chr9.25088_chr9_13747693_13751348_+_2.R 

fitVsDatCorrelation=0.826543299925245
cont.fitVsDatCorrelation=0.260827092172601

fstatistic=9489.8917524834,53,715
cont.fstatistic=3217.21723355804,53,715

residuals=-0.436824249971227,-0.092450159311208,-0.00623753706899183,0.0748927735138272,1.68939616101938
cont.residuals=-0.625358824811333,-0.212196440276154,-0.0197572173281659,0.178033818508367,1.39428128161439

predictedValues:
Include	Exclude	Both
chr9.25088_chr9_13747693_13751348_+_2.R.tl.Lung	57.2026421164439	74.2363565468791	44.5150512986266
chr9.25088_chr9_13747693_13751348_+_2.R.tl.cerebhem	61.38129285966	82.1083503058212	50.7129261336868
chr9.25088_chr9_13747693_13751348_+_2.R.tl.cortex	53.0791936441935	65.7451029672483	48.0987108313672
chr9.25088_chr9_13747693_13751348_+_2.R.tl.heart	53.5745044057828	65.1232250885578	44.5724876767609
chr9.25088_chr9_13747693_13751348_+_2.R.tl.kidney	58.8174072955868	74.2239030645495	45.6780095472204
chr9.25088_chr9_13747693_13751348_+_2.R.tl.liver	60.717068668108	71.8284635087887	46.7657419903152
chr9.25088_chr9_13747693_13751348_+_2.R.tl.stomach	58.1442983093831	68.5725161883892	46.7949468634922
chr9.25088_chr9_13747693_13751348_+_2.R.tl.testicle	58.0066778956385	67.4257264132786	48.6582623800495


diffExp=-17.0337144304351,-20.7270574461612,-12.6659093230548,-11.548720682775,-15.4064957689627,-11.1113948406807,-10.4282178790060,-9.4190485176401
diffExpScore=0.990854262954538
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,-1,-1,-1,-1,0,0,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	58.765691560048	56.8132671788551	57.3652422335561
cerebhem	61.8089804231773	61.5844027275329	61.3494496946968
cortex	59.8363265543469	61.1373438536049	62.5952987324167
heart	63.459320434956	60.4936220701111	59.751325999798
kidney	59.3529702927862	72.9078218853188	58.9016081530847
liver	58.8307837399342	61.6211801362705	54.356050345747
stomach	56.9759291361627	59.9260933144482	61.2305362638007
testicle	61.1855932121217	57.7962660882165	55.4694674883999
cont.diffExp=1.95242438119284,0.224577695644392,-1.30101729925798,2.96569836484485,-13.5548515925326,-2.79039639633624,-2.95016417828550,3.38932712390516
cont.diffExpScore=2.22960509429521

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,-1,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.771940087539242
cont.tran.correlation=-0.0661445665144196

tran.covariance=0.00318921619498284
cont.tran.covariance=-0.000139923259548202

tran.mean=64.3866705798943
cont.tran.mean=60.7809745379932

weightedLogRatios:
wLogRatio
Lung	-1.08873241909227
cerebhem	-1.24012983528891
cortex	-0.872860708286276
heart	-0.796190248416286
kidney	-0.974974036955167
liver	-0.704197003515951
stomach	-0.683843356712207
testicle	-0.622306488048725

cont.weightedLogRatios:
wLogRatio
Lung	0.137067912267096
cerebhem	0.0150050450056672
cortex	-0.0882416377353377
heart	0.197497480337876
kidney	-0.861105886818768
liver	-0.189895308882543
stomach	-0.205358882090184
testicle	0.23281809836254

varWeightedLogRatios=0.0463921578700627
cont.varWeightedLogRatios=0.123874800423037

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.38443667037160	0.0779880677161864	56.2193268632761	1.26330970261945e-264	***
df.mm.trans1	-0.567216701776553	0.0680985154838639	-8.3293548728086	4.13810189049231e-16	***
df.mm.trans2	-0.0682290788783972	0.0613468401839202	-1.11218570791656	0.266432117627253	   
df.mm.exp2	0.0409372656571083	0.0810167616675953	0.505293778898128	0.61350822866275	   
df.mm.exp3	-0.273711903532429	0.0810167616675954	-3.37846018402271	0.000768444914010775	***
df.mm.exp4	-0.197788999164881	0.0810167616675953	-2.44133430038085	0.0148745787890964	*  
df.mm.exp5	0.00188036965487879	0.0810167616675953	0.0232096373167047	0.981489526859886	   
df.mm.exp6	-0.0226719805583809	0.0810167616675954	-0.279843085451898	0.779678831354285	   
df.mm.exp7	-0.112982320901863	0.0810167616675954	-1.39455488686921	0.163583230258376	   
df.mm.exp8	-0.171263579978954	0.0810167616675954	-2.11392774104738	0.0348671050242016	*  
df.mm.trans1:exp2	0.0295677583539792	0.0755485198030922	0.391374423099803	0.695637099154479	   
df.mm.trans2:exp2	0.0598484440034803	0.0609091639154967	0.982585216347937	0.326143985425624	   
df.mm.trans1:exp3	0.198896833384945	0.0755485198030922	2.63270324691132	0.0086537352591742	** 
df.mm.trans2:exp3	0.152243081642696	0.0609091639154966	2.49951028475638	0.0126595863853787	*  
df.mm.trans1:exp4	0.132262201840468	0.0755485198030922	1.75069216690404	0.0804278042177317	.  
df.mm.trans2:exp4	0.0668162343602309	0.0609091639154966	1.09698163732685	0.273018730386593	   
df.mm.trans1:exp5	0.0259573957138282	0.0755485198030922	0.34358576159378	0.731258841457392	   
df.mm.trans2:exp5	-0.00204813821989987	0.0609091639154966	-0.0336261095742735	0.973184686295332	   
df.mm.trans1:exp6	0.0822967480975805	0.0755485198030922	1.08932310404064	0.276378433802795	   
df.mm.trans2:exp6	-0.0103012048334217	0.0609091639154967	-0.169124055744933	0.865746897928803	   
df.mm.trans1:exp7	0.129310055463918	0.0755485198030922	1.71161600254974	0.0874011236833755	.  
df.mm.trans2:exp7	0.0336201261150885	0.0609091639154967	0.551971558199878	0.581140318187967	   
df.mm.trans1:exp8	0.185221631863506	0.0755485198030922	2.45169107675786	0.0144568410475065	*  
df.mm.trans2:exp8	0.0750362120073477	0.0609091639154967	1.23193633246141	0.218377854260487	   
df.mm.trans1:probe2	0.0695705240014809	0.0462638310850315	1.50377784048218	0.133080138619935	   
df.mm.trans1:probe3	0.0388958101936038	0.0462638310850315	0.840739067244873	0.400775194246376	   
df.mm.trans1:probe4	0.189881110770657	0.0462638310850315	4.10431013423124	4.52267651145755e-05	***
df.mm.trans1:probe5	0.563535608901085	0.0462638310850315	12.1809109986011	3.77882299358196e-31	***
df.mm.trans1:probe6	0.352299721064637	0.0462638310850315	7.61501399261814	8.36453044811325e-14	***
df.mm.trans1:probe7	0.161043983151542	0.0462638310850315	3.48099107606433	0.000529903295168658	***
df.mm.trans1:probe8	0.0177952241527296	0.0462638310850315	0.384646574556752	0.700613661624232	   
df.mm.trans1:probe9	0.391673881941266	0.0462638310850315	8.46609268526382	1.43426966054428e-16	***
df.mm.trans1:probe10	0.660137042528856	0.0462638310850315	14.2689662106786	7.86752474084475e-41	***
df.mm.trans1:probe11	0.565895068583708	0.0462638310850315	12.2319110914877	2.24978910528668e-31	***
df.mm.trans1:probe12	0.512925000237608	0.0462638310850315	11.0869547161986	1.80867655670673e-26	***
df.mm.trans1:probe13	0.427308996120593	0.0462638310850315	9.23635129428023	2.85350951315341e-19	***
df.mm.trans1:probe14	0.772714782992772	0.0462638310850315	16.7023518128567	4.00584454739305e-53	***
df.mm.trans1:probe15	0.324713756224679	0.0462638310850315	7.01873901510371	5.209174367955e-12	***
df.mm.trans1:probe16	0.14713199405181	0.0462638310850315	3.18028123916901	0.00153484567162585	** 
df.mm.trans1:probe17	0.313268124924824	0.0462638310850315	6.77133989074632	2.66328502084065e-11	***
df.mm.trans1:probe18	0.185518369185108	0.0462638310850315	4.01000878729931	6.7099817832644e-05	***
df.mm.trans1:probe19	0.203970312693777	0.0462638310850315	4.40885045423251	1.19887932140482e-05	***
df.mm.trans1:probe20	0.0656037971218631	0.0462638310850315	1.41803641383018	0.156615805103103	   
df.mm.trans2:probe2	-0.111824028609853	0.0462638310850315	-2.41709400166891	0.0158942221234740	*  
df.mm.trans2:probe3	-0.0127427988445108	0.0462638310850315	-0.275437605266411	0.783059635544756	   
df.mm.trans2:probe4	-0.00100618989426588	0.0462638310850315	-0.0217489531382849	0.982654281943672	   
df.mm.trans2:probe5	-0.0963642694227138	0.0462638310850315	-2.08292887040849	0.037612504237317	*  
df.mm.trans2:probe6	0.114494315912408	0.0462638310850315	2.47481268254614	0.0135614473432248	*  
df.mm.trans3:probe2	0.099599644592594	0.0462638310850315	2.15286201459479	0.0316633955207042	*  
df.mm.trans3:probe3	0.0707580909192966	0.0462638310850315	1.52944728656918	0.126595868546693	   
df.mm.trans3:probe4	0.0483264146585214	0.0462638310850315	1.04458306900047	0.296568733413530	   
df.mm.trans3:probe5	0.0632875481090101	0.0462638310850315	1.36797032638066	0.171751125630653	   
df.mm.trans3:probe6	-0.000952856091397674	0.0462638310850315	-0.0205961345839767	0.98357357010206	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98906955785109	0.133757585509762	29.8231277325199	1.30484254930487e-127	***
df.mm.trans1	0.0948479946218834	0.116795982701726	0.812082679796497	0.41701447003479	   
df.mm.trans2	0.0393575510825354	0.105216162702174	0.374063737659217	0.708467795291179	   
df.mm.exp2	0.0639816036388083	0.138952108236787	0.460457955267421	0.645327548744911	   
df.mm.exp3	0.00415614128767206	0.138952108236787	0.0299106025839466	0.97614669674014	   
df.mm.exp4	0.0988561342840444	0.138952108236787	0.711440333928469	0.477043448054935	   
df.mm.exp5	0.232940203443333	0.138952108236787	1.67640639929249	0.0940957568285193	.  
df.mm.exp6	0.136225467067414	0.138952108236787	0.980377115511437	0.327231603584028	   
df.mm.exp7	-0.0427946153622927	0.138952108236787	-0.307981044010983	0.758186452781542	   
df.mm.exp8	0.0911136964122086	0.138952108236787	0.655720143928602	0.51221513751049	   
df.mm.trans1:exp2	-0.0134911431871447	0.129573509045932	-0.104119609683197	0.91710362753407	   
df.mm.trans2:exp2	0.0166571568196478	0.10446550272798	0.159451267496618	0.873358380485968	   
df.mm.trans1:exp3	0.0138985949534592	0.129573509045932	0.107264170398691	0.91460949836807	   
df.mm.trans2:exp3	0.0691968550098408	0.10446550272798	0.662389527670431	0.507935060238476	   
df.mm.trans1:exp4	-0.0220152644893742	0.129573509045932	-0.169905597613861	0.865132440129206	   
df.mm.trans2:exp4	-0.0360880706778479	0.10446550272798	-0.345454429792181	0.729854371841548	   
df.mm.trans1:exp5	-0.222996244329532	0.129573509045932	-1.72100181566034	0.0856830954201616	.  
df.mm.trans2:exp5	0.0164858503656299	0.10446550272798	0.157811429946953	0.874649948173013	   
df.mm.trans1:exp6	-0.135118423912116	0.129573509045932	-1.04279358417482	0.297396352240248	   
df.mm.trans2:exp6	-0.0549896977888214	0.10446550272798	-0.526390974559423	0.598779829532503	   
df.mm.trans1:exp7	0.0118652904022646	0.129573509045932	0.0915718844818695	0.927063839203203	   
df.mm.trans2:exp7	0.0961367646849265	0.10446550272798	0.920272838156526	0.35774057601055	   
df.mm.trans1:exp8	-0.0507601473825098	0.129573509045932	-0.391747879302365	0.695361237173198	   
df.mm.trans2:exp8	-0.0739593989152133	0.10446550272798	-0.707979160429619	0.479188918908462	   
df.mm.trans1:probe2	-0.0231788459580372	0.0793472453361086	-0.292119100793650	0.770280302527185	   
df.mm.trans1:probe3	-0.0137289080975464	0.0793472453361086	-0.173023121841115	0.862682220896366	   
df.mm.trans1:probe4	-0.0341517735167895	0.0793472453361086	-0.430409063000553	0.667027806952699	   
df.mm.trans1:probe5	0.0445261183698516	0.0793472453361086	0.561155187949406	0.574867569328952	   
df.mm.trans1:probe6	-0.00464354398857751	0.0793472453361086	-0.0585218046185199	0.953349346146918	   
df.mm.trans1:probe7	-0.0534901466113013	0.0793472453361086	-0.674127329622109	0.500448246959566	   
df.mm.trans1:probe8	-0.110334639191542	0.0793472453361086	-1.3905289178493	0.164800970658226	   
df.mm.trans1:probe9	0.00101787801633801	0.0793472453361086	0.0128281456026150	0.989768479869325	   
df.mm.trans1:probe10	-0.0219776065630044	0.0793472453361086	-0.276980082546142	0.781875454212349	   
df.mm.trans1:probe11	-0.0488542418924541	0.0793472453361086	-0.61570180143635	0.538287283674218	   
df.mm.trans1:probe12	-0.0249490861040394	0.0793472453361086	-0.314429139894612	0.753286852828583	   
df.mm.trans1:probe13	-0.0540425430123283	0.0793472453361086	-0.681089088643321	0.496035637010714	   
df.mm.trans1:probe14	0.0628643716011851	0.0793472453361086	0.79226911198866	0.42846655682235	   
df.mm.trans1:probe15	0.0409992925037666	0.0793472453361086	0.516707194182947	0.605520352216183	   
df.mm.trans1:probe16	-0.0564896315266994	0.0793472453361086	-0.711929333997844	0.476740757939424	   
df.mm.trans1:probe17	0.0343207391067485	0.0793472453361086	0.432538507938978	0.665480497898865	   
df.mm.trans1:probe18	-0.0559287585714721	0.0793472453361086	-0.704860746388388	0.481126435324757	   
df.mm.trans1:probe19	0.0425030021567452	0.0793472453361086	0.535658194266302	0.592361377795802	   
df.mm.trans1:probe20	0.00619536037195716	0.0793472453361086	0.078079085741592	0.93778698836148	   
df.mm.trans2:probe2	0.0118534029710359	0.0793472453361086	0.149386445878819	0.881290832063095	   
df.mm.trans2:probe3	0.0343475227139659	0.0793472453361086	0.432876057240205	0.665235356762118	   
df.mm.trans2:probe4	0.0504624547081762	0.0793472453361086	0.635969837319762	0.52499959131396	   
df.mm.trans2:probe5	0.0451750665316532	0.0793472453361086	0.56933377258776	0.56930846027207	   
df.mm.trans2:probe6	-0.00572524759144053	0.0793472453361086	-0.0721543333632924	0.942499265403028	   
df.mm.trans3:probe2	0.0539904062190537	0.0793472453361086	0.680432017398394	0.496451221136196	   
df.mm.trans3:probe3	-0.142799635166604	0.0793472453361086	-1.79967980692607	0.0723326798713756	.  
df.mm.trans3:probe4	-0.173217050317294	0.0793472453361086	-2.18302537893484	0.0293584654311178	*  
df.mm.trans3:probe5	-0.0044649466743729	0.0793472453361086	-0.0562709726778761	0.955141663176922	   
df.mm.trans3:probe6	-0.176527891911131	0.0793472453361086	-2.22475135921068	0.0264092920815592	*