chr2.14301_chr2_13030392_13084026_+_2.R 

fitVsDatCorrelation=0.815445335745195
cont.fitVsDatCorrelation=0.211959353193328

fstatistic=7489.7851412062,64,968
cont.fstatistic=2617.66988389395,64,968

residuals=-0.625934646855768,-0.107862968376673,-0.00405717903707063,0.0959343541524225,0.797127147195485
cont.residuals=-0.512049630624545,-0.203786779796320,-0.0697724017694178,0.112508424745089,1.67387917224885

predictedValues:
Include	Exclude	Both
chr2.14301_chr2_13030392_13084026_+_2.R.tl.Lung	55.1350998056951	44.3287366122188	55.585201497742
chr2.14301_chr2_13030392_13084026_+_2.R.tl.cerebhem	54.6190497841454	46.2019633503632	51.9648216341853
chr2.14301_chr2_13030392_13084026_+_2.R.tl.cortex	57.7388024618491	48.7945573444344	57.8010580441695
chr2.14301_chr2_13030392_13084026_+_2.R.tl.heart	62.2792375128033	53.6147656878426	67.3887259719988
chr2.14301_chr2_13030392_13084026_+_2.R.tl.kidney	99.8427605591038	79.7142245474377	111.935379541439
chr2.14301_chr2_13030392_13084026_+_2.R.tl.liver	66.7235110143597	51.5419574617695	68.6376099223332
chr2.14301_chr2_13030392_13084026_+_2.R.tl.stomach	56.8951227420453	47.7899171164851	56.5304954511287
chr2.14301_chr2_13030392_13084026_+_2.R.tl.testicle	57.7785842787477	44.060246960341	57.0329749200608


diffExp=10.8063631934763,8.41708643378222,8.9442451174147,8.6644718249607,20.1285360116661,15.1815535525901,9.10520562556025,13.7183373184067
diffExpScore=0.98957962097321
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=0,0,0,0,0,0,0,1
diffExp1.3Score=0.5
diffExp1.2=1,0,0,0,1,1,0,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	61.9320100131647	55.1326850082901	62.1751184691391
cerebhem	57.7760958369273	54.7240766603913	58.7757526788936
cortex	60.0922866116097	58.5803662872182	59.6408693826917
heart	56.6325627584359	60.1845444238117	59.48502396989
kidney	60.6589770952498	58.9189428160011	59.8074532159982
liver	54.2520154993912	67.5233693428206	61.2460805941517
stomach	59.3750207157204	60.3765153538188	59.4446671121999
testicle	61.6265875440887	61.9891598526426	57.8395687646258
cont.diffExp=6.79932500487462,3.05201917653598,1.51192032439140,-3.55198166537576,1.74003427924868,-13.2713538434294,-1.00149463809833,-0.362572308553965
cont.diffExpScore=5.14302565104318

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=0.983168430200677
cont.tran.correlation=-0.557074272235624

tran.covariance=0.0373844299596742
cont.tran.covariance=-0.00167191007858231

tran.mean=57.9411585774776
cont.tran.mean=59.3609509887239

weightedLogRatios:
wLogRatio
Lung	0.850953054468021
cerebhem	0.655500996477717
cortex	0.668491909221864
heart	0.60771218309445
kidney	1.01114697056182
liver	1.05109731473030
stomach	0.689559723030014
testicle	1.06285109816239

cont.weightedLogRatios:
wLogRatio
Lung	0.473072967764038
cerebhem	0.218683128163588
cortex	0.104046165050439
heart	-0.247401493108131
kidney	0.119060225265718
liver	-0.897886750045194
stomach	-0.0684492076017734
testicle	-0.0241920971048671

varWeightedLogRatios=0.0373998932368366
cont.varWeightedLogRatios=0.165179058652764

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.44031953121618	0.086292965632441	39.8679023950803	1.98124928975467e-206	***
df.mm.trans1	0.669032817846878	0.0739194897028298	9.05083112094679	7.6243035373724e-19	***
df.mm.trans2	0.337124580214482	0.064715323168651	5.20934708671578	2.31541856750829e-07	***
df.mm.exp2	0.0993353539424295	0.0819036532482099	1.21283178469943	0.225490033968703	   
df.mm.exp3	0.103038453438816	0.0819036532482099	1.25804465799049	0.208678979179041	   
df.mm.exp4	0.119472203472020	0.0819036532482099	1.45869199643584	0.144974287730590	   
df.mm.exp5	0.48062018722756	0.08190365324821	5.86811660001337	6.04839422491362e-09	***
df.mm.exp6	0.130610253222070	0.0819036532482099	1.59468165389710	0.111109866524722	   
df.mm.exp7	0.0897413879322972	0.0819036532482099	1.09569456762002	0.273484988931593	   
df.mm.exp8	0.0150438548925685	0.0819036532482099	0.183677458769487	0.854304960238248	   
df.mm.trans1:exp2	-0.108739168311279	0.0749298257201821	-1.45121341556772	0.147044426747446	   
df.mm.trans2:exp2	-0.0579462087358547	0.0519409898628609	-1.11561618076300	0.26486333432055	   
df.mm.trans1:exp3	-0.0568955530166119	0.0749298257201821	-0.759317834650818	0.447847314376802	   
df.mm.trans2:exp3	-0.00705282536605614	0.0519409898628609	-0.135785347654668	0.892019205990432	   
df.mm.trans1:exp4	0.00236936711393788	0.0749298257201821	0.0316211480697425	0.974780696532297	   
df.mm.trans2:exp4	0.0707191571788253	0.0519409898628609	1.36152886892499	0.173663425624537	   
df.mm.trans1:exp5	0.113189833427301	0.0749298257201821	1.51061119306478	0.131213890641088	   
df.mm.trans2:exp5	0.106194710032827	0.0519409898628609	2.04452611152025	0.0411716866159046	*  
df.mm.trans1:exp6	0.0601605931460017	0.0749298257201821	0.802892473961775	0.422233972622797	   
df.mm.trans2:exp6	0.0201527820094660	0.0519409898628609	0.387993799553593	0.698105927877658	   
df.mm.trans1:exp7	-0.0583183001971568	0.0749298257201821	-0.778305563060305	0.436579093835336	   
df.mm.trans2:exp7	-0.0145598584105760	0.0519409898628609	-0.280315381917406	0.779295444192442	   
df.mm.trans1:exp8	0.0317878045051238	0.0749298257201821	0.424234331250578	0.671489187666552	   
df.mm.trans2:exp8	-0.0211190571816286	0.0519409898628609	-0.406597125649491	0.684393786263442	   
df.mm.trans1:probe2	-0.0741351379666689	0.0548430167897599	-1.35176987529452	0.176764715848717	   
df.mm.trans1:probe3	-0.0863568046168355	0.0548430167897599	-1.57461805844641	0.115671384559346	   
df.mm.trans1:probe4	-0.308459251724317	0.0548430167897599	-5.62440342964341	2.43563252468657e-08	***
df.mm.trans1:probe5	-0.337965211568567	0.0548430167897599	-6.16241102972422	1.04977409161570e-09	***
df.mm.trans1:probe6	-0.0251211133189171	0.0548430167897599	-0.458054913631366	0.647015794429814	   
df.mm.trans1:probe7	-0.111722551982999	0.0548430167897599	-2.03713359553662	0.0419082596062039	*  
df.mm.trans1:probe8	-0.407307414712121	0.0548430167897599	-7.4267871928623	2.43839679734174e-13	***
df.mm.trans1:probe9	-0.230196069176228	0.0548430167897599	-4.197363359107	2.94894317695496e-05	***
df.mm.trans1:probe10	-0.0107683641295854	0.0548430167897599	-0.196348865542278	0.844378329338862	   
df.mm.trans1:probe11	0.132576005538384	0.0548430167897599	2.41737258996187	0.0158169088112909	*  
df.mm.trans1:probe12	-0.0551217646312146	0.0548430167897599	-1.00508264967486	0.31510822579651	   
df.mm.trans1:probe13	-0.0600025241403835	0.0548430167897599	-1.09407774503730	0.274193051673082	   
df.mm.trans1:probe14	-0.163157839766298	0.0548430167897599	-2.9749975350875	0.00300266606731627	** 
df.mm.trans1:probe15	-0.188441136745223	0.0548430167897599	-3.43600968319466	0.000615438204345519	***
df.mm.trans1:probe16	-0.151892172454623	0.0548430167897599	-2.7695809119491	0.00572018495703115	** 
df.mm.trans1:probe17	-0.323764084962593	0.0548430167897599	-5.90346964689668	4.9204860758322e-09	***
df.mm.trans1:probe18	-0.280811621980276	0.0548430167897599	-5.12028036416676	3.68013532828952e-07	***
df.mm.trans1:probe19	-0.261381526078737	0.0548430167897599	-4.76599467678337	2.16729699876211e-06	***
df.mm.trans1:probe20	-0.266579112196582	0.0548430167897599	-4.86076674480745	1.36363857520731e-06	***
df.mm.trans1:probe21	-0.249281094585941	0.0548430167897599	-4.54535707876095	6.17769127168366e-06	***
df.mm.trans1:probe22	-0.224046387786652	0.0548430167897599	-4.08523091728399	4.7676127411338e-05	***
df.mm.trans2:probe2	0.0347998095083401	0.0548430167897599	0.634534924323088	0.525881714212806	   
df.mm.trans2:probe3	0.112008697745571	0.0548430167897599	2.04235113788424	0.0413872467912752	*  
df.mm.trans2:probe4	0.00905253495596456	0.0548430167897599	0.165062673168899	0.868929115551693	   
df.mm.trans2:probe5	0.0623671122359018	0.0548430167897599	1.13719331806610	0.255738893971693	   
df.mm.trans2:probe6	0.0797416290349425	0.0548430167897599	1.45399785975726	0.146271037690885	   
df.mm.trans3:probe2	-0.685468412803195	0.05484301678976	-12.4987364468102	2.43587527387916e-33	***
df.mm.trans3:probe3	-0.435757551639984	0.0548430167897599	-7.94554306358557	5.351179456874e-15	***
df.mm.trans3:probe4	-0.156404724365439	0.0548430167897599	-2.85186216077453	0.00443884844349883	** 
df.mm.trans3:probe5	-0.484208704038948	0.0548430167897599	-8.82899469033872	4.86964253614525e-18	***
df.mm.trans3:probe6	-0.661466362467261	0.0548430167897599	-12.0610863731837	2.62554218667318e-31	***
df.mm.trans3:probe7	-0.439700857851033	0.0548430167897599	-8.01744476487537	3.09776229895918e-15	***
df.mm.trans3:probe8	-0.471395612976521	0.0548430167897599	-8.59536255606819	3.29388052116055e-17	***
df.mm.trans3:probe9	-0.585463986825956	0.0548430167897599	-10.6752695438022	3.15555272810896e-25	***
df.mm.trans3:probe10	-0.511631778019614	0.0548430167897599	-9.3290232370139	7.06663526279738e-20	***
df.mm.trans3:probe11	-0.292014930721640	0.0548430167897599	-5.32455994973938	1.25829719152274e-07	***
df.mm.trans3:probe12	-0.161133109237757	0.0548430167897599	-2.93807887803582	0.0033808638411857	** 
df.mm.trans3:probe13	-0.367903640286597	0.0548430167897599	-6.70830420757033	3.34469215294083e-11	***
df.mm.trans3:probe14	-0.488079525756754	0.05484301678976	-8.89957471938133	2.71062168966875e-18	***
df.mm.trans3:probe15	-0.687769656415941	0.0548430167897599	-12.5406970052814	1.54548694490444e-33	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97375997488207	0.145693316767860	27.2748267596491	5.59028587444836e-122	***
df.mm.trans1	0.146459225610420	0.124802474334528	1.17352822042488	0.240872708680782	   
df.mm.trans2	0.0336458149319948	0.109262557023537	0.307935452441835	0.758197694216964	   
df.mm.exp2	-0.0206755164016324	0.138282591282857	-0.149516408463453	0.881177307501103	   
df.mm.exp3	0.0721150077559948	0.138282591282857	0.521504602184405	0.602134592334703	   
df.mm.exp4	0.0424501226897785	0.138282591282857	0.306980960480751	0.75892388863869	   
df.mm.exp5	0.0844749888561378	0.138282591282857	0.610886649378332	0.541418034254401	   
df.mm.exp6	0.0853890269981434	0.138282591282857	0.6174965786075	0.537052465596126	   
df.mm.exp7	0.093602896407415	0.138282591282857	0.676895736036291	0.498633829302069	   
df.mm.exp8	0.184554789741027	0.138282591282857	1.33462056234917	0.182314395733520	   
df.mm.trans1:exp2	-0.0487865309321128	0.126508282036688	-0.385639028106986	0.699848755545681	   
df.mm.trans2:exp2	0.0132365526820765	0.087694924306566	0.150938640824911	0.880055566051664	   
df.mm.trans1:exp3	-0.102270687654896	0.126508282036688	-0.808411006840147	0.419052646205091	   
df.mm.trans2:exp3	-0.0114581482382650	0.087694924306566	-0.130659195259801	0.896072060665827	   
df.mm.trans1:exp4	-0.131903159791428	0.126508282036688	-1.04264446301765	0.297373308751197	   
df.mm.trans2:exp4	0.0452227248849042	0.087694924306566	0.515682352684558	0.606194037206126	   
df.mm.trans1:exp5	-0.105244520292823	0.126508282036688	-0.831918026222994	0.405660391785555	   
df.mm.trans2:exp5	-0.0180550747561142	0.087694924306566	-0.205885060040611	0.836923986720964	   
df.mm.trans1:exp6	-0.217786053839746	0.126508282036688	-1.72151617533298	0.0854766916713342	.  
df.mm.trans2:exp6	0.117341988817374	0.087694924306566	1.33807047266689	0.181187698677377	   
df.mm.trans1:exp7	-0.135766455790886	0.126508282036688	-1.07318235300605	0.283456763985687	   
df.mm.trans2:exp7	-0.00274542035308310	0.087694924306566	-0.0313064909376692	0.975031567429793	   
df.mm.trans1:exp8	-0.189498566980029	0.126508282036688	-1.49791431777623	0.134481519039908	   
df.mm.trans2:exp8	-0.0673379957003131	0.087694924306566	-0.767866512603526	0.442753759740614	   
df.mm.trans1:probe2	-0.0825361283979541	0.092594581251148	-0.891371042265292	0.372951651376173	   
df.mm.trans1:probe3	0.108265068885374	0.092594581251148	1.16923763164631	0.242595736671651	   
df.mm.trans1:probe4	-0.0286373845719477	0.092594581251148	-0.309277110874052	0.757177297966342	   
df.mm.trans1:probe5	-0.0476141953610404	0.092594581251148	-0.51422226568415	0.607213972926165	   
df.mm.trans1:probe6	-0.00209806629327775	0.092594581251148	-0.0226586293164076	0.981927246128141	   
df.mm.trans1:probe7	-0.0210259150475658	0.092594581251148	-0.227075005507464	0.82041336534021	   
df.mm.trans1:probe8	0.0191251848343192	0.092594581251148	0.206547560082864	0.836406653649527	   
df.mm.trans1:probe9	0.164034876000096	0.092594581251148	1.77153861255852	0.0767857505234885	.  
df.mm.trans1:probe10	0.035340654711821	0.092594581251148	0.381670873546748	0.702789271248134	   
df.mm.trans1:probe11	-0.00418592471658662	0.092594581251148	-0.0452070160048888	0.963951625897702	   
df.mm.trans1:probe12	0.00410833701755879	0.092594581251148	0.044369086852022	0.96461935321825	   
df.mm.trans1:probe13	0.0145519841939535	0.092594581251148	0.157158053930646	0.87515305861729	   
df.mm.trans1:probe14	-0.0915452144870258	0.092594581251148	-0.98866708235036	0.323073111017465	   
df.mm.trans1:probe15	-0.00393420927490730	0.092594581251148	-0.0424885476206905	0.966118003351705	   
df.mm.trans1:probe16	0.0213684187529537	0.092594581251148	0.230773966081182	0.817539171800524	   
df.mm.trans1:probe17	-0.0681599965844194	0.092594581251148	-0.73611215325383	0.461840784412873	   
df.mm.trans1:probe18	0.0311162490197555	0.092594581251148	0.336048271932432	0.736907291852448	   
df.mm.trans1:probe19	-0.0268625969609449	0.092594581251148	-0.290109816341027	0.771794359794386	   
df.mm.trans1:probe20	0.0672012635372371	0.092594581251148	0.725758058724456	0.468162528260658	   
df.mm.trans1:probe21	0.0298849229188925	0.092594581251148	0.322750235651851	0.746954102651216	   
df.mm.trans1:probe22	0.0968675670802433	0.092594581251148	1.04614725582597	0.295754203601293	   
df.mm.trans2:probe2	0.0615302326505188	0.092594581251148	0.664512240555717	0.506520779124708	   
df.mm.trans2:probe3	-0.0358858600121462	0.092594581251148	-0.387558964328718	0.698427641074516	   
df.mm.trans2:probe4	0.0195792721888907	0.092594581251148	0.211451598185698	0.832579406017205	   
df.mm.trans2:probe5	0.00977229249706304	0.092594581251148	0.105538492264005	0.91597039318608	   
df.mm.trans2:probe6	-0.00592009615856413	0.092594581251148	-0.0639356653334477	0.949034653583408	   
df.mm.trans3:probe2	-0.0386510004798071	0.092594581251148	-0.417421839999173	0.676462445675107	   
df.mm.trans3:probe3	-0.0650792119306741	0.092594581251148	-0.702840393588013	0.482324208557645	   
df.mm.trans3:probe4	-0.0479113500008332	0.092594581251148	-0.517431466868253	0.604973214689148	   
df.mm.trans3:probe5	-0.0654921382334377	0.092594581251148	-0.707299901878715	0.479550365865456	   
df.mm.trans3:probe6	0.042685001923506	0.092594581251148	0.460988119895804	0.644910682463245	   
df.mm.trans3:probe7	0.0577587747282121	0.092594581251148	0.623781369792587	0.532918136740984	   
df.mm.trans3:probe8	-0.0840367841681001	0.092594581251148	-0.907577776502534	0.364327217803126	   
df.mm.trans3:probe9	0.0810327203983075	0.092594581251148	0.87513458458783	0.381717766250017	   
df.mm.trans3:probe10	0.0269531461491531	0.092594581251148	0.291087726570597	0.771046584425881	   
df.mm.trans3:probe11	-0.0491179905161216	0.092594581251148	-0.530462904550504	0.59591268731257	   
df.mm.trans3:probe12	-0.0964247743876428	0.092594581251148	-1.04136519745260	0.297966103664538	   
df.mm.trans3:probe13	-0.0496305446178425	0.092594581251148	-0.535998369961063	0.592082814130291	   
df.mm.trans3:probe14	-0.0254850668538320	0.092594581251148	-0.275232810705281	0.783196117287814	   
df.mm.trans3:probe15	-0.0452525868476923	0.092594581251148	-0.488717441520167	0.625152453576526