chr17.10886_chr17_31761550_31767276_+_2.R 

fitVsDatCorrelation=0.7869736458444
cont.fitVsDatCorrelation=0.245972668859838

fstatistic=11418.7229219925,65,991
cont.fstatistic=4617.65347840132,65,991

residuals=-0.549340654957061,-0.0867259872410653,-0.00456051546578999,0.0741195525242577,1.20271737640548
cont.residuals=-0.557558096491418,-0.158905765352234,-0.0307740228312334,0.130000521991028,1.50359997480704

predictedValues:
Include	Exclude	Both
chr17.10886_chr17_31761550_31767276_+_2.R.tl.Lung	65.460576600417	63.7157853745578	54.9406022829643
chr17.10886_chr17_31761550_31767276_+_2.R.tl.cerebhem	65.0202164032387	95.109414639921	54.1799946714059
chr17.10886_chr17_31761550_31767276_+_2.R.tl.cortex	57.9148416237352	56.7883034301454	52.6879123506361
chr17.10886_chr17_31761550_31767276_+_2.R.tl.heart	61.1293609075261	53.0586962821564	54.9920023846283
chr17.10886_chr17_31761550_31767276_+_2.R.tl.kidney	62.5456637962593	63.0889261716717	55.7930128252762
chr17.10886_chr17_31761550_31767276_+_2.R.tl.liver	60.8116857819427	60.623667355045	56.1154329398424
chr17.10886_chr17_31761550_31767276_+_2.R.tl.stomach	67.8241987314401	63.8507349169002	54.9834014268298
chr17.10886_chr17_31761550_31767276_+_2.R.tl.testicle	60.5480529528056	63.7251774805271	53.5774234784472


diffExp=1.74479122585918,-30.0891982366823,1.12653819358983,8.0706646253697,-0.543262375412354,0.188018426897642,3.97346381453992,-3.17712452772159
diffExpScore=2.48212682623217
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	59.7890728799229	63.9233949559676	62.5187028978807
cerebhem	59.564639851391	70.1741636869435	61.2119005557129
cortex	61.9324926614578	64.1540391216996	60.7068651144513
heart	60.5127643019532	68.3130651111116	58.336111597304
kidney	60.3805376445799	64.640962181479	59.212380604961
liver	63.1020007158612	62.1017484040647	58.8140204972792
stomach	62.8359465138111	61.9994765981228	61.1896393638934
testicle	63.4839770008659	65.950506331077	61.8834400998479
cont.diffExp=-4.13432207604464,-10.6095238355525,-2.22154646024180,-7.8003008091584,-4.26042453689906,1.00025231179651,0.836469915688376,-2.46652933021112
cont.diffExpScore=1.08720808361232

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=0.449595134400596
cont.tran.correlation=-0.571483644710922

tran.covariance=0.00430388854329143
cont.tran.covariance=-0.000640891022839614

tran.mean=63.8259564030181
cont.tran.mean=63.3036742475193

weightedLogRatios:
wLogRatio
Lung	0.112599978663705
cerebhem	-1.66008715633852
cortex	0.079538727338362
heart	0.572348660036433
kidney	-0.0358060683847955
liver	0.0127153780495226
stomach	0.252757092333484
testicle	-0.211167495982821

cont.weightedLogRatios:
wLogRatio
Lung	-0.275757928100535
cerebhem	-0.683378017213922
cortex	-0.146031446187067
heart	-0.504807993263943
kidney	-0.281913851895224
liver	0.066098581341998
stomach	0.0553989127921967
testicle	-0.158942077222672

varWeightedLogRatios=0.44517655556176
cont.varWeightedLogRatios=0.0665841692789914

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.38371379469293	0.0703623317876355	62.3019971527332	0	***
df.mm.trans1	-0.248149450437213	0.0602083156478859	-4.12151457430658	4.07860594778127e-05	***
df.mm.trans2	-0.249715470043571	0.0526463880198851	-4.74325930867756	2.41145678896079e-06	***
df.mm.exp2	0.407786692471978	0.0664784792605306	6.13411583730508	1.2357981118758e-09	***
df.mm.exp3	-0.195709773598748	0.0664784792605306	-2.94395683799801	0.00331607134873925	** 
df.mm.exp4	-0.252424470620723	0.0664784792605305	-3.79708551441838	0.000155305166085052	***
df.mm.exp5	-0.0708342492581125	0.0664784792605305	-1.06552150479423	0.286899528370015	   
df.mm.exp6	-0.144571307819111	0.0664784792605305	-2.17470840830358	0.0298878986503862	*  
df.mm.exp7	0.0368080154676645	0.0664784792605305	0.5536831750229	0.57992059045346	   
df.mm.exp8	-0.052738501031696	0.0664784792605305	-0.793316899218058	0.427783108733123	   
df.mm.trans1:exp2	-0.414536526818482	0.0607281093115143	-6.82610625488195	1.51816742124825e-11	***
df.mm.trans2:exp2	-0.00719107051193239	0.0416806518971779	-0.172527784106450	0.863057847992731	   
df.mm.trans1:exp3	0.0732353798731698	0.0607281093115143	1.20595521091390	0.228122599163278	   
df.mm.trans2:exp3	0.0806078126380729	0.0416806518971779	1.93393838553496	0.0534051664292965	.  
df.mm.trans1:exp4	0.183968682488055	0.0607281093115142	3.02938267918666	0.00251407310280007	** 
df.mm.trans2:exp4	0.0693909084377866	0.0416806518971779	1.66482301210085	0.0962641668917465	.  
df.mm.trans1:exp5	0.0252830824939752	0.0607281093115142	0.416332449348649	0.677256900988136	   
df.mm.trans2:exp5	0.0609471669603923	0.0416806518971779	1.4622412123194	0.143992159229768	   
df.mm.trans1:exp6	0.070905201210767	0.0607281093115142	1.16758453399311	0.243255232779228	   
df.mm.trans2:exp6	0.0948243351061038	0.0416806518971779	2.27502044209928	0.0231176029190296	*  
df.mm.trans1:exp7	-0.00133704822993765	0.0607281093115142	-0.0220169579638822	0.982438860464365	   
df.mm.trans2:exp7	-0.0346922629587016	0.0416806518971779	-0.83233494150437	0.405420440903135	   
df.mm.trans1:exp8	-0.0252722629824898	0.0607281093115142	-0.416154286194747	0.677387214997464	   
df.mm.trans2:exp8	0.0528858964338631	0.0416806518971779	1.26883563540051	0.204797634242908	   
df.mm.trans1:probe2	-0.00286290947701204	0.0448506811170611	-0.0638320178358004	0.949116856434438	   
df.mm.trans1:probe3	0.186772861619019	0.0448506811170611	4.16432609198372	3.39466223535145e-05	***
df.mm.trans1:probe4	0.00853547995840486	0.0448506811170611	0.190308814622616	0.849106122641122	   
df.mm.trans1:probe5	-0.115799120913725	0.0448506811170611	-2.58188098886362	0.00996893187447213	** 
df.mm.trans1:probe6	0.118784548479819	0.0448506811170611	2.64844469518286	0.00821484125911314	** 
df.mm.trans1:probe7	0.0508186675345578	0.0448506811170611	1.13306345118640	0.2574616568441	   
df.mm.trans1:probe8	0.223533892034459	0.0448506811170611	4.98395757805843	7.34936769822164e-07	***
df.mm.trans1:probe9	-0.222220763756659	0.0448506811170611	-4.95467980021661	8.51572873677526e-07	***
df.mm.trans1:probe10	-0.0442006318939795	0.0448506811170611	-0.985506368980552	0.324615948633966	   
df.mm.trans1:probe11	-0.116264916251067	0.0448506811170611	-2.59226645739477	0.00967493951052498	** 
df.mm.trans1:probe12	-0.0772094340171031	0.0448506811170611	-1.72147740221793	0.0854763098106048	.  
df.mm.trans1:probe13	-0.0123445609309340	0.0448506811170611	-0.275236866497399	0.783191639387281	   
df.mm.trans1:probe14	-0.139112290770648	0.0448506811170611	-3.10167621329011	0.00197871821252057	** 
df.mm.trans1:probe15	0.0880480162684385	0.0448506811170611	1.96313665869715	0.0499096501937292	*  
df.mm.trans1:probe16	-0.0598152015638366	0.0448506811170611	-1.33365202208898	0.182624363182028	   
df.mm.trans1:probe17	0.141613249574536	0.0448506811170611	3.15743810456128	0.00163972428918250	** 
df.mm.trans1:probe18	0.309842021234507	0.0448506811170611	6.90830135724837	8.76103516972215e-12	***
df.mm.trans1:probe19	0.185542734404123	0.0448506811170611	4.13689892289155	3.8189933562993e-05	***
df.mm.trans1:probe20	0.417316970523453	0.0448506811170611	9.30458490550562	8.37812162681115e-20	***
df.mm.trans1:probe21	0.332581843740975	0.0448506811170611	7.41531311136458	2.60088545270761e-13	***
df.mm.trans1:probe22	0.470021406468445	0.0448506811170611	10.4796938365703	1.92188830683557e-24	***
df.mm.trans2:probe2	0.288571020830065	0.0448506811170611	6.43403876246359	1.93107162991791e-10	***
df.mm.trans2:probe3	0.0103986351833309	0.0448506811170611	0.231850106271301	0.816702303617393	   
df.mm.trans2:probe4	0.0607759936235372	0.0448506811170611	1.35507403923055	0.175702762386224	   
df.mm.trans2:probe5	0.126092058907157	0.0448506811170611	2.81137444887525	0.00503032074784382	** 
df.mm.trans2:probe6	-0.0362893717848335	0.0448506811170611	-0.809115288352424	0.418643046760617	   
df.mm.trans3:probe2	-0.0565231933883096	0.0448506811170611	-1.26025273152002	0.207874873201167	   
df.mm.trans3:probe3	0.0726034534396227	0.0448506811170611	1.61878151304160	0.105812467710268	   
df.mm.trans3:probe4	0.0688736895968213	0.0448506811170611	1.53562193218559	0.124950321052032	   
df.mm.trans3:probe5	-0.00450736483955892	0.0448506811170611	-0.100497132424692	0.919969986433049	   
df.mm.trans3:probe6	0.373908063349228	0.0448506811170611	8.33673099352317	2.53083478494106e-16	***
df.mm.trans3:probe7	-0.0114961545619038	0.0448506811170611	-0.256320623802761	0.79775647225145	   
df.mm.trans3:probe8	0.189263091295936	0.0448506811170611	4.21984876443584	2.6689550862441e-05	***
df.mm.trans3:probe9	0.237617527792705	0.0448506811170611	5.2979692141691	1.44293790565832e-07	***
df.mm.trans3:probe10	0.0792592622871448	0.0448506811170611	1.76718079442933	0.077505690468942	.  
df.mm.trans3:probe11	0.126589579557343	0.0448506811170611	2.82246727149899	0.00486090838188475	** 
df.mm.trans3:probe12	-0.0271443766346614	0.0448506811170611	-0.605216597799576	0.545173651422551	   
df.mm.trans3:probe13	0.224542447088977	0.0448506811170611	5.00644452874455	6.55979215536328e-07	***
df.mm.trans3:probe14	0.0733155802281087	0.0448506811170611	1.63465923821209	0.102438088778566	   
df.mm.trans3:probe15	0.352677883534639	0.0448506811170611	7.86337854299567	9.72842305815013e-15	***
df.mm.trans3:probe16	0.227480814508274	0.0448506811170611	5.07195897236309	4.69855564779135e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14222637208701	0.110538173517313	37.4732659341277	4.03626049782275e-192	***
df.mm.trans1	-0.0494602751388755	0.094586365647431	-0.522911254707024	0.601152899168187	   
df.mm.trans2	0.0287968141313480	0.0827066901586747	0.348179984909331	0.727778965060752	   
df.mm.exp2	0.110658116641012	0.104436699139623	1.05957118094160	0.289597909091276	   
df.mm.exp3	0.0682326550485117	0.104436699139623	0.653339827959237	0.513688771291509	   
df.mm.exp4	0.147691479039422	0.104436699139623	1.4141722235205	0.157625421503408	   
df.mm.exp5	0.0753419052761921	0.104436699139623	0.721412165425361	0.470826125572251	   
df.mm.exp6	0.0861037782142546	0.104436699139623	0.824459016070025	0.40987718339031	   
df.mm.exp7	0.0406327949493787	0.104436699139623	0.389066250505067	0.697310720819413	   
df.mm.exp8	0.101396911973966	0.104436699139623	0.97089349634085	0.331838220509455	   
df.mm.trans1:exp2	-0.114418926219899	0.095402953738297	-1.19932268065583	0.230689171677946	   
df.mm.trans2:exp2	-0.0173633246249318	0.0654796823054488	-0.265171180030099	0.79093274191824	   
df.mm.trans1:exp3	-0.0330106077476604	0.095402953738297	-0.34601242890459	0.729406817239313	   
df.mm.trans2:exp3	-0.0646310148889366	0.0654796823054488	-0.987039225197316	0.323864332717962	   
df.mm.trans1:exp4	-0.135660072557593	0.095402953738297	-1.42196931270836	0.155349802138524	   
df.mm.trans2:exp4	-0.0812758533214012	0.0654796823054488	-1.24123774672984	0.214811658652998	   
df.mm.trans1:exp5	-0.0654979931877014	0.095402953738297	-0.686540517051192	0.492532926425756	   
df.mm.trans2:exp5	-0.0641790185661715	0.0654796823054488	-0.980136376758671	0.327258016728944	   
df.mm.trans1:exp6	-0.0321742186031483	0.095402953738297	-0.337245518534011	0.736003239183545	   
df.mm.trans2:exp6	-0.115015047641642	0.0654796823054488	-1.75649978118589	0.0793119450560115	.  
df.mm.trans1:exp7	0.00907159498564915	0.095402953738297	0.0950871501372352	0.924264827426065	   
df.mm.trans2:exp7	-0.0711922645309943	0.0654796823054488	-1.08724205775614	0.277194212408531	   
df.mm.trans1:exp8	-0.0414322851104396	0.095402953738297	-0.434287236264129	0.664174408341611	   
df.mm.trans2:exp8	-0.0701777679661528	0.0654796823054488	-1.07174875465627	0.284093826343708	   
df.mm.trans1:probe2	0.0365811041348359	0.0704597509168757	0.519177312704272	0.603753021583065	   
df.mm.trans1:probe3	-0.0271890023386323	0.0704597509168757	-0.385879909946151	0.699668432126856	   
df.mm.trans1:probe4	-0.0439649279732061	0.0704597509168757	-0.623972230970179	0.532789423725112	   
df.mm.trans1:probe5	-0.0933191952557275	0.0704597509168757	-1.32443265894908	0.185664747820873	   
df.mm.trans1:probe6	0.0299769560821522	0.0704597509168757	0.425447942861978	0.670602533903577	   
df.mm.trans1:probe7	0.129569414737335	0.0704597509168757	1.83891389128231	0.0662269143870152	.  
df.mm.trans1:probe8	-0.0629047939325858	0.0704597509168757	-0.892776274596787	0.372193730291634	   
df.mm.trans1:probe9	0.0221936415109671	0.0704597509168757	0.314983252455006	0.752840678798521	   
df.mm.trans1:probe10	-0.0215784171341133	0.0704597509168757	-0.306251680616502	0.759477353065256	   
df.mm.trans1:probe11	0.0458121642084909	0.0704597509168757	0.650189130849149	0.515720755244476	   
df.mm.trans1:probe12	-0.0382758048708490	0.0704597509168757	-0.543229352542058	0.587094062859123	   
df.mm.trans1:probe13	0.0328611225665661	0.0704597509168757	0.466381475082614	0.641044968801106	   
df.mm.trans1:probe14	0.0202616182909528	0.0704597509168757	0.287563013313178	0.77374138202704	   
df.mm.trans1:probe15	-0.0726188647495163	0.0704597509168757	-1.03064322261354	0.302959625538946	   
df.mm.trans1:probe16	-0.038008567244931	0.0704597509168757	-0.539436582592682	0.589706819116769	   
df.mm.trans1:probe17	0.0402076947748564	0.0704597509168757	0.570647699596484	0.568367842465047	   
df.mm.trans1:probe18	-0.0821918338278206	0.0704597509168757	-1.16650758423466	0.243689920113105	   
df.mm.trans1:probe19	0.0394909573077007	0.0704597509168757	0.560475403245319	0.575281895009469	   
df.mm.trans1:probe20	0.0587178201575789	0.0704597509168757	0.833352650179685	0.404846674456218	   
df.mm.trans1:probe21	-0.0252748376187626	0.0704597509168757	-0.358713127563854	0.719886111510556	   
df.mm.trans1:probe22	-0.0241871058793242	0.0704597509168757	-0.343275495081707	0.731464022219814	   
df.mm.trans2:probe2	-0.130427437974266	0.0704597509168757	-1.85109138589117	0.064453786197395	.  
df.mm.trans2:probe3	-0.083885709785225	0.0704597509168757	-1.19054791840222	0.234116209177199	   
df.mm.trans2:probe4	-0.0294470640005479	0.0704597509168757	-0.417927449605773	0.676090700119125	   
df.mm.trans2:probe5	0.0217670283622489	0.0704597509168757	0.308928545431965	0.757440815574625	   
df.mm.trans2:probe6	-0.0714378356493025	0.0704597509168757	-1.01388146735830	0.310886786194738	   
df.mm.trans3:probe2	0.0532798111203839	0.0704597509168757	0.756173708068317	0.449724764136159	   
df.mm.trans3:probe3	-0.0292916013581952	0.0704597509168757	-0.415721046087031	0.677704140476559	   
df.mm.trans3:probe4	-0.0191851178293431	0.0704597509168757	-0.272284780739243	0.785459777111954	   
df.mm.trans3:probe5	0.0449033578437424	0.0704597509168757	0.637290896709481	0.524082577738223	   
df.mm.trans3:probe6	0.0333086018650464	0.0704597509168757	0.472732324931747	0.636508255100773	   
df.mm.trans3:probe7	0.0865892771306553	0.0704597509168757	1.22891829738099	0.219394171368507	   
df.mm.trans3:probe8	-0.082269010979581	0.0704597509168757	-1.16760292094471	0.243247816027797	   
df.mm.trans3:probe9	-0.0331410036916254	0.0704597509168757	-0.470353687890882	0.638205841944645	   
df.mm.trans3:probe10	-0.0151184945665636	0.0704597509168757	-0.214569230941499	0.830147328945936	   
df.mm.trans3:probe11	0.129714293206492	0.0704597509168757	1.84097007892522	0.06592472560173	.  
df.mm.trans3:probe12	0.043515232557935	0.0704597509168757	0.617589928883962	0.53698756838162	   
df.mm.trans3:probe13	-0.0486832694843396	0.0704597509168757	-0.690937291869983	0.489766755403432	   
df.mm.trans3:probe14	0.0197140162272248	0.0704597509168757	0.279791171139424	0.779696112687517	   
df.mm.trans3:probe15	-0.000528085834566064	0.0704597509168757	-0.00749485809549723	0.994021532864153	   
df.mm.trans3:probe16	0.03963755001191	0.0704597509168757	0.562555920168836	0.573864545286183