chr1.1085_chr1_151275720_151281824_-_2.R 

fitVsDatCorrelation=0.754854579400428
cont.fitVsDatCorrelation=0.246362541059618

fstatistic=10513.8637861071,53,715
cont.fstatistic=4807.9553033294,53,715

residuals=-0.579339544200259,-0.0874378152709888,-0.00302559220156509,0.0809889499683888,0.743282451362024
cont.residuals=-0.450688120744249,-0.146160319129805,-0.0390735393099357,0.108330085623975,0.816438624765662

predictedValues:
Include	Exclude	Both
chr1.1085_chr1_151275720_151281824_-_2.R.tl.Lung	57.253682599628	48.3967920876022	72.94311292985
chr1.1085_chr1_151275720_151281824_-_2.R.tl.cerebhem	60.035636363081	54.5242080439415	62.2716361100284
chr1.1085_chr1_151275720_151281824_-_2.R.tl.cortex	64.7032046126327	49.4464840103735	85.3658805529223
chr1.1085_chr1_151275720_151281824_-_2.R.tl.heart	58.5451950910704	48.6763124308255	69.6375549678722
chr1.1085_chr1_151275720_151281824_-_2.R.tl.kidney	62.0151883287842	48.2852233508162	79.5937131684472
chr1.1085_chr1_151275720_151281824_-_2.R.tl.liver	60.7137200304671	51.533687480815	70.8091789152673
chr1.1085_chr1_151275720_151281824_-_2.R.tl.stomach	57.3392606862095	50.2097736576717	69.9629609556352
chr1.1085_chr1_151275720_151281824_-_2.R.tl.testicle	63.1809761059507	55.6469605213525	74.0807524627843


diffExp=8.8568905120258,5.51142831913945,15.2567206022592,9.86888266024496,13.7299649779681,9.18003254965215,7.12948702853785,7.53401558459816
diffExpScore=0.987190559501284
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,1,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	62.3372359638665	58.4441869877058	54.2919761167451
cerebhem	57.7417090904415	64.7464959346826	60.78967310799
cortex	61.1800753068158	58.8574123686964	59.3172682752296
heart	60.2193877506826	58.9846594188191	63.3784821551589
kidney	59.5297374367483	56.9151650655602	57.6552368873966
liver	58.2439866267107	59.5982166072242	64.4879028438666
stomach	61.3176839142302	58.8920167374959	58.919722625825
testicle	59.0238062137624	57.0151091796509	55.0161527673543
cont.diffExp=3.89304897616063,-7.00478684424111,2.32266293811938,1.23472833186347,2.61457237118815,-1.35422998051357,2.42566717673427,2.00869703411148
cont.diffExpScore=3.20129428235761

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=0.290790227374549
cont.tran.correlation=-0.461745064194096

tran.covariance=0.000726251310123223
cont.tran.covariance=-0.00049266395579961

tran.mean=55.6566440875763
cont.tran.mean=59.5654302876933

weightedLogRatios:
wLogRatio
Lung	0.666093338442849
cerebhem	0.389679876318328
cortex	1.08518564852885
heart	0.734271602642522
kidney	1.00157868096972
liver	0.659703578909247
stomach	0.528792844832317
testicle	0.518380597241236

cont.weightedLogRatios:
wLogRatio
Lung	0.264415841532014
cerebhem	-0.470964011320526
cortex	0.158471495525053
heart	0.0846833721352948
kidney	0.182531952796218
liver	-0.093689098137934
stomach	0.165321220983131
testicle	0.140597444006722

varWeightedLogRatios=0.0574040713724961
cont.varWeightedLogRatios=0.0557059737866625

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83915448233381	0.0774834786591145	49.5480397727627	2.06672427266453e-233	***
df.mm.trans1	0.237430527261534	0.0688068286092148	3.45068261480280	0.000592019100239457	***
df.mm.trans2	0.0160384485475721	0.0625887582906048	0.256251265971826	0.797830534445462	   
df.mm.exp2	0.324831434474144	0.084378947046545	3.8496739511926	0.000128869069941372	***
df.mm.exp3	-0.0134904467516550	0.084378947046545	-0.159879297192621	0.873021311550626	   
df.mm.exp4	0.0744418957948771	0.084378947046545	0.882233049836633	0.377947289156339	   
df.mm.exp5	-0.00967583743300005	0.084378947046545	-0.114671227500181	0.908737914208596	   
df.mm.exp6	0.151171149179997	0.084378947046545	1.79157425485066	0.0736239812171832	.  
df.mm.exp7	0.0799836610087162	0.084378947046545	0.94791015778611	0.343495450696129	   
df.mm.exp8	0.222629320429393	0.084378947046545	2.63844629758875	0.00850993662921712	** 
df.mm.trans1:exp2	-0.277385074429471	0.0801217688801567	-3.46204381538772	0.000567979093560413	***
df.mm.trans2:exp2	-0.205620179477330	0.067468567239045	-3.04764407918737	0.00239139940348873	** 
df.mm.trans1:exp3	0.135809212116354	0.0801217688801567	1.69503511984979	0.0905041647925952	.  
df.mm.trans2:exp3	0.0349478680346847	0.067468567239045	0.517987404577043	0.604627293387417	   
df.mm.trans1:exp4	-0.0521348394228999	0.0801217688801567	-0.650695062672435	0.515452390976384	   
df.mm.trans2:exp4	-0.0686829141046516	0.067468567239045	-1.01799870540164	0.309022905038906	   
df.mm.trans1:exp5	0.0895632002720784	0.0801217688801567	1.11783852907746	0.264011411653977	   
df.mm.trans2:exp5	0.00736788417397852	0.067468567239045	0.109204693022066	0.913070778706632	   
df.mm.trans1:exp6	-0.0924934117494696	0.0801217688801567	-1.15441050593651	0.248717600925228	   
df.mm.trans2:exp6	-0.0883689618605473	0.067468567239045	-1.30977973116653	0.190691036070663	   
df.mm.trans1:exp7	-0.0784900593952667	0.0801217688801567	-0.979634629792926	0.32759785057223	   
df.mm.trans2:exp7	-0.0432074912298443	0.067468567239045	-0.640409200876575	0.522111777218489	   
df.mm.trans1:exp8	-0.124118040821198	0.0801217688801567	-1.54911758135107	0.121795945357115	   
df.mm.trans2:exp8	-0.0830353943091474	0.067468567239045	-1.23072710311082	0.218829659934606	   
df.mm.trans1:probe2	-0.0449547178990917	0.0438845001628773	-1.02438714653790	0.305998897301621	   
df.mm.trans1:probe3	-0.0475840669771173	0.0438845001628773	-1.08430235733594	0.27859624081051	   
df.mm.trans1:probe4	-0.0486904112919643	0.0438845001628773	-1.10951272342740	0.267582082431431	   
df.mm.trans1:probe5	0.157830612379713	0.0438845001628773	3.59650017190408	0.000344724001967615	***
df.mm.trans1:probe6	-0.106062101608314	0.0438845001628773	-2.41684652245473	0.0159049421326201	*  
df.mm.trans1:probe7	-0.110514308929627	0.0438845001628773	-2.51829936582287	0.0120095320955000	*  
df.mm.trans1:probe8	0.144128356682837	0.0438845001628773	3.28426565525197	0.00107229723058001	** 
df.mm.trans1:probe9	-0.267712040167481	0.0438845001628773	-6.10037801897863	1.73273157214509e-09	***
df.mm.trans1:probe10	0.308005713903569	0.0438845001628773	7.01855353850235	5.21564626933677e-12	***
df.mm.trans1:probe11	-0.166987236437925	0.0438845001628773	-3.80515297697712	0.000153847139573389	***
df.mm.trans1:probe12	-0.0253829312225263	0.0438845001628773	-0.578403106525484	0.563174131277392	   
df.mm.trans1:probe13	-0.0819849844412579	0.0438845001628773	-1.86819911670341	0.0621431130419669	.  
df.mm.trans1:probe14	-0.141245016247863	0.0438845001628773	-3.21856272086118	0.00134652162363069	** 
df.mm.trans1:probe15	-0.0166880364936207	0.0438845001628773	-0.380271768658253	0.703856627031106	   
df.mm.trans1:probe16	-0.0655432318549263	0.0438845001628773	-1.49353944129847	0.135737178832979	   
df.mm.trans1:probe17	-0.230806157668826	0.0438845001628773	-5.25940039905181	1.91137862234954e-07	***
df.mm.trans1:probe18	-0.0975264239105158	0.0438845001628773	-2.222343277206	0.0265722393793763	*  
df.mm.trans1:probe19	0.176863842553902	0.0438845001628773	4.03021207709949	6.17027347670433e-05	***
df.mm.trans1:probe20	-0.0456271738837905	0.0438845001628773	-1.03971046074230	0.298825891672718	   
df.mm.trans1:probe21	-0.0783799191107971	0.0438845001628772	-1.78605017306544	0.0745148047100405	.  
df.mm.trans1:probe22	0.0324410796445657	0.0438845001628773	0.73923776103546	0.460005248689431	   
df.mm.trans2:probe2	0.141317699122645	0.0438845001628773	3.22021895197951	0.00133887758105280	** 
df.mm.trans2:probe3	0.0353572444893187	0.0438845001628773	0.805688668165078	0.420690246962845	   
df.mm.trans2:probe4	0.105778074537482	0.0438845001628773	2.41037437238402	0.0161875662721314	*  
df.mm.trans2:probe5	-0.0232708171257696	0.0438845001628773	-0.53027417515068	0.596086487879457	   
df.mm.trans2:probe6	-0.0167761863950360	0.0438845001628773	-0.382280448285186	0.702366954285194	   
df.mm.trans3:probe2	0.414694006305154	0.0438845001628773	9.44966912613832	4.73565088401265e-20	***
df.mm.trans3:probe3	0.346645504716559	0.0438845001628773	7.89904188107383	1.06099109497128e-14	***
df.mm.trans3:probe4	0.0268861060957351	0.0438845001628773	0.612656085769403	0.540298566600502	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18365591583687	0.114493302752259	36.5406169205326	1.03140663427688e-165	***
df.mm.trans1	-0.0503901297953776	0.101672268665637	-0.495613311837195	0.620319550211805	   
df.mm.trans2	-0.135230608307639	0.0924841498583291	-1.46220307495707	0.144124947686598	   
df.mm.exp2	-0.0872152874893075	0.124682377421635	-0.699499715139164	0.48446728942763	   
df.mm.exp3	-0.100215835543684	0.124682377421635	-0.80376904592368	0.421797505290274	   
df.mm.exp4	-0.180107368258231	0.124682377421635	-1.44452946745767	0.149028163030631	   
df.mm.exp5	-0.13269799736249	0.124682377421635	-1.06428831488951	0.287557397710623	   
df.mm.exp6	-0.220465868215783	0.124682377421635	-1.76821995838465	0.0774505197332028	.  
df.mm.exp7	-0.0906567779010378	0.124682377421635	-0.727101774731693	0.467401605442006	   
df.mm.exp8	-0.0926243840653615	0.124682377421635	-0.742882723130439	0.457796687888488	   
df.mm.trans1:exp2	0.0106361257786130	0.118391766866849	0.0898383904564501	0.928440799617798	   
df.mm.trans2:exp2	0.189622639527241	0.0996947895066206	1.90203159528862	0.057568630579056	.  
df.mm.trans1:exp3	0.0814784698988671	0.118391766866849	0.688210608348325	0.491543379239154	   
df.mm.trans2:exp3	0.10726138466874	0.0996947895066206	1.07589759905774	0.282335957009729	   
df.mm.trans1:exp4	0.145542789110795	0.118391766866849	1.22933201321746	0.219351744282772	   
df.mm.trans2:exp4	0.189312538266540	0.0996947895066206	1.89892108909029	0.0579771429515009	.  
df.mm.trans1:exp5	0.0866150386415382	0.118391766866849	0.731596807225212	0.464654402330097	   
df.mm.trans2:exp5	0.106187594108897	0.0996947895066206	1.06512681991114	0.287178094765494	   
df.mm.trans1:exp6	0.1525477861838	0.118391766866849	1.28849995418487	0.197988760702022	   
df.mm.trans2:exp6	0.240019288903349	0.0996947895066206	2.40754095666564	0.0163126808550936	*  
df.mm.trans1:exp7	0.0741661254397708	0.118391766866849	0.626446647452968	0.531221965964487	   
df.mm.trans2:exp7	0.0982900898723078	0.0996947895066206	0.985909999496819	0.324510783247027	   
df.mm.trans1:exp8	0.0380063064647205	0.118391766866849	0.321021532751216	0.74828788211583	   
df.mm.trans2:exp8	0.0678684598629273	0.0996947895066206	0.680762356777134	0.496242264980961	   
df.mm.trans1:probe2	-0.0323438370038721	0.0648458413358658	-0.498780435839344	0.61808748165416	   
df.mm.trans1:probe3	0.075824055387678	0.0648458413358658	1.16929711799021	0.242673603299136	   
df.mm.trans1:probe4	-0.0233318132471583	0.0648458413358658	-0.359804310754676	0.719099725847427	   
df.mm.trans1:probe5	-0.0924107940455788	0.0648458413358658	-1.42508435609528	0.154569176460063	   
df.mm.trans1:probe6	0.025998173958206	0.0648458413358658	0.400922764245586	0.688596767672752	   
df.mm.trans1:probe7	0.0258769188660089	0.0648458413358658	0.399052866505049	0.689973406056348	   
df.mm.trans1:probe8	0.0322413028287235	0.0648458413358658	0.497199236906054	0.61920141072048	   
df.mm.trans1:probe9	-0.0136346065494805	0.0648458413358658	-0.210261849774771	0.833523211496578	   
df.mm.trans1:probe10	0.0227322533879465	0.0648458413358658	0.350558384618775	0.726022934752255	   
df.mm.trans1:probe11	-0.0146691534989515	0.0648458413358658	-0.226215794209120	0.82109823872846	   
df.mm.trans1:probe12	0.0141957667655107	0.0648458413358658	0.218915607740895	0.826778261540636	   
df.mm.trans1:probe13	0.0339601953025704	0.0648458413358658	0.52370660327584	0.600644904087873	   
df.mm.trans1:probe14	-0.0278398304362278	0.0648458413358658	-0.429323297573283	0.66781729895547	   
df.mm.trans1:probe15	-0.03017938616239	0.0648458413358658	-0.465402029500664	0.641785298962145	   
df.mm.trans1:probe16	0.0162871360678588	0.0648458413358658	0.251167009824121	0.801757154418179	   
df.mm.trans1:probe17	-0.0473261377828592	0.0648458413358658	-0.729825333558953	0.465735987559791	   
df.mm.trans1:probe18	-0.0160543066665261	0.0648458413358658	-0.247576503532024	0.804533169413524	   
df.mm.trans1:probe19	-0.020003041766739	0.0648458413358658	-0.308470695339339	0.757814045655117	   
df.mm.trans1:probe20	0.0890395200132658	0.0648458413358658	1.37309530077789	0.170153170018312	   
df.mm.trans1:probe21	-0.0350605863330184	0.0648458413358658	-0.540675941752746	0.588899350610584	   
df.mm.trans1:probe22	-0.00167994907625053	0.0648458413358658	-0.0259068128602005	0.9793388946515	   
df.mm.trans2:probe2	0.0137688888041797	0.0648458413358658	0.212332641855388	0.83190804794571	   
df.mm.trans2:probe3	0.0672985553897881	0.0648458413358658	1.03782376793014	0.299702950338971	   
df.mm.trans2:probe4	0.0580810172506852	0.0648458413358658	0.895678366633528	0.370726003257332	   
df.mm.trans2:probe5	0.0115572596289327	0.0648458413358658	0.178226689496901	0.85859543388146	   
df.mm.trans2:probe6	0.0457635060187475	0.0648458413358658	0.705727693187258	0.480587359969789	   
df.mm.trans3:probe2	-0.062281166067038	0.0648458413358658	-0.960449656971151	0.337153631357661	   
df.mm.trans3:probe3	-0.0705914478267838	0.0648458413358658	-1.08860408582192	0.276695302477134	   
df.mm.trans3:probe4	0.118237689085856	0.0648458413358658	1.82336579571001	0.0686653908616511	.