chr11.3283_chr11_32186007_32186680_+_1.R 

fitVsDatCorrelation=0.904609609144281
cont.fitVsDatCorrelation=0.249915281767767

fstatistic=9868.52453083606,53,715
cont.fstatistic=1901.49373078054,53,715

residuals=-1.13361843284068,-0.0812120355000372,-0.010803319614817,0.0742498324381164,0.808180161774327
cont.residuals=-0.629692771740744,-0.22319314701221,-0.0737376279958738,0.113784192769076,2.07642772702950

predictedValues:
Include	Exclude	Both
chr11.3283_chr11_32186007_32186680_+_1.R.tl.Lung	86.8525329148755	47.4366473153066	63.6314940818473
chr11.3283_chr11_32186007_32186680_+_1.R.tl.cerebhem	61.1995342616873	71.8056786290582	59.3218682369215
chr11.3283_chr11_32186007_32186680_+_1.R.tl.cortex	71.6673157069548	48.8115122279369	74.1258310474733
chr11.3283_chr11_32186007_32186680_+_1.R.tl.heart	82.4365400163131	46.3136710713526	65.041441303831
chr11.3283_chr11_32186007_32186680_+_1.R.tl.kidney	75.7715607426703	46.9558934146393	68.5285653257679
chr11.3283_chr11_32186007_32186680_+_1.R.tl.liver	79.8496328096108	50.2046770834883	63.944214239725
chr11.3283_chr11_32186007_32186680_+_1.R.tl.stomach	77.7377731897774	50.3694329740921	70.2020045744813
chr11.3283_chr11_32186007_32186680_+_1.R.tl.testicle	78.3448079138505	55.8454647001946	73.4787230097272


diffExp=39.4158855995689,-10.6061443673709,22.8558034790179,36.1228689449606,28.815667328031,29.6449557261225,27.3683402156853,22.4993432136559
diffExpScore=1.10253969688832
diffExp1.5=1,0,0,1,1,1,1,0
diffExp1.5Score=0.833333333333333
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	59.4968559691507	66.3472560513965	69.3847965748077
cerebhem	60.8070519687553	60.8049239424247	63.2033701923742
cortex	61.4619738482575	70.4054621477905	65.8798895789774
heart	61.5692767336719	73.4991449080347	63.4616328339546
kidney	60.2620440676855	65.1971695693972	62.176756687625
liver	65.2870949977628	67.7256840899987	72.6920663337971
stomach	59.7146232687954	72.5685870652636	67.4549056286376
testicle	65.2415687338828	74.1246635618356	65.3456446077017
cont.diffExp=-6.85040008224581,0.00212802633063092,-8.94348829953302,-11.9298681743628,-4.93512550171173,-2.43858909223583,-12.8539637964682,-8.88309482795287
cont.diffExpScore=0.982782248060966

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

tran.correlation=-0.80196952786252
cont.tran.correlation=0.32341492489539

tran.covariance=-0.0125167754660415
cont.tran.covariance=0.00081221516965941

tran.mean=64.475167185738
cont.tran.mean=65.2820863077565

weightedLogRatios:
wLogRatio
Lung	2.51712766495838
cerebhem	-0.670310027316009
cortex	1.56700009570853
heart	2.37770978620751
kidney	1.95638943313074
liver	1.92488463689733
stomach	1.79500271391449
testicle	1.41907412856511

cont.weightedLogRatios:
wLogRatio
Lung	-0.451217788032727
cerebhem	0.000143756710342818
cortex	-0.568724042228741
heart	-0.745409397334848
kidney	-0.325721551528155
liver	-0.153913365599093
stomach	-0.816288083940073
testicle	-0.541487759808244

varWeightedLogRatios=0.986247497313239
cont.varWeightedLogRatios=0.078520219502045

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22554601775174	0.0782160822354215	54.0240049998072	1.30693231564403e-254	***
df.mm.trans1	0.258613464888791	0.0615262005743075	4.2033062739906	2.96387283312704e-05	***
df.mm.trans2	-0.386013562033518	0.0615262005743075	-6.27397041309767	6.09518092094141e-10	***
df.mm.exp2	0.134626932014484	0.081253631210624	1.65687280689655	0.0979838979839702	.  
df.mm.exp3	-0.316261280395027	0.081253631210624	-3.89227257518154	0.00010859595396875	***
df.mm.exp4	-0.0980568496619085	0.081253631210624	-1.20679960022621	0.227908440380768	   
df.mm.exp5	-0.220817111975903	0.081253631210624	-2.71762761474014	0.00673436914584593	** 
df.mm.exp6	-0.0322557820949074	0.081253631210624	-0.396976499564612	0.691503254555882	   
df.mm.exp7	-0.149149281502352	0.081253631210624	-1.83560142827009	0.0668317739026256	.  
df.mm.exp8	-0.083786064336188	0.081253631210624	-1.03116701478854	0.302811178548208	   
df.mm.trans1:exp2	-0.484699009137425	0.0610872446672566	-7.93453709980847	8.1615835395832e-15	***
df.mm.trans2:exp2	0.279941550367081	0.0610872446672566	4.58265145026475	5.4173606951448e-06	***
df.mm.trans1:exp3	0.124084419680568	0.0610872446672566	2.03126561619301	0.0425974584248959	*  
df.mm.trans2:exp3	0.344832391656391	0.0610872446672566	5.64491643934344	2.38381509767844e-08	***
df.mm.trans1:exp4	0.0458739785746601	0.0610872446672566	0.750958384594632	0.452924777133411	   
df.mm.trans2:exp4	0.0740989586031342	0.0610872446672566	1.21300214155594	0.225529597001489	   
df.mm.trans1:exp5	0.0843284896230291	0.0610872446672566	1.38045986657883	0.167876501607627	   
df.mm.trans2:exp5	0.210630755004559	0.0610872446672566	3.44803168242189	0.000597763354523258	***
df.mm.trans1:exp6	-0.0518105982658179	0.0610872446672566	-0.848141024333823	0.396643272747101	   
df.mm.trans2:exp6	0.0889688933600186	0.0610872446672566	1.45642341285212	0.145714581669391	   
df.mm.trans1:exp7	0.0382789056769479	0.0610872446672566	0.626626816865843	0.531103897829364	   
df.mm.trans2:exp7	0.209138703895379	0.0610872446672566	3.4236067616826	0.000653189992579362	***
df.mm.trans1:exp8	-0.0193058934180114	0.0610872446672566	-0.316038045637366	0.75206586400434	   
df.mm.trans2:exp8	0.246979301457746	0.0610872446672566	4.04305846176312	5.84879930609761e-05	***
df.mm.trans1:probe2	0.0127906953218991	0.0463990931259019	0.27566692493735	0.782883551683859	   
df.mm.trans1:probe3	-0.080873097614851	0.0463990931259019	-1.74298875616826	0.081765459889186	.  
df.mm.trans1:probe4	-0.0882273593021743	0.0463990931259019	-1.90148887312891	0.0576397347494411	.  
df.mm.trans1:probe5	-0.349492901298438	0.0463990931259019	-7.53232181392219	1.50823859996067e-13	***
df.mm.trans1:probe6	-0.0128408165231845	0.0463990931259019	-0.27674714435348	0.782054251710296	   
df.mm.trans2:probe2	-0.00895766741762619	0.0463990931259019	-0.193056950344265	0.846969192203924	   
df.mm.trans2:probe3	0.212204533420579	0.0463990931259019	4.57346295206183	5.65346901917085e-06	***
df.mm.trans2:probe4	0.0678393819757264	0.0463990931259019	1.46208422202665	0.144157502197068	   
df.mm.trans2:probe5	0.147700541998293	0.0463990931259019	3.18326355210249	0.00151934397252333	** 
df.mm.trans2:probe6	0.0976414435957188	0.0463990931259019	2.10438258633144	0.0356935908650538	*  
df.mm.trans3:probe2	-0.059817030049482	0.0463990931259019	-1.28918532711773	0.197750570986600	   
df.mm.trans3:probe3	-0.0520435832338585	0.0463990931259019	-1.12165087133579	0.262387461049491	   
df.mm.trans3:probe4	-0.144071831364550	0.0463990931259019	-3.10505705302511	0.00197754112464603	** 
df.mm.trans3:probe5	-0.0525775731934003	0.0463990931259019	-1.13315950056897	0.257527100795047	   
df.mm.trans3:probe6	-0.00517390646938234	0.0463990931259019	-0.111508784349366	0.911244195010418	   
df.mm.trans3:probe7	-0.0419469242848719	0.0463990931259019	-0.90404620993455	0.366275390816695	   
df.mm.trans3:probe8	1.33622521950418	0.0463990931259019	28.7985201753489	1.11288308970679e-121	***
df.mm.trans3:probe9	-0.173448782076251	0.0463990931259019	-3.7381933652368	0.000200154961926678	***
df.mm.trans3:probe10	0.132096340425140	0.0463990931259019	2.8469595314439	0.00454044444808412	** 
df.mm.trans3:probe11	-0.25642782345812	0.0463990931259019	-5.52656972760899	4.57788606785476e-08	***
df.mm.trans3:probe12	0.115680964010744	0.0463990931259019	2.49317295268786	0.0128857776956647	*  
df.mm.trans3:probe13	-0.256149066589909	0.0463990931259019	-5.52056191906294	4.73056799379935e-08	***
df.mm.trans3:probe14	-0.198194003598806	0.0463990931259019	-4.27150597665811	2.20409034090892e-05	***
df.mm.trans3:probe15	-0.059949309746368	0.0463990931259019	-1.29203623837428	0.196762039764473	   
df.mm.trans3:probe16	0.623327959842822	0.0463990931259019	13.4340548025680	7.50906907279412e-37	***
df.mm.trans3:probe17	-0.308672763710768	0.0463990931259019	-6.65256027468464	5.72867712965732e-11	***
df.mm.trans3:probe18	0.000636212541447012	0.0463990931259019	0.0137117451783093	0.989063778032023	   
df.mm.trans3:probe19	-0.0906207325788254	0.0463990931259019	-1.95307120190755	0.0512008847404803	.  
df.mm.trans3:probe20	0.590378976812822	0.0463990931259019	12.7239335305726	1.40890437024662e-33	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06096232468124	0.177679043164103	22.8556066734926	3.20463924786637e-87	***
df.mm.trans1	0.0238191577010771	0.139765584456938	0.170422195089205	0.864726329686538	   
df.mm.trans2	0.141530726839540	0.139765584456938	1.01262930634505	0.311579802545822	   
df.mm.exp2	0.0278608140336178	0.184579271097453	0.150942269237308	0.880063830380668	   
df.mm.exp3	0.143698189865564	0.184579271097453	0.77851748471634	0.43652156177443	   
df.mm.exp4	0.225843147521789	0.184579271097453	1.22355639492449	0.221522687086877	   
df.mm.exp5	0.104979166627560	0.184579271097453	0.568748408222579	0.569705485517244	   
df.mm.exp6	0.0668695289742471	0.184579271097453	0.362280816132066	0.71724924384593	   
df.mm.exp7	0.121491668992260	0.184579271097453	0.658208629115867	0.51061595064155	   
df.mm.exp8	0.262996250307882	0.184579271097453	1.42484174276009	0.154639288117211	   
df.mm.trans1:exp2	-0.00607851576002558	0.138768433189232	-0.0438033032464709	0.965073423838035	   
df.mm.trans2:exp2	-0.115092446700104	0.138768433189232	-0.829384926059931	0.407163430165859	   
df.mm.trans1:exp3	-0.111202987997719	0.138768433189232	-0.801356514893244	0.42319150425759	   
df.mm.trans2:exp3	-0.0843297466393093	0.138768433189232	-0.607701223550695	0.54357861073827	   
df.mm.trans1:exp4	-0.191603626025979	0.138768433189232	-1.38074360012913	0.167789249568131	   
df.mm.trans2:exp4	-0.123471779529532	0.138768433189232	-0.88976849195349	0.373889476910597	   
df.mm.trans1:exp5	-0.0922001829969451	0.138768433189232	-0.66441755432387	0.50663731279376	   
df.mm.trans2:exp5	-0.122465514404143	0.138768433189232	-0.882517094050793	0.377793840863521	   
df.mm.trans1:exp6	0.0260013911023969	0.138768433189232	0.187372520571303	0.851421723878316	   
df.mm.trans2:exp6	-0.0463064443120152	0.138768433189232	-0.333695807092303	0.73870695326227	   
df.mm.trans1:exp7	-0.117838203012925	0.138768433189232	-0.849171532060426	0.396070072575552	   
df.mm.trans2:exp7	-0.0318619301027338	0.138768433189232	-0.229605028827306	0.818464364888705	   
df.mm.trans1:exp8	-0.170822897561272	0.138768433189232	-1.23099247887543	0.218730449721102	   
df.mm.trans2:exp8	-0.152150336049660	0.138768433189232	-1.09643333539826	0.273258329294835	   
df.mm.trans1:probe2	0.0219834275542342	0.105402191399441	0.208567082546926	0.834845609928681	   
df.mm.trans1:probe3	-0.112046377351264	0.105402191399441	-1.06303650677094	0.288124290931005	   
df.mm.trans1:probe4	0.052199426171571	0.105402191399441	0.495240426014972	0.62058257724507	   
df.mm.trans1:probe5	0.0669863368684037	0.105402191399441	0.63553077956934	0.52528564378362	   
df.mm.trans1:probe6	0.000568872638851834	0.105402191399441	0.00539716139957645	0.99569521461438	   
df.mm.trans2:probe2	-0.0321411331387435	0.105402191399441	-0.304937997132704	0.76050211711636	   
df.mm.trans2:probe3	-0.0980992052376488	0.105402191399441	-0.93071314680625	0.352316098607941	   
df.mm.trans2:probe4	0.101551606918263	0.105402191399441	0.96346769995905	0.335638597507557	   
df.mm.trans2:probe5	-0.173193908258963	0.105402191399441	-1.64317179708923	0.100787037639103	   
df.mm.trans2:probe6	0.00452581079567825	0.105402191399441	0.0429384886176308	0.965762555480674	   
df.mm.trans3:probe2	0.107158418728012	0.105402191399441	1.01666215194631	0.309658066267495	   
df.mm.trans3:probe3	-0.129659317095411	0.105402191399441	-1.23013872267648	0.219049740319836	   
df.mm.trans3:probe4	-0.115866446081475	0.105402191399441	-1.09927928957736	0.272016260403557	   
df.mm.trans3:probe5	-0.0660368119302496	0.105402191399441	-0.626522191364986	0.531172459107274	   
df.mm.trans3:probe6	-0.0505929435281727	0.105402191399441	-0.479998972093866	0.631374927771386	   
df.mm.trans3:probe7	0.0232223888516050	0.105402191399441	0.220321689172471	0.825683521928601	   
df.mm.trans3:probe8	0.0266613688613353	0.105402191399441	0.252948904641812	0.800380403173808	   
df.mm.trans3:probe9	-0.00614815369527158	0.105402191399441	-0.058330416224194	0.953501737918614	   
df.mm.trans3:probe10	0.0903480760273443	0.105402191399441	0.857174550431816	0.391635632775597	   
df.mm.trans3:probe11	0.0396227092254121	0.105402191399441	0.375919216662721	0.70708846091961	   
df.mm.trans3:probe12	0.0521697183815267	0.105402191399441	0.494958574284473	0.620781422718963	   
df.mm.trans3:probe13	0.0202071522790296	0.105402191399441	0.191714726332879	0.848020101004795	   
df.mm.trans3:probe14	0.0710973381557265	0.105402191399441	0.674533775927767	0.500190054736507	   
df.mm.trans3:probe15	0.104415262047413	0.105402191399441	0.990636538586866	0.322198209930254	   
df.mm.trans3:probe16	0.00646199776899458	0.105402191399441	0.0613080020746971	0.951131050847055	   
df.mm.trans3:probe17	-0.0428511018208691	0.105402191399441	-0.406548490614174	0.684461293270198	   
df.mm.trans3:probe18	0.0779075104084243	0.105402191399441	0.73914507254578	0.460061488433768	   
df.mm.trans3:probe19	-0.0455334539515769	0.105402191399441	-0.431997222704976	0.665873674612186	   
df.mm.trans3:probe20	0.104527582656241	0.105402191399441	0.99170217685621	0.321678313051608