fitVsDatCorrelation=0.778903412998317
cont.fitVsDatCorrelation=0.263145998208896

fstatistic=10789.2530497745,51,669
cont.fstatistic=4551.64800539226,51,669

residuals=-0.530072571964474,-0.0918069970268404,-0.0088024720512595,0.0740669521351484,0.609151449715834
cont.residuals=-0.564565473477122,-0.147212091884801,-0.0143117286972820,0.120243236812250,1.07287264581154

predictedValues:
Include	Exclude	Both
Lung	58.0081760301282	67.2996652150943	66.5501291507162
cerebhem	60.7236917480318	72.300349001536	60.9550640151528
cortex	68.1893261909044	68.6772515676265	83.1275580160602
heart	57.0081121089447	68.0694684733622	73.9739487525773
kidney	54.1860105604185	65.1616770369587	64.5912845272598
liver	53.6711168928858	67.1505695128564	66.0793754871047
stomach	55.9455407685439	75.7203120492214	68.2482102901858
testicle	59.859076346965	68.5495454735993	77.2940737471965


diffExp=-9.29148918496615,-11.5766572535042,-0.487925376722131,-11.0613563644175,-10.9756664765402,-13.4794526199706,-19.7747712806775,-8.69046912663432
diffExpScore=0.988417586009192
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,-1,-1,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	61.3328495295284	57.9831661567136	54.8251141842725
cerebhem	60.9481632950383	57.9122860962794	58.1139601343711
cortex	58.6420674067784	58.9786291161198	58.0541914342278
heart	61.7915127259684	60.0470752315989	57.0421905348358
kidney	60.6725711752873	62.502559714177	59.8992855188645
liver	60.9213595338142	56.0263031111039	61.9203897708463
stomach	64.1071821931522	55.1702851439176	63.5891702956384
testicle	61.9635305733264	62.173166764404	57.9952194744438
cont.diffExp=3.34968337281485,3.03587719875888,-0.33656170934141,1.74443749436954,-1.82998853888969,4.89505642271032,8.9368970492346,-0.209636191077557
cont.diffExpScore=1.18227998136811

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=0.130402024896942
cont.tran.correlation=-0.31153091826419

tran.covariance=0.000575762469204743
cont.tran.covariance=-0.000352606760461097

tran.mean=63.7824930610673
cont.tran.mean=60.0732942354505

weightedLogRatios:
wLogRatio
Lung	-0.614322935226525
cerebhem	-0.731759100946382
cortex	-0.0301302144171697
heart	-0.732724442644038
kidney	-0.753408073093413
liver	-0.917514542896862
stomach	-1.26385339267288
testicle	-0.563914493639903

cont.weightedLogRatios:
wLogRatio
Lung	0.229606885971544
cerebhem	0.208692917968347
cortex	-0.0233166743362798
heart	0.117682980498839
kidney	-0.122439446358569
liver	0.340721202710306
stomach	0.613362478889643
testicle	-0.0139431489653233

varWeightedLogRatios=0.120679037007641
cont.varWeightedLogRatios=0.0557455513683801

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.88301665862976	0.0759616203570204	51.1181388756524	3.1062894705622e-233	***
df.mm.trans1	0.226779863414167	0.0672839230928389	3.3704910919248	0.000793476274856732	***
df.mm.trans2	0.409056656265367	0.0614423547246871	6.65756802613248	5.8058842252046e-11	***
df.mm.exp2	0.205242206567267	0.0829161836430723	2.47529731289575	0.0135591331042571	*  
df.mm.exp3	-0.0404539524819663	0.0829161836430723	-0.48788970626178	0.625787819049873	   
df.mm.exp4	-0.111774376304416	0.0829161836430723	-1.34804053193727	0.178101602507921	   
df.mm.exp5	-0.0705689425161062	0.0829161836430723	-0.851087681747184	0.395025170149650	   
df.mm.exp6	-0.0728280210961761	0.0829161836430723	-0.878333009267255	0.380078365771082	   
df.mm.exp7	0.0564902245723378	0.0829161836430723	0.681293109382725	0.495921799558789	   
df.mm.exp8	-0.099851182815681	0.0829161836430723	-1.20424238584724	0.228921788318952	   
df.mm.trans1:exp2	-0.159492242437054	0.0783484291364753	-2.03567887952462	0.0421761353380281	*  
df.mm.trans2:exp2	-0.133568512406310	0.0664808466452585	-2.00912772845736	0.0449248109841714	*  
df.mm.trans1:exp3	0.202158030969733	0.0783484291364753	2.58024357600831	0.0100851021285751	*  
df.mm.trans2:exp3	0.0607167076441768	0.0664808466452584	0.91329624558124	0.361415770382857	   
df.mm.trans1:exp4	0.094383985065213	0.0783484291364753	1.20466978222123	0.228756797166916	   
df.mm.trans2:exp4	0.123147893081156	0.0664808466452585	1.85238153987829	0.0644113340922071	.  
df.mm.trans1:exp5	0.00240774325647776	0.0783484291364753	0.0307312256673801	0.975493053960733	   
df.mm.trans2:exp5	0.03828520101158	0.0664808466452584	0.575883174531	0.564887814603133	   
df.mm.trans1:exp6	-0.00488094920384671	0.0783484291364753	-0.0622979842434949	0.950344141111254	   
df.mm.trans2:exp6	0.07061016311095	0.0664808466452585	1.06211287421963	0.28856769843598	   
df.mm.trans1:exp7	-0.0926954599330991	0.0783484291364753	-1.18311829547511	0.237182530777438	   
df.mm.trans2:exp7	0.061400960731454	0.0664808466452585	0.923588730135904	0.356033471530405	   
df.mm.trans1:exp8	0.131260288251753	0.0783484291364753	1.67534039544189	0.0943347575108725	.  
df.mm.trans2:exp8	0.118252696097332	0.0664808466452585	1.77874834729960	0.0757350442097309	.  
df.mm.trans1:probe2	-0.318955641738897	0.0429132019831425	-7.43257615370186	3.2662716349847e-13	***
df.mm.trans1:probe3	-0.231030011934058	0.0429132019831425	-5.38365820441021	1.01122924086757e-07	***
df.mm.trans1:probe4	-0.302856788940157	0.0429132019831425	-7.05742696755949	4.25200994551532e-12	***
df.mm.trans1:probe5	-0.262104343234240	0.0429132019831425	-6.10777875156464	1.71349978578494e-09	***
df.mm.trans1:probe6	-0.111659463215038	0.0429132019831425	-2.60198395959594	0.00947374749199827	** 
df.mm.trans1:probe7	-0.232227804278547	0.0429132019831425	-5.41157018228964	8.71225556120454e-08	***
df.mm.trans1:probe8	-0.183792569193843	0.0429132019831425	-4.28289106149761	2.11514805335251e-05	***
df.mm.trans1:probe9	-0.0550094869028802	0.0429132019831425	-1.28187793873991	0.200329528064362	   
df.mm.trans1:probe10	-0.0977144702792213	0.0429132019831425	-2.27702585133606	0.0230995659324170	*  
df.mm.trans1:probe11	-0.0374822855699502	0.0429132019831425	-0.873444157923109	0.382734437989375	   
df.mm.trans1:probe12	-0.0705431428838823	0.0429132019831425	-1.64385642701734	0.100675685237925	   
df.mm.trans1:probe13	0.0658641353327665	0.0429132019831425	1.53482220596449	0.125300273330188	   
df.mm.trans1:probe14	-0.0327189030381856	0.0429132019831425	-0.76244375917319	0.446063928756315	   
df.mm.trans1:probe15	0.145348076341792	0.0429132019831425	3.38702472956665	0.000747927023392725	***
df.mm.trans1:probe16	0.0781163025262398	0.0429132019831425	1.82033264627808	0.0691550288793743	.  
df.mm.trans1:probe17	0.0716988449791437	0.0429132019831425	1.67078758204314	0.0952313466708046	.  
df.mm.trans1:probe18	0.124796425294007	0.0429132019831425	2.90811264428674	0.00375667501757947	** 
df.mm.trans1:probe19	0.158974335198331	0.0429132019831425	3.7045554247101	0.000229250747516807	***
df.mm.trans1:probe20	0.110195462042494	0.0429132019831425	2.56786855676213	0.0104486072437606	*  
df.mm.trans2:probe2	-0.179897580899375	0.0429132019831425	-4.19212672524514	3.13504623826549e-05	***
df.mm.trans2:probe3	-0.214958286862146	0.0429132019831425	-5.00914117167458	7.00391280836119e-07	***
df.mm.trans2:probe4	-0.189901492274257	0.0429132019831425	-4.42524639268017	1.12452934716213e-05	***
df.mm.trans2:probe5	-0.136088540745434	0.0429132019831425	-3.17125114082358	0.00158719279536967	** 
df.mm.trans2:probe6	-0.108334626943801	0.0429132019831425	-2.52450579162929	0.011816277200687	*  
df.mm.trans3:probe2	-0.519606829437596	0.0429132019831425	-12.1083211092407	1.18218953875383e-30	***
df.mm.trans3:probe3	-0.356394784535963	0.0429132019831425	-8.30501496196822	5.53506516915834e-16	***
df.mm.trans3:probe4	-0.407589157279086	0.0429132019831425	-9.4979898596054	3.72021286789198e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12150506538141	0.116854250781628	35.2704761513852	8.97533078846245e-155	***
df.mm.trans1	-0.0151391454620177	0.103505064606428	-0.146264779598789	0.883756430049748	   
df.mm.trans2	-0.0803825234309344	0.0945187884864385	-0.850439629179839	0.39538497519157	   
df.mm.exp2	-0.0657725682116081	0.127552683470208	-0.515650211521974	0.606269093812883	   
df.mm.exp3	-0.0850693701511123	0.127552683470208	-0.666935166212961	0.505043500390631	   
df.mm.exp4	0.00278374989470228	0.127552683470208	0.0218243146201818	0.982594606078408	   
df.mm.exp5	-0.0242852786227533	0.127552683470208	-0.190394101966703	0.84905807773176	   
df.mm.exp6	-0.162764334190854	0.127552683470208	-1.2760557423229	0.202378652431025	   
df.mm.exp7	-0.15378221094076	0.127552683470208	-1.20563681419277	0.228383799516615	   
df.mm.exp8	0.0237889817466456	0.127552683470208	0.186503185189372	0.8521067209223	   
df.mm.trans1:exp2	0.0594807082626707	0.120525956971825	0.493509529043403	0.621814575554941	   
df.mm.trans2:exp2	0.0645493956385073	0.102269665780530	0.631168540014882	0.528145906744167	   
df.mm.trans1:exp3	0.0402061022174656	0.120525956971825	0.333588740779420	0.73879444446021	   
df.mm.trans2:exp3	0.102091800327390	0.102269665780530	0.998260819063183	0.318513827284588	   
df.mm.trans1:exp4	0.00466668980218682	0.120525956971825	0.0387193756385419	0.969125676636018	   
df.mm.trans2:exp4	0.0321923621514073	0.102269665780530	0.314779186044195	0.753027465482594	   
df.mm.trans1:exp5	0.0134614185563923	0.120525956971825	0.111688958085097	0.911103535695822	   
df.mm.trans2:exp5	0.0993400602116197	0.102269665780530	0.971354110267677	0.331723029827785	   
df.mm.trans1:exp6	0.156032597871942	0.120525956971825	1.29459746093049	0.195905777263145	   
df.mm.trans2:exp6	0.128432883323131	0.102269665780530	1.25582578512329	0.209617548483265	   
df.mm.trans1:exp7	0.198023034481414	0.120525956971825	1.64299076694081	0.100854725526305	   
df.mm.trans2:exp7	0.104053976929699	0.102269665780530	1.01744712017537	0.30930858110309	   
df.mm.trans1:exp8	-0.013558567132287	0.120525956971825	-0.112494996703960	0.910464682334157	   
df.mm.trans2:exp8	0.0459817926744426	0.102269665780530	0.449613209581803	0.653134994456286	   
df.mm.trans1:probe2	0.0132803928097032	0.0660147853983655	0.201173005858654	0.840624446112236	   
df.mm.trans1:probe3	-0.0676501190601777	0.0660147853983655	-1.02477223324962	0.305841065627038	   
df.mm.trans1:probe4	0.0971803791460165	0.0660147853983655	1.47210020542493	0.141464078556041	   
df.mm.trans1:probe5	0.0576601502633527	0.0660147853983655	0.873442970622463	0.382735084421524	   
df.mm.trans1:probe6	0.0356402774789085	0.0660147853983655	0.539883258937186	0.589457200051268	   
df.mm.trans1:probe7	-0.0124054063483946	0.0660147853983655	-0.187918604499497	0.850997441095474	   
df.mm.trans1:probe8	0.0519095408324772	0.0660147853983655	0.786332039394352	0.431951421266397	   
df.mm.trans1:probe9	0.00945022155222658	0.0660147853983655	0.143153105705022	0.88621236996407	   
df.mm.trans1:probe10	0.054123606621972	0.0660147853983655	0.819870977317637	0.412581662401163	   
df.mm.trans1:probe11	0.0566522400731598	0.0660147853983655	0.85817502444782	0.391103172535615	   
df.mm.trans1:probe12	0.0776671206786047	0.0660147853983655	1.17651099234699	0.239809192536706	   
df.mm.trans1:probe13	-0.0108951889413595	0.0660147853983655	-0.165041647497793	0.868961057983526	   
df.mm.trans1:probe14	0.0540570895945941	0.0660147853983655	0.818863369295033	0.413155964161128	   
df.mm.trans1:probe15	-0.068509473330831	0.0660147853983655	-1.03778983628306	0.299742853338677	   
df.mm.trans1:probe16	-0.0458044589879105	0.0660147853983655	-0.693851516921611	0.488016176418044	   
df.mm.trans1:probe17	0.0369803347202187	0.0660147853983655	0.560182609048281	0.575542415948919	   
df.mm.trans1:probe18	-0.0380458356265997	0.0660147853983655	-0.576322946397728	0.564590753798152	   
df.mm.trans1:probe19	-0.0646726999699857	0.0660147853983655	-0.979669926664444	0.327603209164831	   
df.mm.trans1:probe20	0.00217368973071046	0.0660147853983655	0.0329273164730198	0.973742370397394	   
df.mm.trans2:probe2	0.0299120437160103	0.0660147853983655	0.453111277049625	0.65061553458154	   
df.mm.trans2:probe3	-0.039606280607338	0.0660147853983655	-0.599960756796139	0.548735544187988	   
df.mm.trans2:probe4	0.0473151410632819	0.0660147853983655	0.716735512775801	0.473787446861089	   
df.mm.trans2:probe5	0.152445160910144	0.0660147853983655	2.30925784262140	0.0212330160404488	*  
df.mm.trans2:probe6	0.000235812829890005	0.0660147853983655	0.00357212143411503	0.997150930398815	   
df.mm.trans3:probe2	-0.0146723462815791	0.0660147853983655	-0.222258486383603	0.824180500046718	   
df.mm.trans3:probe3	-0.036926473874331	0.0660147853983655	-0.55936671840253	0.576098680491267	   
df.mm.trans3:probe4	-0.0357412577729000	0.0660147853983655	-0.541412920714955	0.588403250205367