fitVsDatCorrelation=0.765691570394549
cont.fitVsDatCorrelation=0.214060124523445

fstatistic=8686.42978633647,51,669
cont.fstatistic=3758.86684527889,51,669

residuals=-0.531579475520998,-0.0867457143735106,-0.00982526779810398,0.0759367389067734,1.17867999313876
cont.residuals=-0.475533683612506,-0.152182850597086,-0.0459256003702806,0.101120898728106,1.25620866180145

predictedValues:
Include	Exclude	Both
Lung	49.588651748682	41.4278405520022	55.8347539981288
cerebhem	53.616479662106	46.3954749249259	54.3581216868649
cortex	49.8340009335024	41.3458164116337	56.7852366119513
heart	50.5992564988963	45.7077456692922	57.7655524042009
kidney	47.5949274652037	40.4748757047205	59.8688880831179
liver	50.3100430830221	43.6254133623434	59.735035806423
stomach	51.6419166854032	41.3196785090454	63.9636391856906
testicle	50.0101413367719	45.3722088304894	56.6368847809612


diffExp=8.1608111966798,7.22100473718015,8.48818452186867,4.89151082960409,7.12005176048321,6.6846297206787,10.3222381763578,4.6379325062825
diffExpScore=0.982913682978627
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,1,0,0,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	51.4826101108505	51.0750723940613	51.7730792256214
cerebhem	50.556930129401	53.2157033281543	52.6413107543664
cortex	50.2194854160881	45.3375504457098	50.1034902960815
heart	50.5504068271828	47.4221706849012	52.1538597508813
kidney	50.120322904449	48.781644251142	49.687447333049
liver	47.7826492135607	46.8002987225791	56.0712845911662
stomach	52.3213252386325	51.2686312140472	53.6779900154317
testicle	49.5165809257446	47.8783327003802	50.7592875271641
cont.diffExp=0.407537716789172,-2.65877319875326,4.88193497037829,3.12823614228156,1.33867865330696,0.98235049098166,1.0526940245853,1.63824822536432
cont.diffExpScore=1.36679810556321

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=0.619548379973819
cont.tran.correlation=0.581266333970684

tran.covariance=0.00115109681406214
cont.tran.covariance=0.000838285762929337

tran.mean=46.8040294611275
cont.tran.mean=49.6456071566803

weightedLogRatios:
wLogRatio
Lung	0.685765459432907
cerebhem	0.565531171714556
cortex	0.712423115581484
heart	0.393774864541527
kidney	0.612805290148251
liver	0.548435956786408
stomach	0.85470262657794
testicle	0.376025244982656

cont.weightedLogRatios:
wLogRatio
Lung	0.0312915534651812
cerebhem	-0.202386058175323
cortex	0.395291395088805
heart	0.248563452025663
kidney	0.105606748023438
liver	0.0801064253484181
stomach	0.0802274813617191
testicle	0.130725304335874

varWeightedLogRatios=0.0258933261599955
cont.varWeightedLogRatios=0.0295122816334172

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.60251165708474	0.0749488177035275	48.066290669655	3.58585729991183e-219	***
df.mm.trans1	0.312063879510301	0.0632409513679448	4.93452221638269	1.01522464438912e-06	***
df.mm.trans2	0.121574751222149	0.0577821116836252	2.10402056414636	0.0357492610342636	*  
df.mm.exp2	0.218145735977708	0.0750223730736464	2.90774241123463	0.00376106868284038	** 
df.mm.exp3	-0.0139262681761056	0.0750223730736464	-0.18562820136914	0.852792602285119	   
df.mm.exp4	0.0844933995921464	0.0750223730736464	1.12624269441867	0.260466640310007	   
df.mm.exp5	-0.13406799130035	0.0750223730736464	-1.78704012959895	0.0743836402347105	.  
df.mm.exp6	-0.00139277307317278	0.0750223730736464	-0.0185647696295284	0.985193842999392	   
df.mm.exp7	-0.0979608759662644	0.0750223730736464	-1.30575549603183	0.192084543879736	   
df.mm.exp8	0.0851465043064078	0.0750223730736464	1.13494816036841	0.256803505425472	   
df.mm.trans1:exp2	-0.140051270937153	0.0658333546571516	-2.12736038846136	0.0337553042667894	*  
df.mm.trans2:exp2	-0.104896936592258	0.0531411679384516	-1.97392982995312	0.0488007183707353	*  
df.mm.trans1:exp3	0.0188617567441653	0.0658333546571516	0.286507604578165	0.774578073369446	   
df.mm.trans2:exp3	0.0119443775547204	0.0531411679384516	0.224766937161682	0.822229221780707	   
df.mm.trans1:exp4	-0.0643185292681298	0.0658333546571516	-0.976990001543887	0.328927247244198	   
df.mm.trans2:exp4	0.0138212415061582	0.0531411679384516	0.260085392217312	0.794877929975978	   
df.mm.trans1:exp5	0.0930321688383677	0.0658333546571516	1.41314641070416	0.158077764613870	   
df.mm.trans2:exp5	0.110796288023096	0.0531411679384516	2.08494265973645	0.0374530974144107	*  
df.mm.trans1:exp6	0.0158354817633501	0.0658333546571516	0.240538885581913	0.809986213274434	   
df.mm.trans2:exp6	0.0530794969840845	0.0531411679384516	0.998839488164082	0.318233588774869	   
df.mm.trans1:exp7	0.138532545385798	0.0658333546571516	2.10429114705229	0.0357255800447231	*  
df.mm.trans2:exp7	0.0953466077492583	0.0531411679384516	1.794213628494	0.073230487381113	.  
df.mm.trans1:exp8	-0.076682704873133	0.0658333546571516	-1.16480020306550	0.244515050372548	   
df.mm.trans2:exp8	0.00580014108757307	0.0531411679384516	0.109145909143187	0.913119491363016	   
df.mm.trans1:probe2	0.0501510599921047	0.0458615258010895	1.09353230438995	0.274553742112058	   
df.mm.trans1:probe3	-0.0797192447939616	0.0458615258010895	-1.73825975916544	0.082625146446513	.  
df.mm.trans1:probe4	0.0191904616381408	0.0458615258010895	0.418443593032068	0.675757111183831	   
df.mm.trans1:probe5	0.0176814778498025	0.0458615258010895	0.385540549315575	0.69995952738725	   
df.mm.trans1:probe6	0.0360326957967821	0.0458615258010895	0.785684627089448	0.432330443461337	   
df.mm.trans1:probe7	-0.134854588571330	0.0458615258010895	-2.94047322272314	0.00339022749741756	** 
df.mm.trans1:probe8	-0.0765989537713338	0.0458615258010895	-1.67022253257684	0.0953430979181214	.  
df.mm.trans1:probe9	-0.0527927668043977	0.0458615258010895	-1.15113411257555	0.250088315718934	   
df.mm.trans1:probe10	-0.114376243632097	0.0458615258010895	-2.49394763114008	0.0128734505358802	*  
df.mm.trans1:probe11	-0.0836380533596411	0.0458615258010895	-1.82370847673921	0.0686421790680932	.  
df.mm.trans1:probe12	-0.088893899520855	0.0458615258010895	-1.93831099092528	0.0530056312703931	.  
df.mm.trans1:probe13	0.248293468825855	0.0458615258010895	5.4139818614573	8.60053012644203e-08	***
df.mm.trans2:probe2	0.00355655925627278	0.0458615258010895	0.0775499548728117	0.938209240045551	   
df.mm.trans2:probe3	-0.00314339962929053	0.0458615258010895	-0.0685411044308485	0.94537538080692	   
df.mm.trans2:probe4	-0.03889147504095	0.0458615258010895	-0.848019649621559	0.396730324917182	   
df.mm.trans2:probe5	0.0101540716242774	0.0458615258010895	0.221407191472818	0.824842955135279	   
df.mm.trans2:probe6	0.0260585452358289	0.0458615258010895	0.568200572934489	0.570089439636459	   
df.mm.trans3:probe2	-0.179978432748865	0.0458615258010895	-3.92438824494124	9.59446931502832e-05	***
df.mm.trans3:probe3	0.458361740026893	0.0458615258010895	9.99447209878927	5.14709044650729e-22	***
df.mm.trans3:probe4	-0.165272442462382	0.0458615258010895	-3.60372751615813	0.000336972264116633	***
df.mm.trans3:probe5	-0.0355206775905606	0.0458615258010895	-0.774520188111944	0.43889687391584	   
df.mm.trans3:probe6	-0.217506559535485	0.0458615258010895	-4.74268040009951	2.57911557329330e-06	***
df.mm.trans3:probe7	0.191923884165072	0.0458615258010895	4.18485605990267	3.23443216945464e-05	***
df.mm.trans3:probe8	-0.117685609623620	0.0458615258010895	-2.56610759384774	0.0105012725673025	*  
df.mm.trans3:probe9	-0.0178177548669802	0.0458615258010895	-0.388512038266223	0.697760754185238	   
df.mm.trans3:probe10	-0.247680136819921	0.0458615258010895	-5.40060829842773	9.23808502332724e-08	***
df.mm.trans3:probe11	0.179884400641764	0.0458615258010895	3.92233789651827	9.67464467986679e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86856596447088	0.113822512331734	33.9877049383342	7.36312506105438e-148	***
df.mm.trans1	0.0597793473227473	0.0960421283151168	0.622428390243599	0.533872302432681	   
df.mm.trans2	0.0465328641414811	0.0877519528817533	0.530277248691943	0.596095676120935	   
df.mm.exp2	0.00628203879115679	0.113934218657183	0.0551374193389506	0.956045446942637	   
df.mm.exp3	-0.111222280793743	0.113934218657183	-0.976197336538553	0.329319534882718	   
df.mm.exp4	-0.0998076753282523	0.113934218657183	-0.876011408201813	0.381338253684609	   
df.mm.exp5	-0.0316419930151629	0.113934218657183	-0.277721595742632	0.781311883377898	   
df.mm.exp6	-0.241741980964988	0.113934218657183	-2.12176801503652	0.0342241760858354	*  
df.mm.exp7	-0.0161902568886046	0.113934218657183	-0.142101794170542	0.88704238375051	   
df.mm.exp8	-0.083794300243003	0.113934218657183	-0.735462104630144	0.462315865332422	   
df.mm.trans1:exp2	-0.024426091069904	0.099979133119139	-0.244311890970258	0.807064183216972	   
df.mm.trans2:exp2	0.0347749312323608	0.0807038913798965	0.430895346404861	0.666683263372533	   
df.mm.trans1:exp3	0.0863813049808362	0.099979133119139	0.863993338268906	0.387901211168147	   
df.mm.trans2:exp3	-0.00793866003676865	0.0807038913798965	-0.0983677478375743	0.921669748898417	   
df.mm.trans1:exp4	0.0815345858812103	0.099979133119139	0.815516031570813	0.415067234853008	   
df.mm.trans2:exp4	0.0256009725336355	0.0807038913798965	0.317221042206308	0.751174857682158	   
df.mm.trans1:exp5	0.00482448275233061	0.099979133119139	0.0482548968151341	0.961527501629729	   
df.mm.trans2:exp5	-0.0143004653893181	0.0807038913798966	-0.177196726760073	0.859407500003048	   
df.mm.trans1:exp6	0.167160484505988	0.099979133119139	1.67195372965270	0.0950010471686644	.  
df.mm.trans2:exp6	0.154335008719652	0.0807038913798965	1.91236142496714	0.0562566190235946	.  
df.mm.trans1:exp7	0.0323502103047058	0.099979133119139	0.323569621934569	0.746365011004854	   
df.mm.trans2:exp7	0.019972786618154	0.0807038913798965	0.247482324292596	0.804610902202175	   
df.mm.trans1:exp8	0.0448577990285642	0.099979133119139	0.448671614056805	0.653813851614235	   
df.mm.trans2:exp8	0.0191607997518954	0.0807038913798965	0.237421014331267	0.812402876796035	   
df.mm.trans1:probe2	-0.00276478983749297	0.0696485180953156	-0.0396963196504668	0.96834707893369	   
df.mm.trans1:probe3	-0.0130686991689361	0.0696485180953156	-0.187637864039710	0.851217437090859	   
df.mm.trans1:probe4	0.0660687695848923	0.0696485180953156	0.948602660784192	0.343165269261667	   
df.mm.trans1:probe5	0.0484042073958419	0.0696485180953156	0.694978281226308	0.487310214819083	   
df.mm.trans1:probe6	0.0649980420748064	0.0696485180953156	0.933229361547291	0.351038243986155	   
df.mm.trans1:probe7	-0.00647880645125486	0.0696485180953156	-0.0930214544175723	0.925914366504738	   
df.mm.trans1:probe8	-0.00231447713400611	0.0696485180953156	-0.0332308166390374	0.973500435376434	   
df.mm.trans1:probe9	0.00516642446665028	0.0696485180953156	0.0741785268077047	0.940890505629638	   
df.mm.trans1:probe10	-0.000672600379084579	0.0696485180953156	-0.00965706661790147	0.992297774382264	   
df.mm.trans1:probe11	0.0809611572910288	0.0696485180953156	1.16242469337584	0.245477496997274	   
df.mm.trans1:probe12	0.0370290142370887	0.0696485180953156	0.531655450104676	0.595141139602381	   
df.mm.trans1:probe13	0.0322422643924152	0.0696485180953156	0.462928218347602	0.643566363636548	   
df.mm.trans2:probe2	0.0354314344971341	0.0696485180953156	0.508717708087419	0.611117829334899	   
df.mm.trans2:probe3	0.0961091670423824	0.0696485180953156	1.37991689802868	0.168073214521595	   
df.mm.trans2:probe4	0.0824351530397427	0.0696485180953156	1.18358804026423	0.236996567650249	   
df.mm.trans2:probe5	0.0271638144762378	0.0696485180953156	0.390012813180942	0.696651210111974	   
df.mm.trans2:probe6	0.0682218323985793	0.0696485180953156	0.979515921720203	0.327679202498166	   
df.mm.trans3:probe2	-0.00529616508514537	0.0696485180953156	-0.0760413176041655	0.939408960354051	   
df.mm.trans3:probe3	-0.0693105595679452	0.0696485180953156	-0.995147656596112	0.320024251993126	   
df.mm.trans3:probe4	0.0199739001267289	0.0696485180953156	0.286781408606485	0.774368493237188	   
df.mm.trans3:probe5	0.0365196112321101	0.0696485180953156	0.524341539932438	0.600214690274793	   
df.mm.trans3:probe6	-0.0760374253062808	0.0696485180953156	-1.09173069845107	0.275344496514358	   
df.mm.trans3:probe7	-0.00824182693843855	0.0696485180953156	-0.118334562799447	0.905838093369002	   
df.mm.trans3:probe8	-0.0358437949733521	0.0696485180953156	-0.514638300333958	0.606975769767274	   
df.mm.trans3:probe9	-0.0530054626989859	0.0696485180953156	-0.76104221810501	0.446900012405821	   
df.mm.trans3:probe10	-0.0766441084804296	0.0696485180953156	-1.10044133854421	0.271535673480280	   
df.mm.trans3:probe11	-0.0402007144686332	0.0696485180953156	-0.577194110772287	0.564002514876667