fitVsDatCorrelation=0.944488208202302
cont.fitVsDatCorrelation=0.237265658194868

fstatistic=7417.94352146663,56,784
cont.fstatistic=836.073936079127,56,784

residuals=-0.829500079031083,-0.0944631889181563,-0.00656446546921283,0.095427457222929,0.71780073317069
cont.residuals=-0.93314673180809,-0.350234081301616,-0.178191174290653,0.113334938288933,1.67885616560565

predictedValues:
Include	Exclude	Both
Lung	77.625744103501	51.0269809372756	70.2833496880682
cerebhem	68.691846097592	63.5221554146053	65.5550362293767
cortex	65.9376330463536	52.0608299114598	68.2242779339677
heart	65.9906258971303	46.8621884451643	63.9444336639247
kidney	74.8387837403902	49.3648126993482	74.1861574771784
liver	72.1328095154846	45.7926528072868	67.1725365340163
stomach	70.1401926609534	53.0745689439769	67.3322197354054
testicle	68.4601703566647	52.3178337074472	76.3540162698116


diffExp=26.5987631662255,5.16969068298674,13.8768031348938,19.128437451966,25.473971041042,26.3401567081977,17.0656237169764,16.1423366492174
diffExpScore=0.99336851480141
diffExp1.5=1,0,0,0,1,1,0,0
diffExp1.5Score=0.75
diffExp1.4=1,0,0,1,1,1,0,0
diffExp1.4Score=0.8
diffExp1.3=1,0,0,1,1,1,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	67.1854437641173	70.8565771840912	69.0965082225059
cerebhem	72.6172100715645	70.2922958131308	64.9869936221058
cortex	62.9419486735173	72.3704932448233	59.8722442039091
heart	63.5302907439934	75.5837430266154	67.4012358758758
kidney	74.2526385762609	71.733838270997	75.1499741270282
liver	65.8144073804934	85.1248697669012	66.4018312946838
stomach	71.7972777142195	71.7521243809792	59.3842849558744
testicle	73.3680168706848	79.4056986108173	82.3248826571675
cont.diffExp=-3.67113341997396,2.32491425843368,-9.42854457130598,-12.053452282622,2.51880030526391,-19.3104623864078,0.0451533332403926,-6.03768174013246
cont.diffExpScore=1.18831329365533

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

tran.correlation=-0.174216465696148
cont.tran.correlation=-0.216921647542007

tran.covariance=-0.00095082167098511
cont.tran.covariance=-0.000974602366324995

tran.mean=61.1149892677896
cont.tran.mean=71.7891796308254

weightedLogRatios:
wLogRatio
Lung	1.73780692377129
cerebhem	0.327872825151747
cortex	0.961859341122746
heart	1.37549157817599
kidney	1.70903546430131
liver	1.84085873605624
stomach	1.14616603844056
testicle	1.10034423789966

cont.weightedLogRatios:
wLogRatio
Lung	-0.225256843397458
cerebhem	0.138909958554075
cortex	-0.587936245945525
heart	-0.736309889626809
kidney	0.148058573528099
liver	-1.11028843618555
stomach	0.00268847126594226
testicle	-0.342822917615417

varWeightedLogRatios=0.253583063142682
cont.varWeightedLogRatios=0.201433893496538

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.64625725415531	0.0903712754124882	29.2820616072619	6.29407972979912e-128	***
df.mm.trans1	1.53173432187286	0.0780033286074593	19.6367815222488	3.93789145134222e-70	***
df.mm.trans2	1.26574655314803	0.069460540537733	18.2225266798844	3.93450902393661e-62	***
df.mm.exp2	0.166410482574493	0.0899435362356558	1.85016611019707	0.0646656892485132	.  
df.mm.exp3	-0.113397033304769	0.0899435362356558	-1.26075800497397	0.207771114338697	   
df.mm.exp4	-0.153009381071897	0.0899435362356559	-1.70117150687744	0.08930730660724	.  
df.mm.exp5	-0.123722181871335	0.0899435362356558	-1.37555389802751	0.169352659175188	   
df.mm.exp6	-0.136350558470976	0.0899435362356559	-1.51595727917270	0.129933225618579	   
df.mm.exp7	-0.0191637252618858	0.0899435362356558	-0.213063951718287	0.831332470667065	   
df.mm.exp8	-0.183509943235162	0.0899435362356559	-2.04027938988699	0.0416571045938676	*  
df.mm.trans1:exp2	-0.288679105008118	0.0827997365025629	-3.48647371600250	0.000516661429200556	***
df.mm.trans2:exp2	0.0526237358193992	0.0630584006428836	0.83452379513114	0.404240092827345	   
df.mm.trans1:exp3	-0.0497927513443859	0.0827997365025629	-0.601363644953686	0.54777164326506	   
df.mm.trans2:exp3	0.133455343367057	0.0630584006428836	2.11637691420132	0.0346273097023459	*  
df.mm.trans1:exp4	-0.00937704493292916	0.0827997365025629	-0.113249695337363	0.909861590041919	   
df.mm.trans2:exp4	0.0678659840169287	0.0630584006428837	1.07624017299886	0.282150926642136	   
df.mm.trans1:exp5	0.0871593055984702	0.0827997365025629	1.05265196823147	0.292824851956602	   
df.mm.trans2:exp5	0.0906055279093195	0.0630584006428837	1.43685102992768	0.151159265027743	   
df.mm.trans1:exp6	0.062960428866596	0.0827997365025629	0.760394072807795	0.447247676746959	   
df.mm.trans2:exp6	0.0281196868488676	0.0630584006428837	0.445930860316563	0.655770311481529	   
df.mm.trans1:exp7	-0.082239409306241	0.0827997365025629	-0.993232741793756	0.320902988939245	   
df.mm.trans2:exp7	0.0585070803597379	0.0630584006428837	0.927823727897554	0.353784561253909	   
df.mm.trans1:exp8	0.0578629386636917	0.0827997365025629	0.698829985550747	0.484865535981466	   
df.mm.trans2:exp8	0.208492714033872	0.0630584006428837	3.30634319786544	0.000988236601101125	***
df.mm.trans1:probe2	-0.098434679265928	0.0555437518016404	-1.77220076197699	0.0767495531561625	.  
df.mm.trans1:probe3	-0.0159758203562535	0.0555437518016404	-0.287625877583978	0.773709144591704	   
df.mm.trans1:probe4	0.169686618337334	0.0555437518016404	3.05500822024635	0.00232677085583640	** 
df.mm.trans1:probe5	-0.178713703933593	0.0555437518016404	-3.21753029164866	0.00134611485818143	** 
df.mm.trans1:probe6	-0.184202204259611	0.0555437518016404	-3.31634429228763	0.000954012771967243	***
df.mm.trans1:probe7	-0.235436759559442	0.0555437518016404	-4.23876227159162	2.51518498223496e-05	***
df.mm.trans1:probe8	0.0282557817136258	0.0555437518016404	0.508712155681052	0.6110971229673	   
df.mm.trans1:probe9	-0.180408312953892	0.0555437518016404	-3.24803973628163	0.00121149015305391	** 
df.mm.trans1:probe10	-0.176464710878164	0.0555437518016404	-3.17703981373748	0.00154616568363933	** 
df.mm.trans1:probe11	-0.218500801214882	0.0555437518016404	-3.93385023747043	9.10068436009582e-05	***
df.mm.trans1:probe12	-0.224926834677468	0.0555437518016404	-4.04954342084657	5.64157724098574e-05	***
df.mm.trans1:probe13	-0.331335455276254	0.0555437518016404	-5.96530562896669	3.69369786059295e-09	***
df.mm.trans1:probe14	-0.272843445526726	0.0555437518016404	-4.9122257081429	1.09640723433862e-06	***
df.mm.trans1:probe15	1.05350559574696	0.0555437518016404	18.9671306236077	2.57282513291063e-66	***
df.mm.trans1:probe16	1.05141499437290	0.0555437518016404	18.9294918018455	4.20186387429354e-66	***
df.mm.trans1:probe17	1.42331521821495	0.0555437518016404	25.625118434526	1.10143984005497e-105	***
df.mm.trans1:probe18	1.24690203282828	0.0555437518016404	22.4490062767322	1.40600589546328e-86	***
df.mm.trans1:probe19	0.945136287778004	0.0555437518016404	17.0160685427464	1.68972702380832e-55	***
df.mm.trans1:probe20	1.24234515047457	0.0555437518016404	22.3669649632468	4.32158238358801e-86	***
df.mm.trans2:probe2	0.0476328229225647	0.0555437518016404	0.857573019062028	0.391390351818455	   
df.mm.trans2:probe3	0.0740421895733508	0.0555437518016404	1.33304264065151	0.182905042302598	   
df.mm.trans2:probe4	0.0165524038041114	0.0555437518016404	0.29800658520915	0.76577709310728	   
df.mm.trans2:probe5	0.0267756865467053	0.0555437518016404	0.482064780973519	0.629894473495138	   
df.mm.trans2:probe6	0.140257749886154	0.0555437518016404	2.52517601596389	0.0117601420090288	*  
df.mm.trans3:probe2	-1.40882190217202	0.0555437518016404	-25.3641833055003	4.20671617608628e-104	***
df.mm.trans3:probe3	-1.43014251527248	0.0555437518016404	-25.7480358975362	1.97813682051276e-106	***
df.mm.trans3:probe4	-1.36317415575428	0.0555437518016404	-24.5423492568973	3.94007769517336e-99	***
df.mm.trans3:probe5	-1.01623969064540	0.0555437518016404	-18.2962017811586	1.52670189048844e-62	***
df.mm.trans3:probe6	-1.36690089508025	0.0555437518016404	-24.6094448203962	1.55001475836489e-99	***
df.mm.trans3:probe7	-1.43113037028692	0.0555437518016404	-25.7658210665678	1.54288896635396e-106	***
df.mm.trans3:probe8	-1.19266340714880	0.0555437518016404	-21.4725035393374	8.42519096257291e-81	***
df.mm.trans3:probe9	-1.5117559082309	0.0555437518016404	-27.2173891607058	2.3163155395544e-115	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31208623516023	0.267210341178102	16.1374227365180	8.47245443803564e-51	***
df.mm.trans1	-0.157211058484869	0.230640720240920	-0.681627504113979	0.495675817936917	   
df.mm.trans2	-0.0164801680689453	0.20538135210315	-0.0802417936204275	0.93606542864627	   
df.mm.exp2	0.131066787483992	0.265945599357707	0.4928330748865	0.622268534871076	   
df.mm.exp3	0.0991882770717178	0.265945599357707	0.372964536022670	0.709275650521422	   
df.mm.exp4	0.0334844677943703	0.265945599357707	0.125907207621557	0.899837657175525	   
df.mm.exp5	0.0283399097057595	0.265945599357707	0.106562807484704	0.915163086598438	   
df.mm.exp6	0.202623159736683	0.265945599357707	0.761897020390802	0.446350601625865	   
df.mm.exp7	0.230424183956834	0.265945599357707	0.866433528185233	0.386517542516944	   
df.mm.exp8	0.0267746263966264	0.265945599357707	0.100677080054307	0.91983254608672	   
df.mm.trans1:exp2	-0.0533214538312171	0.244822768510461	-0.217796139450725	0.827644624715513	   
df.mm.trans2:exp2	-0.139062379161909	0.186451465612512	-0.745836878809589	0.455989517197224	   
df.mm.trans1:exp3	-0.164432038515185	0.244822768510461	-0.671637035703885	0.50201264575855	   
df.mm.trans2:exp3	-0.0780474069676747	0.186451465612512	-0.418593689844599	0.675627744963272	   
df.mm.trans1:exp4	-0.0894242694555644	0.244822768510462	-0.365261245919385	0.715014920548366	   
df.mm.trans2:exp4	0.0310989585876482	0.186451465612512	0.166793854290633	0.867575262528205	   
df.mm.trans1:exp5	0.0716767903136546	0.244822768510461	0.292770115907712	0.769775339427184	   
df.mm.trans2:exp5	-0.0160351253659019	0.186451465612512	-0.0860016053680508	0.93148709917532	   
df.mm.trans1:exp6	-0.223241001635285	0.244822768510462	-0.91184738655443	0.362129349613036	   
df.mm.trans2:exp6	-0.0191617195211331	0.186451465612512	-0.102770549205311	0.918171360215862	   
df.mm.trans1:exp7	-0.164034236877791	0.244822768510461	-0.67001218014076	0.503047316703271	   
df.mm.trans2:exp7	-0.217864516006857	0.186451465612512	-1.16847843105523	0.242969015460526	   
df.mm.trans1:exp8	0.0612568633934698	0.244822768510462	0.250209013508694	0.802491264968777	   
df.mm.trans2:exp8	0.087137715849436	0.186451465612512	0.467347980146897	0.64038079773512	   
df.mm.trans1:probe2	-0.0500061921847594	0.164232105848726	-0.304484874783375	0.76083931744879	   
df.mm.trans1:probe3	0.367805759556298	0.164232105848726	2.23954845890537	0.0253998596886088	*  
df.mm.trans1:probe4	0.122060994041436	0.164232105848726	0.74322248631377	0.457569641811072	   
df.mm.trans1:probe5	0.0816080482561126	0.164232105848726	0.496906788318732	0.619394091461186	   
df.mm.trans1:probe6	0.0582805133183005	0.164232105848726	0.354866747991301	0.722784857987777	   
df.mm.trans1:probe7	0.0899014653631381	0.164232105848726	0.547404935828724	0.584256355721509	   
df.mm.trans1:probe8	0.268241010456372	0.164232105848726	1.63330433516725	0.102806504731529	   
df.mm.trans1:probe9	-0.0606010743504062	0.164232105848726	-0.368996512814771	0.712229952016155	   
df.mm.trans1:probe10	0.133757583419371	0.164232105848726	0.814442357224447	0.415638892695615	   
df.mm.trans1:probe11	0.0288153311381640	0.164232105848726	0.175454920883166	0.860767493728831	   
df.mm.trans1:probe12	0.0388036269709303	0.164232105848726	0.236273089055268	0.813282480516752	   
df.mm.trans1:probe13	-0.125788852109991	0.164232105848726	-0.765921203165082	0.443953718284916	   
df.mm.trans1:probe14	-0.0300340340314697	0.164232105848726	-0.182875533844363	0.85494295983874	   
df.mm.trans1:probe15	0.260829347245095	0.164232105848726	1.58817513723744	0.112649921902215	   
df.mm.trans1:probe16	-0.0287892296962048	0.164232105848726	-0.175295990679938	0.86089232429064	   
df.mm.trans1:probe17	0.0252363888889574	0.164232105848726	0.153662944029973	0.877915047180227	   
df.mm.trans1:probe18	-0.0329441064722463	0.164232105848726	-0.20059480027974	0.841067448658658	   
df.mm.trans1:probe19	0.114025553035083	0.164232105848726	0.694295140684077	0.487702816979853	   
df.mm.trans1:probe20	0.263659532536175	0.164232105848726	1.60540797533846	0.108806745660409	   
df.mm.trans2:probe2	-0.0698413808251287	0.164232105848726	-0.425260216108167	0.670763689354662	   
df.mm.trans2:probe3	-0.0133121112845226	0.164232105848726	-0.0810566923910993	0.935417553919168	   
df.mm.trans2:probe4	-0.0880568161724281	0.164232105848726	-0.536172971279667	0.591991099930983	   
df.mm.trans2:probe5	-0.190019154949133	0.164232105848726	-1.15701588290026	0.247618270389120	   
df.mm.trans2:probe6	-0.162994627907313	0.164232105848726	-0.9924650668332	0.321276916852646	   
df.mm.trans3:probe2	0.232126257029658	0.164232105848726	1.41340364498200	0.157933984079325	   
df.mm.trans3:probe3	0.206996655514288	0.164232105848726	1.26039092322759	0.207903370684627	   
df.mm.trans3:probe4	0.078219910394458	0.164232105848726	0.476276608585329	0.634010027297976	   
df.mm.trans3:probe5	0.164964140679682	0.164232105848726	1.00445731866600	0.31546814355862	   
df.mm.trans3:probe6	-0.0315904239442046	0.164232105848726	-0.192352303959997	0.847516062657417	   
df.mm.trans3:probe7	-0.0078547486931035	0.164232105848726	-0.0478271203581747	0.961866202841664	   
df.mm.trans3:probe8	0.121003983864646	0.164232105848726	0.736786411154603	0.461472663588226	   
df.mm.trans3:probe9	0.110116923046082	0.164232105848726	0.670495713837528	0.502739295537462