.mm.trans2 0.00159058371636402 0.0517244183470467 0.0307511184696548 0.975475217159001 df.mm.exp2 0.141828481367689 0.0661660982680059 2.1435219104686 0.0323534117425219 * df.mm.exp3 0.0479410730194531 0.0661660982680059 0.724556446191941 0.468922874056136 df.mm.exp4 -0.095012684799165 0.0661660982680059 -1.43597230736375 0.151376833825068 df.mm.exp5 -0.141009826403758 0.0661660982680059 -2.13114918507960 0.0333622050871882 * df.mm.exp6 -0.0872092398257725 0.0661660982680059 -1.31803509816358 0.187845735617658 df.mm.exp7 -0.0146568552042550 0.0661660982680059 -0.221516087360741 0.82474362930418 df.mm.exp8 -0.0562674272957881 0.0661660982680059 -0.850396634661407 0.395343241391611 df.mm.trans1:exp2 -0.045966070063997 0.0562303710344766 -0.817459839199954 0.413893956760153 df.mm.trans2:exp2 0.0350479730109630 0.0441107321786706 0.794545256446914 0.427099073233524 df.mm.trans1:exp3 -0.0685778822130475 0.0562303710344766 -1.21958793711321 0.222958228952258 df.mm.trans2:exp3 0.167816037854003 0.0441107321786706 3.80442648683009 0.000152277125874631 *** df.mm.trans1:exp4 0.0877248905805881 0.0562303710344766 1.56009802117082 0.119107632537319 df.mm.trans2:exp4 0.0239555478734574 0.0441107321786706 0.543077538963203 0.58721829441919 df.mm.trans1:exp5 0.127270082864805 0.0562303710344766 2.26336907481495 0.0238634937821707 * df.mm.trans2:exp5 -0.0153103244171407 0.0441107321786706 -0.347088421818215 0.728610504883128 df.mm.trans1:exp6 0.106588561714059 0.0562303710344766 1.89556924048582 0.0583548300887018 . df.mm.trans2:exp6 -0.0258351383233175 0.0441107321786706 -0.585688267849924 0.558239940351348 df.mm.trans1:exp7 0.0364443090885981 0.0562303710344766 0.64812499754364 0.517078524968298 df.mm.trans2:exp7 0.00909240843773585 0.0441107321786706 0.206126899025549 0.836740973926784 df.mm.trans1:exp8 0.126270645243257 0.0562303710344766 2.24559509247835 0.0249852897063842 * df.mm.trans2:exp8 -0.0954146298462662 0.0441107321786706 -2.16307064366534 0.0308129089637122 * df.mm.trans1:probe2 0.0166854316291150 0.0427100327792517 0.390667731756477 0.696140399935903 df.mm.trans1:probe3 0.0778034576639788 0.0427100327792517 1.82166700892291 0.068855630098377 . df.mm.trans1:probe4 -0.0196319672312277 0.0427100327792517 -0.459657039663168 0.64587953668813 df.mm.trans1:probe5 0.00970874977680999 0.0427100327792517 0.227317778635058 0.820231119716321 df.mm.trans1:probe6 -0.0153142789681825 0.0427100327792517 -0.358563971311728 0.720010026625102 df.mm.trans1:probe7 0.198339925254423 0.0427100327792517 4.64387199793431 3.95818300303663e-06 *** df.mm.trans1:probe8 0.124492069877113 0.0427100327792517 2.91482028404320 0.00365205308773019 ** df.mm.trans1:probe9 0.16842818720323 0.0427100327792517 3.94352746282723 8.68811426067055e-05 *** df.mm.trans1:probe10 0.216637405426311 0.0427100327792517 5.07228375463932 4.82416946685747e-07 *** df.mm.trans1:probe11 0.210564741729922 0.0427100327792517 4.93010021364848 9.87663686213827e-07 *** df.mm.trans1:probe12 0.128555051863495 0.0427100327792517 3.00994973541549 0.00268984816588337 ** df.mm.trans2:probe2 0.166219609582206 0.0427100327792517 3.8918164835255 0.000107252266366416 *** df.mm.trans2:probe3 0.189056115552979 0.0427100327792517 4.42650363979166 1.08197067359355e-05 *** df.mm.trans2:probe4 0.412219941219772 0.0427100327792517 9.65159505613927 5.46220193530967e-21 *** df.mm.trans2:probe5 0.177515147816098 0.0427100327792517 4.15628685497835 3.56097425544072e-05 *** df.mm.trans2:probe6 0.128046554159866 0.0427100327792517 2.99804392147577 0.00279603323679749 ** df.mm.trans3:probe2 -0.0479897694508407 0.0427100327792517 -1.12361818355133 0.261491160327820 df.mm.trans3:probe3 0.125006943605614 0.0427100327792517 2.92687538433222 0.00351481158746323 ** df.mm.trans3:probe4 0.384233437439308 0.0427100327792517 8.99632738343312 1.48648273587486e-18 *** df.mm.trans3:probe5 -0.0276212906121519 0.0427100327792517 -0.646716680244979 0.51798929948227 df.mm.trans3:probe6 0.602827947248659 0.0427100327792517 14.1144341978007 8.26760893658567e-41 *** df.mm.trans3:probe7 0.0206361435037654 0.0427100327792517 0.483168524136332 0.629100070761454 df.mm.trans3:probe8 0.319750311709843 0.0427100327792517 7.48653866323363 1.76347070991563e-13 *** df.mm.trans3:probe9 0.259895985621244 0.0427100327792517 6.08512728062105 1.75672263338286e-09 *** df.mm.trans3:probe10 0.955446777062402 0.0427100327792517 22.3705465645663 1.38790472017755e-87 *** df.mm.trans3:probe11 -0.0434690455734800 0.0427100327792517 -1.01777129973539 0.309075234279033 df.mm.trans3:probe12 0.504104116404328 0.0427100327792517 11.8029437956606 6.90829652942249e-30 *** df.mm.trans3:probe13 0.254288895494497 0.0427100327792517 5.95384454066796 3.82423624734762e-09 *** df.mm.trans3:probe14 0.364846708576644 0.0427100327792517 8.54241228196586 5.99686949598215e-17 *** df.mm.trans3:probe15 -0.0504180817385576 0.0427100327792517 -1.18047396496147 0.238140829574328 df.mm.trans3:probe16 0.363781119506473 0.0427100327792517 8.51746289652103 7.31515193392244e-17 *** df.mm.trans3:probe17 0.144304930562779 0.0427100327792517 3.37871270922747 0.000761275112384662 *** df.mm.trans3:probe18 0.0187980833274356 0.0427100327792517 0.440132730044814 0.65995247807403 df.mm.trans3:probe19 0.292429774879625 0.0427100327792517 6.84686374255573 1.44299929693151e-11 *** df.mm.trans3:probe20 0.529496589136902 0.0427100327792517 12.3974755972121 1.42902676140496e-32 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97175686827273 0.130090429759490 30.5307383149989 6.16538316064344e-139 *** df.mm.trans1 0.140591166427963 0.108025394951682 1.30146403529325 0.193451058000393 df.mm.trans2 0.0678400738992037 0.0986599677283644 0.687615001922405 0.491882179784159 df.mm.exp2 -0.0436140345386022 0.126206254771852 -0.345577440812620 0.72974550783966 df.mm.exp3 -0.117211114547541 0.126206254771852 -0.928726668574612 0.353293483804604 df.mm.exp4 -0.220858744284906 0.126206254771852 -1.74998255581041 0.0804808765305066 . df.mm.exp5 -0.0792103802819754 0.126206254771852 -0.627626423311324 0.530416668669483 df.mm.exp6 0.0347687492467365 0.126206254771852 0.275491490573025 0.783005383342139 df.mm.exp7 0.00151375091849456 0.126206254771852 0.0119942622592757 0.99043299738743 df.mm.exp8 -0.138434712612498 0.126206254771852 -1.09689264500204 0.272997883670036 df.mm.trans1:exp2 -0.0490341322728338 0.107254692636523 -0.457174703199293 0.647661886870891 df.mm.trans2:exp2 0.095764615377798 0.0841375031812348 1.13819179030685 0.255359982023086 df.mm.trans1:exp3 0.106414136367911 0.107254692636523 0.992162988416181 0.321399380430765 df.mm.trans2:exp3 -0.0106301017523497 0.0841375031812348 -0.126342015753096 0.899490972164912 df.mm.trans1:exp4 0.194720269127928 0.107254692636523 1.81549416945158 0.0697990187451488 . df.mm.trans2:exp4 0.0730771900056876 0.0841375031812348 0.868544789691192 0.385340462134356 df.mm.trans1:exp5 0.0244167706031550 0.107254692636523 0.227652236027578 0.819971161531116 df.mm.trans2:exp5 0.0379226625093025 0.0841375031812348 0.450722461155234 0.652304135516332 df.mm.trans1:exp6 -0.117674858362478 0.107254692636523 -1.0971534715154 0.272883933596196 df.mm.trans2:exp6 -0.0136893837721852 0.0841375031812348 -0.162702519739596 0.870791198303222 df.mm.trans1:exp7 -0.0107769560362786 0.107254692636523 -0.100480042144178 0.919986837848907 df.mm.trans2:exp7 0.0120967018918817 0.0841375031812348 0.143773007689865 0.885713676036272 df.mm.trans1:exp8 0.0811927688922927 0.107254692636523 0.757009011880237 0.449253506858791 df.mm.trans2:exp8 0.0826395291054928 0.0841375031812348 0.98219611922028 0.326281734359274 df.mm.trans1:probe2 -0.00544433712797017 0.081465787153099 -0.0668297369758253 0.946732910021442 df.mm.trans1:probe3 0.0629124601307516 0.081465787153099 0.77225621121809 0.440176600523106 df.mm.trans1:probe4 -0.0538584787832494 0.081465787153099 -0.661117760785064 0.50871531616766 df.mm.trans1:probe5 -0.0416008179516761 0.081465787153099 -0.510653851211129 0.609725586594228 df.mm.trans1:probe6 -0.070991364951006 0.081465787153099 -0.871425507956998 0.383767056599717 df.mm.trans1:probe7 0.174899469594576 0.081465787153099 2.14690701098716 0.0320820160139007 * df.mm.trans1:probe8 0.0148265177996161 0.081465787153099 0.181996864177506 0.85562842717112 df.mm.trans1:probe9 0.000491402128243297 0.081465787153099 0.00603200613921281 0.995188594997847 df.mm.trans1:probe10 -0.0709592910966592 0.081465787153099 -0.871031798456757 0.383981862158128 df.mm.trans1:probe11 0.0156057200805976 0.081465787153099 0.191561643555591 0.848131178707841 df.mm.trans1:probe12 0.171441720850782 0.081465787153099 2.10446282840907 0.035630146651496 * df.mm.trans2:probe2 -0.0167824444756814 0.081465787153099 -0.206006043299404 0.836835347113656 df.mm.trans2:probe3 -0.0161814490321712 0.081465787153099 -0.198628769175966 0.842600478418329 df.mm.trans2:probe4 0.0954610758532038 0.081465787153099 1.17179345083604 0.241607180066203 df.mm.trans2:probe5 0.0416857566379655 0.081465787153099 0.511696481366162 0.608995886102017 df.mm.trans2:probe6 0.0619530971427966 0.081465787153099 0.760479942658233 0.447177904702748 df.mm.trans3:probe2 -0.0958058759331007 0.081465787153099 -1.17602590340228 0.23991263489031 df.mm.trans3:probe3 -0.203756184402355 0.081465787153099 -2.5011258286799 0.0125663803088101 * df.mm.trans3:probe4 -0.0714315205041912 0.081465787153099 -0.876828457692917 0.380826688829246 df.mm.trans3:probe5 -0.044090259266933 0.081465787153099 -0.54121197140186 0.588502799354647 df.mm.trans3:probe6 -0.0657551746675868 0.081465787153099 -0.807150792565385 0.419804612850315 df.mm.trans3:probe7 -0.0767506834758315 0.081465787153099 -0.94212167043318 0.346397089547202 df.mm.trans3:probe8 -0.0722363534075382 0.081465787153099 -0.886707855308439 0.375486130569521 df.mm.trans3:probe9 -0.185542623524381 0.081465787153099 -2.27755270044455 0.0229999524176821 * df.mm.trans3:probe10 -0.151293316947656 0.081465787153099 -1.85713932479815 0.0636358382632239 . df.mm.trans3:probe11 -0.103167089287586 0.081465787153099 -1.26638547165455 0.205720819470011 df.mm.trans3:probe12 -0.0320344383444906 0.081465787153099 -0.393225665202107 0.694251072736455 df.mm.trans3:probe13 -0.0758513722497942 0.081465787153099 -0.93108254275192 0.352074303814317 df.mm.trans3:probe14 -0.12480862839086 0.081465787153099 -1.53203734662634 0.125883991388522 df.mm.trans3:probe15 -0.165934667022583 0.081465787153099 -2.03686323819275 0.0419720564566396 * df.mm.trans3:probe16 -0.164363932680740 0.081465787153099 -2.01758233025909 0.043947501407367 * df.mm.trans3:probe17 -0.0649657126956763 0.081465787153099 -0.797460074541303 0.425405807474494 df.mm.trans3:probe18 -0.0681400120923528 0.081465787153099 -0.836424890418072 0.403150163770235 df.mm.trans3:probe19 -0.101902750496739 0.081465787153099 -1.25086559717680 0.211326500939547 df.mm.trans3:probe20 -0.155792528983591 0.081465787153099 -1.91236756469080 0.0561637429933226 .