fitVsDatCorrelation=0.84301388542738
cont.fitVsDatCorrelation=0.234279300427966

fstatistic=4749.28440301292,53,715
cont.fstatistic=1444.53814940224,53,715

residuals=-0.932902355823827,-0.106649864652143,-0.0104334371326778,0.0812897036834606,1.69040776705598
cont.residuals=-0.811038779754542,-0.307497769968484,-0.0739166682146851,0.239759009480594,2.40422919381720

predictedValues:
Include	Exclude	Both
Lung	69.2320679283937	60.4575155765381	70.0472309158702
cerebhem	85.4747097655261	116.312090003825	57.5941852227373
cortex	60.6823707022268	54.4825628318059	60.4426680313615
heart	64.724268768309	57.0410958037007	61.8254815042683
kidney	66.0789733366606	53.7662009037523	73.1593452712906
liver	67.296303131355	57.2988220021355	66.9198571556156
stomach	72.0113879446693	63.272145133365	60.752356571543
testicle	68.3061849337779	64.2559772344922	58.0559806408663


diffExp=8.77455235185553,-30.8373802382984,6.19980787042092,7.68317296460828,12.3127724329083,9.99748112921947,8.73924281130432,4.05020769928566
diffExpScore=3.17317590238013
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,1,0,0,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	63.0683919928561	55.905591128661	66.0097592896885
cerebhem	66.5599417424403	64.2087450041423	74.5369141249351
cortex	66.6679443217766	69.9955581047396	64.5007577879138
heart	63.5387466199623	54.0593317674373	73.0232143268244
kidney	68.1022032986638	55.742590873161	77.6620454065779
liver	64.8802021718639	62.4981610469505	63.7590074716348
stomach	67.2306541430287	61.7469927810506	67.1348307490857
testicle	67.1059380689686	72.3041483803562	76.2124629633099
cont.diffExp=7.16280086419515,2.35119673829797,-3.32761378296301,9.47941485252504,12.3596124255027,2.38204112491341,5.48366136197804,-5.19821031138758
cont.diffExpScore=1.50647452681764

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

tran.correlation=0.940676198245015
cont.tran.correlation=0.479205056694805

tran.covariance=0.023642896190221
cont.tran.covariance=0.00148133652700053

tran.mean=67.5432922500333
cont.tran.mean=63.9759463403787

weightedLogRatios:
wLogRatio
Lung	0.565091714216207
cerebhem	-1.41775250916382
cortex	0.436669136836694
heart	0.518972138590874
kidney	0.842916321475713
liver	0.663997987233861
stomach	0.54496213887808
testicle	0.256326249219828

cont.weightedLogRatios:
wLogRatio
Lung	0.492340980324628
cerebhem	0.150331807768200
cortex	-0.205744456687585
heart	0.657720556831928
kidney	0.825267703580066
liver	0.155376010577604
stomach	0.354424962068451
testicle	-0.316608906218778

varWeightedLogRatios=0.510960775717372
cont.varWeightedLogRatios=0.158977088706708

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91299423831933	0.118863085901678	32.9201804633955	2.22646666376989e-145	***
df.mm.trans1	0.2787893434519	0.105552720671984	2.64123313617151	0.0084409323716351	** 
df.mm.trans2	0.23972151817072	0.0960139255738025	2.49673697578852	0.0127581339854236	*  
df.mm.exp2	1.06084061855439	0.129441039620991	8.19555082113505	1.15163223611679e-15	***
df.mm.exp3	-0.088396674074236	0.129441039620991	-0.682910723933192	0.494884463287466	   
df.mm.exp4	-0.000642705681063248	0.129441039620991	-0.00496523886817595	0.996039713824645	   
df.mm.exp5	-0.20737956728461	0.129441039620991	-1.60211605138391	0.109571616835029	   
df.mm.exp6	-0.036345691768874	0.129441039620991	-0.280789553879479	0.778953045380814	   
df.mm.exp7	0.22722826740534	0.129441039620991	1.75545768228280	0.0796092541576013	.  
df.mm.exp8	0.23523202044405	0.129441039620991	1.81729087724280	0.0695910482965868	.  
df.mm.trans1:exp2	-0.850084243235398	0.122910339878967	-6.91629560273363	1.02988468224311e-11	***
df.mm.trans2:exp2	-0.40650450559994	0.103499768494899	-3.92758854934034	9.41143652743714e-05	***
df.mm.trans1:exp3	-0.0434142679384603	0.122910339878967	-0.353219004855176	0.724028373792939	   
df.mm.trans2:exp3	-0.0156635199792450	0.103499768494899	-0.151338695796378	0.87975123417584	   
df.mm.trans1:exp4	-0.0666852309131708	0.122910339878967	-0.542551838835018	0.587607471201434	   
df.mm.trans2:exp4	-0.0575262037407313	0.103499768494899	-0.555809975010395	0.578514639320735	   
df.mm.trans1:exp5	0.160765995051312	0.122910339878967	1.30799406469481	0.191295681140444	   
df.mm.trans2:exp5	0.090083704551569	0.103499768494899	0.870375903845708	0.384387124233502	   
df.mm.trans1:exp6	0.00798683107483769	0.122910339878967	0.0649809534551975	0.948207328528628	   
df.mm.trans2:exp6	-0.0173151396368391	0.103499768494899	-0.167296409341172	0.867184132794249	   
df.mm.trans1:exp7	-0.187868159650520	0.122910339878967	-1.52849760106039	0.126831292079681	   
df.mm.trans2:exp7	-0.181723976878167	0.103499768494899	-1.75579114350506	0.0795522320827335	.  
df.mm.trans1:exp8	-0.248695867220328	0.122910339878967	-2.02339255969209	0.0434042416664829	*  
df.mm.trans2:exp8	-0.174298166596912	0.103499768494899	-1.68404402378449	0.0926096188585849	.  
df.mm.trans1:probe2	0.150520276838315	0.0673207657023373	2.23586697608059	0.0256682990118595	*  
df.mm.trans1:probe3	0.0705693541737837	0.0673207657023373	1.04825537020494	0.294875170368381	   
df.mm.trans1:probe4	0.472065154978733	0.0673207657023373	7.0121774470896	5.4429964982194e-12	***
df.mm.trans1:probe5	-0.326014174256699	0.0673207657023373	-4.84269854710491	1.57085256930811e-06	***
df.mm.trans1:probe6	-0.326558124956618	0.0673207657023373	-4.85077853095899	1.51014693076797e-06	***
df.mm.trans1:probe7	-0.386546819609507	0.0673207657023372	-5.7418660583667	1.38443964843402e-08	***
df.mm.trans1:probe8	0.476938507757923	0.0673207657023373	7.08456748496793	3.34685170041765e-12	***
df.mm.trans1:probe9	0.265432044989073	0.0673207657023373	3.94279598902212	8.84589796773737e-05	***
df.mm.trans1:probe10	0.113794001969083	0.0673207657023373	1.69032542606883	0.0914015800740972	.  
df.mm.trans1:probe11	-0.343680697890201	0.0673207657023373	-5.10512164121551	4.24268962495069e-07	***
df.mm.trans1:probe12	-0.403375567305238	0.0673207657023373	-5.99184461283145	3.2877214576357e-09	***
df.mm.trans1:probe13	-0.375515441315202	0.0673207657023373	-5.57800312277441	3.452533472272e-08	***
df.mm.trans1:probe14	-0.406626505631681	0.0673207657023373	-6.04013488838799	2.47546723604356e-09	***
df.mm.trans1:probe15	-0.327086041424741	0.0673207657023372	-4.8586203382025	1.45339584355626e-06	***
df.mm.trans1:probe16	-0.335733730742166	0.0673207657023373	-4.98707534353713	7.70155986964831e-07	***
df.mm.trans1:probe17	0.586179016624268	0.0673207657023373	8.70725415120935	2.14111080000133e-17	***
df.mm.trans1:probe18	0.371147341496294	0.0673207657023373	5.51311824255452	4.92661276896604e-08	***
df.mm.trans1:probe19	0.341122851393285	0.0673207657023373	5.06712673028081	5.14701792842635e-07	***
df.mm.trans1:probe20	0.538830049242776	0.0673207657023373	8.00392038951613	4.87379165401636e-15	***
df.mm.trans1:probe21	0.332079440501662	0.0673207657023373	4.93279357471914	1.00898920641058e-06	***
df.mm.trans1:probe22	0.700154218080203	0.0673207657023373	10.4002711611448	1.09431418392971e-23	***
df.mm.trans2:probe2	-0.0926818867182232	0.0673207657023373	-1.37672062626295	0.169029562294273	   
df.mm.trans2:probe3	-0.120147373697489	0.0673207657023373	-1.78470004676904	0.0747338653203738	.  
df.mm.trans2:probe4	0.0887638090517804	0.0673207657023373	1.31852049105108	0.187751657713394	   
df.mm.trans2:probe5	-0.190785240194842	0.0673207657023373	-2.83397311668156	0.00472694293457777	** 
df.mm.trans2:probe6	-0.192897908460459	0.0673207657023373	-2.86535523546134	0.00428768622139834	** 
df.mm.trans3:probe2	-0.0188560396249432	0.0673207657023373	-0.280092471145029	0.779487574746776	   
df.mm.trans3:probe3	-0.297051778251714	0.0673207657023373	-4.41248365422856	1.17945610560198e-05	***
df.mm.trans3:probe4	-0.413433614320099	0.0673207657023373	-6.14124943480471	1.35792684979168e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.71357891426386	0.214829537621794	17.2861653726667	3.33566025215550e-56	***
df.mm.trans1	0.268416889549208	0.190772787065632	1.40699778871954	0.159862567408391	   
df.mm.trans2	0.282591109240458	0.173532658014073	1.62846067405676	0.103867748078054	   
df.mm.exp2	0.0708663224205563	0.233947810458698	0.302915091539475	0.762042677833794	   
df.mm.exp3	0.303397533544249	0.233947810458698	1.29685989772412	0.195097740162351	   
df.mm.exp4	-0.127126833424835	0.233947810458698	-0.543398261242879	0.587024993551678	   
df.mm.exp5	-0.0886941140339616	0.233947810458698	-0.379119231165534	0.7047118843476	   
df.mm.exp6	0.174487670236381	0.233947810458698	0.745840150819387	0.456009109765962	   
df.mm.exp7	0.146390063327632	0.233947810458698	0.625738120996335	0.531686404957049	   
df.mm.exp8	0.175547514460964	0.233947810458698	0.750370410036198	0.453278499106649	   
df.mm.trans1:exp2	-0.0169831259278665	0.222144421750734	-0.0764508322739839	0.939081816191088	   
df.mm.trans2:exp2	0.0676086986666475	0.187062343544690	0.361423348951551	0.717889768651103	   
df.mm.trans1:exp3	-0.247893015777378	0.222144421750734	-1.11590925319537	0.264835870554038	   
df.mm.trans2:exp3	-0.0786301445049436	0.187062343544690	-0.420341919249816	0.674361984936997	   
df.mm.trans1:exp4	0.134557011473005	0.222144421750734	0.605718615000785	0.544893843786984	   
df.mm.trans2:exp4	0.0935446179039515	0.187062343544690	0.500071880483006	0.61717833191968	   
df.mm.trans1:exp5	0.165483955781706	0.222144421750734	0.744938605604032	0.456553619605258	   
df.mm.trans2:exp5	0.08577422105611	0.187062343544690	0.458532804789853	0.646709039213488	   
df.mm.trans1:exp6	-0.146164869100993	0.222144421750734	-0.657972268441667	0.510767731716445	   
df.mm.trans2:exp6	-0.0630149325257716	0.187062343544690	-0.336865941758700	0.736316813422049	   
df.mm.trans1:exp7	-0.0824804817225425	0.222144421750734	-0.3712921579237	0.710529929319154	   
df.mm.trans2:exp7	-0.0470091842575216	0.187062343544690	-0.251302230939338	0.80165265639404	   
df.mm.trans1:exp8	-0.113494703343161	0.222144421750734	-0.510905033980608	0.609575267048221	   
df.mm.trans2:exp8	0.0816695950293194	0.187062343544690	0.436590248372506	0.662540342923243	   
df.mm.trans1:probe2	0.159742770621099	0.121673510816818	1.31288042523566	0.189644460818952	   
df.mm.trans1:probe3	0.298169837310885	0.121673510816818	2.45057313879752	0.0145014280849721	*  
df.mm.trans1:probe4	0.20147476309733	0.121673510816818	1.65586380917909	0.0981881805338145	.  
df.mm.trans1:probe5	0.214823298180479	0.121673510816818	1.76557162473843	0.0778945109083845	.  
df.mm.trans1:probe6	0.100752698798031	0.121673510816818	0.828057792708193	0.407914073581924	   
df.mm.trans1:probe7	0.317404489395083	0.121673510816818	2.60865727687386	0.00927973279554757	** 
df.mm.trans1:probe8	0.161359646148848	0.121673510816818	1.32616906560524	0.185207172607029	   
df.mm.trans1:probe9	0.233933816471204	0.121673510816818	1.92263554245095	0.0549227921056222	.  
df.mm.trans1:probe10	0.178616829018134	0.121673510816818	1.46800094629508	0.142543724849388	   
df.mm.trans1:probe11	0.101758161661208	0.121673510816818	0.83632140618024	0.403253521793079	   
df.mm.trans1:probe12	0.161599881395959	0.121673510816818	1.32814349081494	0.184554501988962	   
df.mm.trans1:probe13	0.229342483818234	0.121673510816818	1.88490068445147	0.0598485643764086	.  
df.mm.trans1:probe14	0.239990170861015	0.121673510816818	1.9724109976766	0.0489476303621662	*  
df.mm.trans1:probe15	0.305977226393018	0.121673510816818	2.51473985043197	0.0121303564013591	*  
df.mm.trans1:probe16	0.0699717387375311	0.121673510816818	0.575077831384967	0.56541957341608	   
df.mm.trans1:probe17	0.225021687206428	0.121673510816818	1.84938928527510	0.0648141508661628	.  
df.mm.trans1:probe18	0.199771602386521	0.121673510816818	1.64186601541630	0.101057501242376	   
df.mm.trans1:probe19	0.227936063323764	0.121673510816818	1.87334171417907	0.0614289497250596	.  
df.mm.trans1:probe20	0.131311069257085	0.121673510816818	1.0792083533677	0.280858777562209	   
df.mm.trans1:probe21	0.311589675730769	0.121673510816818	2.56086697621369	0.0106453098804142	*  
df.mm.trans1:probe22	0.147274034705192	0.121673510816818	1.21040342895107	0.226524102658266	   
df.mm.trans2:probe2	0.0793537850173553	0.121673510816818	0.652186202934705	0.514490660592444	   
df.mm.trans2:probe3	-0.0326477322117052	0.121673510816818	-0.268322431008479	0.788528536172323	   
df.mm.trans2:probe4	0.0443320851486458	0.121673510816818	0.364352806547915	0.715702297666321	   
df.mm.trans2:probe5	0.123827974397660	0.121673510816818	1.01770692376984	0.309161492757573	   
df.mm.trans2:probe6	0.0600776061056661	0.121673510816818	0.493760767667122	0.621626781017278	   
df.mm.trans3:probe2	-0.176770511256499	0.121673510816818	-1.45282658542360	0.146710619258352	   
df.mm.trans3:probe3	-0.0933328157902553	0.121673510816818	-0.767075883351223	0.443289608921347	   
df.mm.trans3:probe4	-0.0290339286849156	0.121673510816818	-0.238621607036775	0.81146738298837