fitVsDatCorrelation=0.879371514262508
cont.fitVsDatCorrelation=0.286388605901578

fstatistic=9242.69289445055,46,554
cont.fstatistic=2273.51642159053,46,554

residuals=-0.419495450481174,-0.0777826357811284,-0.00744203727108005,0.0697209731623168,1.87399033028693
cont.residuals=-0.553807348986985,-0.207280248425949,-0.0874478586055525,0.182042641799485,2.00962808247743

predictedValues:
Include	Exclude	Both
Lung	56.1814234586427	45.5128708161056	57.063128072181
cerebhem	54.2319701301755	42.9922096172228	54.6142964940022
cortex	55.2057525227498	45.6468526033872	55.5191956972291
heart	61.7460454477619	48.7077659378961	53.1822881169002
kidney	60.7982486369894	44.0376655794123	56.6478658342949
liver	69.1000994608556	47.2274085333719	66.5757427581451
stomach	61.0879936045828	45.6281193623362	53.4583885197092
testicle	61.1891434017006	43.2823507486422	53.6820205578045


diffExp=10.6685526425372,11.2397605129528,9.55889991936257,13.0382795098658,16.7605830575771,21.8726909274837,15.4598742422467,17.9067926530584
diffExpScore=0.991489755235045
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,1,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,1,1,1,1
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	59.0527879713685	61.2219879769312	56.0690541521224
cerebhem	58.2956580754843	58.4803992389441	57.4662106757859
cortex	54.7105757952115	54.2398698485314	53.93457823138
heart	55.9037382339731	62.0161994681583	60.7192403599563
kidney	56.3111861478012	53.527149590278	64.5391631210299
liver	58.2708984499793	57.2363209800192	56.6433342893059
stomach	60.2248873091761	58.270292429393	54.1060025778502
testicle	59.7833302587607	56.9177141990207	55.2371373149595
cont.diffExp=-2.16920000556265,-0.184741163459734,0.470705946680042,-6.11246123418518,2.78403655752319,1.03457746996006,1.95459487978315,2.86561605974006
cont.diffExpScore=10.6966273208728

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=0.480639761656366
cont.tran.correlation=0.319123993174164

tran.covariance=0.00159942848028215
cont.tran.covariance=0.00060175364104615

tran.mean=52.6609949913645
cont.tran.mean=57.7789372483144

weightedLogRatios:
wLogRatio
Lung	0.826209694666806
cerebhem	0.900473313047846
cortex	0.744559049995775
heart	0.949817555653695
kidney	1.27274464029260
liver	1.53955417155244
stomach	1.15736623778700
testicle	1.36442267230057

cont.weightedLogRatios:
wLogRatio
Lung	-0.147778689919599
cerebhem	-0.0128684459611118
cortex	0.0345435965122355
heart	-0.422893130150836
kidney	0.203097794989616
liver	0.0726622845069116
stomach	0.134665000479106
testicle	0.199731125139543

varWeightedLogRatios=0.0799532015403265
cont.varWeightedLogRatios=0.0437320204583206

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.61835042327165	0.07460441614755	48.5004857636766	1.52317923146310e-201	***
df.mm.trans1	0.250935760225076	0.0635074964690454	3.95127778887309	8.77840634043996e-05	***
df.mm.trans2	0.211722276777309	0.0588944293857968	3.59494571872648	0.000353411924034430	***
df.mm.exp2	-0.0484293269048058	0.0780023939724261	-0.620869750765926	0.534940670899561	   
df.mm.exp3	0.0128498344411043	0.078002393972426	0.164736411111264	0.86921154567126	   
df.mm.exp4	0.232719848748071	0.0780023939724261	2.9834962351327	0.00297531927382242	** 
df.mm.exp5	0.0533288181162776	0.0780023939724261	0.683681812831659	0.494461957420522	   
df.mm.exp6	0.0897671403047601	0.078002393972426	1.15082545205591	0.250300519231620	   
df.mm.exp7	0.151512800594148	0.0780023939724261	1.94241218606326	0.0525936052769951	.  
df.mm.exp8	0.0962134387778903	0.0780023939724261	1.23346776782135	0.217924364774185	   
df.mm.trans1:exp2	0.0131137574371282	0.0692369033893379	0.189404158695341	0.84984543598869	   
df.mm.trans2:exp2	-0.00854690654223531	0.0591871955448089	-0.144404654817015	0.885233456741559	   
df.mm.trans1:exp3	-0.0303688330367321	0.0692369033893379	-0.438622057748018	0.661106407849756	   
df.mm.trans2:exp3	-0.00991033706814435	0.0591871955448089	-0.167440558332275	0.867084516087598	   
df.mm.trans1:exp4	-0.138276075440110	0.0692369033893379	-1.99714413370780	0.0462981929786784	*  
df.mm.trans2:exp4	-0.164876527563367	0.0591871955448089	-2.78567899772416	0.00552447616480001	** 
df.mm.trans1:exp5	0.0256460063106881	0.0692369033893379	0.370409493423956	0.711218976664007	   
df.mm.trans2:exp5	-0.0862786755265793	0.0591871955448089	-1.45772535313420	0.145482876856687	   
df.mm.trans1:exp6	0.117202871025852	0.0692369033893379	1.69278037128247	0.0910594975952719	.  
df.mm.trans2:exp6	-0.0527878880162042	0.059187195544809	-0.891880203653846	0.372844350861442	   
df.mm.trans1:exp7	-0.067783616560291	0.0692369033893379	-0.979009938950119	0.328002387376057	   
df.mm.trans2:exp7	-0.148983782477673	0.059187195544809	-2.51716238801822	0.0121115278193885	*  
df.mm.trans1:exp8	-0.0108298192106840	0.0692369033893379	-0.156416862692212	0.875761428413932	   
df.mm.trans2:exp8	-0.146463651854511	0.059187195544809	-2.47458340450727	0.0136374613683467	*  
df.mm.trans1:probe2	-0.00865700885304493	0.0439992842183551	-0.196753401943605	0.844092675425548	   
df.mm.trans1:probe3	-0.0712192706157453	0.0439992842183551	-1.61864611847560	0.106092555582639	   
df.mm.trans1:probe4	0.115961759529168	0.0439992842183551	2.63553740905614	0.00863573038248771	** 
df.mm.trans1:probe5	-0.0557768625354573	0.0439992842183551	-1.26767658897935	0.205446012037174	   
df.mm.trans1:probe6	0.0134478619174066	0.0439992842183551	0.30563819744587	0.759994942035016	   
df.mm.trans1:probe7	0.256708607393118	0.0439992842183551	5.83438144400603	9.1897048324469e-09	***
df.mm.trans1:probe8	0.651701391675583	0.0439992842183551	14.8116362175663	4.6589078245341e-42	***
df.mm.trans1:probe9	0.523605376199559	0.0439992842183551	11.9003157778900	3.07741401853101e-29	***
df.mm.trans1:probe10	0.61173499972379	0.0439992842183551	13.9032943510611	6.53502113881358e-38	***
df.mm.trans1:probe11	0.503795179459934	0.0439992842183551	11.4500767094244	2.15489982318704e-27	***
df.mm.trans1:probe12	0.485397497202257	0.0439992842183551	11.0319407650674	1.01961694343732e-25	***
df.mm.trans2:probe2	-0.0090425518473857	0.0439992842183551	-0.205515885270094	0.837244581772385	   
df.mm.trans2:probe3	-0.0815255742719147	0.0439992842183551	-1.85288410300786	0.0644305475572197	.  
df.mm.trans2:probe4	-0.0248164284628361	0.0439992842183551	-0.564018913118671	0.572969510621857	   
df.mm.trans2:probe5	-0.0459248344426379	0.0439992842183551	-1.04376321702705	0.297050184645230	   
df.mm.trans2:probe6	0.00430138183723579	0.0439992842183551	0.0977602684600355	0.922158015522266	   
df.mm.trans3:probe2	-0.0492256700176325	0.0439992842183551	-1.11878342777897	0.263717403023451	   
df.mm.trans3:probe3	-0.289491133967852	0.0439992842183551	-6.57945098677506	1.09514325605251e-10	***
df.mm.trans3:probe4	-0.081234179855154	0.0439992842183551	-1.84626139489028	0.065387489890996	.  
df.mm.trans3:probe5	-0.174343186340378	0.0439992842183551	-3.96240960364641	8.38967107190447e-05	***
df.mm.trans3:probe6	0.352526597828667	0.0439992842183551	8.01209847140206	6.69884741523012e-15	***
df.mm.trans3:probe7	-0.0161844943138461	0.0439992842183551	-0.367835400083495	0.713136461310202	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07567706984329	0.150124045145951	27.1487293449954	7.443491786953e-104	***
df.mm.trans1	0.0115871548748405	0.127794073854412	0.0906705180088483	0.927787169730532	   
df.mm.trans2	0.0491629809473678	0.118511348691102	0.414837747526699	0.678421277713221	   
df.mm.exp2	-0.0833319972619908	0.156961685633315	-0.530906615367693	0.5956962577885	   
df.mm.exp3	-0.158652641246892	0.156961685633315	-1.01077304698121	0.312566315347211	   
df.mm.exp4	-0.121587858277929	0.156961685633315	-0.774633999293151	0.438886437724405	   
df.mm.exp5	-0.322544141896872	0.156961685633315	-2.05492277045484	0.0403548361383796	*  
df.mm.exp6	-0.090836956932311	0.156961685633315	-0.578720574806513	0.563012930203353	   
df.mm.exp7	0.00587882393310684	0.156961685633315	0.0374538786926679	0.97013660568941	   
df.mm.exp8	-0.0456561463431598	0.156961685633315	-0.290874465057793	0.771256192060098	   
df.mm.trans1:exp2	0.0704278570266401	0.139323173438281	0.50549994870623	0.613408786673236	   
df.mm.trans2:exp2	0.037517233950293	0.119100472530444	0.315004912685824	0.752876534101338	   
df.mm.trans1:exp3	0.0822779184946878	0.139323173438281	0.590554438749821	0.555059823780446	   
df.mm.trans2:exp3	0.0375624796671827	0.119100472530444	0.315384808045838	0.752588265086827	   
df.mm.trans1:exp4	0.066787354529066	0.139323173438281	0.479370035011816	0.631864578191589	   
df.mm.trans2:exp4	0.134477085278133	0.119100472530444	1.12910622788468	0.259341697155727	   
df.mm.trans1:exp5	0.275005590275453	0.139323173438281	1.97386826246302	0.0488931185586524	*  
df.mm.trans2:exp5	0.18822673039591	0.119100472530444	1.58040288503302	0.114585371088922	   
df.mm.trans1:exp6	0.0775080014392156	0.139323173438281	0.556318087841655	0.578218051505835	   
df.mm.trans2:exp6	0.02351923030911	0.119100472530444	0.197473862272864	0.843529164658368	   
df.mm.trans1:exp7	0.0137750981333413	0.139323173438281	0.098871550176422	0.921276021772122	   
df.mm.trans2:exp7	-0.0552928299279705	0.119100472530444	-0.464253657044364	0.64264840022439	   
df.mm.trans1:exp8	0.0579512549555477	0.139323173438281	0.415948427855899	0.677608817151447	   
df.mm.trans2:exp8	-0.0272436450366534	0.119100472530444	-0.228745062532723	0.819151481009512	   
df.mm.trans1:probe2	0.0243364046219155	0.0885383315288202	0.274868570501509	0.783519689296017	   
df.mm.trans1:probe3	0.0127724153126826	0.0885383315288202	0.144258595030391	0.885348733865683	   
df.mm.trans1:probe4	0.0270385817142193	0.0885383315288202	0.305388426089981	0.760185042711977	   
df.mm.trans1:probe5	-0.0215585168251086	0.0885383315288202	-0.24349359709914	0.807713180034213	   
df.mm.trans1:probe6	-0.000382602808117527	0.0885383315288202	-0.00432132390017972	0.99655364869087	   
df.mm.trans1:probe7	-0.0369582686689133	0.0885383315288202	-0.417426757775336	0.676528004759632	   
df.mm.trans1:probe8	-0.00878980121896552	0.0885383315288202	-0.0992767885636556	0.920954419301068	   
df.mm.trans1:probe9	-0.0481541818445877	0.0885383315288202	-0.543879481497943	0.586743212232405	   
df.mm.trans1:probe10	-0.160868706451338	0.0885383315288202	-1.81693853581342	0.0697667033764847	.  
df.mm.trans1:probe11	0.0794975015870643	0.0885383315288202	0.897887956711574	0.369635329479866	   
df.mm.trans1:probe12	-0.0347497449484942	0.0885383315288202	-0.392482491463968	0.694852701797288	   
df.mm.trans2:probe2	-0.212165502772257	0.0885383315288202	-2.39631241190936	0.0168915404065412	*  
df.mm.trans2:probe3	-0.0428094716521406	0.0885383315288202	-0.483513421960133	0.628922275773526	   
df.mm.trans2:probe4	0.178479827114396	0.0885383315288202	2.01584809689235	0.0442981232804964	*  
df.mm.trans2:probe5	-0.0998727466028502	0.0885383315288202	-1.12801703938074	0.259800996724365	   
df.mm.trans2:probe6	0.042030507230495	0.0885383315288202	0.474715374739286	0.635176931017807	   
df.mm.trans3:probe2	-0.117561804876560	0.0885383315288202	-1.32780687016101	0.18478874205479	   
df.mm.trans3:probe3	-0.176827026565050	0.0885383315288202	-1.99718046987921	0.0462942347345119	*  
df.mm.trans3:probe4	-0.104899773795917	0.0885383315288202	-1.18479501459513	0.236606513758948	   
df.mm.trans3:probe5	-0.127456581489696	0.0885383315288202	-1.43956385092040	0.150555626057150	   
df.mm.trans3:probe6	-0.0838187546611567	0.0885383315288202	-0.946694535731936	0.344207188182526	   
df.mm.trans3:probe7	-0.158338211697505	0.0885383315288202	-1.78835775379350	0.0742646468844472	.