chr12.5473_chr12_17367235_17367672_+_0.R 

fitVsDatCorrelation=0.955895236307771
cont.fitVsDatCorrelation=0.290716815841365

fstatistic=6479.22997250581,37,347
cont.fstatistic=602.030889807553,37,347

residuals=-0.639139268536736,-0.108022074966905,-0.00934925956228213,0.084322000820334,0.559960360142594
cont.residuals=-1.407620574344,-0.568667063470509,0.0668526687695045,0.522413116660351,1.34790916839751

predictedValues:
Include	Exclude	Both
chr12.5473_chr12_17367235_17367672_+_0.R.tl.Lung	313.555699373482	63.6957019599966	200.907870386525
chr12.5473_chr12_17367235_17367672_+_0.R.tl.cerebhem	147.362785068571	56.6091996643103	95.9794675680665
chr12.5473_chr12_17367235_17367672_+_0.R.tl.cortex	223.535510079134	60.9440479599272	165.447804890814
chr12.5473_chr12_17367235_17367672_+_0.R.tl.heart	203.353279072094	59.5076797289033	140.642662905332
chr12.5473_chr12_17367235_17367672_+_0.R.tl.kidney	208.097114905459	64.470989770287	141.733193336349
chr12.5473_chr12_17367235_17367672_+_0.R.tl.liver	293.036418664211	61.1875333874709	169.873833753584
chr12.5473_chr12_17367235_17367672_+_0.R.tl.stomach	262.145114710557	61.6784286511864	177.12608135894
chr12.5473_chr12_17367235_17367672_+_0.R.tl.testicle	210.053603604975	57.6384359643794	144.671887831597


diffExp=249.859997413486,90.7535854042602,162.591462119207,143.845599343190,143.626125135172,231.84888527674,200.466686059370,152.415167640596
diffExpScore=0.999273470978687
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
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	117.326743386441	128.584282565547	176.357467351932
cerebhem	131.949063668073	124.296383795123	142.251720411763
cortex	148.239504387762	125.558526013915	115.867874805185
heart	103.676266670856	138.382976351657	164.024552012250
kidney	152.397570780445	98.0191456631033	157.081348809725
liver	102.534686357362	155.786886466424	124.173766486116
stomach	120.083539670745	109.909443136800	90.9384579399363
testicle	129.474359117243	125.148272697660	105.881630976900
cont.diffExp=-11.2575391791056,7.6526798729504,22.6809783738473,-34.7067096808006,54.3784251173414,-53.2522001090625,10.1740965339454,4.32608641958359
cont.diffExpScore=197.602214128691

cont.diffExp1.5=0,0,0,0,1,-1,0,0
cont.diffExp1.5Score=2
cont.diffExp1.4=0,0,0,0,1,-1,0,0
cont.diffExp1.4Score=2
cont.diffExp1.3=0,0,0,-1,1,-1,0,0
cont.diffExp1.3Score=1.5
cont.diffExp1.2=0,0,0,-1,1,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.639988150083518
cont.tran.correlation=-0.743338472313655

tran.covariance=0.00727814818864772
cont.tran.covariance=-0.0152922854538631

tran.mean=146.679471410309
cont.tran.mean=125.710478170572

weightedLogRatios:
wLogRatio
Lung	7.89127548840754
cerebhem	4.319172353355
cortex	6.1858553552018
heart	5.77619031854752
kidney	5.56847281441113
liver	7.67062099894952
stomach	7.0111427176659
testicle	6.07891740508517

cont.weightedLogRatios:
wLogRatio
Lung	-0.440772048153828
cerebhem	0.289925150280520
cortex	0.81630431238068
heart	-1.38186484111788
kidney	2.12095538949151
liver	-2.02423907328278
stomach	0.41998361842249
testicle	0.164701438311275

varWeightedLogRatios=1.38718361816672
cont.varWeightedLogRatios=1.66479928232809

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43166283995866	0.105667243307296	41.9397980041054	6.30028863092517e-138	***
df.mm.trans1	1.37868110302475	0.0880353145839874	15.6605461062953	3.45426819113469e-42	***
df.mm.trans2	-0.314012809818663	0.0880353145839874	-3.56689598148808	0.000411967117671149	***
df.mm.exp2	-0.134312960960123	0.121294169711700	-1.10733237450215	0.26891725329577	   
df.mm.exp3	-0.188376941060872	0.121294169711699	-1.55305849826599	0.121320706637116	   
df.mm.exp4	-0.144419889366455	0.121294169711699	-1.19065813063993	0.234601690517224	   
df.mm.exp5	-0.0489738033018531	0.121294169711699	-0.403760571660266	0.686637497322955	   
df.mm.exp6	0.0599367017025663	0.121294169711699	0.494143303384063	0.621517635971702	   
df.mm.exp7	-0.0852769807703578	0.121294169711699	-0.703059190503963	0.482490280218827	   
df.mm.exp8	-0.172163600360233	0.121294169711699	-1.41938891844054	0.156683334813057	   
df.mm.trans1:exp2	-0.620766580696426	0.102512283605606	-6.05553362838638	3.63744260159781e-09	***
df.mm.trans2:exp2	0.0163673840538184	0.102512283605606	0.159662661664903	0.873239709871466	   
df.mm.trans1:exp3	-0.150029789464565	0.102512283605606	-1.46352987356884	0.144227684854645	   
df.mm.trans2:exp3	0.144216050601339	0.102512283605606	1.40681726646711	0.160376566195120	   
df.mm.trans1:exp4	-0.288612366892518	0.102512283605606	-2.81539301185498	0.0051499975980344	** 
df.mm.trans2:exp4	0.0764081775168588	0.102512283605606	0.745356310769772	0.456560908636142	   
df.mm.trans1:exp5	-0.360998341120643	0.102512283605606	-3.5215130170108	0.000486396367351651	***
df.mm.trans2:exp5	0.0610720677767287	0.102512283605606	0.595753656329525	0.551728179876208	   
df.mm.trans1:exp6	-0.127616818371811	0.102512283605606	-1.24489294241839	0.214011184858935	   
df.mm.trans2:exp6	-0.100110322899859	0.102512283605606	-0.976569044984038	0.329462865040466	   
df.mm.trans1:exp7	-0.093801809603012	0.102512283605606	-0.915029948643957	0.360811318879818	   
df.mm.trans2:exp7	0.0530941467308588	0.102512283605606	0.51792960671062	0.604837433886781	   
df.mm.trans1:exp8	-0.228450659977115	0.102512283605606	-2.22851986066403	0.0264860444389012	*  
df.mm.trans2:exp8	0.0722361494244224	0.102512283605606	0.704658474903707	0.481495467620933	   
df.mm.trans1:probe2	-0.188792045981113	0.0561482901521572	-3.36238281645803	0.000858925426366332	***
df.mm.trans1:probe3	-0.227228253626805	0.0561482901521572	-4.04693095748838	6.40169626064284e-05	***
df.mm.trans1:probe4	-0.0625794114931105	0.0561482901521572	-1.11453815109108	0.265819730343271	   
df.mm.trans1:probe5	0.055724556091503	0.0561482901521572	0.992453304285742	0.321667990997928	   
df.mm.trans1:probe6	-0.200794137379389	0.0561482901521572	-3.57613984032734	0.00039817976911305	***
df.mm.trans2:probe2	0.171459549348412	0.0561482901521572	3.05369137481783	0.00243511922874229	** 
df.mm.trans2:probe3	0.0196782240308909	0.0561482901521572	0.350468802835572	0.726199550547307	   
df.mm.trans2:probe4	0.058226677046165	0.0561482901521572	1.03701603180392	0.300450485652432	   
df.mm.trans2:probe5	0.0161915955964381	0.0561482901521572	0.288372015471178	0.773234161987268	   
df.mm.trans2:probe6	0.0991145240404096	0.0561482901521572	1.76522782388952	0.078404999689788	.  
df.mm.trans3:probe2	-0.0268862143426526	0.0561482901521572	-0.478842975801992	0.632351931543525	   
df.mm.trans3:probe3	-0.08820315442593	0.0561482901521572	-1.57089653463902	0.117118042081047	   
df.mm.trans3:probe4	-0.658849377107296	0.0561482901521572	-11.7340951135265	5.25980328367952e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32503410587075	0.344231135299402	12.5643315271553	4.24478185199421e-30	***
df.mm.trans1	0.490547293868212	0.286791775172519	1.71046500051518	0.0880737216360718	.  
df.mm.trans2	0.545396043526165	0.286791775172519	1.90171438214392	0.0580364841345913	.  
df.mm.exp2	0.298452399033016	0.395138819166806	0.755310246819932	0.45057521652214	   
df.mm.exp3	0.630116393529643	0.395138819166806	1.59467094338722	0.111696207241983	   
df.mm.exp4	0.0222478260339407	0.395138819166805	0.0563038227447577	0.95513217895237	   
df.mm.exp5	0.105857404321812	0.395138819166805	0.267899277891817	0.788936024008745	   
df.mm.exp6	0.407973872321874	0.395138819166805	1.03248238981463	0.302565252770669	   
df.mm.exp7	0.528627149568065	0.395138819166805	1.33782641422762	0.181829128283748	   
df.mm.exp8	0.581625991013969	0.395138819166805	1.47195355860098	0.141939768834755	   
df.mm.trans1:exp2	-0.180999153253078	0.333953254227228	-0.541989487935708	0.588173606553998	   
df.mm.trans2:exp2	-0.332368078278474	0.333953254227228	-0.995253299889464	0.320306590187853	   
df.mm.trans1:exp3	-0.396249875900492	0.333953254227228	-1.18654293942252	0.23621969032687	   
df.mm.trans2:exp3	-0.653928985703017	0.333953254227228	-1.95814527160760	0.0510138841176819	.  
df.mm.trans1:exp4	-0.145937323300938	0.333953254227228	-0.436999254996448	0.662383707018248	   
df.mm.trans2:exp4	0.0511926215630504	0.333953254227228	0.153292776504038	0.878256500882806	   
df.mm.trans1:exp5	0.155672578010225	0.333953254227228	0.46615080416106	0.641400042685143	   
df.mm.trans2:exp5	-0.377279165604653	0.333953254227228	-1.1297364551146	0.259367621762658	   
df.mm.trans1:exp6	-0.542735448535108	0.333953254227228	-1.62518388925721	0.105031254888372	   
df.mm.trans2:exp6	-0.21606949623089	0.333953254227228	-0.647005212543528	0.518056327011601	   
df.mm.trans1:exp7	-0.505402206105451	0.333953254227228	-1.51339206822511	0.131090347028561	   
df.mm.trans2:exp7	-0.685554951819758	0.333953254227228	-2.05284704712982	0.0408368750639566	*  
df.mm.trans1:exp8	-0.48310584960604	0.333953254227228	-1.44662716560123	0.148904061800354	   
df.mm.trans2:exp8	-0.608711359869514	0.333953254227228	-1.82274420795234	0.069202554242746	.  
df.mm.trans1:probe2	-0.161500497521538	0.18291373049251	-0.882932610289476	0.377883943500360	   
df.mm.trans1:probe3	-0.119905748428319	0.18291373049251	-0.65553169849777	0.512559850922883	   
df.mm.trans1:probe4	-0.0846443538620613	0.18291373049251	-0.462755604153661	0.64382961410184	   
df.mm.trans1:probe5	-0.0643968552432459	0.18291373049251	-0.352061351927229	0.725005962832006	   
df.mm.trans1:probe6	-0.0757393320083421	0.18291373049251	-0.414071331902793	0.679077690350439	   
df.mm.trans2:probe2	0.00152195481327775	0.18291373049251	0.00832061545724183	0.99336596749291	   
df.mm.trans2:probe3	0.0848636270407049	0.18291373049251	0.463954383370798	0.642971341177217	   
df.mm.trans2:probe4	-0.0689799425097137	0.18291373049251	-0.377117356493576	0.706316918082536	   
df.mm.trans2:probe5	-0.0234093303700854	0.18291373049251	-0.127980170253233	0.8982388025668	   
df.mm.trans2:probe6	-0.132451955127438	0.18291373049251	-0.72412254001272	0.469478421099168	   
df.mm.trans3:probe2	-0.251304537306621	0.18291373049251	-1.37389651739081	0.170360648433070	   
df.mm.trans3:probe3	-0.140465104880010	0.18291373049251	-0.767930895629304	0.443050495324054	   
df.mm.trans3:probe4	-0.362088132014334	0.18291373049251	-1.97955687109646	0.04854323888961	*