chr11.3923_chr11_87148421_87151528_+_2.R 

fitVsDatCorrelation=0.857684374603724
cont.fitVsDatCorrelation=0.250880073153169

fstatistic=15020.5706632066,56,784
cont.fstatistic=4227.78371707618,56,784

residuals=-0.488209603721948,-0.0735424063696944,-0.000619407871734583,0.074604678237909,0.672823374824322
cont.residuals=-0.551414074233936,-0.180521855666533,-0.0377809775884873,0.149903963964149,0.897765838472592

predictedValues:
Include	Exclude	Both
chr11.3923_chr11_87148421_87151528_+_2.R.tl.Lung	59.0166093323535	44.901311358378	60.9308096389153
chr11.3923_chr11_87148421_87151528_+_2.R.tl.cerebhem	59.8415013713203	47.8243078283376	59.2453954667153
chr11.3923_chr11_87148421_87151528_+_2.R.tl.cortex	59.6251059342449	43.43737294848	61.712380717403
chr11.3923_chr11_87148421_87151528_+_2.R.tl.heart	61.489615805308	47.6236426870233	65.9372615302779
chr11.3923_chr11_87148421_87151528_+_2.R.tl.kidney	62.6914336222196	46.2102270024674	68.2670348968059
chr11.3923_chr11_87148421_87151528_+_2.R.tl.liver	61.6652312792051	48.9413385103318	68.8709594134858
chr11.3923_chr11_87148421_87151528_+_2.R.tl.stomach	63.8088797448234	44.346209721992	63.4859397940037
chr11.3923_chr11_87148421_87151528_+_2.R.tl.testicle	65.8789235283552	46.9892103039578	75.455732901365


diffExp=14.1152979739756,12.0171935429827,16.1877329857649,13.8659731182847,16.4812066197522,12.7238927688733,19.4626700228313,18.8897132243974
diffExpScore=0.991983561829017
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,1,1
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,1,0,1,0,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	56.1117978452919	55.8931602642307	58.5864508443449
cerebhem	54.8054671844659	54.4416690140022	55.091409090782
cortex	56.5287419938757	54.0719954894597	58.2206433435271
heart	57.252893125552	54.8792785538595	58.1049466101726
kidney	61.3807255057812	55.9266042982964	57.3862157867341
liver	58.0617344909056	51.3538435686238	60.6039649592349
stomach	57.2610054463193	51.9422134313868	58.7024112653703
testicle	57.3013248905026	51.480818217451	63.0317814799987
cont.diffExp=0.218637581061152,0.363798170463681,2.45674650441597,2.37361457169244,5.45412120748485,6.7078909222818,5.31879201493252,5.82050667305158
cont.diffExpScore=0.966345952167426

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=0.166254297229275
cont.tran.correlation=0.100444405881062

tran.covariance=0.000274182915463151
cont.tran.covariance=9.50555330566611e-05

tran.mean=54.0181825611749
cont.tran.mean=55.5433295825003

weightedLogRatios:
wLogRatio
Lung	1.07731898658394
cerebhem	0.892092300714972
cortex	1.24475763894264
heart	1.01988131750162
kidney	1.21573682394461
liver	0.925817589617217
stomach	1.44598535773902
testicle	1.35797783622060

cont.weightedLogRatios:
wLogRatio
Lung	0.0157154388763247
cerebhem	0.0266435427261096
cortex	0.178288101175446
heart	0.170483254809442
kidney	0.378789457653285
liver	0.491083331281667
stomach	0.389842561188082
testicle	0.427897293442655

varWeightedLogRatios=0.0404425434498987
cont.varWeightedLogRatios=0.0344827286584042

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.66726446165689	0.0598543146966736	61.2698429552097	3.77543754704672e-301	***
df.mm.trans1	0.56552644585508	0.051440105235624	10.9938819771977	3.01466597153958e-26	***
df.mm.trans2	0.118940172416018	0.0458237849372739	2.59559904488095	0.0096192643365536	** 
df.mm.exp2	0.104998435244193	0.0591278024986307	1.77578788331638	0.0761557101500921	.  
df.mm.exp3	-0.035634613159427	0.0591278024986307	-0.602671021982464	0.546901840727243	   
df.mm.exp4	0.0209469711033953	0.0591278024986307	0.354266017308531	0.723234792788307	   
df.mm.exp5	-0.0245480144665201	0.0591278024986307	-0.41516872654093	0.678131981616767	   
df.mm.exp6	0.00756110027670173	0.0591278024986307	0.127877241452984	0.898278914807036	   
df.mm.exp7	0.0245541763167119	0.0591278024986307	0.415272938940705	0.678055731705159	   
df.mm.exp8	-0.0583565591980815	0.0591278024986307	-0.986956334110895	0.323968533787525	   
df.mm.trans1:exp2	-0.09111793052709	0.0540353337953482	-1.68626571036254	0.0921424922718982	.  
df.mm.trans2:exp2	-0.0419313935534261	0.040880450301249	-1.02570772201462	0.305345735793652	   
df.mm.trans1:exp3	0.045892420645576	0.0540353337953482	0.849303916940489	0.395971460725457	   
df.mm.trans2:exp3	0.00248781103141113	0.040880450301249	0.0608557638939492	0.9514895888962	   
df.mm.trans1:exp4	0.0201024223626066	0.0540353337953482	0.372023654720853	0.709975768218973	   
df.mm.trans2:exp4	0.0379153614056564	0.040880450301249	0.927469270183113	0.353968371189671	   
df.mm.trans1:exp5	0.0849539095306426	0.0540353337953482	1.57219181531097	0.116309528337098	   
df.mm.trans2:exp5	0.0532821512249863	0.040880450301249	1.30336507627359	0.192832773827750	   
df.mm.trans1:exp6	0.0363402409134339	0.0540353337953482	0.67252736979599	0.501446180171335	   
df.mm.trans2:exp6	0.0785943068693602	0.040880450301249	1.92254014547778	0.0548998527718384	.  
df.mm.trans1:exp7	0.0535192669068615	0.0540353337953482	0.990449454972533	0.322260060704119	   
df.mm.trans2:exp7	-0.0369939345987173	0.040880450301249	-0.904929723770364	0.365780613946953	   
df.mm.trans1:exp8	0.168356206092961	0.0540353337953482	3.11566884606631	0.00190215186061600	** 
df.mm.trans2:exp8	0.103807566030805	0.040880450301249	2.53929605143398	0.0112997207494902	*  
df.mm.trans1:probe2	0.0290443057719698	0.0369954640275418	0.785077482751597	0.432645310090728	   
df.mm.trans1:probe3	0.178839004105291	0.0369954640275418	4.83407922582487	1.60978104158812e-06	***
df.mm.trans1:probe4	-0.269517728895269	0.0369954640275418	-7.28515605844604	7.84050012360861e-13	***
df.mm.trans1:probe5	-0.080494199312627	0.0369954640275418	-2.17578563827992	0.0298692796413434	*  
df.mm.trans1:probe6	-0.495658513977201	0.0369954640275418	-13.3978185435977	5.23486191618059e-37	***
df.mm.trans1:probe7	-0.00117511503512247	0.0369954640275418	-0.0317637598557389	0.974668531844297	   
df.mm.trans1:probe8	-0.437627183110152	0.0369954640275418	-11.8292118943110	7.99465091795824e-30	***
df.mm.trans1:probe9	0.0800975372202044	0.0369954640275418	2.16506372674700	0.0306838275199305	*  
df.mm.trans1:probe10	-0.427968540985407	0.0369954640275418	-11.5681355062015	1.09699317930963e-28	***
df.mm.trans1:probe11	-0.421775651771688	0.0369954640275418	-11.4007396003383	5.75710471416359e-28	***
df.mm.trans1:probe12	-0.278575238531124	0.0369954640275418	-7.52998363052654	1.39737544786500e-13	***
df.mm.trans1:probe13	-0.387846816645524	0.0369954640275418	-10.4836316245901	3.72658445839546e-24	***
df.mm.trans1:probe14	-0.376314367928423	0.0369954640275418	-10.1719056057324	6.50621900688008e-23	***
df.mm.trans1:probe15	-0.329386131620027	0.0369954640275418	-8.90341938608503	3.71616159978444e-18	***
df.mm.trans1:probe16	-0.274272104601686	0.0369954640275418	-7.41366845398938	3.18969943582899e-13	***
df.mm.trans1:probe17	-0.285851182568381	0.0369954640275418	-7.726654877354	3.37839492647311e-14	***
df.mm.trans1:probe18	-0.450186650803475	0.0369954640275418	-12.1686985860841	2.50034927745420e-31	***
df.mm.trans1:probe19	-0.265519105681885	0.0369954640275418	-7.17707191033514	1.65352041913025e-12	***
df.mm.trans2:probe2	0.122329143381710	0.0369954640275418	3.30659843300357	0.000987349202192977	***
df.mm.trans2:probe3	0.0717169774133144	0.0369954640275418	1.93853433923477	0.0529166729906882	.  
df.mm.trans2:probe4	-0.00129720868477752	0.0369954640275418	-0.0350639928130593	0.972037638613468	   
df.mm.trans2:probe5	0.0437646126108898	0.0369954640275418	1.18297239300225	0.237178693140060	   
df.mm.trans2:probe6	0.0556843384590285	0.0369954640275418	1.50516664468848	0.132683992634237	   
df.mm.trans3:probe2	-0.36591091912782	0.0369954640275418	-9.89069683935879	8.12100052038246e-22	***
df.mm.trans3:probe3	-0.358641214074506	0.0369954640275418	-9.69419423439345	4.5899522676857e-21	***
df.mm.trans3:probe4	-0.230617700695032	0.0369954640275418	-6.2336750398196	7.43776768670814e-10	***
df.mm.trans3:probe5	-0.518390328364798	0.0369954640275418	-14.0122672330552	5.66546388928188e-40	***
df.mm.trans3:probe6	-0.419074406519476	0.0369954640275418	-11.3277240206391	1.18019781884560e-27	***
df.mm.trans3:probe7	-0.188721086216276	0.0369954640275418	-5.10119527290643	4.23672465341765e-07	***
df.mm.trans3:probe8	-0.0650632326388468	0.0369954640275418	-1.75868135051393	0.0790217497229611	.  
df.mm.trans3:probe9	-0.0797528792549028	0.0369954640275418	-2.15574750449216	0.0314070308741429	*  
df.mm.trans3:probe10	-0.193749510490564	0.0369954640275418	-5.23711529462975	2.09719976350166e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00876435837674	0.112685157927708	35.5749100600146	9.09358893864093e-166	***
df.mm.trans1	0.031354582545643	0.0968440857049247	0.323763524818414	0.746203419982824	   
df.mm.trans2	-0.0192520684305464	0.0862704797251482	-0.223159399274029	0.823469579767875	   
df.mm.exp2	0.0116413500128634	0.111317384490024	0.104578005189357	0.916737415825218	   
df.mm.exp3	-0.0194590145750894	0.111317384490024	-0.174806609625590	0.861276726904506	   
df.mm.exp4	0.0100785625799207	0.111317384490024	0.090538981185135	0.927882044357559	   
df.mm.exp5	0.111047280051930	0.111317384490024	0.997573564638343	0.318793973427757	   
df.mm.exp6	-0.0843983542036037	0.111317384490024	-0.758177661020838	0.448572474937365	   
df.mm.exp7	-0.0550137755939289	0.111317384490024	-0.494206505533366	0.621298783778168	   
df.mm.exp8	-0.134390696328536	0.111317384490024	-1.20727500869893	0.227690352977127	   
df.mm.trans1:exp2	-0.0351974855865451	0.101730011499799	-0.345989202867774	0.729443653215338	   
df.mm.trans2:exp2	-0.0379535308797787	0.0769638750639319	-0.493134354893795	0.622055750939785	   
df.mm.trans1:exp3	0.0268621405444847	0.101730011499799	0.264053253788708	0.791808276575112	   
df.mm.trans2:exp3	-0.0136665930863065	0.0769638750639319	-0.177571530473927	0.859105351735938	   
df.mm.trans1:exp4	0.0100535228304900	0.101730011499799	0.098825535181521	0.921302055348051	   
df.mm.trans2:exp4	-0.0283847411753286	0.0769638750639319	-0.368806029474869	0.712371881388004	   
df.mm.trans1:exp5	-0.0212975017162237	0.101730011499799	-0.209353183020782	0.834226923035894	   
df.mm.trans2:exp5	-0.110449102525913	0.0769638750639318	-1.43507720257284	0.151663896493274	   
df.mm.trans1:exp6	0.118559095773997	0.101730011499799	1.16542890368424	0.244199870432242	   
df.mm.trans2:exp6	-0.000303877853756991	0.0769638750639318	-0.00394831800639674	0.996850710615086	   
df.mm.trans1:exp7	0.0752875437324993	0.101730011499799	0.740072104805058	0.459477800137046	   
df.mm.trans2:exp7	-0.0182964199620685	0.0769638750639319	-0.237727374652981	0.812154662171497	   
df.mm.trans1:exp8	0.155368351088990	0.101730011499799	1.52726170771441	0.127099265123266	   
df.mm.trans2:exp8	0.0521579567778897	0.0769638750639319	0.677694005591109	0.498165668746508	   
df.mm.trans1:probe2	-0.0209959333444999	0.0696497775921251	-0.301450113271773	0.763151222223817	   
df.mm.trans1:probe3	-0.0230172475705214	0.0696497775921251	-0.330471228570353	0.741132220889513	   
df.mm.trans1:probe4	-0.00659852393210044	0.0696497775921251	-0.0947386217188222	0.924546647895806	   
df.mm.trans1:probe5	0.0680769087206945	0.0696497775921251	0.977417460244576	0.328664017737686	   
df.mm.trans1:probe6	-0.0586031423062802	0.0696497775921251	-0.841397407605018	0.400381897598865	   
df.mm.trans1:probe7	-0.0287726871658956	0.0696497775921251	-0.413105226758811	0.679642478078355	   
df.mm.trans1:probe8	-0.0762852333491308	0.0696497775921251	-1.09526887215439	0.273735211287255	   
df.mm.trans1:probe9	-0.0320640869176471	0.0696497775921251	-0.460361655501861	0.645384307914427	   
df.mm.trans1:probe10	-0.00315225033300451	0.0696497775921251	-0.0452585843341001	0.963912723462355	   
df.mm.trans1:probe11	0.0639513339652933	0.0696497775921251	0.918184324145264	0.358804754787337	   
df.mm.trans1:probe12	0.0696092589206565	0.0696497775921251	0.999418251244018	0.317900480274105	   
df.mm.trans1:probe13	0.00628745020411369	0.0696497775921251	0.0902723658492282	0.928093836410855	   
df.mm.trans1:probe14	-0.0426659230550214	0.0696497775921251	-0.612578022931769	0.540333027820708	   
df.mm.trans1:probe15	-0.0832308957782255	0.0696497775921251	-1.19499155138201	0.232451692639905	   
df.mm.trans1:probe16	-0.0626371134726175	0.0696497775921251	-0.899315340810213	0.368760845457863	   
df.mm.trans1:probe17	0.0142203831133947	0.0696497775921251	0.204169828031188	0.838273775397634	   
df.mm.trans1:probe18	-0.097002959793158	0.0696497775921251	-1.39272461659842	0.164097900945746	   
df.mm.trans1:probe19	-0.0575319946094907	0.0696497775921251	-0.826018353517263	0.409044972302849	   
df.mm.trans2:probe2	0.0504191638871614	0.0696497775921251	0.723895547555373	0.469345803934515	   
df.mm.trans2:probe3	0.099483758397551	0.0696497775921251	1.42834280075001	0.153591458321561	   
df.mm.trans2:probe4	0.166040009946589	0.0696497775921251	2.38392735320610	0.0173659964182907	*  
df.mm.trans2:probe5	0.0785578104061847	0.0696497775921251	1.12789750552006	0.259708255264261	   
df.mm.trans2:probe6	0.148374874897737	0.0696497775921251	2.13029933514838	0.0334576345305292	*  
df.mm.trans3:probe2	0.0613750658097915	0.0696497775921251	0.881195431365323	0.378482111913704	   
df.mm.trans3:probe3	0.118888295148257	0.0696497775921251	1.70694436160983	0.0882283590363815	.  
df.mm.trans3:probe4	0.0568138705380145	0.0696497775921251	0.815707852948522	0.414914993331209	   
df.mm.trans3:probe5	0.075926643518559	0.0696497775921251	1.09012040157819	0.275995047045135	   
df.mm.trans3:probe6	0.102545558664208	0.0696497775921251	1.47230274394735	0.141340451851386	   
df.mm.trans3:probe7	0.0700472082399122	0.0696497775921251	1.00570612945980	0.314867246619073	   
df.mm.trans3:probe8	0.00378139439301191	0.0696497775921251	0.0542915501490337	0.956716712991607	   
df.mm.trans3:probe9	-0.0292468496535421	0.0696497775921251	-0.419913037265016	0.674664032190802	   
df.mm.trans3:probe10	0.0362346443084543	0.0696497775921251	0.520240631932056	0.603042739700075