chr13.6715_chr13_74255255_74264172_+_2.R 

fitVsDatCorrelation=0.75730165495924
cont.fitVsDatCorrelation=0.242273592927068

fstatistic=14158.6439835066,52,692
cont.fstatistic=6407.84680347727,52,692

residuals=-0.476795042889804,-0.0780854705199291,-0.00299229477722524,0.0699893500878407,0.582252786139747
cont.residuals=-0.359356718214112,-0.113867116760029,-0.0278442462133946,0.085287876426437,0.808286667986106

predictedValues:
Include	Exclude	Both
chr13.6715_chr13_74255255_74264172_+_2.R.tl.Lung	51.1059373629706	40.8277414558407	48.1606974292581
chr13.6715_chr13_74255255_74264172_+_2.R.tl.cerebhem	53.8868150666764	45.8915279266935	48.0363923212691
chr13.6715_chr13_74255255_74264172_+_2.R.tl.cortex	50.7715540699177	43.4124450951021	67.0424056943022
chr13.6715_chr13_74255255_74264172_+_2.R.tl.heart	50.5141817531526	41.431609109087	48.5064385031223
chr13.6715_chr13_74255255_74264172_+_2.R.tl.kidney	51.0407496552647	42.8921804447219	53.153200319925
chr13.6715_chr13_74255255_74264172_+_2.R.tl.liver	51.2924153522965	44.1333023572121	43.6985116890221
chr13.6715_chr13_74255255_74264172_+_2.R.tl.stomach	51.6215456598412	41.6280020641144	47.460842778426
chr13.6715_chr13_74255255_74264172_+_2.R.tl.testicle	51.5383752064101	44.5329572587915	41.8752917013778


diffExp=10.2781959071299,7.99528713998283,7.35910897481555,9.0825726440656,8.14856921054279,7.15911299508439,9.99354359572677,7.00541794761857
diffExpScore=0.98529883248767
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=1,0,0,1,0,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	47.1018308368218	45.0759379130732	49.9318471375574
cerebhem	48.5886237239969	47.6093720005619	45.0113923335892
cortex	49.008234608792	46.7814480080333	50.3918553227164
heart	48.7729129183258	47.2215148015265	52.7012701895988
kidney	49.001690259006	49.3171759333418	51.9552262800856
liver	50.6026330756781	44.1166947593922	51.8273683677237
stomach	48.8040286920097	47.471958227577	48.7480666720856
testicle	49.5214228760598	49.5006341948173	51.4631048791666
cont.diffExp=2.02589292374859,0.979251723434935,2.2267866007587,1.55139811679924,-0.315485674335868,6.48593831628584,1.33207046443267,0.0207886812425144
cont.diffExpScore=0.97589094513585

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=0.687536710285009
cont.tran.correlation=0.00203044972486847

tran.covariance=0.000543482419053257
cont.tran.covariance=3.32269509887676e-06

tran.mean=47.2825837398808
cont.tran.mean=48.0310070518133

weightedLogRatios:
wLogRatio
Lung	0.858104848877958
cerebhem	0.627417987604801
cortex	0.602721688619776
heart	0.757786496138867
kidney	0.668893535412137
liver	0.580624600806243
stomach	0.825453663788344
testicle	0.565290512066974

cont.weightedLogRatios:
wLogRatio
Lung	0.168393936943481
cerebhem	0.0788577369862558
cortex	0.179902573847245
heart	0.125132350995842
kidney	-0.0249970536281605
liver	0.528830106255933
stomach	0.107207176715405
testicle	0.00163845309526456

varWeightedLogRatios=0.0129364079965574
cont.varWeightedLogRatios=0.0292481837088081

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81131719029937	0.0659690802491727	57.7742963203911	5.86876751247142e-267	***
df.mm.trans1	0.261996413328623	0.0592498813174359	4.42188925113549	1.13590757644259e-05	***
df.mm.trans2	-0.126310480130470	0.0544883127431723	-2.31812059818824	0.0207334198686440	*  
df.mm.exp2	0.172488268715825	0.0746354943885272	2.31107558312549	0.0211216931604688	*  
df.mm.exp3	-0.275962135268927	0.0746354943885273	-3.69746509391847	0.000235006211483043	***
df.mm.exp4	-0.00411751067869826	0.0746354943885272	-0.0551682642746879	0.956020335535638	   
df.mm.exp5	-0.050583641236225	0.0746354943885273	-0.677742428728396	0.498161553910685	   
df.mm.exp6	0.178724314143119	0.0746354943885273	2.39462893101157	0.0169022256198481	*  
df.mm.exp7	0.0440880086209992	0.0746354943885272	0.590711014674759	0.5549069362605	   
df.mm.exp8	0.235141064193369	0.0746354943885273	3.15052598123493	0.00169998818615737	** 
df.mm.trans1:exp2	-0.119503120670028	0.071458045528372	-1.67235361373801	0.0949066392124044	.  
df.mm.trans2:exp2	-0.0555695334466013	0.0621962453237727	-0.893454792284084	0.371924280948924	   
df.mm.trans1:exp3	0.269397692247864	0.071458045528372	3.77001204351302	0.000177177703146081	***
df.mm.trans2:exp3	0.337346500612371	0.0621962453237727	5.42390459192928	8.067294140788e-08	***
df.mm.trans1:exp4	-0.00752904718552776	0.071458045528372	-0.105363183807461	0.916118161357602	   
df.mm.trans2:exp4	0.0187998172382701	0.0621962453237727	0.302266111730773	0.762540035566271	   
df.mm.trans1:exp5	0.0493072862518681	0.0714580455283721	0.690017280591461	0.490414659872542	   
df.mm.trans2:exp5	0.0999113885400621	0.0621962453237727	1.60638938926228	0.108644565670115	   
df.mm.trans1:exp6	-0.175082103296404	0.071458045528372	-2.45013842740617	0.0145267557787600	*  
df.mm.trans2:exp6	-0.100871449184826	0.0621962453237727	-1.62182537964668	0.105296238211040	   
df.mm.trans1:exp7	-0.0340495532527373	0.071458045528372	-0.476497125004882	0.633870672446425	   
df.mm.trans2:exp7	-0.0246767292610267	0.0621962453237727	-0.396755931689573	0.69166977543389	   
df.mm.trans1:exp8	-0.226715065886980	0.071458045528372	-3.17270174702671	0.0015770767868305	** 
df.mm.trans2:exp8	-0.148273324582608	0.0621962453237727	-2.38395941444289	0.0173960863814309	*  
df.mm.trans1:probe2	-0.207605815527876	0.0357290227641860	-5.81056517828905	9.49907393494642e-09	***
df.mm.trans1:probe3	-0.202707494174641	0.0357290227641860	-5.67346875151118	2.05759906987610e-08	***
df.mm.trans1:probe4	-0.202086565437711	0.0357290227641860	-5.65608991803376	2.26684646839034e-08	***
df.mm.trans1:probe5	-0.279615831682179	0.0357290227641860	-7.82601398106135	1.88937923365595e-14	***
df.mm.trans1:probe6	-0.16088460349396	0.0357290227641860	-4.50291082842677	7.86491315340901e-06	***
df.mm.trans1:probe7	-0.201590818253736	0.035729022764186	-5.6422147223071	2.44863642365657e-08	***
df.mm.trans1:probe8	0.270289033908526	0.0357290227641860	7.56497135934705	1.23900402674205e-13	***
df.mm.trans1:probe9	-0.0163325575254101	0.0357290227641861	-0.457122984672883	0.647726116950357	   
df.mm.trans1:probe10	-0.288373007828421	0.0357290227641860	-8.07111377581476	3.0842520898412e-15	***
df.mm.trans1:probe11	-0.192397984549998	0.0357290227641860	-5.38492154738846	9.93896192176905e-08	***
df.mm.trans1:probe12	-0.285500286952051	0.0357290227641860	-7.99071076856403	5.61722224483873e-15	***
df.mm.trans1:probe13	-0.254404864544758	0.035729022764186	-7.1203980647287	2.70224288586043e-12	***
df.mm.trans1:probe14	-0.239086450913912	0.0357290227641860	-6.69165939667308	4.55874423260736e-11	***
df.mm.trans1:probe15	-0.294468596921077	0.035729022764186	-8.24171987195367	8.50724247531037e-16	***
df.mm.trans1:probe16	-0.345428924643411	0.0357290227641860	-9.66802050319891	7.96506442260823e-21	***
df.mm.trans1:probe17	-0.145300609211191	0.0357290227641860	-4.06673896932990	5.31572846137286e-05	***
df.mm.trans1:probe18	-0.152622075491461	0.0357290227641860	-4.27165546896642	2.21179815141712e-05	***
df.mm.trans1:probe19	-0.0126269885569756	0.0357290227641860	-0.353409849474882	0.723888838891155	   
df.mm.trans1:probe20	-0.109641671839642	0.0357290227641860	-3.06870055090185	0.00223390233105897	** 
df.mm.trans1:probe21	-0.168675743289126	0.0357290227641860	-4.72097276218264	2.84197719546308e-06	***
df.mm.trans1:probe22	0.00373880413368753	0.0357290227641860	0.104643335989453	0.916689148342178	   
df.mm.trans2:probe2	0.0279766018433429	0.035729022764186	0.783021747557732	0.433882225716767	   
df.mm.trans2:probe3	0.0137528240459955	0.0357290227641860	0.384920240801576	0.700414782447323	   
df.mm.trans2:probe4	0.0131681063582665	0.0357290227641860	0.368554898497419	0.712572239821043	   
df.mm.trans2:probe5	0.0815190240977317	0.0357290227641860	2.28159120488021	0.0228163610644943	*  
df.mm.trans2:probe6	0.0827791441382046	0.0357290227641860	2.31686001278435	0.0208024332334197	*  
df.mm.trans3:probe2	-0.137022536002452	0.0357290227641860	-3.8350485236277	0.000136997568439842	***
df.mm.trans3:probe3	-0.0803569872893918	0.0357290227641860	-2.24906759470454	0.0248218908879031	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69032971687232	0.098005105572635	37.6544639721579	1.12922586274349e-169	***
df.mm.trans1	0.122295027533221	0.088022916974869	1.38935440605924	0.165171882550177	   
df.mm.trans2	0.0987775132291193	0.080949026766768	1.22024337011140	0.2227882963598	   
df.mm.exp2	0.189502005807585	0.110880119586102	1.70907108068575	0.0878861513803323	.  
df.mm.exp3	0.0676440603295693	0.110880119586102	0.610064821196748	0.542019147673324	   
df.mm.exp4	0.0273837190104254	0.110880119586102	0.246966896434136	0.805007091915354	   
df.mm.exp5	0.0897434647086141	0.110880119586102	0.809373808791084	0.418578364725136	   
df.mm.exp6	0.0129221023178633	0.110880119586102	0.116541201128746	0.90725744776194	   
df.mm.exp7	0.111285079705555	0.110880119586102	1.00365223379055	0.315897084427208	   
df.mm.exp8	0.113524272150848	0.110880119586102	1.02384694907091	0.306265344301401	   
df.mm.trans1:exp2	-0.158424453592235	0.106159632202998	-1.49232293202840	0.136070249794133	   
df.mm.trans2:exp2	-0.134820949888464	0.0924000996550849	-1.45909961560355	0.144991448492221	   
df.mm.trans1:exp3	-0.0279675946346595	0.106159632202998	-0.263448488415821	0.792283347388675	   
df.mm.trans2:exp3	-0.0305059227878926	0.0924000996550849	-0.330150323449503	0.741386302645432	   
df.mm.trans1:exp4	0.0074795050580304	0.106159632202998	0.0704552653661055	0.943851660560538	   
df.mm.trans2:exp4	0.0191173150860872	0.0924000996550849	0.206897126274205	0.836151064203283	   
df.mm.trans1:exp5	-0.0502005436481815	0.106159632202998	-0.472877897242409	0.636449407708103	   
df.mm.trans2:exp5	0.000180375172073834	0.0924000996550849	0.00195211014649493	0.99844300503642	   
df.mm.trans1:exp6	0.0587696381534054	0.106159632202998	0.553596851588805	0.58003360100168	   
df.mm.trans2:exp6	-0.0344324022363868	0.0924000996550849	-0.372644643944298	0.70952704464637	   
df.mm.trans1:exp7	-0.0757840866268174	0.106159632202998	-0.713869152088839	0.475548793942349	   
df.mm.trans2:exp7	-0.0594944728015262	0.0924000996550849	-0.643878881338979	0.519867315500454	   
df.mm.trans1:exp8	-0.0634307823628432	0.106159632202998	-0.59750378789511	0.550366542938488	   
df.mm.trans2:exp8	-0.0198873673476846	0.0924000996550849	-0.21523101622099	0.829650626951692	   
df.mm.trans1:probe2	-0.00297933106794553	0.0530798161014992	-0.056129265072216	0.95525503244696	   
df.mm.trans1:probe3	0.0193967787870556	0.0530798161014992	0.365426638818135	0.714904626328694	   
df.mm.trans1:probe4	0.0890423001956827	0.0530798161014992	1.67751711922694	0.0938929731002568	.  
df.mm.trans1:probe5	0.0212624789893848	0.0530798161014992	0.400575596357129	0.688856254769613	   
df.mm.trans1:probe6	0.0267809586655329	0.0530798161014992	0.504541285793499	0.614041673126674	   
df.mm.trans1:probe7	0.0511857228397608	0.0530798161014992	0.964316129918074	0.335224316892132	   
df.mm.trans1:probe8	0.0755869594143338	0.0530798161014992	1.42402451564256	0.154890127753555	   
df.mm.trans1:probe9	0.0705504986119905	0.0530798161014992	1.32913984624747	0.184239878519170	   
df.mm.trans1:probe10	0.136841134735814	0.0530798161014992	2.57802578807257	0.0101423049824836	*  
df.mm.trans1:probe11	0.0488224501855534	0.0530798161014992	0.919793129881895	0.358001384394934	   
df.mm.trans1:probe12	0.0582421899546744	0.0530798161014992	1.09725681496906	0.272910809423896	   
df.mm.trans1:probe13	0.0820564152773659	0.0530798161014992	1.54590617119806	0.122584404782399	   
df.mm.trans1:probe14	0.0277250939204828	0.0530798161014992	0.522328371814003	0.601608882820359	   
df.mm.trans1:probe15	0.0328433349119422	0.0530798161014992	0.618753743402939	0.536282220556461	   
df.mm.trans1:probe16	0.0110200678498451	0.0530798161014992	0.207613150519823	0.835592117755278	   
df.mm.trans1:probe17	0.044013169901931	0.0530798161014992	0.829188439872683	0.4072837078663	   
df.mm.trans1:probe18	-0.0114304583955753	0.0530798161014992	-0.215344724889740	0.829562013836875	   
df.mm.trans1:probe19	0.0462258816164724	0.0530798161014992	0.870874939130899	0.3841244522976	   
df.mm.trans1:probe20	0.0395121437861753	0.0530798161014992	0.744391120546088	0.456892591600902	   
df.mm.trans1:probe21	0.0445027392993644	0.0530798161014992	0.8384117084028	0.402089018009658	   
df.mm.trans1:probe22	0.0809776488069044	0.0530798161014992	1.52558269327947	0.127570713244132	   
df.mm.trans2:probe2	0.000963116834228138	0.0530798161014992	0.0181446904862381	0.985528656135579	   
df.mm.trans2:probe3	0.0644157970980565	0.0530798161014992	1.21356481293908	0.225327993251734	   
df.mm.trans2:probe4	0.0416296394108527	0.0530798161014992	0.784283791248419	0.433141987667285	   
df.mm.trans2:probe5	0.0359332809327176	0.0530798161014992	0.676966944723508	0.498653158468877	   
df.mm.trans2:probe6	0.0302302851644878	0.0530798161014992	0.569525054621166	0.569184695287233	   
df.mm.trans3:probe2	0.0310359572807438	0.0530798161014992	0.584703557024329	0.558937642823528	   
df.mm.trans3:probe3	-0.0332656750694797	0.0530798161014992	-0.626710443115875	0.531055747789046