fitVsDatCorrelation=0.819550039886296
cont.fitVsDatCorrelation=0.24504589117271

fstatistic=9070.42079720737,52,692
cont.fstatistic=3159.75772946941,52,692

residuals=-0.692221197416808,-0.0870136921835257,-0.00313492629568235,0.0733442005000487,1.12527128710683
cont.residuals=-0.474726848984793,-0.193094657707611,-0.0545194559327551,0.162869058270644,1.743902057615

predictedValues:
Include	Exclude	Both
Lung	57.6569045337721	48.8822770109381	71.1252256166398
cerebhem	71.6479372167931	56.4609886822984	105.624560427791
cortex	61.5852007728981	49.2968189496597	90.2722388565096
heart	56.8844860179596	50.010675045006	66.6742038894726
kidney	58.0494468308504	51.7979107213547	68.5220450178345
liver	63.0975117117718	51.0781538122623	67.2692754165008
stomach	55.8180024076848	47.7427656579632	75.9565872355824
testicle	61.7778428486591	53.0514610730242	71.6583426535109


diffExp=8.774627522834,15.1869485344947,12.2883818232384,6.87381097295366,6.2515361094957,12.0193578995095,8.07523674972161,8.72638177563495
diffExpScore=0.987373144515432
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=0,1,1,0,0,1,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	60.9093754882958	59.7526014040273	56.1497421996922
cerebhem	66.0319116172905	60.4119247440189	62.7749339902024
cortex	62.3948921489793	62.0820224501627	56.0395438015633
heart	62.3797134194436	60.3270348823709	63.6617957707926
kidney	63.1637865977846	61.1833986443832	54.6165236103055
liver	60.7331903508998	58.4680969718169	64.3576255176081
stomach	60.3204177371688	61.8775129457197	59.9773581988398
testicle	60.9911535583506	56.9206016018042	62.5603460129475
cont.diffExp=1.15677408426855,5.61998687327166,0.312869698816506,2.05267853707267,1.98038795340140,2.26509337908289,-1.55709520855091,4.07055195654650
cont.diffExpScore=1.12509079258106

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=0.856454746223572
cont.tran.correlation=0.264317001570637

tran.covariance=0.00363136406176717
cont.tran.covariance=0.000235533460215599

tran.mean=55.927398955806
cont.tran.mean=61.1217271601573

weightedLogRatios:
wLogRatio
Lung	0.655751571867698
cerebhem	0.98922285686524
cortex	0.892282504369917
heart	0.512134879325852
kidney	0.456273984589845
liver	0.853543481196275
stomach	0.616318564289615
testicle	0.616348186895668

cont.weightedLogRatios:
wLogRatio
Lung	0.078611137023873
cerebhem	0.368763377834462
cortex	0.0207662287438086
heart	0.13773750248345
kidney	0.131555928845047
liver	0.155361604615909
stomach	-0.104809734532300
testicle	0.281548720231743

varWeightedLogRatios=0.0364062256056625
cont.varWeightedLogRatios=0.0214685531005626

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.29804548540395	0.0872517532222322	37.799188710899	1.89021691683688e-170	***
df.mm.trans1	0.502855652731953	0.0783648340044925	6.41685341543792	2.58060220841984e-10	***
df.mm.trans2	0.616785253908215	0.0720671077875563	8.55848490168929	7.35737435304432e-17	***
df.mm.exp2	-0.0340594648979231	0.0987140901981689	-0.345031442112758	0.73017557719025	   
df.mm.exp3	-0.164031670552818	0.098714090198169	-1.66168446899043	0.0970289682103528	.  
df.mm.exp4	0.0739581653277629	0.098714090198169	0.74921589389409	0.453981691142795	   
df.mm.exp5	0.102006636356467	0.098714090198169	1.03335436867917	0.301799087874101	   
df.mm.exp6	0.189851782590701	0.098714090198169	1.92324907426663	0.0548587849543805	.  
df.mm.exp7	-0.121720803314403	0.098714090198169	-1.23306412559796	0.217970545152818	   
df.mm.exp8	0.143414753766613	0.098714090198169	1.45282961610351	0.146724354566461	   
df.mm.trans1:exp2	0.251313823083168	0.0945115458732311	2.65908065264593	0.00801663745695325	** 
df.mm.trans2:exp2	0.178194501297147	0.0822617418318075	2.16618925552882	0.0306374644879385	*  
df.mm.trans1:exp3	0.229943259077368	0.0945115458732312	2.43296474470740	0.0152280516939883	*  
df.mm.trans2:exp3	0.172476327700153	0.0822617418318075	2.0966773114627	0.0363845991792688	*  
df.mm.trans1:exp4	-0.0874455207928461	0.0945115458732311	-0.925236382337215	0.35516549920058	   
df.mm.trans2:exp4	-0.0511365792594215	0.0822617418318075	-0.621632585460875	0.534388220317312	   
df.mm.trans1:exp5	-0.0952214634000188	0.0945115458732312	-1.00751143704431	0.314041221701205	   
df.mm.trans2:exp5	-0.0440717189408823	0.0822617418318075	-0.535749887608646	0.592303565844165	   
df.mm.trans1:exp6	-0.0996804538236285	0.0945115458732312	-1.05469075659106	0.291934874726716	   
df.mm.trans2:exp6	-0.145909792615948	0.0822617418318075	-1.77372602824622	0.0765481888928383	.  
df.mm.trans1:exp7	0.0893072385057063	0.0945115458732311	0.944934692164432	0.345022027274937	   
df.mm.trans2:exp7	0.0981334567460466	0.0822617418318075	1.19294163435890	0.233301090551041	   
df.mm.trans1:exp8	-0.0743799895049023	0.0945115458732312	-0.786993682281618	0.431555006858657	   
df.mm.trans2:exp8	-0.0615672451246629	0.0822617418318075	-0.748431090245371	0.454454466881248	   
df.mm.trans1:probe2	0.155385455909063	0.0472557729366156	3.28817933244859	0.00105938592513306	** 
df.mm.trans1:probe3	0.160611005482462	0.0472557729366156	3.39875946369326	0.000715730244704278	***
df.mm.trans1:probe4	0.171795979415378	0.0472557729366156	3.63544957027385	0.000298079147833984	***
df.mm.trans1:probe5	0.169986175765369	0.0472557729366156	3.59715152672186	0.000344630611847369	***
df.mm.trans1:probe6	0.0300693484135419	0.0472557729366156	0.636310582706457	0.524784412407947	   
df.mm.trans1:probe7	0.155612776690575	0.0472557729366156	3.29298976654766	0.00104170328889626	** 
df.mm.trans1:probe8	0.268296391233513	0.0472557729366156	5.67753682906383	2.01140239943506e-08	***
df.mm.trans1:probe9	0.0603324989874026	0.0472557729366156	1.27672229736517	0.202128567494191	   
df.mm.trans1:probe10	0.0997328046185962	0.0472557729366156	2.11048933116316	0.0351744553760977	*  
df.mm.trans1:probe11	0.752983440400301	0.0472557729366156	15.9342106499936	6.31017389780331e-49	***
df.mm.trans1:probe12	0.487284984999068	0.0472557729366156	10.3116498729725	2.7357068072524e-23	***
df.mm.trans1:probe13	0.428537473762969	0.0472557729366156	9.06846819197664	1.22977887919195e-18	***
df.mm.trans1:probe14	0.672449872171852	0.0472557729366156	14.2300047250060	1.71111855099476e-40	***
df.mm.trans1:probe15	0.556759732591718	0.0472557729366156	11.7818352762636	2.48897711946617e-29	***
df.mm.trans1:probe16	0.494917601467071	0.0472557729366156	10.4731669955099	6.32096440269299e-24	***
df.mm.trans1:probe17	0.275348467984232	0.0472557729366156	5.82676889770818	8.66071466850621e-09	***
df.mm.trans1:probe18	0.279622782672981	0.0472557729366156	5.91721953311483	5.14997895893301e-09	***
df.mm.trans1:probe19	0.170114974764009	0.0472557729366156	3.59987709844859	0.000341104498538802	***
df.mm.trans1:probe20	0.359623116274074	0.0472557729366156	7.61014144782772	8.98162191172714e-14	***
df.mm.trans1:probe21	0.247030523498430	0.0472557729366156	5.22752053658658	2.27760222201467e-07	***
df.mm.trans1:probe22	0.343726288443187	0.0472557729366156	7.27374174800248	9.49564458832904e-13	***
df.mm.trans2:probe2	-0.0697118984812371	0.0472557729366156	-1.47520385656885	0.140612568642882	   
df.mm.trans2:probe3	-0.00964057549781777	0.0472557729366156	-0.204008418415856	0.838406901162889	   
df.mm.trans2:probe4	-0.0506265626092194	0.0472557729366156	-1.07133074888279	0.284394235551881	   
df.mm.trans2:probe5	-0.0611262000957048	0.0472557729366156	-1.29351815232593	0.196263510854799	   
df.mm.trans2:probe6	-0.0376373398788263	0.0472557729366156	-0.796460147404835	0.426037782177076	   
df.mm.trans3:probe2	-0.410541011220347	0.0472557729366156	-8.68763720722561	2.65628865628461e-17	***
df.mm.trans3:probe3	-0.0491919712442793	0.0472557729366156	-1.04097273597156	0.298251753292481	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29406434058396	0.147627315181813	29.0871938929156	3.93084733367116e-122	***
df.mm.trans1	-0.198965411149117	0.132590917907239	-1.50059607618309	0.133916172634634	   
df.mm.trans2	-0.154880168405804	0.121935356513666	-1.27018260194651	0.204446502612827	   
df.mm.exp2	-0.0198089538790298	0.167021241045478	-0.118601405156821	0.905625570906298	   
df.mm.exp3	0.0643045381771326	0.167021241045478	0.385008144919861	0.700349684294044	   
df.mm.exp4	-0.0921419201240783	0.167021241045478	-0.551677855746437	0.581347183797871	   
df.mm.exp5	0.0876928117196604	0.167021241045478	0.525039876190253	0.599723638786497	   
df.mm.exp6	-0.161061581107181	0.167021241045478	-0.964317952010218	0.335223404312806	   
df.mm.exp7	-0.0407173951781402	0.167021241045478	-0.243785730026118	0.807469027696965	   
df.mm.exp8	-0.155323204562187	0.167021241045478	-0.929960785765532	0.352715678334758	   
df.mm.trans1:exp2	0.100559976633731	0.159910663748046	0.628850973892351	0.529654142536856	   
df.mm.trans2:exp2	0.0307827408633094	0.139184367537899	0.221165217099022	0.825029033444952	   
df.mm.trans1:exp3	-0.0402082345106838	0.159910663748047	-0.251441858649498	0.801547157572	   
df.mm.trans2:exp3	-0.0260608127117828	0.139184367537899	-0.187239509528157	0.851527732107631	   
df.mm.trans1:exp4	0.115994925411961	0.159910663748046	0.725373297147471	0.468468272288269	   
df.mm.trans2:exp4	0.101709535050914	0.139184367537899	0.73075401246637	0.465176755809628	   
df.mm.trans1:exp5	-0.051348783434417	0.159910663748047	-0.321109188286039	0.748224602558539	   
df.mm.trans2:exp5	-0.0640296509873533	0.139184367537899	-0.460034787814218	0.645635756794485	   
df.mm.trans1:exp6	0.158164811224335	0.159910663748047	0.989082325826238	0.322968570722801	   
df.mm.trans2:exp6	0.139330107671408	0.139184367537899	1.00104710130949	0.317153945487236	   
df.mm.trans1:exp7	0.0310009324331726	0.159910663748046	0.193864072017219	0.846339208486033	   
df.mm.trans2:exp7	0.0756615004998757	0.139184367537899	0.543606310380178	0.58688749317687	   
df.mm.trans1:exp8	0.156664922813330	0.159910663748047	0.979702786176723	0.327575229664332	   
df.mm.trans2:exp8	0.106767818973060	0.139184367537899	0.76709634035581	0.443285868309044	   
df.mm.trans1:probe2	-0.043400122605527	0.0799553318740233	-0.542804608376934	0.587439117109326	   
df.mm.trans1:probe3	0.114150660720353	0.0799553318740233	1.42768040660762	0.153834976974851	   
df.mm.trans1:probe4	0.0713662258241776	0.0799553318740233	0.892576194125759	0.372394449086719	   
df.mm.trans1:probe5	-0.0516647130897582	0.0799553318740233	-0.646169703493453	0.518383685428924	   
df.mm.trans1:probe6	0.000731497242264341	0.0799553318740232	0.0091488237884733	0.992703033391084	   
df.mm.trans1:probe7	-0.0688848341226049	0.0799553318740233	-0.861541469568772	0.389238321837607	   
df.mm.trans1:probe8	-0.0409630277192861	0.0799553318740233	-0.512323903349272	0.608587812268413	   
df.mm.trans1:probe9	0.109050399047494	0.0799553318740233	1.36389151907108	0.173045171438203	   
df.mm.trans1:probe10	0.0753092479170443	0.0799553318740233	0.941891505568393	0.346576881275904	   
df.mm.trans1:probe11	0.076843315340195	0.0799553318740233	0.961078061201327	0.336848609510508	   
df.mm.trans1:probe12	0.0382470477015587	0.0799553318740232	0.478355186641215	0.632548511722705	   
df.mm.trans1:probe13	0.0427910310008759	0.0799553318740232	0.535186709853159	0.592692691411853	   
df.mm.trans1:probe14	0.0228464067208216	0.0799553318740233	0.285739627181063	0.77516306138769	   
df.mm.trans1:probe15	-0.00826612752247372	0.0799553318740233	-0.103384318828139	0.917687910592189	   
df.mm.trans1:probe16	-0.0479102903806971	0.0799553318740232	-0.599213201393299	0.549226838631367	   
df.mm.trans1:probe17	0.0577911554367999	0.0799553318740233	0.722793015578436	0.470051266523255	   
df.mm.trans1:probe18	-0.0584952151759207	0.0799553318740233	-0.731598678973469	0.46466122509216	   
df.mm.trans1:probe19	-0.0249828878093746	0.0799553318740233	-0.312460560463151	0.75478466453455	   
df.mm.trans1:probe20	0.033765123481159	0.0799553318740233	0.422299835292522	0.67293735243397	   
df.mm.trans1:probe21	-0.0289020187019222	0.0799553318740233	-0.361477065062527	0.717853185741766	   
df.mm.trans1:probe22	0.0877816851142957	0.0799553318740233	1.09788406922760	0.272636975193269	   
df.mm.trans2:probe2	-0.092612996437541	0.0799553318740233	-1.15830919923466	0.247137469566353	   
df.mm.trans2:probe3	-0.107528088565470	0.0799553318740233	-1.34485200730441	0.179113630890774	   
df.mm.trans2:probe4	-0.0793812525929149	0.0799553318740233	-0.99282000002185	0.321144705810272	   
df.mm.trans2:probe5	-0.0562730833418021	0.0799553318740233	-0.703806513247489	0.481790024934642	   
df.mm.trans2:probe6	-0.104947576606514	0.0799553318740233	-1.31257758734425	0.189760483165276	   
df.mm.trans3:probe2	0.112856324006069	0.0799553318740233	1.41149215894550	0.158549000303367	   
df.mm.trans3:probe3	0.150553662591968	0.0799553318740233	1.88297214286070	0.0601233579179906	.