fitVsDatCorrelation=0.870436518728673
cont.fitVsDatCorrelation=0.311966589465166

fstatistic=6178.58053289025,45,531
cont.fstatistic=1650.121954925,45,531

residuals=-0.677997157890916,-0.0882818022442577,-0.00335322412585663,0.0822458035641433,2.6823083552717
cont.residuals=-0.70508952626902,-0.226475174279884,-0.0760940929879508,0.146819662916825,2.63154949084074

predictedValues:
Include	Exclude	Both
Lung	56.8722778301205	44.5046561373442	64.0172615577466
cerebhem	49.1370276875886	52.4273013780821	62.7641435615211
cortex	55.2579249702396	46.2971018313839	64.5087844032671
heart	65.1658281788646	46.3814642920639	64.9308134733682
kidney	59.4440090347842	45.2212011395525	64.2510356953988
liver	61.2943928279501	44.612815526489	64.5183221106868
stomach	63.5498782004569	46.5346176750567	69.3476456054379
testicle	56.6229097684454	47.8814487836582	65.553248918294


diffExp=12.3676216927763,-3.29027369049351,8.96082313885564,18.7843638868007,14.2228078952316,16.6815773014610,17.0152605254001,8.74146098478723
diffExpScore=1.05906363555132
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,1,1,1,1,0
diffExp1.3Score=0.8
diffExp1.2=1,0,0,1,1,1,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	55.8732287336415	56.1584589155639	54.1891394955777
cerebhem	59.3061596079067	54.2539231258136	57.7141065376997
cortex	61.8027317038078	50.4373177159879	60.8050367433755
heart	58.8747973503898	63.3670959416519	70.6611491190753
kidney	58.8537310149197	57.7089317488493	72.199070678255
liver	68.7454433524804	59.4742312653432	56.6950356042153
stomach	62.0047816898583	53.3230179532236	55.1134148372349
testicle	56.7471801883227	65.1036074240055	53.9248847734336
cont.diffExp=-0.285230181922351,5.0522364820931,11.3654139878198,-4.49229859126208,1.14479926607049,9.27121208713712,8.68176373663468,-8.35642723568283
cont.diffExpScore=2.08068109075612

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=0,0,1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.667515746172176
cont.tran.correlation=-0.194944832406038

tran.covariance=-0.00326304462534536
cont.tran.covariance=-0.00116553956757064

tran.mean=52.57530345388
cont.tran.mean=58.8771648582354

weightedLogRatios:
wLogRatio
Lung	0.960798494164595
cerebhem	-0.254528224902774
cortex	0.694202352305647
heart	1.36249349948407
kidney	1.07973704666537
liver	1.25696299906957
stomach	1.24527116576368
testicle	0.662788949578105

cont.weightedLogRatios:
wLogRatio
Lung	-0.0204983797553406
cerebhem	0.35955220194562
cortex	0.817404674059028
heart	-0.302375198338295
kidney	0.0798544914530137
liver	0.602354975775131
stomach	0.611185796640168
testicle	-0.564235240817098

varWeightedLogRatios=0.274781841289593
cont.varWeightedLogRatios=0.233866349458522

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.7961465068287	0.092695386888841	40.9529172296458	1.87118362431169e-166	***
df.mm.trans1	0.00323904838373709	0.079328751428234	0.0408306991528455	0.967446212645843	   
df.mm.trans2	0.0284216350168624	0.0737964047152072	0.385135768152206	0.700291049090969	   
df.mm.exp2	0.0374073339754320	0.098395206286943	0.380174353884108	0.703968013723066	   
df.mm.exp3	0.00304065439551750	0.098395206286943	0.0309024647669345	0.975358935628953	   
df.mm.exp4	0.163263760938277	0.098395206286943	1.65926539614300	0.0976527856160227	.  
df.mm.exp5	0.056553959131278	0.0983952062869429	0.57476335753953	0.565694779285272	   
df.mm.exp6	0.0695112109988195	0.098395206286943	0.706449161721445	0.480218713795609	   
df.mm.exp7	0.0756403150913836	0.098395206286943	0.768739839528352	0.442389537795894	   
df.mm.exp8	0.045029964496575	0.098395206286943	0.457643885264668	0.64739550479854	   
df.mm.trans1:exp2	-0.183602469127972	0.0880073479670876	-2.08621749625536	0.0374355372704004	*  
df.mm.trans2:exp2	0.126426324335668	0.0762165990591946	1.65877677430185	0.0977512919226233	.  
df.mm.trans1:exp3	-0.0318368997067037	0.0880073479670876	-0.361752744993632	0.717680713303728	   
df.mm.trans2:exp3	0.0364448933045351	0.0762165990591946	0.478175276178745	0.632722258481687	   
df.mm.trans1:exp4	-0.0271365508823617	0.0880073479670876	-0.308344149769291	0.757941398050965	   
df.mm.trans2:exp4	-0.121957673980674	0.0762165990591946	-1.60014584074991	0.110160997735972	   
df.mm.trans1:exp5	-0.0123271280449319	0.0880073479670876	-0.140069304776027	0.888658345904525	   
df.mm.trans2:exp5	-0.040581746501959	0.0762165990591946	-0.532452864637015	0.594635105746707	   
df.mm.trans1:exp6	0.00536914296557407	0.0880073479670876	0.061007894108819	0.951375877007232	   
df.mm.trans2:exp6	-0.0670838656033148	0.0762165990591946	-0.88017395726636	0.379163249866524	   
df.mm.trans1:exp7	0.0353767521502058	0.0880073479670876	0.401974982400740	0.687864183012658	   
df.mm.trans2:exp7	-0.0310376293437594	0.0762165990591946	-0.407229261432324	0.68400372154532	   
df.mm.trans1:exp8	-0.0494243087543634	0.0880073479670876	-0.561592979404933	0.574630306209043	   
df.mm.trans2:exp8	0.028104358376403	0.0762165990591946	0.368743275393007	0.712466061978671	   
df.mm.trans1:probe2	0.0353045574902134	0.0538932740337328	0.655082811783074	0.512698113138921	   
df.mm.trans1:probe3	0.0796567640806985	0.0538932740337328	1.47804648184558	0.139988228331321	   
df.mm.trans1:probe4	0.195193313093842	0.0538932740337328	3.62184923060467	0.000320569826557494	***
df.mm.trans1:probe5	0.146207327692896	0.0538932740337328	2.71290490908720	0.00688609229705138	** 
df.mm.trans1:probe6	0.317422858334458	0.0538932740337328	5.88984180355748	6.86504374882422e-09	***
df.mm.trans1:probe7	0.950965892606273	0.0538932740337328	17.6453538898202	3.52433854854984e-55	***
df.mm.trans1:probe8	0.35656649799391	0.0538932740337328	6.61615951873194	9.01884306165906e-11	***
df.mm.trans1:probe9	0.505233117915522	0.0538932740337328	9.37469706515303	1.98795603242181e-19	***
df.mm.trans1:probe10	0.453444885089089	0.0538932740337328	8.4137565070786	3.69588119001274e-16	***
df.mm.trans1:probe11	0.294885604311474	0.0538932740337328	5.47165874774837	6.88013598182502e-08	***
df.mm.trans1:probe12	1.01072343593402	0.0538932740337328	18.7541665273740	1.36313716450561e-60	***
df.mm.trans2:probe2	-0.0103517468857704	0.0538932740337328	-0.192078641933891	0.847754036164924	   
df.mm.trans2:probe3	-0.0672505071642487	0.0538932740337328	-1.24784601362603	0.212637443583101	   
df.mm.trans2:probe4	-0.0975413066672124	0.0538932740337328	-1.80989758770565	0.0708768937521099	.  
df.mm.trans2:probe5	-0.105496949434983	0.0538932740337328	-1.95751605977678	0.0508102247540976	.  
df.mm.trans2:probe6	-0.0670514000126646	0.0538932740337328	-1.24415154237421	0.213992701747013	   
df.mm.trans3:probe2	0.672967143939057	0.0538932740337328	12.4870339760363	1.48406001665182e-31	***
df.mm.trans3:probe3	0.239312071057590	0.0538932740337328	4.44048121678042	1.09251584155502e-05	***
df.mm.trans3:probe4	0.233982038721704	0.0538932740337328	4.34158144809036	1.69449843528741e-05	***
df.mm.trans3:probe5	0.07758709559107	0.0538932740337328	1.43964338745697	0.150557573030466	   
df.mm.trans3:probe6	0.764225062317376	0.0538932740337328	14.1803420931346	6.1606572453196e-39	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05052983988268	0.178900470267947	22.6412475820551	6.04835529804698e-80	***
df.mm.trans1	-0.0139766991746686	0.153103098359133	-0.0912894600074228	0.927297019602846	   
df.mm.trans2	-0.0604194806179288	0.142425766273204	-0.424217346333463	0.671579119858808	   
df.mm.exp2	-0.0378953065231227	0.189901021697606	-0.199552936494815	0.841906630925387	   
df.mm.exp3	-0.121775889171618	0.189901021697606	-0.641259789352432	0.521630586454292	   
df.mm.exp4	-0.0923202191258324	0.189901021697606	-0.486149143909511	0.627061987057215	   
df.mm.exp5	-0.207742146264703	0.189901021697606	-1.09394959757250	0.274473079957824	   
df.mm.exp6	0.219484774949139	0.189901021697606	1.15578511893760	0.248288832994381	   
df.mm.exp7	0.0354042903490717	0.189901021697606	0.186435491671281	0.852174465307842	   
df.mm.exp8	0.168211712357257	0.189901021697606	0.885786241977749	0.376133679470521	   
df.mm.trans1:exp2	0.0975231271891491	0.169852637405003	0.574163160955876	0.566100527033853	   
df.mm.trans2:exp2	0.00339329363337431	0.14709669889252	0.0230684553693058	0.981604333922908	   
df.mm.trans1:exp3	0.222638103070577	0.169852637405003	1.31077212854641	0.190501369198879	   
df.mm.trans2:exp3	0.0143299027591511	0.14709669889252	0.0974182484517996	0.922431012518704	   
df.mm.trans1:exp4	0.144647977510117	0.169852637405003	0.851608663368661	0.394815222463271	   
df.mm.trans2:exp4	0.213087636010872	0.14709669889252	1.44862282848761	0.148033178161068	   
df.mm.trans1:exp5	0.259712024770806	0.169852637405003	1.52904322675625	0.126849190337725	   
df.mm.trans2:exp5	0.234976785760716	0.14709669889252	1.59743072094642	0.110764610512579	   
df.mm.trans1:exp6	-0.0121596710231310	0.169852637405003	-0.0715895331912742	0.942955525012742	   
df.mm.trans2:exp6	-0.162118962563529	0.14709669889252	-1.10212509039367	0.270906576195641	   
df.mm.trans1:exp7	0.0687218638373084	0.169852637405003	0.404596978223219	0.685936704017473	   
df.mm.trans2:exp7	-0.0872135142460556	0.14709669889252	-0.592899194221758	0.553501034880249	   
df.mm.trans1:exp8	-0.152691097456523	0.169852637405003	-0.8989621815082	0.369080264392357	   
df.mm.trans2:exp8	-0.0204090694776796	0.14709669889252	-0.138745938089284	0.889703517848735	   
df.mm.trans1:probe2	-0.111433629544203	0.104013073277057	-1.07134253448488	0.284502132380306	   
df.mm.trans1:probe3	-0.158275356643050	0.104013073277057	-1.52168714620572	0.128682740195361	   
df.mm.trans1:probe4	0.0430537876704622	0.104013073277057	0.413926695116306	0.679094927820097	   
df.mm.trans1:probe5	0.141684189815694	0.104013073277057	1.36217674713153	0.17371964588726	   
df.mm.trans1:probe6	0.0321454216866775	0.104013073277057	0.309051743919274	0.75740338874656	   
df.mm.trans1:probe7	-0.0101609676320448	0.104013073277057	-0.0976893318494619	0.922215849265013	   
df.mm.trans1:probe8	0.0355836262243739	0.104013073277057	0.34210724770713	0.732405542824854	   
df.mm.trans1:probe9	-0.0923441981267139	0.104013073277057	-0.887813379773323	0.375043102787343	   
df.mm.trans1:probe10	-0.143352931504906	0.104013073277057	-1.37822032354588	0.168715710426913	   
df.mm.trans1:probe11	0.0364246476469715	0.104013073277057	0.350192975742080	0.726332741688393	   
df.mm.trans1:probe12	-0.0157447878714731	0.104013073277057	-0.151373162770935	0.879738848684495	   
df.mm.trans2:probe2	0.0267168975934922	0.104013073277057	0.256860957490672	0.797385605038759	   
df.mm.trans2:probe3	-0.00507247716277224	0.104013073277057	-0.0487676885506578	0.961122769407774	   
df.mm.trans2:probe4	0.167566976650707	0.104013073277057	1.61101841692884	0.107769931345844	   
df.mm.trans2:probe5	0.201335766038394	0.104013073277057	1.93567750375092	0.0534376991629843	.  
df.mm.trans2:probe6	0.0662563462495205	0.104013073277057	0.637000178554819	0.524399252537397	   
df.mm.trans3:probe2	0.0406141636768678	0.104013073277057	0.390471720498874	0.696344358549686	   
df.mm.trans3:probe3	0.109273422975056	0.104013073277057	1.05057392818293	0.293932201415217	   
df.mm.trans3:probe4	-0.0689356349005815	0.104013073277057	-0.662759331386735	0.507772275830195	   
df.mm.trans3:probe5	0.0839752560011354	0.104013073277057	0.807352896663797	0.419824687268985	   
df.mm.trans3:probe6	-0.0668461079521169	0.104013073277057	-0.642670251402541	0.52071547772921