chr17.10852_chr17_55454063_55455656_+_1.R 

fitVsDatCorrelation=0.85942673842309
cont.fitVsDatCorrelation=0.301334204724433

fstatistic=9197.83100067782,50,646
cont.fstatistic=2635.08321560573,50,646

residuals=-0.631509198991595,-0.0888731441057865,-0.00366815915310633,0.0914476122186088,0.920790903078364
cont.residuals=-0.587113926350878,-0.200136033157805,-0.0494452437467432,0.133310200877481,1.20468158789672

predictedValues:
Include	Exclude	Both
chr17.10852_chr17_55454063_55455656_+_1.R.tl.Lung	48.2696099831466	72.1741915403258	66.0249881761443
chr17.10852_chr17_55454063_55455656_+_1.R.tl.cerebhem	56.1543823141615	90.3876327245176	62.8580907466972
chr17.10852_chr17_55454063_55455656_+_1.R.tl.cortex	53.9540402656983	70.7655119855413	65.4942920990426
chr17.10852_chr17_55454063_55455656_+_1.R.tl.heart	48.1824210432343	71.8100139109784	63.3640690495707
chr17.10852_chr17_55454063_55455656_+_1.R.tl.kidney	47.382045523731	68.0093354665112	64.6792778625873
chr17.10852_chr17_55454063_55455656_+_1.R.tl.liver	50.6064986520949	68.4492933667373	61.7066330261559
chr17.10852_chr17_55454063_55455656_+_1.R.tl.stomach	48.5490075697007	76.7975884107135	60.9865577759143
chr17.10852_chr17_55454063_55455656_+_1.R.tl.testicle	53.6772027700991	70.7439657521247	62.7114641212949


diffExp=-23.9045815571792,-34.2332504103561,-16.8114717198431,-23.6275928677442,-20.6272899427802,-17.8427947146424,-28.2485808410128,-17.0667629820256
diffExpScore=0.994546316972115
diffExp1.5=0,-1,0,0,0,0,-1,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,-1,0,-1,-1,0,-1,0
diffExp1.4Score=0.833333333333333
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	54.8574639708124	54.0170063696667	60.8836881647132
cerebhem	61.6715220285775	58.2685856360091	69.2144577242427
cortex	58.078976270469	56.4005377083902	55.5305668650567
heart	61.9833206103062	53.5582671746119	59.3185340524445
kidney	54.2399725871952	53.5705125900222	60.8357594556786
liver	52.8402349332469	56.5525262115041	62.7726511247793
stomach	56.1963086998053	51.977306116649	55.9855157062342
testicle	50.1232374130403	54.8142006967644	63.8247087534808
cont.diffExp=0.84045760114568,3.40293639256846,1.67843856207886,8.42505343569427,0.669459997172986,-3.71229127825725,4.2190025831563,-4.69096328372414
cont.diffExpScore=2.33590124544514

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=0.555755926087226
cont.tran.correlation=0.206446121180063

tran.covariance=0.00316755346871092
cont.tran.covariance=0.000514863318348493

tran.mean=62.2445463299573
cont.tran.mean=55.5718736885669

weightedLogRatios:
wLogRatio
Lung	-1.64047609240851
cerebhem	-2.03067813254821
cortex	-1.11852317868623
heart	-1.62585009924183
kidney	-1.45968093406940
liver	-1.23073006444360
stomach	-1.88570307024176
testicle	-1.13772866549602

cont.weightedLogRatios:
wLogRatio
Lung	0.0617112786678856
cerebhem	0.232340371139719
cortex	0.118682626144810
heart	0.592243148749409
kidney	0.0495185763793852
liver	-0.271670721196457
stomach	0.311381709573888
testicle	-0.354209671047193

varWeightedLogRatios=0.11639469239561
cont.varWeightedLogRatios=0.0932654956201367

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23030532669793	0.0782338148302	54.0725942596492	5.48526613254606e-242	***
df.mm.trans1	-0.350962870759769	0.0617436262031335	-5.68419596227016	1.99054877806381e-08	***
df.mm.trans2	0.100804720195807	0.0617436262031335	1.63263362381997	0.103033371527790	   
df.mm.exp2	0.425481104044282	0.0817590218070028	5.20408750790409	2.6221019150082e-07	***
df.mm.exp3	0.0996899310878603	0.0817590218070028	1.21931413664885	0.223169997146368	   
df.mm.exp4	0.0342698004326389	0.0817590218070028	0.419156194328436	0.675241321995449	   
df.mm.exp5	-0.057403932067277	0.0817590218070028	-0.702111287519834	0.482862652031819	   
df.mm.exp6	0.0619303838875637	0.0817590218070028	0.757474618932625	0.449041769434031	   
df.mm.exp7	0.147242101578148	0.0817590218070028	1.80092787711823	0.072180511569707	.  
df.mm.exp8	0.137659938601986	0.0817590218070028	1.68372780837496	0.0927173889850266	.  
df.mm.trans1:exp2	-0.274178549086647	0.0619968478789997	-4.42245950345356	1.14487213465306e-05	***
df.mm.trans2:exp2	-0.200456176254937	0.0619968478789997	-3.23332851770418	0.00128586406006029	** 
df.mm.trans1:exp3	0.0116404771510335	0.0619968478789997	0.187759177269018	0.851124397880274	   
df.mm.trans2:exp3	-0.119400692022658	0.0619968478789997	-1.92591552808772	0.0545534566570192	.  
df.mm.trans1:exp4	-0.0360777242831430	0.0619968478789997	-0.581928364383243	0.560817951476702	   
df.mm.trans2:exp4	-0.0393283886964492	0.0619968478789997	-0.634361101280619	0.526069762696318	   
df.mm.trans1:exp5	0.0388451328533407	0.0619968478789997	0.626566255903129	0.53116493829639	   
df.mm.trans2:exp5	-0.00203361001254720	0.0619968478789997	-0.0328018291593828	0.973842751530736	   
df.mm.trans1:exp6	-0.0146525537193201	0.0619968478789997	-0.236343527463166	0.813241030310215	   
df.mm.trans2:exp6	-0.114919679660994	0.0619968478789997	-1.85363746049291	0.0642467323099513	.  
df.mm.trans1:exp7	-0.141470518194618	0.0619968478789997	-2.28189856475814	0.0228197066413688	*  
df.mm.trans2:exp7	-0.0851513869249641	0.0619968478789997	-1.37347929512732	0.170079856977101	   
df.mm.trans1:exp8	-0.031473726384261	0.0619968478789997	-0.50766655823678	0.61186048253863	   
df.mm.trans2:exp8	-0.157675219543889	0.0619968478789997	-2.54327800425638	0.0112137880441798	*  
df.mm.trans1:probe2	-0.0666010958344386	0.0461594667964179	-1.44284803219623	0.149547973836081	   
df.mm.trans1:probe3	-0.0131174106144097	0.0461594667964179	-0.28417595619687	0.776366602368236	   
df.mm.trans1:probe4	0.0246915717239943	0.0461594667964179	0.534918911279764	0.59289000002469	   
df.mm.trans1:probe5	-0.0560597738116755	0.0461594667964179	-1.21448053243167	0.225008032871403	   
df.mm.trans1:probe6	0.0526601259349985	0.0461594667964179	1.14083046425235	0.254363230016577	   
df.mm.trans2:probe2	-0.442085146415876	0.0461594667964179	-9.57734517094081	2.08850748816660e-20	***
df.mm.trans2:probe3	0.0148178253126514	0.0461594667964179	0.321013788525852	0.748303765827877	   
df.mm.trans2:probe4	-0.228221029667942	0.0461594667964179	-4.9441868701493	9.75786258424304e-07	***
df.mm.trans2:probe5	-0.416124341867294	0.0461594667964179	-9.01492956369215	2.21294014111657e-18	***
df.mm.trans2:probe6	-0.1250203314966	0.0461594667964179	-2.70844401318565	0.00693899335071346	** 
df.mm.trans3:probe2	0.0800706216092835	0.0461594667964179	1.73465222123184	0.0832794030916295	.  
df.mm.trans3:probe3	0.00650497196487591	0.0461594667964179	0.140923897443736	0.887973994482599	   
df.mm.trans3:probe4	-0.0379129919014303	0.0461594667964179	-0.821348133604795	0.411751006023655	   
df.mm.trans3:probe5	0.155677223658323	0.0461594667964179	3.37259579589432	0.00078909266533227	***
df.mm.trans3:probe6	-0.0271222578075995	0.0461594667964179	-0.587577363647196	0.557021328906545	   
df.mm.trans3:probe7	0.0439463082318019	0.0461594667964179	0.952054069983587	0.341425633473738	   
df.mm.trans3:probe8	-0.00608785030873808	0.0461594667964179	-0.131887362035354	0.895114450492953	   
df.mm.trans3:probe9	0.0278651455775968	0.0461594667964179	0.603671305400765	0.546274103590135	   
df.mm.trans3:probe10	0.0718911154150682	0.0461594667964179	1.55745116667264	0.119853151930880	   
df.mm.trans3:probe11	0.300791220806353	0.0461594667964179	6.51634955258399	1.45090487558118e-10	***
df.mm.trans3:probe12	0.316053641890875	0.0461594667964179	6.84699507654196	1.75785934380614e-11	***
df.mm.trans3:probe13	0.326119563504894	0.0461594667964179	7.06506348834606	4.16836012499229e-12	***
df.mm.trans3:probe14	0.312853345481814	0.0461594667964179	6.77766376422035	2.75618255633074e-11	***
df.mm.trans3:probe15	0.508144645322654	0.0461594667964179	11.0084600319102	5.9707569621331e-26	***
df.mm.trans3:probe16	0.324587237321987	0.0461594667964179	7.03186713038844	5.20176193870994e-12	***
df.mm.trans3:probe17	1.16468180968345	0.0461594667964179	25.2316998118755	2.72576454996221e-98	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.8130663935992	0.145909233057555	26.1331398547966	2.84019115711979e-103	***
df.mm.trans1	0.166942263557957	0.115154363430248	1.44972590343116	0.147620262937626	   
df.mm.trans2	0.147460407928462	0.115154363430248	1.28054555238614	0.200812911035584	   
df.mm.exp2	0.064603715430114	0.152483886837009	0.423675686462329	0.671943376662405	   
df.mm.exp3	0.192276918696849	0.152483886837009	1.26096548747065	0.207776485006602	   
df.mm.exp4	0.139641795037431	0.152483886837009	0.915780663347696	0.360123667059082	   
df.mm.exp5	-0.0188327426336018	0.152483886837009	-0.123506444020097	0.901744487969463	   
df.mm.exp6	-0.0221485817619398	0.152483886837009	-0.145251949050948	0.884557258893217	   
df.mm.exp7	0.0694933722616346	0.152483886837009	0.455742398119196	0.648728382427474	   
df.mm.exp8	-0.122778275842041	0.152483886837009	-0.805188524432611	0.421007151329306	   
df.mm.trans1:exp2	0.0524802963292903	0.115626632110989	0.453877237200118	0.65006962886948	   
df.mm.trans2:exp2	0.0111604630823744	0.115626632110989	0.096521561500309	0.923136286113298	   
df.mm.trans1:exp3	-0.135211431682690	0.115626632110989	-1.16937965946203	0.242681987296771	   
df.mm.trans2:exp3	-0.149097156156541	0.115626632110989	-1.28947071651645	0.197695968519099	   
df.mm.trans1:exp4	-0.0175147257496582	0.115626632110989	-0.151476571010439	0.879647082067839	   
df.mm.trans2:exp4	-0.148170557468672	0.115626632110989	-1.28145700314477	0.200492966085353	   
df.mm.trans1:exp5	0.00751262371741325	0.115626632110989	0.0649731258297131	0.948215498142281	   
df.mm.trans2:exp5	0.0105325913279721	0.115626632110989	0.09109139594987	0.927448206817789	   
df.mm.trans1:exp6	-0.0153167496186663	0.115626632110989	-0.132467316041548	0.894655922378087	   
df.mm.trans2:exp6	0.0680195257464722	0.115626632110989	0.588268675690396	0.556557569934125	   
df.mm.trans1:exp7	-0.0453805561671198	0.115626632110989	-0.392474945768197	0.694836779639819	   
df.mm.trans2:exp7	-0.107985099528133	0.115626632110989	-0.933912002422406	0.350698226187300	   
df.mm.trans1:exp8	0.0325247398710095	0.115626632110989	0.281291076953528	0.778577228643273	   
df.mm.trans2:exp8	0.137428643273175	0.115626632110989	1.18855527281343	0.235051461119820	   
df.mm.trans1:probe2	0.139457953927497	0.086089274992265	1.61992250416822	0.105736950573730	   
df.mm.trans1:probe3	0.0338915682437135	0.086089274992265	0.393679331679336	0.693947704688318	   
df.mm.trans1:probe4	0.184926388732443	0.086089274992265	2.14807696718387	0.0320788461990234	*  
df.mm.trans1:probe5	0.0527850878938249	0.086089274992265	0.613143598881133	0.539997197908683	   
df.mm.trans1:probe6	0.157719801035145	0.086089274992265	1.83204935863748	0.0674042951924366	.  
df.mm.trans2:probe2	0.168151944698679	0.086089274992265	1.95322756189765	0.0512239707500435	.  
df.mm.trans2:probe3	0.142173145069153	0.086089274992265	1.65146175388197	0.0991303089902592	.  
df.mm.trans2:probe4	0.168515113717997	0.086089274992265	1.9574460783081	0.0507252352816627	.  
df.mm.trans2:probe5	0.17513138505888	0.086089274992265	2.0342996856997	0.0423293565863169	*  
df.mm.trans2:probe6	0.0077873606832047	0.086089274992265	0.0904568041013746	0.927952257764565	   
df.mm.trans3:probe2	-0.0622809270833458	0.086089274992265	-0.72344583095794	0.469667889230986	   
df.mm.trans3:probe3	-0.0164799483491218	0.086089274992265	-0.191428587946669	0.84824997493712	   
df.mm.trans3:probe4	-0.0211057353107616	0.086089274992265	-0.245161029787485	0.806409608301747	   
df.mm.trans3:probe5	0.0420407163273332	0.086089274992265	0.488338603514903	0.625475739788893	   
df.mm.trans3:probe6	0.00202891439754306	0.086089274992265	0.0235675628320177	0.981204823844635	   
df.mm.trans3:probe7	-0.0502844774648927	0.0860892749922651	-0.58409688627777	0.559359030473956	   
df.mm.trans3:probe8	0.0382432448199168	0.086089274992265	0.444227748733543	0.65702670347107	   
df.mm.trans3:probe9	-0.0972842771767262	0.086089274992265	-1.13003945248078	0.25887900064681	   
df.mm.trans3:probe10	0.0280064698642023	0.086089274992265	0.325318918839990	0.745045048976583	   
df.mm.trans3:probe11	0.142011189795941	0.086089274992265	1.64958050591901	0.0995148930761603	.  
df.mm.trans3:probe12	-0.0700576875579697	0.086089274992265	-0.81377950463939	0.416071126130826	   
df.mm.trans3:probe13	-0.0305254466306898	0.086089274992265	-0.354578972043062	0.723020749268198	   
df.mm.trans3:probe14	-0.0416063202554057	0.086089274992265	-0.483292724432212	0.629051662369196	   
df.mm.trans3:probe15	-0.0753637469424103	0.086089274992265	-0.875413888073533	0.381674113762294	   
df.mm.trans3:probe16	-0.115025348847813	0.086089274992265	-1.33611705822993	0.181981476563164	   
df.mm.trans3:probe17	0.0131192483391274	0.086089274992265	0.152391204831335	0.878925976321632