chr11.4181_chr11_122161829_122162337_+_2.R 

fitVsDatCorrelation=0.738270188205248
cont.fitVsDatCorrelation=0.275211815482295

fstatistic=9941.26915717375,53,715
cont.fstatistic=4886.64193822874,53,715

residuals=-0.434053108972885,-0.0901499904239839,-0.00526153656565536,0.0698392747512825,1.46747656444484
cont.residuals=-0.463265546769686,-0.148549354856568,-0.02223811057696,0.117229071683455,1.36686204714322

predictedValues:
Include	Exclude	Both
chr11.4181_chr11_122161829_122162337_+_2.R.tl.Lung	59.9403639336974	48.8972670431661	62.3867149631013
chr11.4181_chr11_122161829_122162337_+_2.R.tl.cerebhem	66.2403433060339	60.4077871317621	75.522206288418
chr11.4181_chr11_122161829_122162337_+_2.R.tl.cortex	60.7837067114519	49.4245980406358	77.504162877438
chr11.4181_chr11_122161829_122162337_+_2.R.tl.heart	57.4666139069282	44.4353135690152	62.8191662825901
chr11.4181_chr11_122161829_122162337_+_2.R.tl.kidney	59.8829189691072	45.9623553136978	65.640668869294
chr11.4181_chr11_122161829_122162337_+_2.R.tl.liver	59.3657783740819	47.9788726671554	68.7874208398874
chr11.4181_chr11_122161829_122162337_+_2.R.tl.stomach	59.9540504577111	48.0248266901253	72.4498463955464
chr11.4181_chr11_122161829_122162337_+_2.R.tl.testicle	60.7983980302372	48.3245878602142	60.6634518079966


diffExp=11.0430968905313,5.8325561742718,11.3591086708161,13.0313003379131,13.9205636554094,11.3869057069265,11.9292237675858,12.4738101700231
diffExpScore=0.989127665335845
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=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	58.1756205344258	59.4711116461297	60.6557532245671
cerebhem	56.6811909700394	59.529248151669	57.8864277619721
cortex	59.3258384504966	61.8309322549558	58.7772578043488
heart	58.771246339887	60.4806604820596	57.0919741606242
kidney	58.2666429229657	64.9887745088239	52.5283963129993
liver	56.520789214548	65.8124320303349	58.0770484811494
stomach	62.8992766326943	60.2518766632288	62.429764163754
testicle	57.4198053974942	61.263810088681	62.4273062485577
cont.diffExp=-1.29549111170391,-2.84805718162959,-2.50509380445923,-1.70941414217266,-6.72213158585819,-9.29164281578694,2.64739996946555,-3.84400469118675
cont.diffExpScore=1.16165046530586

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

tran.correlation=0.97254579069521
cont.tran.correlation=-0.301430819142239

tran.covariance=0.00360438937122499
cont.tran.covariance=-0.000392413737117469

tran.mean=54.8679863753138
cont.tran.mean=60.1055785180271

weightedLogRatios:
wLogRatio
Lung	0.812790959707212
cerebhem	0.382254706106005
cortex	0.828297844590562
heart	1.00877862978678
kidney	1.04771999167271
liver	0.846981666027586
stomach	0.883591712433523
testicle	0.916827461191413

cont.weightedLogRatios:
wLogRatio
Lung	-0.0897375385398147
cerebhem	-0.199138691153063
cortex	-0.169725220345466
heart	-0.117206402813795
kidney	-0.449800072563442
liver	-0.62565082476318
stomach	0.177165357664062
testicle	-0.264564783833767

varWeightedLogRatios=0.0414123899253108
cont.varWeightedLogRatios=0.0582182360360646

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.71525797144039	0.0773877340920058	48.0083570740466	7.16488869287627e-226	***
df.mm.trans1	0.352534038079932	0.0680841125739395	5.17791926416136	2.91977768537778e-07	***
df.mm.trans2	0.177729174081514	0.0615594185170681	2.88711586891023	0.00400524041757037	** 
df.mm.exp2	0.120261691183956	0.082026861542347	1.46612571690153	0.143053679418677	   
df.mm.exp3	-0.192280930073308	0.082026861542347	-2.34412150432016	0.0193448021200966	*  
df.mm.exp4	-0.144740855316165	0.082026861542347	-1.76455434957049	0.0780656075109225	.  
df.mm.exp5	-0.113700740348814	0.082026861542347	-1.38614032294915	0.166136173131806	   
df.mm.exp6	-0.126261479908349	0.082026861542347	-1.53926991151753	0.124180831202799	   
df.mm.exp7	-0.167317293850086	0.082026861542347	-2.03978661019119	0.0417386345395883	*  
df.mm.exp8	0.0304432439088075	0.082026861542347	0.371137494917942	0.710645065735072	   
df.mm.trans1:exp2	-0.0203221318635505	0.0771688150279771	-0.263346428945200	0.792359449209643	   
df.mm.trans2:exp2	0.091134825237934	0.0634158971288676	1.43709746867948	0.151127746348445	   
df.mm.trans1:exp3	0.20625256790903	0.0771688150279771	2.67274504389182	0.00769485403217114	** 
df.mm.trans2:exp3	0.203007660152245	0.0634158971288675	3.20121088470470	0.00142906732348197	** 
df.mm.trans1:exp4	0.102594873280988	0.0771688150279771	1.32948618225889	0.184111634260565	   
df.mm.trans2:exp4	0.0490538530529306	0.0634158971288675	0.773526123161961	0.43946673659883	   
df.mm.trans1:exp5	0.112741912187882	0.0771688150279771	1.46097762609169	0.144460876473007	   
df.mm.trans2:exp5	0.0518019328194285	0.0634158971288675	0.816860364115984	0.414280313970911	   
df.mm.trans1:exp6	0.116629285103308	0.0771688150279771	1.51135254650501	0.131140472944442	   
df.mm.trans2:exp6	0.107300734921796	0.0634158971288675	1.69201635204734	0.0910785589353022	.  
df.mm.trans1:exp7	0.167545603469722	0.0771688150279771	2.17115687741194	0.0302475371550164	*  
df.mm.trans2:exp7	0.149313887524174	0.0634158971288675	2.35451825621505	0.01881620430214	*  
df.mm.trans1:exp8	-0.0162299369740353	0.0771688150279771	-0.210317301984633	0.833479951018662	   
df.mm.trans2:exp8	-0.0422242535824485	0.0634158971288675	-0.665830737940118	0.50573404114133	   
df.mm.trans1:probe2	0.129829253589403	0.045056880766107	2.88145231942164	0.00407707425590298	** 
df.mm.trans1:probe3	0.0491954231719915	0.045056880766107	1.09185150715088	0.275266145232786	   
df.mm.trans1:probe4	-0.125512538122026	0.045056880766107	-2.78564640933687	0.00548358234458121	** 
df.mm.trans1:probe5	-0.0812569804873494	0.045056880766107	-1.80343110987108	0.071741378776884	.  
df.mm.trans1:probe6	-0.0990574856666419	0.045056880766107	-2.19849851970125	0.0282332703262276	*  
df.mm.trans1:probe7	0.206891614833656	0.045056880766107	4.59178734337254	5.19199991080823e-06	***
df.mm.trans1:probe8	-0.0609347562799938	0.045056880766107	-1.35239624323552	0.176676149695472	   
df.mm.trans1:probe9	-0.0364823718298216	0.045056880766107	-0.809695904587888	0.418384349615295	   
df.mm.trans1:probe10	0.256959767968784	0.045056880766107	5.70300836630654	1.72296972119500e-08	***
df.mm.trans1:probe11	0.359375841427957	0.045056880766107	7.97604794911345	5.9979598205874e-15	***
df.mm.trans1:probe12	0.0831501586814136	0.045056880766107	1.84544862555069	0.0653855937218656	.  
df.mm.trans1:probe13	0.0162711567762124	0.045056880766107	0.361124793806232	0.718112834913016	   
df.mm.trans1:probe14	0.0169203927554619	0.045056880766107	0.375534046471096	0.707374711416362	   
df.mm.trans1:probe15	0.240395475812242	0.045056880766107	5.33537767650073	1.28097045736137e-07	***
df.mm.trans1:probe16	-0.0804892370220435	0.045056880766107	-1.78639168210218	0.0744594775924214	.  
df.mm.trans1:probe17	-0.101808305738002	0.045056880766107	-2.25955068364574	0.0241493707156425	*  
df.mm.trans1:probe18	0.0412978249225385	0.045056880766107	0.916570881524578	0.359676587876381	   
df.mm.trans1:probe19	-0.0979463585763804	0.045056880766107	-2.17383797792896	0.0300446935584822	*  
df.mm.trans1:probe20	0.00831096620970262	0.045056880766107	0.184454983753655	0.853708842187997	   
df.mm.trans1:probe21	-0.060598618868535	0.045056880766107	-1.34493595291485	0.179072362734315	   
df.mm.trans2:probe2	-0.0412156969630378	0.045056880766107	-0.914748119759799	0.360632255832149	   
df.mm.trans2:probe3	0.00607734181695226	0.045056880766107	0.134881547803988	0.89274348919016	   
df.mm.trans2:probe4	0.0544409336489019	0.045056880766107	1.20827125010069	0.227342407981282	   
df.mm.trans2:probe5	0.0197682779875130	0.045056880766107	0.438740490939248	0.660982124855403	   
df.mm.trans2:probe6	-0.074992888688193	0.045056880766107	-1.66440480151047	0.0964696939113453	.  
df.mm.trans3:probe2	-0.132665353591878	0.045056880766107	-2.94439720051976	0.00334080341966936	** 
df.mm.trans3:probe3	-0.0516066332733492	0.045056880766107	-1.14536631022556	0.252440670129005	   
df.mm.trans3:probe4	-0.277614721794798	0.045056880766107	-6.16142789013543	1.20335279277786e-09	***
df.mm.trans3:probe5	-0.0989574519674055	0.045056880766107	-2.19627835493317	0.0283923951090869	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96967290864997	0.110302056536389	35.9891105687622	1.18817715549600e-162	***
df.mm.trans1	0.0620768584688497	0.097041446199112	0.639694284249215	0.522576278380159	   
df.mm.trans2	0.110449542763348	0.0877416885412893	1.25880347870639	0.20851216307228	   
df.mm.exp2	0.0216846736878575	0.116914283968952	0.185474973217269	0.852909109929076	   
df.mm.exp3	0.089951126919872	0.116914283968952	0.76937670801422	0.441923795896165	   
df.mm.exp4	0.0875702691909965	0.116914283968952	0.749012577575652	0.454095959732112	   
df.mm.exp5	0.234147855053486	0.116914283968952	2.00273095044286	0.0455832633772717	*  
df.mm.exp6	0.115904165637799	0.116914283968952	0.991360180323033	0.321845104324862	   
df.mm.exp7	0.0622836701422632	0.116914283968952	0.532729346901729	0.594386469842543	   
df.mm.exp8	-0.0121667609576034	0.116914283968952	-0.104065650017875	0.917146433207478	   
df.mm.trans1:exp2	-0.0477086227287412	0.109990027462781	-0.433754075976435	0.664597874169794	   
df.mm.trans2:exp2	-0.0207075924376287	0.0903877591518313	-0.229097309546579	0.818858798519384	   
df.mm.trans1:exp3	-0.070372567341688	0.109990027462781	-0.63980861688121	0.522501979009518	   
df.mm.trans2:exp3	-0.0510380418475382	0.0903877591518313	-0.564656567730655	0.572484479696604	   
df.mm.trans1:exp4	-0.0773839175316982	0.109990027462781	-0.703553943178021	0.481939640450438	   
df.mm.trans2:exp4	-0.0707372928072189	0.0903877591518313	-0.782598146817602	0.434122238883599	   
df.mm.trans1:exp5	-0.232584463817744	0.109990027462781	-2.11459592458460	0.034809867376633	*  
df.mm.trans2:exp5	-0.145423976033281	0.0903877591518313	-1.60889015722805	0.108081778573717	   
df.mm.trans1:exp6	-0.144762020279609	0.109990027462781	-1.31613768646976	0.188549614040603	   
df.mm.trans2:exp6	-0.0145860846184205	0.0903877591518313	-0.161372344610504	0.87184573342541	   
df.mm.trans1:exp7	0.0157846174059607	0.109990027462781	0.143509532364669	0.88592821857135	   
df.mm.trans2:exp7	-0.0492406264160239	0.0903877591518313	-0.544770961002702	0.586080920753674	   
df.mm.trans1:exp8	-0.000910329278817882	0.109990027462781	-0.00827647105666853	0.993398715567298	   
df.mm.trans2:exp8	0.0418653797064729	0.0903877591518313	0.463175324837387	0.643379647373383	   
df.mm.trans1:probe2	0.0402941259674156	0.0642203401860538	0.627435573381874	0.530574070062314	   
df.mm.trans1:probe3	0.106216789762339	0.0642203401860538	1.65394311918337	0.098577987109605	.  
df.mm.trans1:probe4	0.0198330573735032	0.0642203401860538	0.308828282691194	0.757542116120006	   
df.mm.trans1:probe5	-0.0172434749108536	0.0642203401860538	-0.268504882734929	0.788388167391127	   
df.mm.trans1:probe6	0.0375077965213876	0.0642203401860538	0.584048549302654	0.559371859495355	   
df.mm.trans1:probe7	0.0641207060681784	0.0642203401860538	0.998448558235807	0.318399674351858	   
df.mm.trans1:probe8	0.00328689834835021	0.0642203401860538	0.0511815779677853	0.95919512608034	   
df.mm.trans1:probe9	0.0159384223186111	0.0642203401860538	0.248183399098100	0.804063771035388	   
df.mm.trans1:probe10	0.0811127452993793	0.0642203401860538	1.26303823779797	0.206987158920338	   
df.mm.trans1:probe11	0.178101148148427	0.0642203401860538	2.77328253996238	0.00569399462249196	** 
df.mm.trans1:probe12	0.124772342471600	0.0642203401860538	1.9428788777842	0.0524229874200121	.  
df.mm.trans1:probe13	0.082125300137117	0.0642203401860538	1.27880512465662	0.201380589552278	   
df.mm.trans1:probe14	0.00128305765878410	0.0642203401860537	0.0199789919372419	0.984065705114128	   
df.mm.trans1:probe15	-0.0218495583601341	0.0642203401860537	-0.340228007152149	0.733784757759115	   
df.mm.trans1:probe16	0.0243381718074047	0.0642203401860538	0.378979179133811	0.70481583752785	   
df.mm.trans1:probe17	-0.00996236413328415	0.0642203401860538	-0.155127862985808	0.876764297848486	   
df.mm.trans1:probe18	-0.0182291264388207	0.0642203401860538	-0.28385284764934	0.776605328712124	   
df.mm.trans1:probe19	0.0432791665659645	0.0642203401860538	0.673916806428924	0.500582008208999	   
df.mm.trans1:probe20	-0.0194289853189699	0.0642203401860538	-0.30253631890896	0.762331240571829	   
df.mm.trans1:probe21	0.0891356068641052	0.0642203401860538	1.38796534876441	0.165579933574363	   
df.mm.trans2:probe2	0.026954161199137	0.0642203401860538	0.41971377169675	0.674820645263797	   
df.mm.trans2:probe3	-0.0242740587117611	0.0642203401860538	-0.377980849080468	0.705557004807549	   
df.mm.trans2:probe4	-0.00837787439338548	0.0642203401860538	-0.130455154381210	0.89624302619438	   
df.mm.trans2:probe5	0.0155857202670086	0.0642203401860538	0.242691337695425	0.808314119099842	   
df.mm.trans2:probe6	0.0491625231578583	0.0642203401860538	0.765528849822794	0.444209314733516	   
df.mm.trans3:probe2	-0.0303523510277496	0.0642203401860538	-0.472628312771551	0.636622609647322	   
df.mm.trans3:probe3	-0.0454873152387544	0.0642203401860537	-0.708300751864167	0.478989352484564	   
df.mm.trans3:probe4	-0.0393479892831868	0.0642203401860537	-0.612702909532885	0.540267617356453	   
df.mm.trans3:probe5	-0.0697364422438378	0.0642203401860538	-1.08589337960221	0.277892131333487