fitVsDatCorrelation=0.889322616392117
cont.fitVsDatCorrelation=0.321236335694892

fstatistic=9619.20905661778,60,876
cont.fstatistic=2231.68029433331,60,876

residuals=-1.03201699484834,-0.0911592364752596,-0.00609409223784733,0.0822666972158706,0.974854847770822
cont.residuals=-0.865824787808134,-0.269766747101248,-0.0404450683187867,0.255621886221596,0.998747236065138

predictedValues:
Include	Exclude	Both
Lung	82.1123166626147	59.3937586646982	79.8642784947259
cerebhem	74.8754365457411	67.723857479701	80.1858690440551
cortex	90.6359584769619	58.2613533880924	71.0556629397104
heart	84.43272281489	58.1664166237121	67.678718177989
kidney	88.0277041109356	59.0191581554659	77.3478603249038
liver	89.09390937556	53.1274926385938	82.8115854251302
stomach	87.3653359235896	56.2435122063583	76.9428700725708
testicle	92.015245040169	57.5664203412759	79.9743632036073


diffExp=22.7185579979165,7.15157906604013,32.3746050888695,26.266306191178,29.0085459554697,35.9664167369662,31.1218237172313,34.4488246988931
diffExpScore=0.995455715802977
diffExp1.5=0,0,1,0,0,1,1,1
diffExp1.5Score=0.8
diffExp1.4=0,0,1,1,1,1,1,1
diffExp1.4Score=0.857142857142857
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	82.3367911367549	103.477031562780	71.9915832447517
cerebhem	87.9311879545052	81.1599470044102	69.0953708818813
cortex	91.8406037860354	75.1456868556287	74.7663664753437
heart	80.2895081373089	88.253631862228	85.1674855207294
kidney	83.02377847257	98.2432501545595	68.3237343831378
liver	79.4081356929729	81.8760079808696	99.6663627863708
stomach	78.3000974895411	66.5113540743217	83.9166882289637
testicle	90.7470043090816	91.1311160239884	86.1498019848184
cont.diffExp=-21.1402404260254,6.771240950095,16.6949169304067,-7.96412372491906,-15.2194716819894,-2.46787228789671,11.7887434152194,-0.38411171490678
cont.diffExpScore=6.37963321850297

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=-1,0,1,0,0,0,0,0
cont.diffExp1.2Score=2

tran.correlation=-0.813836541425904
cont.tran.correlation=0.0261822089780604

tran.covariance=-0.00371263896167773
cont.tran.covariance=0.00061301471391093

tran.mean=72.3787874030225
cont.tran.mean=84.9796957810973

weightedLogRatios:
wLogRatio
Lung	1.37531952232954
cerebhem	0.428215593526544
cortex	1.89398862847034
heart	1.58361196940495
kidney	1.71020217899087
liver	2.18751154583790
stomach	1.87168714923048
testicle	2.01087625021952

cont.weightedLogRatios:
wLogRatio
Lung	-1.03412477234069
cerebhem	0.355507741111682
cortex	0.886714135749384
heart	-0.419248029688439
kidney	-0.757990952463004
liver	-0.134353773026289
stomach	0.698224458387902
testicle	-0.0190503220830913

varWeightedLogRatios=0.300237620904468
cont.varWeightedLogRatios=0.459400871950906

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.11904444942112	0.0946048549525766	32.9691795519863	1.20071277904982e-155	***
df.mm.trans1	0.935399050538564	0.0855026768673275	10.9399972586823	3.36291887093362e-26	***
df.mm.trans2	0.841648987379415	0.0779108644523506	10.8027165825397	1.26636699640244e-25	***
df.mm.exp2	0.0349686035108251	0.106501989862717	0.32833756022681	0.742734914151938	   
df.mm.exp3	0.196377955260957	0.106501989862717	1.8438900110138	0.0655367307497558	.  
df.mm.exp4	0.172542917922060	0.106501989862717	1.62009102500780	0.105572626690770	   
df.mm.exp5	0.09525227750598	0.106501989862717	0.89437087165002	0.371369150472729	   
df.mm.exp6	-0.0661309490137307	0.106501989862717	-0.620936276392343	0.534803076184588	   
df.mm.exp7	0.0447775881758603	0.106501989862717	0.42043898178409	0.674267908952716	   
df.mm.exp8	0.0812390662549263	0.106501989862717	0.762793881688451	0.445791683576468	   
df.mm.trans1:exp2	-0.127230742222676	0.103120108269064	-1.23381117764833	0.217604195193613	   
df.mm.trans2:exp2	0.0962807664615262	0.088306784950217	1.09029862785521	0.275881434573952	   
df.mm.trans1:exp3	-0.0976149537289543	0.103120108269064	-0.946614150891451	0.344096469232558	   
df.mm.trans2:exp3	-0.215628121720318	0.088306784950217	-2.44180695562497	0.0148108250462856	*  
df.mm.trans1:exp4	-0.144675905841333	0.103120108269064	-1.40298442534448	0.160975808589694	   
df.mm.trans2:exp4	-0.193423911552839	0.088306784950217	-2.19036296771399	0.0287608595446656	*  
df.mm.trans1:exp5	-0.0256887185881292	0.103120108269064	-0.249114542443085	0.80333057668302	   
df.mm.trans2:exp5	-0.101579319672350	0.088306784950217	-1.15030028247111	0.250334129566530	   
df.mm.trans1:exp6	0.147733898519818	0.103120108269064	1.43263909434953	0.152317752722251	   
df.mm.trans2:exp6	-0.0453636524496942	0.088306784950217	-0.51370517537546	0.607587677503181	   
df.mm.trans1:exp7	0.0172329762093022	0.103120108269064	0.167115575211940	0.867317679156861	   
df.mm.trans2:exp7	-0.0992760401601322	0.088306784950217	-1.12421758097183	0.261228697034781	   
df.mm.trans1:exp8	0.0326271785670285	0.103120108269064	0.316399770274647	0.751774468911937	   
df.mm.trans2:exp8	-0.112488796601453	0.088306784950217	-1.27384092473606	0.203057664282608	   
df.mm.trans1:probe2	0.305645356817506	0.0515600541345319	5.92794871820746	4.40850440578523e-09	***
df.mm.trans1:probe3	0.477126422780602	0.0515600541345319	9.25379988034284	1.62018431262886e-19	***
df.mm.trans1:probe4	0.320123249476351	0.0515600541345319	6.20874541056681	8.23780672872096e-10	***
df.mm.trans1:probe5	-0.0393671705900431	0.0515600541345319	-0.763520738114922	0.445358478113076	   
df.mm.trans1:probe6	0.128555934065147	0.0515600541345319	2.49332426474408	0.0128388677924299	*  
df.mm.trans1:probe7	-0.0172297295595299	0.0515600541345319	-0.334168182108065	0.738332635693446	   
df.mm.trans1:probe8	0.16015246607965	0.0515600541345319	3.10613456032796	0.00195655105832066	** 
df.mm.trans1:probe9	0.058075404429491	0.0515600541345319	1.12636430283721	0.260319814708870	   
df.mm.trans1:probe10	-0.100491882199899	0.0515600541345319	-1.94902592494749	0.0516112014139467	.  
df.mm.trans1:probe11	0.838917951449521	0.0515600541345319	16.2706957068081	3.28027005333673e-52	***
df.mm.trans1:probe12	0.132661169237684	0.0515600541345319	2.57294472367196	0.0102472861918234	*  
df.mm.trans1:probe13	0.97362483410155	0.0515600541345319	18.8833167545003	5.4766841627873e-67	***
df.mm.trans1:probe14	0.335029569831828	0.0515600541345319	6.49785139786043	1.36534786667325e-10	***
df.mm.trans1:probe15	-0.111324279597243	0.0515600541345319	-2.15911874930877	0.0311118292157385	*  
df.mm.trans1:probe16	0.278031225879692	0.0515600541345319	5.39237653153437	8.94767461494201e-08	***
df.mm.trans1:probe17	0.238324679911321	0.0515600541345319	4.62227365567688	4.3661002646196e-06	***
df.mm.trans1:probe18	-0.0317015322036667	0.0515600541345319	-0.614846759488463	0.538815597543777	   
df.mm.trans1:probe19	0.645115598188564	0.0515600541345319	12.5119263161615	3.64354096814944e-33	***
df.mm.trans1:probe20	0.942448522114556	0.0515600541345319	18.2786565672622	1.77162268825925e-63	***
df.mm.trans1:probe21	0.83437637488992	0.0515600541345319	16.1826124680327	9.88511259720434e-52	***
df.mm.trans1:probe22	0.463349545320352	0.0515600541345319	8.98659927918943	1.53973540941358e-18	***
df.mm.trans1:probe23	0.500981387021801	0.0515600541345319	9.71646355751735	2.90080075955713e-21	***
df.mm.trans1:probe24	0.74077554800681	0.0515600541345319	14.3672375919924	3.46884419284174e-42	***
df.mm.trans1:probe25	0.647707587164839	0.0515600541345319	12.5621975778928	2.12810684182161e-33	***
df.mm.trans1:probe26	0.743889011890555	0.0515600541345319	14.4276227862286	1.71161189334899e-42	***
df.mm.trans1:probe27	0.496773126966541	0.0515600541345319	9.63484494547555	5.96495300693133e-21	***
df.mm.trans1:probe28	0.494510361416871	0.0515600541345319	9.59095892581067	8.7718960472749e-21	***
df.mm.trans1:probe29	0.695706109833366	0.0515600541345319	13.4931221759021	7.77797966077516e-38	***
df.mm.trans1:probe30	0.518482498705917	0.0515600541345319	10.0558951577723	1.37515056332805e-22	***
df.mm.trans2:probe2	0.315773342099824	0.0515600541345319	6.1243795686463	1.37331138178464e-09	***
df.mm.trans2:probe3	0.205546061054995	0.0515600541345319	3.98653695201092	7.26302936480517e-05	***
df.mm.trans2:probe4	0.114607452994103	0.0515600541345319	2.22279543568877	0.0264841639461431	*  
df.mm.trans2:probe5	0.152655692352002	0.0515600541345319	2.96073568801321	0.00315184852798212	** 
df.mm.trans2:probe6	0.322878851062797	0.0515600541345319	6.26218991586651	5.94072363859812e-10	***
df.mm.trans3:probe2	-0.686784359862643	0.0515600541345319	-13.3200860897211	5.38787904941337e-37	***
df.mm.trans3:probe3	-0.860507076734022	0.0515600541345319	-16.6894137560206	1.65966257136642e-54	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.85199366223648	0.195920764923914	24.7650812517028	4.69819688019378e-103	***
df.mm.trans1	-0.388488722377469	0.177070720771004	-2.19397493095361	0.0284992217791783	*  
df.mm.trans2	-0.180365423609805	0.161348549892493	-1.11786206774082	0.263932379496438	   
df.mm.exp2	-0.136129824136697	0.220558990659434	-0.617203695617628	0.537260766894867	   
df.mm.exp3	-0.248503412881325	0.220558990659434	-1.12669817783597	0.260178655419403	   
df.mm.exp4	-0.352384435036594	0.220558990659434	-1.59768792005723	0.110473074167587	   
df.mm.exp5	0.008697891202985	0.220558990659434	0.0394356683306346	0.968552028259334	   
df.mm.exp6	-0.595639905700805	0.220558990659434	-2.70059227202637	0.00705518829062085	** 
df.mm.exp7	-0.645521483455226	0.220558990659434	-2.92675207446872	0.0035137871020696	** 
df.mm.exp8	-0.209331400004792	0.220558990659434	-0.949094840246259	0.342834134068916	   
df.mm.trans1:exp2	0.201866332975733	0.213555324420077	0.945264809125759	0.344784347956108	   
df.mm.trans2:exp2	-0.106797984692932	0.182877854039211	-0.583985334112863	0.559380521182859	   
df.mm.trans1:exp3	0.357739875065869	0.213555324420077	1.67516251836537	0.094259241510311	.  
df.mm.trans2:exp3	-0.0714175371811079	0.182877854039211	-0.39052042444568	0.696246701294856	   
df.mm.trans1:exp4	0.327205344447412	0.213555324420077	1.53218069058198	0.125838893499677	   
df.mm.trans2:exp4	0.193249613439077	0.182877854039211	1.05671413553246	0.290933396178101	   
df.mm.trans1:exp5	-0.000388881505203059	0.213555324420077	-0.00182098716695120	0.998547477849574	   
df.mm.trans2:exp5	-0.0606010143408874	0.182877854039211	-0.331374264310285	0.740441057615072	   
df.mm.trans1:exp6	0.559422688661366	0.213555324420077	2.61956797462444	0.00895639758054788	** 
df.mm.trans2:exp6	0.361496239986847	0.182877854039211	1.97670867194963	0.0483878261240333	*  
df.mm.trans1:exp7	0.595252286837962	0.213555324420077	2.78734463050457	0.00542918366518236	** 
df.mm.trans2:exp7	0.203544483692373	0.182877854039211	1.11300783116544	0.266010386989232	   
df.mm.trans1:exp8	0.306588817546251	0.213555324420077	1.43564117812943	0.151461436765670	   
df.mm.trans2:exp8	0.0822810341323852	0.182877854039211	0.449923445157789	0.652876954193587	   
df.mm.trans1:probe2	-0.0078329965540435	0.106777662210038	-0.0733580075824802	0.941537979094649	   
df.mm.trans1:probe3	-0.0902675922309509	0.106777662210038	-0.84537898997441	0.398130110659256	   
df.mm.trans1:probe4	0.0460510618012847	0.106777662210038	0.431279921737745	0.666370958241027	   
df.mm.trans1:probe5	0.0552642128677838	0.106777662210038	0.517563427817661	0.604893572053779	   
df.mm.trans1:probe6	-0.193570693833387	0.106777662210038	-1.81283884500694	0.070198858368361	.  
df.mm.trans1:probe7	-0.192426834199798	0.106777662210038	-1.80212630822805	0.0718692019834098	.  
df.mm.trans1:probe8	0.0409944275114816	0.106777662210038	0.383923253824784	0.701128496607952	   
df.mm.trans1:probe9	0.0190775950920101	0.106777662210038	0.178666536587805	0.858240857753413	   
df.mm.trans1:probe10	0.059123812091647	0.106777662210038	0.553709557485411	0.57991893320377	   
df.mm.trans1:probe11	-0.0298487643129448	0.106777662210038	-0.279541279469394	0.779895451489443	   
df.mm.trans1:probe12	-0.136342864727234	0.106777662210038	-1.27688565103663	0.201980980947883	   
df.mm.trans1:probe13	0.0328089912175978	0.106777662210038	0.307264558321762	0.758715039997396	   
df.mm.trans1:probe14	-0.116934288366104	0.106777662210038	-1.09511939057147	0.273765335012958	   
df.mm.trans1:probe15	-0.189615193529598	0.106777662210038	-1.77579457730225	0.0761138725788172	.  
df.mm.trans1:probe16	-0.096902763601547	0.106777662210038	-0.90751905966	0.364381951295647	   
df.mm.trans1:probe17	-0.12233400213571	0.106777662210038	-1.14568908518592	0.252236705466094	   
df.mm.trans1:probe18	0.0363210168256767	0.106777662210038	0.340155572560026	0.733820920715683	   
df.mm.trans1:probe19	0.00295334693978188	0.106777662210038	0.0276588462292091	0.977940546700715	   
df.mm.trans1:probe20	0.0163555080355273	0.106777662210038	0.153173498061374	0.878296732037786	   
df.mm.trans1:probe21	-0.103875946623770	0.106777662210038	-0.972824694545563	0.330908979500864	   
df.mm.trans1:probe22	-0.0205131241275436	0.106777662210038	-0.192110631596269	0.847700070695056	   
df.mm.trans1:probe23	-0.203352828331735	0.106777662210038	-1.90445102583092	0.0571788047419145	.  
df.mm.trans1:probe24	-0.176965869729781	0.106777662210038	-1.6573304384739	0.0978107420334334	.  
df.mm.trans1:probe25	-0.0153592012962336	0.106777662210038	-0.143842831715318	0.885657662551555	   
df.mm.trans1:probe26	-0.0306433497799512	0.106777662210038	-0.286982774727487	0.774193375635643	   
df.mm.trans1:probe27	-0.0701561916649613	0.106777662210038	-0.657030601840296	0.511333815764643	   
df.mm.trans1:probe28	-0.220146556408622	0.106777662210038	-2.06172856618251	0.0395280991774299	*  
df.mm.trans1:probe29	-0.0994523110482184	0.106777662210038	-0.931396220799342	0.351905257660806	   
df.mm.trans1:probe30	0.068923859555131	0.106777662210038	0.645489497789842	0.518779052495723	   
df.mm.trans2:probe2	0.145408692043874	0.106777662210038	1.36178943267971	0.173614448745669	   
df.mm.trans2:probe3	-0.142370281383792	0.106777662210038	-1.33333394304645	0.182768852675875	   
df.mm.trans2:probe4	-0.171642040329569	0.106777662210038	-1.60747142030455	0.108311339452632	   
df.mm.trans2:probe5	-0.00677080632581803	0.106777662210038	-0.0634103255838232	0.949454231002133	   
df.mm.trans2:probe6	-0.115132674351915	0.106777662210038	-1.07824681650589	0.281220438745658	   
df.mm.trans3:probe2	-0.0236945749087624	0.106777662210038	-0.221905728392458	0.824438991635495	   
df.mm.trans3:probe3	0.00392366457416205	0.106777662210038	0.0367461179890224	0.970695808402879