chr5.18662_chr5_142591122_142599041_+_2.R 

fitVsDatCorrelation=0.821162159363203
cont.fitVsDatCorrelation=0.226615745467690

fstatistic=11028.3015680199,64,968
cont.fstatistic=3776.34847281274,64,968

residuals=-0.522082266364726,-0.0851265530170151,-0.00284365493754893,0.0813429703380819,0.997149084867782
cont.residuals=-0.526175997610506,-0.186931919241445,-0.0519648466826774,0.135311017209421,1.01953860550081

predictedValues:
Include	Exclude	Both
chr5.18662_chr5_142591122_142599041_+_2.R.tl.Lung	65.8691897453173	50.6573456933868	87.228207603601
chr5.18662_chr5_142591122_142599041_+_2.R.tl.cerebhem	77.6580498396248	65.2002359620867	85.116332961271
chr5.18662_chr5_142591122_142599041_+_2.R.tl.cortex	75.6184664220459	54.4963756679818	88.6703316887157
chr5.18662_chr5_142591122_142599041_+_2.R.tl.heart	65.438319966034	49.5566926923264	75.2633540863653
chr5.18662_chr5_142591122_142599041_+_2.R.tl.kidney	62.6594837676064	49.7705449331446	90.3471300592474
chr5.18662_chr5_142591122_142599041_+_2.R.tl.liver	64.3233674145385	48.7436226344251	93.3534925090076
chr5.18662_chr5_142591122_142599041_+_2.R.tl.stomach	64.2212157320748	50.5792140293341	77.6023501860881
chr5.18662_chr5_142591122_142599041_+_2.R.tl.testicle	63.6100447511458	52.9492230690489	79.833371864354


diffExp=15.2118440519305,12.4578138775381,21.1220907540641,15.8816272737076,12.8889388344618,15.5797447801134,13.6420017027407,10.6608216820969
diffExpScore=0.991557254521785
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=1,0,1,1,0,1,0,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	63.90747052816	58.7850900975348	63.3478547037179
cerebhem	64.9212718150009	63.5309272255935	66.6998110711497
cortex	62.511531456277	65.769582643456	63.4529801969237
heart	61.9819816297674	73.9608063559333	55.8008046265385
kidney	63.5498038363218	63.4866522004388	60.2682749502784
liver	64.8952340753379	66.6116495949125	65.5264171105012
stomach	65.1607912677127	58.7968709029543	60.0569366332292
testicle	65.623101344525	64.6469251166013	58.5314618096011
cont.diffExp=5.12238043062514,1.39034458940739,-3.25805118717903,-11.9788247261659,0.0631516358829884,-1.71641551957460,6.3639203647584,0.976176227923716
cont.diffExpScore=7.64598262316596

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=0.843813079111983
cont.tran.correlation=-0.571497947162251

tran.covariance=0.0065989978781708
cont.tran.covariance=-0.000850164140006961

tran.mean=60.0844620200076
cont.tran.mean=64.258730630658

weightedLogRatios:
wLogRatio
Lung	1.06515019236127
cerebhem	0.745724928653228
cortex	1.36330395415553
heart	1.12366972234386
kidney	0.926364318343068
liver	1.11639711045134
stomach	0.965424758874232
testicle	0.744951310002825

cont.weightedLogRatios:
wLogRatio
Lung	0.34385539665588
cerebhem	0.0901086686119265
cortex	-0.211393022114681
heart	-0.744788517259152
kidney	0.0041273679707625
liver	-0.109272244198955
stomach	0.423971958347199
testicle	0.0625932483899461

varWeightedLogRatios=0.0430107364311947
cont.varWeightedLogRatios=0.131271809497146

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.70008832247055	0.078263394742723	47.2773808832844	8.93416710157605e-254	***
df.mm.trans1	0.855603558797081	0.0695330079232105	12.3049985086504	1.96261288518004e-32	***
df.mm.trans2	0.177711967131592	0.0618971497791126	2.87108481999218	0.00417983090454763	** 
df.mm.exp2	0.441532066455316	0.082100805291898	5.37792613465251	9.44878526371127e-08	***
df.mm.exp3	0.194682071918420	0.082100805291898	2.37125654524647	0.0179226298845089	*  
df.mm.exp4	0.119004699235309	0.082100805291898	1.44949490826800	0.147523308658204	   
df.mm.exp5	-0.102748153270526	0.082100805291898	-1.251487764404	0.211058921303758	   
df.mm.exp6	-0.130123223772851	0.082100805291898	-1.58492018817862	0.113311079730356	   
df.mm.exp7	0.0900493184100561	0.082100805291898	1.09681407009223	0.272995454108848	   
df.mm.exp8	0.0979358922714196	0.0821008052918981	1.19287371083904	0.233211139515101	   
df.mm.trans1:exp2	-0.276887655745609	0.0788110415765182	-3.51331044745522	0.000463022438158441	***
df.mm.trans2:exp2	-0.189153227155197	0.0625881655626549	-3.02218838744909	0.00257558028011998	** 
df.mm.trans1:exp3	-0.0566523554808213	0.0788110415765182	-0.718837796678745	0.472414346796095	   
df.mm.trans2:exp3	-0.121632122641815	0.0625881655626549	-1.94337254572596	0.0522607259140756	.  
df.mm.trans1:exp4	-0.125567481923016	0.0788110415765182	-1.59327271168091	0.111425479604326	   
df.mm.trans2:exp4	-0.140971626784946	0.0625881655626549	-2.25236872686138	0.0245223362041907	*  
df.mm.trans1:exp5	0.0527923983712558	0.0788110415765182	0.669860432183215	0.503106519411925	   
df.mm.trans2:exp5	0.0850872464331229	0.0625881655626549	1.35947819636837	0.174311702083844	   
df.mm.trans1:exp6	0.106375399592924	0.0788110415765182	1.34975248981633	0.177410940633274	   
df.mm.trans2:exp6	0.0916133462275877	0.0625881655626549	1.46374870399224	0.143587266082591	   
df.mm.trans1:exp7	-0.115386500950586	0.0788110415765182	-1.46409054673584	0.143493869988287	   
df.mm.trans2:exp7	-0.091592865124393	0.0625881655626549	-1.46342146795631	0.143676715164739	   
df.mm.trans1:exp8	-0.132835299819169	0.0788110415765182	-1.68549098149145	0.092216174575608	.  
df.mm.trans2:exp8	-0.053686741924925	0.062588165562655	-0.857777847334109	0.391227469665011	   
df.mm.trans1:probe2	-0.542284617077202	0.046015734491692	-11.7847649954411	4.73624572653652e-30	***
df.mm.trans1:probe3	-0.577947822014396	0.046015734491692	-12.5597869598005	1.25607821885377e-33	***
df.mm.trans1:probe4	-0.61366896426923	0.046015734491692	-13.3360680003929	2.27365869716659e-37	***
df.mm.trans1:probe5	-0.199490560304057	0.046015734491692	-4.33526841433064	1.6081286181326e-05	***
df.mm.trans1:probe6	-0.41886103540994	0.046015734491692	-9.10256111386343	4.92099066792953e-19	***
df.mm.trans1:probe7	-0.350010774445975	0.046015734491692	-7.60632810303546	6.66611287509667e-14	***
df.mm.trans1:probe8	-0.353816066388053	0.046015734491692	-7.68902355458292	3.63565049530709e-14	***
df.mm.trans1:probe9	-0.217424816156643	0.046015734491692	-4.725010228749	2.64152129715501e-06	***
df.mm.trans1:probe10	-0.38948225312573	0.046015734491692	-8.46411031852702	9.46178777322754e-17	***
df.mm.trans1:probe11	-0.565255859906887	0.046015734491692	-12.2839690847256	2.45803105034573e-32	***
df.mm.trans1:probe12	-0.518956239451357	0.046015734491692	-11.2777997609720	8.39601532262933e-28	***
df.mm.trans1:probe13	-0.679438344397206	0.046015734491692	-14.7653482423469	1.19856278003453e-44	***
df.mm.trans1:probe14	-0.645129805027651	0.046015734491692	-14.0197654596631	8.61975028674097e-41	***
df.mm.trans1:probe15	-0.497459457759996	0.046015734491692	-10.8106382144093	8.51100917691536e-26	***
df.mm.trans1:probe16	-0.401237930140809	0.046015734491692	-8.71958112965146	1.19838839617514e-17	***
df.mm.trans1:probe17	-0.445833519564486	0.046015734491692	-9.6887189673128	2.99258015714862e-21	***
df.mm.trans1:probe18	-0.603667277799253	0.046015734491692	-13.1187143803658	2.63118832775806e-36	***
df.mm.trans1:probe19	-0.55162803693289	0.046015734491692	-11.9878133648499	5.68070121552753e-31	***
df.mm.trans1:probe20	-0.574597620632887	0.046015734491692	-12.4869813984308	2.76646129863614e-33	***
df.mm.trans1:probe21	-0.395746004764993	0.046015734491692	-8.60023227134283	3.16659280126192e-17	***
df.mm.trans1:probe22	-0.578471964039824	0.046015734491692	-12.5711774554911	1.10978987466781e-33	***
df.mm.trans1:probe23	-0.0129808572146187	0.046015734491692	-0.282096056012369	0.777930157139375	   
df.mm.trans1:probe24	-0.549336038728501	0.046015734491692	-11.9380043543080	9.58078385143802e-31	***
df.mm.trans1:probe25	-0.62993686676346	0.046015734491692	-13.6895971285038	3.99825834856031e-39	***
df.mm.trans1:probe26	-0.193540807368773	0.046015734491692	-4.20597018621351	2.84085265092156e-05	***
df.mm.trans1:probe27	-0.303307692260953	0.046015734491692	-6.59139087121851	7.14891701099874e-11	***
df.mm.trans1:probe28	-0.440938459198713	0.046015734491692	-9.58234099856265	7.70048411862675e-21	***
df.mm.trans1:probe29	-0.443247282677968	0.046015734491692	-9.63251565088014	4.93591162650429e-21	***
df.mm.trans1:probe30	-0.31680817808303	0.046015734491692	-6.88477933868966	1.03958841228550e-11	***
df.mm.trans1:probe31	-0.233910080441175	0.046015734491692	-5.08326299742985	4.45243909491046e-07	***
df.mm.trans1:probe32	-0.372364709362913	0.046015734491692	-8.09211704379385	1.74817054242071e-15	***
df.mm.trans2:probe2	0.138469916236954	0.046015734491692	3.00918626566646	0.00268732772163783	** 
df.mm.trans2:probe3	0.0992529455676364	0.046015734491692	2.15693494114619	0.0312563365813384	*  
df.mm.trans2:probe4	0.178949593843238	0.046015734491692	3.88887835476247	0.000107584513897822	***
df.mm.trans2:probe5	0.0298226345232852	0.046015734491692	0.648096457716428	0.517076257534488	   
df.mm.trans2:probe6	0.0736284596451594	0.046015734491692	1.60007137685595	0.109909048910603	   
df.mm.trans3:probe2	-0.0260814697741553	0.046015734491692	-0.566794598896694	0.570985136968607	   
df.mm.trans3:probe3	-0.130179782035153	0.046015734491692	-2.82902758096053	0.00476541649949517	** 
df.mm.trans3:probe4	-0.599627258883539	0.046015734491692	-13.0309179133455	7.01948845823541e-36	***
df.mm.trans3:probe5	-0.568491929809848	0.046015734491692	-12.3542943753834	1.15669680922921e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98904252225612	0.133569283050884	29.8649691840936	1.98083288068535e-139	***
df.mm.trans1	0.131686349048113	0.118669450094844	1.10969039582525	0.267408056949285	   
df.mm.trans2	0.0612123984431303	0.105637609332784	0.579456491203776	0.562416021166206	   
df.mm.exp2	0.0418165676056219	0.140118451758811	0.298437265618675	0.765433499132113	   
df.mm.exp3	0.0885258671451585	0.140118451758811	0.631793072460863	0.527671274410033	   
df.mm.exp4	0.325907268675086	0.140118451758811	2.32594112041772	0.0202274668658096	*  
df.mm.exp5	0.121164272850684	0.140118451758811	0.864727459729902	0.387402629341352	   
df.mm.exp6	0.106516816017150	0.140118451758811	0.760191214505424	0.447325404236707	   
df.mm.exp7	0.0729700333254374	0.140118451758811	0.520773905288664	0.602643382060946	   
df.mm.exp8	0.200620437105580	0.140118451758811	1.43179170614098	0.152526236981862	   
df.mm.trans1:exp2	-0.0260774988790651	0.134503931940005	-0.193879082216697	0.846311239150427	   
df.mm.trans2:exp2	0.0358220096683323	0.106816940782538	0.335358880397634	0.737427046239254	   
df.mm.trans1:exp3	-0.110611088344962	0.134503931940005	-0.822363233175222	0.411072717333149	   
df.mm.trans2:exp3	0.023743341469657	0.106816940782538	0.222280672856889	0.824142284842226	   
df.mm.trans1:exp4	-0.356499808966512	0.134503931940005	-2.65047871704991	0.00816894850435182	** 
df.mm.trans2:exp4	-0.096260212601014	0.106816940782538	-0.901169907093522	0.367722141424454	   
df.mm.trans1:exp5	-0.126776626205197	0.134503931940005	-0.942549592243487	0.346146503431106	   
df.mm.trans2:exp5	-0.0442228435958927	0.106816940782538	-0.414005898988656	0.678961500104496	   
df.mm.trans1:exp6	-0.0911788941221708	0.134503931940005	-0.677890176198277	0.498003326826249	   
df.mm.trans2:exp6	0.0184744120946895	0.106816940782538	0.172953952428767	0.862723778908956	   
df.mm.trans1:exp7	-0.0535483704314367	0.134503931940005	-0.398117509719507	0.690631354973316	   
df.mm.trans2:exp7	-0.0727696487535404	0.106816940782538	-0.68125569053403	0.495872653976399	   
df.mm.trans1:exp8	-0.174128912729231	0.134503931940005	-1.29460091030574	0.19576666205889	   
df.mm.trans2:exp8	-0.105568148000864	0.106816940782538	-0.988309038130794	0.323248287601509	   
df.mm.trans1:probe2	0.0248144553373678	0.0785333767506503	0.315973365262972	0.752090839851109	   
df.mm.trans1:probe3	0.0688214691685002	0.0785333767506502	0.876334012569125	0.381065892458452	   
df.mm.trans1:probe4	0.0496778883425399	0.0785333767506502	0.632570384694793	0.527163620232245	   
df.mm.trans1:probe5	0.0693366228951149	0.0785333767506502	0.882893691369775	0.377512913513316	   
df.mm.trans1:probe6	0.0163549952385275	0.0785333767506502	0.208255342062470	0.835073408028751	   
df.mm.trans1:probe7	0.0842111108450409	0.0785333767506502	1.07229708347341	0.283853870673737	   
df.mm.trans1:probe8	0.0514743229308744	0.0785333767506502	0.655445175804798	0.51233690942547	   
df.mm.trans1:probe9	0.0628616942018981	0.0785333767506502	0.800445578718574	0.423649084266057	   
df.mm.trans1:probe10	0.0326683675333865	0.0785333767506502	0.415980680890766	0.677516344202115	   
df.mm.trans1:probe11	0.0897217563529032	0.0785333767506502	1.14246655454250	0.253542638582801	   
df.mm.trans1:probe12	0.0473876151145432	0.0785333767506502	0.603407329153853	0.546379106040683	   
df.mm.trans1:probe13	0.0295567877029705	0.0785333767506502	0.376359567433547	0.706732074748238	   
df.mm.trans1:probe14	0.0341897521458844	0.0785333767506502	0.435353139779531	0.663403169665482	   
df.mm.trans1:probe15	-0.0222798538607363	0.0785333767506503	-0.283699170754833	0.776701596192195	   
df.mm.trans1:probe16	0.0665755308002996	0.0785333767506502	0.847735492282247	0.396794851841621	   
df.mm.trans1:probe17	0.151454772384678	0.0785333767506502	1.92854017808962	0.0540801319061457	.  
df.mm.trans1:probe18	-0.0250260469402093	0.0785333767506502	-0.318667654132192	0.750047289082888	   
df.mm.trans1:probe19	0.0278594826905902	0.0785333767506502	0.354747036779615	0.722856379855534	   
df.mm.trans1:probe20	-0.00582551215183886	0.0785333767506502	-0.0741788064243732	0.940883434971266	   
df.mm.trans1:probe21	-0.0599274143428306	0.0785333767506503	-0.763082103716295	0.445600353425265	   
df.mm.trans1:probe22	0.124089911902036	0.0785333767506502	1.58009138326029	0.114412624619195	   
df.mm.trans1:probe23	0.0775866110805635	0.0785333767506502	0.987944416638383	0.323426745898719	   
df.mm.trans1:probe24	0.0647002490667801	0.0785333767506502	0.823856705820872	0.410223917554791	   
df.mm.trans1:probe25	0.102336768417470	0.0785333767506502	1.30309904720381	0.192850823901903	   
df.mm.trans1:probe26	0.00227995947266102	0.0785333767506503	0.0290317259615625	0.976845271769316	   
df.mm.trans1:probe27	0.0594046922768771	0.0785333767506502	0.756426053924712	0.449577842007685	   
df.mm.trans1:probe28	-0.0380171017869424	0.0785333767506502	-0.484088464802038	0.628432586285505	   
df.mm.trans1:probe29	0.0751068203009056	0.0785333767506502	0.956368150823003	0.339125048985616	   
df.mm.trans1:probe30	0.0303230178254179	0.0785333767506502	0.386116312325342	0.699495376747144	   
df.mm.trans1:probe31	0.0294780127420926	0.0785333767506502	0.375356491236683	0.707477589305723	   
df.mm.trans1:probe32	0.0369768003421111	0.0785333767506502	0.470841849313514	0.637859756940943	   
df.mm.trans2:probe2	0.013686324033464	0.0785333767506502	0.174273978781266	0.861686588244461	   
df.mm.trans2:probe3	0.0322532021997726	0.0785333767506502	0.410694198241075	0.681387673416898	   
df.mm.trans2:probe4	0.0941999040877374	0.0785333767506502	1.19948877770568	0.230631457874023	   
df.mm.trans2:probe5	-0.0386364034346238	0.0785333767506502	-0.49197430485254	0.622849054656188	   
df.mm.trans2:probe6	0.158463628207538	0.0785333767506502	2.01778701953276	0.0438889575910198	*  
df.mm.trans3:probe2	-0.0494780230106576	0.0785333767506503	-0.630025411587156	0.528826643151054	   
df.mm.trans3:probe3	-0.0638380300581706	0.0785333767506502	-0.812877692256395	0.41648806102184	   
df.mm.trans3:probe4	-0.0504561639546834	0.0785333767506502	-0.642480510100638	0.520713373586063	   
df.mm.trans3:probe5	-0.00272892168241755	0.0785333767506502	-0.0347485590882217	0.972287403595427