fitVsDatCorrelation=0.859469579208373
cont.fitVsDatCorrelation=0.250815058164301

fstatistic=5126.99337787851,53,715
cont.fstatistic=1419.95528092229,53,715

residuals=-1.24276967878238,-0.111329853307615,-0.0137477743255022,0.0983361350850493,1.49699627332442
cont.residuals=-0.783760033141286,-0.279819730386953,-0.103147145479376,0.170106858493351,2.79955527985452

predictedValues:
Include	Exclude	Both
Lung	60.1761762030592	59.3676995607176	55.56678651563
cerebhem	59.3615679051881	82.4283244504278	55.1606550753224
cortex	56.7466257601699	55.0800969006394	59.5354413913235
heart	56.6054427920079	59.2506921037769	77.966869544461
kidney	60.6026212703383	60.9749880329875	58.7690658183038
liver	61.7758552159418	60.2530373207027	55.7954441633607
stomach	61.0810026842037	122.9316290118	271.104320557478
testicle	56.9964373980823	58.5943302157658	53.2443234191393


diffExp=0.808476642341674,-23.0667565452397,1.66652885953052,-2.64524931176893,-0.37236676264925,1.52281789523916,-61.8506263275964,-1.59789281768354
diffExpScore=1.08084175498062
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,0,0,0,0,-1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	60.8475633113971	60.709733146385	85.2713322765742
cerebhem	64.8650307360705	59.4919381166988	63.3371160467372
cortex	63.2613997969099	58.4041024286906	75.9200097983161
heart	64.3261486395147	61.3952248725022	52.6187899106009
kidney	69.3145920219466	56.0776699068894	57.2516385086614
liver	58.8897517994873	70.0749471149386	63.1394610865133
stomach	63.0035420278213	54.5598905200773	55.6582891688174
testicle	61.730995424198	59.6294180821888	72.9971688685275
cont.diffExp=0.137830165012112,5.37309261937168,4.85729736821923,2.93092376701253,13.2369221150572,-11.1851953154513,8.44365150774406,2.10157734200911
cont.diffExpScore=1.79455352163976

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

tran.correlation=0.423437659738677
cont.tran.correlation=-0.659499544921112

tran.covariance=0.00425330854918219
cont.tran.covariance=-0.00243578431552704

tran.mean=64.514157926613
cont.tran.mean=61.6613717466072

weightedLogRatios:
wLogRatio
Lung	0.0553292245477561
cerebhem	-1.39447282581807
cortex	0.119937268892326
heart	-0.185381258029091
kidney	-0.0251602969498119
liver	0.102609786479906
stomach	-3.12078553188915
testicle	-0.112167537463092

cont.weightedLogRatios:
wLogRatio
Lung	0.0093141512217859
cerebhem	0.357032220355214
cortex	0.328131411619602
heart	0.193095667490470
kidney	0.87579177793718
liver	-0.723871716608625
stomach	0.585819340177984
testicle	0.142201563555061

varWeightedLogRatios=1.30858695865211
cont.varWeightedLogRatios=0.219486943293894

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03606411142155	0.113044323849854	35.7033769938087	4.6387177625183e-161	***
df.mm.trans1	-0.0831871911393294	0.100385547357798	-0.828676969233729	0.407563756725074	   
df.mm.trans2	-0.0369255967974442	0.09131370950304	-0.404381740687195	0.686052958312378	   
df.mm.exp2	0.321885032475901	0.123104449891878	2.61473109021333	0.00911790155628459	** 
df.mm.exp3	-0.202628331047319	0.123104449891878	-1.64598705591298	0.10020588595566	   
df.mm.exp4	-0.401842554960071	0.123104449891878	-3.26424069408545	0.00114983253753944	** 
df.mm.exp5	-0.0222549016218149	0.123104449891878	-0.180780643115349	0.856590986547844	   
df.mm.exp6	0.0369321867064126	0.123104449891878	0.300006918830716	0.76425907636743	   
df.mm.exp7	-0.842115630193628	0.123104449891878	-6.84065954507132	1.69436680625684e-11	***
df.mm.exp8	-0.0247056663958570	0.123104449891878	-0.200688654370788	0.840999103496474	   
df.mm.trans1:exp2	-0.335514550320673	0.116893450648478	-2.87025961214569	0.00422249390740804	** 
df.mm.trans2:exp2	0.0062937886753701	0.0984330943401585	0.063939762511381	0.949036062299544	   
df.mm.trans1:exp3	0.143947997818390	0.116893450648478	1.23144621892693	0.218560894897432	   
df.mm.trans2:exp3	0.127666464148707	0.0984330943401586	1.29698720744799	0.195053955247279	   
df.mm.trans1:exp4	0.340671168096697	0.116893450648478	2.91437344185486	0.00367536194512332	** 
df.mm.trans2:exp4	0.399869715955106	0.0984330943401585	4.06235035722094	5.3957327491353e-05	***
df.mm.trans1:exp5	0.0293165191955392	0.116893450648478	0.250796935439093	0.802043163733618	   
df.mm.trans2:exp5	0.0489683494344263	0.0984330943401585	0.497478513326064	0.619004601170904	   
df.mm.trans1:exp6	-0.0106961207337347	0.116893450648478	-0.0915031652705683	0.927118420561745	   
df.mm.trans2:exp6	-0.022129503672804	0.0984330943401586	-0.224817718280097	0.822185315585961	   
df.mm.trans1:exp7	0.85703999647404	0.116893450648478	7.33180509018703	6.16235682087225e-13	***
df.mm.trans2:exp7	1.56999366929659	0.0984330943401586	15.9498558876054	3.18684707280485e-49	***
df.mm.trans1:exp8	-0.0295820993631120	0.116893450648478	-0.253068920448514	0.800287697250486	   
df.mm.trans2:exp8	0.0115933042722975	0.0984330943401585	0.117778521035152	0.9062762264191	   
df.mm.trans1:probe2	-0.0688461515905364	0.0640251797447884	-1.07529806030947	0.282604020369221	   
df.mm.trans1:probe3	0.41547028680655	0.0640251797447884	6.48917017433862	1.61249358652547e-10	***
df.mm.trans1:probe4	-0.101682809091016	0.0640251797447884	-1.58816905311840	0.112690197991857	   
df.mm.trans1:probe5	-0.0522823700204277	0.0640251797447884	-0.816590757399371	0.414434320069152	   
df.mm.trans1:probe6	-0.135542124113344	0.0640251797447884	-2.11701278549519	0.0346035062167234	*  
df.mm.trans1:probe7	0.117684199004898	0.0640251797447884	1.83809244228599	0.0664634674155511	.  
df.mm.trans1:probe8	-0.145159790492632	0.0640251797447884	-2.26722972229451	0.0236739433301339	*  
df.mm.trans1:probe9	0.0459347191808132	0.0640251797447884	0.717447719224126	0.473332204680463	   
df.mm.trans1:probe10	-0.118512504981057	0.0640251797447884	-1.85102963323900	0.0645775010010646	.  
df.mm.trans1:probe11	0.759428236948306	0.0640251797447884	11.8613995302391	9.42760552045374e-30	***
df.mm.trans1:probe12	0.59325359146231	0.0640251797447884	9.26594183455768	2.22839392951987e-19	***
df.mm.trans1:probe13	0.592507083362425	0.0640251797447884	9.25428223277505	2.45661607810031e-19	***
df.mm.trans1:probe14	0.706907886735111	0.0640251797447884	11.0410917947115	2.79916813375e-26	***
df.mm.trans1:probe15	0.749345747415913	0.0640251797447884	11.7039225880019	4.50799496950937e-29	***
df.mm.trans1:probe16	0.605630750168491	0.0640251797447884	9.45925888193682	4.36513118722033e-20	***
df.mm.trans1:probe17	-0.0633905923906169	0.0640251797447884	-0.990088472118298	0.322465810983618	   
df.mm.trans1:probe18	-0.0375540173026955	0.0640251797447884	-0.586550751632249	0.557690599051269	   
df.mm.trans1:probe19	-0.0140065905467398	0.0640251797447884	-0.218766907060185	0.826894055915979	   
df.mm.trans1:probe20	-0.0584548011390644	0.0640251797447884	-0.912997064156193	0.361551830740833	   
df.mm.trans1:probe21	0.0142378245224767	0.0640251797447884	0.222378517002690	0.824082739735389	   
df.mm.trans1:probe22	-0.050578725120502	0.0640251797447884	-0.789981774703554	0.429800323417859	   
df.mm.trans2:probe2	0.0225552052989566	0.0640251797447884	0.352286481488442	0.724727236513994	   
df.mm.trans2:probe3	0.274423167489814	0.0640251797447884	4.2861756668813	2.06694042410429e-05	***
df.mm.trans2:probe4	0.107941947346867	0.0640251797447884	1.68592962608049	0.0922456380646503	.  
df.mm.trans2:probe5	0.115656145919796	0.0640251797447884	1.80641657517894	0.0712736342331338	.  
df.mm.trans2:probe6	0.325541387987336	0.0640251797447884	5.08458374166196	4.71051039634551e-07	***
df.mm.trans3:probe2	0.187553716570306	0.0640251797447884	2.92937430738837	0.00350456645495003	** 
df.mm.trans3:probe3	-0.0337507714867195	0.0640251797447884	-0.527148406630858	0.598254049680941	   
df.mm.trans3:probe4	0.252734392722428	0.0640251797447884	3.94742183824951	8.68035419036328e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.63536726038173	0.214072124287659	16.9819740542057	1.35911357954116e-54	***
df.mm.trans1	0.407829590236141	0.190100189366483	2.14534026291741	0.0322618265086067	*  
df.mm.trans2	0.403776776900362	0.172920842941794	2.33503821766746	0.0198172172378493	*  
df.mm.exp2	0.341040470451632	0.233122992823774	1.46292077980243	0.143928484158020	   
df.mm.exp3	0.116343705191609	0.233122992823774	0.499065766882795	0.617886564120447	   
df.mm.exp4	0.549587506364518	0.233122992823774	2.35750021783553	0.0186669489592713	*  
df.mm.exp5	0.449299386078397	0.233122992823774	1.92730618561524	0.054337356039226	.  
df.mm.exp6	0.411248985591549	0.233122992823774	1.76408590422664	0.0781444988789108	.  
df.mm.exp7	0.354621444771413	0.233122992823774	1.52117747149670	0.12865740222585	   
df.mm.exp8	0.151877057043073	0.233122992823774	0.651488963844428	0.514940237667171	   
df.mm.trans1:exp2	-0.277103584029643	0.221361218709847	-1.25181631021313	0.211046166203631	   
df.mm.trans2:exp2	-0.361303694503559	0.186402827563398	-1.93829513868653	0.0529805125337399	.  
df.mm.trans1:exp3	-0.077440134328566	0.221361218709847	-0.349836049782829	0.7265647625476	   
df.mm.trans2:exp3	-0.155061604344413	0.186402827563398	-0.831862940982883	0.405764045305279	   
df.mm.trans1:exp4	-0.493993065990493	0.221361218709847	-2.23161522542032	0.0259495748873814	*  
df.mm.trans2:exp4	-0.538359478628277	0.186402827563398	-2.88815081651685	0.00399223873859286	** 
df.mm.trans1:exp5	-0.319015713408222	0.221361218709847	-1.44115448617211	0.149978832842837	   
df.mm.trans2:exp5	-0.52866572725085	0.186402827563398	-2.83614650143139	0.00469525454350134	** 
df.mm.trans1:exp6	-0.443953677737756	0.221361218709847	-2.00556213199962	0.0452793244791779	*  
df.mm.trans2:exp6	-0.267787676819656	0.186402827563398	-1.43660737511387	0.151266990475228	   
df.mm.trans1:exp7	-0.319802271744124	0.221361218709847	-1.44470776592222	0.148978068139263	   
df.mm.trans2:exp7	-0.461426471421439	0.186402827563398	-2.47542635191251	0.0135383668151455	*  
df.mm.trans1:exp8	-0.137462669762175	0.221361218709847	-0.620988041913325	0.534805397477247	   
df.mm.trans2:exp8	-0.169832046351582	0.186402827563398	-0.911102307682646	0.362548528571077	   
df.mm.trans1:probe2	0.0866788932145592	0.121244532844218	0.714909705049788	0.474898194720669	   
df.mm.trans1:probe3	0.0238618397491086	0.121244532844218	0.196807552384796	0.844034068953696	   
df.mm.trans1:probe4	0.161797837231264	0.121244532844218	1.33447532384121	0.182472944598022	   
df.mm.trans1:probe5	0.0909869908973168	0.121244532844218	0.750442009737645	0.453235416802052	   
df.mm.trans1:probe6	0.0290324104477264	0.121244532844218	0.239453357332234	0.810822684358606	   
df.mm.trans1:probe7	0.0486137069138171	0.121244532844218	0.40095586805781	0.688572405590668	   
df.mm.trans1:probe8	0.0333623038392554	0.121244532844218	0.275165428548611	0.783268641782082	   
df.mm.trans1:probe9	0.121866981958242	0.121244532844218	1.00513383242462	0.315172445101863	   
df.mm.trans1:probe10	0.0375520504139104	0.121244532844218	0.309721597609349	0.756862920655188	   
df.mm.trans1:probe11	0.180079298402485	0.121244532844218	1.48525706007595	0.137916487272623	   
df.mm.trans1:probe12	0.040901453486756	0.121244532844218	0.337346786096397	0.735954500854336	   
df.mm.trans1:probe13	0.0335397044029924	0.121244532844218	0.276628591955451	0.782145253981494	   
df.mm.trans1:probe14	0.220622610434975	0.121244532844218	1.81964996902948	0.0692303727576846	.  
df.mm.trans1:probe15	0.148521292850665	0.121244532844218	1.22497311315054	0.220988747518259	   
df.mm.trans1:probe16	0.119262557052048	0.121244532844218	0.983653070817501	0.325618850610658	   
df.mm.trans1:probe17	0.126306927487637	0.121244532844218	1.04175359106644	0.297878048797163	   
df.mm.trans1:probe18	0.0940158521051337	0.121244532844218	0.775423434769886	0.438345874927599	   
df.mm.trans1:probe19	-0.0184957702781488	0.121244532844218	-0.152549313723805	0.878796736048706	   
df.mm.trans1:probe20	0.00997469818549294	0.121244532844218	0.0822692615617482	0.934455622013594	   
df.mm.trans1:probe21	-0.0146313043060028	0.121244532844218	-0.120675992251147	0.903981578144382	   
df.mm.trans1:probe22	0.120697685084650	0.121244532844218	0.995489712016374	0.319834918818422	   
df.mm.trans2:probe2	0.0837560415620837	0.121244532844218	0.69080262505278	0.489913783404174	   
df.mm.trans2:probe3	0.105765158039604	0.121244532844218	0.872329296492873	0.383321597016603	   
df.mm.trans2:probe4	0.26843939920013	0.121244532844218	2.21403301990562	0.0271412698859389	*  
df.mm.trans2:probe5	0.0295137791383806	0.121244532844218	0.243423587406631	0.807747094883897	   
df.mm.trans2:probe6	0.182125585102792	0.121244532844218	1.50213441241756	0.133503896989272	   
df.mm.trans3:probe2	-0.0705709483122189	0.121244532844218	-0.582054684501878	0.560713323937662	   
df.mm.trans3:probe3	-0.08141121104723	0.121244532844218	-0.671462944657734	0.502142531592809	   
df.mm.trans3:probe4	0.147440916662906	0.121244532844218	1.21606239229233	0.224362474861555