chr11.3896_chr11_103886743_103887390_+_2.R 

fitVsDatCorrelation=0.802415211496103
cont.fitVsDatCorrelation=0.268562373002878

fstatistic=10932.8452121124,58,830
cont.fstatistic=4187.34588383369,58,830

residuals=-0.641101606757657,-0.0912837143053189,-0.00368123654434732,0.0828856507640495,0.878091226244105
cont.residuals=-0.611226473764605,-0.163370718709024,-0.0131445251266146,0.140241679105096,1.26034955700643

predictedValues:
Include	Exclude	Both
chr11.3896_chr11_103886743_103887390_+_2.R.tl.Lung	62.4715668249765	62.5119935143556	71.0090911116334
chr11.3896_chr11_103886743_103887390_+_2.R.tl.cerebhem	64.5553694317495	88.5827730833147	71.2029991588855
chr11.3896_chr11_103886743_103887390_+_2.R.tl.cortex	59.8560426763614	69.5076260425948	78.4212421663657
chr11.3896_chr11_103886743_103887390_+_2.R.tl.heart	60.5241723418482	59.6476267797803	73.4518913591621
chr11.3896_chr11_103886743_103887390_+_2.R.tl.kidney	68.7689314608533	72.9995549700732	90.0097947626205
chr11.3896_chr11_103886743_103887390_+_2.R.tl.liver	64.9623550291807	62.1809479463526	81.1400574138668
chr11.3896_chr11_103886743_103887390_+_2.R.tl.stomach	59.9471686040925	61.3252025749367	76.7653900605206
chr11.3896_chr11_103886743_103887390_+_2.R.tl.testicle	61.5920915261406	61.8820363698938	75.5077731458193


diffExp=-0.0404266893790606,-24.0274036515652,-9.65158336623337,0.876545562067868,-4.23062350921994,2.78140708282805,-1.37803397084419,-0.289944843753219
diffExpScore=1.17088459031613
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	65.3664407739495	70.307868983365	76.4352268075446
cerebhem	71.2766441439179	68.3091144976211	64.30052641267
cortex	68.5367243518628	68.9632173537518	65.5517867086458
heart	64.709451921111	65.9127741887875	66.2355312972436
kidney	68.1803450615658	62.726755038536	64.2415811661055
liver	68.3411616068396	62.6508863892616	75.123654007706
stomach	67.379580794028	67.2972125208562	70.2246246767177
testicle	64.1341260618101	67.5089844091019	65.7044674550489
cont.diffExp=-4.94142820941546,2.96752964629681,-0.426493001889057,-1.20332226767660,5.45359002302979,5.69027521757796,0.0823682731717668,-3.37485834729179
cont.diffExpScore=4.60011869112999

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=0.457523375579431
cont.tran.correlation=-0.128191812534975

tran.covariance=0.00307046936794522
cont.tran.covariance=-0.000204385848422914

tran.mean=65.0822161985315
cont.tran.mean=66.9750805060229

weightedLogRatios:
wLogRatio
Lung	-0.00267500429693629
cerebhem	-1.36872210857946
cortex	-0.622895416616059
heart	0.0597506894620723
kidney	-0.254363432874719
liver	0.18168547192541
stomach	-0.093291404309634
testicle	-0.0193629306736087

cont.weightedLogRatios:
wLogRatio
Lung	-0.307272424174185
cerebhem	0.180533743403215
cortex	-0.0262439715136491
heart	-0.0770002211029217
kidney	0.348518676485653
liver	0.363476702246252
stomach	0.00514933953574016
testicle	-0.214706891089509

varWeightedLogRatios=0.258575927564143
cont.varWeightedLogRatios=0.0605791054627478

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21027709908852	0.0735916987134775	57.2113047081688	2.92168599090738e-290	***
df.mm.trans1	-0.0933209252338658	0.0634593359534625	-1.47056258676111	0.141788430466018	   
df.mm.trans2	-0.113198665139362	0.0562065883124825	-2.01397502566834	0.0443343859579054	*  
df.mm.exp2	0.378663715464562	0.07236848279747	5.23243960391277	2.12011059033980e-07	***
df.mm.exp3	-0.0359780166062241	0.07236848279747	-0.497150350752992	0.619214692370486	   
df.mm.exp4	-0.112395506825102	0.07236848279747	-1.55310022374866	0.120780392697711	   
df.mm.exp5	0.0140248707469132	0.07236848279747	0.193798048608579	0.84638143276501	   
df.mm.exp6	-0.0995822112541264	0.07236848279747	-1.3760439269235	0.169179192991678	   
df.mm.exp7	-0.138361388139509	0.07236848279747	-1.91190118669098	0.056232915284844	.  
df.mm.exp8	-0.0857342393365191	0.07236848279747	-1.18469029641611	0.236479033419534	   
df.mm.trans1:exp2	-0.345851941539658	0.0666609522855672	-5.18822383541825	2.67041843819225e-07	***
df.mm.trans2:exp2	-0.0300847460462943	0.0495973356405971	-0.606579882925583	0.544295624833265	   
df.mm.trans1:exp3	-0.00679111523395663	0.0666609522855672	-0.101875460837468	0.918880133736447	   
df.mm.trans2:exp3	0.14205605582664	0.0495973356405971	2.86418723892826	0.00428626342444483	** 
df.mm.trans1:exp4	0.0807268124768084	0.0666609522855672	1.21100598939818	0.226237808767841	   
df.mm.trans2:exp4	0.0654914342613425	0.0495973356405971	1.32046275098164	0.187044558253444	   
df.mm.trans1:exp5	0.0820156721670679	0.0666609522855672	1.23034054202711	0.218918188712414	   
df.mm.trans2:exp5	0.141070039520210	0.0495973356405971	2.84430680999604	0.00456026351617838	** 
df.mm.trans1:exp6	0.138678637548865	0.0666609522855672	2.08035788259944	0.0377989596147926	*  
df.mm.trans2:exp6	0.0942724264032597	0.0495973356405971	1.90075586088730	0.0576801850842049	.  
df.mm.trans1:exp7	0.0971135167785938	0.0666609522855672	1.45682762470256	0.145542162239187	   
df.mm.trans2:exp7	0.119193847017335	0.0495973356405971	2.40323084854926	0.0164694010909661	*  
df.mm.trans1:exp8	0.0715561948885532	0.0666609522855672	1.07343493357274	0.283387997088146	   
df.mm.trans2:exp8	0.075605738306922	0.0495973356405971	1.52439112566031	0.127791954243612	   
df.mm.trans1:probe2	0.196101653554416	0.0456396340894759	4.29674026680322	1.93861525623724e-05	***
df.mm.trans1:probe3	-0.086880379103366	0.0456396340894759	-1.90361690746771	0.0573057375776337	.  
df.mm.trans1:probe4	-0.31386248987159	0.0456396340894759	-6.87697209088633	1.202672602838e-11	***
df.mm.trans1:probe5	-0.270924303051086	0.0456396340894759	-5.93616290875475	4.28487503812474e-09	***
df.mm.trans1:probe6	-0.0698507204800473	0.0456396340894759	-1.53048379711165	0.126278043024875	   
df.mm.trans1:probe7	0.205210647514154	0.0456396340894759	4.49632543310583	7.89699949984378e-06	***
df.mm.trans1:probe8	0.335185742399781	0.0456396340894759	7.34418119441215	4.94776900386993e-13	***
df.mm.trans1:probe9	-0.100147408373675	0.0456396340894759	-2.19430787234923	0.0284897427842097	*  
df.mm.trans1:probe10	0.116790764740145	0.0456396340894759	2.55897679878806	0.0106743613197930	*  
df.mm.trans1:probe11	-0.151390761007838	0.0456396340894759	-3.31708971879658	0.000949154446013998	***
df.mm.trans1:probe12	-0.193424801210423	0.0456396340894759	-4.23808834293493	2.50712071311834e-05	***
df.mm.trans1:probe13	-0.0649806804697952	0.0456396340894759	-1.42377741991536	0.154886722683112	   
df.mm.trans1:probe14	-0.0277291494174269	0.0456396340894759	-0.607567303520977	0.543640678523172	   
df.mm.trans1:probe15	0.133877719988396	0.0456396340894759	2.93336532291058	0.00344539500066392	** 
df.mm.trans1:probe16	0.0755673560065232	0.0456396340894759	1.65573974275023	0.0981525330067088	.  
df.mm.trans1:probe17	0.149042842140235	0.0456396340894759	3.26564498409515	0.00113692888829449	** 
df.mm.trans1:probe18	0.153443620583172	0.0456396340894759	3.36206947414057	0.000808995821850069	***
df.mm.trans1:probe19	0.209862651166437	0.0456396340894759	4.59825446354378	4.92414534623417e-06	***
df.mm.trans1:probe20	0.153784454107212	0.0456396340894759	3.36953740263823	0.000787678565020214	***
df.mm.trans1:probe21	0.100739046607234	0.0456396340894759	2.20727112776006	0.0275680605979555	*  
df.mm.trans2:probe2	0.256358483683452	0.0456396340894759	5.61701443926707	2.65121836168344e-08	***
df.mm.trans2:probe3	0.0499761058564217	0.0456396340894759	1.09501548058962	0.273827504597022	   
df.mm.trans2:probe4	0.0400612997050185	0.0456396340894759	0.877774340313045	0.380320201502921	   
df.mm.trans2:probe5	0.0624794283263422	0.0456396340894759	1.36897303347902	0.171378055298775	   
df.mm.trans2:probe6	0.203604692239401	0.0456396340894759	4.46113769975098	9.27566915230929e-06	***
df.mm.trans3:probe2	0.00108513391273596	0.0456396340894759	0.0237761308648655	0.98103689426694	   
df.mm.trans3:probe3	0.110634938636457	0.0456396340894759	2.42409784485912	0.0155591610292817	*  
df.mm.trans3:probe4	0.338183250870982	0.0456396340894759	7.40985894426713	3.11487414983582e-13	***
df.mm.trans3:probe5	0.417662175370633	0.0456396340894759	9.15130420528378	4.2749375034439e-19	***
df.mm.trans3:probe6	0.46649274739876	0.0456396340894759	10.2212201457226	3.51670848980444e-23	***
df.mm.trans3:probe7	0.153583097046473	0.0456396340894759	3.36512551229871	0.000800208320666983	***
df.mm.trans3:probe8	0.418579405524387	0.0456396340894759	9.17140143375751	3.60967281604078e-19	***
df.mm.trans3:probe9	0.0533341734512051	0.0456396340894759	1.16859336222206	0.242903032478938	   
df.mm.trans3:probe10	0.630949117337753	0.0456396340894759	13.8245875525817	2.84838829132159e-39	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97457969598827	0.118787082079407	33.459696344181	4.53787571025291e-156	***
df.mm.trans1	0.173301509538417	0.102432060686053	1.69186784272138	0.0910465250645028	.  
df.mm.trans2	0.277469582892633	0.0907251325353027	3.05835412017354	0.00229702085047767	** 
df.mm.exp2	0.230595047915163	0.116812644038762	1.97405897120730	0.0487062430575739	*  
df.mm.exp3	0.181653495276911	0.116812644038762	1.55508418435108	0.120307221352613	   
df.mm.exp4	0.0685734566420626	0.116812644038762	0.58703796328168	0.557337851872356	   
df.mm.exp5	0.101844674871852	0.116812644038762	0.871863450313282	0.383535000887714	   
df.mm.exp6	-0.0534944443262904	0.116812644038762	-0.457950804611009	0.647107649184397	   
df.mm.exp7	0.0713127632719714	0.116812644038762	0.610488392406457	0.541705452270473	   
df.mm.exp8	0.0916213386665118	0.116812644038762	0.784344361181562	0.433061864512489	   
df.mm.trans1:exp2	-0.144035334522050	0.107599907993250	-1.3386194952052	0.181061113835917	   
df.mm.trans2:exp2	-0.259435569135168	0.0800568934085572	-3.24063997601282	0.00124012494887997	** 
df.mm.trans1:exp3	-0.134292760495524	0.107599907993250	-1.24807504950608	0.212355541754620	   
df.mm.trans2:exp3	-0.200963941529926	0.0800568934085572	-2.51026405064632	0.0122532800518939	*  
df.mm.trans1:exp4	-0.0786751661296571	0.107599907993250	-0.731182466574162	0.464874072879915	   
df.mm.trans2:exp4	-0.133124918785507	0.0800568934085572	-1.66287889921141	0.0967142148157582	.  
df.mm.trans1:exp5	-0.0596973358867589	0.107599907993250	-0.554808428744233	0.579175174905304	   
df.mm.trans2:exp5	-0.215940330003776	0.0800568934085572	-2.69733586715337	0.00713145237757761	** 
df.mm.trans1:exp6	0.0979976995853912	0.107599907993250	0.91076006860098	0.362686225125642	   
df.mm.trans2:exp6	-0.0618114530413406	0.0800568934085572	-0.772094074721285	0.440278489954585	   
df.mm.trans1:exp7	-0.040979735582025	0.107599907993250	-0.380852886831427	0.703409870210541	   
df.mm.trans2:exp7	-0.115077673070440	0.0800568934085572	-1.43744864646646	0.150967433305898	   
df.mm.trans1:exp8	-0.110653717298055	0.107599907993250	-1.02838115163627	0.304070162369552	   
df.mm.trans2:exp8	-0.132244374170251	0.0800568934085572	-1.65187991364295	0.098937268401388	.  
df.mm.trans1:probe2	0.0083513939664089	0.073668620991729	0.113364331434228	0.909769130452118	   
df.mm.trans1:probe3	0.0178840273612655	0.073668620991729	0.242763161852498	0.808248851990545	   
df.mm.trans1:probe4	0.096327684657223	0.073668620991729	1.30758093962473	0.191377556749347	   
df.mm.trans1:probe5	-0.0252449348334393	0.073668620991729	-0.342682331956148	0.73192422049127	   
df.mm.trans1:probe6	0.098328803925728	0.073668620991729	1.33474473394537	0.182325918186721	   
df.mm.trans1:probe7	0.0931891373312983	0.073668620991729	1.26497735503643	0.206234483366314	   
df.mm.trans1:probe8	0.107366898125684	0.073668620991729	1.45743054071473	0.145375802772502	   
df.mm.trans1:probe9	2.22526911573954e-05	0.073668620991729	0.000302064717078032	0.999759059857064	   
df.mm.trans1:probe10	0.086540176654145	0.073668620991729	1.17472236468036	0.240442716787116	   
df.mm.trans1:probe11	0.0784873296490479	0.073668620991729	1.06541059941735	0.286999988027250	   
df.mm.trans1:probe12	0.117315335087733	0.073668620991729	1.59247361371002	0.111659069428573	   
df.mm.trans1:probe13	0.0155277024262244	0.073668620991729	0.210777699069020	0.833112476821416	   
df.mm.trans1:probe14	0.0516720590584363	0.073668620991729	0.701412057981073	0.483242509566185	   
df.mm.trans1:probe15	0.106788024596321	0.073668620991729	1.44957273746593	0.147555432292433	   
df.mm.trans1:probe16	0.0624627129838824	0.073668620991729	0.84788763713787	0.396744988258438	   
df.mm.trans1:probe17	0.0820291778773839	0.073668620991729	1.11348871165368	0.265820951382085	   
df.mm.trans1:probe18	-0.00103854100876632	0.073668620991729	-0.0140974677520151	0.98875560851919	   
df.mm.trans1:probe19	-0.00225485274669219	0.073668620991729	-0.0306080488047326	0.97558948140417	   
df.mm.trans1:probe20	0.0346594946909838	0.073668620991729	0.470478396695862	0.638136870531531	   
df.mm.trans1:probe21	-0.0324526025781624	0.073668620991729	-0.440521379948268	0.659674234872489	   
df.mm.trans2:probe2	0.0854521962528984	0.073668620991729	1.15995379175745	0.246401174144567	   
df.mm.trans2:probe3	0.0304776804206242	0.073668620991729	0.413713193084556	0.679191038627688	   
df.mm.trans2:probe4	-0.0682860142385275	0.073668620991729	-0.926934878368282	0.354229798326467	   
df.mm.trans2:probe5	-0.0709220855036042	0.073668620991729	-0.962717701904137	0.335969597990582	   
df.mm.trans2:probe6	0.0366293910853966	0.073668620991729	0.497218362340582	0.619166756748128	   
df.mm.trans3:probe2	-0.146694411566694	0.073668620991729	-1.99127402674150	0.0467781686022512	*  
df.mm.trans3:probe3	-0.180467778985398	0.073668620991729	-2.44972386554731	0.0145022088496634	*  
df.mm.trans3:probe4	-0.0208485867502864	0.073668620991729	-0.28300498189897	0.777243584041085	   
df.mm.trans3:probe5	-0.136863500969852	0.073668620991729	-1.85782629194618	0.0635476210779088	.  
df.mm.trans3:probe6	-0.114692685870633	0.073668620991729	-1.55687298508696	0.119881842833766	   
df.mm.trans3:probe7	-0.0385728263593475	0.073668620991729	-0.523599136784142	0.600697150262048	   
df.mm.trans3:probe8	-0.0737923074067524	0.073668620991729	-1.00167895656737	0.316790605254465	   
df.mm.trans3:probe9	-0.109708999823096	0.073668620991729	-1.48922293299631	0.136808535665727	   
df.mm.trans3:probe10	-0.067430048717608	0.073668620991729	-0.915315745155303	0.36029173699467