chr11.3720_chr11_121730891_121731712_-_0.R 

fitVsDatCorrelation=0.917562514562602
cont.fitVsDatCorrelation=0.318910134096312

fstatistic=9598.1414237631,43,485
cont.fstatistic=1679.75299153696,43,485

residuals=-0.460860438721498,-0.0877497200308222,0.00179900143625167,0.0780428517116793,1.01009472079082
cont.residuals=-0.75528928746899,-0.277334994255510,-0.0242239683204816,0.259409179011931,1.15253201157592

predictedValues:
Include	Exclude	Both
chr11.3720_chr11_121730891_121731712_-_0.R.tl.Lung	52.0594827590445	103.463188725823	77.7388240456008
chr11.3720_chr11_121730891_121731712_-_0.R.tl.cerebhem	72.135552063482	97.7313856385914	74.498620335326
chr11.3720_chr11_121730891_121731712_-_0.R.tl.cortex	52.8169282923243	87.7388128044253	77.3764510628463
chr11.3720_chr11_121730891_121731712_-_0.R.tl.heart	52.8248969861483	95.633002678121	76.8978671040674
chr11.3720_chr11_121730891_121731712_-_0.R.tl.kidney	55.6183537703421	104.990905485500	86.3024133375848
chr11.3720_chr11_121730891_121731712_-_0.R.tl.liver	60.2838578156477	110.272257027396	86.9955971441964
chr11.3720_chr11_121730891_121731712_-_0.R.tl.stomach	51.0791814504993	174.649613059076	79.0166896391118
chr11.3720_chr11_121730891_121731712_-_0.R.tl.testicle	65.9422404876942	114.874635978022	95.9391256324683


diffExp=-51.4037059667784,-25.5958335751095,-34.921884512101,-42.8081056919728,-49.3725517151582,-49.988399211748,-123.570431608576,-48.9323954903274
diffExpScore=0.99766132916062
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	75.9766719999854	74.12210549164	78.5796554155445
cerebhem	84.2240734004488	78.6715404596155	86.414680792433
cortex	73.0774302359663	77.8849782537943	78.4395039783956
heart	77.0764782201478	78.9070062368521	88.778578382613
kidney	68.0754520136884	89.6357826507156	66.631297393812
liver	71.2336705605522	88.835818984617	84.4190042001988
stomach	69.5444149649675	77.266619975013	79.1761657214266
testicle	68.8213419901046	72.2809598515357	84.6660097365872
cont.diffExp=1.85456650834546,5.5525329408333,-4.80754801782793,-1.83052801670432,-21.5603306370272,-17.6021484240648,-7.72220501004546,-3.4596178614311
cont.diffExpScore=1.27314133116365

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,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,-1,-1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.234415707874893
cont.tran.correlation=-0.238281472204285

tran.covariance=-0.00526910097452847
cont.tran.covariance=-0.00126116272947823

tran.mean=84.5071434388835
cont.tran.mean=76.6021465806028

weightedLogRatios:
wLogRatio
Lung	-2.95048065686354
cerebhem	-1.34540039019445
cortex	-2.14209116447114
heart	-2.53068658075367
kidney	-2.75504598412533
liver	-2.65771615086616
stomach	-5.59143039225713
testicle	-2.4790800153271

cont.weightedLogRatios:
wLogRatio
Lung	0.106710471983097
cerebhem	0.300034793999182
cortex	-0.275457582402752
heart	-0.102256002994064
kidney	-1.19910213929670
liver	-0.966410679827524
stomach	-0.452207483613145
testicle	-0.208745203236961

varWeightedLogRatios=1.50869713247057
cont.varWeightedLogRatios=0.261342626003505

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.92323404872464	0.0788135067377413	49.7787017874935	9.94963432049671e-193	***
df.mm.trans1	0.040394639420189	0.0630943337387607	0.64022610314646	0.522327951541535	   
df.mm.trans2	0.71236553804352	0.0630943337387607	11.2904835637545	2.08827218496023e-26	***
df.mm.exp2	0.311741112375998	0.0844876112927689	3.68978489989194	0.000249869889857414	***
df.mm.exp3	-0.145734419181634	0.0844876112927688	-1.72492057654028	0.0851788533382065	.  
df.mm.exp4	-0.0532256004210889	0.0844876112927688	-0.629981125122002	0.5290036115434	   
df.mm.exp5	-0.0237186301107708	0.0844876112927688	-0.280735006563038	0.779033371455496	   
df.mm.exp6	0.0979111814472896	0.0844876112927688	1.15888211240823	0.247074511374854	   
df.mm.exp7	0.488251620054064	0.0844876112927688	5.77897294743203	1.34596937152508e-08	***
df.mm.exp8	0.130658693767151	0.0844876112927689	1.54648346388193	0.122640085351086	   
df.mm.trans1:exp2	0.0144189401514405	0.06627753517518	0.21755395871813	0.82786815532178	   
df.mm.trans2:exp2	-0.368734244850995	0.0662775351751801	-5.56348759615733	4.37863969831265e-08	***
df.mm.trans1:exp3	0.160179206066361	0.0662775351751801	2.41679485579822	0.01602590569783	*  
df.mm.trans2:exp3	-0.0191171009651231	0.0662775351751801	-0.288440131555800	0.773133081770607	   
df.mm.trans1:exp4	0.0678212497851064	0.0662775351751801	1.02329167199483	0.306680082656260	   
df.mm.trans2:exp4	-0.0254723077536612	0.0662775351751801	-0.384327927801397	0.700903861050065	   
df.mm.trans1:exp5	0.0898449166385482	0.0662775351751801	1.35558626917969	0.175861741590224	   
df.mm.trans2:exp5	0.0383764771362353	0.0662775351751801	0.579026921185306	0.562839823363661	   
df.mm.trans1:exp6	0.0487662244924523	0.0662775351751801	0.735788142446108	0.462215323421536	   
df.mm.trans2:exp6	-0.0341746946524151	0.06627753517518	-0.515630138659909	0.606347715635669	   
df.mm.trans1:exp7	-0.507261577910933	0.0662775351751801	-7.65359750585436	1.06406951288880e-13	***
df.mm.trans2:exp7	0.0353142507172746	0.0662775351751801	0.532823838785412	0.594399570883444	   
df.mm.trans1:exp8	0.105733556931620	0.0662775351751801	1.59531516451468	0.111293158016035	   
df.mm.trans2:exp8	-0.0260331669062413	0.0662775351751801	-0.392790209192787	0.694647013305707	   
df.mm.trans1:probe2	0.243417223003615	0.0453771263391103	5.36431551845131	1.26052114752424e-07	***
df.mm.trans1:probe3	-0.142617752272067	0.0453771263391103	-3.14294367620951	0.00177499563771189	** 
df.mm.trans1:probe4	-0.172000426162247	0.0453771263391103	-3.79046537404905	0.000169302772740262	***
df.mm.trans1:probe5	-0.112557371051271	0.0453771263391103	-2.48048697949958	0.0134584931210812	*  
df.mm.trans1:probe6	0.00389074079873195	0.0453771263391103	0.085742335679343	0.93170663091385	   
df.mm.trans2:probe2	0.0424871950374047	0.0453771263391103	0.936313038421412	0.349578017020371	   
df.mm.trans2:probe3	0.0766923263823603	0.0453771263391103	1.69010981015472	0.09164952404397	.  
df.mm.trans2:probe4	-0.0472413970626131	0.0453771263391103	-1.04108393091204	0.298355284128344	   
df.mm.trans2:probe5	0.0261625318817352	0.0453771263391103	0.576557706325838	0.564505755215648	   
df.mm.trans2:probe6	-0.040239885217287	0.0453771263391103	-0.886787870094904	0.375632627755482	   
df.mm.trans3:probe2	-0.410876764239144	0.0453771263391103	-9.0547109829873	3.34488008575889e-18	***
df.mm.trans3:probe3	-0.370276177970702	0.0453771263391103	-8.1599741509317	2.90410307758979e-15	***
df.mm.trans3:probe4	-0.557248750655225	0.0453771263391103	-12.2803887247248	2.26100818686170e-30	***
df.mm.trans3:probe5	-0.569433549075941	0.0453771263391103	-12.5489116437316	1.77283316540153e-31	***
df.mm.trans3:probe6	0.237383321175441	0.0453771263391103	5.23134319704246	2.50840170670010e-07	***
df.mm.trans3:probe7	-0.0460574923384112	0.0453771263391103	-1.01499358937400	0.310614951666306	   
df.mm.trans3:probe8	-0.514200305907866	0.0453771263391103	-11.331707126299	1.43987041203039e-26	***
df.mm.trans3:probe9	-0.26912542787828	0.0453771263391103	-5.93086097755649	5.73288047923162e-09	***
df.mm.trans3:probe10	-0.726559129506522	0.0453771263391103	-16.0115720875939	1.24904327841202e-46	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30559635122682	0.187876875736967	22.9171170445413	2.65615242129313e-79	***
df.mm.trans1	0.00822636847257355	0.150405264150839	0.0546946845179798	0.956404226772343	   
df.mm.trans2	-0.0294965123608803	0.150405264150839	-0.196113563759967	0.844603422763973	   
df.mm.exp2	0.0675773548720908	0.201402895330929	0.335533184669730	0.737368005980644	   
df.mm.exp3	0.0123976656613721	0.201402895330928	0.0615565413843794	0.950941344499455	   
df.mm.exp4	-0.0451045353988516	0.201402895330928	-0.223951772514192	0.822889135891443	   
df.mm.exp5	0.245169556312156	0.201402895330928	1.21730899602666	0.22407863573096	   
df.mm.exp6	0.0449356745142769	0.201402895330928	0.223113349192137	0.823541225938204	   
df.mm.exp7	-0.0544749893831915	0.201402895330929	-0.270477687491447	0.786907841419796	   
df.mm.exp8	-0.198666898218515	0.201402895330929	-0.986415303971089	0.324421192789231	   
df.mm.trans1:exp2	0.0354770870814866	0.157993429751768	0.224547863396767	0.822425596306786	   
df.mm.trans2:exp2	-0.00800969297740347	0.157993429751768	-0.0506963675007749	0.959588343019526	   
df.mm.trans1:exp3	-0.0513044442810183	0.157993429751768	-0.324725175987543	0.745529093109621	   
df.mm.trans2:exp3	0.0371216273691996	0.157993429751768	0.234956779073177	0.814341444547397	   
df.mm.trans1:exp4	0.059476342170848	0.157993429751768	0.376448199550415	0.706748346094993	   
df.mm.trans2:exp4	0.107660750715653	0.157993429751768	0.68142549272337	0.495927535242508	   
df.mm.trans1:exp5	-0.354979223637446	0.157993429751768	-2.24679737755661	0.0251019718709346	*  
df.mm.trans2:exp5	-0.0551287636519995	0.157993429751768	-0.348930735528784	0.727292740331039	   
df.mm.trans1:exp6	-0.109396412547228	0.157993429751768	-0.692411151014995	0.489010541418601	   
df.mm.trans2:exp6	0.136140453558362	0.157993429751768	0.861684272391958	0.389287103750186	   
df.mm.trans1:exp7	-0.0339857437162547	0.157993429751768	-0.215108588816963	0.829773080289546	   
df.mm.trans2:exp7	0.09602321978943	0.157993429751768	0.607767170700056	0.543626375185138	   
df.mm.trans1:exp8	0.0997544525995145	0.157993429751768	0.631383550292213	0.528087220730785	   
df.mm.trans2:exp8	0.173513835943904	0.157993429751768	1.09823450390640	0.272647008382920	   
df.mm.trans1:probe2	-0.034252904507946	0.108170706765814	-0.316656010967021	0.751640878678508	   
df.mm.trans1:probe3	0.220076120085406	0.108170706765814	2.03452604374550	0.0424420442004911	*  
df.mm.trans1:probe4	0.00435405267994057	0.108170706765814	0.0402516800538889	0.967909040110533	   
df.mm.trans1:probe5	0.153696431464597	0.108170706765814	1.42086925434762	0.155997364602602	   
df.mm.trans1:probe6	-0.0782156821166302	0.108170706765814	-0.723076371183973	0.469981518648565	   
df.mm.trans2:probe2	0.144986818688068	0.108170706765814	1.34035195870505	0.180758477201831	   
df.mm.trans2:probe3	-0.0281196802472488	0.108170706765814	-0.259956517693159	0.795007632529612	   
df.mm.trans2:probe4	0.151030888491615	0.108170706765814	1.39622725049391	0.163284779001051	   
df.mm.trans2:probe5	0.0613240404030364	0.108170706765814	0.566919106258601	0.571031416074631	   
df.mm.trans2:probe6	0.144601431430144	0.108170706765814	1.33678918954650	0.181918167667662	   
df.mm.trans3:probe2	0.186775026500665	0.108170706765814	1.72666918877610	0.0848638510267092	.  
df.mm.trans3:probe3	-0.0118962162613186	0.108170706765814	-0.109976319994595	0.912473645228609	   
df.mm.trans3:probe4	0.200140573926499	0.108170706765814	1.85022895671558	0.0648885466285442	.  
df.mm.trans3:probe5	0.0347627500268516	0.108170706765814	0.321369352814824	0.748068950998925	   
df.mm.trans3:probe6	0.00950065363442198	0.108170706765814	0.0878301891379022	0.930047894982964	   
df.mm.trans3:probe7	0.128824800437888	0.108170706765814	1.19093980514327	0.234259743501553	   
df.mm.trans3:probe8	0.0140499674353058	0.108170706765814	0.129886989328114	0.896709688967094	   
df.mm.trans3:probe9	0.151166280317341	0.108170706765814	1.39747890012969	0.162908526161654	   
df.mm.trans3:probe10	0.0845423803845369	0.108170706765814	0.781564463358537	0.434851932381683