chr4.17100_chr4_19093795_19095579_-_0.R 

fitVsDatCorrelation=0.766287575345617
cont.fitVsDatCorrelation=0.340798699402690

fstatistic=5500.34580632228,38,370
cont.fstatistic=2563.73661062126,38,370

residuals=-0.618792269736178,-0.095229581957679,-0.002129646259282,0.086915320875227,1.06025017744526
cont.residuals=-0.577163135247089,-0.178228745056713,-0.0502855309002966,0.140396078149040,1.3950205157243

predictedValues:
Include	Exclude	Both
chr4.17100_chr4_19093795_19095579_-_0.R.tl.Lung	95.7363058817574	54.2116275294163	66.4622010504495
chr4.17100_chr4_19093795_19095579_-_0.R.tl.cerebhem	94.0283394518626	98.3854507312644	62.8109669875986
chr4.17100_chr4_19093795_19095579_-_0.R.tl.cortex	70.8273699844747	58.9398837100069	58.1147490250969
chr4.17100_chr4_19093795_19095579_-_0.R.tl.heart	76.9267611576366	57.2698715242657	62.7579355627745
chr4.17100_chr4_19093795_19095579_-_0.R.tl.kidney	80.1248359633053	53.0081660590047	62.3485315588863
chr4.17100_chr4_19093795_19095579_-_0.R.tl.liver	76.7906309868485	57.1092590688831	61.8015876474457
chr4.17100_chr4_19093795_19095579_-_0.R.tl.stomach	84.3875715009038	61.8353773593563	67.5030302065955
chr4.17100_chr4_19093795_19095579_-_0.R.tl.testicle	76.6207314287493	58.9576202941642	62.2741422826023


diffExp=41.5246783523411,-4.35711127940176,11.8874862744678,19.6568896333709,27.1166699043006,19.6813719179655,22.5521941415475,17.6631111345851
diffExpScore=1.04922130024393
diffExp1.5=1,0,0,0,1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,0,0,1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,0,1,1,1,1,0
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	64.1826687499071	70.656842709563	74.2594726705542
cerebhem	70.1975811435692	80.051437297368	70.5602999673991
cortex	77.1923138844347	85.2999886845489	66.6222097842212
heart	73.2022089314883	63.5672258567111	60.9014252884764
kidney	71.6403442743251	60.2966742697663	70.0056740445287
liver	69.4726855778831	70.2107678338177	70.3703845189284
stomach	63.7418585031838	68.8512178409517	61.1204403047419
testicle	65.1504340022704	66.793663656865	63.8855519475616
cont.diffExp=-6.47417395965599,-9.8538561537988,-8.10767480011414,9.63498307477725,11.3436700045588,-0.738082255934586,-5.1093593377679,-1.64322965459471
cont.diffExpScore=4.42804280579282

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=0.488332453801848
cont.tran.correlation=0.356447267842532

tran.covariance=0.00952903759199905
cont.tran.covariance=0.00234682763704533

tran.mean=72.1974876644937
cont.tran.mean=70.0317445760408

weightedLogRatios:
wLogRatio
Lung	2.43247942494128
cerebhem	-0.206835876237556
cortex	0.765846373302768
heart	1.23794968861836
kidney	1.72569189779775
liver	1.24162322761842
stomach	1.33082618790495
testicle	1.10266028233578

cont.weightedLogRatios:
wLogRatio
Lung	-0.404567580028407
cerebhem	-0.567060768035542
cortex	-0.439071678889995
heart	0.595934026912643
kidney	0.721496916783042
liver	-0.0448741352298638
stomach	-0.323337766278877
testicle	-0.104348697062980

varWeightedLogRatios=0.570383275179693
cont.varWeightedLogRatios=0.233020170300963

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0611496145583	0.0987569661652124	41.122664782597	4.68035886146498e-140	***
df.mm.trans1	0.519804836444411	0.0813302933393873	6.39128195782218	4.95455490963855e-10	***
df.mm.trans2	-0.0557571467489412	0.0813302933393873	-0.685564313856209	0.493417195710085	   
df.mm.exp2	0.634499830260155	0.111176254270170	5.70715243489186	2.35781128880676e-08	***
df.mm.exp3	-0.0835156085326852	0.111176254270170	-0.75120005689105	0.453009776261976	   
df.mm.exp4	-0.106516167694888	0.111176254270170	-0.958083795808075	0.338646115627886	   
df.mm.exp5	-0.136567907038916	0.111176254270170	-1.22839097193401	0.220081012939996	   
df.mm.exp6	-0.0957397880496742	0.111176254270170	-0.86115320828328	0.389711376581658	   
df.mm.exp7	-0.0101363383218339	0.111176254270170	-0.0911735908749143	0.927403997866855	   
df.mm.exp8	-0.0737193498929347	0.111176254270170	-0.663085389743292	0.5076887478161	   
df.mm.trans1:exp2	-0.652501208217543	0.092182480252825	-7.0783646353185	7.37072851520656e-12	***
df.mm.trans2:exp2	-0.0385023110565386	0.092182480252825	-0.417674930756256	0.676427074193142	   
df.mm.trans1:exp3	-0.217836482090292	0.092182480252825	-2.36310068347957	0.0186392502465659	*  
df.mm.trans2:exp3	0.167138197316643	0.092182480252825	1.81312324053595	0.07062298570238	.  
df.mm.trans1:exp4	-0.112227615202986	0.092182480252825	-1.21745059251155	0.224208705641112	   
df.mm.trans2:exp4	0.161395435263858	0.092182480252825	1.75082548029958	0.0808046966443292	.  
df.mm.trans1:exp5	-0.0414438233030038	0.092182480252825	-0.449584597738503	0.653273249677087	   
df.mm.trans2:exp5	0.114118469807105	0.092182480252825	1.23796267462231	0.216514847075831	   
df.mm.trans1:exp6	-0.124775169917932	0.092182480252825	-1.35356707235168	0.176700825122243	   
df.mm.trans2:exp6	0.147810631387484	0.092182480252825	1.60345687143685	0.109687079411826	   
df.mm.trans1:exp7	-0.116041126338759	0.092182480252825	-1.25881974557962	0.208889096110168	   
df.mm.trans2:exp7	0.141716572679125	0.092182480252825	1.53734822810631	0.12506275174345	   
df.mm.trans1:exp8	-0.149010563015232	0.092182480252825	-1.61647378771484	0.106844170867706	   
df.mm.trans2:exp8	0.157642820098276	0.092182480252825	1.7101169296586	0.0880825712167173	.  
df.mm.trans1:probe2	-0.0353253225219332	0.053822972660261	-0.656324256649891	0.512023468905558	   
df.mm.trans1:probe3	0.123276533147736	0.053822972660261	2.29040736798164	0.0225596438313926	*  
df.mm.trans1:probe4	-0.220849973434391	0.053822972660261	-4.10326599440040	5.01402476729225e-05	***
df.mm.trans1:probe5	-0.0329543326439712	0.053822972660261	-0.612272622918545	0.540733479909996	   
df.mm.trans1:probe6	-0.047072284048282	0.053822972660261	-0.874576072663426	0.382371939232848	   
df.mm.trans2:probe2	-0.0431541162726596	0.053822972660261	-0.801778759881122	0.423195571104589	   
df.mm.trans2:probe3	0.0204232714539517	0.053822972660261	0.379452684318768	0.704569417266671	   
df.mm.trans2:probe4	0.0629512525599487	0.053822972660261	1.16959821147945	0.242915576647862	   
df.mm.trans2:probe5	-0.108768040787462	0.053822972660261	-2.02084789099299	0.0440153305008738	*  
df.mm.trans2:probe6	-0.0689199423529084	0.053822972660261	-1.28049304871996	0.2011739172887	   
df.mm.trans3:probe2	-0.387042340715897	0.053822972660261	-7.19102497662045	3.5880826830576e-12	***
df.mm.trans3:probe3	-0.397272557067978	0.053822972660261	-7.38109653615054	1.04562552311282e-12	***
df.mm.trans3:probe4	-0.409679756200525	0.053822972660261	-7.6116151886758	2.27355282240502e-13	***
df.mm.trans3:probe5	-0.448824963274955	0.053822972660261	-8.33891071212302	1.49627986667313e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20342882357447	0.144506356168167	29.0881933157515	1.23677418381484e-97	***
df.mm.trans1	-0.00973317117160054	0.119006737376901	-0.0817867238959338	0.934860537303362	   
df.mm.trans2	0.0859845402805026	0.119006737376901	0.72251825548485	0.470432326059181	   
df.mm.exp2	0.265512757579012	0.162678907836549	1.63212773622617	0.103503322619230	   
df.mm.exp3	0.481433383073472	0.162678907836549	2.95940874865714	0.00328070643270022	** 
df.mm.exp4	0.224064212220980	0.162678907836549	1.37734027847118	0.169239683617064	   
df.mm.exp5	0.0103562078304313	0.162678907836549	0.0636604214286751	0.94927499760416	   
df.mm.exp6	0.126660021496032	0.162678907836549	0.778589081894334	0.436719446264549	   
df.mm.exp7	0.16194020313437	0.162678907836549	0.995459124283515	0.320163575033658	   
df.mm.exp8	0.109211126466675	0.162678907836549	0.671329356208888	0.502429632913759	   
df.mm.trans1:exp2	-0.175932120869568	0.13488622464966	-1.30430013388333	0.192942032578701	   
df.mm.trans2:exp2	-0.140678321381318	0.13488622464966	-1.04294060973759	0.297656746817836	   
df.mm.trans1:exp3	-0.296866709166091	0.13488622464966	-2.20086750842900	0.0283625494811373	*  
df.mm.trans2:exp3	-0.293094019309677	0.13488622464966	-2.17289808556011	0.0304219989257190	*  
df.mm.trans1:exp4	-0.092571832133707	0.13488622464966	-0.686295671586508	0.492956517517509	   
df.mm.trans2:exp4	-0.329801149493643	0.13488622464966	-2.44503210279801	0.0149495596468158	*  
df.mm.trans1:exp5	0.0995689578820977	0.13488622464966	0.738169951310507	0.460879108467534	   
df.mm.trans2:exp5	-0.168914216769215	0.13488622464966	-1.25227181061621	0.211261810742984	   
df.mm.trans1:exp6	-0.0474595765274739	0.13488622464966	-0.351848950111404	0.725151844608176	   
df.mm.trans2:exp6	-0.132993292359942	0.13488622464966	-0.985966452136722	0.324793878082712	   
df.mm.trans1:exp7	-0.168831954089764	0.13488622464966	-1.25166194345102	0.211483794739653	   
df.mm.trans2:exp7	-0.187827247873473	0.13488622464966	-1.39248650750895	0.16461136954602	   
df.mm.trans1:exp8	-0.0942453784172567	0.13488622464966	-0.698702767180564	0.485176736229586	   
df.mm.trans2:exp8	-0.165437863938357	0.13488622464966	-1.22649932836395	0.22079076899362	   
df.mm.trans1:probe2	-0.0528790584254296	0.0787565876037707	-0.671423940959293	0.502369462284123	   
df.mm.trans1:probe3	-0.0178842459299531	0.0787565876037707	-0.227082539684551	0.820484975432575	   
df.mm.trans1:probe4	-0.0718791991400987	0.0787565876037707	-0.912675387889168	0.362007581794416	   
df.mm.trans1:probe5	-0.0583012751605034	0.0787565876037707	-0.74027172753879	0.459604606308432	   
df.mm.trans1:probe6	-0.15064300984754	0.0787565876037708	-1.91276710216845	0.0565495990069623	.  
df.mm.trans2:probe2	-0.106239341320893	0.0787565876037708	-1.34895815770219	0.178175377810150	   
df.mm.trans2:probe3	-0.0870898436691197	0.0787565876037707	-1.10581027338658	0.269527137909035	   
df.mm.trans2:probe4	-0.0173776687976486	0.0787565876037708	-0.220650352261028	0.825486395503437	   
df.mm.trans2:probe5	-0.113861894626218	0.0787565876037707	-1.44574438901624	0.149095382385294	   
df.mm.trans2:probe6	-0.0227937151210339	0.0787565876037708	-0.289419790960350	0.772422256304754	   
df.mm.trans3:probe2	0.0220272589278909	0.0787565876037707	0.279687828003816	0.779873303187715	   
df.mm.trans3:probe3	0.04631708965973	0.0787565876037707	0.588104323320281	0.556821055021773	   
df.mm.trans3:probe4	-0.0178717114913666	0.0787565876037707	-0.226923385524019	0.820608640056778	   
df.mm.trans3:probe5	0.0889531374870193	0.0787565876037707	1.12946916814817	0.25943168091766