fitVsDatCorrelation=0.907669489170286
cont.fitVsDatCorrelation=0.266209715454581

fstatistic=7515.81154471342,50,646
cont.fstatistic=1414.30546151043,50,646

residuals=-0.766426850611743,-0.110206978922339,-0.00785695934002491,0.109086026871195,0.732733837872535
cont.residuals=-0.772803827052693,-0.336533145166203,-0.0483749955345259,0.322137309377695,1.36905723545630

predictedValues:
Include	Exclude	Both
Lung	75.2274299926195	46.4521455338266	91.4036437591885
cerebhem	86.5085481212635	46.5420445178782	94.9206224764944
cortex	77.0195682147499	44.7604220973833	103.047308930578
heart	74.9635397895064	46.543600884096	88.2399599079971
kidney	82.896153598635	45.8616342493537	100.852472014187
liver	71.1255655168463	48.3316077934977	69.2299272149593
stomach	78.8143617904465	44.3885189809561	75.4699691790295
testicle	73.338639339429	44.6426889693822	76.2278374273486


diffExp=28.7752844587929,39.9665036033853,32.2591461173666,28.4199389054104,37.0345193492813,22.7939577233485,34.4258428094904,28.6959503700469
diffExpScore=0.996053220635826
diffExp1.5=1,1,1,1,1,0,1,1
diffExp1.5Score=0.875
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	78.8098154478345	68.6330094196295	82.3368086753849
cerebhem	73.1453175314505	90.5291520814636	82.1430663956079
cortex	79.3916579188108	68.720545961037	73.1079576033046
heart	74.2556253431125	72.395127650341	76.8696873217914
kidney	77.0675377803659	80.9675613874744	79.9029207825256
liver	77.4089394621768	89.1563203434505	73.7701716215
stomach	81.3713066163907	68.7828563565099	80.740161587863
testicle	65.512825277619	63.6761205971987	76.2773980165095
cont.diffExp=10.1768060282050,-17.3838345500131,10.6711119577737,1.86049769277159,-3.90002360710851,-11.7473808812737,12.5884502598808,1.83670468042039
cont.diffExpScore=13.7515189964238

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,-1,0,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.191422547976073
cont.tran.correlation=0.0932388639564616

tran.covariance=-0.000377732409724474
cont.tran.covariance=0.00130401979375563

tran.mean=61.7135293368669
cont.tran.mean=75.613982448429

weightedLogRatios:
wLogRatio
Lung	1.96668469223358
cerebhem	2.57271740260464
cortex	2.21039329760455
heart	1.94395666777766
kidney	2.43982650990048
liver	1.57297888921259
stomach	2.34240852098340
testicle	2.00886382864642

cont.weightedLogRatios:
wLogRatio
Lung	0.594245379428315
cerebhem	-0.937984327385017
cortex	0.621004384268015
heart	0.108979230328162
kidney	-0.215700041207795
liver	-0.624461451305236
stomach	0.725212281390833
testicle	0.118523329165092

varWeightedLogRatios=0.104251770031359
cont.varWeightedLogRatios=0.369704213754742

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.18819306708973	0.0907384667428655	35.1360694260398	5.00350023655723e-152	***
df.mm.trans1	1.06291531579666	0.0788049981448135	13.4879175283201	1.04124350498018e-36	***
df.mm.trans2	0.583341761860208	0.071612537174325	8.14580497881524	1.95021970530249e-15	***
df.mm.exp2	0.103905093440211	0.0948271319411638	1.09573168895037	0.273604537825807	   
df.mm.exp3	-0.133457592542069	0.0948271319411637	-1.40737771785478	0.159796377893732	   
df.mm.exp4	0.0336782296733081	0.0948271319411638	0.355153941534412	0.722590191202254	   
df.mm.exp5	-0.0140944361217668	0.0948271319411637	-0.148632947482918	0.881889657615092	   
df.mm.exp6	0.261446116775747	0.0948271319411637	2.75708134817325	0.00599674418295067	** 
df.mm.exp7	0.192688037442078	0.0948271319411637	2.03199267443450	0.0425630673286761	*  
df.mm.exp8	0.116398210555777	0.0948271319411638	1.22747791874585	0.220090118628666	   
df.mm.trans1:exp2	0.0358222128391743	0.0874262958689138	0.409741857219775	0.682131154889737	   
df.mm.trans2:exp2	-0.101971660566472	0.0719062330226487	-1.41811990810802	0.156637958029078	   
df.mm.trans1:exp3	0.157001189781613	0.0874262958689138	1.79581198335361	0.0729916199635397	.  
df.mm.trans2:exp3	0.0963592518054012	0.0719062330226487	1.34006813811329	0.180694340488187	   
df.mm.trans1:exp4	-0.0371922954663896	0.0874262958689138	-0.425413144829508	0.670677203631352	   
df.mm.trans2:exp4	-0.0317113573533217	0.0719062330226487	-0.44100985436594	0.659353415358163	   
df.mm.trans1:exp5	0.111167174113168	0.0874262958689138	1.27155306087600	0.203989565072145	   
df.mm.trans2:exp5	0.00130069416030243	0.0719062330226487	0.0180887540012386	0.985573634747371	   
df.mm.trans1:exp6	-0.317515198320139	0.0874262958689138	-3.63180431201407	0.000303759861486971	***
df.mm.trans2:exp6	-0.221783018857131	0.0719062330226487	-3.08433649677177	0.00212712762482949	** 
df.mm.trans1:exp7	-0.146108725947946	0.0874262958689137	-1.67122173592966	0.0951622032714582	.  
df.mm.trans2:exp7	-0.238129837324233	0.0719062330226487	-3.31167170513894	0.000979081666026106	***
df.mm.trans1:exp8	-0.141826525847412	0.0874262958689138	-1.62224104816320	0.105239647768488	   
df.mm.trans2:exp8	-0.156130311899944	0.0719062330226487	-2.17130428527339	0.030271458788312	*  
df.mm.trans1:probe2	0.328308619323813	0.0535374537451079	6.13231665605339	1.50850933369948e-09	***
df.mm.trans1:probe3	0.531688649362933	0.0535374537451079	9.93115309320284	1.00015606369631e-21	***
df.mm.trans1:probe4	0.214969628485578	0.0535374537451079	4.01531289681892	6.63379393396975e-05	***
df.mm.trans1:probe5	0.338739180677831	0.0535374537451079	6.32714402688201	4.66791707552072e-10	***
df.mm.trans1:probe6	-0.436280762606559	0.0535374537451079	-8.14907568603644	1.90298641041561e-15	***
df.mm.trans1:probe7	-0.344593536110163	0.0535374537451079	-6.43649467811402	2.38423866067460e-10	***
df.mm.trans1:probe8	-0.369500969533011	0.0535374537451079	-6.90172848511262	1.22921278280121e-11	***
df.mm.trans1:probe9	-0.399823211344384	0.0535374537451079	-7.46810285838292	2.64710409735745e-13	***
df.mm.trans1:probe10	-0.367291609898076	0.0535374537451079	-6.86046093351307	1.61011507242920e-11	***
df.mm.trans1:probe11	-0.432852971894721	0.0535374537451079	-8.08504965431372	3.07087590024714e-15	***
df.mm.trans1:probe12	0.51910659844138	0.0535374537451079	9.69613909755307	7.59592548839703e-21	***
df.mm.trans1:probe13	0.379286662327293	0.0535374537451079	7.08451067047526	3.6596975044194e-12	***
df.mm.trans1:probe14	0.365829131019038	0.0535374537451079	6.83314400346256	1.92370048935135e-11	***
df.mm.trans1:probe15	0.309255253777292	0.0535374537451079	5.77642812916837	1.18648638935137e-08	***
df.mm.trans1:probe16	0.308397816204221	0.0535374537451079	5.76041247072572	1.29868267833169e-08	***
df.mm.trans1:probe17	0.651134990081524	0.0535374537451079	12.1622330636339	8.62744508202207e-31	***
df.mm.trans2:probe2	0.163503444580145	0.0535374537451079	3.05400113644899	0.00235100049109109	** 
df.mm.trans2:probe3	0.0547074520542866	0.0535374537451079	1.02185382806491	0.30723259940625	   
df.mm.trans2:probe4	0.325502283574042	0.0535374537451079	6.07989847861947	2.05738565668790e-09	***
df.mm.trans2:probe5	0.0642865821769792	0.0535374537451079	1.20077772998036	0.230277535180424	   
df.mm.trans2:probe6	0.194654142683823	0.0535374537451079	3.63584984094635	0.000299126405395314	***
df.mm.trans3:probe2	-0.0415874939713632	0.0535374537451079	-0.776792526767549	0.437565521325658	   
df.mm.trans3:probe3	-0.693954585369674	0.0535374537451079	-12.9620394102715	2.55673824416360e-34	***
df.mm.trans3:probe4	-0.502951762387799	0.0535374537451079	-9.39439079008791	9.74017477706576e-20	***
df.mm.trans3:probe5	-0.312987777386963	0.0535374537451079	-5.84614611813814	7.98671572163147e-09	***
df.mm.trans3:probe6	-0.363507186925294	0.0535374537451079	-6.78977354163973	2.54865009239421e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43161237081194	0.208403999766759	21.2645264763234	1.80271300171255e-76	***
df.mm.trans1	0.0848671249867494	0.180995749702620	0.468890154195268	0.639306346502234	   
df.mm.trans2	-0.165072970822923	0.164476431179597	-1.00362690045649	0.31593426123595	   
df.mm.exp2	0.204664696863027	0.217794660768858	0.93971402301838	0.34771550092968	   
df.mm.exp3	0.12751140232816	0.217794660768858	0.585466153660606	0.558438777472895	   
df.mm.exp4	0.0625480219604327	0.217794660768858	0.287188040972289	0.774060437144096	   
df.mm.exp5	0.172925442827224	0.217794660768858	0.793983847982143	0.427496391551321	   
df.mm.exp6	0.353546138124296	0.217794660768858	1.62330029981547	0.105013069666246	   
df.mm.exp7	0.0537482297614864	0.217794660768858	0.246783964178665	0.805153823438363	   
df.mm.exp8	-0.183314019348086	0.217794660768858	-0.841682797461384	0.400276996840734	   
df.mm.trans1:exp2	-0.279254133946715	0.200796755751951	-1.39073030787252	0.164786090451842	   
df.mm.trans2:exp2	0.0722336185460427	0.165150978498971	0.437379295009703	0.661982477336795	   
df.mm.trans1:exp3	-0.120155654216938	0.200796755751951	-0.598394400183281	0.549786545234062	   
df.mm.trans2:exp3	-0.126236785839111	0.165150978498971	-0.764372012727112	0.444924690070939	   
df.mm.trans1:exp4	-0.122072035578066	0.200796755751951	-0.60793828625829	0.54344206700717	   
df.mm.trans2:exp4	-0.00918262835420107	0.165150978498971	-0.0556014165805155	0.955676527662536	   
df.mm.trans1:exp5	-0.195280842009978	0.200796755751951	-0.97252986622559	0.331151018069118	   
df.mm.trans2:exp5	-0.00765045093846697	0.165150978498971	-0.0463239818982642	0.963066341442192	   
df.mm.trans1:exp6	-0.371481417897955	0.200796755751951	-1.85003695157732	0.0647646885510533	.  
df.mm.trans2:exp6	-0.0919285065937766	0.165150978498971	-0.556633133084036	0.577970883325352	   
df.mm.trans1:exp7	-0.0217630680927883	0.200796755751951	-0.108383564322487	0.913725092914064	   
df.mm.trans2:exp7	-0.0515673026112045	0.165150978498971	-0.312243397404549	0.75495631179752	   
df.mm.trans1:exp8	-0.00147760204041207	0.200796755751951	-0.00735869479005621	0.994130935860396	   
df.mm.trans2:exp8	0.108350032884752	0.165150978498971	0.656066551161408	0.51201490500283	   
df.mm.trans1:probe2	-0.328323804060505	0.122962398399636	-2.67011548516995	0.00777326499166129	** 
df.mm.trans1:probe3	-0.280693822474343	0.122962398399636	-2.28276144681295	0.0227684926000447	*  
df.mm.trans1:probe4	-0.194101576075275	0.122962398399636	-1.57854416147961	0.114930165365063	   
df.mm.trans1:probe5	-0.248004762301458	0.122962398399636	-2.01691545976052	0.0441175443928863	*  
df.mm.trans1:probe6	-0.250982533145501	0.122962398399636	-2.04113238202943	0.041643547382842	*  
df.mm.trans1:probe7	-0.255045858789681	0.122962398399636	-2.07417765194173	0.0384583478492252	*  
df.mm.trans1:probe8	-0.211144026429094	0.122962398399636	-1.71714303866180	0.0864323388512272	.  
df.mm.trans1:probe9	-0.263202190017834	0.122962398399636	-2.14050956587891	0.0326873695195024	*  
df.mm.trans1:probe10	-0.251644640085132	0.122962398399636	-2.04651701138156	0.0411097477274056	*  
df.mm.trans1:probe11	-0.120354563632408	0.122962398399636	-0.978791607831587	0.328049359178256	   
df.mm.trans1:probe12	-0.236941232306931	0.122962398399636	-1.92694055573686	0.0544253094656858	.  
df.mm.trans1:probe13	-0.230922144917565	0.122962398399636	-1.87798992149659	0.0608327778964611	.  
df.mm.trans1:probe14	-0.188007077405716	0.122962398399636	-1.52898023991595	0.126758830215473	   
df.mm.trans1:probe15	-0.150599912522417	0.122962398399636	-1.22476394802383	0.221110584609671	   
df.mm.trans1:probe16	-0.0384711543489727	0.122962398399636	-0.312869257998196	0.75448097117582	   
df.mm.trans1:probe17	-0.188725440426075	0.122962398399636	-1.53482237563962	0.12531705921382	   
df.mm.trans2:probe2	-0.0185820640725941	0.122962398399636	-0.151119889612117	0.879928320126734	   
df.mm.trans2:probe3	-0.0644849799139132	0.122962398399636	-0.524428449291732	0.600160470933783	   
df.mm.trans2:probe4	-0.182198326998513	0.122962398399636	-1.48174018537241	0.138897043909744	   
df.mm.trans2:probe5	-0.0315212338764203	0.122962398399636	-0.256348560915136	0.797763316761814	   
df.mm.trans2:probe6	-0.156402924523266	0.122962398399636	-1.27195733459058	0.203845970543819	   
df.mm.trans3:probe2	0.0172271468264061	0.122962398399636	0.140100933705089	0.888623912847616	   
df.mm.trans3:probe3	0.0893114505836985	0.122962398399636	0.726331396801731	0.467898718354644	   
df.mm.trans3:probe4	0.123676578914624	0.122962398399636	1.00580812121659	0.314884470386036	   
df.mm.trans3:probe5	-0.0194311366052990	0.122962398399636	-0.158025029262576	0.87448645897551	   
df.mm.trans3:probe6	0.145686356633351	0.122962398399636	1.18480412328865	0.236530596086234