fitVsDatCorrelation=0.793373413058116
cont.fitVsDatCorrelation=0.224875662014694

fstatistic=8646.6316876202,52,692
cont.fstatistic=3366.62772650939,52,692

residuals=-0.473139583557604,-0.0846766177982776,-0.00327711707761482,0.072770458127476,1.30293196030144
cont.residuals=-0.507507142258489,-0.172967892155185,-0.0351371769518721,0.119793598061163,1.56705863954371

predictedValues:
Include	Exclude	Both
Lung	59.3441754865521	58.9727963684026	76.3884062803421
cerebhem	69.8790857392567	75.6090475786832	74.6744652034269
cortex	63.1453523507612	54.8414006390086	96.397042664369
heart	61.2614883887499	56.4351963992635	90.1654555829713
kidney	60.1373908320365	53.2923655146115	85.3160183152676
liver	56.9147649834476	56.5047953763982	72.0346725925908
stomach	59.4278288115436	55.9163546023851	73.3462186290653
testicle	59.7553409171979	59.3930161772053	76.9685656800658


diffExp=0.371379118149562,-5.72996183942658,8.30395171175256,4.82629198948642,6.84502531742499,0.409969607049362,3.51147420915848,0.362324739992573
diffExpScore=1.52561229156868
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	62.4005774100014	59.5757343543872	65.6618914118962
cerebhem	65.3380212733737	60.5258044065996	63.7761940952348
cortex	64.6922134247593	60.1415316538676	59.54817574924
heart	62.9134282386427	65.7513141550825	62.9949376482984
kidney	63.4605695288272	58.7414814954398	57.4525491559606
liver	60.8222564160668	64.722053623254	58.2767656552662
stomach	58.8989957995352	60.0824667718457	61.3028896335855
testicle	60.4833756720866	72.284072384712	56.0930843180069
cont.diffExp=2.82484305561424,4.81221686677414,4.55068177089175,-2.83788591643985,4.71908803338746,-3.89979720718717,-1.18347097231050,-11.8006967126253
cont.diffExpScore=9.60117382025941

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=0.815839777066278
cont.tran.correlation=-0.385233041184628

tran.covariance=0.00526329499248748
cont.tran.covariance=-0.000948327044981693

tran.mean=60.051900010344
cont.tran.mean=62.5521185380301

weightedLogRatios:
wLogRatio
Lung	0.0256144430555221
cerebhem	-0.337791226463692
cortex	0.574541823933426
heart	0.334315883862164
kidney	0.487730691929404
liver	0.0291914365092454
stomach	0.246930610002305
testicle	0.0248581626977744

cont.weightedLogRatios:
wLogRatio
Lung	0.190419657061642
cerebhem	0.316829057346808
cortex	0.301474300666951
heart	-0.183707854087606
kidney	0.31772817854916
liver	-0.257225007056676
stomach	-0.0812824251995265
testicle	-0.747070632172484

varWeightedLogRatios=0.0881480912092959
cont.varWeightedLogRatios=0.141508952708838

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.72018213466085	0.089720343164586	41.4641986805203	9.41818445674972e-190	***
df.mm.trans1	0.34916085887254	0.0805819887769014	4.33298884989329	1.68902972882401e-05	***
df.mm.trans2	0.353132837944413	0.0741060827180171	4.76523417501539	2.29980212681820e-06	***
df.mm.exp2	0.434604849929243	0.101506980899301	4.2815267095806	2.11833544347532e-05	***
df.mm.exp3	-0.243190181974782	0.101506980899301	-2.39579760741813	0.0168488876344516	*  
df.mm.exp4	-0.178001269575017	0.101506980899301	-1.75358648240757	0.0799440896638493	.  
df.mm.exp5	-0.198536650872066	0.101506980899301	-1.95589159595854	0.0508798981773453	.  
df.mm.exp6	-0.0258665634289878	0.101506980899301	-0.254825463232411	0.798933618218604	   
df.mm.exp7	-0.0111707318969507	0.101506980899301	-0.110048903021090	0.912402497869832	   
df.mm.exp8	0.00643881812916606	0.101506980899301	0.0634322691121476	0.94944060984428	   
df.mm.trans1:exp2	-0.271192425895012	0.0971855351394952	-2.79046079754313	0.0054082622922896	** 
df.mm.trans2:exp2	-0.186105156012453	0.0845891507494172	-2.20010668464756	0.0281291147682441	*  
df.mm.trans1:exp3	0.305275453233887	0.0971855351394952	3.14116141662142	0.00175449628666740	** 
df.mm.trans2:exp3	0.170559317731376	0.0845891507494172	2.01632616263795	0.0441517649518684	*  
df.mm.trans1:exp4	0.209798689092482	0.0971855351394952	2.15874398171855	0.0312131472872927	*  
df.mm.trans2:exp4	0.134018023998671	0.0845891507494172	1.58434057809233	0.113573099584576	   
df.mm.trans1:exp5	0.211814464740240	0.0971855351394952	2.17948550096691	0.0296320883921175	*  
df.mm.trans2:exp5	0.0972534765862892	0.0845891507494172	1.14971572269815	0.250658163078993	   
df.mm.trans1:exp6	-0.0159326169071967	0.0971855351394952	-0.163940208636273	0.86982608316068	   
df.mm.trans2:exp6	-0.0168841871763418	0.0845891507494172	-0.199602277913378	0.84185028344724	   
df.mm.trans1:exp7	0.0125793692154550	0.0971855351394952	0.129436640930146	0.897049768522124	   
df.mm.trans2:exp7	-0.0420486208909709	0.0845891507494172	-0.497092363718531	0.619281810741792	   
df.mm.trans1:exp8	0.000465778573370613	0.0971855351394952	0.00479267385524047	0.996177395431059	   
df.mm.trans2:exp8	0.000661569478200783	0.0845891507494172	0.00782097316664858	0.993762083958565	   
df.mm.trans1:probe2	-0.184024870557952	0.0485927675697476	-3.78708354682230	0.000165671437023945	***
df.mm.trans1:probe3	0.136885829585661	0.0485927675697476	2.81700006876913	0.0049856383669929	** 
df.mm.trans1:probe4	-0.23239211197118	0.0485927675697476	-4.78244240025259	2.11713927208577e-06	***
df.mm.trans1:probe5	-0.201947737222242	0.0485927675697476	-4.15592170033073	3.64589005132383e-05	***
df.mm.trans1:probe6	-0.085430015758905	0.0485927675697476	-1.75808088387399	0.079175803785718	.  
df.mm.trans1:probe7	-0.175268724101731	0.0485927675697476	-3.60688910855219	0.000332187780545918	***
df.mm.trans1:probe8	-0.286834213906054	0.0485927675697476	-5.90281698802907	5.59693565836813e-09	***
df.mm.trans1:probe9	0.260065308420087	0.0485927675697476	5.35193448380568	1.18461587719344e-07	***
df.mm.trans1:probe10	-0.0731859450662765	0.0485927675697476	-1.50610777542623	0.132495828160299	   
df.mm.trans1:probe11	0.118886793618065	0.0485927675697476	2.44659441237671	0.0146690947403142	*  
df.mm.trans1:probe12	0.0439306950820448	0.0485927675697476	0.904058304952252	0.366279099240786	   
df.mm.trans1:probe13	0.251240677873807	0.0485927675697476	5.17033069814739	3.06229917146402e-07	***
df.mm.trans1:probe14	-0.055767300656715	0.0485927675697476	-1.14764610961229	0.251511288159335	   
df.mm.trans1:probe15	0.073398652493733	0.0485927675697476	1.51048512288131	0.131376154988472	   
df.mm.trans1:probe16	-0.131224651940241	0.0485927675697476	-2.70049759466546	0.00709289468683647	** 
df.mm.trans1:probe17	0.112322523833429	0.0485927675697476	2.31150702976954	0.0210977335489793	*  
df.mm.trans1:probe18	0.145738448012979	0.0485927675697476	2.99917982246625	0.00280421287531493	** 
df.mm.trans1:probe19	0.125363978309835	0.0485927675697476	2.57988965394684	0.0100882586528563	*  
df.mm.trans1:probe20	0.140283536612042	0.0485927675697476	2.88692214146243	0.00401160960675267	** 
df.mm.trans1:probe21	0.250908582820387	0.0485927675697476	5.16349644955385	3.17197879655171e-07	***
df.mm.trans1:probe22	0.117325152428557	0.0485927675697476	2.41445709508425	0.0160171085660308	*  
df.mm.trans2:probe2	-0.0878604556174486	0.0485927675697476	-1.8080973776877	0.0710253637058732	.  
df.mm.trans2:probe3	0.0394452149215752	0.0485927675697476	0.811750737698928	0.417213804317464	   
df.mm.trans2:probe4	0.00807793875863977	0.0485927675697476	0.166237470361101	0.868018630589598	   
df.mm.trans2:probe5	0.00177531041626131	0.0485927675697476	0.0365344578020406	0.9708667399707	   
df.mm.trans2:probe6	0.0724135697457838	0.0485927675697476	1.49021291371900	0.136623902788372	   
df.mm.trans3:probe2	0.311660026533366	0.0485927675697476	6.41371220698688	2.63131621421495e-10	***
df.mm.trans3:probe3	-0.571594717766971	0.0485927675697476	-11.7629586943475	2.99914948223247e-29	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01427203412244	0.143613131084767	27.9519846395734	1.16278271787826e-115	***
df.mm.trans1	0.100764071956685	0.128985593557741	0.781204080063234	0.434949645603286	   
df.mm.trans2	0.0698155788925876	0.118619771126355	0.58856612375537	0.556344416340192	   
df.mm.exp2	0.090959767627755	0.162479710171954	0.559822315854031	0.575781801485247	   
df.mm.exp3	0.143251668969378	0.162479710171954	0.88165881646252	0.378267467270004	   
df.mm.exp4	0.148280755716501	0.162479710171954	0.912610907291586	0.361765041278404	   
df.mm.exp5	0.136301372023623	0.162479710171954	0.838882417253045	0.401824979928948	   
df.mm.exp6	0.176550141113838	0.162479710171954	1.08659808001253	0.277592819428031	   
df.mm.exp7	0.0194109646101202	0.162479710171954	0.119467006616256	0.90494005122964	   
df.mm.exp8	0.319655659835631	0.162479710171954	1.96735739802426	0.0495410584574009	*  
df.mm.trans1:exp2	-0.0449601741532413	0.155562478979022	-0.289016827504429	0.772655060102171	   
df.mm.trans2:exp2	-0.0751383238687381	0.135399758476628	-0.554936912104669	0.579117136209798	   
df.mm.trans1:exp3	-0.107185352324816	0.155562478979022	-0.689018026893683	0.491042871423539	   
df.mm.trans2:exp3	-0.133799373056204	0.135399758476628	-0.988180293388773	0.323409745452606	   
df.mm.trans1:exp4	-0.140095657981860	0.155562478979022	-0.90057486163326	0.368127706089068	   
df.mm.trans2:exp4	-0.0496494471705264	0.135399758476628	-0.366687856234962	0.713963957843071	   
df.mm.trans1:exp5	-0.119457139995615	0.155562478979022	-0.767904579430906	0.442805824852522	   
df.mm.trans2:exp5	-0.150403574796271	0.135399758476628	-1.11081124876771	0.267035413801109	   
df.mm.trans1:exp6	-0.202168888348333	0.155562478979022	-1.29959929717754	0.194171103456553	   
df.mm.trans2:exp6	-0.0936964874378973	0.135399758476628	-0.691998925936566	0.489170119709251	   
df.mm.trans1:exp7	-0.0771614520397823	0.155562478979022	-0.496015829435244	0.620040741353592	   
df.mm.trans2:exp7	-0.0109412493388719	0.135399758476628	-0.0808070077965504	0.935618799181901	   
df.mm.trans1:exp8	-0.350861643535941	0.155562478979022	-2.25543875257514	0.0244173844532275	*  
df.mm.trans2:exp8	-0.126300203368638	0.135399758476628	-0.932794894094575	0.351251216836175	   
df.mm.trans1:probe2	0.079663935074063	0.077781239489511	1.02420500877729	0.306096348769065	   
df.mm.trans1:probe3	0.00763759202683784	0.077781239489511	0.0981932414161102	0.921807283894564	   
df.mm.trans1:probe4	-0.0119697839501267	0.077781239489511	-0.153890372905935	0.877741042105894	   
df.mm.trans1:probe5	-0.0293396548362758	0.077781239489511	-0.377207344969507	0.706135171776178	   
df.mm.trans1:probe6	-0.0581337855497543	0.077781239489511	-0.747401120518191	0.455075355536274	   
df.mm.trans1:probe7	0.0687669934754693	0.077781239489511	0.884107709349922	0.376945133237593	   
df.mm.trans1:probe8	-0.01750337764963	0.077781239489511	-0.225033411199244	0.82201971741325	   
df.mm.trans1:probe9	0.129444675800913	0.077781239489511	1.66421461846682	0.0965222564911549	.  
df.mm.trans1:probe10	-0.0470795043816155	0.077781239489511	-0.605280973800427	0.545190763625868	   
df.mm.trans1:probe11	0.0552160225584278	0.077781239489511	0.70988869450806	0.478012297970979	   
df.mm.trans1:probe12	0.0446147343733241	0.077781239489511	0.573592484076324	0.566429961047276	   
df.mm.trans1:probe13	0.0670198122577298	0.077781239489511	0.861644950602356	0.389181397262448	   
df.mm.trans1:probe14	0.0258359640646973	0.077781239489511	0.332161897062354	0.73986759845961	   
df.mm.trans1:probe15	-0.0185478965147951	0.077781239489511	-0.23846234177454	0.811593116362538	   
df.mm.trans1:probe16	0.000156442410869319	0.077781239489511	0.00201131290650640	0.99839578522413	   
df.mm.trans1:probe17	0.0319816406838824	0.077781239489511	0.411174222650376	0.681072090638832	   
df.mm.trans1:probe18	0.0629668033989562	0.077781239489511	0.809537155903095	0.418484505360114	   
df.mm.trans1:probe19	0.0165414211032737	0.077781239489511	0.212665948907954	0.831650157731334	   
df.mm.trans1:probe20	0.0028148034350952	0.077781239489511	0.0361887192023313	0.971142317314697	   
df.mm.trans1:probe21	0.0366866674358258	0.077781239489511	0.471664731452024	0.637314791108901	   
df.mm.trans1:probe22	0.0166870602539816	0.077781239489511	0.214538368937048	0.830190453536457	   
df.mm.trans2:probe2	0.0148330396890239	0.077781239489511	0.19070202257479	0.848814986620052	   
df.mm.trans2:probe3	-0.0362994212736739	0.077781239489511	-0.466686073813069	0.640871382359441	   
df.mm.trans2:probe4	0.0070383630903578	0.077781239489511	0.0904892122644425	0.927924651316343	   
df.mm.trans2:probe5	-0.0221043155647291	0.077781239489511	-0.28418569451712	0.776353090840066	   
df.mm.trans2:probe6	0.0649789620790682	0.077781239489511	0.835406616113784	0.403777142863875	   
df.mm.trans3:probe2	-0.0293149237524727	0.077781239489511	-0.376889388043577	0.706371349632095	   
df.mm.trans3:probe3	0.0283160222669775	0.077781239489511	0.364046940532441	0.71593416001289