fitVsDatCorrelation=0.891129415546112
cont.fitVsDatCorrelation=0.228481010925402

fstatistic=12052.8590545950,70,1106
cont.fstatistic=2605.85630189926,70,1106

residuals=-0.689379992499336,-0.0866204894161968,-0.00876860554115634,0.0756816270133537,0.705588710748786
cont.residuals=-0.51959843976675,-0.18730647102911,-0.0663081402487445,0.105890943256946,1.98892833112575

predictedValues:
Include	Exclude	Both
Lung	54.7939306874799	44.3756718210872	52.8316457470115
cerebhem	57.7586803420364	47.3787237884331	61.6459371694192
cortex	60.0803753095879	43.6095853920377	67.773840413516
heart	57.3949009922016	44.489825286681	59.4359952290786
kidney	59.1343453698689	45.2088119989533	55.6452190757601
liver	56.2212990185834	46.7702101209555	52.0881196971949
stomach	52.3490551629975	44.8526671815902	53.9994435062943
testicle	53.7806858823688	45.5914195899847	53.873254632471


diffExp=10.4182588663926,10.3799565536032,16.4707899175502,12.9050757055206,13.9255333709156,9.4510888976279,7.49638798140732,8.18926629238413
diffExpScore=0.98891799240618
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,1,0,1,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,1,1,1,0,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	57.4035473579865	60.9696153099347	54.4595346040142
cerebhem	64.7591594592935	54.8346745706632	54.7921844591508
cortex	56.5962333907261	62.2341544175722	57.1928095300375
heart	58.7784477721349	61.4168679511516	57.4515560926033
kidney	58.364283119371	60.1845294561533	54.520636885376
liver	57.3653041757313	58.9759964151526	56.7614124280198
stomach	54.1582092409465	56.8353124108587	59.4334815212089
testicle	58.6577468739858	59.0261905578049	57.1083191096976
cont.diffExp=-3.56606795194822,9.92448488863033,-5.63792102684609,-2.63842017901669,-1.82024633678222,-1.61069223942121,-2.67710316991217,-0.368443683819017
cont.diffExpScore=3.00640278431073

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=-0.0924846958205796
cont.tran.correlation=-0.456156128521133

tran.covariance=-0.000117082666600041
cont.tran.covariance=-0.000943686434138725

tran.mean=50.8618867465529
cont.tran.mean=58.7850170299667

weightedLogRatios:
wLogRatio
Lung	0.822070162250537
cerebhem	0.783927487378058
cortex	1.2609525420444
heart	0.999068441349137
kidney	1.05945910706994
liver	0.724651617087678
stomach	0.599758999553062
testicle	0.644642753275416

cont.weightedLogRatios:
wLogRatio
Lung	-0.245914192458843
cerebhem	0.679965291915995
cortex	-0.387768074674661
heart	-0.179840495214233
kidney	-0.125365039082972
liver	-0.112515846452719
stomach	-0.193766662500765
testicle	-0.0255150847271325

varWeightedLogRatios=0.0513585028793196
cont.varWeightedLogRatios=0.104069605635937

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83942481667603	0.0685693351070619	55.9933213685283	0	***
df.mm.trans1	0.0234025113973154	0.059266781351234	0.394867257235122	0.693016991518784	   
df.mm.trans2	-0.0562640887893468	0.0512460259842767	-1.09792101355547	0.272477943890634	   
df.mm.exp2	-0.0361208749319403	0.0646369739256569	-0.558826825239146	0.576393036993224	   
df.mm.exp3	-0.174376515795138	0.0646369739256569	-2.69778278908447	0.00708649710665363	** 
df.mm.exp4	-0.0688444976584411	0.0646369739256569	-1.06509468926598	0.287065545807805	   
df.mm.exp5	0.0429472968576929	0.0646369739256569	0.664438544216029	0.506548170455076	   
df.mm.exp6	0.0924447828466143	0.0646369739256569	1.43021520396269	0.1529376981224	   
df.mm.exp7	-0.0568172555236318	0.0646369739256569	-0.879020970087197	0.379580838406339	   
df.mm.exp8	-0.0111606739254311	0.0646369739256569	-0.172667023958573	0.86294475688547	   
df.mm.trans1:exp2	0.0888150880493972	0.0604102973224084	1.47019783027044	0.141792594000360	   
df.mm.trans2:exp2	0.101602750328780	0.0402908870894995	2.52173028861555	0.0118174898607688	*  
df.mm.trans1:exp3	0.266480336119999	0.0604102973224084	4.41117405361869	1.12849460686681e-05	***
df.mm.trans2:exp3	0.156962103117120	0.0402908870894995	3.89572219565321	0.000103780107470190	***
df.mm.trans1:exp4	0.115220530207416	0.0604102973224085	1.90729950545494	0.0567406511068846	.  
df.mm.trans2:exp4	0.0714136281330923	0.0402908870894995	1.7724511246044	0.0765949342519499	.  
df.mm.trans1:exp5	0.0332851647458449	0.0604102973224084	0.550984951591989	0.581755270550921	   
df.mm.trans2:exp5	-0.0243466606029628	0.0402908870894995	-0.604272140965296	0.545786655773781	   
df.mm.trans1:exp6	-0.0667285455904699	0.0604102973224085	-1.10458892851232	0.269577994716514	   
df.mm.trans2:exp6	-0.0398897057861159	0.0402908870894995	-0.990042877375909	0.322369704115811	   
df.mm.trans1:exp7	0.0111717102727795	0.0604102973224085	0.184930562634981	0.853317403710908	   
df.mm.trans2:exp7	0.0675089238808947	0.0402908870894995	1.67553828564099	0.0941113233212666	.  
df.mm.trans1:exp8	-0.00750435577339082	0.0604102973224084	-0.124223122646463	0.90116118719442	   
df.mm.trans2:exp8	0.0381888185427484	0.0402908870894995	0.947827692597543	0.343424223983275	   
df.mm.trans1:probe2	-0.0825863323335554	0.043771418892417	-1.88676388436343	0.0594538308856333	.  
df.mm.trans1:probe3	0.241284672073407	0.043771418892417	5.51237949737122	4.40396141316304e-08	***
df.mm.trans1:probe4	0.503854828088956	0.043771418892417	11.5110462680533	4.85696891982966e-29	***
df.mm.trans1:probe5	0.342978050247995	0.043771418892417	7.83566214956338	1.08966698916957e-14	***
df.mm.trans1:probe6	-0.0549883086082408	0.043771418892417	-1.25626059194912	0.209286791185572	   
df.mm.trans1:probe7	0.135019941427361	0.043771418892417	3.08465991836403	0.00208842218581253	** 
df.mm.trans1:probe8	0.289478696459625	0.043771418892417	6.61341815697399	5.83422753564142e-11	***
df.mm.trans1:probe9	0.203550478006093	0.043771418892417	4.65030568249083	3.71417094861127e-06	***
df.mm.trans1:probe10	1.76030110400225	0.043771418892417	40.2157651852406	1.20488325751111e-218	***
df.mm.trans1:probe11	0.0465993720058939	0.043771418892417	1.06460729821045	0.287286040507451	   
df.mm.trans1:probe12	0.0676859097042163	0.043771418892417	1.54634945397994	0.122306267061708	   
df.mm.trans1:probe13	0.468954001901744	0.043771418892417	10.7137034569146	1.49647230714174e-25	***
df.mm.trans1:probe14	0.202729245334272	0.043771418892417	4.63154383531746	4.06015445028804e-06	***
df.mm.trans1:probe15	0.0847095678161785	0.043771418892417	1.93527123313916	0.0532116926610589	.  
df.mm.trans1:probe16	0.0912747184113579	0.043771418892417	2.08525838825779	0.0372747029745066	*  
df.mm.trans1:probe17	0.00825042587454027	0.043771418892417	0.188488883461111	0.850528007645334	   
df.mm.trans1:probe18	0.163847615007693	0.043771418892417	3.74325573978774	0.000190995444621594	***
df.mm.trans1:probe19	0.0782523298272848	0.043771418892417	1.78774944489728	0.0740901487622101	.  
df.mm.trans1:probe20	0.0235329828676021	0.043771418892417	0.537633539489371	0.59093822673212	   
df.mm.trans1:probe21	0.0688909078923854	0.043771418892417	1.57387879204254	0.115801468789052	   
df.mm.trans1:probe22	0.00473518026322767	0.043771418892417	0.108179729673968	0.913872745495913	   
df.mm.trans1:probe23	-0.00587997373174795	0.043771418892417	-0.134333633236792	0.893163199200012	   
df.mm.trans1:probe24	0.491908859192126	0.043771418892417	11.2381291637166	7.98946071791917e-28	***
df.mm.trans1:probe25	0.306118985046957	0.043771418892417	6.99358149205415	4.62926344994491e-12	***
df.mm.trans1:probe26	-0.0125047529323861	0.043771418892417	-0.285683060974575	0.775174373905625	   
df.mm.trans1:probe27	0.0894587538184063	0.043771418892417	2.04377093733884	0.0412126864053832	*  
df.mm.trans1:probe28	0.329443705035194	0.043771418892417	7.52645706653725	1.07783120545597e-13	***
df.mm.trans1:probe29	0.205439589661958	0.043771418892417	4.69346424814088	3.02244151539019e-06	***
df.mm.trans2:probe2	0.0331152172936396	0.043771418892417	0.75654886525455	0.449481306149063	   
df.mm.trans2:probe3	0.0752014663972607	0.043771418892417	1.71804954694509	0.0860674666892284	.  
df.mm.trans2:probe4	0.0885836306559369	0.043771418892417	2.02377791027659	0.0432330755591836	*  
df.mm.trans2:probe5	-0.0179371647809919	0.043771418892417	-0.409791714202332	0.68203810145667	   
df.mm.trans2:probe6	0.0116500389227519	0.043771418892417	0.26615630056192	0.790168444557725	   
df.mm.trans3:probe2	0.0664664153988147	0.043771418892417	1.51848893823109	0.129176971487180	   
df.mm.trans3:probe3	0.138145293504744	0.043771418892417	3.15606158082932	0.00164234904486245	** 
df.mm.trans3:probe4	0.153602399678833	0.043771418892417	3.50919398012576	0.000467542436657838	***
df.mm.trans3:probe5	0.135027913869687	0.043771418892417	3.08484205644701	0.00208715478167683	** 
df.mm.trans3:probe6	0.497180050478379	0.043771418892417	11.3585545787393	2.3371044492273e-28	***
df.mm.trans3:probe7	0.154978815995792	0.043771418892417	3.54063952956848	0.000415763197674429	***
df.mm.trans3:probe8	-0.000537734123052612	0.043771418892417	-0.0122850512197074	0.990200409424847	   
df.mm.trans3:probe9	0.495627616497845	0.043771418892417	11.3230877371377	3.36016322943515e-28	***
df.mm.trans3:probe10	0.117214041598031	0.043771418892417	2.67786707774139	0.00751902126501946	** 
df.mm.trans3:probe11	0.0399264854564233	0.043771418892417	0.912158811999128	0.361883898089599	   
df.mm.trans3:probe12	0.16305380449078	0.043771418892417	3.72512037801515	0.000205079108358808	***
df.mm.trans3:probe13	0.251857682252961	0.043771418892417	5.7539300444426	1.12799520652789e-08	***
df.mm.trans3:probe14	0.0351169391146929	0.043771418892417	0.802280118015014	0.422563290404844	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15739664995268	0.147119950421732	28.2585511892517	1.09675360661177e-132	***
df.mm.trans1	-0.0906418994006904	0.127160718715372	-0.712813676396225	0.476111418423608	   
df.mm.trans2	-0.041985855977725	0.109951668487757	-0.381857379293886	0.702640509031596	   
df.mm.exp2	0.00842653555638124	0.138682814767064	0.0607612094586825	0.951560361014085	   
df.mm.exp3	-0.0426055833249722	0.138682814767064	-0.307216026704778	0.75873678237967	   
df.mm.exp4	-0.0225061078977154	0.138682814767064	-0.162284764233531	0.871111255110718	   
df.mm.exp5	0.00251636646256069	0.138682814767064	0.0181447605226881	0.985526642728677	   
df.mm.exp6	-0.0753103427140562	0.138682814767064	-0.543040194565922	0.587211546987441	   
df.mm.exp7	-0.215814126714467	0.138682814767064	-1.55617065515258	0.119953603392309	   
df.mm.exp8	-0.0582726846014434	0.138682814767064	-0.420186774398256	0.674430633357278	   
df.mm.trans1:exp2	0.112142511771972	0.129614206308958	0.86520231821395	0.387115376997591	   
df.mm.trans2:exp2	-0.114479424465232	0.0864467083106407	-1.32427742712721	0.185684518729703	   
df.mm.trans1:exp3	0.0284419163749483	0.129614206308958	0.219435177554164	0.826351560041905	   
df.mm.trans2:exp3	0.0631339083559182	0.0864467083106407	0.730321715999301	0.465348221292871	   
df.mm.trans1:exp4	0.0461752590468859	0.129614206308958	0.356251527990836	0.721720151373839	   
df.mm.trans2:exp4	0.0298149972507257	0.0864467083106407	0.344894534833963	0.730239234441946	   
df.mm.trans1:exp5	0.0140816437744570	0.129614206308958	0.108642749706702	0.913505558538428	   
df.mm.trans2:exp5	-0.0154766633547496	0.0864467083106408	-0.179031262811479	0.857945944363671	   
df.mm.trans1:exp6	0.0746439043353573	0.129614206308958	0.575892924556669	0.564804717112153	   
df.mm.trans2:exp6	0.0420652330143393	0.0864467083106407	0.486603062584877	0.626636061536879	   
df.mm.trans1:exp7	0.157617588457544	0.129614206308958	1.21605179668219	0.224224666779913	   
df.mm.trans2:exp7	0.145596326239817	0.0864467083106407	1.68423215973274	0.0924189654806937	.  
df.mm.trans1:exp8	0.0798862354135211	0.129614206308958	0.616338576522224	0.537797914900808	   
df.mm.trans2:exp8	0.0258783073663905	0.0864467083106408	0.299355613095163	0.764724969302535	   
df.mm.trans1:probe2	0.0350680522869032	0.0939144147640718	0.373404363696455	0.70891907057954	   
df.mm.trans1:probe3	-0.0691742797891583	0.0939144147640719	-0.736567224136308	0.461541774218864	   
df.mm.trans1:probe4	-0.0511442714044958	0.0939144147640719	-0.544583827019296	0.586149560445861	   
df.mm.trans1:probe5	0.0214886985624114	0.0939144147640718	0.228811504776924	0.819057724941936	   
df.mm.trans1:probe6	0.0613983137431065	0.0939144147640719	0.653768794677036	0.513396672784134	   
df.mm.trans1:probe7	0.108782756898134	0.0939144147640719	1.15831799805615	0.246984342344487	   
df.mm.trans1:probe8	0.0404711590796359	0.0939144147640719	0.430936605219826	0.666598437974066	   
df.mm.trans1:probe9	-0.0624463355234038	0.0939144147640719	-0.664928122911472	0.50623508684816	   
df.mm.trans1:probe10	-0.147416611965648	0.0939144147640719	-1.56969100362263	0.116773045076164	   
df.mm.trans1:probe11	0.0194371313151266	0.0939144147640718	0.206966431766154	0.836074171225767	   
df.mm.trans1:probe12	0.00958745962887761	0.0939144147640719	0.102087199850660	0.918705965854398	   
df.mm.trans1:probe13	-0.0447422966480787	0.0939144147640719	-0.476415646740477	0.633872391881907	   
df.mm.trans1:probe14	-0.100925775783385	0.0939144147640718	-1.07465692073924	0.282762746053413	   
df.mm.trans1:probe15	-0.0545686069484958	0.0939144147640718	-0.581046126790876	0.561327748794957	   
df.mm.trans1:probe16	0.0298805352988391	0.0939144147640718	0.318167720832886	0.75041778190984	   
df.mm.trans1:probe17	-0.0425845279958508	0.0939144147640719	-0.453439741948348	0.650321074405011	   
df.mm.trans1:probe18	-0.106215246525257	0.0939144147640719	-1.13097916642601	0.258309040206740	   
df.mm.trans1:probe19	0.0127004008562967	0.0939144147640719	0.135233775221857	0.892451650668618	   
df.mm.trans1:probe20	-0.0078074259857297	0.0939144147640718	-0.0831334146663557	0.933760499982439	   
df.mm.trans1:probe21	0.0426634212723968	0.0939144147640718	0.454279796978709	0.649716592269896	   
df.mm.trans1:probe22	-0.0859842832411881	0.0939144147640719	-0.91556001767348	0.360097282390003	   
df.mm.trans1:probe23	-0.121636982997271	0.0939144147640719	-1.29518970333620	0.195525079101541	   
df.mm.trans1:probe24	-0.0130714342305670	0.0939144147640718	-0.139184535871352	0.889329667309738	   
df.mm.trans1:probe25	0.00675472701664745	0.0939144147640718	0.0719242837600214	0.942675168201307	   
df.mm.trans1:probe26	-0.0756329025262478	0.0939144147640718	-0.805338591698088	0.420797415890065	   
df.mm.trans1:probe27	-0.101308433167432	0.0939144147640718	-1.07873145375963	0.280942660635138	   
df.mm.trans1:probe28	0.0587337967180185	0.0939144147640718	0.62539703692524	0.531839523126641	   
df.mm.trans1:probe29	-0.078198922337783	0.0939144147640718	-0.832661551841976	0.405215395021884	   
df.mm.trans2:probe2	-0.0119358407055792	0.0939144147640718	-0.127092744341366	0.898890095957846	   
df.mm.trans2:probe3	-0.0671608550060555	0.0939144147640719	-0.715128291804558	0.474680699473108	   
df.mm.trans2:probe4	0.100198341767488	0.0939144147640718	1.06691120867018	0.28624476405425	   
df.mm.trans2:probe5	-0.0348880528762504	0.0939144147640718	-0.371487731291249	0.710345444892831	   
df.mm.trans2:probe6	-0.086916864633268	0.0939144147640718	-0.925490137500374	0.354912900552979	   
df.mm.trans3:probe2	-0.0350099460632611	0.0939144147640718	-0.372785649053042	0.709379411928048	   
df.mm.trans3:probe3	-0.111507620668625	0.0939144147640718	-1.18733232751064	0.235351502639580	   
df.mm.trans3:probe4	-0.0120610830015862	0.0939144147640718	-0.128426323391203	0.897834948730244	   
df.mm.trans3:probe5	0.0919614760749101	0.0939144147640718	0.979205123153162	0.327692774592505	   
df.mm.trans3:probe6	0.0287511546279366	0.0939144147640718	0.306142083727659	0.759554083997923	   
df.mm.trans3:probe7	-0.0628651063084776	0.0939144147640719	-0.669387191161281	0.503388228933178	   
df.mm.trans3:probe8	0.00463417098616909	0.0939144147640719	0.0493446186915063	0.960653571518101	   
df.mm.trans3:probe9	0.00653012154445131	0.0939144147640718	0.0695326863384714	0.944578184922788	   
df.mm.trans3:probe10	-0.0619420654352916	0.0939144147640718	-0.659558658709635	0.509674401232228	   
df.mm.trans3:probe11	-0.116950919970695	0.0939144147640718	-1.24529253857882	0.213288002722764	   
df.mm.trans3:probe12	-0.0454262568476812	0.0939144147640718	-0.483698449932306	0.628695643186434	   
df.mm.trans3:probe13	-0.0122534358608517	0.0939144147640719	-0.130474495226684	0.896214758080956	   
df.mm.trans3:probe14	-0.0562138312602678	0.0939144147640718	-0.598564463202864	0.54958592246911