chr6.20261_chr6_51963005_51967503_+_2.R 

fitVsDatCorrelation=0.794216581120543
cont.fitVsDatCorrelation=0.272960171034062

fstatistic=5911.03293152139,59,853
cont.fstatistic=2349.48284436717,59,853

residuals=-0.699264549436594,-0.101586052596364,-0.00759637445167264,0.0912990423231894,1.05943421886581
cont.residuals=-0.533826521092994,-0.201172885675738,-0.072010675054446,0.112053915444362,1.70801755367324

predictedValues:
Include	Exclude	Both
chr6.20261_chr6_51963005_51967503_+_2.R.tl.Lung	60.3939870344059	43.9066718820761	62.4104749215726
chr6.20261_chr6_51963005_51967503_+_2.R.tl.cerebhem	64.5323710907016	43.7045505658094	66.9079925582761
chr6.20261_chr6_51963005_51967503_+_2.R.tl.cortex	91.4543640536217	45.2194684925272	100.070119312410
chr6.20261_chr6_51963005_51967503_+_2.R.tl.heart	61.5450915806077	46.9186814813535	62.8340157671433
chr6.20261_chr6_51963005_51967503_+_2.R.tl.kidney	55.9557730459107	42.5332670426284	60.466319506309
chr6.20261_chr6_51963005_51967503_+_2.R.tl.liver	66.0961524891404	51.0845810507041	71.8154497910841
chr6.20261_chr6_51963005_51967503_+_2.R.tl.stomach	62.2130910721512	45.6165532168853	60.2004905441557
chr6.20261_chr6_51963005_51967503_+_2.R.tl.testicle	56.4448382747982	45.2583384291814	61.5922197970784


diffExp=16.4873151523298,20.8278205248922,46.2348955610945,14.6264100992542,13.4225060032823,15.0115714384363,16.5965378552659,11.1864998456169
diffExpScore=0.993564726732234
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,1,1,1,0,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	54.9014427383908	68.1315786682984	56.0759144058528
cerebhem	50.9827031322732	56.5365675902056	56.6025386303643
cortex	54.8942020287915	54.329797553766	58.9667413386073
heart	54.7602344041146	50.2144969914346	54.3508988631773
kidney	52.5590797830814	58.9658059122547	57.4695561033457
liver	60.5454900622211	67.3537029471992	59.0649770737831
stomach	55.8469987270341	57.1772617022958	55.0390253486063
testicle	53.6212611334732	55.0901526663181	55.0404845996227
cont.diffExp=-13.2301359299076,-5.55386445793244,0.564404475025576,4.54573741267998,-6.40672612917326,-6.80821288497809,-1.33026297526172,-1.46889153284491
cont.diffExpScore=1.30045288680989

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

tran.correlation=0.147585260100878
cont.tran.correlation=0.494744402085314

tran.covariance=0.00167403046410855
cont.tran.covariance=0.00246408365752875

tran.mean=55.1798613001564
cont.tran.mean=56.619423502572

weightedLogRatios:
wLogRatio
Lung	1.25663483871214
cerebhem	1.54806763016984
cortex	2.93253412551415
heart	1.08110029485258
kidney	1.06622357819689
liver	1.04656065095409
stomach	1.23355149067661
testicle	0.866461588074328

cont.weightedLogRatios:
wLogRatio
Lung	-0.888107427227368
cerebhem	-0.411866405976369
cortex	0.0413420790642515
heart	0.343143909139813
kidney	-0.462316277918163
liver	-0.442947485296911
stomach	-0.0949715077562593
testicle	-0.107978580861259

varWeightedLogRatios=0.433995059917724
cont.varWeightedLogRatios=0.142360336514066

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32967183545301	0.095377173817181	45.3952624319959	9.40772934701374e-230	***
df.mm.trans1	0.0369047726060788	0.0817852518226884	0.451239945877882	0.651931315246056	   
df.mm.trans2	-0.546597021900884	0.0723335983148233	-7.55661317334009	1.06669232879109e-13	***
df.mm.exp2	-0.00792193290551132	0.0925294498637958	-0.0856152599758506	0.931792358142757	   
df.mm.exp3	-0.0277260326527537	0.0925294498637958	-0.299645493338247	0.764520577751727	   
df.mm.exp4	0.078466738802887	0.0925294498637958	0.848019078448977	0.396665235736866	   
df.mm.exp5	-0.0764610788739192	0.0925294498637958	-0.826343169514901	0.408840523609603	   
df.mm.exp6	0.101270911089565	0.0925294498637958	1.09447220575326	0.274056881964931	   
df.mm.exp7	0.103932897354489	0.0925294498637958	1.12324127623670	0.261651067717110	   
df.mm.exp8	-0.0241074917709135	0.0925294498637958	-0.260538583190541	0.79451123842186	   
df.mm.trans1:exp2	0.0741993606095292	0.0844674448732405	0.878437375735462	0.379953776201662	   
df.mm.trans2:exp2	0.00330787390237526	0.0616862999091972	0.0536241257336634	0.957247190570592	   
df.mm.trans1:exp3	0.442676579428394	0.0844674448732405	5.24079519740078	2.01651749921277e-07	***
df.mm.trans2:exp3	0.0571874579953737	0.0616862999091972	0.9270690263406	0.354152925132949	   
df.mm.trans1:exp4	-0.0595861837895637	0.0844674448732405	-0.70543371921554	0.480732957180703	   
df.mm.trans2:exp4	-0.0121171044569382	0.0616862999091972	-0.196431046679323	0.844319586345477	   
df.mm.trans1:exp5	0.000133142966874296	0.0844674448732405	0.00157626369631641	0.998742692605758	   
df.mm.trans2:exp5	0.0446813149323476	0.0616862999091972	0.724331253424487	0.469061009689448	   
df.mm.trans1:exp6	-0.0110499209266578	0.0844674448732405	-0.130818695217315	0.895949578791918	   
df.mm.trans2:exp6	0.0501455122724455	0.0616862999091972	0.812911656984779	0.416495536882572	   
df.mm.trans1:exp7	-0.074256999931182	0.0844674448732405	-0.879119760786168	0.379583923071643	   
df.mm.trans2:exp7	-0.0657285251878503	0.0616862999091972	-1.06552873627699	0.286938233965014	   
df.mm.trans1:exp8	-0.043518208276348	0.0844674448732405	-0.515206874573457	0.606541951278948	   
df.mm.trans2:exp8	0.0544281319943529	0.0616862999091972	0.88233744080082	0.377842928738892	   
df.mm.trans1:probe2	0.0281287416470649	0.0597275030593692	0.470951240320643	0.637795966346142	   
df.mm.trans1:probe3	-0.466045102528472	0.0597275030593692	-7.80285594837646	1.76721232510460e-14	***
df.mm.trans1:probe4	-0.560559252661456	0.0597275030593692	-9.38527853917247	5.53705862375763e-20	***
df.mm.trans1:probe5	-0.5268333031071	0.0597275030593692	-8.82061489467301	6.33488932287824e-18	***
df.mm.trans1:probe6	-0.562948624549728	0.0597275030593692	-9.42528308926887	3.92277555011584e-20	***
df.mm.trans1:probe7	-0.541330873836613	0.0597275030593692	-9.0633434533255	8.49986386910999e-19	***
df.mm.trans1:probe8	-0.0782926120525576	0.0597275030593692	-1.31083015432161	0.190267944754272	   
df.mm.trans1:probe9	-0.341082835705239	0.0597275030593692	-5.71064950373368	1.55377671799253e-08	***
df.mm.trans1:probe10	-0.439275099164256	0.0597275030593692	-7.35465366311422	4.49381353002659e-13	***
df.mm.trans1:probe11	-0.595368461096468	0.0597275030593692	-9.96807886820032	3.26166547631332e-22	***
df.mm.trans1:probe12	-0.417446971352046	0.0597275030593692	-6.98919174533552	5.57500462687383e-12	***
df.mm.trans1:probe13	-0.373654788777019	0.0597275030593692	-6.2559921248609	6.24352806700004e-10	***
df.mm.trans1:probe14	-0.519234344660921	0.0597275030593692	-8.69338777053515	1.78369557248905e-17	***
df.mm.trans1:probe15	-0.585195180692467	0.0597275030593692	-9.7977506294007	1.49955003220824e-21	***
df.mm.trans1:probe16	-0.53913426477111	0.0597275030593692	-9.02656627442152	1.15559729377961e-18	***
df.mm.trans1:probe17	-0.369394536005245	0.0597275030593692	-6.18466396691766	9.6448048631151e-10	***
df.mm.trans1:probe18	-0.569372193981682	0.0597275030593692	-9.53283102117503	1.54415324921690e-20	***
df.mm.trans1:probe19	-0.500635457004183	0.0597275030593692	-8.38199206999413	2.13402889159120e-16	***
df.mm.trans1:probe20	-0.544310775204186	0.0597275030593692	-9.1132350646425	5.59442849424636e-19	***
df.mm.trans2:probe2	0.0390654703931611	0.0597275030593692	0.654061669953454	0.51324836543424	   
df.mm.trans2:probe3	-0.00556572447645297	0.0597275030593692	-0.0931852863649871	0.925778245131104	   
df.mm.trans2:probe4	-0.0495860167285768	0.0597275030593692	-0.830204080007969	0.406655698681971	   
df.mm.trans2:probe5	-0.0410185684453262	0.0597275030593692	-0.68676181565055	0.492419485961999	   
df.mm.trans2:probe6	0.0389513725516611	0.0597275030593692	0.652151363383521	0.514479219750232	   
df.mm.trans3:probe2	0.553451259660182	0.0597275030593692	9.26627150493887	1.53315016351557e-19	***
df.mm.trans3:probe3	0.0229413137719461	0.0597275030593692	0.384099662581614	0.701000307991022	   
df.mm.trans3:probe4	0.575758678157609	0.0597275030593692	9.6397580455574	6.0610357875436e-21	***
df.mm.trans3:probe5	0.186997894909107	0.0597275030593692	3.13085070245161	0.0018023940249014	** 
df.mm.trans3:probe6	0.102761895644936	0.0597275030593692	1.72051216577380	0.0857020472028295	.  
df.mm.trans3:probe7	0.415185231975475	0.0597275030593692	6.95132410880764	7.19188642469263e-12	***
df.mm.trans3:probe8	0.199281826490121	0.0597275030593692	3.33651695253415	0.00088503037759586	***
df.mm.trans3:probe9	0.383395631920573	0.0597275030593692	6.41908019391799	2.27281790207841e-10	***
df.mm.trans3:probe10	0.361130265835641	0.0597275030593692	6.04629772446166	2.21459318018444e-09	***
df.mm.trans3:probe11	0.565391927042069	0.0597275030593692	9.4661905835921	2.75428774453313e-20	***
df.mm.trans3:probe12	0.398746584135427	0.0597275030593692	6.6760966675448	4.4175764385822e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12794980858292	0.151003960029467	27.3366990360874	1.14694427657025e-118	***
df.mm.trans1	-0.167551590548766	0.129484827479848	-1.29398628248428	0.196020395654602	   
df.mm.trans2	0.0899757022022046	0.114520690345215	0.785672020758686	0.432277837122005	   
df.mm.exp2	-0.269953828992198	0.146495359314833	-1.84274662525002	0.0657128587989321	.  
df.mm.exp3	-0.276767067629705	0.146495359314833	-1.88925484687133	0.0591966353650103	.  
df.mm.exp4	-0.27646715754846	0.146495359314833	-1.88720761423101	0.0594717207278218	.  
df.mm.exp5	-0.212633823718423	0.146495359314833	-1.45147139617885	0.147016309942740	   
df.mm.exp6	0.0344406657309198	0.146495359314833	0.235097315655599	0.814189627473747	   
df.mm.exp7	-0.139544459154463	0.146495359314833	-0.952552079513786	0.341086902118471	   
df.mm.exp8	-0.217426419400595	0.146495359314833	-1.48418639619378	0.138128889849888	   
df.mm.trans1:exp2	0.195900622363557	0.133731354777598	1.46488176007302	0.143321642966231	   
df.mm.trans2:exp2	0.083400655848782	0.0976635728765553	0.853958680727345	0.393367633511899	   
df.mm.trans1:exp3	0.276635173334169	0.133731354777598	2.06858872995213	0.0388855158389923	*  
df.mm.trans2:exp3	0.0503990860241758	0.0976635728765553	0.516047944384332	0.605954660954308	   
df.mm.trans1:exp4	0.2738918109734	0.133731354777598	2.04807474977650	0.0408583674295128	*  
df.mm.trans2:exp4	-0.0286698886833033	0.0976635728765553	-0.293557647328154	0.769167350469333	   
df.mm.trans1:exp5	0.169032062251273	0.133731354777598	1.26396732114456	0.206587049877877	   
df.mm.trans2:exp5	0.0681507228755087	0.0976635728765553	0.697811076005276	0.48548551545068	   
df.mm.trans1:exp6	0.0634146910661907	0.133731354777598	0.474194635743074	0.635482476570427	   
df.mm.trans2:exp6	-0.0459235996587728	0.0976635728765553	-0.470222400288584	0.638316330270968	   
df.mm.trans1:exp7	0.156620617566284	0.133731354777598	1.17115853515993	0.241862107102861	   
df.mm.trans2:exp7	-0.0357400601091154	0.0976635728765553	-0.365950774238929	0.714492544962511	   
df.mm.trans1:exp8	0.19383244420734	0.133731354777598	1.44941658992159	0.147588815302319	   
df.mm.trans2:exp8	0.00495658616879465	0.0976635728765553	0.0507516366932395	0.95953531110762	   
df.mm.trans1:probe2	0.0988094247484215	0.0945623478205033	1.04491298096765	0.296359208080614	   
df.mm.trans1:probe3	0.0827910966659693	0.0945623478205033	0.875518624211002	0.381538244486626	   
df.mm.trans1:probe4	-0.00676447169141315	0.0945623478205033	-0.0715345150297385	0.942989124156246	   
df.mm.trans1:probe5	0.0765008095605949	0.0945623478205033	0.808998627083662	0.418741522743869	   
df.mm.trans1:probe6	-0.0769300590610259	0.0945623478205033	-0.813537954948551	0.416136718945706	   
df.mm.trans1:probe7	0.114371223673532	0.0945623478205033	1.20947952657256	0.226813774022263	   
df.mm.trans1:probe8	0.0223852335773670	0.0945623478205033	0.236724595923298	0.81292728786632	   
df.mm.trans1:probe9	0.041365873751833	0.0945623478205033	0.437445502414481	0.661899021964178	   
df.mm.trans1:probe10	0.0788074390957132	0.0945623478205033	0.833391311786211	0.404857367131372	   
df.mm.trans1:probe11	0.00105125435200772	0.0945623478205033	0.0111170500335207	0.991132659585674	   
df.mm.trans1:probe12	0.190934251957771	0.0945623478205033	2.01913611874569	0.0437854446625512	*  
df.mm.trans1:probe13	0.171315915810793	0.0945623478205033	1.81167155595567	0.070388535656222	.  
df.mm.trans1:probe14	0.070047206760209	0.0945623478205033	0.740751561003662	0.459047945449568	   
df.mm.trans1:probe15	0.0714792035885884	0.0945623478205033	0.755894975495628	0.449920854714885	   
df.mm.trans1:probe16	0.130940272441825	0.0945623478205033	1.38469777305417	0.166506976347649	   
df.mm.trans1:probe17	0.00607506901156237	0.0945623478205033	0.0642440585664599	0.948790948112363	   
df.mm.trans1:probe18	0.0150233348069933	0.0945623478205033	0.158872269494729	0.873807115081241	   
df.mm.trans1:probe19	0.135259582058789	0.0945623478205033	1.43037461713130	0.152975724201969	   
df.mm.trans1:probe20	0.221062443653629	0.0945623478205033	2.33774275648535	0.0196309521889076	*  
df.mm.trans2:probe2	0.134070258099487	0.0945623478205033	1.41779747636952	0.156615088097545	   
df.mm.trans2:probe3	-0.0819223684438973	0.0945623478205033	-0.86633179412382	0.386551845460314	   
df.mm.trans2:probe4	-0.00153299012447890	0.0945623478205033	-0.0162114219857231	0.987069514162966	   
df.mm.trans2:probe5	0.0345195676654163	0.0945623478205033	0.365045585912701	0.715167867913684	   
df.mm.trans2:probe6	-0.0218589746047598	0.0945623478205033	-0.231159389636266	0.817246412568613	   
df.mm.trans3:probe2	0.0147161763338240	0.0945623478205033	0.155624058338294	0.876366179573822	   
df.mm.trans3:probe3	0.0692834146797864	0.0945623478205033	0.732674434134176	0.463958298225876	   
df.mm.trans3:probe4	-0.129255403787292	0.0945623478205033	-1.36688023051884	0.172022978882766	   
df.mm.trans3:probe5	-0.0499107303803634	0.0945623478205033	-0.527807647871678	0.597770113806425	   
df.mm.trans3:probe6	-0.0498714965528848	0.0945623478205033	-0.527392748830116	0.598058018458628	   
df.mm.trans3:probe7	-0.0113599038936783	0.0945623478205033	-0.120131364708091	0.90440737549546	   
df.mm.trans3:probe8	0.00554258200114408	0.0945623478205033	0.0586129905706753	0.95327409140591	   
df.mm.trans3:probe9	-0.0465791463011881	0.0945623478205033	-0.492576034486833	0.622438944893836	   
df.mm.trans3:probe10	-0.100178620017764	0.0945623478205033	-1.05939226686631	0.289721057472618	   
df.mm.trans3:probe11	-0.098544520516441	0.0945623478205033	-1.04211160982907	0.297655198200005	   
df.mm.trans3:probe12	0.112147250915582	0.0945623478205033	1.18596094006102	0.235967967395490