fitVsDatCorrelation=0.926744660230986
cont.fitVsDatCorrelation=0.214977231219879

fstatistic=9111.05223198174,69,1083
cont.fstatistic=1334.91170895958,69,1083

residuals=-0.90591437493794,-0.106409323734849,-0.00499641142816429,0.103023500034364,1.68576285025840
cont.residuals=-0.907505092535102,-0.383723325086916,-0.143380181972456,0.374826442152252,1.76023902329852

predictedValues:
Include	Exclude	Both
Lung	88.012873833104	198.249155509106	70.4187201943397
cerebhem	81.4120356233157	136.660023082780	65.979921565305
cortex	76.1233552768058	166.688554237161	63.8131279877242
heart	81.237170105296	188.984622751592	66.7416605265157
kidney	90.5598803304185	211.909950260286	72.8606530961953
liver	89.675830489479	214.402425539963	66.3370717194207
stomach	95.93525931305	184.527458091108	87.386813266542
testicle	86.8652474450121	175.553220983739	74.6243942516838


diffExp=-110.236281676002,-55.2479874594645,-90.5651989603554,-107.747452646296,-121.350069929867,-124.726595050484,-88.5921987780579,-88.6879735387269
diffExpScore=0.998731212038514
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	92.8955440570356	108.795405032206	99.019695150147
cerebhem	90.808714856324	121.881002914870	94.7751252923787
cortex	91.3004944624221	106.963857811502	90.3756409834187
heart	81.8160378099556	114.370136286738	89.91605938233
kidney	93.2879960990715	117.762229481706	93.9305085883403
liver	95.573208325056	100.278849646514	104.623797566828
stomach	94.298314271635	100.402416440522	90.514133082384
testicle	87.2580879992868	106.885318575011	84.8925700332046
cont.diffExp=-15.8998609751701,-31.0722880585464,-15.6633633490795,-32.5540984767823,-24.4742333826341,-4.70564132145759,-6.10410216888711,-19.6272305757245
cont.diffExpScore=0.993381902155157

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,-1,0,-1,0,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,-1,0,-1,-1,0,0,-1
cont.diffExp1.2Score=0.8

tran.correlation=0.559115082737559
cont.tran.correlation=-0.380050411863092

tran.covariance=0.00601641731802298
cont.tran.covariance=-0.00137959850663208

tran.mean=135.424816429513
cont.tran.mean=100.286100879366

weightedLogRatios:
wLogRatio
Lung	-3.96560780765573
cerebhem	-2.41298288391036
cortex	-3.70272794361017
heart	-4.06908517544723
kidney	-4.19216370025799
liver	-4.29901927186201
stomach	-3.19915114775012
testicle	-3.38856720620915

cont.weightedLogRatios:
wLogRatio
Lung	-0.728424303675271
cerebhem	-1.37018451850174
cortex	-0.72728295797902
heart	-1.53145328737607
kidney	-1.08384685743652
liver	-0.220313770889925
stomach	-0.287134729919889
testicle	-0.9272535784919

varWeightedLogRatios=0.399272027365629
cont.varWeightedLogRatios=0.219723596933457

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.39815069711147	0.0871425200370575	61.9462312406837	0	***
df.mm.trans1	-1.07344605100710	0.0743197573582206	-14.4436161952615	2.24158047703876e-43	***
df.mm.trans2	-0.0626680769284512	0.0647360789650856	-0.968054876512527	0.333233094903002	   
df.mm.exp2	-0.384879657364905	0.0811614422219072	-4.7421490652247	2.39679590921889e-06	***
df.mm.exp3	-0.220025248672067	0.0811614422219071	-2.71095784708318	0.00681500864602507	** 
df.mm.exp4	-0.0743393589989732	0.0811614422219071	-0.915944282948036	0.359900020699522	   
df.mm.exp5	0.0610754008236004	0.0811614422219071	0.752517441183603	0.451903472908133	   
df.mm.exp6	0.156758576269090	0.0811614422219072	1.93144148228034	0.0536891581747082	.  
df.mm.exp7	-0.201421093205547	0.0811614422219071	-2.4817337850507	0.0132252850489216	*  
df.mm.exp8	-0.192715724537390	0.0811614422219071	-2.37447387899389	0.0177474692668810	*  
df.mm.trans1:exp2	0.306919679887459	0.0737895263964763	4.15939354642768	3.44336062926487e-05	***
df.mm.trans2:exp2	0.0128513148453519	0.0489775412328933	0.262391996859185	0.793069143886719	   
df.mm.trans1:exp3	0.0748972715731743	0.0737895263964763	1.01501222776178	0.310326700699161	   
df.mm.trans2:exp3	0.0466277744478844	0.0489775412328932	0.952023586201776	0.341297439368543	   
df.mm.trans1:exp4	-0.00577083599241334	0.0737895263964763	-0.0782067086513909	0.93767807222028	   
df.mm.trans2:exp4	0.0264804088671306	0.0489775412328932	0.540664316757216	0.588850153136698	   
df.mm.trans1:exp5	-0.0325472051456742	0.0737895263964763	-0.441081637667598	0.659241934439619	   
df.mm.trans2:exp5	0.00556141990910362	0.0489775412328932	0.113550410435234	0.90961524496139	   
df.mm.trans1:exp6	-0.138040389126224	0.0737895263964763	-1.87073146918606	0.0616518279921744	.  
df.mm.trans2:exp6	-0.0784284347624277	0.0489775412328933	-1.60131425114814	0.109598961088489	   
df.mm.trans1:exp7	0.287611577683374	0.0737895263964764	3.89772901018522	0.000103062237458718	***
df.mm.trans2:exp7	0.12969476917613	0.0489775412328932	2.64804573507311	0.00821351299659908	** 
df.mm.trans1:exp8	0.179590665158655	0.0737895263964764	2.43382325282455	0.0151011403262500	*  
df.mm.trans2:exp8	0.0711333741610674	0.0489775412328932	1.45236719464582	0.146689178919313	   
df.mm.trans1:probe2	-0.0976196379784766	0.0560471686951282	-1.74174075606717	0.081837732263222	.  
df.mm.trans1:probe3	-0.24246951630945	0.0560471686951282	-4.32616886730491	1.65739237809172e-05	***
df.mm.trans1:probe4	-0.183190913813935	0.0560471686951282	-3.26851325551184	0.00111512979497091	** 
df.mm.trans1:probe5	-0.286480369194924	0.0560471686951282	-5.11141554274134	3.77804101908907e-07	***
df.mm.trans1:probe6	-0.389222405125109	0.0560471686951282	-6.94455070946235	6.53503839411522e-12	***
df.mm.trans1:probe7	-0.0486196926889887	0.0560471686951282	-0.86747812281934	0.38587225897956	   
df.mm.trans1:probe8	-0.157604554120300	0.0560471686951282	-2.8119984967947	0.00501235942589618	** 
df.mm.trans1:probe9	0.97547521391981	0.0560471686951282	17.4045404367518	5.1729880222928e-60	***
df.mm.trans1:probe10	-0.151493127555034	0.0560471686951282	-2.7029577243962	0.00698007770484763	** 
df.mm.trans1:probe11	0.918696170220154	0.0560471686951282	16.3914822391378	4.13737249363902e-54	***
df.mm.trans1:probe12	1.12753004896596	0.0560471686951282	20.1175202105075	9.9557863237083e-77	***
df.mm.trans1:probe13	1.09751075421549	0.0560471686951282	19.5819125170348	2.46638541947756e-73	***
df.mm.trans1:probe14	1.05129160676116	0.0560471686951282	18.7572651971007	3.40079789697841e-68	***
df.mm.trans1:probe15	0.784043473567936	0.0560471686951282	13.9889934107606	5.3394242681638e-41	***
df.mm.trans1:probe16	0.869748446510872	0.0560471686951282	15.5181513493022	3.42764703295676e-49	***
df.mm.trans1:probe17	0.121093236398078	0.0560471686951282	2.16055938626929	0.0309481541515115	*  
df.mm.trans1:probe18	0.208383211216895	0.0560471686951282	3.71799710972748	0.000211090956741476	***
df.mm.trans1:probe19	0.096973812484598	0.0560471686951282	1.73021786367288	0.0838762353113415	.  
df.mm.trans1:probe20	0.059109061674737	0.0560471686951282	1.05463064506013	0.291829501206162	   
df.mm.trans1:probe21	0.269975533295144	0.0560471686951282	4.81693437118459	1.66511125410156e-06	***
df.mm.trans1:probe22	0.3935645992982	0.0560471686951282	7.02202463498231	3.85459340788199e-12	***
df.mm.trans2:probe2	-0.275060562642591	0.0560471686951282	-4.90766204692334	1.06314307904305e-06	***
df.mm.trans2:probe3	-0.12730678293657	0.0560471686951282	-2.27142219492054	0.0233169906701261	*  
df.mm.trans2:probe4	-0.283974127542299	0.0560471686951282	-5.06669889226684	4.75626828364237e-07	***
df.mm.trans2:probe5	-0.309136490369303	0.0560471686951282	-5.51564865035144	4.34480539248332e-08	***
df.mm.trans2:probe6	-0.199430541018939	0.0560471686951282	-3.55826254317597	0.000389481996731530	***
df.mm.trans3:probe2	0.0417655714273714	0.0560471686951282	0.745186106626681	0.456320905375912	   
df.mm.trans3:probe3	0.089183140540754	0.0560471686951282	1.59121580299392	0.111852848645175	   
df.mm.trans3:probe4	-0.148464331425487	0.0560471686951282	-2.64891759712372	0.00819249324019737	** 
df.mm.trans3:probe5	-0.182223408582001	0.0560471686951282	-3.25125091640607	0.00118439753938677	** 
df.mm.trans3:probe6	-0.289249116901104	0.0560471686951282	-5.16081585627441	2.92338680123149e-07	***
df.mm.trans3:probe7	-0.205133916264855	0.0560471686951282	-3.66002281722191	0.000264352510205402	***
df.mm.trans3:probe8	0.553822284957086	0.0560471686951282	9.88136060841242	4.17701864801879e-22	***
df.mm.trans3:probe9	-0.23236503405001	0.0560471686951282	-4.1458835380958	3.64944316366976e-05	***
df.mm.trans3:probe10	0.225911233866353	0.0560471686951282	4.03073409640386	5.9492937716141e-05	***
df.mm.trans3:probe11	0.0945290400242982	0.0560471686951282	1.68659795356469	0.091968687327666	.  
df.mm.trans3:probe12	-0.147586317003546	0.0560471686951282	-2.63325196329452	0.00857761983914469	** 
df.mm.trans3:probe13	-0.0803387036579408	0.0560471686951282	-1.43341234764146	0.152028653542192	   
df.mm.trans3:probe14	0.00692134692196047	0.0560471686951282	0.123491464120329	0.90174084711938	   
df.mm.trans3:probe15	-0.259023351220994	0.0560471686951282	-4.62152428483883	4.26732282753084e-06	***
df.mm.trans3:probe16	-0.188924608800780	0.0560471686951282	-3.37081449784638	0.000775911893951048	***
df.mm.trans3:probe17	-0.191938173524804	0.0560471686951282	-3.42458286463788	0.000638808818540702	***
df.mm.trans3:probe18	0.515279952719385	0.0560471686951282	9.19368390439632	1.89054997627777e-19	***
df.mm.trans3:probe19	-0.0197054827502915	0.0560471686951282	-0.351587479065724	0.725216048514229	   
df.mm.trans3:probe20	0.265991283548562	0.0560471686951282	4.745846931099	2.35429442980573e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.62708902026766	0.226528884121507	20.4260442910484	1.05748625730511e-78	***
df.mm.trans1	-0.0702857715567935	0.193195832475116	-0.363805837094578	0.716074011583764	   
df.mm.trans2	0.113369586270726	0.168282851174449	0.673684724732897	0.500655523329145	   
df.mm.exp2	0.134667450146811	0.210980941707932	0.638292013755612	0.523418553560019	   
df.mm.exp3	0.0570464215447767	0.210980941707932	0.270386609723962	0.786914357674541	   
df.mm.exp4	0.0194107136300379	0.210980941707932	0.0920022134364574	0.926713295593315	   
df.mm.exp5	0.136177780244550	0.210980941707932	0.645450622895905	0.518771925239292	   
df.mm.exp6	-0.108149709849344	0.210980941707932	-0.512604166868583	0.608332766305158	   
df.mm.exp7	0.0245175743422158	0.210980941707932	0.116207531086653	0.907509612351034	   
df.mm.exp8	0.0736141425895056	0.210980941707932	0.348913707530096	0.727221916108525	   
df.mm.trans1:exp2	-0.157387870280362	0.191817485509255	-0.820508463357835	0.412106863506743	   
df.mm.trans2:exp2	-0.0210913677257789	0.127318188156417	-0.165658717196534	0.86845645410801	   
df.mm.trans1:exp3	-0.0743658978888345	0.191817485509255	-0.387690922396366	0.698320956770797	   
df.mm.trans2:exp3	-0.074024521975251	0.127318188156416	-0.581413567433966	0.561082741415913	   
df.mm.trans1:exp4	-0.146413107721455	0.191817485509255	-0.763293853700272	0.445454402486053	   
df.mm.trans2:exp4	0.0305601844223835	0.127318188156416	0.240029997794493	0.810352424976727	   
df.mm.trans1:exp5	-0.131962019571517	0.191817485509255	-0.687956153846829	0.491627709554822	   
df.mm.trans2:exp5	-0.0569792933939472	0.127318188156416	-0.447534592025025	0.654578615129722	   
df.mm.trans1:exp6	0.136566563255107	0.191817485509255	0.711960971089457	0.476642280003846	   
df.mm.trans2:exp6	0.0266354112886132	0.127318188156416	0.209203505597255	0.834328718977148	   
df.mm.trans1:exp7	-0.00952994082029512	0.191817485509255	-0.0496823362843806	0.96038469357513	   
df.mm.trans2:exp7	-0.104800399611652	0.127318188156416	-0.823137692494493	0.410610907660884	   
df.mm.trans1:exp8	-0.136219566400066	0.191817485509255	-0.710151976178905	0.477762778977904	   
df.mm.trans2:exp8	-0.0913267722928875	0.127318188156416	-0.717311278265194	0.473336728706638	   
df.mm.trans1:probe2	-0.109545986381149	0.145695839152667	-0.751881364754428	0.452285773534189	   
df.mm.trans1:probe3	-0.0798433983067116	0.145695839152667	-0.548014265685705	0.58379503895437	   
df.mm.trans1:probe4	-0.07794530516465	0.145695839152667	-0.534986487040135	0.592769024221013	   
df.mm.trans1:probe5	-0.177436163279840	0.145695839152667	-1.21785333274970	0.223544977442010	   
df.mm.trans1:probe6	-0.120863516668949	0.145695839152667	-0.82956052397833	0.40697011167971	   
df.mm.trans1:probe7	0.0339254681227221	0.145695839152667	0.232851317649321	0.81592086051017	   
df.mm.trans1:probe8	-0.137810091187459	0.145695839152667	-0.945875269938593	0.344423191463294	   
df.mm.trans1:probe9	0.0978007511919605	0.145695839152667	0.67126660418545	0.50219384957998	   
df.mm.trans1:probe10	-0.172595082503200	0.145695839152667	-1.18462602300088	0.236425244854625	   
df.mm.trans1:probe11	-0.0895377979485012	0.145695839152667	-0.61455288269886	0.538979124364705	   
df.mm.trans1:probe12	-0.0257832134901411	0.145695839152667	-0.176966024837018	0.859568165460327	   
df.mm.trans1:probe13	-0.142418109840576	0.145695839152667	-0.97750293123569	0.32853852273767	   
df.mm.trans1:probe14	-0.0774636924307787	0.145695839152667	-0.53168088314182	0.595056071571961	   
df.mm.trans1:probe15	0.0723423598698804	0.145695839152667	0.496530033325636	0.61962126742135	   
df.mm.trans1:probe16	-0.129748240833987	0.145695839152667	-0.890541841061294	0.373372799218639	   
df.mm.trans1:probe17	-0.00793852796004605	0.145695839152667	-0.0544869915724063	0.95655722174109	   
df.mm.trans1:probe18	0.0660893554704587	0.145695839152667	0.453611824845643	0.650199118776696	   
df.mm.trans1:probe19	0.0395386873520678	0.145695839152667	0.271378287684916	0.786151817721777	   
df.mm.trans1:probe20	-0.0415712553781243	0.145695839152667	-0.285329050025677	0.775446616600165	   
df.mm.trans1:probe21	0.0242991241381136	0.145695839152667	0.166779808396943	0.867574439576727	   
df.mm.trans1:probe22	-0.00725326200824311	0.145695839152667	-0.0497835905982379	0.960304022886585	   
df.mm.trans2:probe2	-0.0898666744072743	0.145695839152667	-0.616810163762518	0.537489587843383	   
df.mm.trans2:probe3	-0.364743695129313	0.145695839152667	-2.50345992892162	0.0124448525731272	*  
df.mm.trans2:probe4	-0.251749137432726	0.145695839152667	-1.72790890183851	0.0842896216283608	.  
df.mm.trans2:probe5	-0.30328609940092	0.145695839152667	-2.08163871504334	0.0376098914546684	*  
df.mm.trans2:probe6	-0.316081554004517	0.145695839152667	-2.16946177627840	0.0302645853520340	*  
df.mm.trans3:probe2	-0.0974854648749152	0.145695839152667	-0.66910260060869	0.503572635368772	   
df.mm.trans3:probe3	-0.100491325281646	0.145695839152667	-0.689733666150521	0.490509451881753	   
df.mm.trans3:probe4	0.0336303593920599	0.145695839152667	0.230825805236760	0.817493729997859	   
df.mm.trans3:probe5	-0.170988116496069	0.145695839152667	-1.1735964286317	0.240814741523428	   
df.mm.trans3:probe6	-0.0171360500190746	0.145695839152667	-0.117615232656841	0.906394343380145	   
df.mm.trans3:probe7	-0.103282423123401	0.145695839152667	-0.708890684346699	0.478544881138682	   
df.mm.trans3:probe8	-0.112102109455544	0.145695839152667	-0.769425606849884	0.441808467777133	   
df.mm.trans3:probe9	-0.193591591321968	0.145695839152667	-1.32873795468595	0.184214326745092	   
df.mm.trans3:probe10	-0.0425790395817317	0.145695839152667	-0.292246091785197	0.770154447823044	   
df.mm.trans3:probe11	-0.0561225280798963	0.145695839152667	-0.385203368924546	0.700162387390505	   
df.mm.trans3:probe12	-0.163180997571194	0.145695839152667	-1.12001137795160	0.262957231653187	   
df.mm.trans3:probe13	-0.185113837886164	0.145695839152667	-1.27054992759397	0.204161677366464	   
df.mm.trans3:probe14	-0.0192148632737205	0.145695839152667	-0.131883404395552	0.895101011772314	   
df.mm.trans3:probe15	-0.0199228336623388	0.145695839152667	-0.136742639859898	0.89125963934227	   
df.mm.trans3:probe16	-0.0320506038801837	0.145695839152667	-0.219982973203507	0.825925872939876	   
df.mm.trans3:probe17	-0.00543846337185799	0.145695839152667	-0.0373275132871797	0.970230747459906	   
df.mm.trans3:probe18	0.0966585382799118	0.145695839152667	0.663426895661915	0.507198374093148	   
df.mm.trans3:probe19	-0.0841956903542149	0.145695839152667	-0.577886718274709	0.563460732761139	   
df.mm.trans3:probe20	-0.224474233888898	0.145695839152667	-1.54070449227917	0.123680841080749