chr3.15356_chr3_67990057_67998479_+_2.R 

fitVsDatCorrelation=0.80979333406168
cont.fitVsDatCorrelation=0.250827184726367

fstatistic=7990.49876993055,50,646
cont.fstatistic=2927.10441772851,50,646

residuals=-0.597957865544092,-0.0864527826446878,-0.000304412771359097,0.0788212528356301,0.879564061389595
cont.residuals=-0.494754198938692,-0.144964859975682,-0.0544618075427837,0.0667961537425279,1.66271638852492

predictedValues:
Include	Exclude	Both
chr3.15356_chr3_67990057_67998479_+_2.R.tl.Lung	47.6946718288664	48.8829567301297	54.7415466521388
chr3.15356_chr3_67990057_67998479_+_2.R.tl.cerebhem	53.2427129010297	52.422918248383	49.9709855735985
chr3.15356_chr3_67990057_67998479_+_2.R.tl.cortex	47.4929770402631	43.0877557105624	45.7640225974068
chr3.15356_chr3_67990057_67998479_+_2.R.tl.heart	77.0675786130275	46.593181289489	96.1260833629181
chr3.15356_chr3_67990057_67998479_+_2.R.tl.kidney	47.9088693836880	44.0381776044529	44.7292436932579
chr3.15356_chr3_67990057_67998479_+_2.R.tl.liver	52.0067871973574	47.9448105007095	48.0145731469148
chr3.15356_chr3_67990057_67998479_+_2.R.tl.stomach	48.8428171906632	47.4936135814577	49.5017100038654
chr3.15356_chr3_67990057_67998479_+_2.R.tl.testicle	49.4534502410472	47.7915177109478	47.2676087176364


diffExp=-1.18828490126332,0.819794652646685,4.40522132970074,30.4743973235385,3.87069177923503,4.0619766966479,1.34920360920553,1.66193253009934
diffExpScore=1.02963237083863
diffExp1.5=0,0,0,1,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,1,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	54.614760964302	54.8723341693054	53.9563199495842
cerebhem	53.2600187328486	56.6271150215175	51.9489161418458
cortex	58.8683779523365	51.0375031342053	55.713225655771
heart	53.9589056113112	49.9869235471974	52.362556404878
kidney	55.6212941017375	51.3116749969811	48.9082048400704
liver	54.1539911548484	55.2380423001398	51.5129627908903
stomach	52.3847853718374	55.7697137999178	49.6481069711488
testicle	55.2403905200945	49.6409622280783	49.6402023292239
cont.diffExp=-0.257573205003474,-3.36709628866895,7.83087481813117,3.97198206411379,4.30961910475641,-1.08405114529134,-3.38492842808044,5.59942829201619
cont.diffExpScore=2.03892687012667

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=0.0618253058304997
cont.tran.correlation=-0.575683818691994

tran.covariance=0.00104130272910398
cont.tran.covariance=-0.00108492054018018

tran.mean=50.1227997357547
cont.tran.mean=53.9116746004162

weightedLogRatios:
wLogRatio
Lung	-0.0954125873291965
cerebhem	0.0615578511219884
cortex	0.371062720618567
heart	2.05974859144925
kidney	0.322415783551111
liver	0.318033464879025
stomach	0.108535635621173
testicle	0.132767276409404

cont.weightedLogRatios:
wLogRatio
Lung	-0.0188328866747966
cerebhem	-0.245565825173753
cortex	0.571534729322498
heart	0.302021560212505
kidney	0.320836507369955
liver	-0.0793153694858562
stomach	-0.249828126804693
testicle	0.423050630341516

varWeightedLogRatios=0.469129673916086
cont.varWeightedLogRatios=0.0997274899329482

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76926116718057	0.0782538467752663	48.1671038869864	5.49954384795944e-216	***
df.mm.trans1	0.0615209835149416	0.0661867510510394	0.92950601952799	0.352974099834627	   
df.mm.trans2	0.138121477169536	0.0605720214745621	2.28028508554138	0.0229157395663149	*  
df.mm.exp2	0.271136731860033	0.0789192197525605	3.43562357446186	0.00062906712568213	***
df.mm.exp3	0.0486969303829167	0.0789192197525605	0.61704779311806	0.537420615315679	   
df.mm.exp4	-0.131149345507342	0.0789192197525605	-1.66181756381451	0.0970345063319731	.  
df.mm.exp5	0.102104538533975	0.0789192197525605	1.29378545370961	0.196201915320754	   
df.mm.exp6	0.198294694372346	0.0789192197525605	2.51262867263602	0.0122260077096427	*  
df.mm.exp7	0.0955698313604717	0.0789192197525605	1.21098297297055	0.226344763691095	   
df.mm.exp8	0.160429207339166	0.0789192197525605	2.03282809741617	0.0424783091599323	*  
df.mm.trans1:exp2	-0.161095473518196	0.0695259694646919	-2.31705468846438	0.0208125325960828	*  
df.mm.trans2:exp2	-0.20122166760243	0.0566310910860778	-3.55320132004129	0.000408306220832521	***
df.mm.trans1:exp3	-0.0529347720116895	0.0695259694646919	-0.761366902457534	0.446715793027	   
df.mm.trans2:exp3	-0.174886866479522	0.0566310910860778	-3.08817759159361	0.00210022298360125	** 
df.mm.trans1:exp4	0.611012336827186	0.0695259694646918	8.78826058135706	1.36486004097411e-17	***
df.mm.trans2:exp4	0.0831747490697512	0.0566310910860778	1.46871175311328	0.142397752530211	   
df.mm.trans1:exp5	-0.0976235766258466	0.0695259694646919	-1.40413110924579	0.160760382995259	   
df.mm.trans2:exp5	-0.206476410600563	0.0566310910860778	-3.64599033217856	0.000287802131533419	***
df.mm.trans1:exp6	-0.111740151236485	0.0695259694646919	-1.60717142237379	0.108505398451176	   
df.mm.trans2:exp6	-0.217672928808083	0.0566310910860778	-3.84370007064187	0.000133172874514707	***
df.mm.trans1:exp7	-0.0717821916992828	0.0695259694646919	-1.03245150340171	0.302247019803196	   
df.mm.trans2:exp7	-0.124403382878750	0.0566310910860778	-2.19673293402840	0.0283940102292615	*  
df.mm.trans1:exp8	-0.124217069342278	0.0695259694646919	-1.78662836776927	0.074466400259556	.  
df.mm.trans2:exp8	-0.183009839937256	0.0566310910860778	-3.23161423217302	0.00129347752170405	** 
df.mm.trans1:probe2	-0.0690402273927198	0.0476011772602839	-1.45038907368208	0.147435402243188	   
df.mm.trans1:probe3	-0.0261393377498498	0.0476011772602839	-0.549132169713357	0.583104505486684	   
df.mm.trans1:probe4	-0.140559187567525	0.0476011772602839	-2.95285107758881	0.0032628754223951	** 
df.mm.trans1:probe5	-0.0649020827557105	0.0476011772602839	-1.36345541205473	0.173213987029572	   
df.mm.trans1:probe6	-0.044107039631555	0.0476011772602839	-0.926595562760499	0.354482595853164	   
df.mm.trans1:probe7	-0.0315932391798121	0.0476011772602839	-0.663707097138792	0.507114585246087	   
df.mm.trans1:probe8	0.126011192246271	0.0476011772602839	2.64722848254867	0.00831336958552926	** 
df.mm.trans1:probe9	0.113059498039051	0.0476011772602839	2.37514079580090	0.0178331900427416	*  
df.mm.trans1:probe10	0.181587982252537	0.0476011772602839	3.81477922824494	0.000149385738916832	***
df.mm.trans1:probe11	0.14539997020668	0.0476011772602839	3.05454567670944	0.00234679807894720	** 
df.mm.trans1:probe12	0.406703674227685	0.0476011772602839	8.54398352384909	9.31874234314808e-17	***
df.mm.trans1:probe13	0.186442200256689	0.0476011772602839	3.91675607595208	9.93010460627919e-05	***
df.mm.trans2:probe2	-0.0635055915753291	0.0476011772602839	-1.33411808762795	0.182635268768735	   
df.mm.trans2:probe3	-0.0509693928587319	0.0476011772602839	-1.07075908186116	0.284677654923487	   
df.mm.trans2:probe4	0.00588449170023651	0.0476011772602839	0.123620717783092	0.901654040164732	   
df.mm.trans2:probe5	-0.120609338373865	0.0476011772602839	-2.53374696416375	0.0115202610195999	*  
df.mm.trans2:probe6	-0.058061636848251	0.0476011772602839	-1.21975211938077	0.223003982583805	   
df.mm.trans3:probe2	0.337122932803796	0.0476011772602839	7.08223939421506	3.71580444263384e-12	***
df.mm.trans3:probe3	0.0693492876584738	0.0476011772602839	1.45688177582817	0.145634899740782	   
df.mm.trans3:probe4	0.0239365515136828	0.0476011772602839	0.502856292456748	0.615236701317962	   
df.mm.trans3:probe5	0.0787404985641598	0.0476011772602839	1.65417124315236	0.0985784975313247	.  
df.mm.trans3:probe6	-0.0153821819222834	0.0476011772602839	-0.323147090211097	0.746688421206193	   
df.mm.trans3:probe7	-0.0147833694406778	0.0476011772602839	-0.310567307187428	0.75622975869355	   
df.mm.trans3:probe8	-0.124473524361678	0.0476011772602839	-2.6149253343264	0.00913295351346595	** 
df.mm.trans3:probe9	0.0635408684462895	0.0476011772602839	1.33485918003345	0.182392680497369	   
df.mm.trans3:probe10	-0.0808577646306589	0.0476011772602839	-1.69865052262316	0.0898665578674633	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13173609210948	0.129112412969283	32.0010756292847	2.31386104171206e-135	***
df.mm.trans1	-0.0707978262610607	0.109202697208464	-0.648315729106124	0.517011133650162	   
df.mm.trans2	-0.096662816594624	0.0999388550631586	-0.9672195717425	0.333796239965347	   
df.mm.exp2	0.0442743584604316	0.130210223673334	0.34002213659893	0.733950385251334	   
df.mm.exp3	-0.0294914298116854	0.130210223673334	-0.226490892801723	0.82089128862704	   
df.mm.exp4	-0.0753462259400199	0.130210223673334	-0.578650614478979	0.563026628918071	   
df.mm.exp5	0.0494005996129108	0.130210223673334	0.379391097098835	0.70452213789814	   
df.mm.exp6	0.04451142932202	0.130210223673334	0.341842814383673	0.73258036138023	   
df.mm.exp7	0.057748235678571	0.130210223673334	0.443500011361991	0.657552606501811	   
df.mm.exp8	-0.00542894823915959	0.130210223673334	-0.0416937171752313	0.966755745387875	   
df.mm.trans1:exp2	-0.0693926198953168	0.114712133032831	-0.604928337227037	0.54543903820937	   
df.mm.trans2:exp2	-0.0127957141384712	0.0934366439544518	-0.136945352454106	0.891116658847431	   
df.mm.trans1:exp3	0.104491305883745	0.114712133032831	0.910900208383708	0.362687717947979	   
df.mm.trans2:exp3	-0.0429571422898414	0.0934366439544518	-0.459746203114722	0.645853084786719	   
df.mm.trans1:exp4	0.0632647820482421	0.114712133032830	0.55150907210605	0.581475459839278	   
df.mm.trans2:exp4	-0.0179016220496440	0.0934366439544518	-0.191591021380976	0.848122779590868	   
df.mm.trans1:exp5	-0.0311386777811649	0.114712133032831	-0.271450603854198	0.786131207544681	   
df.mm.trans2:exp5	-0.116491580699137	0.0934366439544518	-1.24674405852937	0.212943215699045	   
df.mm.trans1:exp6	-0.0529839465989147	0.114712133032831	-0.461886159712075	0.644318462342158	   
df.mm.trans2:exp6	-0.0378688314621016	0.0934366439544518	-0.405288865903208	0.685399372634353	   
df.mm.trans1:exp7	-0.0994362355573228	0.114712133032831	-0.866832765884183	0.3863554957688	   
df.mm.trans2:exp7	-0.0415265672401062	0.0934366439544518	-0.444435560638815	0.656876558394577	   
df.mm.trans1:exp8	0.0168191527266738	0.114712133032831	0.146620521142783	0.883477298355624	   
df.mm.trans2:exp8	-0.0947639976420928	0.0934366439544518	-1.01420592212503	0.310864205225127	   
df.mm.trans1:probe2	-0.0929190702954939	0.0785380286020184	-1.18310927775320	0.237201058332772	   
df.mm.trans1:probe3	-0.132344387856745	0.0785380286020184	-1.6850994379727	0.0924523526344538	.  
df.mm.trans1:probe4	-0.109113186449730	0.0785380286020184	-1.38930386198827	0.165219047201413	   
df.mm.trans1:probe5	-0.141055315611125	0.0785380286020184	-1.79601293948827	0.0729596185594711	.  
df.mm.trans1:probe6	-0.153655410972147	0.0785380286020184	-1.95644598810567	0.0508431010481979	.  
df.mm.trans1:probe7	-0.0706305563209869	0.0785380286020184	-0.899316644155895	0.368819117726553	   
df.mm.trans1:probe8	-0.207068105090132	0.0785380286020184	-2.63653301179004	0.00857711984586329	** 
df.mm.trans1:probe9	-0.143936359563794	0.0785380286020184	-1.83269636538973	0.0673078341749335	.  
df.mm.trans1:probe10	-0.159520394636167	0.0785380286020184	-2.03112297921961	0.0426514548541703	*  
df.mm.trans1:probe11	-0.0976936888764512	0.0785380286020184	-1.24390299343394	0.213986439664812	   
df.mm.trans1:probe12	0.0249737080747021	0.0785380286020184	0.317982364966826	0.750601076478506	   
df.mm.trans1:probe13	-0.111620895377247	0.0785380286020184	-1.42123373051381	0.155731360613421	   
df.mm.trans2:probe2	-0.131718662350033	0.0785380286020184	-1.67713227202965	0.0940003768288048	.  
df.mm.trans2:probe3	-0.105048873497095	0.0785380286020184	-1.33755424431923	0.181512500501311	   
df.mm.trans2:probe4	-0.06983389402007	0.0785380286020184	-0.88917299381099	0.374241094922361	   
df.mm.trans2:probe5	-0.0759563794344512	0.0785380286020184	-0.96712867366904	0.333841637707869	   
df.mm.trans2:probe6	-0.0984659564228637	0.0785380286020184	-1.25373603304748	0.210391486202628	   
df.mm.trans3:probe2	-0.00585422096614488	0.0785380286020184	-0.0745399530692374	0.940603827167285	   
df.mm.trans3:probe3	0.0348442842067927	0.0785380286020184	0.443661304300898	0.657436032688076	   
df.mm.trans3:probe4	0.0419333548116118	0.0785380286020184	0.533924209176472	0.593577644716024	   
df.mm.trans3:probe5	0.0342843156262654	0.0785380286020184	0.436531400603354	0.662597081911942	   
df.mm.trans3:probe6	0.121384570725989	0.0785380286020184	1.54555153581827	0.122702631312316	   
df.mm.trans3:probe7	0.0239727314544983	0.0785380286020184	0.30523724469807	0.760283789877913	   
df.mm.trans3:probe8	0.0670276793902636	0.0785380286020184	0.853442346126587	0.393730380619724	   
df.mm.trans3:probe9	-0.0693346021697078	0.0785380286020184	-0.882815667821918	0.377664265643411	   
df.mm.trans3:probe10	-0.00926431147888585	0.0785380286020184	-0.117959562313839	0.906136365731312