fitVsDatCorrelation=0.879873463427689
cont.fitVsDatCorrelation=0.235390024621057

fstatistic=8933.31387669827,70,1106
cont.fstatistic=2123.65697834095,70,1106

residuals=-0.616173921516154,-0.107591283872260,-0.00572444332884236,0.092501757941542,1.07199637146672
cont.residuals=-0.843900581186323,-0.265266302436580,-0.0738484442099044,0.188249781758750,1.49657935154755

predictedValues:
Include	Exclude	Both
Lung	63.9610445570218	46.7190652131061	67.4915273475183
cerebhem	68.1423357678026	51.0630819008	62.5091163332022
cortex	66.215602263957	48.5360310216933	69.4872502994541
heart	91.6911340738869	46.413153122941	115.526358028965
kidney	81.1791200898298	46.6676210344946	102.461545895253
liver	88.0802124164057	53.7135385668434	103.354421134539
stomach	69.3968787704405	50.8306304579539	64.6210054144773
testicle	64.6195378914928	48.9044174060041	64.6267305449447


diffExp=17.2419793439157,17.0792538670025,17.6795712422637,45.2779809509459,34.5114990553352,34.3666738495623,18.5662483124867,15.7151204854887
diffExpScore=0.99503570142603
diffExp1.5=0,0,0,1,1,1,0,0
diffExp1.5Score=0.75
diffExp1.4=0,0,0,1,1,1,0,0
diffExp1.4Score=0.75
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	72.7177657090925	67.7387543289968	76.6222517054589
cerebhem	71.2828887858574	65.5660021956017	71.5193232306873
cortex	74.6043542105341	67.8355324391293	68.9315949160046
heart	71.462043616308	67.9867646765309	71.7883881293313
kidney	69.2722870193076	68.0780926236064	74.5360622302527
liver	74.2612927758394	87.4134974566403	74.2233493235447
stomach	72.604089378237	68.1691607535528	77.2585086784532
testicle	76.9569917169957	68.9052753329869	70.814319273351
cont.diffExp=4.97901138009571,5.71688659025567,6.7688217714048,3.47527893977713,1.19419439570122,-13.1522046808009,4.43492862468429,8.05171638400884
cont.diffExpScore=2.1262104332446

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=0.0524219305746917
cont.tran.correlation=0.288928038254809

tran.covariance=0.000346572622607957
cont.tran.covariance=0.000883905888433013

tran.mean=61.6333377846671
cont.tran.mean=71.553424563701

weightedLogRatios:
wLogRatio
Lung	1.25686867293955
cerebhem	1.17646031516549
cortex	1.25412118276112
heart	2.84456413580546
kidney	2.2807822051785
liver	2.09255839198495
stomach	1.27157690916993
testicle	1.12273072401462

cont.weightedLogRatios:
wLogRatio
Lung	0.301520469961484
cerebhem	0.353193834740372
cortex	0.405621825143951
heart	0.211589786645576
kidney	0.0735461174493612
liver	-0.715689197721459
stomach	0.268094165138644
testicle	0.473882796178273

varWeightedLogRatios=0.425173579145373
cont.varWeightedLogRatios=0.143400515543621

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.60840005087095	0.0829581524234365	43.4966298725277	9.81402374036828e-242	***
df.mm.trans1	0.600572717848872	0.0708525450965125	8.47637465994758	7.33673687449524e-17	***
df.mm.trans2	0.236668285913757	0.0615557700825322	3.84477824899987	0.000127541368212680	***
df.mm.exp2	0.228923571510940	0.0770828076846416	2.96983955809581	0.00304384795857639	** 
df.mm.exp3	0.0436547818350899	0.0770828076846417	0.56633616686212	0.57128017459118	   
df.mm.exp4	-0.183914609937695	0.0770828076846416	-2.38593553429085	0.0172032128046586	*  
df.mm.exp5	-0.180203389427751	0.0770828076846416	-2.33778964260088	0.0195757381420701	*  
df.mm.exp6	0.03332444507478	0.0770828076846416	0.432320073382845	0.665593075479922	   
df.mm.exp7	0.209377027449799	0.0770828076846416	2.71626104106632	0.00670533661257454	** 
df.mm.exp8	0.0993319463167407	0.0770828076846416	1.28863944244382	0.197792951517616	   
df.mm.trans1:exp2	-0.16559910015643	0.0703470508044971	-2.35403045703578	0.0187452538149011	*  
df.mm.trans2:exp2	-0.140014133140234	0.0462067215874753	-3.03016808658821	0.00250096892494215	** 
df.mm.trans1:exp3	-0.00901288215860014	0.0703470508044971	-0.128120256009709	0.898077097790166	   
df.mm.trans2:exp3	-0.00550068208585946	0.0462067215874753	-0.119045063074773	0.90526127269311	   
df.mm.trans1:exp4	0.544066081246786	0.0703470508044971	7.73402829293883	2.33485802695216e-14	***
df.mm.trans2:exp4	0.177345171437665	0.0462067215874753	3.83808167610263	0.000131022049230868	***
df.mm.trans1:exp5	0.418587242534915	0.070347050804497	5.95031686116053	3.58745208321848e-09	***
df.mm.trans2:exp5	0.179101643860317	0.0462067215874753	3.87609502918865	0.000112390992463783	***
df.mm.trans1:exp6	0.286649239724142	0.0703470508044971	4.07478688084274	4.93465825556885e-05	***
df.mm.trans2:exp6	0.106188309464546	0.0462067215874753	2.29811390672931	0.0217412099924697	*  
df.mm.trans1:exp7	-0.127809354680748	0.070347050804497	-1.81684026862684	0.0695121354765209	.  
df.mm.trans2:exp7	-0.125030222841712	0.0462067215874753	-2.70588820297528	0.00691696658263881	** 
df.mm.trans1:exp8	-0.0890893563574329	0.0703470508044971	-1.26642631551141	0.205627146695785	   
df.mm.trans2:exp8	-0.0536165484534987	0.0462067215874753	-1.16036253193154	0.246151642197596	   
df.mm.trans1:probe2	0.249657713251266	0.0534324207809617	4.67240131744547	3.34295517804944e-06	***
df.mm.trans1:probe3	-0.139930104749912	0.0534324207809617	-2.61882397811498	0.0089440044509196	** 
df.mm.trans1:probe4	0.000503040144790188	0.0534324207809617	0.00941451159123646	0.992490115339458	   
df.mm.trans1:probe5	-0.299661641894473	0.0534324207809617	-5.60823630138135	2.58106505790057e-08	***
df.mm.trans1:probe6	0.0096946615432601	0.0534324207809617	0.181437812503422	0.856057185851368	   
df.mm.trans1:probe7	-0.502449445076122	0.0534324207809617	-9.40345651071733	2.93905465291248e-20	***
df.mm.trans1:probe8	-0.414106537880756	0.0534324207809617	-7.75009875705098	2.07098373015747e-14	***
df.mm.trans1:probe9	0.204619081287543	0.0534324207809617	3.82949299876097	0.000135617482346932	***
df.mm.trans1:probe10	-0.5066066481391	0.0534324207809617	-9.48125951874536	1.47816099272597e-20	***
df.mm.trans1:probe11	0.0461208485994742	0.0534324207809617	0.86316225103369	0.388235398966089	   
df.mm.trans1:probe12	0.0521860297859807	0.0534324207809617	0.976673506894055	0.328944386615865	   
df.mm.trans1:probe13	0.111944634242844	0.0534324207809617	2.09506948415728	0.0363918095828439	*  
df.mm.trans1:probe14	-0.113835223786659	0.0534324207809617	-2.13045230073535	0.0333544539356209	*  
df.mm.trans1:probe15	0.257140557569871	0.0534324207809617	4.8124444637083	1.69754717426342e-06	***
df.mm.trans1:probe16	0.055780372836324	0.0534324207809617	1.04394246079524	0.296740128878609	   
df.mm.trans1:probe17	-0.0936373268058715	0.0534324207809617	-1.75244402999677	0.079974571826466	.  
df.mm.trans1:probe18	-0.181569085647535	0.0534324207809617	-3.39810704800089	0.000702703770932474	***
df.mm.trans1:probe19	-0.241684495236524	0.0534324207809617	-4.52318071508072	6.74902249923362e-06	***
df.mm.trans1:probe20	-0.125244758124970	0.0534324207809617	-2.34398435059478	0.0192552458068632	*  
df.mm.trans1:probe21	-0.286444052813750	0.0534324207809617	-5.36086609266657	1.00774549696675e-07	***
df.mm.trans1:probe22	-0.128520494583906	0.0534324207809617	-2.40529050912286	0.0163230461328467	*  
df.mm.trans1:probe23	-0.133994752025540	0.0534324207809617	-2.50774249167619	0.0122928968775612	*  
df.mm.trans2:probe2	-0.0140831102358969	0.0534324207809617	-0.263568635484970	0.792161425817103	   
df.mm.trans2:probe3	0.0176066881293337	0.0534324207809617	0.329513203257433	0.741830208553874	   
df.mm.trans2:probe4	-0.0340824880814595	0.0534324207809617	-0.637861575113275	0.523695826238098	   
df.mm.trans2:probe5	0.0269596682404361	0.0534324207809617	0.504556369455041	0.613970966479376	   
df.mm.trans2:probe6	-0.0202169334490735	0.0534324207809617	-0.378364542605131	0.7052324153009	   
df.mm.trans3:probe2	-0.443486830675402	0.0534324207809617	-8.29995766977151	3.00663011121307e-16	***
df.mm.trans3:probe3	-0.215461310470801	0.0534324207809617	-4.03240780263453	5.89945763037319e-05	***
df.mm.trans3:probe4	-0.0160946692245089	0.0534324207809617	-0.301215422944932	0.763306849223991	   
df.mm.trans3:probe5	-0.0410053550046345	0.0534324207809617	-0.767424616090853	0.442992892802101	   
df.mm.trans3:probe6	-0.33087903006708	0.0534324207809617	-6.19247687510678	8.3355437067603e-10	***
df.mm.trans3:probe7	0.242234599457404	0.0534324207809617	4.53347604164163	6.43385946323514e-06	***
df.mm.trans3:probe8	-0.114194876909597	0.0534324207809617	-2.13718329135268	0.0328019299596057	*  
df.mm.trans3:probe9	0.536882725094459	0.0534324207809617	10.0478832373201	8.60945941604551e-23	***
df.mm.trans3:probe10	-0.624987994179711	0.0534324207809617	-11.6967935392962	7.01116518474427e-30	***
df.mm.trans3:probe11	-0.508108640555445	0.0534324207809617	-9.5093696510282	1.15180227713023e-20	***
df.mm.trans3:probe12	-0.0419939587527159	0.0534324207809617	-0.785926561794082	0.432078853503722	   
df.mm.trans3:probe13	-0.405369633398918	0.0534324207809617	-7.58658558744083	6.9460300095118e-14	***
df.mm.trans3:probe14	-0.370804829021185	0.0534324207809617	-6.93969735231058	6.68006767611216e-12	***
df.mm.trans3:probe15	-0.574172316612584	0.0534324207809617	-10.7457664882211	1.09292842879598e-25	***
df.mm.trans3:probe16	-0.59329649215602	0.0534324207809617	-11.1036798161953	3.11368190346672e-27	***
df.mm.trans3:probe17	-0.227358268895669	0.0534324207809617	-4.25506210597664	2.26683341004577e-05	***
df.mm.trans3:probe18	-0.365256073206839	0.0534324207809617	-6.83585111563918	1.34483612792394e-11	***
df.mm.trans3:probe19	-0.0302069172458906	0.0534324207809617	-0.565329378762745	0.571964407318714	   
df.mm.trans3:probe20	-0.549219836394789	0.0534324207809617	-10.2787751025961	9.86426456485766e-24	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24630133486122	0.169666972151642	25.0272712538657	6.74953373833176e-110	***
df.mm.trans1	0.102315827566690	0.144908444132210	0.706072224979108	0.480291923300399	   
df.mm.trans2	-0.0192697538178132	0.125894572423184	-0.153062625710658	0.87837680880044	   
df.mm.exp2	0.0163892876287489	0.157650649185693	0.103959531491966	0.917220311520009	   
df.mm.exp3	0.132813716807832	0.157650649185693	0.842455882635747	0.399714961016421	   
df.mm.exp4	0.05140010535351	0.157650649185693	0.326038018993293	0.744457268362042	   
df.mm.exp5	-0.0159393266083718	0.157650649185693	-0.101105366141545	0.919485143434098	   
df.mm.exp6	0.307804123430473	0.157650649185693	1.95244437634962	0.0511374700095555	.  
df.mm.exp7	-0.00350018867482784	0.157650649185693	-0.0222021837075028	0.98229068045193	   
df.mm.exp8	0.152561570714287	0.157650649185693	0.967719267267892	0.33339615475315	   
df.mm.trans1:exp2	-0.0363187028133972	0.143874601363769	-0.252433038695758	0.800753269727455	   
df.mm.trans2:exp2	-0.0489904434532501	0.0945025210395841	-0.518403561241817	0.604280423128774	   
df.mm.trans1:exp3	-0.107200568692774	0.143874601363769	-0.745097242158334	0.456371237338296	   
df.mm.trans2:exp3	-0.131386040087152	0.0945025210395841	-1.39029137679955	0.164720125137826	   
df.mm.trans1:exp4	-0.0688193799467687	0.143874601363769	-0.478328900962633	0.63251066365903	   
df.mm.trans2:exp4	-0.0477455144499348	0.0945025210395841	-0.505230060793148	0.613497919113949	   
df.mm.trans1:exp5	-0.0326014707126523	0.143874601363769	-0.226596427747685	0.820779442224592	   
df.mm.trans2:exp5	0.0209363355705507	0.0945025210395841	0.221542614315877	0.824710855553684	   
df.mm.trans1:exp6	-0.286799990880050	0.143874601363769	-1.99340250580372	0.0464626952315877	*  
df.mm.trans2:exp6	-0.0528128777686477	0.0945025210395841	-0.558851522559129	0.576376186033576	   
df.mm.trans1:exp7	0.00193571161839008	0.143874601363769	0.0134541579962114	0.989267885360268	   
df.mm.trans2:exp7	0.00983400467349372	0.0945025210395841	0.104060765419946	0.917139992806384	   
df.mm.trans1:exp8	-0.0959005788619814	0.143874601363769	-0.66655669557345	0.505194355073024	   
df.mm.trans2:exp8	-0.135487288778939	0.0945025210395841	-1.43368967609010	0.15194347647441	   
df.mm.trans1:probe2	-0.24063627074127	0.109280604543419	-2.20200347304687	0.0278708995794199	*  
df.mm.trans1:probe3	-0.206609587198849	0.109280604543419	-1.89063364045319	0.0589344697142477	.  
df.mm.trans1:probe4	-0.171940146963843	0.109280604543419	-1.57338209906707	0.115916369010542	   
df.mm.trans1:probe5	-0.0551475010009233	0.109280604543419	-0.50464125112899	0.613911356058762	   
df.mm.trans1:probe6	-0.136303073283813	0.109280604543419	-1.24727598143601	0.212560365193701	   
df.mm.trans1:probe7	-0.022235736671254	0.109280604543419	-0.203473770703925	0.838802174021591	   
df.mm.trans1:probe8	-0.105805931647475	0.109280604543419	-0.968204120845948	0.333154108325616	   
df.mm.trans1:probe9	-0.170121922958024	0.109280604543419	-1.55674397729408	0.119817368389846	   
df.mm.trans1:probe10	-0.268767077068784	0.109280604543419	-2.45942157981015	0.0140683279044223	*  
df.mm.trans1:probe11	-0.123702418968602	0.109280604543419	-1.13197048538886	0.257892204114212	   
df.mm.trans1:probe12	-0.0327689204596175	0.109280604543419	-0.2998603512172	0.76434002406159	   
df.mm.trans1:probe13	-0.0981209454776122	0.109280604543419	-0.897880697929587	0.369444569586727	   
df.mm.trans1:probe14	-0.18644135576719	0.109280604543419	-1.70607910293097	0.0882740998234128	.  
df.mm.trans1:probe15	0.0428872213146478	0.109280604543419	0.392450439799755	0.694801040871437	   
df.mm.trans1:probe16	-0.0227590644476106	0.109280604543419	-0.208262614786031	0.835062265523898	   
df.mm.trans1:probe17	-0.187339597979292	0.109280604543419	-1.7142986970287	0.0867540463573606	.  
df.mm.trans1:probe18	-0.150382138343360	0.109280604543419	-1.37611005147405	0.169066127467583	   
df.mm.trans1:probe19	-0.0790788061162122	0.109280604543419	-0.72363075265376	0.469445473758097	   
df.mm.trans1:probe20	-0.124683081808488	0.109280604543419	-1.14094429042941	0.254140092056895	   
df.mm.trans1:probe21	-0.0522274431513742	0.109280604543419	-0.477920518188782	0.63280121908815	   
df.mm.trans1:probe22	-0.183005292402429	0.109280604543419	-1.67463653012387	0.0942882750452265	.  
df.mm.trans1:probe23	-0.0921627257932938	0.109280604543419	-0.84335849145743	0.399210334514352	   
df.mm.trans2:probe2	-0.0239315153328349	0.109280604543419	-0.218991425173956	0.826697131990648	   
df.mm.trans2:probe3	-0.0129807875127749	0.109280604543419	-0.118784001671746	0.905468053962427	   
df.mm.trans2:probe4	-0.0416044308716425	0.109280604543419	-0.380711939190567	0.703490118534706	   
df.mm.trans2:probe5	-0.115741879982116	0.109280604543419	-1.05912554625492	0.289773856148489	   
df.mm.trans2:probe6	-0.101442579750039	0.109280604543419	-0.928276158188112	0.353466874428923	   
df.mm.trans3:probe2	0.0635716612888291	0.109280604543419	0.581728675041974	0.560867998641437	   
df.mm.trans3:probe3	-0.0215667417082145	0.109280604543419	-0.197351961936171	0.843588381115897	   
df.mm.trans3:probe4	-0.0893999221397637	0.109280604543419	-0.818076753082416	0.413489572028479	   
df.mm.trans3:probe5	0.0348567026099656	0.109280604543419	0.318965133434236	0.749813180757468	   
df.mm.trans3:probe6	-0.00836546526593672	0.109280604543419	-0.0765503201678664	0.938995126511246	   
df.mm.trans3:probe7	-0.086185972212738	0.109280604543419	-0.788666685848127	0.430475857514937	   
df.mm.trans3:probe8	0.122454668419098	0.109280604543419	1.12055262624801	0.262721595987354	   
df.mm.trans3:probe9	-0.00677127530102658	0.109280604543419	-0.0619622789361148	0.950604066545357	   
df.mm.trans3:probe10	-0.00890500492084536	0.109280604543419	-0.0814875151729899	0.935068996754104	   
df.mm.trans3:probe11	0.0898470367281412	0.109280604543419	0.822168188980356	0.411158388313884	   
df.mm.trans3:probe12	0.0638037409448136	0.109280604543419	0.583852379032762	0.559438684995218	   
df.mm.trans3:probe13	0.00467766742540566	0.109280604543419	0.0428041869364581	0.965865352436542	   
df.mm.trans3:probe14	-0.0440834189962983	0.109280604543419	-0.403396551295461	0.68673449622958	   
df.mm.trans3:probe15	0.0644412801738575	0.109280604543419	0.58968634409644	0.555521393056725	   
df.mm.trans3:probe16	-0.022559517803543	0.109280604543419	-0.206436612405267	0.836487869563002	   
df.mm.trans3:probe17	-0.0216921842147504	0.109280604543419	-0.198499855535955	0.842690478381675	   
df.mm.trans3:probe18	-0.050763081676874	0.109280604543419	-0.464520505619138	0.642366297654449	   
df.mm.trans3:probe19	-0.0650036956490198	0.109280604543419	-0.594832870120084	0.552076863441747	   
df.mm.trans3:probe20	0.172445886101181	0.109280604543419	1.57800999382892	0.114849266410126