chr16.9650_chr16_93320234_93375758_-_2.R 

fitVsDatCorrelation=0.885974166443344
cont.fitVsDatCorrelation=0.221048909155582

fstatistic=10262.5825781419,58,830
cont.fstatistic=2309.26937367238,58,830

residuals=-0.688403837006096,-0.104681811592139,-0.0109147053846522,0.0988906599197351,0.96073097079315
cont.residuals=-0.698371468135192,-0.236343347491256,-0.0409559039171906,0.191305263059886,1.36100772335535

predictedValues:
Include	Exclude	Both
chr16.9650_chr16_93320234_93375758_-_2.R.tl.Lung	94.4699035049897	119.194429278529	91.0062096062942
chr16.9650_chr16_93320234_93375758_-_2.R.tl.cerebhem	68.2220078643941	71.2681703462324	47.9670114730037
chr16.9650_chr16_93320234_93375758_-_2.R.tl.cortex	65.5869790989326	84.4605789568785	52.1101856205177
chr16.9650_chr16_93320234_93375758_-_2.R.tl.heart	74.289594657631	106.310748136199	62.4470647171483
chr16.9650_chr16_93320234_93375758_-_2.R.tl.kidney	73.1972388228735	100.245331256228	56.6693858495325
chr16.9650_chr16_93320234_93375758_-_2.R.tl.liver	75.4745055961268	105.710222710235	53.3900405422
chr16.9650_chr16_93320234_93375758_-_2.R.tl.stomach	88.0398109357356	101.998927519375	74.1748690033386
chr16.9650_chr16_93320234_93375758_-_2.R.tl.testicle	82.5339020231426	111.578184529883	63.4316306984575


diffExp=-24.7245257735389,-3.04616248183829,-18.8735998579459,-32.0211534785676,-27.0480924333541,-30.2357171141081,-13.9591165836389,-29.0442825067407
diffExpScore=0.99444298264725
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,-1,0,-1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,-1,-1,-1,0,-1
diffExp1.3Score=0.8
diffExp1.2=-1,0,-1,-1,-1,-1,0,-1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	74.3274525657851	64.2873100599376	85.8536752559958
cerebhem	70.5560483379864	71.0162650349416	70.6825633019579
cortex	70.0230875847591	76.9185937631208	78.7812741873249
heart	76.6363946945776	75.5915722330623	75.3170502561005
kidney	73.8717650904256	73.0338306878478	75.3670882603399
liver	76.4467702563716	72.3390602114504	73.3340809464162
stomach	73.0419150987069	75.573689334797	87.384897982759
testicle	73.5142772010041	70.808991113354	75.0078795444994
cont.diffExp=10.0401425058475,-0.460216696955214,-6.89550617836163,1.04482246151537,0.837934402577844,4.10771004492122,-2.53177423609006,2.70528608765011
cont.diffExpScore=2.90640076459244

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=0.775817727310527
cont.tran.correlation=-0.146030103937039

tran.covariance=0.0158614284754014
cont.tran.covariance=-0.000276155316414327

tran.mean=88.9112834523365
cont.tran.mean=72.999188954258

weightedLogRatios:
wLogRatio
Lung	-1.08438265539936
cerebhem	-0.185415536865536
cortex	-1.08998953205648
heart	-1.60818102584518
kidney	-1.39948179344435
liver	-1.51346843739924
stomach	-0.669840479137553
testicle	-1.37611104608207

cont.weightedLogRatios:
wLogRatio
Lung	0.61470889329945
cerebhem	-0.0276942951937000
cortex	-0.403471542788239
heart	0.0594694898812119
kidney	0.049015565072712
liver	0.237986873893562
stomach	-0.146796402520907
testicle	0.160425307118319

varWeightedLogRatios=0.230709258139304
cont.varWeightedLogRatios=0.0877631513646984

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.88288396395384	0.0782502209016973	62.4008968624896	0	***
df.mm.trans1	-0.346577054835606	0.067744997283829	-5.11590624741725	3.88055005288941e-07	***
df.mm.trans2	-0.0261320698539854	0.0600180595391768	-0.435403444473703	0.663382809842989	   
df.mm.exp2	-0.199406075887335	0.0775714109786659	-2.57061297933818	0.0103251263649722	*  
df.mm.exp3	-0.151807919837258	0.0775714109786659	-1.95700861853614	0.0506813273035535	.  
df.mm.exp4	0.0219084751526221	0.0775714109786659	0.282429762153578	0.777684409399264	   
df.mm.exp5	0.0454344805148797	0.0775714109786659	0.585711668018715	0.558228512096651	   
df.mm.exp6	0.188762725694592	0.0775714109786659	2.43340585549626	0.0151676442782438	*  
df.mm.exp7	-0.0217835722621358	0.0775714109786659	-0.280819595612704	0.778918757339062	   
df.mm.exp8	0.159862470290835	0.0775714109786659	2.06084262583288	0.0396291219848153	*  
df.mm.trans1:exp2	-0.126108017704569	0.0719106225927323	-1.75367717811018	0.079854920279681	.  
df.mm.trans2:exp2	-0.314900134371967	0.0539874172026822	-5.83284310841828	7.80442217534652e-09	***
df.mm.trans1:exp3	-0.213096195604892	0.0719106225927322	-2.96334794390208	0.00313008574182815	** 
df.mm.trans2:exp3	-0.192663195173508	0.0539874172026822	-3.56866850010998	0.000379386688817508	***
df.mm.trans1:exp4	-0.262218880565938	0.0719106225927322	-3.64645543470012	0.000282451724197368	***
df.mm.trans2:exp4	-0.136298102867648	0.0539874172026822	-2.52462721741163	0.0117674581671265	*  
df.mm.trans1:exp5	-0.300558083603099	0.0719106225927322	-4.17960619400165	3.23002921738035e-05	***
df.mm.trans2:exp5	-0.218570005721500	0.0539874172026822	-4.04853606722719	5.63614769363606e-05	***
df.mm.trans1:exp6	-0.413249103032913	0.0719106225927323	-5.74670456371043	1.27760921602583e-08	***
df.mm.trans2:exp6	-0.308817142411029	0.0539874172026822	-5.7201688543768	1.48518800759567e-08	***
df.mm.trans1:exp7	-0.0487086209083669	0.0719106225927322	-0.677349453421222	0.498373010665029	   
df.mm.trans2:exp7	-0.134010148323446	0.0539874172026822	-2.48224781378851	0.0132523920095522	*  
df.mm.trans1:exp8	-0.294934630091479	0.0719106225927322	-4.10140559847255	4.51087902227792e-05	***
df.mm.trans2:exp8	-0.225892937873707	0.0539874172026822	-4.18417752836089	3.16704007073532e-05	***
df.mm.trans1:probe2	-0.322151804857681	0.0482391121265044	-6.67822832254573	4.42615965004938e-11	***
df.mm.trans1:probe3	1.03466265893277	0.0482391121265044	21.4486256757676	1.51995665158646e-81	***
df.mm.trans1:probe4	-0.232793197407877	0.0482391121265044	-4.82581845199368	1.65925502673760e-06	***
df.mm.trans1:probe5	-0.383648599118765	0.0482391121265044	-7.95306095420388	5.94276743508242e-15	***
df.mm.trans1:probe6	-0.0320984853862552	0.0482391121265044	-0.66540373508697	0.505977156073345	   
df.mm.trans1:probe7	-0.311688878434427	0.0482391121265044	-6.46133116250233	1.76732631186050e-10	***
df.mm.trans1:probe8	-0.00514067677646349	0.0482391121265044	-0.106566571187760	0.915158590913602	   
df.mm.trans1:probe9	0.341715393134163	0.0482391121265044	7.08378280756979	2.99426156684384e-12	***
df.mm.trans1:probe10	-0.112173853502786	0.0482391121265044	-2.32537143736428	0.0202923186641766	*  
df.mm.trans1:probe11	0.0430858311550656	0.0482391121265044	0.893172143012818	0.372023884659854	   
df.mm.trans1:probe12	-0.029908599220417	0.0482391121265044	-0.620007249345372	0.535423190327615	   
df.mm.trans1:probe13	-0.0623606552290552	0.0482391121265044	-1.29274052693005	0.196460554110900	   
df.mm.trans1:probe14	-0.0975760909940311	0.0482391121265044	-2.02275885049756	0.0434182027873751	*  
df.mm.trans1:probe15	-0.0126413065361884	0.0482391121265044	-0.262055124543695	0.793343965149358	   
df.mm.trans1:probe16	-0.0283820448566077	0.0482391121265044	-0.58836167594	0.556449617676767	   
df.mm.trans1:probe17	0.243415474464989	0.0482391121265044	5.04601896126623	5.54445301296854e-07	***
df.mm.trans1:probe18	-0.136950181126149	0.0482391121265044	-2.83898635544958	0.00463624006147819	** 
df.mm.trans1:probe19	0.397929465485648	0.0482391121265044	8.249104262991	6.23038355832197e-16	***
df.mm.trans1:probe20	0.153133072739969	0.0482391121265044	3.17445877400035	0.00155652075727927	** 
df.mm.trans1:probe21	-0.18620208855407	0.0482391121265044	-3.85998166935091	0.000122200190263994	***
df.mm.trans1:probe22	0.110980756118819	0.0482391121265044	2.30063844931013	0.0216591991351390	*  
df.mm.trans2:probe2	-0.146767772968234	0.0482391121265044	-3.04250568674115	0.00242006825953767	** 
df.mm.trans2:probe3	-0.373612609260949	0.0482391121265044	-7.74501421753307	2.78315785285124e-14	***
df.mm.trans2:probe4	-0.0154206646947744	0.0482391121265044	-0.319671403866941	0.749297884935002	   
df.mm.trans2:probe5	-0.197070248541558	0.0482391121265044	-4.08527934810973	4.82923406510512e-05	***
df.mm.trans2:probe6	-0.407066826552787	0.0482391121265044	-8.43852236511477	1.42042043484473e-16	***
df.mm.trans3:probe2	0.0938932889594612	0.0482391121265044	1.94641411958892	0.0519421121913416	.  
df.mm.trans3:probe3	0.065710197288432	0.0482391121265044	1.36217675640693	0.173511654751125	   
df.mm.trans3:probe4	0.0431045270396383	0.0482391121265044	0.893559709942402	0.371816524779001	   
df.mm.trans3:probe5	0.174542671901852	0.0482391121265044	3.61828118735112	0.000314505457547814	***
df.mm.trans3:probe6	0.00396882304314254	0.0482391121265044	0.0822739654232135	0.934448693004532	   
df.mm.trans3:probe7	-0.085530976784384	0.0482391121265044	-1.77306283250164	0.0765849784676468	.  
df.mm.trans3:probe8	-0.0279944434301587	0.0482391121265044	-0.580326672612564	0.561851856597091	   
df.mm.trans3:probe9	-0.260917966810988	0.0482391121265044	-5.40884679068604	8.30088498664067e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97222464069693	0.164565085399815	24.1377120246760	6.15983566509219e-98	***
df.mm.trans1	0.311255941740949	0.142471946212509	2.18468231827679	0.0291912072919586	*  
df.mm.trans2	0.185131968215052	0.126221715156609	1.46672042909059	0.142830896943482	   
df.mm.exp2	0.241919202440226	0.163137505877781	1.48291590666752	0.138476343982807	   
df.mm.exp3	0.205699091522861	0.163137505877780	1.26089393371606	0.207701374382108	   
df.mm.exp4	0.323512122723066	0.163137505877780	1.98306404761045	0.0476895028382641	*  
df.mm.exp5	0.251684543058115	0.163137505877780	1.54277547461509	0.12326642088223	   
df.mm.exp6	0.303735241689772	0.163137505877781	1.86183575662438	0.0629794907915169	.  
df.mm.exp7	0.126620951849482	0.163137505877780	0.776160890582354	0.437875105150519	   
df.mm.exp8	0.220674226017713	0.163137505877781	1.35268848711613	0.176523544022206	   
df.mm.trans1:exp2	-0.293992162787827	0.151232515534919	-1.94397455962402	0.0522361227444059	.  
df.mm.trans2:exp2	-0.142372523191243	0.11353889893341	-1.25395370686785	0.210211885095878	   
df.mm.trans1:exp3	-0.265354447411215	0.151232515534919	-1.75461240244956	0.0796945995160247	.  
df.mm.trans2:exp3	-0.0263137093356472	0.11353889893341	-0.231759419748117	0.816782002752468	   
df.mm.trans1:exp4	-0.292920398449939	0.151232515534919	-1.93688769517494	0.053098134822085	.  
df.mm.trans2:exp4	-0.161529580705952	0.11353889893341	-1.42268052820107	0.155204505431811	   
df.mm.trans1:exp5	-0.257834223450307	0.151232515534919	-1.70488616511026	0.0885898668966622	.  
df.mm.trans2:exp5	-0.124124031737573	0.11353889893341	-1.09322913031217	0.274610397671275	   
df.mm.trans1:exp6	-0.275620922887748	0.151232515534919	-1.82249777379460	0.0687391698643493	.  
df.mm.trans2:exp6	-0.18573326307683	0.11353889893341	-1.63585577120809	0.102248899645462	   
df.mm.trans1:exp7	-0.144067862242289	0.151232515534919	-0.952624914904785	0.341057481473408	   
df.mm.trans2:exp7	0.0351249895338195	0.11353889893341	0.309365247186518	0.75712134856025	   
df.mm.trans1:exp8	-0.231674957227441	0.151232515534919	-1.53191234310949	0.125925109958690	   
df.mm.trans2:exp8	-0.124050496810062	0.11353889893341	-1.09258146745651	0.274894622909115	   
df.mm.trans1:probe2	0.0340602792023409	0.101449855543312	0.335735117807039	0.737155468094964	   
df.mm.trans1:probe3	0.090396787368558	0.101449855543312	0.8910489510748	0.373161129844322	   
df.mm.trans1:probe4	0.0273840973121911	0.101449855543312	0.269927415525003	0.78728319199893	   
df.mm.trans1:probe5	0.0991662641906749	0.101449855543312	0.977490442540236	0.328611219711048	   
df.mm.trans1:probe6	0.0613143396124274	0.101449855543312	0.604380748341731	0.545755699669946	   
df.mm.trans1:probe7	-0.05182095508134	0.101449855543312	-0.510803635981681	0.609624395385764	   
df.mm.trans1:probe8	0.0102753316719671	0.101449855543312	0.101284832954545	0.91934881745204	   
df.mm.trans1:probe9	0.0080313823339139	0.101449855543312	0.0791660302609802	0.936919645845216	   
df.mm.trans1:probe10	0.0184022027656300	0.101449855543312	0.181392104178735	0.856104102195306	   
df.mm.trans1:probe11	0.196721189992988	0.101449855543312	1.93909778322949	0.0528280418230058	.  
df.mm.trans1:probe12	-0.020161234674768	0.101449855543312	-0.198731033837309	0.842521815006368	   
df.mm.trans1:probe13	0.000297367510185663	0.101449855543312	0.00293117726578436	0.99766196659746	   
df.mm.trans1:probe14	0.0124389587217254	0.101449855543312	0.1226118919057	0.902444118520102	   
df.mm.trans1:probe15	0.0434538719767171	0.101449855543312	0.428328574190679	0.668523045179137	   
df.mm.trans1:probe16	0.0142664574906580	0.101449855543312	0.140625705322638	0.888199760249205	   
df.mm.trans1:probe17	0.0928511958764062	0.101449855543312	0.915242268006635	0.360330277929023	   
df.mm.trans1:probe18	-0.0550050484338634	0.101449855543312	-0.542189519534408	0.587833486323002	   
df.mm.trans1:probe19	0.0886935128280318	0.101449855543312	0.874259626620819	0.382229770784837	   
df.mm.trans1:probe20	-0.0252391734332914	0.101449855543312	-0.248784715346550	0.803588860599548	   
df.mm.trans1:probe21	0.101904666189435	0.101449855543312	1.00448310787322	0.315438555844491	   
df.mm.trans1:probe22	0.0275618031307212	0.101449855543312	0.271679077147175	0.78593635025178	   
df.mm.trans2:probe2	-0.00836620081555452	0.101449855543312	-0.0824663649913503	0.93429574695537	   
df.mm.trans2:probe3	0.100429894743051	0.101449855543312	0.989946158180333	0.322488935665890	   
df.mm.trans2:probe4	-0.00761564446539782	0.101449855543312	-0.0750680661358506	0.94017864437978	   
df.mm.trans2:probe5	0.0055681854947846	0.101449855543312	0.0548860859876472	0.956242425360252	   
df.mm.trans2:probe6	6.84796966066748e-05	0.101449855543312	0.000675010291931257	0.999461581939696	   
df.mm.trans3:probe2	-0.0535628857955119	0.101449855543312	-0.527973997682474	0.597658503298421	   
df.mm.trans3:probe3	0.0347136902862811	0.101449855543312	0.342175847371816	0.732305184912765	   
df.mm.trans3:probe4	0.0744933432160582	0.101449855543312	0.734287326651292	0.46298102313072	   
df.mm.trans3:probe5	-0.120865111561483	0.101449855543312	-1.19137785770314	0.233845846604464	   
df.mm.trans3:probe6	0.0713386658980483	0.101449855543312	0.703191399494816	0.482133671049775	   
df.mm.trans3:probe7	-0.0329275745229552	0.101449855543312	-0.32456994982016	0.745588356221965	   
df.mm.trans3:probe8	-0.0059624000791214	0.101449855543312	-0.0587718932391765	0.953147942341868	   
df.mm.trans3:probe9	-0.110941113639163	0.101449855543312	-1.09355615190402	0.274466961473436