fitVsDatCorrelation=0.962839494253486
cont.fitVsDatCorrelation=0.2214057986424

fstatistic=11699.0478099669,59,853
cont.fstatistic=883.968027779275,59,853

residuals=-1.12820548693118,-0.0873661962481598,0.00177363921815896,0.0904578056671651,0.621097954989542
cont.residuals=-0.870249200075297,-0.390126998316957,-0.186870335681227,0.329730548528001,2.38508898882241

predictedValues:
Include	Exclude	Both
Lung	93.3290295225972	51.1321344420617	68.5749646382431
cerebhem	103.068364210741	56.6665029465835	75.3937120086207
cortex	96.7960771644457	47.7745691317389	84.4237977646884
heart	92.6848449609629	47.5536276071212	71.7645699075136
kidney	94.1906595417059	50.7613616053383	69.1153230545928
liver	93.4083566266841	50.6581947649555	66.3473934169738
stomach	89.636474070887	49.4679488345952	63.7041398602761
testicle	95.5099718760076	51.7893351340259	67.3904178658807


diffExp=42.1968950805355,46.4018612641572,49.0215080327069,45.1312173538417,43.4292979363676,42.7501618617287,40.1685252362918,43.7206367419817
diffExpScore=0.997173704970163
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	76.6300409353172	92.5775831327293	74.3966496943815
cerebhem	76.1448544074797	57.6595638221751	73.2511086675473
cortex	78.8278179110537	73.3127012861289	81.174196041286
heart	80.8160956090156	106.935256489795	76.6375760449372
kidney	82.5311606955427	68.4678333223479	74.7456941720072
liver	78.8249494557967	77.6698071110171	80.9454775468819
stomach	79.7420285731504	84.805584961787	71.4711257585434
testicle	82.4608154480485	87.9124818034227	70.8959998563789
cont.diffExp=-15.9475421974121,18.4852905853046,5.51511662492481,-26.1191608807798,14.0633273731948,1.15514234477962,-5.0635563886366,-5.45166635537413
cont.diffExpScore=6.39145653739043

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=0,1,0,-1,0,0,0,0
cont.diffExp1.3Score=2
cont.diffExp1.2=-1,1,0,-1,1,0,0,0
cont.diffExp1.2Score=4

tran.correlation=0.721000380178558
cont.tran.correlation=0.272886556749074

tran.covariance=0.00155944250286530
cont.tran.covariance=0.0017580158893524

tran.mean=72.7767157775282
cont.tran.mean=80.3324109353005

weightedLogRatios:
wLogRatio
Lung	2.5484397054867
cerebhem	2.59400747227783
cortex	2.97947907482798
heart	2.79987494886038
kidney	2.61877510468989
liver	2.58888847917272
stomach	2.49577116336517
testicle	2.60316038103964

cont.weightedLogRatios:
wLogRatio
Lung	-0.838191402382046
cerebhem	1.16616118034262
cortex	0.314136439706156
heart	-1.26923089149327
kidney	0.806984348231847
liver	0.0643642856128703
stomach	-0.271474059146527
testicle	-0.284519848960981

varWeightedLogRatios=0.0250082742434411
cont.varWeightedLogRatios=0.652855947209541

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15014862245942	0.073823548542037	56.2171380869917	4.37855886598366e-289	***
df.mm.trans1	0.55851671067095	0.0637522107361302	8.76074263499105	1.03268704223987e-17	***
df.mm.trans2	-0.236738563089315	0.0563247900305182	-4.20309712581341	2.91057420329510e-05	***
df.mm.exp2	0.107235047396517	0.0724516892759723	1.48009036736263	0.139218361801188	   
df.mm.exp3	-0.239366219842857	0.0724516892759723	-3.30380453837450	0.000993559595587284	***
df.mm.exp4	-0.124944681696284	0.0724516892759723	-1.72452406486153	0.0849755456946222	.  
df.mm.exp5	-0.00593680378306417	0.0724516892759723	-0.0819415508788287	0.934712415618574	   
df.mm.exp6	0.0245605139275373	0.0724516892759723	0.338991598028654	0.734699492758499	   
df.mm.exp7	0.000220891336306309	0.0724516892759723	0.00304880864081612	0.997568119272365	   
df.mm.exp8	0.0532952289227315	0.0724516892759723	0.735596774282614	0.462178336515452	   
df.mm.trans1:exp2	-0.00797375031703781	0.0669686030294605	-0.119066994924921	0.905250313455673	   
df.mm.trans2:exp2	-0.0044649418123696	0.0494595911741961	-0.09027453940418	0.928090244897418	   
df.mm.trans1:exp3	0.275841486963487	0.0669686030294605	4.11896731431205	4.1762765962104e-05	***
df.mm.trans2:exp3	0.171446537615381	0.0494595911741961	3.46639617403120	0.000553834537093503	***
df.mm.trans1:exp4	0.118018454943694	0.0669686030294605	1.76229530862061	0.078377532147708	.  
df.mm.trans2:exp4	0.0523896045324097	0.0494595911741961	1.05924054947996	0.289790089518291	   
df.mm.trans1:exp5	0.0151266236608719	0.0669686030294605	0.225876350656702	0.821351700734613	   
df.mm.trans2:exp5	-0.00134088297016179	0.0494595911741961	-0.0271106763790105	0.978377800509468	   
df.mm.trans1:exp6	-0.0237109024973401	0.0669686030294605	-0.354059983704741	0.723381430247884	   
df.mm.trans2:exp6	-0.0338726579325832	0.0494595911741961	-0.684855194481573	0.493621346347821	   
df.mm.trans1:exp7	-0.040589778684699	0.0669686030294605	-0.606101618497895	0.54460852662996	   
df.mm.trans2:exp7	-0.0333090833861114	0.0494595911741961	-0.67346054820383	0.500836767786405	   
df.mm.trans1:exp8	-0.0301957705174119	0.0669686030294605	-0.450894436369359	0.652180226843079	   
df.mm.trans2:exp8	-0.0405241399140205	0.0494595911741961	-0.8193383518132	0.412822256689559	   
df.mm.trans1:probe2	-0.394039123083161	0.0458502681548054	-8.59404184404674	3.96904608014728e-17	***
df.mm.trans1:probe3	-0.567223951013033	0.0458502681548054	-12.3712242881089	1.88574420136929e-32	***
df.mm.trans1:probe4	1.48746195524456	0.0458502681548054	32.4417285897306	4.94420417923527e-151	***
df.mm.trans1:probe5	-0.446214698260799	0.0458502681548054	-9.73199756987752	2.68746941705616e-21	***
df.mm.trans1:probe6	-0.507117077783296	0.0458502681548054	-11.0602859741431	1.14894937071655e-26	***
df.mm.trans1:probe7	-0.870970771387292	0.0458502681548054	-18.9959798805672	2.25094579971233e-67	***
df.mm.trans1:probe8	-0.423260482572229	0.0458502681548054	-9.23136329635333	2.06288402002072e-19	***
df.mm.trans1:probe9	-0.585806301785038	0.0458502681548054	-12.7765076489229	2.49350725046834e-34	***
df.mm.trans1:probe10	-0.580372003503758	0.0458502681548054	-12.6579849335719	8.9214649365555e-34	***
df.mm.trans1:probe11	0.587680984506425	0.0458502681548054	12.8173947101514	1.60334257097316e-34	***
df.mm.trans1:probe12	0.254774108003103	0.0458502681548054	5.55665469922449	3.67654185828535e-08	***
df.mm.trans1:probe13	0.298451304713094	0.0458502681548054	6.50925974315843	1.28737733753146e-10	***
df.mm.trans1:probe14	0.57339053534252	0.0458502681548054	12.5057182524335	4.53505711651757e-33	***
df.mm.trans1:probe15	0.237282730722183	0.0458502681548054	5.17516560472535	2.84072082034953e-07	***
df.mm.trans1:probe16	0.530987998058341	0.0458502681548054	11.5809136876921	6.57721776880553e-29	***
df.mm.trans1:probe17	-0.847887769621067	0.0458502681548054	-18.4925367668150	1.83297420365597e-64	***
df.mm.trans1:probe18	-0.820988265385908	0.0458502681548054	-17.9058552637901	4.10301300902689e-61	***
df.mm.trans1:probe19	-0.844287040697482	0.0458502681548054	-18.4140044251627	5.17930331747272e-64	***
df.mm.trans1:probe20	-0.845852579413663	0.0458502681548054	-18.4481490175322	3.29787196429728e-64	***
df.mm.trans1:probe21	-0.907440070522918	0.0458502681548054	-19.7913797899525	4.91086695522789e-72	***
df.mm.trans1:probe22	-0.849661703970918	0.0458502681548054	-18.5312264936420	1.09802481538000e-64	***
df.mm.trans2:probe2	0.0211667334652938	0.0458502681548054	0.461649065907924	0.644450706618972	   
df.mm.trans2:probe3	0.0359927372284302	0.0458502681548054	0.785006035447973	0.432667996187872	   
df.mm.trans2:probe4	0.0645116841059156	0.0458502681548054	1.40700778211598	0.159789326728009	   
df.mm.trans2:probe5	0.0790629723382998	0.0458502681548054	1.72437317206865	0.0850027797798233	.  
df.mm.trans2:probe6	0.135315380794598	0.0458502681548054	2.951245134221	0.00325162949398129	** 
df.mm.trans3:probe2	-0.478391425420079	0.0458502681548054	-10.4337759553526	4.54363445395513e-24	***
df.mm.trans3:probe3	-0.548894137283581	0.0458502681548054	-11.9714487913208	1.22430905789439e-30	***
df.mm.trans3:probe4	-0.607442301512239	0.0458502681548054	-13.2483914698452	1.44177296493025e-36	***
df.mm.trans3:probe5	-0.320796447569426	0.0458502681548054	-6.99661006313666	5.30296649200505e-12	***
df.mm.trans3:probe6	0.42877420685994	0.0458502681548054	9.35161830269473	7.39325584571443e-20	***
df.mm.trans3:probe7	0.149876566139308	0.0458502681548054	3.26882638141344	0.00112317306137999	** 
df.mm.trans3:probe8	-0.287985152919427	0.0458502681548054	-6.28099168247164	5.35539896308424e-10	***
df.mm.trans3:probe9	-0.249327215842409	0.0458502681548054	-5.43785730981113	7.04501740885977e-08	***
df.mm.trans3:probe10	-0.52580658128164	0.0458502681548054	-11.4679063491264	2.04675024044337e-28	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.5441674674671	0.266561415710679	17.0473564426116	2.66795389136155e-56	***
df.mm.trans1	-0.0741277272410809	0.230195918296066	-0.322020163475452	0.747516319290747	   
df.mm.trans2	-0.0442648072550096	0.203377053347582	-0.217648975272342	0.8277546741515	   
df.mm.exp2	-0.464325049273633	0.261607918414219	-1.77488912448911	0.076272761227622	.  
df.mm.exp3	-0.292222887063821	0.261607918414219	-1.11702615438852	0.264297672372443	   
df.mm.exp4	0.167686916320706	0.261607918414219	0.640985629705586	0.521704188782238	   
df.mm.exp5	-0.232176930576766	0.261607918414219	-0.887499629155517	0.375060132353308	   
df.mm.exp6	-0.231704947012555	0.261607918414219	-0.885695465248428	0.376031262911825	   
df.mm.exp7	-0.00776064897691175	0.261607918414219	-0.0296651914206353	0.976341012053425	   
df.mm.exp8	0.069825699574572	0.261607918414219	0.266909732693996	0.789603175565268	   
df.mm.trans1:exp2	0.457973375355806	0.241809639122597	1.89394176765474	0.0585708384825302	.  
df.mm.trans2:exp2	-0.00916585274202343	0.178588254076649	-0.0513239394685471	0.95907940824067	   
df.mm.trans1:exp3	0.32049966163028	0.241809639122596	1.32542136365287	0.185386311948686	   
df.mm.trans2:exp3	0.0589097295191287	0.178588254076649	0.329863404643875	0.741584164903432	   
df.mm.trans1:exp4	-0.114499946726654	0.241809639122596	-0.473512748053036	0.635968567172348	   
df.mm.trans2:exp4	-0.0235103740733855	0.178588254076649	-0.131645690781517	0.895295587578916	   
df.mm.trans1:exp5	0.306363678828617	0.241809639122597	1.26696222673444	0.205514605174408	   
df.mm.trans2:exp5	-0.0695060505258704	0.178588254076649	-0.38919721168246	0.69722740428515	   
df.mm.trans1:exp6	0.259945332046433	0.241809639122596	1.07499987589263	0.28267875970667	   
df.mm.trans2:exp6	0.0561245164530133	0.178588254076649	0.314267680946840	0.753394588281878	   
df.mm.trans1:exp7	0.0475682512738309	0.241809639122596	0.196717762974345	0.84409526809416	   
df.mm.trans2:exp7	-0.0799249795409637	0.178588254076649	-0.447537717159498	0.654600487830044	   
df.mm.trans1:exp8	0.00350833749215434	0.241809639122596	0.0145086751085867	0.988427550920263	   
df.mm.trans2:exp8	-0.121530934471018	0.178588254076649	-0.680509113543702	0.496366817536657	   
df.mm.trans1:probe2	-0.170522340750075	0.165555742462037	-1.02999955310627	0.303302175621456	   
df.mm.trans1:probe3	-0.181219715230925	0.165555742462037	-1.09461449380096	0.273994549960468	   
df.mm.trans1:probe4	-0.0354442352580462	0.165555742462037	-0.214092454486583	0.830526125290223	   
df.mm.trans1:probe5	-0.158105985859655	0.165555742462037	-0.955001520988674	0.339847486618002	   
df.mm.trans1:probe6	-0.30177367477659	0.165555742462037	-1.82279195084875	0.0686848421410206	.  
df.mm.trans1:probe7	-0.151612483213334	0.165555742462037	-0.9157790660635	0.360041601233222	   
df.mm.trans1:probe8	-0.216536919826991	0.165555742462037	-1.30793964985325	0.191246144722032	   
df.mm.trans1:probe9	-0.144778986549579	0.165555742462037	-0.874502958317964	0.38209055833614	   
df.mm.trans1:probe10	-0.309804897414231	0.165555742462037	-1.87130263684614	0.0616452534740885	.  
df.mm.trans1:probe11	-0.224124934550194	0.165555742462037	-1.35377324408778	0.176167309524028	   
df.mm.trans1:probe12	-0.167835351681742	0.165555742462037	-1.01376943611743	0.310980246361325	   
df.mm.trans1:probe13	-0.182264723149090	0.165555742462037	-1.10092661503955	0.271239165595205	   
df.mm.trans1:probe14	-0.153724565382389	0.165555742462037	-0.928536594963707	0.353391964565796	   
df.mm.trans1:probe15	-0.270736688652606	0.165555742462037	-1.63532043423192	0.102350820252073	   
df.mm.trans1:probe16	-0.237561833842618	0.165555742462037	-1.43493563140579	0.151671977494012	   
df.mm.trans1:probe17	-0.231088906435045	0.165555742462037	-1.3958374563059	0.163126644911353	   
df.mm.trans1:probe18	-0.0456219031480838	0.165555742462037	-0.275568231398226	0.782946453463366	   
df.mm.trans1:probe19	-0.298505100279495	0.165555742462037	-1.80304890570585	0.0717333390253869	.  
df.mm.trans1:probe20	-0.102900414057993	0.165555742462037	-0.621545423479278	0.534406886963041	   
df.mm.trans1:probe21	-0.371416163631676	0.165555742462037	-2.24345080459437	0.0251236695654745	*  
df.mm.trans1:probe22	-0.238038129891789	0.165555742462037	-1.43781258415952	0.150853983303952	   
df.mm.trans2:probe2	0.219782406680861	0.165555742462037	1.32754323959049	0.184684216240353	   
df.mm.trans2:probe3	-0.00303286975685492	0.165555742462037	-0.0183193268427422	0.985388393464088	   
df.mm.trans2:probe4	0.248388724316832	0.165555742462037	1.50033288258659	0.133898224957430	   
df.mm.trans2:probe5	-0.075373249931638	0.165555742462037	-0.455274150027876	0.649027878690756	   
df.mm.trans2:probe6	0.0605448980248298	0.165555742462037	0.365707024863321	0.714674374115482	   
df.mm.trans3:probe2	-0.124614973935646	0.165555742462037	-0.752707046475423	0.45183364547666	   
df.mm.trans3:probe3	-0.142466461956977	0.165555742462037	-0.860534704736352	0.389736165180425	   
df.mm.trans3:probe4	-0.144184066047690	0.165555742462037	-0.870909482833267	0.384048611838735	   
df.mm.trans3:probe5	-0.100525161685083	0.165555742462037	-0.607198277692689	0.543880926807964	   
df.mm.trans3:probe6	-0.129791650248178	0.165555742462037	-0.783975525813852	0.433272109476194	   
df.mm.trans3:probe7	-0.131496068275385	0.165555742462037	-0.794270656637222	0.427258795304279	   
df.mm.trans3:probe8	-0.0195370007758045	0.165555742462037	-0.118008596290669	0.906088628566881	   
df.mm.trans3:probe9	-0.131845828397386	0.165555742462037	-0.79638329928434	0.426030866111184	   
df.mm.trans3:probe10	-0.239179020031656	0.165555742462037	-1.44470385910353	0.148908310906609