fitVsDatCorrelation=0.779858351286212
cont.fitVsDatCorrelation=0.275112437045777

fstatistic=11234.0381345878,59,853
cont.fstatistic=4753.8358148929,59,853

residuals=-0.461734362653377,-0.088784229548452,-0.0063731016079968,0.077202480803332,0.762884492084852
cont.residuals=-0.475502345514084,-0.151911825938973,-0.0414188913340944,0.115461399807204,1.15978234667675

predictedValues:
Include	Exclude	Both
Lung	66.2936303184018	51.7124279618186	57.8302691727344
cerebhem	64.2539620702097	48.7702918000105	56.7576266084224
cortex	52.0535889067106	51.9200191429472	50.8780200381549
heart	53.9061498060377	57.758841331502	54.0286382633882
kidney	54.8945267652998	51.4430507918736	53.4339113137962
liver	56.9710740581888	53.5705876487208	59.648013093887
stomach	54.2989002916168	52.2222252698047	55.1009225519069
testicle	55.0009273553157	49.8223675823621	55.6301925468939


diffExp=14.5812023565832,15.4836702701992,0.13356976376339,-3.85269152546425,3.4514759734262,3.40048640946801,2.07667502181208,5.17855977295365
diffExpScore=1.16175889454267
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=1,1,0,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	55.0987671895904	55.9020704067367	55.4923968526819
cerebhem	56.8370789328202	63.1664184198632	59.9457370872495
cortex	57.0140409997479	59.3704350928298	55.2080812010219
heart	55.4542392889393	52.4530001658242	53.0888270700858
kidney	58.5989798750557	56.5396193479503	54.4407755799418
liver	53.6995404782704	53.45081458233	53.7449078081006
stomach	54.7730212721937	64.9847560278402	57.7336538334317
testicle	54.5270553504291	54.495277064247	54.7361225751242
cont.diffExp=-0.8033032171463,-6.32933948704304,-2.35639409308191,3.00123912311513,2.05936052710537,0.248725895940368,-10.2117347556466,0.0317782861820817
cont.diffExpScore=1.63036569806235

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=-0.39655061049497
cont.tran.correlation=0.25289476683771

tran.covariance=-0.00179051991521957
cont.tran.covariance=0.000610161494471009

tran.mean=54.6807856938013
cont.tran.mean=56.6478196559168

weightedLogRatios:
wLogRatio
Lung	1.01094458005867
cerebhem	1.10977633185894
cortex	0.0101512870414242
heart	-0.277629633366952
kidney	0.257996115878871
liver	0.246898432366987
stomach	0.155008536145936
testicle	0.391381653466682

cont.weightedLogRatios:
wLogRatio
Lung	-0.0581331604966965
cerebhem	-0.432152861011842
cortex	-0.164568800253444
heart	0.221880244714138
kidney	0.144992682429781
liver	0.0184824446722667
stomach	-0.698979310322311
testicle	0.00233094405078204

varWeightedLogRatios=0.225855276036771
cont.varWeightedLogRatios=0.0942444400816712

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20601479573429	0.069454115519909	60.5581795153469	0	***
df.mm.trans1	-0.0297878519899736	0.0599788752581494	-0.49663905603042	0.61957156750051	   
df.mm.trans2	-0.254257900790251	0.0529910651908942	-4.79812775746847	1.89018296821143e-06	***
df.mm.exp2	-0.0711049266519732	0.0681634532065408	-1.04315323398478	0.297172872265683	   
df.mm.exp3	-0.109732449983420	0.0681634532065408	-1.60984288238628	0.107802142861263	   
df.mm.exp4	-0.0282728728402703	0.0681634532065408	-0.414780524023646	0.678406872608482	   
df.mm.exp5	-0.114836183843449	0.0681634532065408	-1.68471781345182	0.0924088273550657	.  
df.mm.exp6	-0.147196623622322	0.0681634532065408	-2.15946547156736	0.0310921690302114	*  
df.mm.exp7	-0.141433940214570	0.0681634532065408	-2.07492334324692	0.0382929332330376	*  
df.mm.exp8	-0.185191638728595	0.0681634532065408	-2.71687583326286	0.0067236210755511	** 
df.mm.trans1:exp2	0.0398544960408947	0.0630048972566869	0.632561876555791	0.527189311258683	   
df.mm.trans2:exp2	0.0125281408076865	0.0465322004539516	0.269235941680523	0.787813233736527	   
df.mm.trans1:exp3	-0.132087625142565	0.0630048972566869	-2.09646600333986	0.0363349050042764	*  
df.mm.trans2:exp3	0.113738752345071	0.0465322004539516	2.44430203677187	0.0147147901277965	*  
df.mm.trans1:exp4	-0.178576378113842	0.0630048972566869	-2.8343253602384	0.0047007179415156	** 
df.mm.trans2:exp4	0.138851168438513	0.0465322004539516	2.98398027782762	0.00292639699748236	** 
df.mm.trans1:exp5	-0.0738439861795528	0.0630048972566869	-1.17203565746178	0.241509980842670	   
df.mm.trans2:exp5	0.109613431052820	0.0465322004539516	2.35564684204638	0.0187159707566792	*  
df.mm.trans1:exp6	-0.00435352896182875	0.0630048972566869	-0.0690982630142563	0.944927598084508	   
df.mm.trans2:exp6	0.18249866445539	0.0465322004539516	3.92198655286013	9.4876777223563e-05	***
df.mm.trans1:exp7	-0.0581559044545512	0.0630048972566869	-0.923037842877824	0.356248497371652	   
df.mm.trans2:exp7	0.151243977205467	0.0465322004539516	3.25030786702509	0.00119797254276087	** 
df.mm.trans1:exp8	-0.00155213412285488	0.0630048972566869	-0.0246351345758309	0.980351756201927	   
df.mm.trans2:exp8	0.147957531427577	0.0465322004539516	3.17968052196449	0.0015275903069925	** 
df.mm.trans1:probe2	-0.0562866878384848	0.0431365043259785	-1.30485046755601	0.192295673612255	   
df.mm.trans1:probe3	-0.219724716340064	0.0431365043259785	-5.09370705330281	4.32378299313183e-07	***
df.mm.trans1:probe4	-0.137742290570554	0.0431365043259785	-3.1931722962447	0.00145878902973557	** 
df.mm.trans1:probe5	-0.203264408518914	0.0431365043259785	-4.71212055067939	2.86159850646396e-06	***
df.mm.trans1:probe6	-0.195108376282358	0.0431365043259785	-4.52304560443615	6.958377577618e-06	***
df.mm.trans1:probe7	0.188315460903386	0.0431365043259785	4.36557073517836	1.42348505331518e-05	***
df.mm.trans1:probe8	0.0163796686406339	0.0431365043259785	0.379717107275414	0.70424993615057	   
df.mm.trans1:probe9	-0.129327342488189	0.0431365043259785	-2.99809510550217	0.00279556864440238	** 
df.mm.trans1:probe10	-0.0896619558157826	0.0431365043259785	-2.07856332395912	0.0379559169679781	*  
df.mm.trans1:probe11	0.0286660078969573	0.0431365043259785	0.664541745903446	0.506523242771729	   
df.mm.trans1:probe12	-0.0664585850228151	0.0431365043259785	-1.54065764162515	0.123770998298459	   
df.mm.trans1:probe13	0.406161267119644	0.0431365043259785	9.41572047772629	4.26010017112586e-20	***
df.mm.trans1:probe14	0.219727350571804	0.0431365043259785	5.09376812064661	4.32243102345105e-07	***
df.mm.trans1:probe15	-0.035096324513171	0.0431365043259785	-0.813610770310718	0.41609501351316	   
df.mm.trans1:probe16	0.256876896900364	0.0431365043259785	5.95497713396453	3.798897160088e-09	***
df.mm.trans1:probe17	0.0922736102936273	0.0431365043259785	2.13910727666582	0.0327103075562096	*  
df.mm.trans1:probe18	0.00599956053177199	0.0431365043259785	0.139083141425505	0.889417279204038	   
df.mm.trans1:probe19	-0.0131353304340420	0.0431365043259785	-0.304506140200411	0.760816589099334	   
df.mm.trans1:probe20	0.257524022317093	0.0431365043259785	5.96997893874312	3.47832567249098e-09	***
df.mm.trans1:probe21	0.0638316998908027	0.0431365043259785	1.47976060851924	0.139306359333749	   
df.mm.trans1:probe22	0.181790479260760	0.0431365043259785	4.21430716515615	2.77252986774105e-05	***
df.mm.trans2:probe2	-0.00638407229243165	0.0431365043259785	-0.147996978248117	0.88238012919588	   
df.mm.trans2:probe3	0.0874536982680997	0.0431365043259785	2.02737100825835	0.0429349791819342	*  
df.mm.trans2:probe4	-0.0407716641197435	0.0431365043259785	-0.945177750418436	0.344835784997287	   
df.mm.trans2:probe5	-0.0399498736143465	0.0431365043259785	-0.926126820858017	0.354642021712633	   
df.mm.trans2:probe6	-0.09728818758697	0.0431365043259785	-2.25535631843907	0.0243636798871749	*  
df.mm.trans3:probe2	0.444105156511169	0.0431365043259785	10.2953441279133	1.64388976948431e-23	***
df.mm.trans3:probe3	0.432814639436521	0.0431365043259785	10.0336048597212	1.80391154449482e-22	***
df.mm.trans3:probe4	-0.070127116028527	0.0431365043259785	-1.62570234014752	0.104382363357275	   
df.mm.trans3:probe5	0.0125381728423557	0.0431365043259785	0.290662700612130	0.771379954005266	   
df.mm.trans3:probe6	-0.122884932585733	0.0431365043259785	-2.84874573185401	0.00449479786663830	** 
df.mm.trans3:probe7	0.219809003135828	0.0431365043259785	5.09566100847561	4.28072639780022e-07	***
df.mm.trans3:probe8	-0.0279229280002852	0.0431365043259785	-0.647315503112498	0.517601932505353	   
df.mm.trans3:probe9	0.258870070650673	0.0431365043259785	6.00118332942364	2.89376040123812e-09	***
df.mm.trans3:probe10	0.208230771608954	0.0431365043259785	4.82725188011003	1.64009981137982e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98211619749788	0.106675263058616	37.3293309369182	1.49189074584914e-181	***
df.mm.trans1	0.023236433814177	0.0921221478126654	0.252235041907939	0.800920157332673	   
df.mm.trans2	0.0442962180152189	0.0813895012078086	0.544249778630774	0.586411834454393	   
df.mm.exp2	0.0760392042411435	0.104692922044441	0.726307020152385	0.467849822647968	   
df.mm.exp3	0.0995018651492457	0.104692922044441	0.950416352950859	0.342169941135885	   
df.mm.exp4	-0.0129735373760302	0.104692922044440	-0.123919909031893	0.901407883183121	   
df.mm.exp5	0.0920627424758099	0.104692922044441	0.87935975687765	0.379453897773226	   
df.mm.exp6	-0.0385653436475732	0.104692922044440	-0.368366293484509	0.712691524829199	   
df.mm.exp7	0.105027479793044	0.104692922044441	1.0031956099999	0.316050982410489	   
df.mm.exp8	-0.0221955634345665	0.104692922044440	-0.212006341986947	0.83215273294407	   
df.mm.trans1:exp2	-0.0449776352349569	0.0967698449332575	-0.464789783077343	0.642200621541069	   
df.mm.trans2:exp2	0.0461321844141822	0.071469266968041	0.645482825993041	0.518787929349005	   
df.mm.trans1:exp3	-0.0653316361748034	0.0967698449332575	-0.675123910964856	0.499779999004385	   
df.mm.trans2:exp3	-0.0393069055599121	0.071469266968041	-0.549983331681421	0.582474781019832	   
df.mm.trans1:exp4	0.0194043588567024	0.0967698449332575	0.200520718722714	0.841121154304357	   
df.mm.trans2:exp4	-0.050710346087964	0.071469266968041	-0.709540593310412	0.478182969720872	   
df.mm.trans1:exp5	-0.0304727961738145	0.0967698449332575	-0.314899710698428	0.752914814151483	   
df.mm.trans2:exp5	-0.0807225398338933	0.071469266968041	-1.12947205503017	0.259016271544987	   
df.mm.trans1:exp6	0.0128424460701647	0.0967698449332575	0.132711239529445	0.894453052755887	   
df.mm.trans2:exp6	-0.0062741960792639	0.071469266968041	-0.0877887285743332	0.930065201176064	   
df.mm.trans1:exp7	-0.110957061469845	0.0967698449332575	-1.14660782546849	0.251865270207985	   
df.mm.trans2:exp7	0.0455238227864713	0.071469266968041	0.636970612932524	0.524314892332328	   
df.mm.trans1:exp8	0.0117652285182229	0.0967698449332575	0.121579491279927	0.903260691077362	   
df.mm.trans2:exp8	-0.00329181518576320	0.071469266968041	-0.0460591709613477	0.963273868904539	   
df.mm.trans1:probe2	0.0604614651515774	0.0662537836952777	0.91257376981908	0.361724597478247	   
df.mm.trans1:probe3	0.0218286707576325	0.0662537836952777	0.329470553078591	0.741880931810188	   
df.mm.trans1:probe4	-0.0152176258278885	0.0662537836952777	-0.229686894530872	0.818390143978349	   
df.mm.trans1:probe5	0.00775972932967096	0.0662537836952777	0.117121300805407	0.906791500556209	   
df.mm.trans1:probe6	-0.0665065861102075	0.0662537836952777	-1.00381566758651	0.315752137967100	   
df.mm.trans1:probe7	0.0109641009665643	0.0662537836952777	0.165486412323132	0.868600355437474	   
df.mm.trans1:probe8	-0.0778112495064726	0.0662537836952777	-1.17444235131312	0.240545655695819	   
df.mm.trans1:probe9	0.0958693934267756	0.0662537836952777	1.44700254203910	0.148263590644755	   
df.mm.trans1:probe10	0.00441631344914874	0.0662537836952777	0.0666575281113117	0.94686996657905	   
df.mm.trans1:probe11	0.0416871140792396	0.0662537836952777	0.629203522488195	0.529384291294552	   
df.mm.trans1:probe12	0.0548375293707848	0.0662537836952777	0.827689021098027	0.408078135125838	   
df.mm.trans1:probe13	0.0385086252165599	0.0662537836952777	0.581229072043242	0.561239621191476	   
df.mm.trans1:probe14	-0.0309299227428692	0.0662537836952777	-0.466840096033244	0.640733499457949	   
df.mm.trans1:probe15	-0.037146832557353	0.0662537836952777	-0.560674885048123	0.575166425985442	   
df.mm.trans1:probe16	0.00609264134689282	0.0662537836952777	0.091959145683133	0.926752090519004	   
df.mm.trans1:probe17	-0.0514326904813375	0.0662537836952777	-0.776298161594708	0.437788151874626	   
df.mm.trans1:probe18	-0.0742907662189351	0.0662537836952777	-1.12130601567787	0.262473191136582	   
df.mm.trans1:probe19	0.00573267416492758	0.0662537836952777	0.0865259890860571	0.931068603367702	   
df.mm.trans1:probe20	0.0330340196601169	0.0662537836952777	0.498598235718143	0.61819098987029	   
df.mm.trans1:probe21	0.0136588824792605	0.0662537836952777	0.206160036717028	0.83671509795091	   
df.mm.trans1:probe22	0.0792752513862996	0.0662537836952777	1.19653923088999	0.231818659951611	   
df.mm.trans2:probe2	-0.0109648725800579	0.0662537836952777	-0.165498058654112	0.86859119222249	   
df.mm.trans2:probe3	0.0559959961807781	0.0662537836952777	0.845174313942908	0.398250519311647	   
df.mm.trans2:probe4	-0.056347547248746	0.0662537836952777	-0.8504804421119	0.395296690596715	   
df.mm.trans2:probe5	-0.000584028581499828	0.0662537836952777	-0.00881502231157034	0.992968782022465	   
df.mm.trans2:probe6	-0.0330755213756891	0.0662537836952777	-0.499224640932418	0.617749864561805	   
df.mm.trans3:probe2	0.0533122770416076	0.0662537836952777	0.804667659236004	0.421235700633641	   
df.mm.trans3:probe3	-0.0864056771802638	0.0662537836952777	-1.30416215287675	0.192530100660583	   
df.mm.trans3:probe4	0.0214357393740065	0.0662537836952777	0.323539852042207	0.74636573539528	   
df.mm.trans3:probe5	-0.124629173029148	0.0662537836952777	-1.88108763119640	0.0603004013386178	.  
df.mm.trans3:probe6	-0.0775060580012749	0.0662537836952777	-1.16983595016626	0.242393751177322	   
df.mm.trans3:probe7	0.0178933660264332	0.0662537836952777	0.270073119276183	0.787169326989838	   
df.mm.trans3:probe8	-0.0704809616600712	0.0662537836952777	-1.06380281591517	0.287719086997064	   
df.mm.trans3:probe9	-0.0818428402141301	0.0662537836952777	-1.23529307534422	0.217061570859826	   
df.mm.trans3:probe10	0.0141950115953851	0.0662537836952777	0.214252089520992	0.830401682687606