fitVsDatCorrelation=0.872192564821909
cont.fitVsDatCorrelation=0.246307118510096

fstatistic=10392.9223754232,54,738
cont.fstatistic=2637.24679930794,54,738

residuals=-0.431168781286691,-0.0802140801683743,-0.00745369570245986,0.0686513222507454,1.29188261299116
cont.residuals=-0.514587124598395,-0.215133750128338,-0.0698540783947484,0.151278561913825,1.41764600115566

predictedValues:
Include	Exclude	Both
Lung	59.3762038096307	41.9319636995763	71.3332293890558
cerebhem	62.6937121146849	45.5121277556520	76.3648946645177
cortex	57.293062958059	42.7438292072635	76.1045999623978
heart	60.1122322428485	47.8216844915891	68.4195767759201
kidney	60.7331381077677	41.4973551074722	64.2691971445848
liver	62.231658395061	45.1962747590636	61.084891999966
stomach	67.3301789598456	42.7968788060419	66.6364912625566
testicle	61.4014489972731	46.1377232738089	67.0520021038424


diffExp=17.4442401100544,17.1815843590329,14.5492337507955,12.2905477512594,19.2357830002955,17.0353836359973,24.5333001538037,15.2637257234642
diffExpScore=0.992781544930276
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,0,1,0,1,0
diffExp1.4Score=0.75
diffExp1.3=1,1,1,0,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	56.3557886513251	56.1812572364391	48.5745723164368
cerebhem	55.6203853950843	61.3679813191635	56.2371419134288
cortex	56.3385032696496	53.9468853343168	52.6799991148968
heart	56.891092354261	54.3610215400564	51.6484233374479
kidney	55.2265603475284	58.1007666742231	53.9102193412047
liver	59.7826765358543	52.8188828076784	64.8093962565023
stomach	56.6854277150729	51.5565231856997	55.3290942843712
testicle	57.6835908319885	55.9339787542915	55.807953663549
cont.diffExp=0.174531414885926,-5.74759592407921,2.39161793533281,2.53007081420456,-2.87420632669467,6.96379372817588,5.12890452937327,1.749612077697
cont.diffExpScore=2.4353622481951

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=0.040975621042298
cont.tran.correlation=-0.578572753718298

tran.covariance=0.000147478334928355
cont.tran.covariance=-0.000793922520999117

tran.mean=52.8005920428524
cont.tran.mean=56.1782076220396

weightedLogRatios:
wLogRatio
Lung	1.36006441309527
cerebhem	1.27412151282424
cortex	1.14302204344373
heart	0.910784282637007
kidney	1.49146962743528
liver	1.27010166945804
stomach	1.80488681145910
testicle	1.13593133618833

cont.weightedLogRatios:
wLogRatio
Lung	0.0125005159275362
cerebhem	-0.400013158699331
cortex	0.173933248980999
heart	0.182802320990158
kidney	-0.204807047739479
liver	0.498954543172425
stomach	0.378414615871346
testicle	0.124421661495327

varWeightedLogRatios=0.0713972237950668
cont.varWeightedLogRatios=0.0857188713730872

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99989922823111	0.074966540092448	53.3557934419606	1.70249147799281e-255	***
df.mm.trans1	0.435487494996675	0.0660388468014125	6.59441398645624	8.13610182584283e-11	***
df.mm.trans2	-0.270573951997579	0.0595888681022766	-4.54067950297652	6.54683747807393e-06	***
df.mm.exp2	0.0681372346850499	0.0793546353242882	0.858642149971485	0.390816765059648	   
df.mm.exp3	-0.0812839798454266	0.0793546353242882	-1.02431294042565	0.306023117344508	   
df.mm.exp4	0.185453885014346	0.0793546353242882	2.33702649198092	0.0197043606411098	*  
df.mm.exp5	0.116459046606731	0.0793546353242882	1.46757711292874	0.142645160273812	   
df.mm.exp6	0.277034284913306	0.0793546353242881	3.49109139978006	0.000509655827704902	***
df.mm.exp7	0.214241730450495	0.0793546353242881	2.69980108376759	0.00709698319080717	** 
df.mm.exp8	0.191016212150548	0.0793546353242882	2.40712103798282	0.0163234070202532	*  
df.mm.trans1:exp2	-0.013769614165105	0.0748506422543835	-0.183961202608099	0.854094424819839	   
df.mm.trans2:exp2	0.0137932069430121	0.0612414345717683	0.225226711938760	0.821865260888962	   
df.mm.trans1:exp3	0.0455699940949512	0.0748506422543835	0.608812332432358	0.542836185394679	   
df.mm.trans2:exp3	0.100460425887838	0.0612414345717683	1.64039961817206	0.101348217915098	   
df.mm.trans1:exp4	-0.173134069525720	0.0748506422543835	-2.31306057384672	0.0209933977107340	*  
df.mm.trans2:exp4	-0.0540230908260805	0.0612414345717682	-0.882133006906809	0.377992107791161	   
df.mm.trans1:exp5	-0.093863101763386	0.0748506422543835	-1.25400529556430	0.210237140173636	   
df.mm.trans2:exp5	-0.126877746643682	0.0612414345717683	-2.07176313766776	0.0386343899947633	*  
df.mm.trans1:exp6	-0.230063974078478	0.0748506422543834	-3.07364061482058	0.00219250866308183	** 
df.mm.trans2:exp6	-0.202068011198993	0.0612414345717683	-3.29953098930416	0.00101496042828738	** 
df.mm.trans1:exp7	-0.0885267067835611	0.0748506422543834	-1.18271138519692	0.237304434238285	   
df.mm.trans2:exp7	-0.193824948506930	0.0612414345717683	-3.16493155103654	0.00161489276017903	** 
df.mm.trans1:exp8	-0.157476314809582	0.0748506422543834	-2.10387392902243	0.0357272231310174	*  
df.mm.trans2:exp8	-0.0954336973855664	0.0612414345717683	-1.55831910295518	0.119586327705226	   
df.mm.trans1:probe2	-0.194836555318414	0.043703359473637	-4.45815968531994	9.5518504753922e-06	***
df.mm.trans1:probe3	-0.669447236122797	0.043703359473637	-15.3179811388785	3.41260243494144e-46	***
df.mm.trans1:probe4	0.244364108215588	0.043703359473637	5.59142617772884	3.17210556054842e-08	***
df.mm.trans1:probe5	-0.626971302755912	0.043703359473637	-14.3460665337208	2.3814839662062e-41	***
df.mm.trans1:probe6	0.142358095776859	0.043703359473637	3.25737191583025	0.00117592534331359	** 
df.mm.trans1:probe7	-0.487259479440994	0.043703359473637	-11.1492453969110	8.68423189431893e-27	***
df.mm.trans1:probe8	-0.272998579470957	0.043703359473637	-6.24662686710931	7.0812843830457e-10	***
df.mm.trans1:probe9	-0.386440020810730	0.043703359473637	-8.8423413088836	6.82538067115057e-18	***
df.mm.trans1:probe10	-0.330032835115108	0.043703359473637	-7.55165824984673	1.27174320110452e-13	***
df.mm.trans1:probe11	-0.556474134916107	0.043703359473637	-12.7329830387933	1.02736327446392e-33	***
df.mm.trans1:probe12	-0.54644866290967	0.043703359473637	-12.5035848385821	1.12755336891601e-32	***
df.mm.trans1:probe13	-0.415052706037873	0.043703359473637	-9.49704350047148	2.92858477637070e-20	***
df.mm.trans1:probe14	-0.571383539133803	0.043703359473637	-13.0741331104872	2.77117780081129e-35	***
df.mm.trans1:probe15	-0.660374242577997	0.043703359473637	-15.1103771090264	3.8172831263332e-45	***
df.mm.trans1:probe16	-0.557511033120932	0.043703359473637	-12.7567088625587	8.00641119199485e-34	***
df.mm.trans1:probe17	-0.630418046518757	0.043703359473637	-14.4249333257559	9.77673564947516e-42	***
df.mm.trans1:probe18	-0.661280544064036	0.043703359473637	-15.1311146792488	3.00147445821532e-45	***
df.mm.trans1:probe19	-0.614626438507484	0.043703359473637	-14.0635970760610	5.64882545991264e-40	***
df.mm.trans1:probe20	-0.596919838000559	0.043703359473637	-13.6584428563355	4.97721214172516e-38	***
df.mm.trans1:probe21	-0.503367663244049	0.043703359473637	-11.5178253870322	2.40837516181227e-28	***
df.mm.trans1:probe22	-0.59519537845604	0.043703359473637	-13.6189845729154	7.66736329091187e-38	***
df.mm.trans2:probe2	0.00594254685654746	0.043703359473637	0.135974600765695	0.891878440843455	   
df.mm.trans2:probe3	0.0504487498768292	0.043703359473637	1.15434489440706	0.248732455535831	   
df.mm.trans2:probe4	-0.0110879821928831	0.043703359473637	-0.253710065460108	0.799790221416549	   
df.mm.trans2:probe5	-0.0141969103457252	0.043703359473637	-0.324847117400416	0.745388843446651	   
df.mm.trans2:probe6	0.0428478790543495	0.043703359473637	0.980425293854043	0.327197516254946	   
df.mm.trans3:probe2	0.404676013637826	0.043703359473637	9.2596088381246	2.18935096253446e-19	***
df.mm.trans3:probe3	0.190803529101715	0.043703359473637	4.36587785011842	1.44697604279111e-05	***
df.mm.trans3:probe4	-0.0131988428306210	0.043703359473637	-0.302009799465941	0.76272968060988	   
df.mm.trans3:probe5	-0.0695332101104083	0.043703359473637	-1.59102666128796	0.112031810843205	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23920830135967	0.148533321927221	28.5404530536037	2.65155294862358e-121	***
df.mm.trans1	-0.174543296071027	0.130844631212276	-1.33397369425007	0.182623981354408	   
df.mm.trans2	-0.190920980086245	0.118065106355439	-1.61708218439638	0.106287866968176	   
df.mm.exp2	-0.071307398106635	0.157227578870578	-0.45352983629756	0.650300589206276	   
df.mm.exp3	-0.122025677888522	0.157227578870578	-0.776108611256855	0.437933506934271	   
df.mm.exp4	-0.0848414796901886	0.157227578870578	-0.539609401223596	0.589629198784392	   
df.mm.exp5	-0.0908651667577862	0.157227578870578	-0.577921299879468	0.563493541860279	   
df.mm.exp6	-0.291033883604402	0.157227578870578	-1.85103584049950	0.0645637572471043	.  
df.mm.exp7	-0.210270944584023	0.157227578870578	-1.33736680354982	0.181514959229557	   
df.mm.exp8	-0.119939579649280	0.157227578870578	-0.762840593939361	0.445802227418907	   
df.mm.trans1:exp2	0.0581722140208944	0.148303690269275	0.392250617063345	0.694986299862446	   
df.mm.trans2:exp2	0.159612420211985	0.121339382947589	1.31542139357119	0.188776839916508	   
df.mm.trans1:exp3	0.121718912008612	0.148303690269275	0.820740952484774	0.412058862758206	   
df.mm.trans2:exp3	0.0814424353311788	0.121339382947589	0.67119539718079	0.50230607901489	   
df.mm.trans1:exp4	0.0942952974917801	0.148303690269275	0.63582569874403	0.525087151639188	   
df.mm.trans2:exp4	0.0519056607793629	0.121339382947589	0.427772579013221	0.668941497352875	   
df.mm.trans1:exp5	0.0706242080924819	0.148303690269275	0.476213423713526	0.634063284757627	   
df.mm.trans2:exp5	0.124460826153549	0.121339382947589	1.02572489763944	0.305357355250925	   
df.mm.trans1:exp6	0.350064850615497	0.148303690269275	2.36045947325979	0.0185114999573021	*  
df.mm.trans2:exp6	0.229319439171136	0.121339382947589	1.88990114833688	0.059162828103335	.  
df.mm.trans1:exp7	0.216103153555642	0.148303690269275	1.45716639392630	0.145495804122445	   
df.mm.trans2:exp7	0.124366487841269	0.121339382947589	1.02494742284117	0.305723828780805	   
df.mm.trans1:exp8	0.143227363115638	0.148303690269275	0.96577072934315	0.334475281060309	   
df.mm.trans2:exp8	0.115528423914192	0.121339382947589	0.95210986827	0.341352982595080	   
df.mm.trans1:probe2	-0.0620547784807543	0.0865906997174162	-0.716644843883541	0.473819990649129	   
df.mm.trans1:probe3	-0.0340975482281102	0.0865906997174162	-0.39377841199327	0.693858387423632	   
df.mm.trans1:probe4	0.0111835595494810	0.0865906997174162	0.129154280840528	0.897270761826555	   
df.mm.trans1:probe5	-0.115842560934825	0.0865906997174162	-1.33781758679478	0.181368000469788	   
df.mm.trans1:probe6	-0.0993866322657586	0.0865906997174162	-1.14777490642876	0.251433423319467	   
df.mm.trans1:probe7	0.0453048291745408	0.0865906997174162	0.523206641387476	0.600987512185374	   
df.mm.trans1:probe8	-0.0191722400458102	0.0865906997174162	-0.221412231433372	0.824832717017392	   
df.mm.trans1:probe9	-0.147696754283960	0.0865906997174162	-1.70568842573117	0.0884868802989703	.  
df.mm.trans1:probe10	-0.156751184193254	0.0865906997174162	-1.81025427332038	0.0706629681132883	.  
df.mm.trans1:probe11	0.0137532197633349	0.0865906997174162	0.15883021858257	0.873846093441995	   
df.mm.trans1:probe12	-0.0345809475826395	0.0865906997174162	-0.399360990215952	0.689742775244024	   
df.mm.trans1:probe13	-0.0490960378736398	0.0865906997174162	-0.566989734854458	0.570893562303937	   
df.mm.trans1:probe14	-0.104345687786040	0.0865906997174162	-1.20504497742329	0.228572204587367	   
df.mm.trans1:probe15	0.0076035965602577	0.0865906997174162	0.0878107762735676	0.930050893569609	   
df.mm.trans1:probe16	-0.0970298475309978	0.0865906997174162	-1.12055737911403	0.262840828028298	   
df.mm.trans1:probe17	-0.0288887760745947	0.0865906997174162	-0.333624467395131	0.738757721506477	   
df.mm.trans1:probe18	0.0520266102867408	0.0865906997174162	0.600833697574066	0.548135235816915	   
df.mm.trans1:probe19	0.0101236441270278	0.0865906997174162	0.116913758175713	0.906960203955984	   
df.mm.trans1:probe20	0.0172204583544323	0.0865906997174162	0.198871915928966	0.842417713056082	   
df.mm.trans1:probe21	-0.115046180120046	0.0865906997174162	-1.32862051577701	0.184383866204513	   
df.mm.trans1:probe22	0.0163120872076226	0.0865906997174162	0.188381515114858	0.85062937662512	   
df.mm.trans2:probe2	0.0193931045158729	0.0865906997174162	0.223962903396799	0.822848153824374	   
df.mm.trans2:probe3	-0.105525484273257	0.0865906997174162	-1.21866995667703	0.223358895738262	   
df.mm.trans2:probe4	-0.0784436013622657	0.0865906997174162	-0.905912547401302	0.365277806369233	   
df.mm.trans2:probe5	-0.0226351845791618	0.0865906997174162	-0.261404338491667	0.793853645202517	   
df.mm.trans2:probe6	-0.0295341667160302	0.0865906997174162	-0.341077815659341	0.73314208131247	   
df.mm.trans3:probe2	0.0798273581852014	0.0865906997174162	0.921892979797062	0.356885674196758	   
df.mm.trans3:probe3	-0.0467781879597665	0.0865906997174162	-0.540221849603069	0.589207027372567	   
df.mm.trans3:probe4	-0.0659918324507119	0.0865906997174162	-0.762112243763735	0.446236506508773	   
df.mm.trans3:probe5	0.0797234893248455	0.0865906997174162	0.920693441501439	0.357511355061045