fitVsDatCorrelation=0.915423248592151
cont.fitVsDatCorrelation=0.249138836858408

fstatistic=12241.0739773093,53,715
cont.fstatistic=2103.13133136971,53,715

residuals=-0.362675993231755,-0.0830165305754092,-0.0067618401163613,0.074390260234503,1.06222673779867
cont.residuals=-0.635216586666233,-0.252965466521698,-0.0677794710590184,0.204283320491210,1.29005791765517

predictedValues:
Include	Exclude	Both
Lung	60.1020877033256	88.605651045276	52.8853964225788
cerebhem	57.3113411821144	69.2771689404257	50.9572936753983
cortex	54.5510990403401	73.7586051525666	46.9115579989801
heart	58.3862283737868	80.328976063979	53.6916678870323
kidney	59.3924661944496	93.8287714658013	53.5331225773054
liver	62.2102080295117	97.405130916153	52.0262302689069
stomach	60.4800521313895	77.1256724756023	51.2896278130777
testicle	56.5627706245725	77.6316501527878	54.5889194156991


diffExp=-28.5035633419504,-11.9658277583113,-19.2075061122265,-21.9427476901922,-34.4363052713518,-35.1949228866412,-16.6456203442128,-21.0688795282153
diffExpScore=0.99473588273189
diffExp1.5=0,0,0,0,-1,-1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,0,0,-1,-1,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,0,-1,-1,-1,-1,0,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	62.0442432616785	56.9756007152722	54.6709123864854
cerebhem	57.6795247491523	64.7698765072506	56.939787909978
cortex	62.5766779413132	57.0950913945595	57.2935436338345
heart	57.539733512505	56.6621630403389	68.2329709401966
kidney	59.265416377761	58.251361949903	48.8332111229772
liver	58.9546619634679	56.7577938439556	70.7857590504947
stomach	60.7814067082931	60.0310985260541	55.4562553670784
testicle	62.6381774470769	61.8503774790637	62.1099747284866
cont.diffExp=5.06864254640626,-7.0903517580983,5.48158654675373,0.877570472166028,1.01405442785796,2.19686811951231,0.750308182239067,0.787799968013246
cont.diffExpScore=2.30676960347046

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=0.73220515896519
cont.tran.correlation=-0.126709202990908

tran.covariance=0.00363771588405523
cont.tran.covariance=-0.000205660661007711

tran.mean=70.4348674682551
cont.tran.mean=59.6170753386028

weightedLogRatios:
wLogRatio
Lung	-1.66521466312902
cerebhem	-0.785640893463298
cortex	-1.25187848294105
heart	-1.34849990583181
kidney	-1.97227025815889
liver	-1.95247190771549
stomach	-1.02691947950789
testicle	-1.32781522973123

cont.weightedLogRatios:
wLogRatio
Lung	0.348162608044262
cerebhem	-0.476839623338911
cortex	0.374999637137399
heart	0.0621646277077436
kidney	0.070300574127677
liver	0.154097164691414
stomach	0.0509402306470517
testicle	0.0522856387785756

varWeightedLogRatios=0.178192954433154
cont.varWeightedLogRatios=0.0680742244144647

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.76864172801102	0.0723364192682211	65.9231100495734	4.59127680982816e-306	***
df.mm.trans1	-0.793462663074208	0.0642361402575892	-12.3522780150301	6.57992617619332e-32	***
df.mm.trans2	-0.126634551377647	0.0584311228604598	-2.16724487188214	0.0305456221544935	*  
df.mm.exp2	-0.256486947628853	0.0787738366500394	-3.25599156441144	0.001183257479589	** 
df.mm.exp3	-0.160441530920005	0.0787738366500395	-2.03673627873150	0.0420443716821185	*  
df.mm.exp4	-0.142160353630210	0.0787738366500395	-1.80466459011984	0.0715478199684614	.  
df.mm.exp5	0.0332253771446328	0.0787738366500394	0.421781882888856	0.673311011046323	   
df.mm.exp6	0.145536996396288	0.0787738366500395	1.84752961878511	0.0650833081702201	.  
df.mm.exp7	-0.101851724077175	0.0787738366500394	-1.29296386222321	0.196441176661884	   
df.mm.exp8	-0.224617682786657	0.0787738366500394	-2.85142494435739	0.0044778762076366	** 
df.mm.trans1:exp2	0.208940900056135	0.0747994535935147	2.79334794598354	0.00535609593500563	** 
df.mm.trans2:exp2	0.0104067098977278	0.0629867766869503	0.16522055017119	0.868817092183134	   
df.mm.trans1:exp3	0.0635348122920809	0.0747994535935148	0.849402091054707	0.395941897210576	   
df.mm.trans2:exp3	-0.0229564377583213	0.0629867766869504	-0.364464399764680	0.715619015389811	   
df.mm.trans1:exp4	0.113195822040193	0.0747994535935148	1.51332418356071	0.130639213749983	   
df.mm.trans2:exp4	0.0440951199877704	0.0629867766869503	0.700069479772349	0.484111629654528	   
df.mm.trans1:exp5	-0.0451025686611319	0.0747994535935147	-0.602979921567801	0.546713253324314	   
df.mm.trans2:exp5	0.0240505267532109	0.0629867766869503	0.381834537632304	0.702697550921905	   
df.mm.trans1:exp6	-0.111062471984658	0.0747994535935148	-1.4848032525506	0.138036672255606	   
df.mm.trans2:exp6	-0.0508537454511424	0.0629867766869504	-0.807371771124117	0.419720824005091	   
df.mm.trans1:exp7	0.108120739810886	0.0747994535935148	1.44547499502403	0.148762653750582	   
df.mm.trans2:exp7	-0.0369077115670735	0.0629867766869504	-0.585959680878861	0.558087524361587	   
df.mm.trans1:exp8	0.163924110660187	0.0747994535935147	2.19151481441302	0.028736424618522	*  
df.mm.trans2:exp8	0.0923972524895983	0.0629867766869503	1.46693095518795	0.142834529306047	   
df.mm.trans1:probe2	-0.18505917458449	0.0409693480222289	-4.51701536680744	7.33545899977183e-06	***
df.mm.trans1:probe3	0.424950926821080	0.0409693480222289	10.3724112619638	1.41035171375337e-23	***
df.mm.trans1:probe4	-0.0828122372663188	0.0409693480222289	-2.02132182385199	0.0436185743006882	*  
df.mm.trans1:probe5	-0.142444831586208	0.0409693480222289	-3.47686352023276	0.00053798896579522	***
df.mm.trans1:probe6	-0.0449702126818432	0.0409693480222289	-1.09765507270078	0.272724647308354	   
df.mm.trans1:probe7	0.118416588460063	0.0409693480222289	2.89037034213513	0.00396448501896356	** 
df.mm.trans1:probe8	0.671143288993835	0.0409693480222289	16.3815955438122	1.88468221797655e-51	***
df.mm.trans1:probe9	0.225275018945431	0.0409693480222289	5.4986234787823	5.33125884073172e-08	***
df.mm.trans1:probe10	0.00333866366400093	0.0409693480222289	0.081491745052654	0.93507369525767	   
df.mm.trans1:probe11	-0.0444934626443142	0.0409693480222289	-1.08601832326385	0.277836888777045	   
df.mm.trans1:probe12	-0.129136604072108	0.0409693480222289	-3.15202975653999	0.00168910473759992	** 
df.mm.trans1:probe13	-0.168595649625696	0.0409693480222289	-4.11516555094362	4.31970240462504e-05	***
df.mm.trans1:probe14	-0.190473763979419	0.0409693480222289	-4.64917732828145	3.96907427988615e-06	***
df.mm.trans1:probe15	-0.129453237193336	0.0409693480222289	-3.15975829351979	0.00164554339353843	** 
df.mm.trans1:probe16	-0.119447981342596	0.0409693480222289	-2.91554508697055	0.00366175379396579	** 
df.mm.trans1:probe17	0.518755836758809	0.0409693480222289	12.6620476478500	2.68531248971769e-33	***
df.mm.trans1:probe18	0.627425976206692	0.0409693480222289	15.3145218680626	5.37977222346055e-46	***
df.mm.trans1:probe19	0.0796312617368217	0.0409693480222289	1.94367900835561	0.0523261723756503	.  
df.mm.trans1:probe20	0.915768612850853	0.0409693480222289	22.3525307835990	2.46392618693851e-84	***
df.mm.trans1:probe21	0.298597305488897	0.0409693480222289	7.28830991713331	8.32808452472174e-13	***
df.mm.trans1:probe22	0.496087017240715	0.0409693480222289	12.1087359498997	7.85313813057493e-31	***
df.mm.trans2:probe2	-0.0304043055984202	0.0409693480222289	-0.742123247407395	0.458256378113113	   
df.mm.trans2:probe3	-0.612081074750876	0.0409693480222289	-14.9399759649282	4.00711503978966e-44	***
df.mm.trans2:probe4	-0.0572801334817655	0.0409693480222289	-1.39812167503097	0.162510067739346	   
df.mm.trans2:probe5	-0.539538965266897	0.0409693480222289	-13.1693324720266	1.28322042564799e-35	***
df.mm.trans2:probe6	-0.338810915971094	0.0409693480222289	-8.26986350349691	6.53353039618743e-16	***
df.mm.trans3:probe2	0.236215376021993	0.0409693480222289	5.76566109604256	1.21010710727244e-08	***
df.mm.trans3:probe3	0.133605551094712	0.0409693480222289	3.26111001381377	0.00116241352103018	** 
df.mm.trans3:probe4	0.108509981398579	0.0409693480222289	2.64856500376096	0.00826177867719113	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99814728541581	0.174054119375975	22.9707133605921	6.98337397124748e-88	***
df.mm.trans1	-0.00467515365027978	0.154563426525014	-0.0302474767504145	0.975878125701892	   
df.mm.trans2	-0.00171143524611651	0.14059553592107	-0.0121727566590543	0.990291180873146	   
df.mm.exp2	0.0146094746511132	0.189543675325564	0.0770770885708513	0.938583782251397	   
df.mm.exp3	-0.0362161894515979	0.189543675325564	-0.191070418938497	0.848524664761747	   
df.mm.exp4	-0.302484545030097	0.189543675325564	-1.59585670432181	0.110962660319397	   
df.mm.exp5	0.0892437259368505	0.189543675325564	0.470834628396668	0.637902439493758	   
df.mm.exp6	-0.313235194883739	0.189543675325564	-1.65257529350805	0.0988563433042306	.  
df.mm.exp7	0.0174131014497907	0.189543675325564	0.0918685438587259	0.926828216587995	   
df.mm.exp8	-0.035952490509638	0.189543675325564	-0.189679188439738	0.849614363594904	   
df.mm.trans1:exp2	-0.0875549526952496	0.179980612210687	-0.486468801388213	0.626783891888056	   
df.mm.trans2:exp2	0.113608033341961	0.151557238518090	0.749604799168996	0.453739320112801	   
df.mm.trans1:exp3	0.0447611097452949	0.179980612210687	0.248699619339539	0.803664560448335	   
df.mm.trans2:exp3	0.0383112187844152	0.151557238518090	0.252783827146879	0.800507921494115	   
df.mm.trans1:exp4	0.227112540251667	0.179980612210687	1.26187225091671	0.207406237766984	   
df.mm.trans2:exp4	0.296968095930231	0.151557238518090	1.95944514979259	0.0504488378993118	.  
df.mm.trans1:exp5	-0.135065519283744	0.179980612210687	-0.750444826388494	0.45323372204052	   
df.mm.trans2:exp5	-0.0670993712150869	0.151557238518090	-0.442732870242141	0.658092869542523	   
df.mm.trans1:exp6	0.262156170542167	0.179980612210687	1.45658005783026	0.145671321538688	   
df.mm.trans2:exp6	0.309405059707902	0.151557238518090	2.04150631624877	0.0415670998965940	*  
df.mm.trans1:exp7	-0.0379769015139452	0.179980612210687	-0.211005513579925	0.832943091819177	   
df.mm.trans2:exp7	0.0348265166993558	0.151557238518090	0.229791180150058	0.818319760397161	   
df.mm.trans1:exp8	0.0454797145486562	0.179980612210687	0.252692298298315	0.800578627583584	   
df.mm.trans2:exp8	0.118047573902233	0.151557238518090	0.778897630073557	0.436297723323113	   
df.mm.trans1:probe2	0.109382697935293	0.0985794412213829	1.10958934824604	0.267549069563232	   
df.mm.trans1:probe3	0.21911178683232	0.0985794412213829	2.22269252206709	0.0265485532593943	*  
df.mm.trans1:probe4	0.214570347960427	0.0985794412213829	2.17662369863266	0.0298351768942923	*  
df.mm.trans1:probe5	0.273892073159734	0.0985794412213829	2.77838938592324	0.00560621560491423	** 
df.mm.trans1:probe6	0.246621200706321	0.0985794412213829	2.50175084835870	0.0125804632612914	*  
df.mm.trans1:probe7	0.280426672469685	0.0985794412213829	2.84467703402703	0.00457273177960889	** 
df.mm.trans1:probe8	0.0797934311395258	0.0985794412213829	0.809432779805794	0.418535531273803	   
df.mm.trans1:probe9	0.0925100839212893	0.0985794412213829	0.938431814738497	0.348339432777962	   
df.mm.trans1:probe10	0.0425900951989694	0.0985794412213829	0.432038310131253	0.665843826453752	   
df.mm.trans1:probe11	0.141007595149282	0.0985794412213829	1.43039556120649	0.15304037364312	   
df.mm.trans1:probe12	0.223219259170958	0.0985794412213829	2.26435914431355	0.0238507065637777	*  
df.mm.trans1:probe13	0.0873937477144669	0.0985794412213828	0.886531173555793	0.375629442373164	   
df.mm.trans1:probe14	0.236035886647877	0.0985794412213829	2.39437233284579	0.0169054060625834	*  
df.mm.trans1:probe15	0.123140477158576	0.0985794412213829	1.24914967698017	0.212019122054448	   
df.mm.trans1:probe16	0.184343396353245	0.0985794412213829	1.86999838981902	0.0618924652430318	.  
df.mm.trans1:probe17	0.145927896348524	0.0985794412213829	1.48030760309149	0.139231659963147	   
df.mm.trans1:probe18	0.0937025104616877	0.0985794412213829	0.950527912318524	0.342165261191469	   
df.mm.trans1:probe19	0.202160795936525	0.0985794412213829	2.05073992540216	0.0406562745027638	*  
df.mm.trans1:probe20	0.205212665338422	0.0985794412213828	2.08169840278938	0.0377251804196678	*  
df.mm.trans1:probe21	0.170881109076084	0.0985794412213829	1.73343556180574	0.0834493825633855	.  
df.mm.trans1:probe22	0.121841865659324	0.0985794412213828	1.23597642824633	0.216873220643543	   
df.mm.trans2:probe2	0.0715182186082504	0.0985794412213829	0.725488171997646	0.468389978218653	   
df.mm.trans2:probe3	0.134206025983569	0.0985794412213829	1.36139974340266	0.173816250013403	   
df.mm.trans2:probe4	0.0996205645074773	0.0985794412213829	1.01056126179247	0.31256831967816	   
df.mm.trans2:probe5	0.104454796986940	0.0985794412213829	1.05960021372370	0.289684328818443	   
df.mm.trans2:probe6	0.0520730778661127	0.0985794412213829	0.528234662531416	0.597500379905303	   
df.mm.trans3:probe2	-0.00868677838084444	0.0985794412213829	-0.0881195741547802	0.929806313171472	   
df.mm.trans3:probe3	0.0396715101440747	0.0985794412213829	0.402431882880967	0.687486491641002	   
df.mm.trans3:probe4	0.00729968914596553	0.0985794412213829	0.074048798162412	0.940992267433276