chr3.14832_chr3_108434544_108435589_-_0.R 

fitVsDatCorrelation=0.827458784849279
cont.fitVsDatCorrelation=0.359093290946404

fstatistic=9518.975779088,37,347
cont.fstatistic=3439.78882410002,37,347

residuals=-0.446966875184029,-0.0780555140742447,-0.00667229754138507,0.0832408867354955,0.452233855938775
cont.residuals=-0.49054840724916,-0.139300893345916,-0.0274275094798865,0.0948965300907025,0.780887156167499

predictedValues:
Include	Exclude	Both
chr3.14832_chr3_108434544_108435589_-_0.R.tl.Lung	63.6159074222452	56.2435485197932	47.0687065545867
chr3.14832_chr3_108434544_108435589_-_0.R.tl.cerebhem	74.2555079136255	52.5473197580044	46.6598883382047
chr3.14832_chr3_108434544_108435589_-_0.R.tl.cortex	57.1736660679944	53.1976527532978	50.4771952348708
chr3.14832_chr3_108434544_108435589_-_0.R.tl.heart	56.0365046650293	51.2344965439259	46.1719595999371
chr3.14832_chr3_108434544_108435589_-_0.R.tl.kidney	60.9874881626968	55.7147951901404	46.177859418626
chr3.14832_chr3_108434544_108435589_-_0.R.tl.liver	62.7494940163114	101.969102633513	78.703407324543
chr3.14832_chr3_108434544_108435589_-_0.R.tl.stomach	56.242856877766	54.9279026161388	52.3465142493258
chr3.14832_chr3_108434544_108435589_-_0.R.tl.testicle	64.4836811017579	51.7553227719058	46.1121079061417


diffExp=7.37235890245201,21.7081881556211,3.97601331469657,4.80200812110346,5.27269297255643,-39.2196086172017,1.31495426162719,12.7283583298521
diffExpScore=5.08543172904432
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,0,0,0,-1,0,0
diffExp1.4Score=2
diffExp1.3=0,1,0,0,0,-1,0,0
diffExp1.3Score=2
diffExp1.2=0,1,0,0,0,-1,0,1
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	57.2253084607039	54.5819760211125	56.8316218850415
cerebhem	53.1003744322655	56.572203279848	51.9082269667623
cortex	58.7842439366615	58.8310901462141	56.6621242633337
heart	57.0775399336307	57.4247674593386	54.8964306188973
kidney	60.6838353811112	57.0237611216046	55.5059828454605
liver	60.5701960564808	52.3508583163016	56.4800145098003
stomach	51.9593614963632	52.2600162359847	54.701797875207
testicle	55.249629822312	69.0288953288021	53.8879433551117
cont.diffExp=2.64333243959139,-3.47182884758249,-0.0468462095525979,-0.347227525707844,3.66007425950662,8.21933774017923,-0.300654739621521,-13.7792655064901
cont.diffExpScore=7.34071712226659

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,-1
cont.diffExp1.2Score=0.5

tran.correlation=0.0444149817404048
cont.tran.correlation=-0.0740606876391107

tran.covariance=0.00132786390339896
cont.tran.covariance=-0.000241842196342851

tran.mean=60.8209529383841
cont.tran.mean=57.0452535892959

weightedLogRatios:
wLogRatio
Lung	0.503931702456141
cerebhem	1.42974034892392
cortex	0.289041211219429
heart	0.356677564276554
kidney	0.367610825465983
liver	-2.12750212935735
stomach	0.0950526154342485
testicle	0.891956455050373

cont.weightedLogRatios:
wLogRatio
Lung	0.190274429708419
cerebhem	-0.253578997090298
cortex	-0.00324556689675873
heart	-0.0245477672271695
kidney	0.253476382367812
liver	0.58784269625541
stomach	-0.0228094958343890
testicle	-0.918084881037036

varWeightedLogRatios=1.08099407389498
cont.varWeightedLogRatios=0.192826384736652

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27412003684915	0.0724203858057362	59.018189274968	5.14341511794478e-183	***
df.mm.trans1	-0.0652075518436235	0.0603361197581417	-1.08073823946599	0.280564109864388	   
df.mm.trans2	-0.225402182103954	0.0603361197581417	-3.73577523724566	0.000218717581324160	***
df.mm.exp2	0.0953946530066633	0.083130498076514	1.14752894802653	0.251953623071009	   
df.mm.exp3	-0.232360493088004	0.0831304980765139	-2.79512932635310	0.00547641716566369	** 
df.mm.exp4	-0.200902787040958	0.0831304980765139	-2.41671578649807	0.0161765640922517	*  
df.mm.exp5	-0.0325325057952545	0.0831304980765139	-0.391342606480131	0.695784202071267	   
df.mm.exp6	0.0671873954917475	0.0831304980765139	0.80821596220809	0.41951999974304	   
df.mm.exp7	-0.253131363098845	0.0831304980765139	-3.04498792808701	0.00250478651059379	** 
df.mm.exp8	-0.0490826133871535	0.0831304980765139	-0.590428477187488	0.555287742774352	   
df.mm.trans1:exp2	0.059253746805708	0.0702580941470665	0.84337253273162	0.399601111584521	   
df.mm.trans2:exp2	-0.163371902163345	0.0702580941470665	-2.32531075809387	0.0206314418044834	*  
df.mm.trans1:exp3	0.125590346264019	0.0702580941470665	1.78755697530209	0.0747200928445323	.  
df.mm.trans2:exp3	0.17668342575829	0.0702580941470665	2.51477680832689	0.0123622943989126	*  
df.mm.trans1:exp4	0.0740425786460901	0.0702580941470665	1.05386545913275	0.292677772504572	   
df.mm.trans2:exp4	0.107624511309852	0.0702580941470665	1.53184501538811	0.126471832681518	   
df.mm.trans1:exp5	-0.0096623189308107	0.0702580941470665	-0.137526060849092	0.890694758845898	   
df.mm.trans2:exp5	0.0230868987349081	0.0702580941470665	0.328601266732085	0.742655303946671	   
df.mm.trans1:exp6	-0.0809004367715194	0.0702580941470665	-1.1514749688794	0.250329654177423	   
df.mm.trans2:exp6	0.527791115021353	0.0702580941470665	7.51217523658646	4.97856232546538e-13	***
df.mm.trans1:exp7	0.129946851505817	0.0702580941470665	1.84956414037944	0.0652265592370091	.  
df.mm.trans2:exp7	0.229461485350685	0.0702580941470665	3.26597935990647	0.00119965701087627	** 
df.mm.trans1:exp8	0.062631243229045	0.0702580941470665	0.891445234736133	0.373308065642602	   
df.mm.trans2:exp8	-0.0340814457862009	0.0702580941470665	-0.485089244163962	0.62791913901219	   
df.mm.trans1:probe2	-0.0885839288595574	0.03848194301167	-2.30196091794776	0.0219290198849631	*  
df.mm.trans1:probe3	-0.0855641634152557	0.03848194301167	-2.22348864737177	0.0268263332458262	*  
df.mm.trans1:probe4	-0.0747271189287578	0.03848194301167	-1.94187489197456	0.0529612699504229	.  
df.mm.trans1:probe5	-0.210408670621949	0.03848194301167	-5.46772470813495	8.73139319191572e-08	***
df.mm.trans1:probe6	-0.101205410488958	0.03848194301167	-2.62994543852077	0.00892018564031771	** 
df.mm.trans2:probe2	-0.0196239367146533	0.03848194301167	-0.509951815808836	0.610409411507571	   
df.mm.trans2:probe3	-0.113415054457372	0.03848194301167	-2.94722785756888	0.00342331631622607	** 
df.mm.trans2:probe4	-0.044159438366863	0.03848194301167	-1.14753660836386	0.251950463359372	   
df.mm.trans2:probe5	-0.0164085527485458	0.03848194301167	-0.42639616049454	0.67008359177697	   
df.mm.trans2:probe6	0.00334185008881056	0.03848194301167	0.0868420310221112	0.930847167536875	   
df.mm.trans3:probe2	-0.207795833774034	0.03848194301167	-5.39982697108141	1.24003198099367e-07	***
df.mm.trans3:probe3	-0.144934166477433	0.03848194301167	-3.76629024250362	0.000194578640622668	***
df.mm.trans3:probe4	-0.174877707967749	0.03848194301167	-4.54440951473567	7.6168317132661e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96773406368333	0.120368446560573	32.9632405921818	6.30166612288068e-109	***
df.mm.trans1	0.0709377152711501	0.100283434366970	0.707372216746844	0.479809990388603	   
df.mm.trans2	0.0418688837236741	0.100283434366970	0.417505483213331	0.67656691882734	   
df.mm.exp2	0.051617362032721	0.138169505781397	0.373579985980314	0.708944960959022	   
df.mm.exp3	0.104831252032808	0.138169505781397	0.758714822347744	0.448538201435852	   
df.mm.exp4	0.0828309811122173	0.138169505781397	0.599488147864316	0.549238630819991	   
df.mm.exp5	0.126047507208848	0.138169505781397	0.912267193082913	0.36226142930233	   
df.mm.exp6	0.0212773599231991	0.138169505781396	0.153994615547535	0.877703502289836	   
df.mm.exp7	-0.101810183424538	0.138169505781396	-0.736849877610592	0.461711538370089	   
df.mm.exp8	0.252872981939841	0.138169505781397	1.83016491598314	0.0680829418012093	.  
df.mm.trans1:exp2	-0.126429638492440	0.116774545684884	-1.08268148465857	0.279701592100453	   
df.mm.trans2:exp2	-0.0158033243358614	0.116774545684884	-0.135331927374881	0.892427911652238	   
df.mm.trans1:exp3	-0.0779536494346955	0.116774545684884	-0.667556863334357	0.504860210854321	   
df.mm.trans2:exp3	-0.0298645116301274	0.116774545684884	-0.255745046619294	0.79829924168625	   
df.mm.trans1:exp4	-0.0854165441400856	0.116774545684884	-0.731465437430023	0.464988577445398	   
df.mm.trans2:exp4	-0.0320590007618727	0.116774545684884	-0.274537576437111	0.783834902759676	   
df.mm.trans1:exp5	-0.0673664041876281	0.116774545684884	-0.576892881856433	0.564385874679699	   
df.mm.trans2:exp5	-0.0822831832037823	0.116774545684884	-0.704632869442487	0.48151138630616	   
df.mm.trans1:exp6	0.0355293417844373	0.116774545684884	0.304255876792819	0.761115334437616	   
df.mm.trans2:exp6	-0.0630127457634819	0.116774545684884	-0.539610284021331	0.589811911361829	   
df.mm.trans1:exp7	0.00527583064490614	0.116774545684884	0.045179628950499	0.963990119738294	   
df.mm.trans2:exp7	0.058338035444758	0.116774545684884	0.499578355048225	0.617688614661101	   
df.mm.trans1:exp8	-0.28800759775092	0.116774545684884	-2.46635596877516	0.0141323110155265	*  
df.mm.trans2:exp8	-0.0180515108715701	0.116774545684884	-0.154584295453242	0.877238923335969	   
df.mm.trans1:probe2	0.046540712907935	0.0639600528140283	0.727652821726989	0.467316817584902	   
df.mm.trans1:probe3	0.0467068355496077	0.0639600528140283	0.730250109164443	0.465730034603637	   
df.mm.trans1:probe4	-0.0337760996343365	0.0639600528140283	-0.528081171736125	0.597780536255698	   
df.mm.trans1:probe5	0.0236346361678428	0.0639600528140283	0.369521836333742	0.711964199074419	   
df.mm.trans1:probe6	0.000138686842611351	0.0639600528140283	0.00216833533603545	0.998271166076623	   
df.mm.trans2:probe2	-0.0125232851041733	0.0639600528140283	-0.195798542264908	0.844882458383046	   
df.mm.trans2:probe3	-0.0997832038259537	0.0639600528140283	-1.5600863263213	0.119651011965918	   
df.mm.trans2:probe4	-0.105821863733979	0.0639600528140283	-1.65449931759232	0.0989302997159518	.  
df.mm.trans2:probe5	0.065742998953708	0.0639600528140283	1.02787593288679	0.304724145834893	   
df.mm.trans2:probe6	0.053393067133255	0.0639600528140283	0.834787727403882	0.4044115900994	   
df.mm.trans3:probe2	0.0069035896705339	0.0639600528140283	0.107935959505958	0.914108874139229	   
df.mm.trans3:probe3	-0.107558430494313	0.0639600528140283	-1.68165012006872	0.0935364054554814	.  
df.mm.trans3:probe4	-0.0611362333193836	0.0639600528140283	-0.955850263243914	0.339813320977869