fitVsDatCorrelation=0.873322221813724
cont.fitVsDatCorrelation=0.266122132031923

fstatistic=9105.22409667535,60,876
cont.fstatistic=2314.56359391508,60,876

residuals=-0.723015875998561,-0.0977784092123488,-0.00257783993509194,0.0953391483394322,1.28436031856153
cont.residuals=-0.894301217831572,-0.267869165871261,-0.0387124807817783,0.247375449286090,1.27922323960456

predictedValues:
Include	Exclude	Both
Lung	78.2189459834483	82.4465149818215	67.32020659452
cerebhem	69.083654639524	62.40471710931	90.1976575751949
cortex	88.8169336296023	73.0907757594755	86.3060161440598
heart	89.722914502298	74.2378273607087	88.0583711331964
kidney	87.235599682374	90.7286059600352	76.1892283671644
liver	81.6697355673729	94.0506544023865	74.974199067364
stomach	77.4027872590497	73.9047486529554	78.8126590562225
testicle	88.380583252257	78.4615660896463	92.2574350313304


diffExp=-4.22756899837327,6.67893753021404,15.7261578701267,15.4850871415894,-3.49300627766125,-12.3809188350136,3.49803860609424,9.9190171626107
diffExpScore=2.21726695645179
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,1,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	83.0394886973408	89.4960377803988	88.5321461794804
cerebhem	81.1852308202754	99.7823432989213	90.6898221522197
cortex	81.8468613778118	70.393152723195	83.592061561077
heart	85.738145312703	90.5648480712	77.0778688506115
kidney	81.5096276208518	85.7413758538067	89.3995610618508
liver	93.8930832846004	90.1138901576502	78.4369970229187
stomach	86.4906498463996	102.452752159153	94.5465843186367
testicle	82.3729359478768	84.9409433150551	85.5161694918111
cont.diffExp=-6.45654908305801,-18.597112478646,11.4537086546168,-4.82670275849695,-4.23174823295481,3.77919312695016,-15.9621023127532,-2.56800736717835
cont.diffExpScore=1.76715243114819

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=0.385808873053099
cont.tran.correlation=0.240411258693045

tran.covariance=0.0054433881245369
cont.tran.covariance=0.00145122962812977

tran.mean=80.6160353020166
cont.tran.mean=86.8475853917025

weightedLogRatios:
wLogRatio
Lung	-0.230860862036939
cerebhem	0.425466533102247
cortex	0.855334282993387
heart	0.833969336826503
kidney	-0.176209290955107
liver	-0.631400501745738
stomach	0.200054302467271
testicle	0.526425453511538

cont.weightedLogRatios:
wLogRatio
Lung	-0.333713332173185
cerebhem	-0.928132159228814
cortex	0.652685200587276
heart	-0.245289879227765
kidney	-0.224020609936764
liver	0.185758846983983
stomach	-0.769718264435394
testicle	-0.135893700266416

varWeightedLogRatios=0.286004322685737
cont.varWeightedLogRatios=0.250048253020048

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54540213603269	0.0855589713533271	53.1259558657134	3.29242213247868e-276	***
df.mm.trans1	-0.416108070536365	0.0737299713519467	-5.64367600999182	2.24765875871627e-08	***
df.mm.trans2	-0.131445472218447	0.0649867038725235	-2.02265177929761	0.0434124486006190	*  
df.mm.exp2	-0.695245669848855	0.0832496073038809	-8.35133873137734	2.62108428798603e-16	***
df.mm.exp3	-0.241821045503613	0.0832496073038808	-2.90477100535632	0.00376766205885059	** 
df.mm.exp4	-0.236201122299158	0.0832496073038809	-2.837264101883	0.00465522384734809	** 
df.mm.exp5	0.081063870606655	0.0832496073038808	0.973744780690108	0.330452079531761	   
df.mm.exp6	0.0671717354806398	0.0832496073038808	0.806871499530886	0.419959529125815	   
df.mm.exp7	-0.277474993026205	0.0832496073038809	-3.33304867148928	0.00089500870495379	***
df.mm.exp8	-0.242522912128618	0.0832496073038808	-2.91320187545572	0.00366837670799373	** 
df.mm.trans1:exp2	0.571051931969302	0.0767523455590304	7.44018867189022	2.39919707574514e-13	***
df.mm.trans2:exp2	0.416736757217018	0.0558455343094922	7.46231121914765	2.04914917949164e-13	***
df.mm.trans1:exp3	0.368886477138646	0.0767523455590304	4.80619158218343	1.80968140807518e-06	***
df.mm.trans2:exp3	0.121373437845701	0.0558455343094922	2.17337768089132	0.0300191715203396	*  
df.mm.trans1:exp4	0.373415421634721	0.0767523455590304	4.86519882767005	1.35555874447429e-06	***
df.mm.trans2:exp4	0.131325165421647	0.0558455343094922	2.35157863641974	0.0189145673979164	*  
df.mm.trans1:exp5	0.0280367360234846	0.0767523455590304	0.365288328575202	0.714984368641338	   
df.mm.trans2:exp5	0.0146590478981931	0.0558455343094921	0.262492750395290	0.793003290704985	   
df.mm.trans1:exp6	-0.0240001301444265	0.0767523455590304	-0.312695722451478	0.75458626977138	   
df.mm.trans2:exp6	0.0645119984180661	0.0558455343094922	1.15518634060416	0.248329124831920	   
df.mm.trans1:exp7	0.266985889861987	0.0767523455590304	3.47853720843811	0.000528998767384568	***
df.mm.trans2:exp7	0.168102296886128	0.0558455343094922	3.01012961850301	0.00268623796689372	** 
df.mm.trans1:exp8	0.364663316968367	0.0767523455590304	4.75116837553719	2.36267132195596e-06	***
df.mm.trans2:exp8	0.192982033217714	0.0558455343094922	3.45563948136338	0.00057536866198036	***
df.mm.trans1:probe2	0.378445165816325	0.0534680283950444	7.0779712133803	2.99812307770012e-12	***
df.mm.trans1:probe3	0.0210298379646246	0.0534680283950444	0.393316129206173	0.694181709382656	   
df.mm.trans1:probe4	0.00126313298305086	0.0534680283950444	0.0236240800524437	0.981157844500561	   
df.mm.trans1:probe5	-0.0502290584234257	0.0534680283950444	-0.939422303966628	0.347772919055564	   
df.mm.trans1:probe6	-0.0338499755942566	0.0534680283950444	-0.633088157733415	0.526841307907672	   
df.mm.trans1:probe7	0.169004884981015	0.0534680283950444	3.16085874220639	0.00162710908899430	** 
df.mm.trans1:probe8	-0.0756252651177641	0.0534680283950444	-1.41440160387087	0.157599339626063	   
df.mm.trans1:probe9	0.36501908943997	0.0534680283950444	6.82686645452969	1.62051414062061e-11	***
df.mm.trans1:probe10	0.633527866138429	0.0534680283950444	11.8487231557831	3.81347491377751e-30	***
df.mm.trans1:probe11	0.887985821132026	0.0534680283950444	16.6077906327724	4.67667635863953e-54	***
df.mm.trans1:probe12	0.843068246669166	0.0534680283950444	15.7677077680931	1.70894014034703e-49	***
df.mm.trans1:probe13	0.844901567790372	0.0534680283950444	15.8019959432183	1.11942945287403e-49	***
df.mm.trans1:probe14	0.707326734465147	0.0534680283950444	13.2289660886524	1.48321624775519e-36	***
df.mm.trans1:probe15	0.525150154907671	0.0534680283950444	9.82176023076144	1.13642315189479e-21	***
df.mm.trans1:probe16	0.660235791447673	0.0534680283950444	12.3482352214219	2.07719356602492e-32	***
df.mm.trans1:probe17	0.573981065736719	0.0534680283950444	10.7350333080529	2.42361458523131e-25	***
df.mm.trans1:probe18	0.345591986323881	0.0534680283950444	6.46352589944969	1.69640747604565e-10	***
df.mm.trans1:probe19	0.342537957562309	0.0534680283950444	6.4064071155101	2.42922310512771e-10	***
df.mm.trans1:probe20	0.105973204836101	0.0534680283950444	1.98199200563609	0.0477922902629458	*  
df.mm.trans1:probe21	0.381825971670736	0.0534680283950444	7.14120163267745	1.94425919842198e-12	***
df.mm.trans1:probe22	-0.0299758331669981	0.0534680283950444	-0.560630980172374	0.57519248408431	   
df.mm.trans2:probe2	0.396185600010658	0.0534680283950444	7.40976639504025	2.97833044929755e-13	***
df.mm.trans2:probe3	0.292866358620843	0.0534680283950444	5.47741084554348	5.64118533401876e-08	***
df.mm.trans2:probe4	-0.281469001349399	0.0534680283950444	-5.26424874449806	1.77166419149418e-07	***
df.mm.trans2:probe5	-0.0689011948890327	0.0534680283950444	-1.28864289477745	0.197862485674674	   
df.mm.trans2:probe6	-0.36939878977561	0.0534680283950444	-6.90877896312794	9.3994589542096e-12	***
df.mm.trans3:probe2	-0.3067264767366	0.0534680283950444	-5.73663338528914	1.32966480591035e-08	***
df.mm.trans3:probe3	0.71795788464688	0.0534680283950444	13.4277979981290	1.61813986327988e-37	***
df.mm.trans3:probe4	0.240169446324024	0.0534680283950444	4.49183284914772	8.00635381541257e-06	***
df.mm.trans3:probe5	0.580415672897226	0.0534680283950444	10.8553782572432	7.6261325792073e-26	***
df.mm.trans3:probe6	0.20083465538566	0.0534680283950444	3.75616347589645	0.000183944276867769	***
df.mm.trans3:probe7	0.325156420532045	0.0534680283950444	6.0813243033697	1.77837549430015e-09	***
df.mm.trans3:probe8	0.106711510964108	0.0534680283950444	1.99580037205932	0.0462648905113333	*  
df.mm.trans3:probe9	0.0920910118830226	0.0534680283950444	1.72235660538322	0.085357904560647	.  
df.mm.trans3:probe10	0.138558285346659	0.0534680283950444	2.59142312716174	0.00971703204521129	** 
df.mm.trans3:probe11	0.232561882471683	0.0534680283950444	4.34955036593863	1.52459219061378e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21139075599675	0.169300713656959	24.8752097083889	9.41542909668061e-104	***
df.mm.trans1	0.148874506351787	0.145893955599856	1.02042956981786	0.307806500602705	   
df.mm.trans2	0.287679576264096	0.128593123196821	2.23713033101919	0.0255286569487122	*  
df.mm.exp2	0.0621345585244118	0.164731035276297	0.377187931953418	0.706125304528122	   
df.mm.exp3	-0.197147501199860	0.164731035276297	-1.19678420565495	0.231714460319163	   
df.mm.exp4	0.182402886102225	0.164731035276297	1.10727699729616	0.268478136617722	   
df.mm.exp5	-0.0712040169595682	0.164731035276297	-0.432244093167633	0.665670393594752	   
df.mm.exp6	0.250790426678896	0.164731035276297	1.52242366630098	0.128263842529193	   
df.mm.exp7	0.110200486748851	0.164731035276297	0.668972222289603	0.50368945206708	   
df.mm.exp8	-0.0256372047652351	0.164731035276297	-0.155630690490319	0.87635998656235	   
df.mm.trans1:exp2	-0.0847174764429332	0.15187451032257	-0.557812342986323	0.577115051593443	   
df.mm.trans2:exp2	0.0466623345974062	0.110504937864514	0.42226470146173	0.672935436263074	   
df.mm.trans1:exp3	0.182681196163268	0.15187451032257	1.20284302991507	0.229362067292492	   
df.mm.trans2:exp3	-0.0429508565118273	0.110504937864514	-0.388678165354815	0.697608684483482	   
df.mm.trans1:exp4	-0.150421318788360	0.15187451032257	-0.990431629829633	0.322236680991372	   
df.mm.trans2:exp4	-0.170531092415909	0.110504937864514	-1.54319884442621	0.123143688090356	   
df.mm.trans1:exp5	0.0526088984537528	0.15187451032257	0.346397156060062	0.729127446312244	   
df.mm.trans2:exp5	0.0283451711235817	0.110504937864514	0.256505923367284	0.797620434705238	   
df.mm.trans1:exp6	-0.127949965711629	0.15187451032257	-0.842471626343836	0.399753966115002	   
df.mm.trans2:exp6	-0.243910463845488	0.110504937864514	-2.20723588066749	0.0275561807921502	*  
df.mm.trans1:exp7	-0.0694804350078948	0.15187451032257	-0.457485820762969	0.64743533095523	   
df.mm.trans2:exp7	0.0250068973217879	0.110504937864514	0.226296650674994	0.821023494106775	   
df.mm.trans1:exp8	0.0175778784025529	0.15187451032257	0.115739490222676	0.907885533469318	   
df.mm.trans2:exp8	-0.0266009184386062	0.110504937864514	-0.240721536545458	0.809827271726235	   
df.mm.trans1:probe2	0.118932777830116	0.105800423052416	1.12412383995094	0.261268435268777	   
df.mm.trans1:probe3	0.161813563552805	0.105800423052416	1.52942265148258	0.126520703250772	   
df.mm.trans1:probe4	0.0504248194381814	0.105800423052416	0.476603192911618	0.6337635679137	   
df.mm.trans1:probe5	0.077681080434773	0.105800423052416	0.734222777127158	0.463009449032909	   
df.mm.trans1:probe6	0.132045779673057	0.105800423052416	1.24806476064503	0.212340830864274	   
df.mm.trans1:probe7	0.0893082588019248	0.105800423052416	0.844120053827001	0.398832777442179	   
df.mm.trans1:probe8	0.0629904818789696	0.105800423052416	0.595370794006775	0.551749415182484	   
df.mm.trans1:probe9	0.0132582198053700	0.105800423052416	0.125313485739103	0.90030412826322	   
df.mm.trans1:probe10	0.0954549230525084	0.105800423052416	0.902216837121887	0.367189692194838	   
df.mm.trans1:probe11	0.10526329432058	0.105800423052416	0.9949231891864	0.320048361820409	   
df.mm.trans1:probe12	0.0388817955328238	0.105800423052416	0.367501323823260	0.713333872496521	   
df.mm.trans1:probe13	0.0186729974433334	0.105800423052416	0.176492653853400	0.859947713518855	   
df.mm.trans1:probe14	-0.0223908985863947	0.105800423052416	-0.211633355901627	0.83244231093214	   
df.mm.trans1:probe15	0.0163445105774566	0.105800423052416	0.154484359380673	0.877263426844591	   
df.mm.trans1:probe16	0.101424365574837	0.105800423052416	0.958638563520576	0.338005359083767	   
df.mm.trans1:probe17	0.203507257177942	0.105800423052416	1.92350135572823	0.0547409267198317	.  
df.mm.trans1:probe18	0.181777996361838	0.105800423052416	1.71812163994638	0.0861277972712736	.  
df.mm.trans1:probe19	0.218261653036454	0.105800423052416	2.06295633551788	0.0394110055539722	*  
df.mm.trans1:probe20	0.0641941494353918	0.105800423052416	0.606747568519537	0.544175661730946	   
df.mm.trans1:probe21	0.130785611305411	0.105800423052416	1.23615395413510	0.216732693539843	   
df.mm.trans1:probe22	0.090050354919852	0.105800423052416	0.851134166781535	0.394927423562809	   
df.mm.trans2:probe2	-0.0177460954855513	0.105800423052416	-0.167731800814818	0.866832991218222	   
df.mm.trans2:probe3	0.0747297157085499	0.105800423052416	0.706327191825378	0.480172499864434	   
df.mm.trans2:probe4	0.0518726989611612	0.105800423052416	0.490288199844553	0.624052750500356	   
df.mm.trans2:probe5	-0.0741127073947179	0.105800423052416	-0.700495378529827	0.483803994522204	   
df.mm.trans2:probe6	-0.117635247480708	0.105800423052416	-1.11185989702923	0.266503440999380	   
df.mm.trans3:probe2	-0.145238034217743	0.105800423052416	-1.37275475870062	0.170179921436165	   
df.mm.trans3:probe3	-0.230664214704819	0.105800423052416	-2.18018234757477	0.0295094849102095	*  
df.mm.trans3:probe4	-0.312443582505896	0.105800423052416	-2.95314114529678	0.0032296446782157	** 
df.mm.trans3:probe5	-0.246910742128932	0.105800423052416	-2.33374059389732	0.0198346661575780	*  
df.mm.trans3:probe6	-0.124929615114787	0.105800423052416	-1.18080449501505	0.238000900065368	   
df.mm.trans3:probe7	-0.118990783279472	0.105800423052416	-1.12467209342368	0.261036081699915	   
df.mm.trans3:probe8	-0.0769408508761105	0.105800423052416	-0.727226306439174	0.467281634965659	   
df.mm.trans3:probe9	-0.282753675106484	0.105800423052416	-2.67251932410895	0.00766831919676697	** 
df.mm.trans3:probe10	-0.0970986478287356	0.105800423052416	-0.917752926003242	0.359000837154825	   
df.mm.trans3:probe11	-0.174400161979497	0.105800423052416	-1.64838813445098	0.0996316205140523	.