chr13.6481_chr13_64418989_64421043_-_0.R 

fitVsDatCorrelation=0.839728774831857
cont.fitVsDatCorrelation=0.244642512323752

fstatistic=14388.6008945007,69,1083
cont.fstatistic=4501.86760345615,69,1083

residuals=-0.523791065265408,-0.0815097554149527,-0.00293862217594935,0.0756164282355465,1.28139645383953
cont.residuals=-0.603499284550556,-0.178569895211591,-0.0411281696948754,0.151002917684403,1.44917068203218

predictedValues:
Include	Exclude	Both
chr13.6481_chr13_64418989_64421043_-_0.R.tl.Lung	52.9659164354297	43.0002620771204	60.4718538866086
chr13.6481_chr13_64418989_64421043_-_0.R.tl.cerebhem	63.7361057827585	55.3107505068554	63.0673957549974
chr13.6481_chr13_64418989_64421043_-_0.R.tl.cortex	57.7454012319879	43.0135156744864	63.4164331394907
chr13.6481_chr13_64418989_64421043_-_0.R.tl.heart	58.095701466648	41.9473088673164	65.9994617355949
chr13.6481_chr13_64418989_64421043_-_0.R.tl.kidney	53.7312588297441	41.5057448546215	63.6763630438861
chr13.6481_chr13_64418989_64421043_-_0.R.tl.liver	56.0015986166964	43.4374358756138	63.4163305343317
chr13.6481_chr13_64418989_64421043_-_0.R.tl.stomach	54.1927288037769	44.337529589637	59.7066675085134
chr13.6481_chr13_64418989_64421043_-_0.R.tl.testicle	57.7444192837999	45.0701565446546	67.6503580716001


diffExp=9.96565435830927,8.42535527590313,14.7318855575015,16.1483925993316,12.2255139751226,12.5641627410826,9.85519921413994,12.6742627391452
diffExpScore=0.989753093246248
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=0,0,1,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.4491704225604	61.1896646830637	60.8178838532387
cerebhem	62.9422775220366	64.2261571238981	59.0389765274492
cortex	61.8201159664999	58.8428208451326	61.502028264572
heart	62.802629183092	65.9077966411	62.4220251340268
kidney	61.3033177151089	59.0086199354398	61.5085209008482
liver	60.9243967964181	63.0893443463764	60.1332493993635
stomach	61.4192887960074	58.419341682615	60.6352318360584
testicle	63.0535721408925	68.2058659234249	58.2215957449152
cont.diffExp=0.259505739496753,-1.28387960186145,2.97729512136731,-3.10516745800805,2.29469777966906,-2.16494754995823,2.99994711339234,-5.1522937825324
cont.diffExpScore=4.84754418285659

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=0.812960876899255
cont.tran.correlation=0.747361648226245

tran.covariance=0.00430897703273344
cont.tran.covariance=0.000575950703054606

tran.mean=50.7397396525717
cont.tran.mean=62.1627737327291

weightedLogRatios:
wLogRatio
Lung	0.805719006434451
cerebhem	0.579025568490295
cortex	1.15125007275815
heart	1.26989855675465
kidney	0.995196334801489
liver	0.9904095435706
stomach	0.781221738952286
testicle	0.974405366883645

cont.weightedLogRatios:
wLogRatio
Lung	0.0174194690091729
cerebhem	-0.0838455361413758
cortex	0.202349560992266
heart	-0.200959954050319
kidney	0.156293108855634
liver	-0.144110598998137
stomach	0.204949031866343
testicle	-0.32857754946332

varWeightedLogRatios=0.0476690886531816
cont.varWeightedLogRatios=0.0399904592006967

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.604975698865	0.0631350251326216	57.0994577303545	0	***
df.mm.trans1	0.292073996096582	0.0530120425795882	5.50957823702199	4.49308648694039e-08	***
df.mm.trans2	0.138425398544751	0.0470518790969861	2.94197386377323	0.00333070780793131	** 
df.mm.exp2	0.394837777031855	0.0591807377120338	6.67172786782564	4.02537023556475e-11	***
df.mm.exp3	0.0391582732808725	0.0591807377120338	0.661672611642861	0.508321812850812	   
df.mm.exp4	-0.0198174298169854	0.0591807377120337	-0.33486283853734	0.737793400023455	   
df.mm.exp5	-0.0726634450206547	0.0591807377120338	-1.22782256237200	0.219780373528994	   
df.mm.exp6	0.0183036771723270	0.0591807377120338	0.309284369880457	0.757164711529727	   
df.mm.exp7	0.0662577350042861	0.0591807377120338	1.11958278260552	0.263139832459590	   
df.mm.exp8	0.0212175721326229	0.0591807377120338	0.358521589167492	0.720022888433862	   
df.mm.trans1:exp2	-0.209735185812872	0.0518116142891287	-4.04803418481558	5.53238871464076e-05	***
df.mm.trans2:exp2	-0.143076694510258	0.0363489954985501	-3.93619390433953	8.8065320507593e-05	***
df.mm.trans1:exp3	0.0472368200441005	0.0518116142891287	0.911703306144852	0.362127810217793	   
df.mm.trans2:exp3	-0.0388500994552996	0.0363489954985501	-1.06880806257354	0.285394331304989	   
df.mm.trans1:exp4	0.112260485294731	0.0518116142891287	2.16670503003969	0.0304748596509995	*  
df.mm.trans2:exp4	-0.00497450077719616	0.0363489954985501	-0.136853871997497	0.891171736537305	   
df.mm.trans1:exp5	0.087009757733314	0.0518116142891287	1.67934851919043	0.0933725795505572	.  
df.mm.trans2:exp5	0.0372890824168276	0.0363489954985501	1.02586280323249	0.305185331635857	   
df.mm.trans1:exp6	0.0374279393225176	0.0518116142891287	0.722385122255704	0.470213684991358	   
df.mm.trans2:exp6	-0.00818824058501121	0.0363489954985501	-0.225267314067533	0.821813824524468	   
df.mm.trans1:exp7	-0.043359611029703	0.0518116142891287	-0.836870489843064	0.402849990860809	   
df.mm.trans2:exp7	-0.0356324580238134	0.0363489954985501	-0.980287282635766	0.327163260312094	   
df.mm.trans1:exp8	0.0651605162618084	0.0518116142891287	1.25764304308659	0.208791990587995	   
df.mm.trans2:exp8	0.0257965274392463	0.0363489954985501	0.709690242754446	0.478049009504159	   
df.mm.trans1:probe2	0.488834094117785	0.0404454284411748	12.0862632183205	1.22515371115014e-31	***
df.mm.trans1:probe3	-0.0359687559163515	0.0404454284411748	-0.88931573487139	0.374030907257395	   
df.mm.trans1:probe4	0.0421741271633178	0.0404454284411748	1.04274151093880	0.297300731839086	   
df.mm.trans1:probe5	0.0449990126564675	0.0404454284411748	1.11258588153951	0.266133222940831	   
df.mm.trans1:probe6	0.515847359134255	0.0404454284411748	12.7541573674889	8.21881441875955e-35	***
df.mm.trans1:probe7	-0.0426477484893373	0.0404454284411748	-1.05445164343767	0.291911369869066	   
df.mm.trans1:probe8	0.455246531750528	0.0404454284411748	11.2558217157386	7.1405831677834e-28	***
df.mm.trans1:probe9	0.303173368454245	0.0404454284411748	7.4958624531618	1.36618884339592e-13	***
df.mm.trans1:probe10	-0.0368573313369357	0.0404454284411748	-0.91128547174478	0.362347766280081	   
df.mm.trans1:probe11	0.127120650599621	0.0404454284411748	3.14301654102910	0.00171759673093191	** 
df.mm.trans1:probe12	0.06177738572705	0.0404454284411748	1.527425672271	0.126947185630166	   
df.mm.trans1:probe13	0.181023156393825	0.0404454284411748	4.47573838059624	8.41989174843531e-06	***
df.mm.trans1:probe14	0.123299175999591	0.0404454284411748	3.04853183046191	0.0023554309780956	** 
df.mm.trans1:probe15	0.151915897138726	0.0404454284411748	3.75607090822829	0.000181792202655146	***
df.mm.trans1:probe16	0.233624398135034	0.0404454284411748	5.77628689172685	9.97196879149891e-09	***
df.mm.trans2:probe2	0.0890466258593466	0.0404454284411748	2.20164872252148	0.027900417532613	*  
df.mm.trans2:probe3	0.117652338831220	0.0404454284411748	2.90891562694998	0.00370093373801687	** 
df.mm.trans2:probe4	0.114650511474457	0.0404454284411748	2.83469642659884	0.00467219166394628	** 
df.mm.trans2:probe5	0.122310892463412	0.0404454284411748	3.02409684301663	0.00255251287800601	** 
df.mm.trans2:probe6	0.126103249995301	0.0404454284411748	3.11786164358996	0.00186972458084016	** 
df.mm.trans3:probe2	-0.100225315761072	0.0404454284411748	-2.47803817696832	0.0133622644024725	*  
df.mm.trans3:probe3	0.0210992274580573	0.0404454284411748	0.521671503338003	0.602005734991556	   
df.mm.trans3:probe4	0.370269565510578	0.0404454284411748	9.15479399727736	2.642598706118e-19	***
df.mm.trans3:probe5	-0.0672612974277201	0.0404454284411748	-1.66301359684067	0.0965990527714234	.  
df.mm.trans3:probe6	0.419086098643925	0.0404454284411748	10.3617668249814	4.71252178373179e-24	***
df.mm.trans3:probe7	0.0904747433147434	0.0404454284411748	2.23695845987472	0.0254919292975982	*  
df.mm.trans3:probe8	0.059797335055466	0.0404454284411748	1.47846956652807	0.139572908745939	   
df.mm.trans3:probe9	-0.211748072755935	0.0404454284411748	-5.23540189625903	1.97660278221848e-07	***
df.mm.trans3:probe10	0.285478220260411	0.0404454284411748	7.0583556971246	3.00397516638942e-12	***
df.mm.trans3:probe11	-0.117470850607813	0.0404454284411748	-2.90442838993946	0.00375397723658513	** 
df.mm.trans3:probe12	-0.153439606524164	0.0404454284411748	-3.79374412481084	0.000156602989731582	***
df.mm.trans3:probe13	-0.0752967234444949	0.0404454284411748	-1.86168687900065	0.0629179685718005	.  
df.mm.trans3:probe14	-0.0724718379513453	0.0404454284411748	-1.79184250839995	0.0734372865503314	.  
df.mm.trans3:probe15	-0.16011859909715	0.0404454284411748	-3.95888003337713	8.0214267689798e-05	***
df.mm.trans3:probe16	-0.154328181944749	0.0404454284411748	-3.81571386168424	0.000143471805104179	***
df.mm.trans3:probe17	-0.0185536653659917	0.0404454284411748	-0.458733312541782	0.646517733448561	   
df.mm.trans3:probe18	0.394645009725887	0.0404454284411748	9.75746888921876	1.29102280771946e-21	***
df.mm.trans3:probe19	-0.0852455857625706	0.0404454284411748	-2.10766924836894	0.0352889663110323	*  
df.mm.trans3:probe20	0.274249433709399	0.0404454284411748	6.78072761939651	1.96197615134617e-11	***
df.mm.trans3:probe21	0.196036933865200	0.0404454284411748	4.84694912183518	1.43660982593927e-06	***
df.mm.trans3:probe22	-0.075377258357148	0.0404454284411748	-1.86367807839591	0.0626373906973579	.  
df.mm.trans3:probe23	0.24525877337617	0.0404454284411748	6.06394301726541	1.83235017951183e-09	***
df.mm.trans3:probe24	0.396627107502751	0.0404454284411748	9.80647560897072	8.27344259154987e-22	***
df.mm.trans3:probe25	0.333639469826919	0.0404454284411748	8.24912685279562	4.58888357855188e-16	***
df.mm.trans3:probe26	-0.0608485283594426	0.0404454284411748	-1.50445997742224	0.132754534216383	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.078281021159	0.112736426213575	36.1753619316691	1.58857429538863e-188	***
df.mm.trans1	0.0486983836469557	0.0946604236578768	0.514453472371539	0.607039945815294	   
df.mm.trans2	0.0412609690850924	0.0840177173428676	0.491098430069347	0.623456331466083	   
df.mm.exp2	0.102126045873165	0.105675492427900	0.966411828578353	0.334053900343278	   
df.mm.exp3	-0.0442762514433536	0.105675492427900	-0.418983157079322	0.675311524439132	   
df.mm.exp4	0.0700307579093213	0.105675492427900	0.662696300725558	0.507666086366993	   
df.mm.exp5	-0.0499629639071079	0.105675492427900	-0.472796130485943	0.636453901792194	   
df.mm.exp6	0.0333179161521571	0.105675492427900	0.315285175272678	0.752605879629972	   
df.mm.exp7	-0.0438098891494984	0.105675492427900	-0.414570002400403	0.678538813012502	   
df.mm.exp8	0.177954196061343	0.105675492427900	1.68396845827576	0.092475927755601	.  
df.mm.trans1:exp2	-0.0781183051668847	0.092516890886522	-0.84436803288928	0.398650256788657	   
df.mm.trans2:exp2	-0.0536937838477574	0.0649062202850463	-0.827251742775227	0.408276623798034	   
df.mm.trans1:exp3	0.0502947281378485	0.092516890886522	0.543627522022311	0.586809699301702	   
df.mm.trans2:exp3	0.00516778944213551	0.0649062202850464	0.0796193249189416	0.93655473409631	   
df.mm.trans1:exp4	-0.0482441552199384	0.092516890886522	-0.521463213448374	0.602150742259399	   
df.mm.trans2:exp4	0.00424768925554068	0.0649062202850463	0.0654434850294202	0.947832980182282	   
df.mm.trans1:exp5	0.0475865920990582	0.092516890886522	0.514355720810228	0.607108251633086	   
df.mm.trans2:exp5	0.0136682002127349	0.0649062202850464	0.210583826214326	0.833251647279112	   
df.mm.trans1:exp6	-0.0418945533274881	0.092516890886522	-0.452831401120845	0.650760850735712	   
df.mm.trans2:exp6	-0.00274432770670893	0.0649062202850464	-0.0422814284155935	0.966282143629899	   
df.mm.trans1:exp7	0.0433234888587773	0.092516890886522	0.468276532464935	0.639681076619088	   
df.mm.trans2:exp7	-0.00252138018233109	0.0649062202850464	-0.0388465107235953	0.969019922069085	   
df.mm.trans1:exp8	-0.152179815452246	0.092516890886522	-1.64488683086967	0.100283345903504	   
df.mm.trans2:exp8	-0.0694019218323373	0.0649062202850464	-1.06926457167814	0.285188729550965	   
df.mm.trans1:probe2	-0.0271760588082472	0.0722209748005466	-0.376290390475891	0.706774725248087	   
df.mm.trans1:probe3	0.0305207931587550	0.0722209748005466	0.422602896776796	0.672668902498674	   
df.mm.trans1:probe4	-0.00695790697445172	0.0722209748005466	-0.0963419144323023	0.923266847321028	   
df.mm.trans1:probe5	0.0298253954379446	0.0722209748005467	0.412974146642491	0.6797073025263	   
df.mm.trans1:probe6	0.0106073629324023	0.0722209748005466	0.146873715865741	0.883259034253587	   
df.mm.trans1:probe7	-0.0632386688220626	0.0722209748005466	-0.875627461367136	0.381426750143130	   
df.mm.trans1:probe8	-0.118044584071632	0.0722209748005466	-1.63449170268938	0.102446264140232	   
df.mm.trans1:probe9	-0.0510267512308403	0.0722209748005466	-0.70653645110387	0.480006566665233	   
df.mm.trans1:probe10	-0.000536764125663325	0.0722209748005466	-0.00743224703274514	0.994071348217365	   
df.mm.trans1:probe11	-0.0246094177127223	0.0722209748005467	-0.340751669174867	0.73335662651499	   
df.mm.trans1:probe12	-0.0188318407483928	0.0722209748005467	-0.260753067933531	0.794332510452864	   
df.mm.trans1:probe13	-0.0811327459052773	0.0722209748005466	-1.12339588505060	0.26151835702309	   
df.mm.trans1:probe14	0.0362535274595625	0.0722209748005466	0.501980588875797	0.615783212295542	   
df.mm.trans1:probe15	-0.0335084634577541	0.0722209748005466	-0.463971353894	0.642761511093536	   
df.mm.trans1:probe16	0.00216964157785417	0.0722209748005467	0.0300417099581678	0.976039323487278	   
df.mm.trans2:probe2	0.0203264529859653	0.0722209748005466	0.281448056358988	0.778420528805969	   
df.mm.trans2:probe3	-0.0632334829168686	0.0722209748005466	-0.875555655285756	0.381465782793293	   
df.mm.trans2:probe4	-0.0556062741322812	0.0722209748005466	-0.769946324954067	0.441499639328212	   
df.mm.trans2:probe5	-0.0479226087964016	0.0722209748005466	-0.66355527502571	0.50711621161062	   
df.mm.trans2:probe6	-0.031602254019669	0.0722209748005466	-0.437577228872155	0.661780028799917	   
df.mm.trans3:probe2	0.0705284860059336	0.0722209748005466	0.976565135000086	0.329002568166021	   
df.mm.trans3:probe3	-0.136015840094168	0.0722209748005466	-1.88332877629808	0.0599236083452814	.  
df.mm.trans3:probe4	-0.00361049402275448	0.0722209748005466	-0.0499923191666356	0.960137727284281	   
df.mm.trans3:probe5	-0.123950194805829	0.0722209748005467	-1.71626310982569	0.086399864167542	.  
df.mm.trans3:probe6	-0.152368015751698	0.0722209748005466	-2.10974742687279	0.0351090659371626	*  
df.mm.trans3:probe7	-0.0424214772655425	0.0722209748005466	-0.587384445899522	0.557067975420792	   
df.mm.trans3:probe8	0.0247520810787659	0.0722209748005466	0.342727042207945	0.731870324358228	   
df.mm.trans3:probe9	-0.0736629441400676	0.0722209748005467	-1.01996607417033	0.307972339108602	   
df.mm.trans3:probe10	0.0248948397444813	0.0722209748005466	0.344703734797731	0.730384037081609	   
df.mm.trans3:probe11	0.0105702986358911	0.0722209748005466	0.146360509049942	0.883664038925706	   
df.mm.trans3:probe12	-0.0939733118886367	0.0722209748005466	-1.30119140801081	0.193469695747169	   
df.mm.trans3:probe13	-0.0602543946775699	0.0722209748005466	-0.83430602873992	0.404292540968436	   
df.mm.trans3:probe14	-0.0343889898545910	0.0722209748005466	-0.476163468432313	0.634053965536776	   
df.mm.trans3:probe15	-0.140387881332736	0.0722209748005466	-1.94386577750365	0.0521703671791604	.  
df.mm.trans3:probe16	-0.0966171692939854	0.0722209748005466	-1.33779929668374	0.181242717295657	   
df.mm.trans3:probe17	-0.0303605131693573	0.0722209748005466	-0.420383597053407	0.67428864362357	   
df.mm.trans3:probe18	-0.00533361061848699	0.0722209748005466	-0.0738512687376052	0.9411423693892	   
df.mm.trans3:probe19	-0.141538653369187	0.0722209748005466	-1.95979981937485	0.0502753530392148	.  
df.mm.trans3:probe20	0.0141110858604057	0.0722209748005466	0.195387640493312	0.845126141976341	   
df.mm.trans3:probe21	-0.162404325949908	0.0722209748005466	-2.24871412215664	0.0247310764204918	*  
df.mm.trans3:probe22	-0.114517426356823	0.0722209748005466	-1.58565329079380	0.113109903061751	   
df.mm.trans3:probe23	-0.122432662503433	0.0722209748005466	-1.69525076117509	0.0903153094584289	.  
df.mm.trans3:probe24	-0.135767926640293	0.0722209748005466	-1.87989606918551	0.0603905042076747	.  
df.mm.trans3:probe25	-0.0112911396316202	0.0722209748005466	-0.156341556768004	0.875792939174167	   
df.mm.trans3:probe26	-0.03282845198841	0.0722209748005467	-0.454555647844309	0.649520041431112