fitVsDatCorrelation=0.914794685976301
cont.fitVsDatCorrelation=0.258469526513050

fstatistic=11950.0102517478,43,485
cont.fstatistic=2079.91906697313,43,485

residuals=-0.463169481997669,-0.0860658862365574,0.00254870423399316,0.0820199779949687,0.434428238896324
cont.residuals=-0.600494176742345,-0.230101086184877,-0.0567870565939075,0.218851030610388,0.978780188056173

predictedValues:
Include	Exclude	Both
Lung	64.9749916659293	48.6775485366887	92.2624403985055
cerebhem	60.4514590164555	50.0377601817105	69.3784838437008
cortex	75.1832276303375	47.7110921864065	92.5832672808468
heart	69.9311752697134	46.564140469506	89.4145195880543
kidney	70.7915306063958	48.7776332935852	101.287673323083
liver	63.5083678739634	48.3220153298668	81.8177687034454
stomach	65.6691752397893	49.1513869044469	80.4922152831864
testicle	77.5706086252699	49.4627845539894	97.916648118544


diffExp=16.2974431292406,10.4136988347450,27.4721354439310,23.3670348002074,22.0138973128105,15.1863525440966,16.5177883353425,28.1078240712805
diffExpScore=0.993764659848669
diffExp1.5=0,0,1,1,0,0,0,1
diffExp1.5Score=0.75
diffExp1.4=0,0,1,1,1,0,0,1
diffExp1.4Score=0.8
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.0159984429921	61.7274572908296	59.028752522471
cerebhem	63.6886382934275	64.5469993922867	66.5629489733429
cortex	60.8373084849339	64.7305948792905	67.4216617268019
heart	69.2979352183524	65.4317796773844	62.718538827208
kidney	65.5905011949069	64.5907764200004	63.6820732595636
liver	59.4017643050763	63.2691240594876	52.1068167971625
stomach	61.5331609068075	66.8421267734919	64.7078795379818
testicle	62.6918107210936	65.0077551752408	51.8158376911135
cont.diffExp=4.28854115216247,-0.85836109885922,-3.89328639435666,3.86615554096802,0.999724774906454,-3.8673597544113,-5.30896586668444,-2.3159444541472
cont.diffExpScore=3.13966886456268

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=-0.282961643906774
cont.tran.correlation=-0.0583332562105429

tran.covariance=-0.000582414876569189
cont.tran.covariance=-7.0966037344356e-05

tran.mean=58.5490560865034
cont.tran.mean=64.0752332022251

weightedLogRatios:
wLogRatio
Lung	1.16368913897708
cerebhem	0.757633074719153
cortex	1.86114366252183
heart	1.64468683793972
kidney	1.51724843754337
liver	1.09710833314838
stomach	1.1704198872726
testicle	1.85666019166270

cont.weightedLogRatios:
wLogRatio
Lung	0.279172346409676
cerebhem	-0.0557011506113729
cortex	-0.256758773844608
heart	0.241667092959673
kidney	0.0641364737920227
liver	-0.259601782212891
stomach	-0.344349352992235
testicle	-0.150775369320904

varWeightedLogRatios=0.157940106399675
cont.varWeightedLogRatios=0.0555883225353843

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.43043383767158	0.072185377413328	47.5225587313781	1.28351500160189e-184	***
df.mm.trans1	0.821286984693777	0.0638104350143368	12.8707316367496	8.09162352525493e-33	***
df.mm.trans2	0.498446171890731	0.0600557863103526	8.29971935285122	1.04346333470501e-15	***
df.mm.exp2	0.240458617078726	0.0826664537373791	2.90878108600899	0.00379493218440956	** 
df.mm.exp3	0.122400423012724	0.0826664537373791	1.48065409218562	0.139348085283417	   
df.mm.exp4	0.0604759632563362	0.0826664537373791	0.731565955985734	0.464786849610523	   
df.mm.exp5	-0.0055367072790919	0.0826664537373791	-0.0669764702460968	0.946628035703623	   
df.mm.exp6	0.0899812628895477	0.0826664537373791	1.08848582250071	0.276921274074962	   
df.mm.exp7	0.156790998476877	0.0826664537373791	1.89667018951826	0.0584653729082765	.  
df.mm.exp8	0.133709341591249	0.082666453737379	1.61745587897150	0.106430071031451	   
df.mm.trans1:exp2	-0.312620356649417	0.0769582802370588	-4.06220559615463	5.6684225162906e-05	***
df.mm.trans2:exp2	-0.212898601153545	0.0697104746397485	-3.05404033258657	0.00238198050095882	** 
df.mm.trans1:exp3	0.0235252939742991	0.0769582802370588	0.305688925244078	0.759972666844474	   
df.mm.trans2:exp3	-0.142454419670680	0.0697104746397485	-2.04351527380727	0.0415412514624607	*  
df.mm.trans1:exp4	0.0130331324301824	0.0769582802370588	0.169353218263659	0.865589428931432	   
df.mm.trans2:exp4	-0.104863144291717	0.0697104746397485	-1.50426668063345	0.133163615515580	   
df.mm.trans1:exp5	0.09127362435803	0.0769582802370588	1.18601434539435	0.236197333062400	   
df.mm.trans2:exp5	0.00759067280874649	0.0697104746397485	0.108888554381157	0.913335920111304	   
df.mm.trans1:exp6	-0.112812040391809	0.0769582802370588	-1.46588567265676	0.143327446862605	   
df.mm.trans2:exp6	-0.097311910261491	0.0697104746397485	-1.39594387736394	0.163370053705223	   
df.mm.trans1:exp7	-0.146163809777244	0.0769582802370588	-1.89926034374738	0.0581233127982177	.  
df.mm.trans2:exp7	-0.147103842604828	0.0697104746397485	-2.11021146197949	0.0353516262768269	*  
df.mm.trans1:exp8	0.0434768067614616	0.0769582802370588	0.564939947040626	0.572375818813685	   
df.mm.trans2:exp8	-0.117706690086939	0.0697104746397484	-1.68850794224579	0.0919565661640911	.  
df.mm.trans1:probe2	0.112196247213629	0.0384791401185294	2.91576804647985	0.00371243026950114	** 
df.mm.trans1:probe3	0.300794260485471	0.0384791401185294	7.81707334308714	3.39013829756156e-14	***
df.mm.trans1:probe4	0.177413437587775	0.0384791401185294	4.61063935008107	5.14123902706849e-06	***
df.mm.trans1:probe5	0.0334080845351482	0.0384791401185294	0.86821286630209	0.385707207377892	   
df.mm.trans1:probe6	0.176691858677737	0.0384791401185294	4.59188688035812	5.60444263969922e-06	***
df.mm.trans1:probe7	-0.330672649375891	0.0384791401185294	-8.5935561022752	1.16431215948977e-16	***
df.mm.trans1:probe8	-0.324934063694702	0.0384791401185294	-8.44442112515481	3.56803015203775e-16	***
df.mm.trans1:probe9	-0.292395141357993	0.0384791401185294	-7.59879613882515	1.55498495802983e-13	***
df.mm.trans1:probe10	-0.306997148399626	0.0384791401185294	-7.97827465618946	1.07826970821954e-14	***
df.mm.trans1:probe11	-0.209890631664685	0.0384791401185294	-5.45466013580727	7.83368439960368e-08	***
df.mm.trans1:probe12	-0.172657922716511	0.0384791401185294	-4.48705252208504	9.02843134976519e-06	***
df.mm.trans1:probe13	-0.406450252948170	0.0384791401185294	-10.5628725511059	1.29666078184544e-23	***
df.mm.trans2:probe2	-0.0867980138809855	0.0384791401185294	-2.25571604806180	0.0245331989557893	*  
df.mm.trans2:probe3	0.0193610398072198	0.0384791401185294	0.503156768773442	0.615082478204262	   
df.mm.trans2:probe4	-0.116057597123982	0.0384791401185294	-3.01611722004398	0.00269473575483093	** 
df.mm.trans2:probe5	-0.147392041035830	0.0384791401185294	-3.83044009252313	0.000144707036617617	***
df.mm.trans2:probe6	-0.0620723007975621	0.0384791401185294	-1.61314157765369	0.107364190284322	   
df.mm.trans3:probe2	-0.543252876203361	0.0384791401185294	-14.1181137242140	3.68457990291252e-38	***
df.mm.trans3:probe3	-0.133336719274388	0.0384791401185294	-3.46516889056419	0.000576932288898556	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27681822654346	0.172639686018106	24.7730885359402	3.65780753136315e-88	***
df.mm.trans1	-0.166555499095030	0.152610041816029	-1.09137968323088	0.275647720467948	   
df.mm.trans2	-0.0630739075135783	0.143630364815069	-0.439140481156537	0.660755325690422	   
df.mm.exp2	-0.111349587849547	0.197706393300873	-0.563206813853982	0.573554338113776	   
df.mm.exp3	-0.167130468988012	0.197706393300873	-0.845346810478049	0.398334069122418	   
df.mm.exp4	0.0461647981840975	0.197706393300873	0.233501797353832	0.81547029492688	   
df.mm.exp5	-0.0370018931036757	0.197706393300873	-0.187155774205873	0.851616764983856	   
df.mm.exp6	0.0438242838195721	0.197706393300873	0.221663463117652	0.824669173946365	   
df.mm.exp7	-0.082574528469901	0.197706393300873	-0.417662408843995	0.676378796103348	   
df.mm.exp8	0.130440250344237	0.197706393300873	0.659767487365627	0.509716223568152	   
df.mm.trans1:exp2	0.0754586584750684	0.184054635616079	0.409979668387533	0.682001890586742	   
df.mm.trans2:exp2	0.156014374306974	0.166720669548751	0.935783035956164	0.349850608606488	   
df.mm.trans1:exp3	0.0854365828618473	0.184054635616079	0.464191421073795	0.642718891597051	   
df.mm.trans2:exp3	0.214635586926972	0.166720669548751	1.28739638287147	0.198570168803124	   
df.mm.trans1:exp4	0.00235319943538672	0.184054635616079	0.0127853309834330	0.989804317150278	   
df.mm.trans2:exp4	0.0121144253032927	0.166720669548751	0.0726630077487204	0.942104241946101	   
df.mm.trans1:exp5	0.0305356664076953	0.184054635616079	0.165905445986103	0.868300496922934	   
df.mm.trans2:exp5	0.0823446690822195	0.166720669548751	0.493907979766967	0.621594770676246	   
df.mm.trans1:exp6	-0.149397469089940	0.184054635616079	-0.811701746005294	0.417360976517813	   
df.mm.trans2:exp6	-0.0191556899806095	0.166720669548751	-0.114896911297541	0.908574384626753	   
df.mm.trans1:exp7	0.0122536463985283	0.184054635616079	0.0665761357083573	0.946946579379528	   
df.mm.trans2:exp7	0.162179205824352	0.166720669548751	0.972760043870442	0.331157469990948	   
df.mm.trans1:exp8	-0.182106535048061	0.184054635616079	-0.98941563975557	0.322953173479353	   
df.mm.trans2:exp8	-0.0786625219170385	0.166720669548751	-0.471822252933293	0.637265858848856	   
df.mm.trans1:probe2	0.0618433398972353	0.0920273178080395	0.672010674332971	0.501896888107546	   
df.mm.trans1:probe3	0.199158598711630	0.0920273178080395	2.16412477789537	0.0309421595381721	*  
df.mm.trans1:probe4	0.152205781671132	0.0920273178080395	1.65391956754210	0.098790884195091	.  
df.mm.trans1:probe5	0.114163887324613	0.0920273178080395	1.24054346083136	0.215374220696390	   
df.mm.trans1:probe6	0.0209134019901994	0.0920273178080395	0.227252108268795	0.820323470878504	   
df.mm.trans1:probe7	0.156417074466098	0.0920273178080395	1.69968090119034	0.089832142768159	.  
df.mm.trans1:probe8	0.0529504045081492	0.0920273178080395	0.575377026836736	0.56530317731672	   
df.mm.trans1:probe9	0.132329796454459	0.0920273178080395	1.43794038125165	0.151095874542436	   
df.mm.trans1:probe10	0.134869892193850	0.0920273178080395	1.46554192174955	0.143421101518344	   
df.mm.trans1:probe11	0.101799753476422	0.0920273178080395	1.10619059537047	0.269192417628416	   
df.mm.trans1:probe12	0.0595251282553782	0.0920273178080395	0.64682020157908	0.518054303623574	   
df.mm.trans1:probe13	0.0879731147324466	0.0920273178080395	0.9559456564403	0.339575926699347	   
df.mm.trans2:probe2	-0.166105543722871	0.0920273178080395	-1.80495908909735	0.0717011632984217	.  
df.mm.trans2:probe3	-0.172666470322386	0.0920273178080395	-1.87625233936028	0.0612209304075886	.  
df.mm.trans2:probe4	-0.152878067667002	0.0920273178080395	-1.66122485484030	0.0973145314375343	.  
df.mm.trans2:probe5	-0.222989023875223	0.0920273178080395	-2.42307424780496	0.0157548774743205	*  
df.mm.trans2:probe6	-0.10450016302288	0.0920273178080395	-1.1355341599856	0.256712406717501	   
df.mm.trans3:probe2	0.0428712486654562	0.0920273178080395	0.465853506182606	0.641529497203705	   
df.mm.trans3:probe3	0.0496362597937672	0.0920273178080395	0.539364408047879	0.589882950433409