fitVsDatCorrelation=0.860916920455601
cont.fitVsDatCorrelation=0.265684060166369

fstatistic=12841.2661447217,72,1152
cont.fstatistic=3564.48328410457,72,1152

residuals=-0.476319428153373,-0.0924011450284463,-0.00568279367921804,0.0747158195853979,0.788959687586986
cont.residuals=-0.633900370715223,-0.201969881070316,-0.0507379733108565,0.194040988221133,1.26800921038774

predictedValues:
Include	Exclude	Both
Lung	66.1651830334709	112.314302149278	54.0293631420946
cerebhem	67.218088517542	115.634226837117	62.4963874181284
cortex	65.6512182188246	114.633898898816	57.0806755023092
heart	62.8003132162629	104.605817573152	53.8584456609064
kidney	67.3319991638037	118.555649396737	52.209756208773
liver	64.2994203908608	105.362015549964	53.0011193724864
stomach	67.2530597184039	102.316700057650	54.5457440194577
testicle	63.9537194671372	112.925347599706	54.1877702274775


diffExp=-46.1491191158068,-48.4161383195745,-48.9826806799919,-41.8055043568892,-51.2236502329334,-41.0625951591034,-35.0636403392457,-48.9716281325691
diffExpScore=0.9972427100837
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
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	58.522366866008	74.4020334587353	63.935594231192
cerebhem	61.8565831704346	70.8900413759522	63.2025230776068
cortex	56.7311868497884	77.3579025325818	63.1695142511572
heart	61.2473648918885	63.7766275831814	60.6325994097632
kidney	59.8520922518573	73.3433452577072	57.7323497465277
liver	58.4239348556222	81.9410390414552	58.8544363079453
stomach	61.3654054777656	63.9609069966408	64.4876954915043
testicle	61.5466938167635	68.3630384900111	70.8866638857378
cont.diffExp=-15.8796665927273,-9.03345820551754,-20.6267156827934,-2.52926269129289,-13.4912530058499,-23.5171041858329,-2.59550151887525,-6.81634467324766
cont.diffExpScore=0.989527623185617

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,-1,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,-1,0,0,-1,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,0,-1,0,-1,-1,0,0
cont.diffExp1.2Score=0.8

tran.correlation=0.40799009591524
cont.tran.correlation=-0.79275985161002

tran.covariance=0.000570359698241268
cont.tran.covariance=-0.00219360768940757

tran.mean=88.1888099867954
cont.tran.mean=65.8487851822746

weightedLogRatios:
wLogRatio
Lung	-2.35826335033782
cerebhem	-2.42991249624816
cortex	-2.48764778236355
heart	-2.24254149506700
kidney	-2.54162292819988
liver	-2.17812069706531
stomach	-1.85395103898664
testicle	-2.52582787838343

cont.weightedLogRatios:
wLogRatio
Lung	-1.00577815060396
cerebhem	-0.571550326815414
cortex	-1.30044638494153
heart	-0.167332960561288
kidney	-0.852438189354803
liver	-1.43322181200915
stomach	-0.171401784580910
testicle	-0.438243905109728

varWeightedLogRatios=0.053720682210175
cont.varWeightedLogRatios=0.235509773670815

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.1380718429349	0.0675458744096698	76.067885534684	0	***
df.mm.trans1	-0.863868021907672	0.0580447264137169	-14.8827994424576	5.93124895724898e-46	***
df.mm.trans2	-0.368783608248928	0.0500933611606547	-7.36192580622012	3.44110820107199e-13	***
df.mm.exp2	-0.100662313896983	0.0626954701328874	-1.60557554929602	0.108641268332678	   
df.mm.exp3	-0.0422938456880549	0.0626954701328874	-0.674591730445758	0.500070540784061	   
df.mm.exp4	-0.120127937822840	0.0626954701328874	-1.91605450231446	0.0556054304633974	.  
df.mm.exp5	0.105820743531975	0.0626954701328874	1.68785309859996	0.0917101105340023	.  
df.mm.exp6	-0.0732881672563921	0.0626954701328874	-1.16895474427503	0.24266368983226	   
df.mm.exp7	-0.0864322068865696	0.0626954701328875	-1.37860369662067	0.16828461874857	   
df.mm.exp8	-0.0314965314286657	0.0626954701328875	-0.502373319187281	0.61550095592339	   
df.mm.trans1:exp2	0.116450311033553	0.0580447264137169	2.00621689907791	0.0450665220612823	*  
df.mm.trans2:exp2	0.129793095955292	0.0374677097892485	3.46413209361775	0.000551493720164366	***
df.mm.trans1:exp3	0.0344956139903135	0.0580447264137169	0.594293678713277	0.552432394518954	   
df.mm.trans2:exp3	0.0627361978745136	0.0374677097892485	1.67440706217160	0.0943220576676829	.  
df.mm.trans1:exp4	0.0679336102648692	0.0580447264137169	1.17036662005552	0.242095517104015	   
df.mm.trans2:exp4	0.0490258949784115	0.0374677097892485	1.30848389864703	0.190970222122165	   
df.mm.trans1:exp5	-0.0883395379339409	0.0580447264137169	-1.52192185909011	0.128303090176275	   
df.mm.trans2:exp5	-0.0517394882919556	0.0374677097892485	-1.38090875004061	0.167574844194851	   
df.mm.trans1:exp6	0.0446843957805187	0.0580447264137169	0.769826968638431	0.441560363129407	   
df.mm.trans2:exp6	0.00938914437057024	0.0374677097892485	0.250592961869916	0.80217350868304	   
df.mm.trans1:exp7	0.102740333465166	0.0580447264137169	1.77002011746733	0.0769882558451358	.  
df.mm.trans2:exp7	-0.00679609782150176	0.0374677097892485	-0.181385461233927	0.856096937592766	   
df.mm.trans1:exp8	-0.00249816866446041	0.0580447264137169	-0.0430386844560964	0.965678154989196	   
df.mm.trans2:exp8	0.0369222808483947	0.0374677097892485	0.985442693350574	0.324613587446879	   
df.mm.trans1:probe2	-0.153162894159748	0.0435335448102876	-3.51827297380005	0.000451292381182874	***
df.mm.trans1:probe3	-0.168634867295941	0.0435335448102876	-3.87367644952473	0.000113241593203955	***
df.mm.trans1:probe4	-0.138748531045565	0.0435335448102876	-3.18716363783858	0.00147538757437801	** 
df.mm.trans1:probe5	-0.153440740780602	0.0435335448102876	-3.52465533071732	0.00044067248453931	***
df.mm.trans1:probe6	0.129421198453894	0.0435335448102876	2.97290742157321	0.00301126634216111	** 
df.mm.trans1:probe7	-0.0970745174194655	0.0435335448102876	-2.22987854176592	0.0259476222163379	*  
df.mm.trans1:probe8	-0.438191125500677	0.0435335448102876	-10.0655971713364	6.71141501789128e-23	***
df.mm.trans1:probe9	-0.156255359919050	0.0435335448102876	-3.58930936132093	0.000345494316198380	***
df.mm.trans1:probe10	0.264842146282957	0.0435335448102876	6.08363383770142	1.59655827352167e-09	***
df.mm.trans1:probe11	-0.0373588675703029	0.0435335448102876	-0.858162773858801	0.390981096353769	   
df.mm.trans1:probe12	-0.144500458767476	0.0435335448102876	-3.31928997275978	0.000930707494573087	***
df.mm.trans1:probe13	0.139828309972096	0.0435335448102876	3.21196701489497	0.00135478066330837	** 
df.mm.trans1:probe14	0.0375744576216079	0.0435335448102876	0.863115048070436	0.388253866905085	   
df.mm.trans1:probe15	0.00299293726732445	0.0435335448102876	0.068750139240147	0.945200438203326	   
df.mm.trans1:probe16	-0.294653125667115	0.0435335448102876	-6.76841564249286	2.06873331813065e-11	***
df.mm.trans1:probe17	-0.368773210408845	0.0435335448102876	-8.47101268724843	7.33111695929535e-17	***
df.mm.trans1:probe18	-0.462404288467569	0.0435335448102876	-10.6217926999204	3.30953863524091e-25	***
df.mm.trans1:probe19	-0.255353496555289	0.0435335448102876	-5.86567203906964	5.83882803782959e-09	***
df.mm.trans1:probe20	-0.403405932659334	0.0435335448102876	-9.2665537441831	9.15446621429755e-20	***
df.mm.trans1:probe21	-0.314610166329377	0.0435335448102876	-7.22684467117022	8.97719063086723e-13	***
df.mm.trans1:probe22	-0.0473049118762032	0.0435335448102876	-1.08663128817905	0.277427102414349	   
df.mm.trans1:probe23	-0.0860317355387013	0.0435335448102876	-1.97621709680694	0.0483684499436811	*  
df.mm.trans1:probe24	-0.152485198087467	0.0435335448102876	-3.50270575832898	0.000478206816105947	***
df.mm.trans1:probe25	-0.134943968137340	0.0435335448102876	-3.09976981487276	0.00198368275433457	** 
df.mm.trans1:probe26	0.126938072269631	0.0435335448102876	2.91586804664786	0.00361569663350971	** 
df.mm.trans1:probe27	-0.386488187615918	0.0435335448102876	-8.87793974279312	2.55612504024044e-18	***
df.mm.trans2:probe2	-0.0532453645750593	0.0435335448102876	-1.22308819112007	0.221546525773804	   
df.mm.trans2:probe3	-0.363252182554128	0.0435335448102876	-8.3441903051342	2.02988795543518e-16	***
df.mm.trans2:probe4	-0.204904925096873	0.0435335448102876	-4.70682840071528	2.82131893349338e-06	***
df.mm.trans2:probe5	-0.267453199074768	0.0435335448102876	-6.1436117881116	1.10921655968456e-09	***
df.mm.trans2:probe6	-0.262832914855002	0.0435335448102876	-6.037480200622	2.10841663536824e-09	***
df.mm.trans3:probe2	-0.0474225771038212	0.0435335448102876	-1.08933415164056	0.276234377410518	   
df.mm.trans3:probe3	-0.0751672317726009	0.0435335448102876	-1.72665084132634	0.0844984731584041	.  
df.mm.trans3:probe4	-0.00387731031794701	0.0435335448102876	-0.089064888578307	0.929045822198689	   
df.mm.trans3:probe5	0.0482680485030391	0.0435335448102876	1.10875529923841	0.267767174244699	   
df.mm.trans3:probe6	0.0373871986887970	0.0435335448102876	0.858813562086995	0.390622042281187	   
df.mm.trans3:probe7	-0.0332587750674496	0.0435335448102876	-0.763980402064342	0.445035364359273	   
df.mm.trans3:probe8	0.0430841420664873	0.0435335448102876	0.989676863077456	0.322539907256408	   
df.mm.trans3:probe9	0.0712158857359329	0.0435335448102876	1.63588529365758	0.102136757815371	   
df.mm.trans3:probe10	0.0651051529980941	0.0435335448102876	1.49551692337052	0.135053468201224	   
df.mm.trans3:probe11	0.40799300096549	0.0435335448102876	9.37192233583227	3.63591142791390e-20	***
df.mm.trans3:probe12	0.0291420935569152	0.0435335448102876	0.669416967626043	0.503363668829358	   
df.mm.trans3:probe13	0.0579170321076933	0.0435335448102876	1.33040009399847	0.183649779412871	   
df.mm.trans3:probe14	0.567360898411154	0.0435335448102876	13.0327291490648	2.56726378693100e-36	***
df.mm.trans3:probe15	-0.114970887624834	0.0435335448102876	-2.64097233813279	0.00837869253066967	** 
df.mm.trans3:probe16	-0.0145435345311089	0.0435335448102876	-0.33407650570353	0.738382633152791	   
df.mm.trans3:probe17	-0.00943496870342617	0.0435335448102876	-0.216728702993112	0.828458151255843	   
df.mm.trans3:probe18	0.485135894137943	0.0435335448102876	11.1439556841073	1.83420945701711e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08990542004145	0.127997745390210	31.952948917757	6.109167615025e-161	***
df.mm.trans1	-0.0758935612801183	0.10999330718093	-0.689983447404482	0.490343568229051	   
df.mm.trans2	0.254490725234186	0.0949256685714532	2.68094740931557	0.00744623615951815	** 
df.mm.exp2	0.0185882147813011	0.118806350399989	0.156458090991935	0.875699364449017	   
df.mm.exp3	0.0199289573842518	0.118806350399988	0.167743199897618	0.866814750963212	   
df.mm.exp4	-0.055541138886807	0.118806350399989	-0.467493014471155	0.640235604954265	   
df.mm.exp5	0.110194436534754	0.118806350399989	0.927513017307236	0.353854516798072	   
df.mm.exp6	0.177642330532962	0.118806350399989	1.49522588594708	0.135129373751606	   
df.mm.exp7	-0.112372178945298	0.118806350399989	-0.945843202547433	0.344426908139399	   
df.mm.exp8	-0.137469934053904	0.118806350399989	-1.15709247520086	0.247474446842600	   
df.mm.trans1:exp2	0.0368212953173861	0.10999330718093	0.334759416378108	0.737867517124008	   
df.mm.trans2:exp2	-0.0669415241505506	0.0710003744843365	-0.942833395411438	0.345963805637484	   
df.mm.trans1:exp3	-0.0510138859934435	0.10999330718093	-0.463790818740724	0.642885240700058	   
df.mm.trans2:exp3	0.0190305074188825	0.0710003744843365	0.268033902033585	0.788721207298874	   
df.mm.trans1:exp4	0.101052944458379	0.10999330718093	0.91871902980565	0.35843486766918	   
df.mm.trans2:exp4	-0.0985553496026782	0.0710003744843365	-1.38809619411825	0.165376134018182	   
df.mm.trans1:exp5	-0.0877270676363945	0.10999330718093	-0.797567323728985	0.425285980753599	   
df.mm.trans2:exp5	-0.124525934828062	0.0710003744843365	-1.75387715533154	0.0797174578640788	.  
df.mm.trans1:exp6	-0.179325701998001	0.109993307180930	-1.63033285018900	0.103304525872871	   
df.mm.trans2:exp6	-0.0811256508791138	0.0710003744843365	-1.14260877450739	0.253438472569135	   
df.mm.trans1:exp7	0.159809405857376	0.10999330718093	1.45290118056459	0.146523518701031	   
df.mm.trans2:exp7	-0.0388390254042155	0.0710003744843365	-0.547025641572973	0.584467061944735	   
df.mm.trans1:exp8	0.187857049081152	0.109993307180930	1.70789527013805	0.0879251926825861	.  
df.mm.trans2:exp8	0.0528189667751091	0.0710003744843365	0.743925185729289	0.457073441697135	   
df.mm.trans1:probe2	0.0544447855477614	0.0824949803856976	0.659976950030291	0.509400546942288	   
df.mm.trans1:probe3	0.157836188736265	0.0824949803856976	1.91328233546231	0.0559595806871881	.  
df.mm.trans1:probe4	0.0183996557183513	0.0824949803856976	0.223039700504509	0.823544148705088	   
df.mm.trans1:probe5	0.0519604858632816	0.0824949803856976	0.629862394297753	0.52890952109758	   
df.mm.trans1:probe6	0.122891337146797	0.0824949803856976	1.48968260338059	0.136581428604626	   
df.mm.trans1:probe7	0.145378809491331	0.0824949803856976	1.76227461127484	0.0782880776562816	.  
df.mm.trans1:probe8	0.118583441916003	0.0824949803856976	1.43746251422301	0.150858174217496	   
df.mm.trans1:probe9	0.0802167778462802	0.0824949803856976	0.972383743486382	0.331063799088348	   
df.mm.trans1:probe10	0.147769549452632	0.0824949803856976	1.79125504075216	0.073514807719604	.  
df.mm.trans1:probe11	0.0292561318834954	0.0824949803856976	0.354641358137320	0.722923197018422	   
df.mm.trans1:probe12	0.101197042645325	0.0824949803856976	1.22670545737677	0.220183970689783	   
df.mm.trans1:probe13	0.0147857285280745	0.0824949803856976	0.179231857004453	0.857787167878287	   
df.mm.trans1:probe14	0.0866138765457178	0.0824949803856976	1.04992905193459	0.293970936308348	   
df.mm.trans1:probe15	0.190713441426002	0.0824949803856976	2.31181873775033	0.0209633850349250	*  
df.mm.trans1:probe16	0.0473844612716431	0.0824949803856976	0.574392054523821	0.565814667816289	   
df.mm.trans1:probe17	0.0460341702105873	0.0824949803856976	0.558023894246158	0.576936493919182	   
df.mm.trans1:probe18	0.178754464544602	0.0824949803856976	2.16685262192744	0.0304505335276099	*  
df.mm.trans1:probe19	0.110497298409429	0.0824949803856976	1.33944268963771	0.18069078764646	   
df.mm.trans1:probe20	0.122044452220006	0.0824949803856976	1.47941670692446	0.139302368976216	   
df.mm.trans1:probe21	0.0627735436559787	0.0824949803856976	0.760937736605147	0.446849981574921	   
df.mm.trans1:probe22	0.109039157536085	0.0824949803856976	1.32176717936391	0.186508102176355	   
df.mm.trans1:probe23	0.140349954468069	0.0824949803856976	1.70131508380117	0.0891536858290882	.  
df.mm.trans1:probe24	0.0920368287482084	0.0824949803856976	1.1156658055787	0.264797880609634	   
df.mm.trans1:probe25	0.122288184007088	0.0824949803856976	1.48237121137966	0.138515039913078	   
df.mm.trans1:probe26	0.129623757671869	0.0824949803856976	1.57129266612132	0.116389282152734	   
df.mm.trans1:probe27	0.0119987642032304	0.0824949803856976	0.145448415735495	0.884382284898722	   
df.mm.trans2:probe2	-0.0960431803200554	0.0824949803856976	-1.16423059768018	0.244571623793476	   
df.mm.trans2:probe3	-0.221250011338124	0.0824949803856976	-2.68198150122214	0.00742340315514847	** 
df.mm.trans2:probe4	-0.203799001420552	0.0824949803856975	-2.47044123736631	0.0136386071097083	*  
df.mm.trans2:probe5	-0.191919706127132	0.0824949803856976	-2.32644101774229	0.0201677986069489	*  
df.mm.trans2:probe6	-0.12489703780547	0.0824949803856976	-1.51399560581172	0.130301252095190	   
df.mm.trans3:probe2	-0.136963701877953	0.0824949803856976	-1.66026710034467	0.0971328721039251	.  
df.mm.trans3:probe3	-0.01757139063068	0.0824949803856976	-0.212999512800980	0.831365028278138	   
df.mm.trans3:probe4	-0.064397081748806	0.0824949803856976	-0.78061818364855	0.435187463030687	   
df.mm.trans3:probe5	-0.142596982632173	0.0824949803856976	-1.72855344610634	0.0841569885567513	.  
df.mm.trans3:probe6	-0.09262408169254	0.0824949803856976	-1.12278445621157	0.26176299175517	   
df.mm.trans3:probe7	-0.135012826237260	0.0824949803856976	-1.63661868402199	0.101983303641785	   
df.mm.trans3:probe8	-0.107874936928949	0.0824949803856976	-1.30765455576315	0.191251402228291	   
df.mm.trans3:probe9	-0.173346237346008	0.0824949803856975	-2.10129436403941	0.0358319183501344	*  
df.mm.trans3:probe10	-0.0863588566989178	0.0824949803856976	-1.04683771418764	0.29539402112047	   
df.mm.trans3:probe11	-0.111665131531334	0.0824949803856975	-1.35359910396068	0.176129826678808	   
df.mm.trans3:probe12	-0.166719904405303	0.0824949803856975	-2.02097028965665	0.0435137913783530	*  
df.mm.trans3:probe13	-0.173120543864876	0.0824949803856975	-2.098558519021	0.0360731220243634	*  
df.mm.trans3:probe14	-0.0603175298097005	0.0824949803856976	-0.731166060379571	0.464826416807973	   
df.mm.trans3:probe15	-0.0617506699075111	0.0824949803856976	-0.74853851251072	0.454288198664203	   
df.mm.trans3:probe16	-0.104177315199193	0.0824949803856976	-1.26283217126814	0.206905048564844	   
df.mm.trans3:probe17	-0.139119132399509	0.0824949803856976	-1.68639512063728	0.0919904847412341	.  
df.mm.trans3:probe18	-0.217657931615451	0.0824949803856976	-2.63843849162472	0.0084411786203116	**