chr12.5645_chr12_75169159_75174148_+_2.R 

fitVsDatCorrelation=0.750489870350582
cont.fitVsDatCorrelation=0.242444535240633

fstatistic=6435.40554075703,59,853
cont.fstatistic=2978.54321815758,59,853

residuals=-0.653738647078264,-0.107531662450531,-0.0160456588136889,0.0816698464904011,1.32656926700538
cont.residuals=-0.57303987830649,-0.189036385004074,-0.0334270353209055,0.122710003892144,1.69228332388616

predictedValues:
Include	Exclude	Both
chr12.5645_chr12_75169159_75174148_+_2.R.tl.Lung	54.4527764976467	67.1325564869276	60.72912918971
chr12.5645_chr12_75169159_75174148_+_2.R.tl.cerebhem	67.7505779096567	141.908721633556	66.910763829385
chr12.5645_chr12_75169159_75174148_+_2.R.tl.cortex	54.6883028776704	79.6116616550333	82.1840483957743
chr12.5645_chr12_75169159_75174148_+_2.R.tl.heart	55.5463277057706	65.3950290397445	64.7862228064847
chr12.5645_chr12_75169159_75174148_+_2.R.tl.kidney	53.0362544510129	60.9565235435101	61.1255015572535
chr12.5645_chr12_75169159_75174148_+_2.R.tl.liver	55.8354252209363	62.3348061148773	61.0589344402071
chr12.5645_chr12_75169159_75174148_+_2.R.tl.stomach	59.0122992897084	74.151901378667	66.5807863898242
chr12.5645_chr12_75169159_75174148_+_2.R.tl.testicle	55.7428603585394	66.6669541692016	58.3760763596478


diffExp=-12.6797799892809,-74.158143723899,-24.9233587773629,-9.84870133397391,-7.92026909249716,-6.49938089394098,-15.1396020889585,-10.9240938106622
diffExpScore=0.993868541394215
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,-1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,-1,0,0,0,-1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	65.3736575004024	60.2926187699043	66.088549539143
cerebhem	62.8331765762357	55.9667730984645	61.9702826643836
cortex	58.8125840652151	60.7751412368485	61.3856380405203
heart	64.9023247485266	72.671987342138	66.8979349559736
kidney	64.8761221238186	62.6802465606585	60.4254355877115
liver	60.991075384791	61.5394473895402	67.3053243031834
stomach	60.5070396739676	55.6210779014757	65.3731467656594
testicle	61.3261280333614	58.7369119207064	66.2303872295108
cont.diffExp=5.08103873049811,6.86640347777115,-1.96255717163332,-7.76966259361139,2.19587556316014,-0.548372004749226,4.88596177249195,2.58921611265495
cont.diffExpScore=2.58545436241085

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=0.938235401066313
cont.tran.correlation=0.461685628980309

tran.covariance=0.0195896638486662
cont.tran.covariance=0.00147485064289505

tran.mean=67.1389361457787
cont.tran.mean=61.7441445203784

weightedLogRatios:
wLogRatio
Lung	-0.858693628681348
cerebhem	-3.39030056371276
cortex	-1.57316665305417
heart	-0.669047568552071
kidney	-0.56238712692578
liver	-0.448975405736306
stomach	-0.957310506301609
testicle	-0.735566867168095

cont.weightedLogRatios:
wLogRatio
Lung	0.334939011782032
cerebhem	0.472461469544945
cortex	-0.134279460336499
heart	-0.478231264907687
kidney	0.143079362924644
liver	-0.0368345309820714
stomach	0.341897454112244
testicle	0.176632951074287

varWeightedLogRatios=0.937113874075328
cont.varWeightedLogRatios=0.0954400450979523

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9025917714499	0.0949462618166848	41.1031640085498	1.70991859535742e-204	***
df.mm.trans1	-0.0769887937872213	0.08199326924116	-0.938964801620246	0.348014613103555	   
df.mm.trans2	0.306875926638825	0.0724406827715982	4.23623735859018	2.52029990990275e-05	***
df.mm.exp2	0.87007769790211	0.0931818802389387	9.33741297847866	8.35056758346008e-20	***
df.mm.exp3	-0.127730283125494	0.0931818802389387	-1.37076310113046	0.170809386594437	   
df.mm.exp4	-0.0710088056071976	0.0931818802389387	-0.762045211205392	0.446243670970788	   
df.mm.exp5	-0.129372034852064	0.0931818802389387	-1.38838188841356	0.165383243392625	   
df.mm.exp6	-0.0544905410347227	0.0931818802389387	-0.584776148485061	0.558852883192742	   
df.mm.exp7	0.0878660631564197	0.0931818802389387	0.942952244911907	0.345972316131924	   
df.mm.exp8	0.055973083835713	0.0931818802389387	0.600686353314462	0.548208476638775	   
df.mm.trans1:exp2	-0.6515785479181	0.0861299496205084	-7.5650636136324	1.00367844045418e-13	***
df.mm.trans2:exp2	-0.121562772241486	0.0636111835005791	-1.91102830590126	0.0563358744591352	.  
df.mm.trans1:exp3	0.132046288555992	0.0861299496205083	1.5331053731924	0.125620680595356	   
df.mm.trans2:exp3	0.298221748594047	0.0636111835005791	4.68819682613408	3.20774359660578e-06	***
df.mm.trans1:exp4	0.0908923718294375	0.0861299496205083	1.05529345169610	0.291589934521158	   
df.mm.trans2:exp4	0.0447859327597811	0.0636111835005791	0.704057530377082	0.481589099649288	   
df.mm.trans1:exp5	0.103013921025463	0.0861299496205084	1.19602904076162	0.232017581909399	   
df.mm.trans2:exp5	0.0328637962633685	0.0636111835005791	0.516635510532033	0.605544534896377	   
df.mm.trans1:exp6	0.0795652298022582	0.0861299496205083	0.923781218412706	0.355861472982287	   
df.mm.trans2:exp6	-0.0196586234016984	0.0636111835005791	-0.309043509645115	0.757363896981859	   
df.mm.trans1:exp7	-0.00745401820981804	0.0861299496205083	-0.0865438589324702	0.931054402802456	   
df.mm.trans2:exp7	0.0115805275722061	0.0636111835005791	0.182051754658844	0.855585363919964	   
df.mm.trans1:exp8	-0.0325575867854819	0.0861299496205083	-0.378005408443077	0.705520620722857	   
df.mm.trans2:exp8	-0.0629328132822352	0.0636111835005791	-0.989335676825794	0.322779481674311	   
df.mm.trans1:probe2	0.148336297721536	0.0589691453549199	2.51549003854031	0.0120694143316982	*  
df.mm.trans1:probe3	0.0135655383926683	0.0589691453549199	0.230044683724359	0.818112202596769	   
df.mm.trans1:probe4	0.44791979287797	0.0589691453549199	7.59583321382831	8.0370162155928e-14	***
df.mm.trans1:probe5	0.363556846599579	0.0589691453549199	6.16520460677233	1.08515508023529e-09	***
df.mm.trans1:probe6	0.547717059179011	0.0589691453549199	9.28819734256694	1.27183594497148e-19	***
df.mm.trans1:probe7	0.0647729865373686	0.0589691453549199	1.09842166013289	0.272330350963444	   
df.mm.trans1:probe8	0.101695999560219	0.0589691453549199	1.72456288705113	0.0849685399638792	.  
df.mm.trans1:probe9	0.542248902614411	0.0589691453549199	9.19546822920285	2.79643415361923e-19	***
df.mm.trans1:probe10	-0.0629128471519314	0.0589691453549199	-1.06687737753829	0.286329070592618	   
df.mm.trans1:probe11	0.033885707105846	0.0589691453549199	0.574634529666264	0.565690008432594	   
df.mm.trans1:probe12	0.0967651212687118	0.0589691453549199	1.64094494987689	0.101177486736973	   
df.mm.trans1:probe13	0.223545981844787	0.0589691453549199	3.7908974345706	0.000160670069204251	***
df.mm.trans1:probe14	0.142523954425082	0.0589691453549199	2.41692419938033	0.0158611510928213	*  
df.mm.trans1:probe15	0.301630897484915	0.0589691453549199	5.11506306678649	3.87507340662459e-07	***
df.mm.trans1:probe16	0.235001150509106	0.0589691453549199	3.98515442431284	7.32025609803716e-05	***
df.mm.trans1:probe17	0.339248198125824	0.0589691453549199	5.75297803764965	1.22182904294334e-08	***
df.mm.trans1:probe18	0.463621231903632	0.0589691453549199	7.86209854514961	1.13847796696788e-14	***
df.mm.trans1:probe19	0.263096815453538	0.0589691453549199	4.46160129793346	9.22440375749272e-06	***
df.mm.trans1:probe20	0.429283835980314	0.0589691453549199	7.27980426707164	7.59369897570445e-13	***
df.mm.trans1:probe21	0.446265799983092	0.0589691453549199	7.56778476773123	9.84178317010777e-14	***
df.mm.trans1:probe22	0.35361831705866	0.0589691453549199	5.99666681499831	2.9720137622314e-09	***
df.mm.trans2:probe2	0.0701661600947352	0.0589691453549199	1.18987921009239	0.234424941251876	   
df.mm.trans2:probe3	0.0619298620044012	0.0589691453549199	1.05020789485182	0.293919982100386	   
df.mm.trans2:probe4	-0.156643161523786	0.0589691453549199	-2.6563580085991	0.00804649286832971	** 
df.mm.trans2:probe5	0.0441283125940872	0.0589691453549199	0.748328847713331	0.454468101990057	   
df.mm.trans2:probe6	-0.0643584249062803	0.0589691453549199	-1.09139151532422	0.275408817718979	   
df.mm.trans3:probe2	-0.193368507329905	0.0589691453549199	-3.27914719072271	0.00108338641315480	** 
df.mm.trans3:probe3	0.0609448927638822	0.0589691453549199	1.03350476587495	0.301660661908853	   
df.mm.trans3:probe4	-0.26779743653241	0.0589691453549199	-4.54131452848108	6.39476377219596e-06	***
df.mm.trans3:probe5	0.430688978307973	0.0589691453549199	7.30363270004624	6.42887608793818e-13	***
df.mm.trans3:probe6	-0.105486207075533	0.0589691453549199	-1.78883730535077	0.0739957731368834	.  
df.mm.trans3:probe7	-0.108197807271253	0.0589691453549199	-1.83482067817056	0.0668803587308221	.  
df.mm.trans3:probe8	-0.141163836573232	0.0589691453549199	-2.39385929240799	0.0168872513646482	*  
df.mm.trans3:probe9	-0.0918902816207215	0.0589691453549199	-1.55827731719118	0.119538442492920	   
df.mm.trans3:probe10	0.155715869918584	0.0589691453549199	2.64063297816800	0.00842641598290299	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98437436757622	0.139379756497718	28.5864638287085	1.35393903994643e-126	***
df.mm.trans1	0.115700394356198	0.120364948367840	0.961246574896646	0.336700600706542	   
df.mm.trans2	0.103758367985438	0.106341887843124	0.975705529494673	0.329487029165709	   
df.mm.exp2	-0.0497471298509665	0.136789669537263	-0.363676073048884	0.71619002843411	   
df.mm.exp3	-0.0239727809062894	0.136789669537263	-0.175252860741497	0.860922541148621	   
df.mm.exp4	0.167337764150382	0.136789669537263	1.22332164933550	0.221546068496363	   
df.mm.exp5	0.120782281767974	0.136789669537263	0.882978094592672	0.377496878584932	   
df.mm.exp6	-0.0671669801350939	0.136789669537263	-0.491023776592989	0.6235359324952	   
df.mm.exp7	-0.147123201450861	0.136789669537263	-1.07554321864037	0.282435710086276	   
df.mm.exp8	-0.0921986146070038	0.136789669537263	-0.674017379520664	0.500482869276669	   
df.mm.trans1:exp2	0.0101109664523493	0.126437536092206	0.0799680756589222	0.936281403566953	   
df.mm.trans2:exp2	-0.0247043808169881	0.0933803090000573	-0.264556640275981	0.791414935555979	   
df.mm.trans1:exp3	-0.0817907591800082	0.126437536092206	-0.646886689727654	0.517879308356923	   
df.mm.trans2:exp3	0.0319439372545058	0.0933803090000573	0.34208429589247	0.732371719935918	   
df.mm.trans1:exp4	-0.174573707419052	0.126437536092206	-1.38071108323197	0.167729469014165	   
df.mm.trans2:exp4	0.0194085398134579	0.0933803090000573	0.207844030730783	0.835400364018159	   
df.mm.trans1:exp5	-0.128422030532788	0.126437536092206	-1.01569545328006	0.310062435446338	   
df.mm.trans2:exp5	-0.0819456184275573	0.0933803090000573	-0.877547090013453	0.380436645120057	   
df.mm.trans1:exp6	-0.00222485837014083	0.126437536092206	-0.0175965021061334	0.985964862075437	   
df.mm.trans2:exp6	0.0876356825118777	0.0933803090000573	0.938481393457629	0.348262726913809	   
df.mm.trans1:exp7	0.0697635311584196	0.126437536092206	0.551762817550823	0.581255381523519	   
df.mm.trans2:exp7	0.0664757420336366	0.0933803090000573	0.711881795482127	0.476732621685371	   
df.mm.trans1:exp8	0.0282852121223582	0.126437536092206	0.223708979125715	0.82303732468729	   
df.mm.trans2:exp8	0.066057279213976	0.0933803090000573	0.707400520745069	0.479510831046641	   
df.mm.trans1:probe2	0.0931087020274358	0.0865658632913438	1.07558220396961	0.282418276516863	   
df.mm.trans1:probe3	0.0985172150540917	0.0865658632913438	1.13806079334673	0.255414643353078	   
df.mm.trans1:probe4	0.121761901184247	0.0865658632913438	1.40658103038202	0.159915868210241	   
df.mm.trans1:probe5	0.218941316629338	0.0865658632913438	2.52918769945694	0.0116118443417935	*  
df.mm.trans1:probe6	0.0499088681428916	0.0865658632913438	0.576542140808088	0.564400895612235	   
df.mm.trans1:probe7	0.165162294662016	0.0865658632913438	1.90793793745405	0.0567347509752458	.  
df.mm.trans1:probe8	0.0156444163069204	0.0865658632913438	0.180722697286204	0.856628170601818	   
df.mm.trans1:probe9	0.0873834317391271	0.0865658632913438	1.00944446709937	0.313047773842215	   
df.mm.trans1:probe10	0.139971356231450	0.0865658632913438	1.61693479287980	0.106262084585411	   
df.mm.trans1:probe11	0.15247928574748	0.0865658632913438	1.76142511551349	0.0785246973933227	.  
df.mm.trans1:probe12	0.0732943460496804	0.0865658632913438	0.84668879004883	0.397406081571541	   
df.mm.trans1:probe13	0.114484412595925	0.0865658632913438	1.32251222644912	0.186352111671515	   
df.mm.trans1:probe14	0.205612128344145	0.0865658632913438	2.37521027950871	0.0177591211235375	*  
df.mm.trans1:probe15	0.088876265542466	0.0865658632913438	1.02668953052945	0.304857730554573	   
df.mm.trans1:probe16	0.112751784802809	0.0865658632913438	1.30249708737190	0.193098060772244	   
df.mm.trans1:probe17	0.0902688071122624	0.0865658632913438	1.04277602833413	0.29734747752613	   
df.mm.trans1:probe18	0.151249313404898	0.0865658632913438	1.74721660079629	0.0809596375930828	.  
df.mm.trans1:probe19	0.043030173351545	0.0865658632913438	0.497080162034817	0.619260615296747	   
df.mm.trans1:probe20	0.184415009438273	0.0865658632913438	2.13034332965190	0.0334288344790482	*  
df.mm.trans1:probe21	0.197720763192418	0.0865658632913438	2.28405003629403	0.0226135412190467	*  
df.mm.trans1:probe22	0.156846204444518	0.0865658632913438	1.81187131371451	0.070357628350478	.  
df.mm.trans2:probe2	0.0725272762908256	0.0865658632913438	0.83782767863967	0.402362181412753	   
df.mm.trans2:probe3	0.0748814799260609	0.0865658632913438	0.865023198279	0.387269258807069	   
df.mm.trans2:probe4	0.0233299253836235	0.0865658632913438	0.26950491217427	0.787606341917138	   
df.mm.trans2:probe5	0.054963684625397	0.0865658632913438	0.634934863878301	0.525641164690449	   
df.mm.trans2:probe6	-0.0484711307113705	0.0865658632913438	-0.559933545030763	0.575671775054186	   
df.mm.trans3:probe2	0.0251206406601671	0.0865658632913438	0.290191071919676	0.771740596201429	   
df.mm.trans3:probe3	-0.0490124142457055	0.0865658632913438	-0.566186397064518	0.571415997342216	   
df.mm.trans3:probe4	0.0832496786599787	0.0865658632913438	0.961691774271295	0.336476983655583	   
df.mm.trans3:probe5	-0.0249149693971259	0.0865658632913438	-0.287815178522193	0.77355813007546	   
df.mm.trans3:probe6	-0.0340544905109009	0.0865658632913438	-0.39339399176657	0.694126810825059	   
df.mm.trans3:probe7	-0.0270104796592277	0.0865658632913438	-0.312022298770613	0.755099829478938	   
df.mm.trans3:probe8	-0.0105350973065465	0.0865658632913438	-0.121700366703325	0.903164986198783	   
df.mm.trans3:probe9	-0.103622792398811	0.0865658632913438	-1.19703990070613	0.231623567902716	   
df.mm.trans3:probe10	0.0124036499122887	0.0865658632913438	0.143285695315523	0.886098393271132