fitVsDatCorrelation=0.908118052018213 cont.fitVsDatCorrelation=0.278815439489376 fstatistic=7383.95496464226,66,1014 cont.fstatistic=1391.24367479967,66,1014 residuals=-0.877843095385071,-0.124948769361712,0.00473576408003454,0.116513743603330,1.10748934448166 cont.residuals=-1.10960681765359,-0.380716748754623,-0.0930503282756022,0.335700065787269,1.84989122484745 predictedValues: Include Exclude Both Lung 126.334418070119 176.025812675698 121.615080966408 cerebhem 169.958476741905 236.558482917054 135.425701512533 cortex 181.582378095447 137.742698077466 238.516780466166 heart 132.687833021891 158.767658281271 148.948291945473 kidney 127.908515465745 167.578499333905 125.450499493922 liver 109.377702805334 173.680384792670 104.351137939088 stomach 126.90120749939 156.527915659004 143.019058454726 testicle 124.497106133713 168.498160032613 129.63413931666 diffExp=-49.6913946055786,-66.6000061751489,43.8396800179805,-26.0798252593794,-39.6699838681599,-64.302681987335,-29.6267081596140,-44.0010538989 diffExpScore=1.31277285982142 diffExp1.5=0,0,0,0,0,-1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,-1,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,-1,1,0,-1,-1,0,-1 diffExp1.3Score=1.2 diffExp1.2=-1,-1,1,0,-1,-1,-1,-1 diffExp1.2Score=1.16666666666667 cont.predictedValues: Include Exclude Both Lung 153.041829416311 150.546599712782 170.647252977042 cerebhem 149.580695412948 119.023206922316 145.869084654774 cortex 143.241219736003 130.968855552821 157.974335052054 heart 133.088432736769 145.571963378646 149.103673492043 kidney 149.455961840645 142.827699611718 135.41100537515 liver 134.138997085894 135.531259525962 173.647279677789 stomach 143.99605320274 147.985761027022 144.772745170729 testicle 129.848778732928 144.173135344402 164.134503429855 cont.diffExp=2.49522970352871,30.5574884906327,12.2723641831813,-12.4835306418767,6.62826222892659,-1.39226244006798,-3.98970782428188,-14.3243566114737 cont.diffExpScore=4.05246005957706 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.161669067951101 cont.tran.correlation=-0.128025986450643 tran.covariance=0.00209486295036439 cont.tran.covariance=-0.00073638162800898 tran.mean=154.664203100202 cont.tran.mean=140.813778077494 weightedLogRatios: wLogRatio Lung -1.66007676218779 cerebhem -1.75268818876607 cortex 1.39916976089597 heart -0.893215196352932 kidney -1.3470047529455 liver -2.27783561201934 stomach -1.03828395542853 testicle -1.50582842295180 cont.weightedLogRatios: wLogRatio Lung 0.0825627867821736 cerebhem 1.11826816522130 cortex 0.440664048176192 heart -0.442531534132 kidney 0.226102598738762 liver -0.0506379209729617 stomach -0.136198557224326 testicle -0.514713517027192 varWeightedLogRatios=1.23365789901126 cont.varWeightedLogRatios=0.274155923299711 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.33632389264692 0.106757651919086 49.9853996104319 8.01405291182639e-276 *** df.mm.trans1 -0.700321060217108 0.091263571497515 -7.67361005849061 3.91694528033793e-14 *** df.mm.trans2 -0.0842080087701116 0.0797128219377586 -1.05639226818319 0.291040651839721 df.mm.exp2 0.484624286616145 0.100450353335013 4.82451549971029 1.61874991846844e-06 *** df.mm.exp3 -0.556044970497982 0.100450353335013 -5.5355203046774 3.95116345047908e-08 *** df.mm.exp4 -0.256860264607464 0.100450353335013 -2.55708672074858 0.0106999048311527 * df.mm.exp5 -0.0678462416924012 0.100450353335013 -0.675420637557408 0.499562548341088 df.mm.exp6 -0.0044399086077586 0.100450353335013 -0.0442000297694427 0.964753660632838 df.mm.exp7 -0.275036805310067 0.100450353335013 -2.73803721120611 0.00628894053195398 ** df.mm.exp8 -0.122211035059900 0.100450353335013 -1.21663121136382 0.224027669684614 df.mm.trans1:exp2 -0.188002636544713 0.0916378597591746 -2.05158257775537 0.0404666787488297 * df.mm.trans2:exp2 -0.189059471406827 0.0623171535310367 -3.03382713577679 0.00247623369530704 ** df.mm.trans1:exp3 0.918821892352973 0.0916378597591746 10.0266625035509 1.26453719658670e-22 *** df.mm.trans2:exp3 0.310801761399665 0.0623171535310367 4.98741909392367 7.19544568951617e-07 *** df.mm.trans1:exp4 0.305927011204453 0.0916378597591746 3.33843470382692 0.000873164087184983 *** df.mm.trans2:exp4 0.153671482247854 0.0623171535310367 2.46595798332347 0.0138293318861948 * df.mm.trans1:exp5 0.0802290244674906 0.0916378597591746 0.87550085388652 0.381508780548402 df.mm.trans2:exp5 0.0186674886995608 0.0623171535310367 0.299556183840515 0.764577100347381 df.mm.trans1:exp6 -0.139685538341194 0.0916378597591746 -1.52432126534044 0.127740286399303 df.mm.trans2:exp6 -0.00897399751230265 0.0623171535310367 -0.144005253831647 0.885524934824097 df.mm.trans1:exp7 0.279513192662293 0.0916378597591746 3.05019337418898 0.00234636052760354 ** df.mm.trans2:exp7 0.157640526986998 0.0623171535310367 2.52964903007783 0.0115682057008081 * df.mm.trans1:exp8 0.107561004104132 0.0916378597591746 1.17376163505786 0.240766127840454 df.mm.trans2:exp8 0.0785052178906431 0.0623171535310367 1.25976899525014 0.208042576409163 df.mm.trans1:probe2 -0.395715981925010 0.0682285453141761 -5.7998595764109 8.86175707076726e-09 *** df.mm.trans1:probe3 -0.314011202937215 0.0682285453141761 -4.60234351313322 4.70677747810802e-06 *** df.mm.trans1:probe4 0.43826202546444 0.0682285453141761 6.42344085523659 2.04482142168490e-10 *** df.mm.trans1:probe5 0.210435595408291 0.0682285453141761 3.08427498254997 0.00209572913334153 ** df.mm.trans1:probe6 0.214818070882869 0.0682285453141762 3.14850726911564 0.00168902297599766 ** df.mm.trans1:probe7 0.284668171097064 0.0682285453141762 4.17227378637834 3.27415630507301e-05 *** df.mm.trans1:probe8 0.283423968008858 0.0682285453141761 4.1540379719919 3.54170865162831e-05 *** df.mm.trans1:probe9 0.30592629643648 0.0682285453141762 4.48384609444276 8.16703130420113e-06 *** df.mm.trans1:probe10 0.515402903616528 0.0682285453141762 7.55406554900475 9.39927889082433e-14 *** df.mm.trans1:probe11 1.15469856960327 0.0682285453141762 16.9239804877293 8.8410097398936e-57 *** df.mm.trans1:probe12 0.917355694348746 0.0682285453141761 13.4453356747464 4.82302166456211e-38 *** df.mm.trans1:probe13 1.15798017909289 0.0682285453141761 16.9720777976530 4.67389323953206e-57 *** df.mm.trans1:probe14 1.40051117357156 0.0682285453141761 20.5267629131258 1.40568688860238e-78 *** df.mm.trans1:probe15 0.923441197565177 0.0682285453141761 13.5345285952229 1.72933762511514e-38 *** df.mm.trans1:probe16 1.49649666199751 0.0682285453141762 21.9335859368905 1.41316846978199e-87 *** df.mm.trans1:probe17 -0.154007699800174 0.0682285453141761 -2.25723264494363 0.0242056495887182 * df.mm.trans1:probe18 -0.345321976572913 0.0682285453141761 -5.06125368762865 4.94379621329098e-07 *** df.mm.trans1:probe19 -0.176526180179318 0.0682285453141761 -2.58727750044292 0.00981196993645494 ** df.mm.trans1:probe20 0.127186961040241 0.0682285453141761 1.86413121450230 0.0625920885105985 . df.mm.trans1:probe21 0.0705213652462789 0.0682285453141761 1.03360499511670 0.301567309487803 df.mm.trans1:probe22 -0.201288652033845 0.0682285453141762 -2.95021169082469 0.00324854329285062 ** df.mm.trans2:probe2 -0.176754850152599 0.0682285453141761 -2.59062902980982 0.00971757089406483 ** df.mm.trans2:probe3 -0.325300267264601 0.0682285453141761 -4.76780306199805 2.13470711322969e-06 *** df.mm.trans2:probe4 -0.339187900807083 0.0682285453141762 -4.97134885765487 7.80285367792961e-07 *** df.mm.trans2:probe5 -0.568333946544327 0.0682285453141761 -8.32985583859783 2.60364037811072e-16 *** df.mm.trans2:probe6 -0.464583478469021 0.0682285453141761 -6.80922444307914 1.67804343644463e-11 *** df.mm.trans3:probe2 0.0312520530115876 0.0682285453141762 0.458049528502761 0.647014998874491 df.mm.trans3:probe3 0.0160190516466761 0.0682285453141761 0.234785185187699 0.814422868207125 df.mm.trans3:probe4 0.177569540766953 0.0682285453141762 2.60256964221189 0.00938780688304383 ** df.mm.trans3:probe5 0.0101700481462521 0.0682285453141761 0.149058551657835 0.881537070426395 df.mm.trans3:probe6 -0.0844558749151824 0.0682285453141761 -1.2378378364405 0.216062728429431 df.mm.trans3:probe7 0.780462732734034 0.0682285453141762 11.4389472784476 1.38788205310259e-28 *** df.mm.trans3:probe8 -0.0734547822453185 0.0682285453141762 -1.07659897931396 0.281915532473586 df.mm.trans3:probe9 0.829935077432871 0.0682285453141762 12.1640447353995 7.11232626776862e-32 *** df.mm.trans3:probe10 -0.170638019217630 0.0682285453141761 -2.50097695080384 0.0125419417728716 * df.mm.trans3:probe11 0.466398747493349 0.0682285453141762 6.83583015504279 1.40565057684858e-11 *** df.mm.trans3:probe12 0.150247379340453 0.0682285453141762 2.20211904927183 0.0278814060069591 * df.mm.trans3:probe13 0.169335011197767 0.0682285453141762 2.48187925476089 0.0132302012294534 * df.mm.trans3:probe14 0.332925102042274 0.0682285453141762 4.87955738333909 1.23413053377735e-06 *** df.mm.trans3:probe15 0.807110419453722 0.0682285453141762 11.8295123505444 2.44762039561420e-30 *** df.mm.trans3:probe16 -0.0721329111660331 0.0682285453141761 -1.05722481453882 0.290660798301056 df.mm.trans3:probe17 0.863381132169388 0.0682285453141762 12.6542509179072 3.49860398344255e-34 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.82595016401975 0.244854698307167 19.7094448151681 1.8361020192831e-73 *** df.mm.trans1 0.180335697606594 0.209318150631444 0.861538748849924 0.389145071986432 df.mm.trans2 0.183118759497678 0.182825854783467 1.00160209678526 0.31677476079928 df.mm.exp2 -0.100940402891315 0.230388553125302 -0.438131155050993 0.66138452819654 df.mm.exp3 -0.12832834196135 0.230388553125302 -0.557008324504543 0.577644660597008 df.mm.exp4 -0.0383428561658557 0.230388553125302 -0.166426915077687 0.867854173038876 df.mm.exp5 0.154940778137147 0.230388553125302 0.672519428744706 0.501406292753375 df.mm.exp6 -0.254332618174308 0.230388553125302 -1.10392905690928 0.269885811212455 df.mm.exp7 0.0863513424717771 0.230388553125302 0.374807434225316 0.707882059169699 df.mm.exp8 -0.168686165564279 0.230388553125302 -0.732181192494125 0.464227175284499 df.mm.trans1:exp2 0.0780651398431302 0.210176601878181 0.371426405915427 0.710397554170076 df.mm.trans2:exp2 -0.134013775762125 0.142927907769723 -0.937631970224035 0.348656844913987 df.mm.trans1:exp3 0.062147123481547 0.210176601878181 0.295690019375077 0.767527359324097 df.mm.trans2:exp3 -0.01098477588035 0.142927907769723 -0.076855360522369 0.938753770941088 df.mm.trans1:exp4 -0.101354607675096 0.210176601878181 -0.482235447568239 0.629742772400783 df.mm.trans2:exp4 0.00474074526823539 0.142927907769723 0.0331687865736717 0.973546516772761 df.mm.trans1:exp5 -0.178650277233067 0.210176601878181 -0.850000788083031 0.395525297167629 df.mm.trans2:exp5 -0.207574441076084 0.142927907769723 -1.45230168352087 0.146727087510975 df.mm.trans1:exp6 0.122497893291004 0.210176601878181 0.582833161238398 0.560135204504774 df.mm.trans2:exp6 0.149262260590331 0.142927907769723 1.04431851637269 0.296586943283713 df.mm.trans1:exp7 -0.147276730490409 0.210176601878181 -0.700728478690372 0.483633236996825 df.mm.trans2:exp7 -0.103507951518655 0.142927907769723 -0.724196926505219 0.469111895223384 df.mm.trans1:exp8 0.0043454195098307 0.210176601878181 0.0206750869078630 0.983508910049309 df.mm.trans2:exp8 0.125428402796731 0.142927907769723 0.87756411434227 0.380388183419515 df.mm.trans1:probe2 -0.0672544687265106 0.156486018365235 -0.429779410512831 0.667447423641262 df.mm.trans1:probe3 0.144253545755110 0.156486018365235 0.921830252070348 0.356836397700117 df.mm.trans1:probe4 0.0750357335148377 0.156486018365235 0.479504394697461 0.631683242559906 df.mm.trans1:probe5 -0.107058596907469 0.156486018365235 -0.684141612304283 0.494042067058934 df.mm.trans1:probe6 0.0574907355025452 0.156486018365235 0.367385764575868 0.713407950728642 df.mm.trans1:probe7 0.0827280851126133 0.156486018365235 0.528661192717728 0.597156192758484 df.mm.trans1:probe8 0.0233402998663239 0.156486018365235 0.149152621493942 0.881462862389205 df.mm.trans1:probe9 0.110652298453023 0.156486018365235 0.707106613159285 0.479662692712398 df.mm.trans1:probe10 0.104350433770593 0.156486018365235 0.666835509400214 0.505028910520514 df.mm.trans1:probe11 -0.113877363937269 0.156486018365235 -0.727715901566884 0.466955508684653 df.mm.trans1:probe12 -0.103006413587831 0.156486018365235 -0.658246753696659 0.510529003308559 df.mm.trans1:probe13 0.175697637728462 0.156486018365235 1.12276891931896 0.261801418980595 df.mm.trans1:probe14 0.0645988232357613 0.156486018365235 0.412808913605234 0.679833890029767 df.mm.trans1:probe15 0.160595200785558 0.156486018365235 1.02625910265499 0.305014218799284 df.mm.trans1:probe16 -0.0292182858722342 0.156486018365235 -0.186714993310389 0.851921448850983 df.mm.trans1:probe17 -0.193778136584706 0.156486018365235 -1.23830958579592 0.215887897375749 df.mm.trans1:probe18 -0.171070967526289 0.156486018365235 -1.09320289003081 0.274564368684678 df.mm.trans1:probe19 0.112146650985083 0.156486018365235 0.716656044780532 0.473751380595303 df.mm.trans1:probe20 0.469779051079645 0.156486018365235 3.00205127580914 0.00274737538186014 ** df.mm.trans1:probe21 0.0794610368401137 0.156486018365235 0.507783619713893 0.611715529391528 df.mm.trans1:probe22 0.0777259743691349 0.156486018365235 0.496695967992005 0.619511127387289 df.mm.trans2:probe2 0.0752152998234597 0.156486018365235 0.480651885767257 0.630867615540056 df.mm.trans2:probe3 0.178052227030844 0.156486018365235 1.13781556263560 0.255466279078740 df.mm.trans2:probe4 0.0172675188129681 0.156486018365235 0.110345441678158 0.912157255093995 df.mm.trans2:probe5 0.00301138936967318 0.156486018365235 0.0192438238325207 0.984650383525757 df.mm.trans2:probe6 -0.153860291106515 0.156486018365235 -0.983220690984725 0.325733369308387 df.mm.trans3:probe2 0.119134443360282 0.156486018365235 0.761310464697393 0.446648664703997 df.mm.trans3:probe3 0.0840684122421559 0.156486018365235 0.537226348528736 0.591229137209264 df.mm.trans3:probe4 -0.173374384619926 0.156486018365235 -1.10792252516308 0.268158005553396 df.mm.trans3:probe5 0.0209704215134711 0.156486018365235 0.134008275835395 0.893422622223774 df.mm.trans3:probe6 0.104011918888084 0.156486018365235 0.664672281745468 0.506411252532577 df.mm.trans3:probe7 -0.164893086547831 0.156486018365235 -1.05372408519574 0.29226027588615 df.mm.trans3:probe8 0.0247251913290834 0.156486018365235 0.158002558869990 0.874486240974036 df.mm.trans3:probe9 -0.156310128512073 0.156486018365235 -0.998876002757314 0.318093065218425 df.mm.trans3:probe10 -0.0319503675527181 0.156486018365235 -0.204173944014262 0.838258542716261 df.mm.trans3:probe11 -0.192994989698800 0.156486018365235 -1.23330500523282 0.217747807922309 df.mm.trans3:probe12 -0.252672420977970 0.156486018365235 -1.61466451519163 0.106694467499190 df.mm.trans3:probe13 -0.0333855121218992 0.156486018365235 -0.213345016191659 0.831100749915995 df.mm.trans3:probe14 0.188015026808732 0.156486018365235 1.20148131298164 0.229845095616116 df.mm.trans3:probe15 -0.0361126379669995 0.156486018365235 -0.230772297386425 0.817538255981138 df.mm.trans3:probe16 -0.219236188022928 0.156486018365235 -1.40099537526245 0.161521414461165 df.mm.trans3:probe17 -0.126698434399541 0.156486018365235 -0.809646994173178 0.418333039638233