fitVsDatCorrelation=0.75730165495924
cont.fitVsDatCorrelation=0.254144086283617

fstatistic=14158.6439835066,52,692
cont.fstatistic=6448.29757006471,52,692

residuals=-0.476795042889804,-0.0780854705199291,-0.00299229477722524,0.0699893500878407,0.582252786139747
cont.residuals=-0.383377170108405,-0.111808259593956,-0.0266111610845360,0.0765539915375112,0.790469646505808

predictedValues:
Include	Exclude	Both
Lung	51.1059373629706	40.8277414558407	48.1606974292581
cerebhem	53.8868150666764	45.8915279266935	48.0363923212691
cortex	50.7715540699177	43.4124450951021	67.0424056943022
heart	50.5141817531526	41.431609109087	48.5064385031223
kidney	51.0407496552647	42.8921804447219	53.153200319925
liver	51.2924153522965	44.1333023572121	43.6985116890221
stomach	51.6215456598412	41.6280020641144	47.460842778426
testicle	51.5383752064101	44.5329572587915	41.8752917013778


diffExp=10.2781959071299,7.99528713998283,7.35910897481555,9.0825726440656,8.14856921054279,7.15911299508439,9.99354359572677,7.00541794761857
diffExpScore=0.98529883248767
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=1,0,0,1,0,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	50.8937159025839	50.1631062167093	48.2276894714222
cerebhem	49.4200615744693	48.6404365701481	48.0518832537886
cortex	49.0012444395243	45.9658749580087	50.9181312220231
heart	48.6329562890039	46.1434251597376	50.6982203323785
kidney	49.6101949767382	45.2933354251302	52.9243960097241
liver	48.455340648512	48.5559028377440	50.2160174673528
stomach	48.9446822767054	46.2852349876152	47.284749379455
testicle	48.6100710483664	47.8150251844673	45.488750515685
cont.diffExp=0.730609685874633,0.779625004321183,3.03536948151556,2.48953112926628,4.31685955160800,-0.100562189232093,2.65944728909018,0.795045863899155
cont.diffExpScore=0.949135400811295

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=0.687536710285009
cont.tran.correlation=0.454005592795047

tran.covariance=0.000543482419053257
cont.tran.covariance=0.000248607335537282

tran.mean=47.2825837398808
cont.tran.mean=48.2769130309665

weightedLogRatios:
wLogRatio
Lung	0.858104848877958
cerebhem	0.627417987604801
cortex	0.602721688619776
heart	0.757786496138867
kidney	0.668893535412137
liver	0.580624600806243
stomach	0.825453663788344
testicle	0.565290512066974

cont.weightedLogRatios:
wLogRatio
Lung	0.0567180603336494
cerebhem	0.0618940533805043
cortex	0.246825034960689
heart	0.202727524496594
kidney	0.351280380472786
liver	-0.00804752687702119
stomach	0.215803402903488
testicle	0.0639115341160728

varWeightedLogRatios=0.0129364079965574
cont.varWeightedLogRatios=0.0151205297002797

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81131719029937	0.0659690802491727	57.7742963203911	5.86876751247142e-267	***
df.mm.trans1	0.261996413328623	0.0592498813174359	4.42188925113549	1.13590757644259e-05	***
df.mm.trans2	-0.126310480130470	0.0544883127431723	-2.31812059818824	0.0207334198686440	*  
df.mm.exp2	0.172488268715825	0.0746354943885272	2.31107558312549	0.0211216931604688	*  
df.mm.exp3	-0.275962135268927	0.0746354943885273	-3.69746509391847	0.000235006211483043	***
df.mm.exp4	-0.00411751067869826	0.0746354943885272	-0.0551682642746879	0.956020335535638	   
df.mm.exp5	-0.050583641236225	0.0746354943885273	-0.677742428728396	0.498161553910685	   
df.mm.exp6	0.178724314143119	0.0746354943885273	2.39462893101157	0.0169022256198481	*  
df.mm.exp7	0.0440880086209992	0.0746354943885272	0.590711014674759	0.5549069362605	   
df.mm.exp8	0.235141064193369	0.0746354943885273	3.15052598123493	0.00169998818615737	** 
df.mm.trans1:exp2	-0.119503120670028	0.071458045528372	-1.67235361373801	0.0949066392124044	.  
df.mm.trans2:exp2	-0.0555695334466013	0.0621962453237727	-0.893454792284084	0.371924280948924	   
df.mm.trans1:exp3	0.269397692247864	0.071458045528372	3.77001204351302	0.000177177703146081	***
df.mm.trans2:exp3	0.337346500612371	0.0621962453237727	5.42390459192928	8.067294140788e-08	***
df.mm.trans1:exp4	-0.00752904718552776	0.071458045528372	-0.105363183807461	0.916118161357602	   
df.mm.trans2:exp4	0.0187998172382701	0.0621962453237727	0.302266111730773	0.762540035566271	   
df.mm.trans1:exp5	0.0493072862518681	0.0714580455283721	0.690017280591461	0.490414659872542	   
df.mm.trans2:exp5	0.0999113885400621	0.0621962453237727	1.60638938926228	0.108644565670115	   
df.mm.trans1:exp6	-0.175082103296404	0.071458045528372	-2.45013842740617	0.0145267557787600	*  
df.mm.trans2:exp6	-0.100871449184826	0.0621962453237727	-1.62182537964668	0.105296238211040	   
df.mm.trans1:exp7	-0.0340495532527373	0.071458045528372	-0.476497125004882	0.633870672446425	   
df.mm.trans2:exp7	-0.0246767292610267	0.0621962453237727	-0.396755931689573	0.69166977543389	   
df.mm.trans1:exp8	-0.226715065886980	0.071458045528372	-3.17270174702671	0.0015770767868305	** 
df.mm.trans2:exp8	-0.148273324582608	0.0621962453237727	-2.38395941444289	0.0173960863814309	*  
df.mm.trans1:probe2	-0.207605815527876	0.0357290227641860	-5.81056517828905	9.49907393494642e-09	***
df.mm.trans1:probe3	-0.202707494174641	0.0357290227641860	-5.67346875151118	2.05759906987610e-08	***
df.mm.trans1:probe4	-0.202086565437711	0.0357290227641860	-5.65608991803376	2.26684646839034e-08	***
df.mm.trans1:probe5	-0.279615831682179	0.0357290227641860	-7.82601398106135	1.88937923365595e-14	***
df.mm.trans1:probe6	-0.16088460349396	0.0357290227641860	-4.50291082842677	7.86491315340901e-06	***
df.mm.trans1:probe7	-0.201590818253736	0.035729022764186	-5.6422147223071	2.44863642365657e-08	***
df.mm.trans1:probe8	0.270289033908526	0.0357290227641860	7.56497135934705	1.23900402674205e-13	***
df.mm.trans1:probe9	-0.0163325575254101	0.0357290227641861	-0.457122984672883	0.647726116950357	   
df.mm.trans1:probe10	-0.288373007828421	0.0357290227641860	-8.07111377581476	3.0842520898412e-15	***
df.mm.trans1:probe11	-0.192397984549998	0.0357290227641860	-5.38492154738846	9.93896192176905e-08	***
df.mm.trans1:probe12	-0.285500286952051	0.0357290227641860	-7.99071076856403	5.61722224483873e-15	***
df.mm.trans1:probe13	-0.254404864544758	0.035729022764186	-7.1203980647287	2.70224288586043e-12	***
df.mm.trans1:probe14	-0.239086450913912	0.0357290227641860	-6.69165939667308	4.55874423260736e-11	***
df.mm.trans1:probe15	-0.294468596921077	0.035729022764186	-8.24171987195367	8.50724247531037e-16	***
df.mm.trans1:probe16	-0.345428924643411	0.0357290227641860	-9.66802050319891	7.96506442260823e-21	***
df.mm.trans1:probe17	-0.145300609211191	0.0357290227641860	-4.06673896932990	5.31572846137286e-05	***
df.mm.trans1:probe18	-0.152622075491461	0.0357290227641860	-4.27165546896642	2.21179815141712e-05	***
df.mm.trans1:probe19	-0.0126269885569756	0.0357290227641860	-0.353409849474882	0.723888838891155	   
df.mm.trans1:probe20	-0.109641671839642	0.0357290227641860	-3.06870055090185	0.00223390233105897	** 
df.mm.trans1:probe21	-0.168675743289126	0.0357290227641860	-4.72097276218264	2.84197719546308e-06	***
df.mm.trans1:probe22	0.00373880413368753	0.0357290227641860	0.104643335989453	0.916689148342178	   
df.mm.trans2:probe2	0.0279766018433429	0.035729022764186	0.783021747557732	0.433882225716767	   
df.mm.trans2:probe3	0.0137528240459955	0.0357290227641860	0.384920240801576	0.700414782447323	   
df.mm.trans2:probe4	0.0131681063582665	0.0357290227641860	0.368554898497419	0.712572239821043	   
df.mm.trans2:probe5	0.0815190240977317	0.0357290227641860	2.28159120488021	0.0228163610644943	*  
df.mm.trans2:probe6	0.0827791441382046	0.0357290227641860	2.31686001278435	0.0208024332334197	*  
df.mm.trans3:probe2	-0.137022536002452	0.0357290227641860	-3.8350485236277	0.000136997568439842	***
df.mm.trans3:probe3	-0.0803569872893918	0.0357290227641860	-2.24906759470454	0.0248218908879031	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9322422002068	0.097697859508066	40.2490107767627	1.98031073611748e-183	***
df.mm.trans1	0.00229884991912949	0.0877469650775226	0.0261986259820896	0.979106464873182	   
df.mm.trans2	-0.0507202813786522	0.0806952515194537	-0.628541090381567	0.529856935462548	   
df.mm.exp2	-0.0565556189834625	0.110532510344903	-0.511665018798433	0.609048703455164	   
df.mm.exp3	-0.17956005614911	0.110532510344903	-1.62449993751898	0.104724512088588	   
df.mm.exp4	-0.178920860331622	0.110532510344903	-1.61871706137246	0.105963802750857	   
df.mm.exp5	-0.220594097825655	0.110532510344903	-1.99573950810779	0.0463538137750632	*  
df.mm.exp6	-0.122061666860860	0.110532510344903	-1.10430557018910	0.269844439858808	   
df.mm.exp7	-0.0997600318714836	0.110532510344903	-0.90254018080468	0.367084028551014	   
df.mm.exp8	-0.0353803471755059	0.110532510344903	-0.320089963261542	0.748996755534979	   
df.mm.trans1:exp2	0.0271726092249486	0.105826821692568	0.256764861595168	0.797436617296645	   
df.mm.trans2:exp2	0.0257310115609182	0.0921104252874188	0.279349611953564	0.780060005465705	   
df.mm.trans1:exp3	0.141666294401713	0.105826821692568	1.33866152394958	0.181120500305071	   
df.mm.trans2:exp3	0.0921795080155635	0.0921104252874189	1.000749999014	0.317297492677634	   
df.mm.trans1:exp4	0.133482818105325	0.105826821692568	1.2613325806297	0.207614082022696	   
df.mm.trans2:exp4	0.0953955238087927	0.0921104252874189	1.03566478507860	0.300720329411787	   
df.mm.trans1:exp5	0.195050998060618	0.105826821692568	1.84311495839166	0.0657397161753004	.  
df.mm.trans2:exp5	0.118474178055495	0.0921104252874188	1.28621898863034	0.198796818730369	   
df.mm.trans1:exp6	0.0729647729656721	0.105826821692568	0.689473347103237	0.490756566659157	   
df.mm.trans2:exp6	0.089497616281646	0.0921104252874189	0.971633949168947	0.331572181245792	   
df.mm.trans1:exp7	0.0607113028681154	0.105826821692568	0.573685403162583	0.566367105007215	   
df.mm.trans2:exp7	0.0193032227712804	0.0921104252874188	0.209566101893973	0.834068018766515	   
df.mm.trans1:exp8	-0.0105283764289127	0.105826821692568	-0.0994868433212343	0.92078054659175	   
df.mm.trans2:exp8	-0.0125595488944703	0.0921104252874188	-0.136353174521558	0.891581746442416	   
df.mm.trans1:probe2	-0.00723754351173967	0.0529134108462838	-0.136780891573312	0.891243771883132	   
df.mm.trans1:probe3	0.00348793773207208	0.0529134108462838	0.0659178396608134	0.947462276189289	   
df.mm.trans1:probe4	-0.0122549419251845	0.0529134108462838	-0.231603703658146	0.816914297771756	   
df.mm.trans1:probe5	0.0178289582892306	0.0529134108462838	0.336945927394941	0.736259826538218	   
df.mm.trans1:probe6	0.0126750405084060	0.0529134108462838	0.239543063009633	0.810755441528808	   
df.mm.trans1:probe7	0.0262926212826537	0.0529134108462838	0.496899006549307	0.619418092938578	   
df.mm.trans1:probe8	0.080880637343872	0.0529134108462838	1.52854703656951	0.126833724088889	   
df.mm.trans1:probe9	-0.038364354627248	0.0529134108462838	-0.72504028777692	0.468672406070468	   
df.mm.trans1:probe10	-0.0722133227293133	0.0529134108462838	-1.36474518603793	0.172776735416101	   
df.mm.trans1:probe11	0.0611470832467601	0.0529134108462838	1.15560653280121	0.248240973838826	   
df.mm.trans1:probe12	-0.0641043494501607	0.0529134108462838	-1.21149531706408	0.226119162436881	   
df.mm.trans1:probe13	-0.0352763950861103	0.0529134108462838	-0.666681556186013	0.505197775620878	   
df.mm.trans1:probe14	-0.05241537791244	0.0529134108462838	-0.99058777489717	0.322233147439418	   
df.mm.trans1:probe15	-0.0485932671893530	0.0529134108462838	-0.918354466517364	0.358753294207224	   
df.mm.trans1:probe16	-0.0192008917414511	0.0529134108462838	-0.362873824128078	0.716809949319002	   
df.mm.trans1:probe17	0.0313037821366149	0.0529134108462838	0.591603936241306	0.554309047818751	   
df.mm.trans1:probe18	-0.0461708079385661	0.0529134108462838	-0.872572892204863	0.383198581693771	   
df.mm.trans1:probe19	0.0269542343342587	0.0529134108462838	0.509402699677822	0.610632383845981	   
df.mm.trans1:probe20	0.00852615925964324	0.0529134108462838	0.161134183627137	0.872034747462407	   
df.mm.trans1:probe21	-0.008007638958234	0.0529134108462838	-0.151334771850119	0.879755746781517	   
df.mm.trans1:probe22	0.0147025903009705	0.0529134108462838	0.277861322220983	0.781201810658899	   
df.mm.trans2:probe2	0.0127129386520778	0.0529134108462838	0.240259292469531	0.810200406474527	   
df.mm.trans2:probe3	-0.00211661560814982	0.0529134108462838	-0.0400014962992783	0.968103471053475	   
df.mm.trans2:probe4	0.0810718148833591	0.0529134108462838	1.53216006276513	0.125939956441441	   
df.mm.trans2:probe5	0.0948160544356035	0.0529134108462838	1.79190970529284	0.0735842202310087	.  
df.mm.trans2:probe6	0.117336923383609	0.0529134108462838	2.21752711660337	0.0269111878608539	*  
df.mm.trans3:probe2	-0.00467434226280127	0.0529134108462838	-0.0883394623034314	0.929632433409596	   
df.mm.trans3:probe3	-0.0189879845597899	0.0529134108462838	-0.35885013375817	0.719816662851433