fitVsDatCorrelation=0.907178810070627
cont.fitVsDatCorrelation=0.227328769694259

fstatistic=9751.62548150744,63,945
cont.fstatistic=1808.17107505685,63,945

residuals=-0.72983347374799,-0.101794895326091,-0.000109078786779117,0.103932514412837,0.655001585801514
cont.residuals=-0.773163333564848,-0.29627287584901,-0.0669345275919484,0.254518942792264,1.63392973037372

predictedValues:
Include	Exclude	Both
Lung	75.4728362451663	56.9425265926353	89.278690601643
cerebhem	69.5667556316348	86.4210111163835	81.65308136455
cortex	93.5362130078952	58.155168333005	112.073892002850
heart	84.8082053831154	62.3399487076578	94.7970498519982
kidney	73.7835554290703	61.1526896302857	92.0874931158924
liver	66.6805379856512	60.2194370308359	74.0134570816898
stomach	81.1462459479647	58.8721364645288	91.7822997915373
testicle	78.2253276691023	64.059601230146	91.359380215866


diffExp=18.5303096525311,-16.8542554847488,35.3810446748902,22.4682566754576,12.6308657987846,6.46110095481525,22.2741094834360,14.1657264389562
diffExpScore=1.28183105185798
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,1,0,0,1,0
diffExp1.3Score=0.8
diffExp1.2=1,-1,1,1,1,0,1,1
diffExp1.2Score=1.16666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	83.4877082803512	82.9020865151088	84.7741517930518
cerebhem	83.8589080187183	80.9716365699553	80.2168300694115
cortex	81.8454560022071	96.0116703966684	79.3643494992652
heart	78.6481848662749	68.6843692832865	77.6299234717497
kidney	76.944336429793	88.9818420417913	77.1070919125992
liver	81.6921674187782	79.6850930815322	83.9579911496768
stomach	79.7418895995043	71.8282786627094	75.5177493031485
testicle	86.0532959174339	76.525953719096	86.0205768583102
cont.diffExp=0.585621765242323,2.88727144876306,-14.1662143944613,9.9638155829884,-12.0375056119983,2.00707433724602,7.91361093679492,9.52734219833785
cont.diffExpScore=7.69279145536184

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=-0.408234402971489
cont.tran.correlation=0.0175093461907800

tran.covariance=-0.006023576623845
cont.tran.covariance=0.000173992126410353

tran.mean=70.7113872753174
cont.tran.mean=81.1164298002005

weightedLogRatios:
wLogRatio
Lung	1.17845204291240
cerebhem	-0.9438711348805
cortex	2.04385355455313
heart	1.31934037636296
kidney	0.789962736114948
liver	0.422852908079048
stomach	1.35920854609132
testicle	0.85100172696942

cont.weightedLogRatios:
wLogRatio
Lung	0.0311214978838183
cerebhem	0.154568921344167
cortex	-0.715916115762373
heart	0.582118361218292
kidney	-0.641830631442435
liver	0.109216813501183
stomach	0.452195708676243
testicle	0.515847921555943

varWeightedLogRatios=0.77295434685324
cont.varWeightedLogRatios=0.248718182815588

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75249191361605	0.0810490984853497	46.2989963336155	2.96861186694057e-245	***
df.mm.trans1	0.540890323578231	0.0693215561453916	7.80262812398202	1.60158764850612e-14	***
df.mm.trans2	0.299156821213294	0.0608547800885469	4.91591327383001	1.04153835847898e-06	***
df.mm.exp2	0.424985649201496	0.0771066848735421	5.51165764548805	4.58555600403243e-08	***
df.mm.exp3	0.00825258865233164	0.0771066848735421	0.107028186542661	0.914789335379482	   
df.mm.exp4	0.147204034073287	0.0771066848735421	1.90909561621936	0.0565524014443231	.  
df.mm.exp5	0.0177181605480277	0.0771066848735421	0.229787606315667	0.818306543213411	   
df.mm.exp6	0.119608943243592	0.0771066848735421	1.55121366506361	0.121185305134129	   
df.mm.exp7	0.0781490514184706	0.0771066848735421	1.01351849773646	0.311071987287404	   
df.mm.exp8	0.130554007540051	0.0771066848735421	1.69316068709431	0.0907544648528375	.  
df.mm.trans1:exp2	-0.506471651627149	0.0702690473311961	-7.20760663283249	1.16445062892447e-12	***
df.mm.trans2:exp2	-0.00779727249891148	0.0491780887636887	-0.158551759430486	0.874055913698283	   
df.mm.trans1:exp3	0.206323270871748	0.0702690473311961	2.93618995429515	0.00340326023347585	** 
df.mm.trans2:exp3	0.0128197119318757	0.0491780887636887	0.260679344280279	0.79439658881088	   
df.mm.trans1:exp4	-0.0305845410478425	0.0702690473311961	-0.435249120479601	0.663480997694425	   
df.mm.trans2:exp4	-0.0566440362785733	0.0491780887636887	-1.15181451135200	0.249688616312169	   
df.mm.trans1:exp5	-0.0403550866200525	0.0702690473311961	-0.574293919623652	0.565905594798887	   
df.mm.trans2:exp5	0.053613230999581	0.0491780887636887	1.09018533146345	0.275909417550512	   
df.mm.trans1:exp6	-0.243468623907067	0.0702690473311961	-3.46480610103531	0.000554461600095055	***
df.mm.trans2:exp6	-0.0636562225354567	0.0491780887636887	-1.29440212370470	0.195842765132549	   
df.mm.trans1:exp7	-0.00566882586612803	0.0702690473311961	-0.0806731566945742	0.935718966477232	   
df.mm.trans2:exp7	-0.0448235915702304	0.0491780887636887	-0.911454525726231	0.362288374850184	   
df.mm.trans1:exp8	-0.0947333358756902	0.0702690473311961	-1.34815170368239	0.177932643408443	   
df.mm.trans2:exp8	-0.0127825420818358	0.0491780887636887	-0.259923522918075	0.79497938415012	   
df.mm.trans1:probe2	-0.486674631779514	0.0514316763124036	-9.4625465602828	2.3133480965652e-20	***
df.mm.trans1:probe3	-0.444408713445863	0.0514316763124036	-8.64075887292607	2.35492997835633e-17	***
df.mm.trans1:probe4	0.0394220226106432	0.0514316763124037	0.766493053253563	0.443574410798407	   
df.mm.trans1:probe5	-0.191428563081090	0.0514316763124037	-3.72199735272722	0.000209328501896582	***
df.mm.trans1:probe6	0.0966022409339402	0.0514316763124036	1.87826351113201	0.0606528286680907	.  
df.mm.trans1:probe7	0.171382323906987	0.0514316763124037	3.33223289993477	0.000894938409670857	***
df.mm.trans1:probe8	-0.477355262775224	0.0514316763124036	-9.28134754690276	1.11309641912053e-19	***
df.mm.trans1:probe9	-0.444901734604618	0.0514316763124036	-8.6503448167277	2.17866036465507e-17	***
df.mm.trans1:probe10	0.162562595350454	0.0514316763124036	3.16074853098361	0.00162373429887029	** 
df.mm.trans1:probe11	0.150624646317602	0.0514316763124036	2.92863575751811	0.00348629111629883	** 
df.mm.trans1:probe12	0.0179211143586745	0.0514316763124036	0.348445075945396	0.727583565762683	   
df.mm.trans1:probe13	0.269423610442972	0.0514316763124036	5.23847616411437	1.99652632495062e-07	***
df.mm.trans1:probe14	-0.0644519633875416	0.0514316763124036	-1.25315696490332	0.210458560847962	   
df.mm.trans1:probe15	0.122717243655152	0.0514316763124037	2.38602457578379	0.0172276548895391	*  
df.mm.trans1:probe16	0.580612341346205	0.0514316763124036	11.2890028670168	8.19003218238654e-28	***
df.mm.trans1:probe17	0.566695482378421	0.0514316763124036	11.0184136121916	1.20412237192064e-26	***
df.mm.trans1:probe18	0.499815896024312	0.0514316763124036	9.71805571703236	2.42179335988808e-21	***
df.mm.trans1:probe19	0.267200971953664	0.0514316763124037	5.19526080251877	2.50447583890005e-07	***
df.mm.trans1:probe20	0.174190840043483	0.0514316763124036	3.38683963916365	0.00073618312943487	***
df.mm.trans1:probe21	0.0841100414049242	0.0514316763124037	1.63537429528891	0.102303620427517	   
df.mm.trans2:probe2	-0.0884697838414858	0.0514316763124036	-1.72014194723320	0.0857340317193557	.  
df.mm.trans2:probe3	0.0433444011897911	0.0514316763124036	0.842756921367111	0.399577650863595	   
df.mm.trans2:probe4	-0.00490009332424597	0.0514316763124036	-0.0952738404729817	0.92411751881395	   
df.mm.trans2:probe5	-0.062752210346615	0.0514316763124036	-1.22010820657388	0.222728247703529	   
df.mm.trans2:probe6	-0.0889542161265074	0.0514316763124036	-1.72956089523869	0.0840352971135	.  
df.mm.trans3:probe2	-0.340932198775175	0.0514316763124037	-6.6288369973458	5.67967457056453e-11	***
df.mm.trans3:probe3	0.306654280689375	0.0514316763124037	5.96236216036808	3.50844527210753e-09	***
df.mm.trans3:probe4	-0.179237435719213	0.0514316763124036	-3.48496196450021	0.000514820500337712	***
df.mm.trans3:probe5	-0.527041060840099	0.0514316763124036	-10.2474019636998	1.94015794490583e-23	***
df.mm.trans3:probe6	-0.271794652431186	0.0514316763124036	-5.2845769750973	1.56483469466055e-07	***
df.mm.trans3:probe7	0.469571933776765	0.0514316763124036	9.13001417500988	4.05656587509033e-19	***
df.mm.trans3:probe8	-0.592249773094753	0.0514316763124036	-11.5152726016033	8.33471023912168e-29	***
df.mm.trans3:probe9	0.853487637874678	0.0514316763124036	16.5945910977209	1.81283263182602e-54	***
df.mm.trans3:probe10	-0.529739092891594	0.0514316763124036	-10.2998605309670	1.18943168461684e-23	***
df.mm.trans3:probe11	-0.116180762323056	0.0514316763124037	-2.25893400046611	0.0241146924386193	*  
df.mm.trans3:probe12	-0.47839361695391	0.0514316763124037	-9.30153654817852	9.35494294094994e-20	***
df.mm.trans3:probe13	0.101718606260338	0.0514316763124037	1.97774238666623	0.0482479501514937	*  
df.mm.trans3:probe14	-0.50683504479759	0.0514316763124036	-9.85453092601918	7.1137260044197e-22	***
df.mm.trans3:probe15	0.299327787990100	0.0514316763124037	5.81991118026055	8.05859591170875e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43769553480704	0.187588730620246	23.6565145471915	1.52755913808756e-97	***
df.mm.trans1	0.00778934392639849	0.160445248188475	0.048548299275578	0.9612895308367	   
df.mm.trans2	-0.00940983782221183	0.140848833143384	-0.0668080637390344	0.946748635190742	   
df.mm.exp2	0.0361323405179567	0.178463985510926	0.202462925023853	0.839598419052282	   
df.mm.exp3	0.192884305041911	0.178463985510926	1.08080240665757	0.280060759984276	   
df.mm.exp4	-0.159815725113915	0.178463985510926	-0.89550687023142	0.370744235557891	   
df.mm.exp5	0.0839503610859795	0.178463985510926	0.470405055931242	0.638174199176949	   
df.mm.exp6	-0.0516448738264416	0.178463985510926	-0.289385411171823	0.772349933245088	   
df.mm.exp7	-0.0736633914075946	0.178463985510926	-0.412763343801288	0.679873639592357	   
df.mm.exp8	-0.0643587246654786	0.178463985510926	-0.360625839892712	0.718459763298671	   
df.mm.trans1:exp2	-0.0316960351373198	0.162638223460755	-0.194886752098397	0.845523495925479	   
df.mm.trans2:exp2	-0.0596936438558336	0.113823045757599	-0.524442510376666	0.600093786665018	   
df.mm.trans1:exp3	-0.212750933724600	0.162638223460755	-1.30812381737518	0.191149411194067	   
df.mm.trans2:exp3	-0.0460747852156699	0.113823045757599	-0.404793114689551	0.685721198492077	   
df.mm.trans1:exp4	0.100100860930983	0.162638223460755	0.615481765608054	0.538384801999611	   
df.mm.trans2:exp4	-0.0283228537791656	0.113823045757599	-0.248832330839949	0.803544549126046	   
df.mm.trans1:exp5	-0.165567518959787	0.162638223460755	-1.01801111348058	0.308933227829055	   
df.mm.trans2:exp5	-0.013178265056439	0.113823045757599	-0.115778531216813	0.907852663146276	   
df.mm.trans1:exp6	0.0299035862718089	0.162638223460755	0.183865672137182	0.854158279976424	   
df.mm.trans2:exp6	0.0120671733710423	0.113823045757599	0.106016960719369	0.915591395137607	   
df.mm.trans1:exp7	0.0277590152915454	0.162638223460755	0.170679528470401	0.864512296107683	   
df.mm.trans2:exp7	-0.0697185877219126	0.113823045757599	-0.612517326854767	0.540342896942017	   
df.mm.trans1:exp8	0.0946261341444533	0.162638223460755	0.581819772319924	0.560826847930197	   
df.mm.trans2:exp8	-0.0156715586221769	0.113823045757599	-0.137683529006520	0.890519867310126	   
df.mm.trans1:probe2	-0.0355646890322574	0.119038990604678	-0.298765042038753	0.765185010639294	   
df.mm.trans1:probe3	-0.107114278500912	0.119038990604678	-0.89982515776392	0.368442539561276	   
df.mm.trans1:probe4	0.0185228958747009	0.119038990604678	0.155603603328715	0.876378715373643	   
df.mm.trans1:probe5	0.0322723592031257	0.119038990604678	0.271107466882850	0.786367585485293	   
df.mm.trans1:probe6	-0.174639361838799	0.119038990604678	-1.46707697160140	0.142687825273728	   
df.mm.trans1:probe7	-0.099603926333558	0.119038990604678	-0.836733626752071	0.402953849788401	   
df.mm.trans1:probe8	-0.0121212299578606	0.119038990604678	-0.101825711863725	0.918916617716416	   
df.mm.trans1:probe9	0.0913240372413898	0.119038990604678	0.767177517026097	0.443167602511017	   
df.mm.trans1:probe10	-0.136868425978891	0.119038990604678	-1.14977811289935	0.250526165065861	   
df.mm.trans1:probe11	-0.113213616219821	0.119038990604678	-0.951063308288602	0.341815413950801	   
df.mm.trans1:probe12	-0.119145623021189	0.119038990604678	-1.00089577722366	0.317133413385502	   
df.mm.trans1:probe13	0.00309274934601358	0.119038990604678	0.0259809775797279	0.979277996223937	   
df.mm.trans1:probe14	-0.034875649546644	0.119038990604678	-0.292976690826153	0.769604288205253	   
df.mm.trans1:probe15	-0.0685563134922864	0.119038990604678	-0.575914775016517	0.564809910043507	   
df.mm.trans1:probe16	-0.0607772413192562	0.119038990604678	-0.510565832342229	0.609774347374912	   
df.mm.trans1:probe17	-0.0198928439050781	0.119038990604678	-0.167112000900118	0.867317675015382	   
df.mm.trans1:probe18	0.00193583389042624	0.119038990604678	0.0162621833450776	0.987028659498679	   
df.mm.trans1:probe19	0.0821303149178841	0.119038990604678	0.689944651753932	0.490398390190695	   
df.mm.trans1:probe20	-0.0617195342686666	0.119038990604678	-0.518481666848418	0.604243624971339	   
df.mm.trans1:probe21	0.0665378412503655	0.119038990604678	0.558958379203114	0.576322555424523	   
df.mm.trans2:probe2	0.0521868001944137	0.119038990604678	0.438400896456886	0.661195896076988	   
df.mm.trans2:probe3	0.0246629863572195	0.119038990604678	0.207184101880736	0.835910718001947	   
df.mm.trans2:probe4	-0.152813465498629	0.119038990604678	-1.28372615327455	0.199552464083402	   
df.mm.trans2:probe5	-0.185439602194778	0.119038990604678	-1.55780556650226	0.119614161639588	   
df.mm.trans2:probe6	0.0382684930439330	0.119038990604678	0.321478641994038	0.747918785264232	   
df.mm.trans3:probe2	-0.0229226051118753	0.119038990604678	-0.192563839759025	0.847341940249724	   
df.mm.trans3:probe3	0.0343542782764794	0.119038990604678	0.288596854710975	0.772953192957156	   
df.mm.trans3:probe4	-0.0836400429997013	0.119038990604678	-0.702627286864901	0.482461091282004	   
df.mm.trans3:probe5	0.117849631982718	0.119038990604678	0.990008663414247	0.322423300660355	   
df.mm.trans3:probe6	-0.0890077296224018	0.119038990604678	-0.747719122703179	0.454815614874522	   
df.mm.trans3:probe7	-0.0285874937491484	0.119038990604678	-0.240152353476231	0.810264233127264	   
df.mm.trans3:probe8	0.0807493618026961	0.119038990604678	0.678343804769484	0.497719800095752	   
df.mm.trans3:probe9	0.0578847799384611	0.119038990604678	0.486267395619082	0.626890334871533	   
df.mm.trans3:probe10	-0.000958544730099207	0.119038990604678	-0.00805235935914882	0.99357691574871	   
df.mm.trans3:probe11	0.0694246954846255	0.119038990604678	0.58320971248136	0.559891284210394	   
df.mm.trans3:probe12	0.0926721901362306	0.119038990604678	0.778502822188655	0.436467525501985	   
df.mm.trans3:probe13	0.0379722975038291	0.119038990604678	0.318990419113457	0.749804272120716	   
df.mm.trans3:probe14	-0.0543434747013666	0.119038990604678	-0.456518275443365	0.648122237132435	   
df.mm.trans3:probe15	-0.152712719306090	0.119038990604678	-1.28287982391619	0.199848732396108