chr5.18457_chr5_63887156_63892794_+_2.R 

fitVsDatCorrelation=0.882845107470959
cont.fitVsDatCorrelation=0.259288044691971

fstatistic=11795.0146019438,68,1060
cont.fstatistic=2777.42282141193,68,1060

residuals=-0.663803077870363,-0.0909350138704789,-0.00604541630516782,0.0865452407819761,0.731200175610144
cont.residuals=-0.631978779881532,-0.234319997031529,-0.0547679167031482,0.197792895368234,1.34148148050587

predictedValues:
Include	Exclude	Both
chr5.18457_chr5_63887156_63892794_+_2.R.tl.Lung	53.6182787089439	101.777513085954	98.9311247845646
chr5.18457_chr5_63887156_63892794_+_2.R.tl.cerebhem	57.4010267242448	99.1986983362832	58.5753452753507
chr5.18457_chr5_63887156_63892794_+_2.R.tl.cortex	50.7909145053207	73.0529958963412	66.5182913900276
chr5.18457_chr5_63887156_63892794_+_2.R.tl.heart	53.1590048367814	98.1085563305826	76.6176085661584
chr5.18457_chr5_63887156_63892794_+_2.R.tl.kidney	54.8664523884147	97.973776877309	67.3201119994758
chr5.18457_chr5_63887156_63892794_+_2.R.tl.liver	55.6466801094683	95.566266113095	66.1886880292121
chr5.18457_chr5_63887156_63892794_+_2.R.tl.stomach	53.1941651269271	114.539738804123	101.566545471510
chr5.18457_chr5_63887156_63892794_+_2.R.tl.testicle	53.6991695924953	95.065542946598	71.1877426991994


diffExp=-48.1592343770101,-41.7976716120384,-22.2620813910205,-44.9495514938012,-43.1073244888943,-39.9195860036266,-61.3455736771961,-41.3663733541027
diffExpScore=0.99709224049708
diffExp1.5=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	71.9587590222023	70.6334113467116	66.5343731027014
cerebhem	67.9797205583454	66.1996769228178	66.5596242921206
cortex	63.3286915164866	76.5131387425165	67.3396823400425
heart	70.2907364342271	69.8300806322061	68.6410758390063
kidney	67.4357216748547	70.5220137084194	65.4695571893512
liver	76.6364413391773	71.7729767634754	68.2432615469906
stomach	70.4249034326011	69.6062089390827	67.0392710593405
testicle	67.2827228082436	71.3943375552271	71.8943381984225
cont.diffExp=1.32534767549076,1.78004363552758,-13.1844472260299,0.460655802021023,-3.08629203356470,4.86346457570193,0.818694493518436,-4.11161474698356
cont.diffExpScore=2.44191521463537

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

tran.correlation=0.423681820198091
cont.tran.correlation=-0.308696169238106

tran.covariance=0.00222331878169344
cont.tran.covariance=-0.00071762448023968

tran.mean=75.4786737739301
cont.tran.mean=70.1130963372872

weightedLogRatios:
wLogRatio
Lung	-2.75736582408525
cerebhem	-2.36527676621563
cortex	-1.49365264956683
heart	-2.62253320908283
kidney	-2.49011601125842
liver	-2.31970812412796
stomach	-3.34203569897318
testicle	-2.43831039223624

cont.weightedLogRatios:
wLogRatio
Lung	0.0793192774364423
cerebhem	0.111599851838834
cortex	-0.802434248713496
heart	0.0279401155633405
kidney	-0.189451525188461
liver	0.282340472910491
stomach	0.0496807768160657
testicle	-0.251410567499445

varWeightedLogRatios=0.264844898907140
cont.varWeightedLogRatios=0.112111565889404

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0932919994415	0.0712882339554039	57.4189003195417	0	***
df.mm.trans1	-0.160333720650362	0.0608416169596152	-2.63526396342798	0.00852987922331707	** 
df.mm.trans2	0.518604164187285	0.0530398622413254	9.77763030054063	1.12108960734198e-21	***
df.mm.exp2	0.566617885467573	0.0666010253110742	8.50764508235517	5.98161543610761e-17	***
df.mm.exp3	0.0111703523608792	0.0666010253110742	0.167720426355385	0.86683521837027	   
df.mm.exp4	0.210279851097782	0.0666010253110742	3.15730651466137	0.00163730003765937	** 
df.mm.exp5	0.369887602588803	0.0666010253110742	5.55378240591889	3.53354228277811e-08	***
df.mm.exp6	0.37607742102585	0.0666010253110742	5.64672119189308	2.09924699694765e-08	***
df.mm.exp7	0.0839010162620995	0.0666010253110742	1.25975562493553	0.208034823164005	   
df.mm.exp8	0.262388148023885	0.0666010253110742	3.93970133039763	8.69215826283494e-05	***
df.mm.trans1:exp2	-0.498445725751733	0.0606147363050484	-8.22317733501743	5.75102552104866e-16	***
df.mm.trans2:exp2	-0.592282179523555	0.0405381970111538	-14.6104716833014	3.49815140448366e-44	***
df.mm.trans1:exp3	-0.0653428927238402	0.0606147363050484	-1.07800341479664	0.281277486384978	   
df.mm.trans2:exp3	-0.342774390070015	0.0405381970111538	-8.45559041453429	9.0950190727175e-17	***
df.mm.trans1:exp4	-0.21888236821047	0.0606147363050484	-3.61104215827859	0.000319244132423371	***
df.mm.trans2:exp4	-0.246994454485387	0.0405381970111538	-6.09288208889577	1.54971999862110e-09	***
df.mm.trans1:exp5	-0.346875539089181	0.0606147363050484	-5.72262720641895	1.36425224404879e-08	***
df.mm.trans2:exp5	-0.407976929260192	0.0405381970111538	-10.0640126927189	8.11648300618654e-23	***
df.mm.trans1:exp6	-0.33894503232882	0.0606147363050484	-5.5917925737242	2.85840034875405e-08	***
df.mm.trans2:exp6	-0.439046714816429	0.0405381970111538	-10.8304450416388	5.35265548127637e-26	***
df.mm.trans1:exp7	-0.0918423346265302	0.0606147363050484	-1.51518162455292	0.130024539292020	   
df.mm.trans2:exp7	0.0342316236602854	0.0405381970111538	0.844428864235545	0.398620336239129	   
df.mm.trans1:exp8	-0.260880641087206	0.0606147363050484	-4.30391447674215	1.83333247214416e-05	***
df.mm.trans2:exp8	-0.330610755209715	0.0405381970111538	-8.15553674276016	9.75605275713499e-16	***
df.mm.trans1:probe2	-0.103310261476321	0.0457631223440711	-2.25750027936425	0.0241796662728956	*  
df.mm.trans1:probe3	-0.0701527999740744	0.0457631223440711	-1.53295484181846	0.125585370876929	   
df.mm.trans1:probe4	-0.108601226626373	0.0457631223440711	-2.37311662892781	0.0178162504345786	*  
df.mm.trans1:probe5	0.162271057005356	0.0457631223440711	3.5458912917986	0.000408376835164603	***
df.mm.trans1:probe6	0.239163381839718	0.0457631223440711	5.22611591144422	2.08392284234241e-07	***
df.mm.trans1:probe7	-0.041739392792419	0.0457631223440711	-0.912074846611218	0.361936697775600	   
df.mm.trans1:probe8	0.189703091509947	0.0457631223440711	4.14532666900784	3.66409338010474e-05	***
df.mm.trans1:probe9	0.40440226647086	0.0457631223440711	8.83685915113817	4.0186617294419e-18	***
df.mm.trans1:probe10	-0.0253820409012727	0.045763122344071	-0.554639622498598	0.579258185942422	   
df.mm.trans1:probe11	0.110223214276741	0.0457631223440711	2.40855974485364	0.0161854308946063	*  
df.mm.trans1:probe12	0.061397961983262	0.0457631223440711	1.34164713503681	0.179997777806491	   
df.mm.trans1:probe13	0.124263112501698	0.0457631223440710	2.71535476900862	0.00672808248317066	** 
df.mm.trans1:probe14	0.153802338154269	0.0457631223440711	3.36083576198981	0.000804783952360093	***
df.mm.trans1:probe15	0.108516076781348	0.0457631223440711	2.3712559638188	0.0179057132965305	*  
df.mm.trans1:probe16	0.534662281834203	0.0457631223440711	11.6832561776343	9.4651292048629e-30	***
df.mm.trans1:probe17	0.0989495971603326	0.0457631223440711	2.16221254346191	0.0308249659579214	*  
df.mm.trans1:probe18	0.0652873148912157	0.0457631223440711	1.42663593625346	0.153979326107681	   
df.mm.trans1:probe19	-0.0284938106442818	0.0457631223440711	-0.622636944001558	0.533657047204125	   
df.mm.trans1:probe20	0.125691232060128	0.0457631223440711	2.74656154610946	0.00612436385229765	** 
df.mm.trans1:probe21	0.0101034250059293	0.0457631223440711	0.220776566117288	0.82530892749952	   
df.mm.trans1:probe22	-0.00455499359853443	0.0457631223440711	-0.099534152505758	0.920732992971794	   
df.mm.trans2:probe2	0.0117326133594312	0.0457631223440711	0.256377029329846	0.797709470943808	   
df.mm.trans2:probe3	0.0471173061766404	0.0457631223440711	1.02959115906445	0.303436836018736	   
df.mm.trans2:probe4	0.0840405218750951	0.0457631223440711	1.83642456131456	0.0665747968308153	.  
df.mm.trans2:probe5	0.0919332123519934	0.0457631223440711	2.00889291733181	0.0448016883783592	*  
df.mm.trans2:probe6	0.0375019217839846	0.0457631223440711	0.819479088468342	0.41269734012779	   
df.mm.trans3:probe2	-0.126737985651196	0.0457631223440711	-2.76943484533929	0.00571337349636798	** 
df.mm.trans3:probe3	-0.0149416938275254	0.0457631223440711	-0.326500751307701	0.744109974367275	   
df.mm.trans3:probe4	0.209485088821578	0.0457631223440711	4.57759606625089	5.26003537750081e-06	***
df.mm.trans3:probe5	-0.119802088846639	0.0457631223440711	-2.61787401536775	0.00897404357841432	** 
df.mm.trans3:probe6	-0.0594353189843944	0.0457631223440710	-1.29876013567275	0.194308713507987	   
df.mm.trans3:probe7	0.00788876918079896	0.0457631223440711	0.172382669204410	0.86316960257728	   
df.mm.trans3:probe8	0.367937398866969	0.0457631223440711	8.04004141370913	2.38475886512559e-15	***
df.mm.trans3:probe9	0.117959900855928	0.0457631223440711	2.57761915738711	0.0100824975048976	*  
df.mm.trans3:probe10	0.394106017281658	0.0457631223440711	8.6118690573288	2.56808883658263e-17	***
df.mm.trans3:probe11	0.130610921530274	0.0457631223440711	2.85406490729091	0.00440041617649143	** 
df.mm.trans3:probe12	0.0903527760150225	0.0457631223440710	1.97435776640639	0.0486003376599138	*  
df.mm.trans3:probe13	0.0739726475413995	0.0457631223440711	1.61642483625209	0.106300041867613	   
df.mm.trans3:probe14	0.315342862351179	0.0457631223440710	6.89076370227247	9.50456995319452e-12	***
df.mm.trans3:probe15	0.514385020124482	0.0457631223440711	11.2401644332104	8.97730348622884e-28	***
df.mm.trans3:probe16	-0.107071602216768	0.0457631223440711	-2.33969180275218	0.0194845347105344	*  
df.mm.trans3:probe17	0.253352507157142	0.0457631223440711	5.53617179466702	3.89660665815274e-08	***
df.mm.trans3:probe18	0.599063188162774	0.0457631223440711	13.090522619036	2.10198688406413e-36	***
df.mm.trans3:probe19	0.0678992516732021	0.0457631223440711	1.48371107991059	0.138182855302505	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26185703112949	0.146594461061709	29.0724287961702	4.05535686081525e-137	***
df.mm.trans1	0.0459791320436493	0.125112428144836	0.367502515341015	0.713317577399397	   
df.mm.trans2	-0.000466254175266272	0.109069191206482	-0.00427484764587271	0.996589979807018	   
df.mm.exp2	-0.122090877378177	0.136955860314086	-0.891461505175325	0.372883939818494	   
df.mm.exp3	-0.0598264749079077	0.136955860314086	-0.436830339137774	0.662323363257042	   
df.mm.exp4	-0.0660639806913332	0.136955860314086	-0.482374252111784	0.629639699763002	   
df.mm.exp5	-0.0503632156631359	0.136955860314086	-0.367733191903115	0.713145600788797	   
df.mm.exp6	0.0536242970970554	0.136955860314086	0.391544377685459	0.695473588712783	   
df.mm.exp7	-0.0437556098423431	0.136955860314086	-0.319486948145167	0.749420240191444	   
df.mm.exp8	-0.133953212982167	0.136955860314086	-0.978075802488242	0.328259999014534	   
df.mm.trans1:exp2	0.0652071474424724	0.124645879242778	0.523139215179875	0.600986712130537	   
df.mm.trans2:exp2	0.0572631788998417	0.083361233874598	0.686928158788818	0.49227825078081	   
df.mm.trans1:exp3	-0.0679281996807572	0.124645879242778	-0.544969477478277	0.585889147637134	   
df.mm.trans2:exp3	0.139785668172127	0.083361233874598	1.67686659223889	0.0938634174034807	.  
df.mm.trans1:exp4	0.0426108353580448	0.124645879242778	0.341855146892179	0.732527667375402	   
df.mm.trans2:exp4	0.0546255711521068	0.083361233874598	0.655287459327692	0.512424874877573	   
df.mm.trans1:exp5	-0.0145550751906706	0.124645879242778	-0.116771410969159	0.90706331920475	   
df.mm.trans2:exp5	0.0487848468349424	0.083361233874598	0.585222225816983	0.558522858493436	   
df.mm.trans1:exp6	0.00935523847151833	0.124645879242778	0.0750545347214947	0.940185483498316	   
df.mm.trans2:exp6	-0.0376195411733016	0.083361233874598	-0.451283401465643	0.651877614883947	   
df.mm.trans1:exp7	0.0222093888446514	0.124645879242778	0.178179888333037	0.858615749297908	   
df.mm.trans2:exp7	0.0291061010092897	0.083361233874598	0.349156312310283	0.727041308681989	   
df.mm.trans1:exp8	0.0667635340793444	0.124645879242778	0.535625682011567	0.592329636436851	   
df.mm.trans2:exp8	0.144668492144206	0.083361233874598	1.73544086885559	0.082953366580028	.  
df.mm.trans1:probe2	0.0351632328849181	0.0941055751321726	0.373657276261591	0.708734034324959	   
df.mm.trans1:probe3	-0.166920256745732	0.0941055751321726	-1.77375523725656	0.0763906906663871	.  
df.mm.trans1:probe4	-0.137674680084833	0.0941055751321726	-1.46298112403507	0.143768981994577	   
df.mm.trans1:probe5	-0.0484902600268868	0.0941055751321726	-0.515275104145334	0.60646823180622	   
df.mm.trans1:probe6	-0.0208902253482221	0.0941055751321726	-0.221987117329462	0.824366672148085	   
df.mm.trans1:probe7	-0.081678580931247	0.0941055751321726	-0.867946248843687	0.385620210213275	   
df.mm.trans1:probe8	-0.201162303801001	0.0941055751321726	-2.13762365851827	0.0327755576625381	*  
df.mm.trans1:probe9	-0.0701039537891752	0.0941055751321726	-0.744950059448796	0.456467042724594	   
df.mm.trans1:probe10	-0.145295568233986	0.0941055751321726	-1.54396344775447	0.122895662382759	   
df.mm.trans1:probe11	-0.109254718395057	0.0941055751321726	-1.16098029518025	0.24591129557647	   
df.mm.trans1:probe12	-0.0697294008209027	0.0941055751321726	-0.740969923651885	0.458875786120846	   
df.mm.trans1:probe13	-0.0592401645406346	0.0941055751321726	-0.629507491532048	0.529152517167437	   
df.mm.trans1:probe14	-0.0930258963980793	0.0941055751321726	-0.988526941867399	0.32312023406247	   
df.mm.trans1:probe15	-0.0684570924056434	0.0941055751321726	-0.727449912606075	0.467111025096022	   
df.mm.trans1:probe16	-0.0738277293455978	0.0941055751321726	-0.784520250175462	0.432910189498375	   
df.mm.trans1:probe17	0.0875914828995175	0.0941055751321726	0.930778891436496	0.352179880169487	   
df.mm.trans1:probe18	0.0861674503242005	0.0941055751321726	0.915646604392748	0.360060531501562	   
df.mm.trans1:probe19	-0.123387031471385	0.0941055751321726	-1.31115538370693	0.190089225896377	   
df.mm.trans1:probe20	-0.0676238949232482	0.0941055751321726	-0.718596053722317	0.472548209903753	   
df.mm.trans1:probe21	-0.0823710833852341	0.0941055751321726	-0.875305031285796	0.381606247540273	   
df.mm.trans1:probe22	0.10874768999427	0.0941055751321726	1.15559242735122	0.248108313635477	   
df.mm.trans2:probe2	-0.0697974427496292	0.0941055751321726	-0.74169296188454	0.458437680169332	   
df.mm.trans2:probe3	-0.0301518195055349	0.0941055751321726	-0.320404178638579	0.748725107208476	   
df.mm.trans2:probe4	0.0406344793561475	0.0941055751321726	0.431796727229772	0.665976970778915	   
df.mm.trans2:probe5	0.0239008174769407	0.0941055751321726	0.253978762080479	0.799561221573621	   
df.mm.trans2:probe6	-0.0617734311981366	0.0941055751321726	-0.656426902565283	0.511691956568462	   
df.mm.trans3:probe2	-0.0779455930360932	0.0941055751321726	-0.82827816446175	0.407699434104927	   
df.mm.trans3:probe3	-0.0424575508187916	0.0941055751321726	-0.451169346334256	0.651959782878269	   
df.mm.trans3:probe4	-0.0495487056976179	0.0941055751321726	-0.526522532039425	0.598635353604026	   
df.mm.trans3:probe5	-0.176540300720686	0.0941055751321726	-1.87598131643883	0.060932479973271	.  
df.mm.trans3:probe6	-0.213861750117286	0.0941055751321726	-2.27257258474766	0.0232515059041429	*  
df.mm.trans3:probe7	0.0294081279213671	0.0941055751321726	0.31250144191843	0.754720932935405	   
df.mm.trans3:probe8	-0.130178264825235	0.0941055751321726	-1.38332149442154	0.166857579720263	   
df.mm.trans3:probe9	-0.181310550814797	0.0941055751321726	-1.92667172545456	0.054287642994558	.  
df.mm.trans3:probe10	-0.160387855471629	0.0941055751321726	-1.70433957017278	0.0886107213828015	.  
df.mm.trans3:probe11	-0.0710628215377719	0.0941055751321726	-0.755139336197278	0.450333131587159	   
df.mm.trans3:probe12	-0.0721744821433902	0.0941055751321726	-0.766952245305554	0.443280689887565	   
df.mm.trans3:probe13	-0.0672151900373882	0.0941055751321726	-0.714253007252583	0.475227994672025	   
df.mm.trans3:probe14	-0.174320930861880	0.0941055751321726	-1.85239748672748	0.064246612150268	.  
df.mm.trans3:probe15	-0.0676531524489107	0.0941055751321726	-0.718906954809967	0.472356695111353	   
df.mm.trans3:probe16	-0.218978906930459	0.0941055751321726	-2.32694935048109	0.0201556293007486	*  
df.mm.trans3:probe17	-0.0517478872708987	0.0941055751321726	-0.549891833700799	0.582509406595325	   
df.mm.trans3:probe18	-0.181010094856639	0.0941055751321726	-1.92347897138303	0.0546875140424031	.  
df.mm.trans3:probe19	-0.176387006509638	0.0941055751321726	-1.87435235650916	0.0611567382702546	.