fitVsDatCorrelation=0.955667190888602
cont.fitVsDatCorrelation=0.24962264308019

fstatistic=8742.43231427562,57,807
cont.fstatistic=795.490800584338,57,807

residuals=-1.09082640509516,-0.106319034909541,0.00386753563496389,0.110495257663033,1.07794665152225
cont.residuals=-1.34310937769095,-0.537370264670887,-0.00167008499684248,0.482001228670117,1.852247950417

predictedValues:
Include	Exclude	Both
Lung	89.1484513056624	222.730133204857	170.201564037675
cerebhem	112.007504643421	389.500656394822	291.606941074703
cortex	105.418940172172	325.156006545209	225.643857210724
heart	81.6668901575729	300.306154732700	183.926359356322
kidney	75.2088569718617	95.4931711753957	102.469265991588
liver	77.7361008543166	134.630917327437	115.749917275521
stomach	81.081942918423	205.791523182370	145.143998260992
testicle	82.9498220289613	259.141567627116	176.407372284621


diffExp=-133.581681899195,-277.493151751402,-219.737066373037,-218.639264575127,-20.284314203534,-56.8948164731202,-124.709580263947,-176.191745598154
diffExpScore=0.99918602013754
diffExp1.5=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,-1,-1,0,-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	154.838742779514	108.338129479369	149.125495940198
cerebhem	136.889482370203	121.070400302803	143.547624291741
cortex	130.381918918166	174.053216085723	161.945846810153
heart	149.701582048611	130.073416796245	162.731062438147
kidney	132.738067988633	92.8273613789198	148.314475934406
liver	127.511527568199	126.101130616563	171.458507474028
stomach	139.791286559540	118.54470667576	126.499542288945
testicle	140.755593840183	97.657148261375	125.113694063679
cont.diffExp=46.5006133001458,15.8190820673997,-43.6712971675565,19.6281652523656,39.9107066097131,1.41039695163573,21.2465798837797,43.0984455788081
cont.diffExpScore=1.59570160357851

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=1,0,0,0,1,0,0,1
cont.diffExp1.4Score=0.75
cont.diffExp1.3=1,0,-1,0,1,0,0,1
cont.diffExp1.3Score=1.33333333333333
cont.diffExp1.2=1,0,-1,0,1,0,0,1
cont.diffExp1.2Score=1.33333333333333

tran.correlation=0.840897611801619
cont.tran.correlation=-0.293549523595554

tran.covariance=0.0533561246325244
cont.tran.covariance=-0.00345478600455627

tran.mean=164.873039952644
cont.tran.mean=130.079606979363

weightedLogRatios:
wLogRatio
Lung	-4.53079609168021
cerebhem	-6.6573785605805
cortex	-5.88087935259043
heart	-6.58072800612005
kidney	-1.06012792748753
liver	-2.54173845191435
stomach	-4.52770252675728
testicle	-5.68180159094794

cont.weightedLogRatios:
wLogRatio
Lung	1.73700189129359
cerebhem	0.596542813757148
cortex	-1.4487742606524
heart	0.694062545267439
kidney	1.68430963205603
liver	0.0538626336705866
stomach	0.80084542047201
testicle	1.74162726398807

varWeightedLogRatios=3.95564960565899
cont.varWeightedLogRatios=1.16806319888898

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.50695263573088	0.095461315077255	47.2123459862615	2.13626188555643e-234	***
df.mm.trans1	-0.0830163240616663	0.0809097211945258	-1.02603646182485	0.305181842228735	   
df.mm.trans2	0.836491875893814	0.0724192340376035	11.5506865960425	1.14193223594258e-28	***
df.mm.exp2	0.248744181578827	0.0926650637708414	2.68433616140345	0.00741641313482516	** 
df.mm.exp3	0.264009061540258	0.0926650637708414	2.84906793128796	0.00449634497686886	** 
df.mm.exp4	0.133635231458699	0.0926650637708414	1.44213175948570	0.149653173165089	   
df.mm.exp5	-0.509519608304494	0.0926650637708414	-5.49850814935524	5.14150732305654e-08	***
df.mm.exp6	-0.254855562179163	0.0926650637708414	-2.75028745255509	0.0060875852876799	** 
df.mm.exp7	-0.0146828643357466	0.0926650637708414	-0.158450917079785	0.874141135956116	   
df.mm.exp8	0.043534000455494	0.0926650637708414	0.469799498149082	0.638625191246682	   
df.mm.trans1:exp2	-0.0204812791602585	0.0824708243335828	-0.248345755311170	0.803930081762482	   
df.mm.trans2:exp2	0.310160495336231	0.0618531182501871	5.01446821293108	6.5410927773663e-07	***
df.mm.trans1:exp3	-0.0963697159251276	0.0824708243335828	-1.16853101328690	0.242937710662339	   
df.mm.trans2:exp3	0.114335152137256	0.0618531182501871	1.84849455244579	0.0648964754147915	.  
df.mm.trans1:exp4	-0.221289545339054	0.0824708243335828	-2.68324643444776	0.00744040578564169	** 
df.mm.trans2:exp4	0.165206364982429	0.0618531182501871	2.67094642365795	0.00771609315158202	** 
df.mm.trans1:exp5	0.339485638829471	0.0824708243335828	4.11643319407479	4.243593306332e-05	***
df.mm.trans2:exp5	-0.337386526400814	0.0618531182501871	-5.45464054109824	6.52893210590392e-08	***
df.mm.trans1:exp6	0.117872357734133	0.0824708243335828	1.42926130163748	0.153316116475066	   
df.mm.trans2:exp6	-0.248568222778807	0.0618531182501871	-4.01868539227698	6.39992502506832e-05	***
df.mm.trans1:exp7	-0.080159823764075	0.0824708243335828	-0.971977962046794	0.331352748156961	   
df.mm.trans2:exp7	-0.064414376321175	0.0618531182501871	-1.04140871379561	0.297997785539744	   
df.mm.trans1:exp8	-0.115601101719775	0.0824708243335828	-1.40172118629717	0.161383008570396	   
df.mm.trans2:exp8	0.107879631452789	0.0618531182501871	1.74412599566009	0.0815178625978271	.  
df.mm.trans1:probe2	-0.287812991655900	0.0597557892993378	-4.81648715598323	1.74491624200847e-06	***
df.mm.trans1:probe3	0.0201384426975966	0.0597557892993378	0.337012412248728	0.736195169915936	   
df.mm.trans1:probe4	1.3540839621412	0.0597557892993378	22.6602974877985	2.28878184453424e-88	***
df.mm.trans1:probe5	-0.464191807922616	0.0597557892993378	-7.76814787931786	2.41806278970369e-14	***
df.mm.trans1:probe6	-0.469515109487406	0.0597557892993378	-7.8572321609784	1.25315563308212e-14	***
df.mm.trans1:probe7	-0.26509129109703	0.0597557892993378	-4.43624449120895	1.04239855528425e-05	***
df.mm.trans1:probe8	-0.309168632938113	0.0597557892993378	-5.17386911901336	2.89538199148577e-07	***
df.mm.trans1:probe9	-0.439832838615691	0.0597557892993378	-7.36050588190565	4.51874469792247e-13	***
df.mm.trans1:probe10	-0.386580137397313	0.0597557892993378	-6.46933363160507	1.70544888794085e-10	***
df.mm.trans1:probe11	1.03371842305833	0.0597557892993378	17.299050605458	2.87327436300048e-57	***
df.mm.trans1:probe12	0.520071935158799	0.0597557892993378	8.70328952653567	1.79703177476893e-17	***
df.mm.trans1:probe13	0.234422577645665	0.0597557892993378	3.92301031237926	9.48938833095905e-05	***
df.mm.trans1:probe14	0.929808673820302	0.0597557892993378	15.5601437906303	6.10708937671332e-48	***
df.mm.trans1:probe15	0.383951917765209	0.0597557892993378	6.42535095372699	2.24723872909541e-10	***
df.mm.trans1:probe16	0.136996698047644	0.0597557892993378	2.29260963086571	0.0221269726212455	*  
df.mm.trans2:probe2	0.179387149578841	0.0597557892993378	3.00200452010144	0.00276478529886365	** 
df.mm.trans2:probe3	0.312985352932047	0.0597557892993378	5.23774108922222	2.07573658279824e-07	***
df.mm.trans2:probe4	0.288375335315978	0.0597557892993378	4.82589785353524	1.66670491922561e-06	***
df.mm.trans2:probe5	0.15291233219355	0.0597557892993378	2.55895427014709	0.0106800892602151	*  
df.mm.trans2:probe6	0.316667069856015	0.0597557892993378	5.29935381272797	1.50067534198155e-07	***
df.mm.trans3:probe2	0.247436235311242	0.0597557892993378	4.14079101309743	3.82552292736479e-05	***
df.mm.trans3:probe3	-0.491757298708254	0.0597557892993378	-8.22945030890428	7.52126976956174e-16	***
df.mm.trans3:probe4	-0.406342781171517	0.0597557892993378	-6.80005713146894	2.03497999204170e-11	***
df.mm.trans3:probe5	0.561749566919563	0.0597557892993378	9.40075553358623	5.44579007522434e-20	***
df.mm.trans3:probe6	-0.577152831432195	0.0597557892993378	-9.6585257796702	5.84581440932883e-21	***
df.mm.trans3:probe7	-0.214436824425680	0.0597557892993378	-3.58855312497825	0.000352572742335012	***
df.mm.trans3:probe8	-0.349625773458435	0.0597557892993378	-5.85091047341098	7.10345746566469e-09	***
df.mm.trans3:probe9	-0.430387311286309	0.0597557892993378	-7.20243705811243	1.35700497898970e-12	***
df.mm.trans3:probe10	0.338723393182200	0.0597557892993378	5.66846153575875	2.00591884898352e-08	***
df.mm.trans3:probe11	0.0333065658751852	0.0597557892993378	0.55737805935992	0.57742371412339	   
df.mm.trans3:probe12	-0.552990905527179	0.0597557892993378	-9.25418126028012	1.89688567855438e-19	***
df.mm.trans3:probe13	-0.195542075802813	0.0597557892993378	-3.27235365971444	0.00111189919762855	** 
df.mm.trans3:probe14	0.308793091748171	0.0597557892993378	5.16758451974114	2.99118628340275e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.89074144783694	0.313939972404315	15.5785878758322	4.89257476532053e-48	***
df.mm.trans1	0.191624645942274	0.266084702672427	0.72016408315731	0.471632504393740	   
df.mm.trans2	-0.237723025111895	0.23816236259586	-0.998155302629782	0.318503250349148	   
df.mm.exp2	0.0260260776813770	0.304744047780182	0.0854030714330807	0.931962134296789	   
df.mm.exp3	0.219713907990945	0.304744047780182	0.720978505048372	0.471131543640904	   
df.mm.exp4	0.0617908157255088	0.304744047780182	0.202762994636337	0.839371391931377	   
df.mm.exp5	-0.303068810260903	0.304744047780182	-0.994502804791491	0.320276235570561	   
df.mm.exp6	-0.181903481547663	0.304744047780182	-0.596905773460334	0.550737712136876	   
df.mm.exp7	0.152348806102908	0.304744047780182	0.499923812171715	0.617265007225026	   
df.mm.exp8	-0.0235881744863950	0.304744047780182	-0.077403232838233	0.938321955366428	   
df.mm.trans1:exp2	-0.149236381632092	0.271218642803036	-0.550243818381138	0.582304416393919	   
df.mm.trans2:exp2	0.085088954608764	0.203413982102277	0.418304355135140	0.675835895531246	   
df.mm.trans1:exp3	-0.391630132910581	0.271218642803036	-1.44396465103983	0.149137017944452	   
df.mm.trans2:exp3	0.254390019238421	0.203413982102277	1.25060242471687	0.211442043147911	   
df.mm.trans1:exp4	-0.095531162423952	0.271218642803036	-0.35222933584742	0.724758240164957	   
df.mm.trans2:exp4	0.121051055109472	0.203413982102277	0.595097022625549	0.55194544560758	   
df.mm.trans1:exp5	0.149062376823227	0.271218642803036	0.549602251831484	0.582744271374486	   
df.mm.trans2:exp5	0.148553084241928	0.203413982102277	0.730299277889537	0.465419216380960	   
df.mm.trans1:exp6	-0.0122739518363807	0.271218642803036	-0.0452548235974114	0.963915391675226	   
df.mm.trans2:exp6	0.333730525710875	0.203413982102277	1.64064693224026	0.101260397365399	   
df.mm.trans1:exp7	-0.254582512341127	0.271218642803036	-0.938661552576274	0.348185386471366	   
df.mm.trans2:exp7	-0.0623158099502239	0.203413982102277	-0.306349687991907	0.759417429764201	   
df.mm.trans1:exp8	-0.0717710223868305	0.271218642803036	-0.264624222159212	0.79136652848879	   
df.mm.trans2:exp8	-0.0802061327875466	0.203413982102277	-0.394299998252917	0.693463737780382	   
df.mm.trans1:probe2	-0.158116723422627	0.196516576672449	-0.80459738358955	0.421289019646763	   
df.mm.trans1:probe3	0.0682942677431648	0.196516576672449	0.347524208387758	0.728288151791934	   
df.mm.trans1:probe4	-0.0468254026129051	0.196516576672449	-0.238277113339671	0.811726659608991	   
df.mm.trans1:probe5	-0.0812729603871113	0.196516576672449	-0.413567963391587	0.679300416394548	   
df.mm.trans1:probe6	-0.132112864699990	0.196516576672449	-0.672273387502544	0.501602095275247	   
df.mm.trans1:probe7	-0.0980505994940525	0.196516576672449	-0.498943148483	0.617955410755535	   
df.mm.trans1:probe8	0.0126739755390890	0.196516576672449	0.064493162631333	0.948593522943898	   
df.mm.trans1:probe9	-0.0735490447428656	0.196516576672449	-0.374263820326243	0.708306362741296	   
df.mm.trans1:probe10	-0.316472845362409	0.196516576672449	-1.61041297747569	0.107698777012786	   
df.mm.trans1:probe11	-0.0949681335910521	0.196516576672449	-0.483257622329458	0.629043907933718	   
df.mm.trans1:probe12	0.0841980812610418	0.196516576672449	0.428452819027995	0.668435807620903	   
df.mm.trans1:probe13	-0.0323160790386378	0.196516576672449	-0.164444545014143	0.869422377769765	   
df.mm.trans1:probe14	-0.0764840101770987	0.196516576672449	-0.389198771280152	0.697231787163063	   
df.mm.trans1:probe15	-0.0634056936281597	0.196516576672449	-0.322648067159462	0.747045359640586	   
df.mm.trans1:probe16	-0.191048594907535	0.196516576672449	-0.972175468057191	0.331254559315219	   
df.mm.trans2:probe2	-0.0364054611008138	0.196516576672449	-0.185253894186718	0.853076447775664	   
df.mm.trans2:probe3	0.0297859374108155	0.196516576672449	0.151569592322292	0.879564299462306	   
df.mm.trans2:probe4	0.311381479871462	0.196516576672449	1.58450490612030	0.113470633447594	   
df.mm.trans2:probe5	0.0364497106743982	0.196516576672449	0.185479063861122	0.852899907014043	   
df.mm.trans2:probe6	0.303563174621045	0.196516576672449	1.54472044934418	0.122805929377247	   
df.mm.trans3:probe2	0.447595882752929	0.196516576672449	2.27764950077966	0.0230082383877169	*  
df.mm.trans3:probe3	0.274206668686044	0.196516576672449	1.39533607459023	0.163298360013683	   
df.mm.trans3:probe4	0.233158116167053	0.196516576672449	1.18645520960645	0.235791750795727	   
df.mm.trans3:probe5	0.260819806749109	0.196516576672449	1.32721529738349	0.184812834297077	   
df.mm.trans3:probe6	0.162587283122282	0.196516576672449	0.827346404437325	0.408285323544521	   
df.mm.trans3:probe7	0.0440153844080758	0.196516576672449	0.223977972511907	0.82283113323194	   
df.mm.trans3:probe8	-0.0600704671022762	0.196516576672449	-0.305676335907281	0.759929924228111	   
df.mm.trans3:probe9	0.154091747186429	0.196516576672449	0.784115771786862	0.43320226969002	   
df.mm.trans3:probe10	0.165704578373300	0.196516576672449	0.84320916422992	0.399361325538248	   
df.mm.trans3:probe11	0.0714322818024515	0.196516576672449	0.363492398514116	0.716332288997437	   
df.mm.trans3:probe12	0.0610747817597482	0.196516576672449	0.310786920848652	0.75604286726297	   
df.mm.trans3:probe13	0.140528565328221	0.196516576672449	0.715097767871524	0.474755454815153	   
df.mm.trans3:probe14	0.286887294547844	0.196516576672449	1.45986307824823	0.144716867810481