chr11.3889_chr11_103093517_103094652_-_2.R 

fitVsDatCorrelation=0.880214093436258
cont.fitVsDatCorrelation=0.232037719180358

fstatistic=7299.46147403918,53,715
cont.fstatistic=1727.28130001320,53,715

residuals=-1.00545107638422,-0.101565988839363,-0.00538037300655952,0.104145234837119,0.752837131753012
cont.residuals=-0.852117140253654,-0.317619057161975,-0.0700728083304531,0.280332794228489,1.20553061773729

predictedValues:
Include	Exclude	Both
chr11.3889_chr11_103093517_103094652_-_2.R.tl.Lung	91.8998659911266	122.369487264004	101.440766865104
chr11.3889_chr11_103093517_103094652_-_2.R.tl.cerebhem	93.7851031241152	100.614261191445	91.3480580329215
chr11.3889_chr11_103093517_103094652_-_2.R.tl.cortex	102.328865833155	130.685321338883	105.106095122629
chr11.3889_chr11_103093517_103094652_-_2.R.tl.heart	80.9675153658933	106.830014424370	92.6316606604145
chr11.3889_chr11_103093517_103094652_-_2.R.tl.kidney	84.9585764677693	106.914411819499	84.0616573455649
chr11.3889_chr11_103093517_103094652_-_2.R.tl.liver	78.0431813294854	98.2761524044614	75.4665109248919
chr11.3889_chr11_103093517_103094652_-_2.R.tl.stomach	83.3295476059338	105.143028858329	78.5151219871583
chr11.3889_chr11_103093517_103094652_-_2.R.tl.testicle	91.5019974936732	125.016520439076	95.9855112846466


diffExp=-30.4696212728774,-6.82915806732947,-28.3564555057278,-25.862499058477,-21.9558353517299,-20.232971074976,-21.8134812523949,-33.5145229454024
diffExpScore=0.994737798843474
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=-1,0,0,-1,0,0,0,-1
diffExp1.3Score=0.75
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	94.1273947206448	93.7336990204791	83.9669117777854
cerebhem	91.1308948134407	108.462564612079	89.638896292621
cortex	94.4167493538282	88.473545907379	90.8134434481754
heart	94.9265099803613	101.955555322515	110.226869115494
kidney	90.7472480402338	108.655547216595	100.690818253046
liver	108.215554460067	89.386515808818	103.518128772554
stomach	100.585407268812	99.679802950653	109.373708762899
testicle	93.3728790988403	101.081805351832	94.5718431527562
cont.diffExp=0.393695700165651,-17.3316697986386,5.94320344644929,-7.02904534215406,-17.9082991763607,18.8290386512487,0.905604318158964,-7.70892625299132
cont.diffExpScore=3.05341146879391

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=0,0,0,0,0,1,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.746133120531557
cont.tran.correlation=-0.609168888410693

tran.covariance=0.00704464222332078
cont.tran.covariance=-0.00284655546901427

tran.mean=100.166490684451
cont.tran.mean=97.434479620411

weightedLogRatios:
wLogRatio
Lung	-1.33547875936996
cerebhem	-0.321648344974592
cortex	-1.16197275381840
heart	-1.25640748624522
kidney	-1.04751611363781
liver	-1.03100178627482
stomach	-1.05541554245501
testicle	-1.45818741686356

cont.weightedLogRatios:
wLogRatio
Lung	0.0190394545629525
cerebhem	-0.800784763363817
cortex	0.293555976027844
heart	-0.327797644476914
kidney	-0.828144506865306
liver	0.87712503971751
stomach	0.0416615672768638
testicle	-0.363031546164735

varWeightedLogRatios=0.117885213383506
cont.varWeightedLogRatios=0.325124855607975

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.47897939144349	0.103983990388284	43.0737402432688	7.71855559764333e-201	***
df.mm.trans1	-0.134428386785823	0.0923397958966992	-1.45580121203872	0.145886510157635	   
df.mm.trans2	0.187503409496178	0.0839950522760801	2.23231493302582	0.0259031027405388	*  
df.mm.exp2	-0.0706465596567362	0.113237812376269	-0.623877821146796	0.532906811992387	   
df.mm.exp3	0.137744265487964	0.113237812376269	1.21641581197511	0.224227966395065	   
df.mm.exp4	-0.171613613297640	0.113237812376269	-1.51551508896515	0.130083957354884	   
df.mm.exp5	-0.025627691380594	0.113237812376269	-0.226317436223846	0.821019220165351	   
df.mm.exp6	-0.0869148887348909	0.113237812376269	-0.767542986843374	0.443012132062997	   
df.mm.exp7	0.00656398660450999	0.113237812376269	0.0579663847858438	0.953791600294735	   
df.mm.exp8	0.0723398660486078	0.113237812376269	0.63883136322199	0.523137225339407	   
df.mm.trans1:exp2	0.0909530165911026	0.107524615431634	0.845880882493666	0.397902188871178	   
df.mm.trans2:exp2	-0.125104483320698	0.090543829067887	-1.38170082498829	0.167495141300638	   
df.mm.trans1:exp3	-0.0302520350327652	0.107524615431634	-0.281349855670955	0.778523476807327	   
df.mm.trans2:exp3	-0.071997011149274	0.090543829067887	-0.795161988292906	0.426783155601544	   
df.mm.trans1:exp4	0.0449620715071794	0.107524615431634	0.418156078277416	0.67595856281156	   
df.mm.trans2:exp4	0.0358074823348954	0.090543829067887	0.395471261857593	0.69261309017042	   
df.mm.trans1:exp5	-0.0529080777464004	0.107524615431634	-0.492055493842154	0.622831150470142	   
df.mm.trans2:exp5	-0.109388735719476	0.090543829067887	-1.20813021545024	0.227396609795653	   
df.mm.trans1:exp6	-0.0765224021283334	0.107524615431634	-0.711673339366535	0.476899204983221	   
df.mm.trans2:exp6	-0.132348765644491	0.090543829067887	-1.46170939540518	0.144260206393201	   
df.mm.trans1:exp7	-0.10446035832847	0.107524615431634	-0.971501808298841	0.331626984445974	   
df.mm.trans2:exp7	-0.158287435711080	0.090543829067887	-1.74818579400261	0.0808610534861923	.  
df.mm.trans1:exp8	-0.0766786346308532	0.107524615431634	-0.713126332263953	0.47600026326356	   
df.mm.trans2:exp8	-0.050939025908781	0.090543829067887	-0.562589703055174	0.573890649198345	   
df.mm.trans1:probe2	-0.0868516575388178	0.058893657358974	-1.47472005362872	0.140727991476458	   
df.mm.trans1:probe3	0.771362248646121	0.058893657358974	13.0975436615261	2.7546879785188e-35	***
df.mm.trans1:probe4	-0.0145881560653656	0.058893657358974	-0.247703347347687	0.804435057265283	   
df.mm.trans1:probe5	-0.121049721835169	0.058893657358974	-2.05539488059530	0.0402035627941464	*  
df.mm.trans1:probe6	0.0459704064600759	0.058893657358974	0.780566338067151	0.435315933644823	   
df.mm.trans1:probe7	-0.116595933153310	0.058893657358974	-1.97977063035199	0.0481123625661339	*  
df.mm.trans1:probe8	-0.139910319585927	0.058893657358974	-2.37564325022528	0.0177810155342596	*  
df.mm.trans1:probe9	0.728050235272036	0.058893657358974	12.362116192485	5.94886173505477e-32	***
df.mm.trans1:probe10	-0.0350828731695355	0.058893657358974	-0.595698666762962	0.551565059138252	   
df.mm.trans1:probe11	0.0361748688371156	0.058893657358974	0.614240488014171	0.539251813442275	   
df.mm.trans1:probe12	-0.153369801469072	0.058893657358974	-2.6041819840503	0.00940061131230671	** 
df.mm.trans1:probe13	-0.0348127042418266	0.058893657358974	-0.591111263979294	0.554632687927345	   
df.mm.trans1:probe14	-0.0935735562402377	0.058893657358974	-1.58885626120788	0.112534911834554	   
df.mm.trans1:probe15	-0.0971116766229519	0.058893657358974	-1.64893268609602	0.0996006911173226	.  
df.mm.trans1:probe16	-0.0539716193787634	0.058893657358974	-0.916424990382081	0.359753019380131	   
df.mm.trans1:probe17	0.954912012105676	0.058893657358974	16.2141740711603	1.38832021028185e-50	***
df.mm.trans1:probe18	0.904540294733574	0.058893657358974	15.3588745426377	3.21765192768689e-46	***
df.mm.trans1:probe19	0.378940750720161	0.058893657358974	6.43432192384328	2.27117577466614e-10	***
df.mm.trans1:probe20	0.897399354520002	0.058893657358974	15.2376231119438	1.30926792267203e-45	***
df.mm.trans1:probe21	0.293347513322943	0.058893657358974	4.98096953861949	7.9401613256662e-07	***
df.mm.trans1:probe22	0.516083063675935	0.058893657358974	8.76296509368163	1.37163404718579e-17	***
df.mm.trans2:probe2	0.105265124091860	0.058893657358974	1.78737624410450	0.0743001591407263	.  
df.mm.trans2:probe3	0.444664285365191	0.058893657358974	7.55029158156765	1.32747880933068e-13	***
df.mm.trans2:probe4	0.367919551026466	0.058893657358974	6.24718462947358	7.17311344661569e-10	***
df.mm.trans2:probe5	0.373638487925335	0.058893657358974	6.3442907892084	3.9636940996414e-10	***
df.mm.trans2:probe6	0.114135061616976	0.058893657358974	1.93798562927226	0.0530183367669276	.  
df.mm.trans3:probe2	0.0629646864777405	0.058893657358974	1.06912508581276	0.285374109653677	   
df.mm.trans3:probe3	0.339264133094653	0.058893657358974	5.76062259177995	1.24513870464436e-08	***
df.mm.trans3:probe4	-0.0525462743861488	0.058893657358974	-0.892222978543579	0.372573597483954	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.81601424563386	0.213128688523402	22.5967432118133	9.81817845808807e-86	***
df.mm.trans1	-0.240706323704260	0.189262400149241	-1.27181269768561	0.203853186545634	   
df.mm.trans2	-0.262107182469192	0.172158764702231	-1.52247364763878	0.128332569363628	   
df.mm.exp2	0.0482285416859342	0.232095598109808	0.207796020599738	0.835447411792661	   
df.mm.exp3	-0.133069338706499	0.232095598109808	-0.573338485478476	0.566595807661089	   
df.mm.exp4	-0.179584775599770	0.232095598109808	-0.773753475129696	0.439332338554561	   
df.mm.exp5	-0.0704777935562893	0.232095598109808	-0.303658467158628	0.761476443709197	   
df.mm.exp6	-0.117335887521267	0.232095598109808	-0.505549818595668	0.613328524833166	   
df.mm.exp7	-0.136484357118815	0.232095598109808	-0.58805232942954	0.556682854975395	   
df.mm.exp8	-0.0515128047831477	0.232095598109808	-0.221946496196693	0.824418911080932	   
df.mm.trans1:exp2	-0.0805807918542588	0.220385659228456	-0.365635369090541	0.714745322932398	   
df.mm.trans2:exp2	0.0977187725251686	0.185581332963542	0.526554966303457	0.598665975039968	   
df.mm.trans1:exp3	0.136138697999204	0.220385659228456	0.617729386185152	0.536950428732912	   
df.mm.trans2:exp3	0.0753151571179445	0.185581332963542	0.405833689818039	0.684986221436526	   
df.mm.trans1:exp4	0.188038660963848	0.220385659228456	0.853225484916526	0.393820008279364	   
df.mm.trans2:exp4	0.263663989175755	0.185581332963542	1.42074628393553	0.155826471503558	   
df.mm.trans1:exp5	0.0339068139003083	0.220385659228456	0.153852179034752	0.87776970373144	   
df.mm.trans2:exp5	0.218202782174292	0.185581332963542	1.17577979794529	0.240074230992883	   
df.mm.trans1:exp6	0.256811872448584	0.220385659228456	1.16528395426296	0.244292673331521	   
df.mm.trans2:exp6	0.0698479558285188	0.185581332963542	0.376373823342677	0.70675066005637	   
df.mm.trans1:exp7	0.202842419576995	0.220385659228456	0.920397544409752	0.357675473035496	   
df.mm.trans2:exp7	0.197989662719459	0.185581332963542	1.06686194973260	0.28639427310742	   
df.mm.trans1:exp8	0.0434646064616412	0.220385659228456	0.197220665871843	0.843710908992269	   
df.mm.trans2:exp8	0.126985175149971	0.185581332963542	0.68425618634239	0.49403512423062	   
df.mm.trans1:probe2	-0.0926291610851984	0.120710196910072	-0.767368154938943	0.443115977068476	   
df.mm.trans1:probe3	-0.0255116156222389	0.120710196910072	-0.211345986298447	0.832677525153251	   
df.mm.trans1:probe4	-0.129318057133635	0.120710196910072	-1.07131013322740	0.284391485345677	   
df.mm.trans1:probe5	0.0929254023023693	0.120710196910072	0.769822307320051	0.441659559009143	   
df.mm.trans1:probe6	0.00153634237467323	0.120710196910072	0.0127275276985738	0.989848726714056	   
df.mm.trans1:probe7	-0.0228576268876870	0.120710196910072	-0.189359536085553	0.849864776091702	   
df.mm.trans1:probe8	-0.0155963021594766	0.120710196910072	-0.129204512615415	0.897232161827288	   
df.mm.trans1:probe9	-0.0833385027485357	0.120710196910072	-0.690401514385915	0.490165770238375	   
df.mm.trans1:probe10	0.0117269936856352	0.120710196910072	0.0971499838938355	0.922634512308942	   
df.mm.trans1:probe11	0.0397535034349154	0.120710196910072	0.329330118353891	0.742002628931263	   
df.mm.trans1:probe12	-0.0131582815239369	0.120710196910072	-0.109007207847898	0.913227357942467	   
df.mm.trans1:probe13	-0.0901218877636817	0.120710196910072	-0.746597139849103	0.455552190951071	   
df.mm.trans1:probe14	0.0166798589058624	0.120710196910072	0.138181026399027	0.890136255694563	   
df.mm.trans1:probe15	0.0600970236862685	0.120710196910072	0.497862030090467	0.618734376763892	   
df.mm.trans1:probe16	-0.108128977094706	0.120710196910072	-0.895773346929933	0.370675297968841	   
df.mm.trans1:probe17	-0.180650488952289	0.120710196910072	-1.49656361746201	0.134948115548051	   
df.mm.trans1:probe18	-0.0199811561379431	0.120710196910072	-0.165529977163644	0.868573647968859	   
df.mm.trans1:probe19	-0.110645888894601	0.120710196910072	-0.916624210107377	0.359648651899508	   
df.mm.trans1:probe20	-0.128094743796612	0.120710196910072	-1.06117583332286	0.288968309306741	   
df.mm.trans1:probe21	0.0335822534237589	0.120710196910072	0.278205605519618	0.780934964051417	   
df.mm.trans1:probe22	-0.0333973375765904	0.120710196910072	-0.276673706376862	0.782110623247364	   
df.mm.trans2:probe2	-0.0363913213984149	0.120710196910072	-0.301476779343887	0.76313861200594	   
df.mm.trans2:probe3	-0.134848783917952	0.120710196910072	-1.11712835675692	0.264314690947350	   
df.mm.trans2:probe4	0.0477893813649488	0.120710196910072	0.395901776223192	0.692295597457458	   
df.mm.trans2:probe5	-0.0884046764314022	0.120710196910072	-0.732371238672263	0.464182008224383	   
df.mm.trans2:probe6	0.0773624949086262	0.120710196910072	0.640894447105082	0.521796620509265	   
df.mm.trans3:probe2	0.185429901208731	0.120710196910072	1.53615772283823	0.124942079063075	   
df.mm.trans3:probe3	0.186228849316291	0.120710196910072	1.54277645205922	0.123327473954854	   
df.mm.trans3:probe4	0.0972295469697056	0.120710196910072	0.80547915137725	0.420811015246670