fitVsDatCorrelation=0.711443670515447
cont.fitVsDatCorrelation=0.298513352864005

fstatistic=13845.3656461404,55,761
cont.fstatistic=7500.06710014594,55,761

residuals=-0.351079402232428,-0.0760223884926961,-0.00020266199689498,0.0698697875979036,0.661434958895729
cont.residuals=-0.381100203999708,-0.107093588761126,-0.025239722853486,0.0765242730862125,0.86984479384307

predictedValues:
Include	Exclude	Both
Lung	44.2730965604619	43.0044590610165	56.480157777504
cerebhem	51.4220257573048	44.3772393934788	63.3150235764054
cortex	45.1237034517725	44.8499937618916	51.8141905698993
heart	46.4148812748161	45.1919403583041	51.5449680929914
kidney	43.5206022834212	43.5625792345593	58.1395969523406
liver	48.7703089665453	49.5786210040143	55.8353980277662
stomach	45.8258990246973	44.4074153279524	54.2493614232536
testicle	46.3671834576483	44.7610523654109	56.8806020306248


diffExp=1.26863749944546,7.04478636382603,0.273709689880889,1.22294091651192,-0.0419769511381318,-0.808312037468937,1.41848369674489,1.60613109223742
diffExpScore=1.05395535894181
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	49.2099167875026	48.1264893289899	47.2562001314505
cerebhem	49.5152466855303	48.4708765962708	48.7543784467287
cortex	50.0253291178654	50.3009153852154	48.0525241627794
heart	49.5028652482732	55.3323988562622	48.9578232059457
kidney	50.7839022238396	49.9869633727012	47.2662615178576
liver	47.665742131122	59.4025522014117	52.3887612192466
stomach	47.7789837844072	50.2787303116874	48.7053811304465
testicle	50.0597175528144	54.627094316296	45.8994327623256
cont.diffExp=1.08342745851262,1.04437008925950,-0.275586267350043,-5.82953360798901,0.796938851138414,-11.7368100702897,-2.49974652728028,-4.56737676348164
cont.diffExpScore=1.21099051288369

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

tran.correlation=0.467754054069844
cont.tran.correlation=-0.376754686510711

tran.covariance=0.00112404340881463
cont.tran.covariance=-0.000606046936428429

tran.mean=45.7156875802059
cont.tran.mean=50.6917327437618

weightedLogRatios:
wLogRatio
Lung	0.109776453392037
cerebhem	0.569674440863709
cortex	0.0231588205214076
heart	0.102113212949724
kidney	-0.00363811063327285
liver	-0.0640316409098161
stomach	0.119770199549031
testicle	0.134632123608948

cont.weightedLogRatios:
wLogRatio
Lung	0.0864887173727286
cerebhem	0.082959653306659
cortex	-0.0215098077070236
heart	-0.44060245866172
kidney	0.061998139522925
liver	-0.874834303867398
stomach	-0.198481719996176
testicle	-0.345487860001137

varWeightedLogRatios=0.0373220389881065
cont.varWeightedLogRatios=0.113828246021585

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.57239234286681	0.0609985182990156	58.5652314594741	3.83676919226974e-284	***
df.mm.trans1	0.140766431236452	0.0530923954113169	2.65134827965299	0.00818388700741092	** 
df.mm.trans2	0.166966408728169	0.047487282091201	3.5160236883531	0.000464003297719654	***
df.mm.exp2	0.0668790546419469	0.0621754470658998	1.07565056301182	0.282424465769432	   
df.mm.exp3	0.147275458142767	0.0621754470658998	2.3687076666561	0.0180991801115056	*  
df.mm.exp4	0.188292677520321	0.0621754470658998	3.02840890425363	0.00254152706962581	** 
df.mm.exp5	-0.0332056278368647	0.0621754470658998	-0.534063354649787	0.593453671824756	   
df.mm.exp6	0.25048176186046	0.0621754470658998	4.02862823961642	6.1734936655485e-05	***
df.mm.exp7	0.106873044048079	0.0621754470658998	1.7188946616629	0.0860402360968324	.  
df.mm.exp8	0.0791843665701398	0.0621754470658998	1.27356328433332	0.203207084673447	   
df.mm.trans1:exp2	0.0828103512603717	0.057882191177125	1.43067063592952	0.152935110070399	   
df.mm.trans2:exp2	-0.0354561523251272	0.045406526507949	-0.780860265074894	0.435127515016057	   
df.mm.trans1:exp3	-0.128244965724027	0.0578821911771249	-2.21562043723580	0.0270127433513541	*  
df.mm.trans2:exp3	-0.105255818136819	0.0454065265079489	-2.31807685440095	0.0207094041755743	*  
df.mm.trans1:exp4	-0.141049744028031	0.057882191177125	-2.43684181886628	0.0150443485474866	*  
df.mm.trans2:exp4	-0.138677726648948	0.045406526507949	-3.05413642738497	0.00233579215982728	** 
df.mm.trans1:exp5	0.0160628780709833	0.057882191177125	0.27750984792247	0.781464014608457	   
df.mm.trans2:exp5	0.0461003258161657	0.045406526507949	1.01527972654097	0.310295183159184	   
df.mm.trans1:exp6	-0.153737248433154	0.057882191177125	-2.65603712137821	0.0080720133739103	** 
df.mm.trans2:exp6	-0.108225858604374	0.045406526507949	-2.38348684490163	0.0173938247247728	*  
df.mm.trans1:exp7	-0.0724008233142534	0.057882191177125	-1.25083072775701	0.211380668071458	   
df.mm.trans2:exp7	-0.074770386074604	0.0454065265079489	-1.64668808263752	0.100035032674637	   
df.mm.trans1:exp8	-0.0329696020781232	0.057882191177125	-0.569598375728955	0.569118223898928	   
df.mm.trans2:exp8	-0.0391497815218163	0.045406526507949	-0.862206042449925	0.388845756974129	   
df.mm.trans1:probe2	0.102199645316989	0.036783490539175	2.77841074403052	0.00559713947160922	** 
df.mm.trans1:probe3	0.0212367905815418	0.036783490539175	0.577345713260254	0.563876700967104	   
df.mm.trans1:probe4	-0.0190211237369142	0.036783490539175	-0.517110351902477	0.60522938572629	   
df.mm.trans1:probe5	0.119725405587958	0.036783490539175	3.25486798107015	0.00118460845719163	** 
df.mm.trans1:probe6	0.207814338729368	0.036783490539175	5.64966335938245	2.27199166167959e-08	***
df.mm.trans1:probe7	0.0925258257248808	0.036783490539175	2.51541722573444	0.0120940525305163	*  
df.mm.trans1:probe8	0.077074254081053	0.036783490539175	2.09534910774625	0.0364698913263196	*  
df.mm.trans1:probe9	0.312615897736832	0.0367834905391750	8.49881001379928	1.00505420455623e-16	***
df.mm.trans1:probe10	0.0184072345589498	0.036783490539175	0.500421093516011	0.616923287874123	   
df.mm.trans1:probe11	0.07827351651476	0.036783490539175	2.12795238753640	0.0336618250976546	*  
df.mm.trans1:probe12	0.0644805294694227	0.036783490539175	1.75297473198608	0.0800089830268298	.  
df.mm.trans1:probe13	0.0122318833729075	0.0367834905391750	0.332537320238283	0.739575142969892	   
df.mm.trans1:probe14	0.049887879743064	0.036783490539175	1.35625735926101	0.175419548465117	   
df.mm.trans1:probe15	0.136059540047853	0.036783490539175	3.69892954837844	0.000232087882665487	***
df.mm.trans1:probe16	0.152048560546954	0.036783490539175	4.13360880977351	3.96798139635917e-05	***
df.mm.trans1:probe17	0.308355534815538	0.036783490539175	8.38298732109544	2.48724786399154e-16	***
df.mm.trans1:probe18	0.115533033369427	0.036783490539175	3.14089369105367	0.00174950317203194	** 
df.mm.trans1:probe19	0.0636572820464174	0.036783490539175	1.73059383743425	0.0839296045445567	.  
df.mm.trans1:probe20	0.0427556339632045	0.036783490539175	1.16235934481718	0.245453920417587	   
df.mm.trans1:probe21	0.206254023261655	0.036783490539175	5.6072444522901	2.87683181471644e-08	***
df.mm.trans2:probe2	-0.00896591066441658	0.036783490539175	-0.243748228702432	0.807491512610542	   
df.mm.trans2:probe3	0.0728103111373571	0.036783490539175	1.97942908816152	0.0481276854546208	*  
df.mm.trans2:probe4	0.077815738379628	0.036783490539175	2.11550718104778	0.0347110501275380	*  
df.mm.trans2:probe5	0.0739487473554816	0.036783490539175	2.010378739797	0.0447434900491771	*  
df.mm.trans2:probe6	0.0696768653948606	0.036783490539175	1.89424288922917	0.0585717022623027	.  
df.mm.trans3:probe2	0.0979963023026763	0.0367834905391750	2.66413820075908	0.0078819541099478	** 
df.mm.trans3:probe3	0.101374423995300	0.036783490539175	2.75597618685302	0.00599172022606427	** 
df.mm.trans3:probe4	0.381692770292459	0.036783490539175	10.3767414320278	1.10117037479780e-23	***
df.mm.trans3:probe5	0.0724789878177214	0.036783490539175	1.97042169612832	0.0491521752928645	*  
df.mm.trans3:probe6	0.173245790781576	0.036783490539175	4.70987903111223	2.94591134704474e-06	***
df.mm.trans3:probe7	0.249561151835175	0.036783490539175	6.78459678994814	2.34024689269302e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02705808385752	0.0828429571027152	48.6107476688025	1.41451964582827e-235	***
df.mm.trans1	-0.0917776777360815	0.0721055389243955	-1.27282423937380	0.203469132182108	   
df.mm.trans2	-0.130302595744805	0.0644931546356822	-2.02040970829968	0.0436907438080363	*  
df.mm.exp2	-0.0178952747647669	0.084441360835573	-0.211925466237016	0.832221928207755	   
df.mm.exp3	0.0439140308392007	0.084441360835573	0.520053566222263	0.603177486890657	   
df.mm.exp4	0.110085948466354	0.084441360835573	1.30369699608131	0.192731150592223	   
df.mm.exp5	0.0692008659652336	0.084441360835573	0.819513864775151	0.412749876497406	   
df.mm.exp6	0.0755140114886046	0.084441360835573	0.894277528705961	0.371456158215598	   
df.mm.exp7	-0.0159655534837024	0.084441360835573	-0.189072669195739	0.850086274933306	   
df.mm.exp8	0.172949839421180	0.084441360835573	2.04816499532679	0.0408864880086662	*  
df.mm.trans1:exp2	0.0240807468527551	0.0786106288220301	0.306329401171342	0.759437643945984	   
df.mm.trans2:exp2	0.0250256705702419	0.0616672508214355	0.405817840699703	0.684990524622029	   
df.mm.trans1:exp3	-0.0274797352324981	0.07861062882203	-0.349567681168289	0.726759881580198	   
df.mm.trans2:exp3	0.000276505389139469	0.0616672508214355	0.00448382870091163	0.996423609404298	   
df.mm.trans1:exp4	-0.10415056067264	0.0786106288220301	-1.32489158569678	0.185604794161917	   
df.mm.trans2:exp4	0.0294399237146415	0.0616672508214355	0.477399646043702	0.633214644109613	   
df.mm.trans1:exp5	-0.0377166108662857	0.0786106288220301	-0.479790219611064	0.631514431211409	   
df.mm.trans2:exp5	-0.0312713663046947	0.0616672508214355	-0.507098433741509	0.612232662208333	   
df.mm.trans1:exp6	-0.107396229844722	0.0786106288220301	-1.36617950338320	0.172286191353829	   
df.mm.trans2:exp6	0.134990441085707	0.0616672508214355	2.18901344372538	0.0288989839749302	*  
df.mm.trans1:exp7	-0.0135437373934894	0.0786106288220301	-0.172288882514242	0.863256231756446	   
df.mm.trans2:exp7	0.0597149453088376	0.0616672508214355	0.968341291583583	0.33318159558107	   
df.mm.trans1:exp8	-0.155828359515800	0.0786106288220301	-1.98228104584414	0.0478070779096434	*  
df.mm.trans2:exp8	-0.0462525860789459	0.0616672508214355	-0.75003483150036	0.453465541392937	   
df.mm.trans1:probe2	-0.0299580368441628	0.0499561827696754	-0.599686268710428	0.548893845797458	   
df.mm.trans1:probe3	-0.0609188801587442	0.0499561827696753	-1.21944625832628	0.223052677683044	   
df.mm.trans1:probe4	-0.057013969096462	0.0499561827696753	-1.1412795360952	0.254112619617090	   
df.mm.trans1:probe5	-0.00988908668467227	0.0499561827696753	-0.197955210674647	0.843132972495378	   
df.mm.trans1:probe6	-0.0217564513021886	0.0499561827696754	-0.435510683482311	0.663315281414344	   
df.mm.trans1:probe7	-0.0473676484156609	0.0499561827696753	-0.948183904163596	0.34333683580639	   
df.mm.trans1:probe8	-0.077889560751126	0.0499561827696753	-1.55915757435348	0.119374837789745	   
df.mm.trans1:probe9	-0.0583149667069251	0.0499561827696754	-1.16732231074957	0.243445857866868	   
df.mm.trans1:probe10	0.0202974825414794	0.0499561827696753	0.40630571465121	0.684632215987184	   
df.mm.trans1:probe11	-0.0934068829029077	0.0499561827696754	-1.86977622636948	0.0618986710736711	.  
df.mm.trans1:probe12	-0.0660939772877164	0.0499561827696754	-1.32303898383199	0.186219806310653	   
df.mm.trans1:probe13	-0.101059934745487	0.0499561827696754	-2.02297151508607	0.0434252682568468	*  
df.mm.trans1:probe14	-0.0838700260522182	0.0499561827696753	-1.67887179128365	0.0935875491949743	.  
df.mm.trans1:probe15	-0.0408489161782283	0.0499561827696754	-0.81769490608527	0.413787356863785	   
df.mm.trans1:probe16	-0.0861682652050042	0.0499561827696753	-1.72487689066008	0.0849556839836693	.  
df.mm.trans1:probe17	-0.00538377227989857	0.0499561827696753	-0.107769889159078	0.914206634956242	   
df.mm.trans1:probe18	-0.0320273504065087	0.0499561827696753	-0.641108840404638	0.52164499293089	   
df.mm.trans1:probe19	-0.0554166383757142	0.0499561827696753	-1.10930490088113	0.267649097809046	   
df.mm.trans1:probe20	-0.083302138441826	0.0499561827696753	-1.66750407704074	0.0958256341148299	.  
df.mm.trans1:probe21	-0.106797762648051	0.0499561827696753	-2.13782872763610	0.0328486289200186	*  
df.mm.trans2:probe2	-0.0398254227922182	0.0499561827696754	-0.797207083972662	0.425579430356328	   
df.mm.trans2:probe3	-0.0982152950445606	0.0499561827696754	-1.96602881964352	0.0496584306621765	*  
df.mm.trans2:probe4	-0.101471149747496	0.0499561827696754	-2.03120302876887	0.042581475368852	*  
df.mm.trans2:probe5	-0.0231180552420501	0.0499561827696753	-0.462766647896952	0.64366393475755	   
df.mm.trans2:probe6	-0.0353658127141192	0.0499561827696753	-0.707936650748006	0.479201360279268	   
df.mm.trans3:probe2	0.072811518853556	0.0499561827696754	1.45750765604442	0.145388820060909	   
df.mm.trans3:probe3	0.0758508011583253	0.0499561827696753	1.51834661803601	0.129342352162646	   
df.mm.trans3:probe4	0.0521775189836311	0.0499561827696753	1.04446569154808	0.296601642195866	   
df.mm.trans3:probe5	0.0610513423931496	0.0499561827696753	1.22209782670203	0.222049031199750	   
df.mm.trans3:probe6	0.0422622726521291	0.0499561827696754	0.845986829037372	0.397826018684192	   
df.mm.trans3:probe7	0.0500889286338019	0.0499561827696754	1.00265724594568	0.316344967952225