chr9.24752_chr9_112131901_112132387_+_1.R 

fitVsDatCorrelation=0.91958307527686
cont.fitVsDatCorrelation=0.314267186287735

fstatistic=3702.62474557441,37,347
cont.fstatistic=626.42687127378,37,347

residuals=-1.00781313979589,-0.107836567071982,-0.000643331451470528,0.114285446973413,1.20259726868399
cont.residuals=-1.0952191021828,-0.433336125426696,-0.150535233986413,0.442948693249026,1.4976393097654

predictedValues:
Include	Exclude	Both
chr9.24752_chr9_112131901_112132387_+_1.R.tl.Lung	70.2490617058373	189.957429009698	84.923321343603
chr9.24752_chr9_112131901_112132387_+_1.R.tl.cerebhem	73.5674426549543	85.2091485535225	57.5777776656101
chr9.24752_chr9_112131901_112132387_+_1.R.tl.cortex	71.9014663648334	120.267596664598	54.5881892972902
chr9.24752_chr9_112131901_112132387_+_1.R.tl.heart	72.2295643252789	176.550511447578	57.160575150874
chr9.24752_chr9_112131901_112132387_+_1.R.tl.kidney	68.7363368572107	156.630432596222	62.8160315870558
chr9.24752_chr9_112131901_112132387_+_1.R.tl.liver	67.7278734554769	150.282371679515	64.5698273857285
chr9.24752_chr9_112131901_112132387_+_1.R.tl.stomach	79.6702576434013	173.374830051688	57.3616573887882
chr9.24752_chr9_112131901_112132387_+_1.R.tl.testicle	73.1185464306116	261.615764615502	147.466258332595


diffExp=-119.708367303861,-11.6417058985681,-48.3661302997642,-104.320947122299,-87.8940957390109,-82.5544982240385,-93.7045724082867,-188.497218184890
diffExpScore=0.998644412502165
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	76.612760442395	89.5010772827202	110.837560424602
cerebhem	100.454043295851	98.5347004794665	83.1086005482344
cortex	78.4874455865207	103.515735378850	106.372209285562
heart	90.9529891109243	91.9820353331064	85.9651120016987
kidney	102.073645659114	81.3306047027323	93.486605292946
liver	92.0134875513127	75.2370158210907	76.9508911275576
stomach	78.9254260429381	120.407256966091	109.863314348948
testicle	78.1594594530788	92.4972076823199	146.822710316145
cont.diffExp=-12.8883168403252,1.91934281638483,-25.0282897923291,-1.02904622218207,20.7430409563814,16.7764717302220,-41.4818309231526,-14.3377482292411
cont.diffExpScore=2.38261531877719

cont.diffExp1.5=0,0,0,0,0,0,-1,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,0,0,-1,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,-1,0,0,0,-1,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,0,-1,0,1,1,-1,0
cont.diffExp1.2Score=4

tran.correlation=0.0763281054877751
cont.tran.correlation=-0.457219922630954

tran.covariance=0.000512936996716706
cont.tran.covariance=-0.00801731429724969

tran.mean=118.193039628495
cont.tran.mean=90.667805674282

weightedLogRatios:
wLogRatio
Lung	-4.72450342398998
cerebhem	-0.642223468482428
cortex	-2.33162440075411
heart	-4.22454917920672
kidney	-3.82327161259842
liver	-3.67744742112256
stomach	-3.70637459023104
testicle	-6.28407494670029

cont.weightedLogRatios:
wLogRatio
Lung	-0.686709508105762
cerebhem	0.088742260750557
cortex	-1.24590072247487
heart	-0.0508069886778195
kidney	1.02502552703716
liver	0.88996939911462
stomach	-1.93435353242792
testicle	-0.748316849888586

varWeightedLogRatios=2.74801076989197
cont.varWeightedLogRatios=1.04181513429946

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.36001057480376	0.131678548884449	40.7052676401173	3.49505695490321e-134	***
df.mm.trans1	-0.956544855491552	0.109706301708781	-8.71914229713741	1.17923140643384e-16	***
df.mm.trans2	-0.213669466740483	0.109706301708781	-1.94764989259847	0.0522630414245619	.  
df.mm.exp2	-0.366923504073487	0.151152237494567	-2.42750957680447	0.0157110882071377	*  
df.mm.exp3	0.00810015187227545	0.151152237494567	0.0535893613388726	0.957293171284273	   
df.mm.exp4	0.350493851087166	0.151152237494567	2.31881351475041	0.0209855720636945	*  
df.mm.exp5	0.086858563890192	0.151152237494567	0.574642925105981	0.565905169628629	   
df.mm.exp6	0.00316839100264469	0.151152237494567	0.0209615884962243	0.983288344824877	   
df.mm.exp7	0.426877951965252	0.151152237494567	2.82415900049508	0.00501434297468251	** 
df.mm.exp8	-0.191738658092348	0.151152237494567	-1.26851352828461	0.205464957567864	   
df.mm.trans1:exp2	0.41307912508466	0.127746956630269	3.23357311971206	0.00133988107589919	** 
df.mm.trans2:exp2	-0.434767679679105	0.127746956630269	-3.40335058577896	0.000743464754428937	***
df.mm.trans1:exp3	0.0151495555265821	0.127746956630269	0.11859034395965	0.905668536083591	   
df.mm.trans2:exp3	-0.465180908795138	0.127746956630269	-3.64142458705679	0.000312454564939871	***
df.mm.trans1:exp4	-0.322691362436362	0.127746956630269	-2.52601996124503	0.0119806541206620	*  
df.mm.trans2:exp4	-0.423686821029714	0.127746956630269	-3.31660989980347	0.00100757199669770	** 
df.mm.trans1:exp5	-0.108627535340761	0.127746956630269	-0.850333645561167	0.395725960583460	   
df.mm.trans2:exp5	-0.27976945511664	0.127746956630269	-2.19002833802422	0.0291873518309944	*  
df.mm.trans1:exp6	-0.0397175272725027	0.127746956630269	-0.310907815889932	0.756057381873459	   
df.mm.trans2:exp6	-0.237452377895965	0.127746956630269	-1.85877130977931	0.06390601970636	.  
df.mm.trans1:exp7	-0.301028566320046	0.127746956630269	-2.35644413190442	0.0190061843997224	*  
df.mm.trans2:exp7	-0.518222042778218	0.127746956630269	-4.05662926497795	6.15327972833402e-05	***
df.mm.trans1:exp8	0.231773754185286	0.127746956630269	1.81431918457437	0.0704920606340333	.  
df.mm.trans2:exp8	0.511815548939283	0.127746956630269	4.00647939050794	7.54442523888323e-05	***
df.mm.trans1:probe2	-0.434788327414430	0.0699698897990324	-6.2139347176797	1.48074373330861e-09	***
df.mm.trans1:probe3	-0.248840217848650	0.0699698897990324	-3.55639002095572	0.000428180193160211	***
df.mm.trans1:probe4	-0.432586678333674	0.0699698897990324	-6.18246905313343	1.77264191337668e-09	***
df.mm.trans1:probe5	-0.278067888494474	0.0699698897990324	-3.97410785257975	8.59584078773566e-05	***
df.mm.trans1:probe6	-0.119904564789693	0.0699698897990324	-1.71365947744213	0.0874843608186763	.  
df.mm.trans2:probe2	0.0971065648018295	0.0699698897990324	1.38783361072511	0.166078198854731	   
df.mm.trans2:probe3	0.209530704053845	0.0699698897990324	2.99458387966108	0.00294554517995727	** 
df.mm.trans2:probe4	0.204371971364627	0.0699698897990324	2.92085598464746	0.00371912841198834	** 
df.mm.trans2:probe5	0.405811148585974	0.0699698897990324	5.79979688050881	1.49511649945400e-08	***
df.mm.trans2:probe6	0.087768422681751	0.0699698897990324	1.25437417343145	0.21055031829886	   
df.mm.trans3:probe2	0.209300242645135	0.0699698897990324	2.99129015704166	0.00297668600049661	** 
df.mm.trans3:probe3	0.155917822741437	0.0699698897990324	2.2283559855427	0.0264970689327309	*  
df.mm.trans3:probe4	0.642591914372971	0.0699698897990324	9.18383487838304	3.85761396705351e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04720768068651	0.318168473061416	12.7203290814589	1.09106372244972e-30	***
df.mm.trans1	0.240227196269556	0.265078000901483	0.90625097311956	0.365431877109857	   
df.mm.trans2	0.411720234595596	0.265078000901483	1.55320408783607	0.121285932275719	   
df.mm.exp2	0.655012324372389	0.365221799684812	1.79346447812717	0.0737693279319102	.  
df.mm.exp3	0.210769364794150	0.365221799684812	0.577099628160324	0.564246367144037	   
df.mm.exp4	0.453045919872053	0.365221799684812	1.24046790269100	0.215640457008493	   
df.mm.exp5	0.361450188914495	0.365221799684812	0.989673095161427	0.323023519826179	   
df.mm.exp6	0.374462470512927	0.365221799684812	1.02530153138747	0.305935144529458	   
df.mm.exp7	0.335197640765255	0.365221799684812	0.917791985731772	0.359365247102922	   
df.mm.exp8	-0.228244858287908	0.365221799684812	-0.624948616114603	0.532415512753627	   
df.mm.trans1:exp2	-0.384075630378823	0.308668757923225	-1.24429706771410	0.214230061171801	   
df.mm.trans2:exp2	-0.55885421098876	0.308668757923225	-1.81053053360121	0.0710783581058961	.  
df.mm.trans1:exp3	-0.186594329912491	0.308668757923225	-0.604513171880203	0.545897571270732	   
df.mm.trans2:exp3	-0.0652963928854324	0.308668757923225	-0.211541956253550	0.832588584631306	   
df.mm.trans1:exp4	-0.281466798299848	0.308668757923225	-0.911873298073974	0.362468474488893	   
df.mm.trans2:exp4	-0.42570329189553	0.308668757923225	-1.37915898829455	0.168733986467142	   
df.mm.trans1:exp5	-0.0745192681753457	0.308668757923225	-0.241421479377193	0.809371063318129	   
df.mm.trans2:exp5	-0.45717846347588	0.308668757923225	-1.48112969563831	0.139479453281756	   
df.mm.trans1:exp6	-0.191290948673154	0.308668757923225	-0.619728896310048	0.53584300434832	   
df.mm.trans2:exp6	-0.548069790886765	0.308668757923225	-1.77559204428161	0.0766765566188574	.  
df.mm.trans1:exp7	-0.305457856545027	0.308668757923225	-0.989597582211423	0.323060389296118	   
df.mm.trans2:exp7	-0.0385684977889947	0.308668757923225	-0.124951090121624	0.900634626414225	   
df.mm.trans1:exp8	0.248232301813659	0.308668757923225	0.804202872632163	0.421830423287698	   
df.mm.trans2:exp8	0.261172653245674	0.308668757923225	0.846125973366682	0.398065578041927	   
df.mm.trans1:probe2	0.0997883954547732	0.169064841511652	0.590237417564407	0.555415663760083	   
df.mm.trans1:probe3	-0.0552667675743946	0.169064841511652	-0.32689687033827	0.74394298495473	   
df.mm.trans1:probe4	-0.0289227884864588	0.169064841511652	-0.171075122585233	0.86426440935713	   
df.mm.trans1:probe5	0.301925521129428	0.169064841511652	1.78585635209447	0.0749956504583617	.  
df.mm.trans1:probe6	0.195763352741897	0.169064841511652	1.15791876650122	0.247693544029874	   
df.mm.trans2:probe2	0.0741759805515598	0.169064841511652	0.438742791749801	0.661120961267641	   
df.mm.trans2:probe3	0.124376819846618	0.169064841511652	0.735675251782293	0.462425324094469	   
df.mm.trans2:probe4	0.00365640226638166	0.169064841511652	0.0216272184901889	0.98275775209484	   
df.mm.trans2:probe5	-0.103155812771152	0.169064841511652	-0.610155321761815	0.542158295945244	   
df.mm.trans2:probe6	0.254174076153574	0.169064841511652	1.50341179089005	0.133642435143033	   
df.mm.trans3:probe2	-0.0489270355054281	0.169064841511652	-0.289398050286263	0.772449618029236	   
df.mm.trans3:probe3	0.0310308861723763	0.169064841511652	0.183544289249741	0.854478173691364	   
df.mm.trans3:probe4	0.0535385426058348	0.169064841511652	0.316674609144829	0.751680941204637