fitVsDatCorrelation=0.854251657218727
cont.fitVsDatCorrelation=0.261899182156202

fstatistic=11204.6947161062,62,922
cont.fstatistic=3240.55633260441,62,922

residuals=-0.642397709389171,-0.0885239784598013,-0.00435313178463366,0.0865394147943733,0.872910251912048
cont.residuals=-0.739309740955388,-0.192386087417608,-0.0243689604276331,0.183819557628825,1.32621500918584

predictedValues:
Include	Exclude	Both
Lung	69.3911152686823	101.303950559015	96.4356664240204
cerebhem	61.2503090381308	81.0502636713246	88.1963612381414
cortex	64.1404957674141	82.8903622103936	89.3404948145594
heart	62.1387304618703	93.541837698635	90.5737657861214
kidney	71.5690879128523	105.111067455247	100.442880900691
liver	65.8332686739676	103.72752869065	109.707933347072
stomach	61.9972241265861	92.7358673296037	94.6761676866376
testicle	64.0260679170718	101.531377284311	103.289209717118


diffExp=-31.9128352903322,-19.7999546331939,-18.7498664429795,-31.4031072367648,-33.5419795423951,-37.8942600166823,-30.7386432030176,-37.5053093672391
diffExpScore=0.995877069988739
diffExp1.5=0,0,0,-1,0,-1,0,-1
diffExp1.5Score=0.75
diffExp1.4=-1,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	81.1277972017387	78.5382143136981	87.6549606788607
cerebhem	74.8033214486799	76.584687876267	84.3262280268011
cortex	81.0658565047767	84.675260791809	88.5554797526712
heart	79.4577768576337	81.1648085268536	78.3277440751647
kidney	79.2729101653143	73.4992014716168	86.1888481092464
liver	73.631271707635	71.7595526555995	77.8536161528591
stomach	81.2189765593699	75.8826920787764	76.9563281305769
testicle	79.8867268066177	67.1271678509645	85.4915482559356
cont.diffExp=2.58958288804055,-1.78136642758703,-3.60940428703225,-1.70703166921996,5.77370869369747,1.87171905203556,5.33628448059346,12.7595589556532
cont.diffExpScore=1.59351298031121

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=0,0,0,0,0,0,0,0
cont.diffExp1.2Score=0

tran.correlation=0.697214366786504
cont.tran.correlation=0.329671871897240

tran.covariance=0.00387583227845905
cont.tran.covariance=0.000873555125616046

tran.mean=80.1399096291097
cont.tran.mean=77.4810139260844

weightedLogRatios:
wLogRatio
Lung	-1.67576364807003
cerebhem	-1.19183345780399
cortex	-1.09995974117216
heart	-1.77273108572631
kidney	-1.71531203067410
liver	-2.00699377410200
stomach	-1.74290727945518
testicle	-2.02405175513929

cont.weightedLogRatios:
wLogRatio
Lung	0.142082386445827
cerebhem	-0.101826673280123
cortex	-0.192413494756009
heart	-0.0932256586942769
kidney	0.327827473318844
liver	0.110364555332847
stomach	0.296522253408097
testicle	0.74717587549477

varWeightedLogRatios=0.115476439298418
cont.varWeightedLogRatios=0.0929999810892778

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23046635031579	0.0757825590475806	55.8237463010409	5.74739305049375e-298	***
df.mm.trans1	-0.134514107423172	0.0648963064248397	-2.07275444218017	0.0384725017356144	*  
df.mm.trans2	0.414355573737442	0.0570470380033298	7.26340206678665	8.02852552977286e-13	***
df.mm.exp2	-0.258535439569415	0.072461986129793	-3.56787680517519	0.000378424898533629	***
df.mm.exp3	-0.202868262919167	0.072461986129793	-2.79965087564384	0.00522263438189664	** 
df.mm.exp4	-0.127394468073487	0.072461986129793	-1.75808689324772	0.0790645027821032	.  
df.mm.exp5	0.0270834778731593	0.072461986129793	0.373761185963737	0.708667926543383	   
df.mm.exp6	-0.157936954526188	0.072461986129793	-2.17958357149214	0.0295407637433304	*  
df.mm.exp7	-0.182625508893976	0.072461986129793	-2.52029400031706	0.0118935293182389	*  
df.mm.exp8	-0.146882846244928	0.072461986129793	-2.02703312578037	0.0429463411583298	*  
df.mm.trans1:exp2	0.133745497249887	0.0661484406081902	2.02189947367146	0.0434751383990833	*  
df.mm.trans2:exp2	0.0354795318367527	0.0467740109189669	0.758530883704175	0.448327083458411	   
df.mm.trans1:exp3	0.124185349359821	0.0661484406081902	1.87737380077324	0.0607824045912493	.  
df.mm.trans2:exp3	0.00226165118251463	0.0467740109189669	0.0483527313155334	0.961445601864395	   
df.mm.trans1:exp4	0.0170051042890116	0.0661484406081902	0.25707490808039	0.797178304544303	   
df.mm.trans2:exp4	0.0476778572018922	0.0467740109189669	1.01932368563584	0.308316711635552	   
df.mm.trans1:exp5	0.00382093255702872	0.0661484406081902	0.0577630027540759	0.953949924370433	   
df.mm.trans2:exp5	0.0098086894093359	0.0467740109189669	0.209703833744958	0.833945160348654	   
df.mm.trans1:exp6	0.105303430779053	0.0661484406081902	1.59192612570847	0.111744072485191	   
df.mm.trans2:exp6	0.181579090141848	0.0467740109189669	3.88205087770690	0.00011096195013655	***
df.mm.trans1:exp7	0.0699562835327894	0.0661484406081902	1.05756512004801	0.290530754380776	   
df.mm.trans2:exp7	0.0942554158641914	0.0467740109189669	2.01512365547361	0.0441815143891794	*  
df.mm.trans1:exp8	0.0664143206280162	0.0661484406081902	1.00401944501460	0.315632663291733	   
df.mm.trans2:exp8	0.149125323657711	0.0467740109189669	3.18820902308468	0.00147981664827830	** 
df.mm.trans1:probe2	0.245758534867363	0.047929098701311	5.12754342406694	3.57750003880326e-07	***
df.mm.trans1:probe3	0.158626624696452	0.047929098701311	3.30961000716904	0.000970474277124392	***
df.mm.trans1:probe4	-0.0675500986806126	0.047929098701311	-1.40937552574434	0.159061347134815	   
df.mm.trans1:probe5	-0.162316193234872	0.047929098701311	-3.38658972592849	0.000737582929527052	***
df.mm.trans1:probe6	0.234515045206905	0.047929098701311	4.89295754690439	1.17197187161662e-06	***
df.mm.trans1:probe7	0.230665763901295	0.047929098701311	4.81264555669572	1.73964832910478e-06	***
df.mm.trans1:probe8	0.298893976582165	0.047929098701311	6.2361693560073	6.82024623386205e-10	***
df.mm.trans1:probe9	0.274347072463724	0.047929098701311	5.72401901762071	1.40655641589470e-08	***
df.mm.trans1:probe10	0.538333093791089	0.047929098701311	11.2318634895666	1.58740788992089e-27	***
df.mm.trans1:probe11	0.330368129989866	0.047929098701311	6.89285087643072	1.01322944497305e-11	***
df.mm.trans1:probe12	0.364868855338206	0.047929098701311	7.61267925382926	6.63285091281906e-14	***
df.mm.trans1:probe13	0.307532895051077	0.047929098701311	6.41641306396326	2.22783611310091e-10	***
df.mm.trans1:probe14	0.27233237229215	0.047929098701311	5.68198400702871	1.7850135185781e-08	***
df.mm.trans1:probe15	0.168504926547726	0.047929098701311	3.51571239838727	0.000459936435484088	***
df.mm.trans1:probe16	0.287257272059919	0.047929098701311	5.99337938420406	2.94561035705176e-09	***
df.mm.trans1:probe17	0.264011212965944	0.047929098701311	5.50837007412206	4.69834582287064e-08	***
df.mm.trans1:probe18	0.240376174917455	0.047929098701311	5.01524504801254	6.35221463223574e-07	***
df.mm.trans1:probe19	0.290575414679921	0.047929098701311	6.06260961614896	1.95097505731977e-09	***
df.mm.trans1:probe20	0.509262151282742	0.047929098701311	10.6253229266089	5.88966181217329e-25	***
df.mm.trans1:probe21	0.246867577864577	0.047929098701311	5.15068266572316	3.1739369829851e-07	***
df.mm.trans2:probe2	0.0670464206360314	0.047929098701311	1.39886671046867	0.162189250119868	   
df.mm.trans2:probe3	-0.215493012332699	0.047929098701311	-4.49607896187717	7.80321083833358e-06	***
df.mm.trans2:probe4	-0.190384362009942	0.047929098701311	-3.97220826530449	7.67594339902622e-05	***
df.mm.trans2:probe5	-0.0879285968487649	0.047929098701311	-1.83455560883225	0.0668936398281387	.  
df.mm.trans2:probe6	-0.107170748460348	0.047929098701311	-2.23602678465173	0.0255885936004052	*  
df.mm.trans3:probe2	0.446856566652382	0.047929098701311	9.32328332391861	8.11431122946514e-20	***
df.mm.trans3:probe3	0.219592708005979	0.047929098701311	4.58161563551314	5.24798349302676e-06	***
df.mm.trans3:probe4	0.144796907350005	0.047929098701311	3.02106468248785	0.00258839383277462	** 
df.mm.trans3:probe5	0.0880845603090718	0.047929098701311	1.83780965417283	0.0664120661870339	.  
df.mm.trans3:probe6	-0.26705135675812	0.047929098701311	-5.57180009627044	3.3091450297715e-08	***
df.mm.trans3:probe7	0.260011836235977	0.047929098701311	5.42492647016674	7.41087886751284e-08	***
df.mm.trans3:probe8	0.401058871480377	0.047929098701311	8.36775325110394	2.15925817460662e-16	***
df.mm.trans3:probe9	0.383091938923593	0.047929098701311	7.99288843946306	3.925738595596e-15	***
df.mm.trans3:probe10	-0.29514482997749	0.047929098701311	-6.15794659141831	1.09879153522972e-09	***
df.mm.trans3:probe11	-0.568441054640632	0.047929098701311	-11.8600405608104	2.6828770677217e-30	***
df.mm.trans3:probe12	0.282009594717132	0.047929098701311	5.8838910465349	5.60359229802392e-09	***
df.mm.trans3:probe13	-0.348417304726222	0.047929098701311	-7.26943160140608	7.69680164113026e-13	***
df.mm.trans3:probe14	0.0735077653823095	0.047929098701311	1.53367718930836	0.125452138317834	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12849628417976	0.140686779660703	29.3453037601440	3.31670053320231e-134	***
df.mm.trans1	0.218846520326936	0.120476960365676	1.81650101116998	0.0696181612604318	.  
df.mm.trans2	0.212442143166055	0.105905160326285	2.00596592754818	0.0451516022547717	*  
df.mm.exp2	-0.0676362571258733	0.134522291204477	-0.502788471117138	0.615233073790042	   
df.mm.exp3	0.0642533395145922	0.134522291204477	0.477640835130631	0.633019071952056	   
df.mm.exp4	0.124602900625169	0.134522291204477	0.926262105034842	0.354552168514275	   
df.mm.exp5	-0.0725725581895886	0.134522291204477	-0.539483512656477	0.589683524966689	   
df.mm.exp6	-0.0686423020392132	0.134522291204477	-0.510267119483383	0.609986467926812	   
df.mm.exp7	0.0968966888212637	0.134522291204477	0.720302099776004	0.471521578054003	   
df.mm.exp8	-0.147421717047194	0.134522291204477	-1.09589061951903	0.273412811313494	   
df.mm.trans1:exp2	-0.0135271090599500	0.122801488966618	-0.110154275601880	0.912310995748575	   
df.mm.trans2:exp2	0.0424481038332217	0.0868337655881005	0.488843292073454	0.625068901816493	   
df.mm.trans1:exp3	-0.0650171264884681	0.122801488966618	-0.529449007789655	0.596621460699143	   
df.mm.trans2:exp3	0.0109848262191524	0.0868337655881005	0.126504086800282	0.899360495500505	   
df.mm.trans1:exp4	-0.145402783336607	0.122801488966618	-1.18404739682052	0.236699459658287	   
df.mm.trans2:exp4	-0.0917064528060767	0.0868337655881005	-1.05611512048308	0.291192269721266	   
df.mm.trans1:exp5	0.0494433618542683	0.122801488966618	0.402628357932279	0.687314977030066	   
df.mm.trans2:exp5	0.00626178713719607	0.0868337655881005	0.0721123527787463	0.942528135229533	   
df.mm.trans1:exp6	-0.0283135297075472	0.122801488966618	-0.230563407217675	0.817705146518314	   
df.mm.trans2:exp6	-0.0216220270064284	0.0868337655881005	-0.24900482963037	0.803412502664975	   
df.mm.trans1:exp7	-0.0957734220620197	0.122801488966618	-0.779904403993462	0.43564730137725	   
df.mm.trans2:exp7	-0.131293379156978	0.0868337655881005	-1.51200835605557	0.130874399804240	   
df.mm.trans1:exp8	0.132005778874522	0.122801488966618	1.07495259206838	0.282677176181264	   
df.mm.trans2:exp8	-0.00957474779438072	0.0868337655881005	-0.110265260633737	0.912223003106497	   
df.mm.trans1:probe2	0.170673826249108	0.0889781320804142	1.91815474497555	0.0553999958177366	.  
df.mm.trans1:probe3	-0.0449958001719382	0.0889781320804142	-0.505695041240843	0.61319162294115	   
df.mm.trans1:probe4	0.00361406696042608	0.0889781320804143	0.0406174739334813	0.967609646945288	   
df.mm.trans1:probe5	0.0863061909383554	0.0889781320804142	0.969970811034289	0.332315420824185	   
df.mm.trans1:probe6	0.0827169904075894	0.0889781320804143	0.929632803853802	0.352804559708487	   
df.mm.trans1:probe7	0.107760025741791	0.0889781320804142	1.21108437795033	0.226173396895233	   
df.mm.trans1:probe8	0.0831017414177289	0.0889781320804142	0.93395691137487	0.35057064396421	   
df.mm.trans1:probe9	0.0981860301270455	0.0889781320804142	1.10348495558785	0.270104542882751	   
df.mm.trans1:probe10	0.137546128197797	0.0889781320804143	1.54584193870792	0.122485895764146	   
df.mm.trans1:probe11	0.0371710823547714	0.0889781320804142	0.417755256102454	0.676223335170798	   
df.mm.trans1:probe12	0.174771794132653	0.0889781320804143	1.96421064419179	0.0498057296219262	*  
df.mm.trans1:probe13	0.106978531096208	0.0889781320804142	1.20230138119247	0.229555467276768	   
df.mm.trans1:probe14	0.119445747399831	0.0889781320804142	1.34241689061174	0.179791291361936	   
df.mm.trans1:probe15	-0.0103626392169255	0.0889781320804142	-0.116462764216721	0.90731115702438	   
df.mm.trans1:probe16	0.128911047290074	0.0889781320804142	1.44879471254320	0.147734921597308	   
df.mm.trans1:probe17	0.141518061024693	0.0889781320804142	1.59048136565505	0.112069140805648	   
df.mm.trans1:probe18	0.175570078128032	0.0889781320804142	1.97318233169201	0.0487731065388126	*  
df.mm.trans1:probe19	0.0567297605670305	0.0889781320804142	0.637569695391681	0.523912121020177	   
df.mm.trans1:probe20	0.00874819714429176	0.0889781320804142	0.0983185074776076	0.921700752511652	   
df.mm.trans1:probe21	0.0395088925854494	0.0889781320804142	0.444029242485593	0.657125633192494	   
df.mm.trans2:probe2	0.0206022000522591	0.0889781320804142	0.231542285397043	0.816944914423342	   
df.mm.trans2:probe3	0.0966175317112686	0.0889781320804142	1.08585704658253	0.277826059345254	   
df.mm.trans2:probe4	0.134348439523524	0.0889781320804142	1.50990402228388	0.131410527306565	   
df.mm.trans2:probe5	0.115550791618780	0.0889781320804142	1.29864258685887	0.194391317330706	   
df.mm.trans2:probe6	0.085818747786251	0.0889781320804142	0.964492575644228	0.335051926920329	   
df.mm.trans3:probe2	-0.158825025857855	0.0889781320804142	-1.78498943666649	0.0745917035068163	.  
df.mm.trans3:probe3	-0.0186312125209316	0.0889781320804142	-0.209390915332922	0.834189339368954	   
df.mm.trans3:probe4	-0.0645832128834669	0.0889781320804142	-0.725832419420759	0.468125707949948	   
df.mm.trans3:probe5	-0.020148267265451	0.0889781320804142	-0.226440663501926	0.820908895089679	   
df.mm.trans3:probe6	-0.136440375191413	0.0889781320804142	-1.53341469416445	0.125516757538310	   
df.mm.trans3:probe7	-0.13609281100107	0.0889781320804142	-1.52950851876814	0.126481425317821	   
df.mm.trans3:probe8	-0.0306842951165292	0.0889781320804142	-0.344852093419967	0.73028421671052	   
df.mm.trans3:probe9	-0.118514482722426	0.0889781320804142	-1.33195067092798	0.183205551548737	   
df.mm.trans3:probe10	-0.145601581241247	0.0889781320804142	-1.63637489163808	0.102102424376158	   
df.mm.trans3:probe11	-0.0866905868901375	0.0889781320804142	-0.97429092815514	0.330167649904973	   
df.mm.trans3:probe12	-0.00356366344822355	0.0889781320804142	-0.0400510031498849	0.96806113489532	   
df.mm.trans3:probe13	-0.0430376749759989	0.0889781320804142	-0.483688227317510	0.6287220016695	   
df.mm.trans3:probe14	-0.246461191280751	0.0889781320804142	-2.76990745386755	0.00571987651834724	**