fitVsDatCorrelation=0.945791995397126
cont.fitVsDatCorrelation=0.278276895332495

fstatistic=11397.4727392813,55,761
cont.fstatistic=1290.83086970650,55,761

residuals=-1.00886864250011,-0.0767922551774123,-0.00214714683381433,0.086426056159685,0.731033706530208
cont.residuals=-0.871331356701001,-0.294636436706835,-0.115994907826145,0.19237432037878,1.96154809456127

predictedValues:
Include	Exclude	Both
Lung	82.4705222899454	55.5120791694776	83.921912779332
cerebhem	60.8943666992406	64.9782676612056	65.0651884114639
cortex	65.2823609192415	51.7895270559652	72.7253353308152
heart	71.805123124952	51.2170647104468	76.856230646819
kidney	81.4311824866517	53.9858646400645	84.3431997485058
liver	74.3524673590026	53.0526141709984	85.3892723578551
stomach	70.5121451281386	51.6321628099256	76.9892259998756
testicle	72.2468917619858	53.9317216864145	74.2777637404537


diffExp=26.9584431204677,-4.08390096196494,13.4928338632762,20.5880584145052,27.4453178465872,21.2998531880042,18.879982318213,18.3151700755713
diffExpScore=1.04981246167570
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,1,1,1,0,0
diffExp1.4Score=0.8
diffExp1.3=1,0,0,1,1,1,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	75.5890571652764	79.7681239468712	79.5068242304606
cerebhem	63.721693610137	61.4980942055155	67.4401079536009
cortex	72.3237893362684	70.4796706433098	80.2278487727097
heart	80.7377190015008	70.938772088048	67.6551799966016
kidney	82.5330382342	70.6138748263241	63.0289338719763
liver	71.2807242108642	62.3112632161946	74.442365133979
stomach	71.9737644527926	65.9835416933708	64.1099223842178
testicle	71.7555243206496	63.1197663644217	70.8907255147989
cont.diffExp=-4.17906678159481,2.22359940462150,1.84411869295869,9.7989469134528,11.9191634078759,8.9694609946696,5.99022275942175,8.63575795622786
cont.diffExpScore=1.15925936492305

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=-0.398409824174206
cont.tran.correlation=0.60763684218433

tran.covariance=-0.00324608405445906
cont.tran.covariance=0.00452789324448166

tran.mean=63.4433976046035
cont.tran.mean=70.914276082234

weightedLogRatios:
wLogRatio
Lung	1.66827710384147
cerebhem	-0.268840160802608
cortex	0.94071190852014
heart	1.38701477043021
kidney	1.72398386815299
liver	1.39740249263519
stomach	1.27771378306020
testicle	1.20863148465973

cont.weightedLogRatios:
wLogRatio
Lung	-0.234203496402797
cerebhem	0.146933272818202
cortex	0.110243496550043
heart	0.559801801890611
kidney	0.676171894393484
liver	0.564748891380477
stomach	0.367819423977305
testicle	0.539742893429587

varWeightedLogRatios=0.398547915639019
cont.varWeightedLogRatios=0.0960377438113026

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00470335958519	0.0754866801272082	53.0517881146258	6.84691384120489e-258	***
df.mm.trans1	0.144710145274960	0.0660996144573379	2.18927366616275	0.0288799995919658	*  
df.mm.trans2	-0.00589681604522585	0.0592761280864657	-0.099480452512421	0.920783006802326	   
df.mm.exp2	0.108649066991790	0.0781710427974462	1.38988892950190	0.164969038214522	   
df.mm.exp3	-0.159934797793788	0.0781710427974462	-2.04595962993874	0.0411035752106053	*  
df.mm.exp4	-0.131062751225665	0.0781710427974462	-1.67661510625194	0.0940284728223473	.  
df.mm.exp5	-0.0455684699859269	0.0781710427974462	-0.582932865613706	0.560111187123787	   
df.mm.exp6	-0.166274284023530	0.0781710427974462	-2.12705725896959	0.0337363734477372	*  
df.mm.exp7	-0.142890542063558	0.0781710427974462	-1.82792165679317	0.0679526872434887	.  
df.mm.exp8	-0.0391582984069237	0.0781710427974462	-0.500930997023912	0.616564540178169	   
df.mm.trans1:exp2	-0.411949321303258	0.0733309382197947	-5.61767422187513	2.71501547204193e-08	***
df.mm.trans2:exp2	0.0488031635598354	0.0584978519429773	0.834272745731038	0.404389107057222	   
df.mm.trans1:exp3	-0.073784250077402	0.0733309382197947	-1.00618172723017	0.314647978429374	   
df.mm.trans2:exp3	0.0905221064663	0.0584978519429773	1.54744325577184	0.122172137726341	   
df.mm.trans1:exp4	-0.00742234646579917	0.0733309382197947	-0.101217121258591	0.919404766902966	   
df.mm.trans2:exp4	0.0505348831016139	0.0584978519429773	0.863875876175324	0.387928275321115	   
df.mm.trans1:exp5	0.0328858229552971	0.0733309382197947	0.448457687214207	0.653950574019499	   
df.mm.trans2:exp5	0.0176900765938451	0.0584978519429773	0.30240557569685	0.762425537929489	   
df.mm.trans1:exp6	0.0626502181933838	0.0733309382197947	0.8543490607689	0.393180473405994	   
df.mm.trans2:exp6	0.120957785597383	0.0584978519429773	2.06773037948968	0.0390028317949508	*  
df.mm.trans1:exp7	-0.0137654155494726	0.0733309382197947	-0.187716342974006	0.85114905762073	   
df.mm.trans2:exp7	0.0704346908788439	0.0584978519429773	1.20405602153567	0.228942296413845	   
df.mm.trans1:exp8	-0.0931933200366677	0.0733309382197947	-1.27085950758382	0.204166977222226	   
df.mm.trans2:exp8	0.0102764919468565	0.0584978519429773	0.175672979528785	0.860597597614815	   
df.mm.trans1:probe2	0.0844988835059709	0.0449058452495135	1.88169007924166	0.0602595548698533	.  
df.mm.trans1:probe3	0.133433543765753	0.0449058452495135	2.97140701893811	0.00305777035226191	** 
df.mm.trans1:probe4	0.161022606024980	0.0449058452495135	3.58578276681529	0.000357523607861162	***
df.mm.trans1:probe5	0.351041492315849	0.0449058452495135	7.8172783602075	1.79783142917332e-14	***
df.mm.trans1:probe6	0.0353996062931079	0.0449058452495135	0.788307314925586	0.430762457010675	   
df.mm.trans1:probe7	1.25124954049535	0.0449058452495135	27.8638456428767	2.53519904593336e-118	***
df.mm.trans1:probe8	0.0568996920871365	0.0449058452495135	1.26708876697412	0.205511175832855	   
df.mm.trans1:probe9	0.401605449430746	0.0449058452495135	8.94327781159173	2.83285573803311e-18	***
df.mm.trans1:probe10	1.92267735298899	0.0449058452495135	42.8157479790412	7.50862572958264e-205	***
df.mm.trans1:probe11	-0.107493781519216	0.0449058452495135	-2.39375922938185	0.0169178992274237	*  
df.mm.trans1:probe12	-0.155186456198788	0.0449058452495135	-3.45581862086138	0.000579043839291632	***
df.mm.trans1:probe13	-0.0928731187596247	0.0449058452495135	-2.06817438227890	0.0389609571288155	*  
df.mm.trans1:probe14	-0.00101157011779692	0.0449058452495135	-0.0225264687074981	0.982033903477519	   
df.mm.trans1:probe15	-0.0829877218782646	0.0449058452495135	-1.84803829918253	0.0649846358229547	.  
df.mm.trans1:probe16	-0.0634416304214444	0.0449058452495135	-1.41276998726868	0.158132205699941	   
df.mm.trans1:probe17	0.566782573004891	0.0449058452495135	12.6215767647984	2.69303465194662e-33	***
df.mm.trans1:probe18	0.594524735256092	0.0449058452495135	13.2393618682087	3.70984091537803e-36	***
df.mm.trans1:probe19	0.355373878836555	0.0449058452495135	7.91375547797767	8.81453139840113e-15	***
df.mm.trans1:probe20	0.519009472400678	0.0449058452495135	11.5577263832107	1.40096839737980e-28	***
df.mm.trans1:probe21	0.775060415638072	0.0449058452495135	17.2596776952209	1.39688716485469e-56	***
df.mm.trans1:probe22	0.659182770397124	0.0449058452495135	14.6792197482190	3.86623514668240e-43	***
df.mm.trans2:probe2	0.0522450441173291	0.0449058452495135	1.16343526832724	0.245017606459793	   
df.mm.trans2:probe3	-0.0166873746095843	0.0449058452495135	-0.371608072776785	0.710288118472616	   
df.mm.trans2:probe4	0.0187724608984507	0.0449058452495135	0.41804047544688	0.67603545627359	   
df.mm.trans2:probe5	0.0416523756757347	0.0449058452495135	0.927549085075645	0.353935601388509	   
df.mm.trans2:probe6	0.117546648617866	0.0449058452495135	2.61762467591319	0.0090303287171843	** 
df.mm.trans3:probe2	0.178701348494046	0.0449058452495135	3.97946742792871	7.56760032507095e-05	***
df.mm.trans3:probe3	0.147748527334539	0.0449058452495135	3.29018475242128	0.00104731612258551	** 
df.mm.trans3:probe4	0.58100624047077	0.0449058452495135	12.9383209967986	9.42241122383781e-35	***
df.mm.trans3:probe5	0.140271769955636	0.0449058452495135	3.12368621893728	0.00185371379549697	** 
df.mm.trans3:probe6	0.670492521165435	0.0449058452495135	14.9310745057783	2.11050077097226e-44	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.33954222541347	0.223248311471048	19.4381861023672	1.20752266114904e-68	***
df.mm.trans1	-0.0185350801856224	0.195486505587747	-0.0948151389268284	0.92448662142268	   
df.mm.trans2	-0.0288409409112859	0.175306365090432	-0.164517363054149	0.869367566550347	   
df.mm.exp2	-0.2663013092256	0.231187188005248	-1.15188610373839	0.249729621293115	   
df.mm.exp3	-0.176985920312645	0.231187188005248	-0.76555245919868	0.444180004277184	   
df.mm.exp4	0.110006437450486	0.231187188005248	0.475832758725321	0.634330093451588	   
df.mm.exp5	0.198238796095376	0.231187188005248	0.857481756691795	0.391448652371331	   
df.mm.exp6	-0.239849705218459	0.231187188005248	-1.03746971139687	0.299846537457736	   
df.mm.exp7	-0.0234847789052682	0.231187188005248	-0.101583392695339	0.91911412059578	   
df.mm.exp8	-0.171433437648956	0.231187188005248	-0.741535199801228	0.45859777139684	   
df.mm.trans1:exp2	0.0955148466378424	0.216872806017815	0.440418733872960	0.659758928187068	   
df.mm.trans2:exp2	0.0061835196623977	0.173004649945466	0.0357419275397906	0.971497511559725	   
df.mm.trans1:exp3	0.132827505601999	0.216872806017815	0.612467316861698	0.540411578561827	   
df.mm.trans2:exp3	0.0531862535105428	0.173004649945466	0.307426728283361	0.758602698274181	   
df.mm.trans1:exp4	-0.0441120998004417	0.216872806017815	-0.203400788740743	0.838876158021289	   
df.mm.trans2:exp4	-0.227313272784129	0.173004649945466	-1.31391423788772	0.189270999911495	   
df.mm.trans1:exp5	-0.110351645691627	0.216872806017815	-0.50883117951894	0.611018049113583	   
df.mm.trans2:exp5	-0.320136118987410	0.173004649945466	-1.85044806072161	0.0646363922669894	.  
df.mm.trans1:exp6	0.181164122196825	0.216872806017815	0.83534734263522	0.403784348749785	   
df.mm.trans2:exp6	-0.00713207070891731	0.173004649945466	-0.0412247341974071	0.967127548490438	   
df.mm.trans1:exp7	-0.0255250772999009	0.216872806017815	-0.117696071575724	0.906339536035507	   
df.mm.trans2:exp7	-0.166233853815323	0.173004649945466	-0.960863502037215	0.336925996113758	   
df.mm.trans1:exp8	0.119386757123171	0.216872806017815	0.550492057143224	0.582143473466281	   
df.mm.trans2:exp8	-0.0626565626384524	0.173004649945466	-0.362166928219575	0.717327838740774	   
df.mm.trans1:probe2	0.00208741023282751	0.132806928457311	0.0157176305263206	0.987463781298625	   
df.mm.trans1:probe3	0.0259518256384710	0.132806928457311	0.195410178820700	0.845124010420643	   
df.mm.trans1:probe4	0.174059046827589	0.132806928457311	1.31061721590480	0.190382512731402	   
df.mm.trans1:probe5	-0.0149332083275987	0.132806928457311	-0.112442991499489	0.910501822031159	   
df.mm.trans1:probe6	0.178454134533761	0.132806928457311	1.34371102928657	0.179442349618318	   
df.mm.trans1:probe7	-0.143608953954629	0.132806928457311	-1.08133630995607	0.279890085400176	   
df.mm.trans1:probe8	-0.0896315304808206	0.132806928457311	-0.674901012484687	0.499943646438875	   
df.mm.trans1:probe9	-0.0453378771830108	0.132806928457311	-0.341381866967762	0.732910362086179	   
df.mm.trans1:probe10	-0.0535368393148981	0.132806928457311	-0.403117818752255	0.686974780523706	   
df.mm.trans1:probe11	0.0165337807331820	0.132806928457311	0.124494865781770	0.900956341995402	   
df.mm.trans1:probe12	-0.0359274309747489	0.132806928457311	-0.270523770047865	0.78683068382629	   
df.mm.trans1:probe13	0.0376529625760501	0.132806928457311	0.283516553040025	0.776858006234032	   
df.mm.trans1:probe14	-0.137780980389247	0.132806928457311	-1.03745325631512	0.299854197556354	   
df.mm.trans1:probe15	0.0191647291229305	0.132806928457311	0.144305190591700	0.885297685912378	   
df.mm.trans1:probe16	-0.089133156079281	0.132806928457311	-0.671148388978302	0.502329659362569	   
df.mm.trans1:probe17	0.276125213562223	0.132806928457311	2.07914765268424	0.0379381425086694	*  
df.mm.trans1:probe18	0.0176235380078412	0.132806928457311	0.132700441253756	0.894465373328334	   
df.mm.trans1:probe19	-0.100227277744515	0.132806928457311	-0.754684103523494	0.450672024190572	   
df.mm.trans1:probe20	0.0168988597065762	0.132806928457311	0.127243811018550	0.898781044927922	   
df.mm.trans1:probe21	0.0317171817460203	0.132806928457311	0.238821740058654	0.811308110085643	   
df.mm.trans1:probe22	0.0343712394757955	0.132806928457311	0.258806071904928	0.795854858022086	   
df.mm.trans2:probe2	0.077231484731589	0.132806928457311	0.581532045268361	0.561054135114362	   
df.mm.trans2:probe3	0.119486076467061	0.132806928457311	0.899697612579512	0.368565795520451	   
df.mm.trans2:probe4	0.109273651048527	0.132806928457311	0.82280083063326	0.410879010092914	   
df.mm.trans2:probe5	0.224228495917929	0.132806928457311	1.68837950340824	0.0917480989545354	.  
df.mm.trans2:probe6	0.290852582216469	0.132806928457311	2.19004072750586	0.0288241016390424	*  
df.mm.trans3:probe2	0.0550203645549161	0.132806928457311	0.414288359756785	0.678779682563034	   
df.mm.trans3:probe3	0.101241807926953	0.132806928457311	0.762323239479899	0.446103315899377	   
df.mm.trans3:probe4	0.32064089408122	0.132806928457311	2.41433860270540	0.0159987785307596	*  
df.mm.trans3:probe5	0.0231331785526758	0.132806928457311	0.174186533951138	0.861765194611043	   
df.mm.trans3:probe6	0.00202367310381756	0.132806928457311	0.0152377073043146	0.987846532940016