fitVsDatCorrelation=0.808989416262022
cont.fitVsDatCorrelation=0.326273858493049

fstatistic=8952.95625001655,52,692
cont.fstatistic=3453.96794845245,52,692

residuals=-0.510800350308542,-0.0919118061991063,-0.0134444567505621,0.0773513212914,1.53971568901259
cont.residuals=-0.634779847631176,-0.166189590858915,-0.0312267053218264,0.116443221925973,1.71839437716250

predictedValues:
Include	Exclude	Both
Lung	67.9123250921763	47.135297472507	79.8209889753573
cerebhem	77.0354568856703	51.0556326062033	77.9528928473657
cortex	85.1699814291855	51.9415483691635	112.671289578266
heart	69.2650822666188	50.2269472883499	88.978519928124
kidney	61.3603792480276	44.6249847467243	62.9985040143454
liver	56.2699536568302	49.5282263656638	60.5505709439928
stomach	61.2746357860003	45.9410407766202	77.6355662203833
testicle	60.0800111101412	45.9541355855885	64.287142427551


diffExp=20.7770276196693,25.9798242794669,33.228433060022,19.0381349782689,16.7353945013033,6.74172729116639,15.3335950093800,14.1258755245527
diffExpScore=0.993462343620402
diffExp1.5=0,1,1,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,1,1,0,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	63.4974204112284	57.9599557554063	74.276004162506
cerebhem	62.2261676317534	66.5951984003782	69.065450086413
cortex	61.7741390974715	60.9844399009573	53.7351005534636
heart	59.927526026641	72.5095306466033	58.0819554264309
kidney	63.0143548161942	69.3982042216044	54.654214939403
liver	65.2215943403516	66.9595621058039	62.8846598045928
stomach	63.9350147794967	58.432448680622	65.9690485533255
testicle	70.0952910234502	63.4070393512583	64.9956725070781
cont.diffExp=5.53746465582212,-4.36903076862487,0.789699196514213,-12.5820046199623,-6.38384940541022,-1.73796776545232,5.50256609887472,6.68825167219194
cont.diffExpScore=5.76989793099797

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,-1,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.723434958862035
cont.tran.correlation=-0.30035940451172

tran.covariance=0.00551217459464196
cont.tran.covariance=-0.00110793386750588

tran.mean=57.7984774178419
cont.tran.mean=64.1211179493263

weightedLogRatios:
wLogRatio
Lung	1.47378980225992
cerebhem	1.70240901391850
cortex	2.07573242698913
heart	1.31038306704620
kidney	1.26035603736984
liver	0.506177767898077
stomach	1.14378070568709
testicle	1.06185712382137

cont.weightedLogRatios:
wLogRatio
Lung	0.374602929778043
cerebhem	-0.282603764809972
cortex	0.0529702666833052
heart	-0.798239115141244
kidney	-0.404483983546469
liver	-0.110214468835269
stomach	0.370141000441493
testicle	0.421150513711345

varWeightedLogRatios=0.214940797840529
cont.varWeightedLogRatios=0.190367822114540

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.54114225293487	0.088517674457937	40.0049173752028	3.76836708074214e-182	***
df.mm.trans1	0.808280111991951	0.079501816401237	10.1668131444023	1.00372562994393e-22	***
df.mm.trans2	0.206932626707514	0.0731127175177324	2.83032328346058	0.00478496234844227	** 
df.mm.exp2	0.229623779444625	0.100146316582513	2.29288292650717	0.0221538481141941	*  
df.mm.exp3	-0.0211597627825355	0.100146316582513	-0.211288477745475	0.832724380851431	   
df.mm.exp4	-0.0253555770933526	0.100146316582513	-0.253185318827593	0.800200206736112	   
df.mm.exp5	0.0804940730526176	0.100146316582513	0.803764689501054	0.421808896082475	   
df.mm.exp6	0.137771364595502	0.100146316582513	1.37570076760625	0.169359397761521	   
df.mm.exp7	-0.100753994842508	0.100146316582513	-1.00606790424982	0.314734562979845	   
df.mm.exp8	0.0685082073148964	0.100146316582513	0.684081148990148	0.494152939366592	   
df.mm.trans1:exp2	-0.103575520930598	0.0958827982380458	-1.08023047756130	0.280415895900418	   
df.mm.trans2:exp2	-0.149730041736104	0.0834552638187607	-1.79413538325481	0.0732279802136011	.  
df.mm.trans1:exp3	0.247591267505019	0.0958827982380458	2.58222822085700	0.0100208110941781	*  
df.mm.trans2:exp3	0.118256643396947	0.0834552638187607	1.41700640541697	0.156931094630797	   
df.mm.trans1:exp4	0.0450789565244403	0.0958827982380458	0.470146442874184	0.638398525223807	   
df.mm.trans2:exp4	0.0888851224360087	0.0834552638187607	1.06506310529483	0.287218849819072	   
df.mm.trans1:exp5	-0.181947271694625	0.0958827982380459	-1.89760076925279	0.0581646584475803	.  
df.mm.trans2:exp5	-0.135222310665892	0.0834552638187607	-1.62029696484518	0.105624072288141	   
df.mm.trans1:exp6	-0.325828191122796	0.0958827982380458	-3.39819234638804	0.000717191038726574	***
df.mm.trans2:exp6	-0.0882507638335324	0.0834552638187607	-1.05746192385403	0.290669825395027	   
df.mm.trans1:exp7	-0.00209755607230507	0.0958827982380458	-0.0218762500766563	0.982552976197407	   
df.mm.trans2:exp7	0.0750907110727849	0.0834552638187607	0.899772017207434	0.36855458658249	   
df.mm.trans1:exp8	-0.191048551322977	0.0958827982380458	-1.99252164969848	0.0467062492600752	*  
df.mm.trans2:exp8	-0.0938864966034007	0.0834552638187607	-1.12499190952525	0.260982510559898	   
df.mm.trans1:probe2	-0.144255485810906	0.0479413991190229	-3.00899615909763	0.0027163215685347	** 
df.mm.trans1:probe3	0.0315162363241033	0.0479413991190229	0.657390833460216	0.511148235637697	   
df.mm.trans1:probe4	-0.364682040625592	0.0479413991190229	-7.60682932344558	9.1965021889601e-14	***
df.mm.trans1:probe5	-0.252332315257983	0.0479413991190229	-5.26334900305108	1.88934194940556e-07	***
df.mm.trans1:probe6	-0.320802433980156	0.0479413991190229	-6.69155343555385	4.56184611141923e-11	***
df.mm.trans1:probe7	-0.286503454799632	0.0479413991190229	-5.97611792864736	3.65772807523901e-09	***
df.mm.trans1:probe8	-0.203548905153396	0.0479413991190229	-4.24578566528796	2.4757638204975e-05	***
df.mm.trans1:probe9	-0.280743800744426	0.0479413991190229	-5.8559784633617	7.32785353966231e-09	***
df.mm.trans1:probe10	0.0364713040053781	0.0479413991190229	0.760747593428212	0.447066976872138	   
df.mm.trans1:probe11	-0.330206827373206	0.0479413991190229	-6.88771778548661	1.27517951809197e-11	***
df.mm.trans1:probe12	-0.0313435839119194	0.0479413991190229	-0.653789511526426	0.513464614359237	   
df.mm.trans1:probe13	-0.299418299389109	0.0479413991190229	-6.24550607389974	7.37300437871486e-10	***
df.mm.trans1:probe14	-0.0680935930073732	0.0479413991190229	-1.42035055836228	0.155956032963472	   
df.mm.trans1:probe15	-0.269047177566077	0.0479413991190229	-5.61200095345822	2.89489248037855e-08	***
df.mm.trans1:probe16	-0.251360498531879	0.0479413991190229	-5.24307807345865	2.10035087535422e-07	***
df.mm.trans1:probe17	-0.0337916459319682	0.0479413991190229	-0.704853144733521	0.48113879041	   
df.mm.trans1:probe18	-0.0429919730545865	0.0479413991190229	-0.896760917382728	0.370158367221884	   
df.mm.trans1:probe19	-0.061221814403946	0.0479413991190229	-1.27701351084795	0.202025797816338	   
df.mm.trans1:probe20	-0.046197187968236	0.0479413991190229	-0.963617850483325	0.335574162264395	   
df.mm.trans1:probe21	-0.0428241330361245	0.0479413991190229	-0.893259976201489	0.372028501905429	   
df.mm.trans1:probe22	-0.0187430832934035	0.0479413991190229	-0.390958203928729	0.695948466741971	   
df.mm.trans2:probe2	0.146784058210460	0.0479413991190229	3.06173914211479	0.00228578982766430	** 
df.mm.trans2:probe3	0.108035776996945	0.0479413991190229	2.25349653915454	0.0245400851071884	*  
df.mm.trans2:probe4	0.366000854582381	0.0479413991190229	7.63433819846851	7.55496018870187e-14	***
df.mm.trans2:probe5	0.0718448565769582	0.0479413991190229	1.49859741053011	0.134434127579808	   
df.mm.trans2:probe6	0.251859759978115	0.0479413991190229	5.25349206753079	1.98924604337086e-07	***
df.mm.trans3:probe2	-0.435017836912536	0.0479413991190229	-9.0739495489593	1.17570723183014e-18	***
df.mm.trans3:probe3	-0.0946876691500934	0.0479413991190229	-1.97507104277484	0.0486571155782573	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98982354889309	0.142344788324429	28.0292913836759	4.20913705015026e-116	***
df.mm.trans1	0.194157423304945	0.127846436277755	1.5186768513682	0.129300575684065	   
df.mm.trans2	0.0931492252895883	0.117572161295661	0.792272798790729	0.428473140352865	   
df.mm.exp2	0.191389789687208	0.161044744145228	1.18842617747659	0.23507323763262	   
df.mm.exp3	0.347073439208988	0.161044744145228	2.15513670471607	0.031495398203304	*  
df.mm.exp4	0.412035210285873	0.161044744145228	2.55851386192589	0.0107237907945880	*  
df.mm.exp5	0.47923351342124	0.161044744145228	2.97577866303463	0.00302433316111247	** 
df.mm.exp6	0.337613555538806	0.161044744145228	2.09639598815067	0.0364096123754985	*  
df.mm.exp7	0.133589171397211	0.161044744145228	0.829515872164955	0.407098608973733	   
df.mm.exp8	0.322146116909872	0.161044744145228	2.00035163283172	0.0458525861285554	*  
df.mm.trans1:exp2	-0.211613458565263	0.154188603606325	-1.37243255088785	0.170373481855657	   
df.mm.trans2:exp2	-0.0525096649182397	0.134203953454356	-0.391267645748592	0.695719853713204	   
df.mm.trans1:exp3	-0.374587905158898	0.154188603606325	-2.42941369464178	0.0153767330531675	*  
df.mm.trans2:exp3	-0.296207045253802	0.134203953454356	-2.20714097930464	0.0276317061615517	*  
df.mm.trans1:exp4	-0.469898559346677	0.154188603606325	-3.04755700717301	0.00239493664780327	** 
df.mm.trans2:exp4	-0.188069553958365	0.134203953454356	-1.40137119002480	0.161551423205572	   
df.mm.trans1:exp5	-0.486870240387503	0.154188603606325	-3.15762792450331	0.00165969562852807	** 
df.mm.trans2:exp5	-0.299124876100563	0.134203953454356	-2.22888274451838	0.0261420713932639	*  
df.mm.trans1:exp6	-0.310822221530455	0.154188603606325	-2.01585729593899	0.0442009146186284	*  
df.mm.trans2:exp6	-0.193277023105411	0.134203953454356	-1.44017384086338	0.150270473050954	   
df.mm.trans1:exp7	-0.126721279597963	0.154188603606325	-0.821858922346223	0.411440265321093	   
df.mm.trans2:exp7	-0.125470161862231	0.134203953454356	-0.934921502926546	0.350154879200168	   
df.mm.trans1:exp8	-0.223289781883045	0.154188603606325	-1.44816008875176	0.148025202945557	   
df.mm.trans2:exp8	-0.232323584941118	0.134203953454356	-1.73112325651519	0.0838755088396866	.  
df.mm.trans1:probe2	0.0670781564175409	0.0770943018031623	0.870079303510722	0.384558773152877	   
df.mm.trans1:probe3	-0.00350969557641147	0.0770943018031623	-0.0455247079787096	0.963702215026893	   
df.mm.trans1:probe4	0.0907724177209864	0.0770943018031624	1.17742058229864	0.239432468962742	   
df.mm.trans1:probe5	-0.0559305415569259	0.0770943018031623	-0.725482172466237	0.468401542734036	   
df.mm.trans1:probe6	-0.100149248271006	0.0770943018031624	-1.29904864469371	0.194359894265638	   
df.mm.trans1:probe7	-0.0896281891770275	0.0770943018031624	-1.16257864823611	0.245401268200690	   
df.mm.trans1:probe8	-0.00528220423553504	0.0770943018031624	-0.0685161433723285	0.945394562312306	   
df.mm.trans1:probe9	0.0136886950680486	0.0770943018031623	0.177557805802544	0.859122223841746	   
df.mm.trans1:probe10	0.00973087726021753	0.0770943018031624	0.126220447330368	0.899594089279783	   
df.mm.trans1:probe11	-0.0132226461883694	0.0770943018031623	-0.171512626473090	0.863870844088114	   
df.mm.trans1:probe12	-0.04829021026232	0.0770943018031624	-0.626378462906569	0.531273294990646	   
df.mm.trans1:probe13	-0.0457972125411215	0.0770943018031624	-0.594041472196625	0.552678515967367	   
df.mm.trans1:probe14	-0.116987536690778	0.0770943018031624	-1.51746022669057	0.129607214057528	   
df.mm.trans1:probe15	-0.146281617344649	0.0770943018031624	-1.89743747492695	0.0581862258881389	.  
df.mm.trans1:probe16	0.0129676583495100	0.0770943018031623	0.168205146764531	0.866471040143933	   
df.mm.trans1:probe17	-0.0260432580208755	0.0770943018031624	-0.337810414151869	0.735608511299013	   
df.mm.trans1:probe18	-0.0539820292969797	0.0770943018031624	-0.700207771967466	0.484032894214527	   
df.mm.trans1:probe19	-0.088142389510802	0.0770943018031624	-1.14330615167704	0.253306868951151	   
df.mm.trans1:probe20	-0.167620211755407	0.0770943018031623	-2.17422309865878	0.0300265455733264	*  
df.mm.trans1:probe21	-0.0598288293291036	0.0770943018031623	-0.776047359269937	0.437986170270176	   
df.mm.trans1:probe22	0.00191575069698157	0.0770943018031624	0.0248494460961963	0.980182214780353	   
df.mm.trans2:probe2	0.0580651386183827	0.0770943018031624	0.753170302607253	0.451603738078442	   
df.mm.trans2:probe3	-0.13884257625864	0.0770943018031624	-1.80094472627995	0.0721468542324162	.  
df.mm.trans2:probe4	-0.0152143351824318	0.0770943018031624	-0.197347077884915	0.843613891684897	   
df.mm.trans2:probe5	0.0546377159862416	0.0770943018031624	0.708712767459039	0.478741414591156	   
df.mm.trans2:probe6	-0.167629724016442	0.0770943018031624	-2.17434648340723	0.0300172454179389	*  
df.mm.trans3:probe2	0.0573177361386936	0.0770943018031624	0.743475649925951	0.457446100630068	   
df.mm.trans3:probe3	0.0346554937366824	0.0770943018031624	0.449520819647151	0.653196753386723