fitVsDatCorrelation=0.881269214781269
cont.fitVsDatCorrelation=0.204743136624300

fstatistic=16230.0110370996,65,991
cont.fstatistic=3772.13482188672,65,991

residuals=-0.536009076577366,-0.0797418688504887,-0.00250508800084419,0.0735757965857885,0.52620154380441
cont.residuals=-0.582582704079257,-0.201743751064773,-0.0480278734580562,0.192913589478304,1.08489435192795

predictedValues:
Include	Exclude	Both
Lung	56.6637148956904	77.3780489103906	56.9248300459745
cerebhem	56.1151791068436	64.601453635665	70.7797396196682
cortex	57.9940101008691	73.3680569511307	65.3179486646267
heart	55.381727430221	78.0674684117994	53.5692929985222
kidney	57.0287663123704	81.0709002656423	62.1568641918921
liver	57.2639615182973	90.9007268113291	55.7157516478889
stomach	57.999497224681	80.2012733634842	58.9501076199126
testicle	56.8552472642233	74.9661380257374	55.327324025156


diffExp=-20.7143340147002,-8.48627452882138,-15.3740468502616,-22.6857409815784,-24.0421339532719,-33.6367652930318,-22.2017761388032,-18.1108907615140
diffExpScore=0.993985033410551
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,-1,-1,-1,0,0
diffExp1.4Score=0.75
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	61.1030203335658	59.1823804508335	60.6154786364541
cerebhem	62.4286771569076	61.3309570885608	63.1979888901937
cortex	61.9725357937982	57.7710190147924	61.6398659023608
heart	60.1726650803629	62.5858187740109	60.3231249209316
kidney	62.2030487695342	65.8410893392867	60.1494467842555
liver	60.8830778624273	68.5347057185298	59.2032365522797
stomach	60.9575650817515	63.9172641685298	61.8137280316486
testicle	61.166339551429	63.186938268443	58.1849974322995
cont.diffExp=1.92063988273232,1.09772006834683,4.20151677900572,-2.41315369364802,-3.63804056975246,-7.6516278561025,-2.9596990867783,-2.02059871701405
cont.diffExpScore=2.07835121659917

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=0.283892667733078
cont.tran.correlation=-0.223289856459917

tran.covariance=0.000442545068130337
cont.tran.covariance=-0.000156382871800725

tran.mean=67.2410106392734
cont.tran.mean=62.0773189032977

weightedLogRatios:
wLogRatio
Lung	-1.30638377938899
cerebhem	-0.577098532986087
cortex	-0.982432003037924
heart	-1.43712274420805
kidney	-1.48426509539874
liver	-1.97718147964985
stomach	-1.36852931770336
testicle	-1.15554761709476

cont.weightedLogRatios:
wLogRatio
Lung	0.130834628697585
cerebhem	0.0731803050087572
cortex	0.287246208235579
heart	-0.161877861147807
kidney	-0.236387922723481
liver	-0.493446179054719
stomach	-0.195993760833899
testicle	-0.134222416432417

varWeightedLogRatios=0.165545451437728
cont.varWeightedLogRatios=0.060035884769083

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.80286047270298	0.0628744199143895	60.4834283621383	0	***
df.mm.trans1	-0.0662947086524922	0.055546545137734	-1.19349832627946	0.232959900397861	   
df.mm.trans2	0.585276747659586	0.0490966734229705	11.9209043475796	1.03441102221986e-30	***
df.mm.exp2	-0.408035100447550	0.0644472514440004	-6.33130337299335	3.67907905926416e-10	***
df.mm.exp3	-0.167544047768636	0.0644472514440004	-2.5997081956896	0.00946906415136667	** 
df.mm.exp4	0.0467415647788266	0.0644472514440004	0.7252685526774	0.468458514725053	   
df.mm.exp5	-0.034886926159412	0.0644472514440004	-0.541325275752466	0.588405068698285	   
df.mm.exp6	0.193071012397926	0.0644472514440004	2.99579901503930	0.00280537737443005	** 
df.mm.exp7	0.0241767163252873	0.0644472514440004	0.375139603064299	0.707636916688593	   
df.mm.exp8	0.000172577711056322	0.0644472514440004	0.00267781336192869	0.997863955544872	   
df.mm.trans1:exp2	0.398307391743251	0.0616233589750693	6.46357807117255	1.60168322151443e-10	***
df.mm.trans2:exp2	0.227568878519974	0.0474839871219876	4.79253938670616	1.89873634908164e-06	***
df.mm.trans1:exp3	0.190749721909603	0.0616233589750693	3.09541260136070	0.00202057600049223	** 
df.mm.trans2:exp3	0.114329562701315	0.0474839871219876	2.40774984643979	0.0162330371513063	*  
df.mm.trans1:exp4	-0.0696259121601646	0.0616233589750693	-1.12986233334559	0.258807633693348	   
df.mm.trans2:exp4	-0.0378712668931267	0.0474839871219876	-0.797558696910487	0.425317683159879	   
df.mm.trans1:exp5	0.0413086818872874	0.0616233589750692	0.670341288990747	0.502796430392157	   
df.mm.trans2:exp5	0.081507875328238	0.0474839871219876	1.71653393635295	0.0863768545246667	.  
df.mm.trans1:exp6	-0.182533587328993	0.0616233589750692	-2.9620843518582	0.00312857248038554	** 
df.mm.trans2:exp6	-0.0320061500853078	0.0474839871219876	-0.674040914110333	0.500442489993081	   
df.mm.trans1:exp7	-0.000876431305787781	0.0616233589750692	-0.0142223877497875	0.988655421600448	   
df.mm.trans2:exp7	0.0116595412082731	0.0474839871219876	0.245546802510905	0.806083915249391	   
df.mm.trans1:exp8	0.00320188143896557	0.0616233589750692	0.0519588917614982	0.958571919953582	   
df.mm.trans2:exp8	-0.0318391936816044	0.0474839871219876	-0.670524857144128	0.502679494441872	   
df.mm.trans1:probe2	0.66935223088806	0.0377364464313195	17.7375533254379	2.39102655780768e-61	***
df.mm.trans1:probe3	0.170997421024993	0.0377364464313194	4.5313599237864	6.57467778359564e-06	***
df.mm.trans1:probe4	0.165780940896939	0.0377364464313194	4.39312538870508	1.23759113061800e-05	***
df.mm.trans1:probe5	0.193229913812273	0.0377364464313195	5.12051165612407	3.66004674898243e-07	***
df.mm.trans1:probe6	0.250569137578051	0.0377364464313195	6.63997703212696	5.16148262451276e-11	***
df.mm.trans1:probe7	0.872668562127672	0.0377364464313195	23.1253508121368	5.81613400557894e-95	***
df.mm.trans1:probe8	0.220730723456351	0.0377364464313194	5.84927157510929	6.70107199695971e-09	***
df.mm.trans1:probe9	0.26127793389889	0.0377364464313195	6.92375564229179	7.89560403518082e-12	***
df.mm.trans1:probe10	0.137798902339874	0.0377364464313194	3.65161310540114	0.000274229145910356	***
df.mm.trans1:probe11	0.128707500674856	0.0377364464313195	3.41069477511891	0.000674125097236175	***
df.mm.trans1:probe12	0.291802087028406	0.0377364464313195	7.73263289534926	2.58147710523959e-14	***
df.mm.trans1:probe13	0.226528882006465	0.0377364464313194	6.00292034436121	2.71598891758496e-09	***
df.mm.trans1:probe14	0.319099351010922	0.0377364464313194	8.45599893969044	9.8094230279377e-17	***
df.mm.trans1:probe15	0.243488350855555	0.0377364464313195	6.45233915436911	1.71995766550288e-10	***
df.mm.trans1:probe16	0.646407500155505	0.0377364464313194	17.1295275863341	8.1839907897613e-58	***
df.mm.trans1:probe17	0.288743405324724	0.0377364464313195	7.6515791133179	4.69417485623446e-14	***
df.mm.trans1:probe18	0.577855861710558	0.0377364464313194	15.3129379249384	1.14486569253805e-47	***
df.mm.trans1:probe19	0.455817749938586	0.0377364464313194	12.0789791579389	1.95203325594097e-31	***
df.mm.trans1:probe20	0.711036803034305	0.0377364464313195	18.8421770006457	6.31288472849051e-68	***
df.mm.trans1:probe21	0.296282524769266	0.0377364464313195	7.85136261593951	1.06474686943756e-14	***
df.mm.trans1:probe22	0.585132410316556	0.0377364464313195	15.505763410487	1.03138991042589e-48	***
df.mm.trans1:probe23	0.679217705967039	0.0377364464313194	17.9989842764665	6.9094552054294e-63	***
df.mm.trans1:probe24	0.180073003187817	0.0377364464313194	4.77185904389676	2.09964427077374e-06	***
df.mm.trans1:probe25	0.806086493677287	0.0377364464313194	21.3609539293629	1.42124450208880e-83	***
df.mm.trans1:probe26	0.157203610944500	0.0377364464313194	4.16582974315324	3.37274635486336e-05	***
df.mm.trans1:probe27	0.238489727052938	0.0377364464313194	6.3198777205212	3.95028265215554e-10	***
df.mm.trans1:probe28	0.243699611822955	0.0377364464313194	6.45793748138128	1.66001214408311e-10	***
df.mm.trans1:probe29	0.870340897677456	0.0377364464313195	23.0636686806608	1.47089526322047e-94	***
df.mm.trans1:probe30	0.174542247077960	0.0377364464313194	4.62529632713637	4.23580739334309e-06	***
df.mm.trans1:probe31	0.191438060293925	0.0377364464313194	5.07302828956996	4.67288522486993e-07	***
df.mm.trans1:probe32	0.167195579606711	0.0377364464313195	4.43061272107345	1.04428938003289e-05	***
df.mm.trans2:probe2	-0.149391711871484	0.0377364464313195	-3.95881769480807	8.07021701656935e-05	***
df.mm.trans2:probe3	-0.121134495356621	0.0377364464313194	-3.21001331105956	0.00136994866529033	** 
df.mm.trans2:probe4	-0.09972343061906	0.0377364464313194	-2.64262907745056	0.00835624438914738	** 
df.mm.trans2:probe5	0.0623974835514674	0.0377364464313195	1.65350713838493	0.0985444030964851	.  
df.mm.trans2:probe6	-0.165356875298594	0.0377364464313195	-4.38188782824435	1.30190662634792e-05	***
df.mm.trans3:probe2	-0.425150532804129	0.0377364464313194	-11.2663107687658	8.66905514521531e-28	***
df.mm.trans3:probe3	-0.311141502509479	0.0377364464313194	-8.24511929271767	5.2010438136536e-16	***
df.mm.trans3:probe4	-0.0641362818094193	0.0377364464313195	-1.69958456279522	0.0895229122906848	.  
df.mm.trans3:probe5	-0.333906192478371	0.0377364464313194	-8.84837402711149	4.00896657013343e-18	***
df.mm.trans3:probe6	-0.546330802454737	0.0377364464313195	-14.4775370794137	3.11557257613330e-43	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02479051541636	0.130217031364192	30.908326455084	2.02393719278565e-147	***
df.mm.trans1	0.0376345482483447	0.115040523955871	0.327141662383084	0.7436298293378	   
df.mm.trans2	0.0548279323989551	0.101682418250562	0.539207596969714	0.589864733979186	   
df.mm.exp2	0.0154021012580387	0.133474468218493	0.115393613951891	0.90815651733696	   
df.mm.exp3	-0.0267651544591932	0.133474468218493	-0.200526398916829	0.84111005835577	   
df.mm.exp4	0.0454064838220872	0.133474468218493	0.340188535141854	0.73378664471326	   
df.mm.exp5	0.132180986354509	0.133474468218493	0.990309143904085	0.32226476868498	   
df.mm.exp6	0.166684442242991	0.133474468218493	1.24881143538354	0.212028966535906	   
df.mm.exp7	0.0550071069908256	0.133474468218493	0.412117071714276	0.680342743535745	   
df.mm.exp8	0.107432208124455	0.133474468218493	0.804889575949403	0.421076462662989	   
df.mm.trans1:exp2	0.00606134078599736	0.12762600242435	0.0474929925787671	0.962129884216549	   
df.mm.trans2:exp2	0.0202587537406166	0.0983424395609519	0.206002147506829	0.836831545112226	   
df.mm.trans1:exp3	0.0408951727121296	0.12762600242435	0.320429786527006	0.748710099551508	   
df.mm.trans2:exp3	0.00262853319539175	0.0983424395609519	0.0267283708552156	0.978681765740548	   
df.mm.trans1:exp4	-0.060749600643826	0.12762600242435	-0.475997049894556	0.634181394732337	   
df.mm.trans2:exp4	0.0105083613937777	0.0983424395609519	0.106854796776368	0.914925801748695	   
df.mm.trans1:exp5	-0.114338269806950	0.12762600242435	-0.895885380996113	0.370531540724593	   
df.mm.trans2:exp5	-0.0255607549425565	0.0983424395609519	-0.259915811084940	0.794982703494185	   
df.mm.trans1:exp6	-0.170290471335244	0.12762600242435	-1.33429291915794	0.182414388946175	   
df.mm.trans2:exp6	-0.0199680425251607	0.0983424395609519	-0.203046036017691	0.839140730995935	   
df.mm.trans1:exp7	-0.0573904368785435	0.12762600242435	-0.449676678642047	0.653041925700539	   
df.mm.trans2:exp7	0.0219585226130078	0.0983424395609519	0.223286332035703	0.823358680911568	   
df.mm.trans1:exp8	-0.106396474828997	0.12762600242435	-0.833658289125393	0.404674455531536	   
df.mm.trans2:exp8	-0.0419584713695125	0.098342439560952	-0.426656808157652	0.669721980854262	   
df.mm.trans1:probe2	0.0250805060946268	0.0781546459627165	0.320908703323809	0.748347238618954	   
df.mm.trans1:probe3	0.0806074644164477	0.0781546459627165	1.03138416691058	0.302612343923372	   
df.mm.trans1:probe4	0.0333579669445958	0.0781546459627165	0.426820012216665	0.669603135708714	   
df.mm.trans1:probe5	0.0993687660139436	0.0781546459627165	1.27143773463381	0.203871286868699	   
df.mm.trans1:probe6	0.0692737152077498	0.0781546459627165	0.886367206382032	0.375634722170688	   
df.mm.trans1:probe7	-0.0436199638323961	0.0781546459627165	-0.558123746772583	0.576885950413666	   
df.mm.trans1:probe8	0.155307594843554	0.0781546459627165	1.98718314094396	0.0471770561208072	*  
df.mm.trans1:probe9	0.0838209442821858	0.0781546459627165	1.07250110661588	0.283756116142186	   
df.mm.trans1:probe10	0.0877687741216469	0.0781546459627165	1.12301416045716	0.261703467999682	   
df.mm.trans1:probe11	0.113693163959779	0.0781546459627165	1.45472047834516	0.146063316134579	   
df.mm.trans1:probe12	0.106051303118951	0.0781546459627165	1.35694176350747	0.175108703291367	   
df.mm.trans1:probe13	0.0390365300807005	0.0781546459627165	0.499478048935476	0.617553516158327	   
df.mm.trans1:probe14	-0.00904856046736134	0.0781546459627165	-0.115777640035346	0.90785222766006	   
df.mm.trans1:probe15	0.155985846155100	0.0781546459627165	1.99586146458283	0.0462223253034235	*  
df.mm.trans1:probe16	0.101278121452113	0.0781546459627165	1.29586821364948	0.195322622484863	   
df.mm.trans1:probe17	0.0377580039159266	0.0781546459627165	0.483119121721042	0.629117870334299	   
df.mm.trans1:probe18	0.0824224397368721	0.0781546459627165	1.05460703866782	0.291862127496695	   
df.mm.trans1:probe19	0.105506840690438	0.0781546459627165	1.34997528797930	0.177332163446696	   
df.mm.trans1:probe20	0.0115634689455073	0.0781546459627165	0.147956257789506	0.882407387258065	   
df.mm.trans1:probe21	-0.00898874032342829	0.0781546459627165	-0.115012232640864	0.908458724742244	   
df.mm.trans1:probe22	0.0818131938606739	0.0781546459627165	1.04681164955571	0.295441704155105	   
df.mm.trans1:probe23	0.00807950039817741	0.0781546459627165	0.103378376277614	0.917683589332237	   
df.mm.trans1:probe24	0.0562874729613744	0.0781546459627165	0.720206358406718	0.471567780220644	   
df.mm.trans1:probe25	0.0122655487682846	0.0781546459627165	0.156939470676329	0.875324519150908	   
df.mm.trans1:probe26	0.0703382286791312	0.0781546459627165	0.899987810228017	0.368345378181351	   
df.mm.trans1:probe27	0.0678739135961904	0.0781546459627165	0.868456542283737	0.385354716012614	   
df.mm.trans1:probe28	0.0396374695985461	0.0781546459627165	0.507167156990962	0.612150372774035	   
df.mm.trans1:probe29	-0.0208749590191378	0.0781546459627165	-0.267098120169288	0.789449186875875	   
df.mm.trans1:probe30	0.0848284984464334	0.0781546459627165	1.08539290788804	0.278011630606697	   
df.mm.trans1:probe31	0.0286803575868726	0.0781546459627165	0.366969323878129	0.713720243206912	   
df.mm.trans1:probe32	0.150023478688820	0.0781546459627165	1.91957211040772	0.0551988452375726	.  
df.mm.trans2:probe2	-0.0690309466377144	0.0781546459627165	-0.88326094741246	0.377309511038346	   
df.mm.trans2:probe3	0.079114031542723	0.0781546459627165	1.01227547726931	0.311653440582216	   
df.mm.trans2:probe4	-0.00177086256763963	0.0781546459627165	-0.0226584427045376	0.981927286580761	   
df.mm.trans2:probe5	-0.0151504132764854	0.0781546459627165	-0.193851729348411	0.846331707560054	   
df.mm.trans2:probe6	0.0189032580000396	0.0781546459627165	0.241869920427474	0.80893099841991	   
df.mm.trans3:probe2	-0.104178357749759	0.0781546459627165	-1.33297715659101	0.182845660266581	   
df.mm.trans3:probe3	0.0152753860313727	0.0781546459627165	0.195450773824243	0.84508013699613	   
df.mm.trans3:probe4	0.0247604712120542	0.0781546459627165	0.316813810708913	0.751451610195706	   
df.mm.trans3:probe5	-0.00156857394898657	0.0781546459627165	-0.0200701305682435	0.983991467771063	   
df.mm.trans3:probe6	-0.0105052009017340	0.0781546459627165	-0.134415565093155	0.893101267313919