fitVsDatCorrelation=0.91847575471453
cont.fitVsDatCorrelation=0.257346706822572

fstatistic=10673.6876618368,50,646
cont.fstatistic=1777.03374198925,50,646

residuals=-0.575162033012472,-0.0827252242696713,-0.00414770829506124,0.0824628305074477,0.705531528306712
cont.residuals=-0.631497198241648,-0.211734938332283,-0.0719029688378428,0.0933372983338817,1.54106061500028

predictedValues:
Include	Exclude	Both
Lung	59.471741791066	47.7933632555127	72.4783977677692
cerebhem	78.8053596532277	60.7849016828577	82.3228272562304
cortex	56.6964395495106	45.4369645950613	66.4040814609834
heart	54.9580514758279	43.5297409842764	64.0340015172772
kidney	60.3019473230408	46.7318325671773	74.4329248689919
liver	60.5325986083982	46.1980422660209	71.8651084886056
stomach	55.2620340481616	46.9568685713448	65.640932341066
testicle	59.5571810606682	49.8316530614615	64.7594297339143


diffExp=11.6783785355533,18.0204579703700,11.2594749544493,11.4283104915515,13.5701147558634,14.3345563423773,8.30516547681686,9.7255279992067
diffExpScore=0.989931735812228
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,1,0,0
diffExp1.3Score=0.5
diffExp1.2=1,1,1,1,1,1,0,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	60.80404758941	65.7841320347809	59.740649549883
cerebhem	59.7236735060773	68.0657783804672	54.7317430442965
cortex	58.4831777779627	52.6995468884475	59.3633831442956
heart	54.873523364184	66.5488223091196	61.3523040261907
kidney	62.5058798806702	71.9871409847062	58.1694298351476
liver	58.0723978911156	56.5248306242192	56.9679573044481
stomach	57.570898155433	61.9705936067027	55.5967963869246
testicle	52.1048954654283	56.0060802588963	58.8082856928873
cont.diffExp=-4.9800844453708,-8.34210487438987,5.7836308895152,-11.6752989449356,-9.48126110403594,1.54756726689634,-4.39969545126966,-3.90118479346796
cont.diffExpScore=1.37484181806066

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=0.955515922943133
cont.tran.correlation=0.525144642895997

tran.covariance=0.0110004084991355
cont.tran.covariance=0.00317674085888395

tran.mean=54.5530450308509
cont.tran.mean=60.2328386698513

weightedLogRatios:
wLogRatio
Lung	0.869253613953766
cerebhem	1.10013475928350
cortex	0.869384770376952
heart	0.906861019063817
kidney	1.01259024081878
liver	1.07234678580800
stomach	0.640133712371229
testicle	0.712752600415127

cont.weightedLogRatios:
wLogRatio
Lung	-0.326462738911064
cerebhem	-0.543263252172677
cortex	0.418265382210446
heart	-0.791196190344089
kidney	-0.593982452207776
liver	0.109343305038639
stomach	-0.301187067800320
testicle	-0.288037162283804

varWeightedLogRatios=0.0267030677456338
cont.varWeightedLogRatios=0.152902453597706

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85195481938841	0.0723630502075013	53.2309626023629	2.16190581913533e-238	***
df.mm.trans1	0.234092230507287	0.0628462243418232	3.72484159484346	0.000212527903179494	***
df.mm.trans2	0.0235585460269937	0.0571103062355822	0.412509537767383	0.680102852696076	   
df.mm.exp2	0.394574503311666	0.0756237211847237	5.21760232279302	2.44505356647958e-07	***
df.mm.exp3	-0.0108206383113301	0.0756237211847237	-0.143085240210527	0.886267479889955	   
df.mm.exp4	-0.0484991189863154	0.0756237211847237	-0.641321508999116	0.521541279816952	   
df.mm.exp5	-0.0352079154148838	0.0756237211847237	-0.465567084815657	0.641682322666398	   
df.mm.exp6	-0.00777090877414536	0.0756237211847237	-0.102757556126650	0.918187281507506	   
df.mm.exp7	0.00801664588422562	0.0756237211847237	0.106007027406700	0.915609685211936	   
df.mm.exp8	0.155808438072989	0.0756237211847237	2.06031170685718	0.0397686700958654	*  
df.mm.trans1:exp2	-0.113094764713278	0.0697216259488483	-1.62209017896759	0.105271950897903	   
df.mm.trans2:exp2	-0.154119858593277	0.0573445258359443	-2.68761239798539	0.00738184435268605	** 
df.mm.trans1:exp3	-0.0369692193242558	0.0697216259488482	-0.530240349692627	0.596127504512263	   
df.mm.trans2:exp3	-0.0397401756225109	0.0573445258359443	-0.69300731051823	0.488554026934155	   
df.mm.trans1:exp4	-0.0304319590810348	0.0697216259488483	-0.436478046329003	0.662635763903279	   
df.mm.trans2:exp4	-0.0449432615787452	0.0573445258359443	-0.783741096880326	0.433479298727657	   
df.mm.trans1:exp5	0.0490710406846534	0.0697216259488483	0.703813773945185	0.481802358972853	   
df.mm.trans2:exp5	0.0127467016023738	0.0573445258359443	0.222282797120698	0.824163998818498	   
df.mm.trans1:exp6	0.0254516768167337	0.0697216259488482	0.365047092209331	0.71519573375942	   
df.mm.trans2:exp6	-0.0261784550611235	0.0573445258359443	-0.45651184100845	0.648175404474991	   
df.mm.trans1:exp7	-0.0814317902336164	0.0697216259488482	-1.16795598389170	0.243255391888839	   
df.mm.trans2:exp7	-0.0256739413115428	0.0573445258359443	-0.447713900102563	0.654509783263237	   
df.mm.trans1:exp8	-0.154372832638123	0.0697216259488482	-2.21413127616070	0.0271681550430490	*  
df.mm.trans2:exp8	-0.114044838139777	0.0573445258359443	-1.98876591056041	0.0471484841046653	*  
df.mm.trans1:probe2	-0.0949270426755677	0.0426956019029673	-2.22334475788173	0.0265377297833272	*  
df.mm.trans1:probe3	-0.040414325463646	0.0426956019029673	-0.946568818856193	0.344212464935339	   
df.mm.trans1:probe4	-0.222163033097747	0.0426956019029673	-5.20341728880291	2.63119761669156e-07	***
df.mm.trans1:probe5	0.867323208484547	0.0426956019029673	20.3141112861151	2.56172948896140e-71	***
df.mm.trans1:probe6	-0.170502619295064	0.0426956019029673	-3.99344690543439	7.26024255747128e-05	***
df.mm.trans1:probe7	-0.262242605074844	0.0426956019029673	-6.14214563998495	1.42288239960784e-09	***
df.mm.trans1:probe8	-0.142164915667989	0.0426956019029673	-3.32973208788769	0.000918743476602704	***
df.mm.trans1:probe9	-0.203211772362519	0.0426956019029673	-4.7595481338886	2.39611022808536e-06	***
df.mm.trans1:probe10	-0.239380373751190	0.0426956019029673	-5.6066752330889	3.05814531479111e-08	***
df.mm.trans1:probe11	-0.20274122550109	0.0426956019029673	-4.74852716590932	2.52575373201121e-06	***
df.mm.trans1:probe12	0.192528551453954	0.0426956019029673	4.50932983428847	7.72342548353137e-06	***
df.mm.trans1:probe13	0.0917799153086433	0.0426956019029673	2.14963394864951	0.0319548578528360	*  
df.mm.trans1:probe14	-0.00156559646601687	0.0426956019029673	-0.0366687995071469	0.970760414246946	   
df.mm.trans1:probe15	0.388734381518024	0.0426956019029673	9.10478747674025	1.06548442452600e-18	***
df.mm.trans1:probe16	0.00277799995051207	0.0426956019029673	0.0650652485664807	0.948142178655722	   
df.mm.trans1:probe17	0.0236165577355424	0.0426956019029673	0.553137950583643	0.580360316876518	   
df.mm.trans2:probe2	0.118674495106859	0.0426956019029673	2.77954847378814	0.00560184221400583	** 
df.mm.trans2:probe3	0.0729824295510124	0.0426956019029673	1.70936645223732	0.0878633489100246	.  
df.mm.trans2:probe4	-0.100675845809017	0.0426956019029673	-2.35799101832127	0.0186712199854921	*  
df.mm.trans2:probe5	-0.114507387317987	0.0426956019029673	-2.6819480746102	0.00750657548027674	** 
df.mm.trans2:probe6	-0.0799926465466624	0.0426956019029673	-1.87355706399124	0.0614427529574469	.  
df.mm.trans3:probe2	0.00185576445010515	0.0426956019029673	0.0434650026558398	0.96534429310813	   
df.mm.trans3:probe3	-0.0559562364530476	0.0426956019029673	-1.31058549262796	0.190463588685543	   
df.mm.trans3:probe4	-0.103380014420136	0.0426956019029673	-2.42132701759502	0.0157384033955106	*  
df.mm.trans3:probe5	1.13304140719490	0.0426956019029673	26.5376609462006	1.65566884100673e-105	***
df.mm.trans3:probe6	0.0665368375158956	0.0426956019029673	1.55840026959009	0.119628135887845	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23036659327302	0.176813692401070	23.9255599259654	4.47896544827209e-91	***
df.mm.trans1	-0.166416727050943	0.153560041312243	-1.08372416176001	0.278891425997005	   
df.mm.trans2	-0.0388682225462857	0.139544755102410	-0.278535889921201	0.780690152376597	   
df.mm.exp2	0.103736868931907	0.184780897674127	0.56140472439339	0.57471636026579	   
df.mm.exp3	-0.254353907840420	0.184780897674127	-1.37651624730706	0.169138764235501	   
df.mm.exp4	-0.117688243622194	0.184780897674127	-0.636906980664983	0.524411064915298	   
df.mm.exp5	0.144365851827316	0.184780897674127	0.781281256041491	0.434923314018642	   
df.mm.exp6	-0.150140791921292	0.184780897674127	-0.812534162411497	0.416784519393307	   
df.mm.exp7	-0.0424707331600993	0.184780897674127	-0.229843743020445	0.818285950163245	   
df.mm.exp8	-0.299585918051921	0.184780897674127	-1.62130351038915	0.105440515046935	   
df.mm.trans1:exp2	-0.121664744945221	0.170359570096511	-0.714164428075841	0.475383468959446	   
df.mm.trans2:exp2	-0.0696409569548393	0.140117053150294	-0.497019851538985	0.619344155776736	   
df.mm.trans1:exp3	0.215436702423513	0.170359570096511	1.26459994176708	0.206470809415093	   
df.mm.trans2:exp3	0.0325821106463611	0.140117053150294	0.232534940707127	0.816196202803075	   
df.mm.trans1:exp4	0.0150628466240072	0.170359570096511	0.0884179656914722	0.929571885011093	   
df.mm.trans2:exp4	0.129245437212707	0.140117053150294	0.922410472578764	0.356658878569113	   
df.mm.trans1:exp5	-0.116761580369838	0.170359570096511	-0.6853831593006	0.493348031616089	   
df.mm.trans2:exp5	-0.054257000958558	0.140117053150294	-0.387226249329981	0.698716255272025	   
df.mm.trans1:exp6	0.104174904615929	0.170359570096511	0.611500161434504	0.541083638518258	   
df.mm.trans2:exp6	-0.00155784115695915	0.140117053150294	-0.0111181410251910	0.991132622282632	   
df.mm.trans1:exp7	-0.0121684261225717	0.170359570096510	-0.0714278987419266	0.94307931349691	   
df.mm.trans2:exp7	-0.0172479457373755	0.140117053150294	-0.123096692012747	0.902068817621996	   
df.mm.trans1:exp8	0.145188466314578	0.170359570096511	0.852247198277896	0.394392745542755	   
df.mm.trans2:exp8	0.138667524182598	0.140117053150294	0.989654871158749	0.322713370641992	   
df.mm.trans1:probe2	0.0198453736469115	0.104323504884089	0.1902291690541	0.849189315925389	   
df.mm.trans1:probe3	0.112582408809234	0.104323504884089	1.07916628121651	0.280916369791065	   
df.mm.trans1:probe4	0.0646297811691017	0.104323504884089	0.619513131205765	0.535796804365396	   
df.mm.trans1:probe5	-0.0446902152459761	0.104323504884089	-0.428381075728143	0.668516491973301	   
df.mm.trans1:probe6	-0.0992550498842814	0.104323504884089	-0.951415982376755	0.341749073623859	   
df.mm.trans1:probe7	0.0810913244698766	0.104323504884089	0.777306366000406	0.437262590934938	   
df.mm.trans1:probe8	0.137022992797993	0.104323504884089	1.31344314927145	0.189499963456789	   
df.mm.trans1:probe9	0.164468533153373	0.104323504884089	1.57652422947359	0.115394591304609	   
df.mm.trans1:probe10	-0.01294601868217	0.104323504884089	-0.124094936194427	0.901278709474739	   
df.mm.trans1:probe11	0.0559749921393139	0.104323504884089	0.536552066588507	0.591761782468411	   
df.mm.trans1:probe12	0.0139016632229963	0.104323504884089	0.133255331465734	0.894032951557251	   
df.mm.trans1:probe13	0.154705290646377	0.104323504884089	1.48293800920767	0.138578555749218	   
df.mm.trans1:probe14	0.0959145431876403	0.104323504884089	0.919395329884753	0.358231996822979	   
df.mm.trans1:probe15	0.0877750089797682	0.104323504884089	0.841373275153024	0.400450190990811	   
df.mm.trans1:probe16	0.130600443401196	0.104323504884089	1.25187936837727	0.211066905443867	   
df.mm.trans1:probe17	0.0436282609432452	0.104323504884089	0.418201641056055	0.67593867746999	   
df.mm.trans2:probe2	-0.0270676911871272	0.104323504884089	-0.259459181487446	0.795363633989003	   
df.mm.trans2:probe3	0.0673222986566322	0.104323504884089	0.645322439381541	0.51894737953986	   
df.mm.trans2:probe4	-0.116153908280672	0.104323504884089	-1.11340113054798	0.265950298345912	   
df.mm.trans2:probe5	0.0285797052435681	0.104323504884089	0.273952694316801	0.784208547886514	   
df.mm.trans2:probe6	-0.0141169960063159	0.104323504884089	-0.135319418399535	0.892401485453091	   
df.mm.trans3:probe2	0.0504699657841729	0.104323504884089	0.483783264761368	0.628703640660297	   
df.mm.trans3:probe3	0.0531969566895289	0.104323504884089	0.509923020211359	0.610279559124574	   
df.mm.trans3:probe4	0.135594577402580	0.104323504884089	1.29975097705197	0.194149929240757	   
df.mm.trans3:probe5	0.083076033379578	0.104323504884089	0.796330927262095	0.426132255773195	   
df.mm.trans3:probe6	0.0672487187474574	0.104323504884089	0.644617134194023	0.519404160646251