fitVsDatCorrelation=0.828604136007792
cont.fitVsDatCorrelation=0.288332968163685

fstatistic=9043.24080586283,60,876
cont.fstatistic=3081.67660268746,60,876

residuals=-0.826056175812065,-0.0980126232776226,-0.00382166336141778,0.0872082989095215,1.27790345867193
cont.residuals=-0.641486507793375,-0.191510944680667,-0.0363222245969972,0.135966603060373,1.55129143368490

predictedValues:
Include	Exclude	Both
Lung	85.7444836878568	51.3720258886816	70.1687036968073
cerebhem	81.3773599530485	54.8599956779146	71.880490245706
cortex	82.3474048305486	51.0082497612215	65.0549265595147
heart	91.496726370986	49.690685107015	73.8515137303323
kidney	77.8074024731877	55.1077673276847	59.9537235296302
liver	76.8518476151487	53.88910620413	58.5816173631824
stomach	91.7959360893144	50.3970385944941	70.5941543350971
testicle	93.5932230715234	53.0416682901546	74.3564949240988


diffExp=34.3724577991751,26.5173642751339,31.3391550693271,41.806041263971,22.6996351455030,22.9627414110186,41.3988974948202,40.5515547813688
diffExpScore=0.996192620611564
diffExp1.5=1,0,1,1,0,0,1,1
diffExp1.5Score=0.833333333333333
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	65.0851476161588	69.4541704303253	63.129084118468
cerebhem	62.8720668457355	61.9044275931008	60.6862087063096
cortex	65.6773399678949	64.8841064433917	60.0404698082122
heart	61.0168557064199	67.3286249473203	61.4754605352578
kidney	61.2620783132934	62.8876373821712	59.9299566582174
liver	61.988797651778	69.9174475814954	60.8275389690297
stomach	67.4726726844314	69.4311175727244	79.9498729802358
testicle	65.2507030895342	62.4254431368513	65.4353777024678
cont.diffExp=-4.36902281416658,0.967639252634662,0.793233524503194,-6.31176924090038,-1.62555906887783,-7.92864992971741,-1.95844488829303,2.82525995268288
cont.diffExpScore=1.43919642596826

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=-0.662441365578475
cont.tran.correlation=0.18909572039144

tran.covariance=-0.00204775545327749
cont.tran.covariance=0.000350913099129552

tran.mean=68.7738075589319
cont.tran.mean=64.9286648101642

weightedLogRatios:
wLogRatio
Lung	2.14912533794401
cerebhem	1.65687858013349
cortex	1.99796408665419
heart	2.57079212644014
kidney	1.44248221719283
liver	1.47816002727762
stomach	2.53031250717034
testicle	2.41634031989811

cont.weightedLogRatios:
wLogRatio
Lung	-0.273408685577665
cerebhem	0.0641093316022508
cortex	0.0507762225011236
heart	-0.409527181661778
kidney	-0.108113117500716
liver	-0.50396995193384
stomach	-0.120917168615819
testicle	0.183965355084955

varWeightedLogRatios=0.213738326300527
cont.varWeightedLogRatios=0.058186644040857

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45494188161821	0.0803227936964888	55.4629847469182	7.74371985750603e-289	***
df.mm.trans1	0.41182163009596	0.0692177241553542	5.94965574383312	3.88172640367509e-09	***
df.mm.trans2	-0.489282259630369	0.0610095414379305	-8.01976622178272	3.38279905796278e-15	***
df.mm.exp2	-0.0106865137953044	0.0781547618796063	-0.136735286990785	0.891271454500326	   
df.mm.exp3	0.0281392454790822	0.0781547618796062	0.360045182178783	0.718900132109134	   
df.mm.exp4	-0.0194989580741284	0.0781547618796062	-0.249491619002892	0.803039012223293	   
df.mm.exp5	0.130391118695344	0.0781547618796062	1.66837075002807	0.0955995073089894	.  
df.mm.exp6	0.118823767922865	0.0781547618796062	1.52036504321909	0.12878009845792	   
df.mm.exp7	0.0429899621265394	0.0781547618796062	0.550061967980447	0.582417089852274	   
df.mm.exp8	0.0616016120720265	0.0781547618796062	0.788200367968889	0.430792748317277	   
df.mm.trans1:exp2	-0.0415881389433686	0.0720551301698169	-0.577171102811906	0.563972172708649	   
df.mm.trans2:exp2	0.0763771396247932	0.0524278080984326	1.45680588975599	0.145528307739628	   
df.mm.trans1:exp3	-0.0685640570554202	0.0720551301698169	-0.951549971443129	0.341587727713678	   
df.mm.trans2:exp3	-0.0352456467086017	0.0524278080984326	-0.672270079314176	0.501589046214301	   
df.mm.trans1:exp4	0.0844303985432965	0.0720551301698169	1.17174722111131	0.241617147880032	   
df.mm.trans2:exp4	-0.0137773296791794	0.0524278080984326	-0.262786680940630	0.792776792066873	   
df.mm.trans1:exp5	-0.227526298372962	0.0720551301698169	-3.15766965983874	0.00164480779847012	** 
df.mm.trans2:exp5	-0.060194225532992	0.0524278080984326	-1.14813545933444	0.251226079891769	   
df.mm.trans1:exp6	-0.228316010196591	0.0720551301698169	-3.16862948770621	0.00158471573497415	** 
df.mm.trans2:exp6	-0.0709892025718267	0.0524278080984326	-1.35403720175647	0.176073705980623	   
df.mm.trans1:exp7	0.0252063115344048	0.0720551301698169	0.349819804294288	0.726558019893074	   
df.mm.trans2:exp7	-0.0621513277173436	0.0524278080984326	-1.18546492732741	0.236155145378277	   
df.mm.trans1:exp8	0.0259846119022668	0.0720551301698169	0.360621260984849	0.71846953261679	   
df.mm.trans2:exp8	-0.0296175941508346	0.0524278080984326	-0.564921464868947	0.572271817755462	   
df.mm.trans1:probe2	-1.06824678349610	0.0501958046736867	-21.2815949548089	2.45259902556850e-81	***
df.mm.trans1:probe3	-0.844735817832039	0.0501958046736867	-16.8288131512883	2.81214058883914e-55	***
df.mm.trans1:probe4	-0.807189217337664	0.0501958046736867	-16.0808103901321	3.52323103916581e-51	***
df.mm.trans1:probe5	-0.141979347186193	0.0501958046736867	-2.82851023325901	0.00478325589231171	** 
df.mm.trans1:probe6	-0.630932385583279	0.0501958046736867	-12.5694246697478	1.96955499369382e-33	***
df.mm.trans1:probe7	-0.145759755870084	0.0501958046736867	-2.90382347324922	0.00377897217040519	** 
df.mm.trans1:probe8	-0.814639851275783	0.0501958046736867	-16.2292417976283	5.51497075512984e-52	***
df.mm.trans1:probe9	-0.642939427754021	0.0501958046736867	-12.8086287675563	1.49290766343976e-34	***
df.mm.trans1:probe10	-0.785702849922125	0.0501958046736867	-15.6527593297852	7.03171122954882e-49	***
df.mm.trans1:probe11	-0.619887513140923	0.0501958046736867	-12.3493889015366	2.05197980936372e-32	***
df.mm.trans1:probe12	-0.628899493846816	0.0501958046736867	-12.5289254338121	3.03827827880101e-33	***
df.mm.trans1:probe13	-0.677161652362187	0.0501958046736867	-13.4904033666615	8.01914651218448e-38	***
df.mm.trans1:probe14	-0.630895541477955	0.0501958046736867	-12.5686906620839	1.98510650906927e-33	***
df.mm.trans1:probe15	-0.672112881439646	0.0501958046736867	-13.3898218348909	2.47464039025980e-37	***
df.mm.trans1:probe16	-0.621932204717054	0.0501958046736867	-12.3901232136852	1.33255005136709e-32	***
df.mm.trans1:probe17	-0.672563076092226	0.0501958046736867	-13.3987906053988	2.23856926416295e-37	***
df.mm.trans1:probe18	-0.426085614563465	0.0501958046736867	-8.48847064676752	8.87599644998624e-17	***
df.mm.trans1:probe19	-0.6611378334755	0.0501958046736867	-13.1711771087929	2.81251040544298e-36	***
df.mm.trans1:probe20	-0.645251678958881	0.0501958046736867	-12.8546933982539	9.0500197071388e-35	***
df.mm.trans1:probe21	-0.868909429967957	0.0501958046736867	-17.3103994570178	5.76748647037058e-58	***
df.mm.trans1:probe22	-0.700965654669536	0.0501958046736867	-13.9646263114294	3.67562833531035e-40	***
df.mm.trans2:probe2	0.115362115151786	0.0501958046736867	2.29824217186542	0.0217827687583473	*  
df.mm.trans2:probe3	-0.221026589301380	0.0501958046736867	-4.40328809824311	1.19793367879961e-05	***
df.mm.trans2:probe4	-0.118489597116494	0.0501958046736867	-2.36054781643151	0.0184662194854118	*  
df.mm.trans2:probe5	-0.0941530310889029	0.0501958046736867	-1.87571514593647	0.0610267845602344	.  
df.mm.trans2:probe6	-0.133312196205478	0.0501958046736867	-2.65584339313046	0.00805478723989747	** 
df.mm.trans3:probe2	-0.0413104800836505	0.0501958046736867	-0.822986708793734	0.410739526605873	   
df.mm.trans3:probe3	-0.308319487225917	0.0501958046736867	-6.14233578344333	1.23239394442421e-09	***
df.mm.trans3:probe4	0.0999635563957925	0.0501958046736867	1.99147233609734	0.0467391397747103	*  
df.mm.trans3:probe5	-0.093554050068153	0.0501958046736867	-1.86378225583453	0.0626866617547424	.  
df.mm.trans3:probe6	0.130158780910298	0.0501958046736867	2.59302110517871	0.00967234827316208	** 
df.mm.trans3:probe7	-0.218608316436014	0.0501958046736867	-4.35511130575841	1.48719849302379e-05	***
df.mm.trans3:probe8	-0.387654041237625	0.0501958046736867	-7.72283747133232	3.10498414456678e-14	***
df.mm.trans3:probe9	-0.320105611119526	0.0501958046736867	-6.37713875094685	2.91679883707742e-10	***
df.mm.trans3:probe10	-0.250888716483573	0.0501958046736867	-4.99820090771634	6.9881343042974e-07	***
df.mm.trans3:probe11	-0.00204905881871745	0.0501958046736867	-0.0408213162840598	0.967447647278376	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22347386843743	0.137382454561142	30.7424545727401	2.30827125956566e-141	***
df.mm.trans1	-0.112075703304752	0.118388571985318	-0.946676705574682	0.344064600881823	   
df.mm.trans2	0.0140820940598427	0.104349465060489	0.134951281750028	0.892681394241062	   
df.mm.exp2	-0.110204694360783	0.133674297525476	-0.824426957170131	0.40992142068712	   
df.mm.exp3	-0.00884407485470618	0.133674297525476	-0.0661613714709865	0.947264446554402	   
df.mm.exp4	-0.0690843590241937	0.133674297525476	-0.516811087120375	0.60541848842321	   
df.mm.exp5	-0.107847772290688	0.133674297525476	-0.806795130306438	0.420003510336298	   
df.mm.exp6	-0.00495562681885259	0.133674297525476	-0.0370723984385116	0.970435725972885	   
df.mm.exp7	-0.200523945071243	0.133674297525476	-1.50009350176706	0.133950492255141	   
df.mm.exp8	-0.140035207705692	0.133674297525476	-1.04758514013514	0.295118735785311	   
df.mm.trans1:exp2	0.0756102951599062	0.123241612883352	0.613512703955531	0.539696651645177	   
df.mm.trans2:exp2	-0.00487071713692931	0.0896714448334488	-0.0543173709978236	0.95669469479454	   
df.mm.trans1:exp3	0.0179016633311739	0.123241612883352	0.145256645968418	0.884541657646951	   
df.mm.trans2:exp3	-0.0592203412306297	0.0896714448334488	-0.660414709951675	0.509161352391962	   
df.mm.trans1:exp4	0.00453813211562005	0.123241612883352	0.0368230503435182	0.970634484330638	   
df.mm.trans2:exp4	0.0380027219971751	0.0896714448334488	0.423799594929684	0.671816013613438	   
df.mm.trans1:exp5	0.0473124232587251	0.123241612883352	0.383899740938202	0.70114591801874	   
df.mm.trans2:exp5	0.00853025588594736	0.0896714448334488	0.0951278960854375	0.924234996158837	   
df.mm.trans1:exp6	-0.0437870634648052	0.123241612883352	-0.355294469460161	0.72245452721859	   
df.mm.trans2:exp6	0.0116037358683083	0.0896714448334488	0.129402798068666	0.897068638979548	   
df.mm.trans1:exp7	0.236550235888896	0.123241612883352	1.91940230539494	0.0552580200836244	.  
df.mm.trans2:exp7	0.200191975311968	0.0896714448334488	2.23250529400741	0.0258336260124123	*  
df.mm.trans1:exp8	0.142575653201209	0.123241612883352	1.15687915684905	0.247637105941692	   
df.mm.trans2:exp8	0.0333410257642106	0.0896714448334488	0.371813187867515	0.71012186223746	   
df.mm.trans1:probe2	0.151482657160785	0.085853872075221	1.76442428861058	0.0780090351659401	.  
df.mm.trans1:probe3	0.134165219546695	0.085853872075221	1.56271599991606	0.118480574780949	   
df.mm.trans1:probe4	0.0149149324724823	0.0858538720752209	0.173724633635797	0.862122013009915	   
df.mm.trans1:probe5	0.147623230038714	0.085853872075221	1.71947084587372	0.0858819116583165	.  
df.mm.trans1:probe6	0.204449159688193	0.0858538720752209	2.3813621301677	0.0174614935741557	*  
df.mm.trans1:probe7	0.126865370274785	0.0858538720752209	1.47768955794599	0.139850319788875	   
df.mm.trans1:probe8	0.107135669309771	0.085853872075221	1.24788395351469	0.212407014776135	   
df.mm.trans1:probe9	0.151120645472645	0.085853872075221	1.76020768568528	0.0787215550469582	.  
df.mm.trans1:probe10	0.207008156009284	0.085853872075221	2.41116854727197	0.0161063883191971	*  
df.mm.trans1:probe11	0.159589422780883	0.085853872075221	1.85884944875939	0.0633836606118512	.  
df.mm.trans1:probe12	0.0498178900958144	0.0858538720752209	0.580263753883651	0.561885990674817	   
df.mm.trans1:probe13	0.057251723941973	0.0858538720752209	0.66685080775171	0.505043035641198	   
df.mm.trans1:probe14	0.211549960884059	0.085853872075221	2.46407012020039	0.0139280033805241	*  
df.mm.trans1:probe15	0.0184756931698643	0.0858538720752209	0.215199299964908	0.829662027385712	   
df.mm.trans1:probe16	0.0724713694913217	0.085853872075221	0.844124647375553	0.398830212225107	   
df.mm.trans1:probe17	0.0483901992667615	0.085853872075221	0.563634441838155	0.573147193073854	   
df.mm.trans1:probe18	-0.0458007291139223	0.085853872075221	-0.533473074735569	0.593841469156233	   
df.mm.trans1:probe19	0.0242298090242782	0.0858538720752209	0.282221505432501	0.777840332686632	   
df.mm.trans1:probe20	0.155484988059000	0.085853872075221	1.81104223141819	0.0704767458325775	.  
df.mm.trans1:probe21	0.0971719957820901	0.0858538720752209	1.13183009028356	0.258015604864538	   
df.mm.trans1:probe22	0.0284435942022784	0.085853872075221	0.331302403895745	0.7404953127177	   
df.mm.trans2:probe2	0.0405302257680219	0.085853872075221	0.472083841862267	0.636984517311908	   
df.mm.trans2:probe3	-0.0734847494748415	0.085853872075221	-0.855928191688988	0.392271556096638	   
df.mm.trans2:probe4	-0.0359101943374903	0.0858538720752209	-0.418271109613176	0.675851425995582	   
df.mm.trans2:probe5	0.0568351805075016	0.085853872075221	0.661999035497262	0.508145945122825	   
df.mm.trans2:probe6	0.0649191605163906	0.085853872075221	0.756158795721078	0.449757285867304	   
df.mm.trans3:probe2	0.0486371556876301	0.085853872075221	0.566510915722201	0.571191623433049	   
df.mm.trans3:probe3	0.111251480040955	0.085853872075221	1.29582367518010	0.195377562088976	   
df.mm.trans3:probe4	0.00237180007704798	0.085853872075221	0.0276260117303728	0.977966727349498	   
df.mm.trans3:probe5	0.0993666358467628	0.085853872075221	1.15739259563858	0.247427481023600	   
df.mm.trans3:probe6	-0.0128828595427008	0.085853872075221	-0.150055661221820	0.880755242944011	   
df.mm.trans3:probe7	0.0246827112800877	0.085853872075221	0.287496774268515	0.773799967679492	   
df.mm.trans3:probe8	-0.0134894536639946	0.085853872075221	-0.157121086538482	0.875185625101996	   
df.mm.trans3:probe9	-0.0337954079958944	0.085853872075221	-0.393638716332847	0.693943582714237	   
df.mm.trans3:probe10	-0.00607452141979516	0.085853872075221	-0.0707541928274704	0.943609538302881	   
df.mm.trans3:probe11	-0.00335296292815574	0.085853872075221	-0.0390543006053127	0.968855994413352