chr6.20215_chr6_53135751_53138300_+_1.R 

fitVsDatCorrelation=0.922888162325576
cont.fitVsDatCorrelation=0.322505078554099

fstatistic=6242.10507563693,44,508
cont.fstatistic=1023.37100590313,44,508

residuals=-0.73042961525056,-0.102384676257991,0.00526433300444531,0.122120932810850,1.04756550640588
cont.residuals=-0.934917722099545,-0.4141156602453,-0.0139907968429978,0.321909290798286,1.49484861970214

predictedValues:
Include	Exclude	Both
chr6.20215_chr6_53135751_53138300_+_1.R.tl.Lung	77.1322427860367	49.0108271869002	110.622308452910
chr6.20215_chr6_53135751_53138300_+_1.R.tl.cerebhem	73.7951667662081	64.6113472122755	121.226960059998
chr6.20215_chr6_53135751_53138300_+_1.R.tl.cortex	96.0625001658015	47.0092185298766	136.217881705967
chr6.20215_chr6_53135751_53138300_+_1.R.tl.heart	87.2586830510107	45.2241553419162	114.933164622556
chr6.20215_chr6_53135751_53138300_+_1.R.tl.kidney	81.3082848227679	46.0313001503693	121.627959322970
chr6.20215_chr6_53135751_53138300_+_1.R.tl.liver	78.9670970630014	45.2101902755273	102.910760354243
chr6.20215_chr6_53135751_53138300_+_1.R.tl.stomach	106.875698278599	55.0826030045439	124.329085505324
chr6.20215_chr6_53135751_53138300_+_1.R.tl.testicle	155.720792622390	52.6523935876931	221.701793993744


diffExp=28.1214155991365,9.1838195539326,49.0532816359249,42.0345277090944,35.2769846723986,33.7569067874741,51.7930952740549,103.068399034697
diffExpScore=0.997169451602915
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	98.4127846028106	78.1607717523695	104.003805567939
cerebhem	109.082380174378	76.9270015515216	87.901825166547
cortex	111.298992111340	94.0708079323753	114.801857385387
heart	101.702561848023	86.32025050204	105.191767464969
kidney	102.423209150408	95.56848096763	82.840550681038
liver	104.080248540614	89.3510238443157	82.6058305247722
stomach	95.416008204089	86.5031159897278	100.629851795340
testicle	118.942052163414	118.246856852457	88.754889998179
cont.diffExp=20.2520128504411,32.1553786228565,17.2281841789643,15.3823113459833,6.85472818277792,14.7292246962981,8.91289221436122,0.695195310957402
cont.diffExpScore=0.991468299467802

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

tran.correlation=0.0756859251555106
cont.tran.correlation=0.688194490681454

tran.covariance=0.00242652691417875
cont.tran.covariance=0.00620744916431331

tran.mean=72.6220313028073
cont.tran.mean=97.9066591367196

weightedLogRatios:
wLogRatio
Lung	1.86778557061865
cerebhem	0.562823962251137
cortex	3.00703465360456
heart	2.72116436666579
kidney	2.3404403938175
liver	2.28112749388681
stomach	2.87685901604335
testicle	4.88597294782135

cont.weightedLogRatios:
wLogRatio
Lung	1.03081522745304
cerebhem	1.57771385345259
cortex	0.778324530253883
heart	0.744516315920162
kidney	0.318260746046190
liver	0.697161301229145
stomach	0.442200201057596
testicle	0.0279950487052635

varWeightedLogRatios=1.47567336416893
cont.varWeightedLogRatios=0.221669284868058

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.63774994277039	0.102467830855544	35.5013852874351	1.06941513446886e-139	***
df.mm.trans1	0.687177235400831	0.0817854961267665	8.40218948278696	4.4211801193502e-16	***
df.mm.trans2	0.198703632730904	0.0817854961267664	2.42957054907285	0.0154622946544406	*  
df.mm.exp2	0.140577944096601	0.109264337991961	1.28658578526275	0.198824702095493	   
df.mm.exp3	-0.0303537822791967	0.109264337991961	-0.277801365358842	0.781277865499681	   
df.mm.exp4	0.00471679587349882	0.109264337991961	0.0431686674736074	0.965584043707842	   
df.mm.exp5	-0.104838204455567	0.109264337991961	-0.959491508229158	0.337767632839433	   
df.mm.exp6	0.0150507229229769	0.109264337991961	0.137745976405259	0.890495761309402	   
df.mm.exp7	0.32612757508156	0.109264337991961	2.98475770846252	0.00297475497943145	** 
df.mm.exp8	0.0790022524427416	0.109264337991961	0.723037853838039	0.469989386083246	   
df.mm.trans1:exp2	-0.184806093129404	0.0851380862536112	-2.1706629930452	0.0304190879835861	*  
df.mm.trans2:exp2	0.135770868007630	0.0851380862536112	1.59471364675960	0.111398207914036	   
df.mm.trans1:exp3	0.249831417782176	0.0851380862536112	2.93442604568269	0.00349281532519018	** 
df.mm.trans2:exp3	-0.0113437330097885	0.0851380862536112	-0.133239229455987	0.894056949532543	   
df.mm.trans1:exp4	0.118638891944809	0.0851380862536112	1.39348788732936	0.164081656836957	   
df.mm.trans2:exp4	-0.0851266782620776	0.0851380862536112	-0.999866006014047	0.317851379512442	   
df.mm.trans1:exp5	0.157564732634714	0.0851380862536112	1.85069619917644	0.0647935970304674	.  
df.mm.trans2:exp5	0.0421185709032945	0.0851380862536112	0.494708922371484	0.621019481323568	   
df.mm.trans1:exp6	0.00845916239158542	0.0851380862536112	0.0993581458524577	0.920893108401254	   
df.mm.trans2:exp6	-0.0957694496207529	0.0851380862536112	-1.12487200305951	0.261174480181677	   
df.mm.trans1:exp7	1.74982331329280e-05	0.0851380862536112	0.000205527677481543	0.999836093312692	   
df.mm.trans2:exp7	-0.209334880422084	0.0851380862536112	-2.45876892039260	0.0142739023295651	*  
df.mm.trans1:exp8	0.623540972813492	0.0851380862536112	7.32387818720842	9.51177876345666e-13	***
df.mm.trans2:exp8	-0.00733178934161582	0.0851380862536112	-0.086116445227295	0.931407781522926	   
df.mm.trans1:probe2	-0.0826395622698607	0.059309791513886	-1.39335445565531	0.164121960321563	   
df.mm.trans1:probe3	0.106097920597950	0.059309791513886	1.78887697781082	0.0742303322901756	.  
df.mm.trans1:probe4	0.0566404335458702	0.059309791513886	0.954992963221077	0.340035468118989	   
df.mm.trans1:probe5	0.118342559052505	0.059309791513886	1.99532920335419	0.0465406089869588	*  
df.mm.trans1:probe6	0.151660208033618	0.059309791513886	2.55708550245216	0.010844770463685	*  
df.mm.trans2:probe2	0.226834728728352	0.059309791513886	3.82457471082568	0.000147286349271899	***
df.mm.trans2:probe3	0.120859356036068	0.059309791513886	2.03776396697957	0.0420911140211037	*  
df.mm.trans2:probe4	0.267836733371058	0.059309791513886	4.5158940292068	7.84616712755764e-06	***
df.mm.trans2:probe5	0.163893358071354	0.059309791513886	2.76334402613744	0.00592885227878832	** 
df.mm.trans2:probe6	0.165566064168860	0.059309791513886	2.79154689205233	0.0054432120101269	** 
df.mm.trans3:probe2	0.0620896560467328	0.059309791513886	1.04687024624249	0.295657263989115	   
df.mm.trans3:probe3	0.336828863668497	0.059309791513886	5.67914428749318	2.28066779001817e-08	***
df.mm.trans3:probe4	-0.124705492129254	0.059309791513886	-2.10261221538870	0.0359911376644429	*  
df.mm.trans3:probe5	0.515158662976161	0.059309791513886	8.68589569827688	5.16257707706787e-17	***
df.mm.trans3:probe6	0.161186707505724	0.059309791513886	2.71770821295142	0.00679809793450599	** 
df.mm.trans3:probe7	0.352674090078492	0.059309791513886	5.94630466701139	5.10537015919185e-09	***
df.mm.trans3:probe8	-0.649175395863686	0.059309791513886	-10.9455012282702	3.52370355288817e-25	***
df.mm.trans3:probe9	0.815731256799187	0.059309791513886	13.7537367098686	8.16370633694145e-37	***
df.mm.trans3:probe10	0.25948708653765	0.059309791513886	4.37511378668221	1.47362620970965e-05	***
df.mm.trans3:probe11	0.278125143749349	0.059309791513886	4.68936303180618	3.52654138880290e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48654674949299	0.25188464847845	17.8119102398447	1.69743857353648e-55	***
df.mm.trans1	0.141622077252784	0.201043691181169	0.704434326790996	0.481485137691361	   
df.mm.trans2	-0.150159248564561	0.201043691181169	-0.746898585488297	0.455470319647522	   
df.mm.exp2	0.255228638310046	0.268591704699355	0.95024765785575	0.342438285353007	   
df.mm.exp3	0.209549202478485	0.268591704699355	0.780177491754787	0.435649762723582	   
df.mm.exp4	0.120820563074897	0.268591704699355	0.449829838230248	0.653025020542877	   
df.mm.exp5	0.468527611790892	0.268591704699355	1.74438600892508	0.0816965985644546	.  
df.mm.exp6	0.420143545355884	0.268591704699355	1.56424616994842	0.118382468265678	   
df.mm.exp7	0.103466738709086	0.268591704699355	0.385219412583499	0.700236101373131	   
df.mm.exp8	0.762021227315617	0.268591704699355	2.83709889018567	0.00473444763681296	** 
df.mm.trans1:exp2	-0.152295980567562	0.209284970210322	-0.727696692287606	0.467134528509254	   
df.mm.trans2:exp2	-0.271139579613703	0.209284970210322	-1.29555208547092	0.195718226538009	   
df.mm.trans1:exp3	-0.0864997202854256	0.209284970210322	-0.413310713131942	0.679553390305525	   
df.mm.trans2:exp3	-0.0242693095945807	0.209284970210322	-0.115962983725927	0.907727704193667	   
df.mm.trans1:exp4	-0.0879387905356203	0.209284970210322	-0.420186841163250	0.674526634761248	   
df.mm.trans2:exp4	-0.0215242218055636	0.209284970210322	-0.102846476667353	0.918125403236756	   
df.mm.trans1:exp5	-0.428584993451704	0.209284970210322	-2.04785366584612	0.041087965250691	*  
df.mm.trans2:exp5	-0.267452424779411	0.209284970210322	-1.27793421816499	0.201856256535795	   
df.mm.trans1:exp6	-0.364152043621815	0.209284970210322	-1.73998182122614	0.0824678111631463	.  
df.mm.trans2:exp6	-0.286338726702657	0.209284970210322	-1.36817625467753	0.171861745734938	   
df.mm.trans1:exp7	-0.134391093894421	0.209284970210322	-0.642144028591083	0.521069336951666	   
df.mm.trans2:exp7	-0.00205418410998539	0.209284970210322	-0.00981524907364838	0.99217254333638	   
df.mm.trans1:exp8	-0.572555529859946	0.209284970210322	-2.73576993744249	0.00644119033502216	** 
df.mm.trans2:exp8	-0.348014662518304	0.209284970210322	-1.66287460665983	0.0969543421700137	.  
df.mm.trans1:probe2	-0.0460375879441994	0.145794303071236	-0.315770828999437	0.752306149002234	   
df.mm.trans1:probe3	-0.172672073709302	0.145794303071236	-1.18435405274329	0.236826768732887	   
df.mm.trans1:probe4	-0.345415813602104	0.145794303071236	-2.3691996623032	0.0181995726883453	*  
df.mm.trans1:probe5	0.0229366488632025	0.145794303071236	0.157321982958384	0.875053604767902	   
df.mm.trans1:probe6	-0.121778980737439	0.145794303071236	-0.835279418825692	0.403953064497725	   
df.mm.trans2:probe2	0.0381425301494880	0.145794303071236	0.261618796797919	0.793721388982978	   
df.mm.trans2:probe3	0.0728882423719276	0.145794303071236	0.499938892237194	0.617334585097657	   
df.mm.trans2:probe4	0.150939160754112	0.145794303071236	1.03528846857865	0.301026783993659	   
df.mm.trans2:probe5	0.163838364947634	0.145794303071236	1.12376383367724	0.2616439848832	   
df.mm.trans2:probe6	-0.0453418252330498	0.145794303071236	-0.310998607475735	0.755929251458457	   
df.mm.trans3:probe2	0.265091884698188	0.145794303071236	1.81825955550994	0.0696132476317595	.  
df.mm.trans3:probe3	0.0185954446717083	0.145794303071236	0.127545756452654	0.898558932369447	   
df.mm.trans3:probe4	0.257143495501531	0.145794303071236	1.76374172436552	0.0783765239030483	.  
df.mm.trans3:probe5	0.20346504504215	0.145794303071236	1.39556238313876	0.163456010077959	   
df.mm.trans3:probe6	0.361720470636550	0.145794303071236	2.48103295544964	0.0134227907786437	*  
df.mm.trans3:probe7	0.106699189288239	0.145794303071236	0.731847452476279	0.464599158411129	   
df.mm.trans3:probe8	-0.00522281441550782	0.145794303071236	-0.0358231721369518	0.9714374280886	   
df.mm.trans3:probe9	0.142710448105360	0.145794303071236	0.97884790488439	0.328121051170602	   
df.mm.trans3:probe10	0.431074141423653	0.145794303071236	2.95672829694194	0.00325386277589821	** 
df.mm.trans3:probe11	0.0493197302392879	0.145794303071236	0.338282972656276	0.73528971463158