chr11.3882_chr11_75513651_75514967_+_2.R 

fitVsDatCorrelation=0.889322616392117
cont.fitVsDatCorrelation=0.238411954207772

fstatistic=9619.20905661778,60,876
cont.fstatistic=2121.28461519396,60,876

residuals=-1.03201699484834,-0.0911592364752596,-0.00609409223784733,0.0822666972158706,0.974854847770822
cont.residuals=-0.827488686943492,-0.292464044409576,-0.0289745970107108,0.2614814100948,1.26488574467897

predictedValues:
Include	Exclude	Both
chr11.3882_chr11_75513651_75514967_+_2.R.tl.Lung	82.1123166626147	59.3937586646982	79.8642784947259
chr11.3882_chr11_75513651_75514967_+_2.R.tl.cerebhem	74.8754365457411	67.723857479701	80.1858690440551
chr11.3882_chr11_75513651_75514967_+_2.R.tl.cortex	90.6359584769619	58.2613533880924	71.0556629397104
chr11.3882_chr11_75513651_75514967_+_2.R.tl.heart	84.43272281489	58.1664166237121	67.678718177989
chr11.3882_chr11_75513651_75514967_+_2.R.tl.kidney	88.0277041109356	59.0191581554659	77.3478603249038
chr11.3882_chr11_75513651_75514967_+_2.R.tl.liver	89.09390937556	53.1274926385938	82.8115854251302
chr11.3882_chr11_75513651_75514967_+_2.R.tl.stomach	87.3653359235896	56.2435122063583	76.9428700725708
chr11.3882_chr11_75513651_75514967_+_2.R.tl.testicle	92.015245040169	57.5664203412759	79.9743632036073


diffExp=22.7185579979165,7.15157906604013,32.3746050888695,26.266306191178,29.0085459554697,35.9664167369662,31.1218237172313,34.4488246988931
diffExpScore=0.995455715802977
diffExp1.5=0,0,1,0,0,1,1,1
diffExp1.5Score=0.8
diffExp1.4=0,0,1,1,1,1,1,1
diffExp1.4Score=0.857142857142857
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	81.0343411162584	85.2464402504508	102.159754977663
cerebhem	84.3284815561972	84.3680469826199	85.737276783052
cortex	83.4232046919184	86.016976292204	74.1270965347193
heart	85.2394752703424	83.5377057382081	78.9590835920422
kidney	83.6975295789919	79.716517845221	67.8390959444131
liver	81.4880747427372	86.514659943445	89.0445245667426
stomach	85.4999817401473	104.122227186453	82.388148602915
testicle	83.0749702923512	79.3594372046244	81.8582641787793
cont.diffExp=-4.21209913419243,-0.0395654264226835,-2.59377160028563,1.70176953213426,3.98101173377093,-5.0265852007077,-18.6222454463059,3.71553308772688
cont.diffExpScore=1.80542482810308

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=-0.813836541425904
cont.tran.correlation=0.37866002437661

tran.covariance=-0.00371263896167773
cont.tran.covariance=0.000582743975193941

tran.mean=72.3787874030225
cont.tran.mean=84.7917544020107

weightedLogRatios:
wLogRatio
Lung	1.37531952232954
cerebhem	0.428215593526544
cortex	1.89398862847034
heart	1.58361196940495
kidney	1.71020217899087
liver	2.18751154583790
stomach	1.87168714923048
testicle	2.01087625021952

cont.weightedLogRatios:
wLogRatio
Lung	-0.223986742173611
cerebhem	-0.00208031379147015
cortex	-0.135921275017528
heart	0.0894463652708176
kidney	0.214562201763785
liver	-0.265190419539322
stomach	-0.895991246748558
testicle	0.201183349216742

varWeightedLogRatios=0.300237620904468
cont.varWeightedLogRatios=0.129536094750201

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.11904444942112	0.0946048549525766	32.9691795519863	1.20071277904982e-155	***
df.mm.trans1	0.935399050538564	0.0855026768673275	10.9399972586823	3.36291887093362e-26	***
df.mm.trans2	0.841648987379415	0.0779108644523506	10.8027165825397	1.26636699640244e-25	***
df.mm.exp2	0.0349686035108251	0.106501989862717	0.32833756022681	0.742734914151938	   
df.mm.exp3	0.196377955260957	0.106501989862717	1.8438900110138	0.0655367307497558	.  
df.mm.exp4	0.172542917922060	0.106501989862717	1.62009102500780	0.105572626690770	   
df.mm.exp5	0.09525227750598	0.106501989862717	0.89437087165002	0.371369150472729	   
df.mm.exp6	-0.0661309490137307	0.106501989862717	-0.620936276392343	0.534803076184588	   
df.mm.exp7	0.0447775881758603	0.106501989862717	0.42043898178409	0.674267908952716	   
df.mm.exp8	0.0812390662549263	0.106501989862717	0.762793881688451	0.445791683576468	   
df.mm.trans1:exp2	-0.127230742222676	0.103120108269064	-1.23381117764833	0.217604195193613	   
df.mm.trans2:exp2	0.0962807664615262	0.088306784950217	1.09029862785521	0.275881434573952	   
df.mm.trans1:exp3	-0.0976149537289543	0.103120108269064	-0.946614150891451	0.344096469232558	   
df.mm.trans2:exp3	-0.215628121720318	0.088306784950217	-2.44180695562497	0.0148108250462856	*  
df.mm.trans1:exp4	-0.144675905841333	0.103120108269064	-1.40298442534448	0.160975808589694	   
df.mm.trans2:exp4	-0.193423911552839	0.088306784950217	-2.19036296771399	0.0287608595446656	*  
df.mm.trans1:exp5	-0.0256887185881292	0.103120108269064	-0.249114542443085	0.80333057668302	   
df.mm.trans2:exp5	-0.101579319672350	0.088306784950217	-1.15030028247111	0.250334129566530	   
df.mm.trans1:exp6	0.147733898519818	0.103120108269064	1.43263909434953	0.152317752722251	   
df.mm.trans2:exp6	-0.0453636524496942	0.088306784950217	-0.51370517537546	0.607587677503181	   
df.mm.trans1:exp7	0.0172329762093022	0.103120108269064	0.167115575211940	0.867317679156861	   
df.mm.trans2:exp7	-0.0992760401601322	0.088306784950217	-1.12421758097183	0.261228697034781	   
df.mm.trans1:exp8	0.0326271785670285	0.103120108269064	0.316399770274647	0.751774468911937	   
df.mm.trans2:exp8	-0.112488796601453	0.088306784950217	-1.27384092473606	0.203057664282608	   
df.mm.trans1:probe2	0.305645356817506	0.0515600541345319	5.92794871820746	4.40850440578523e-09	***
df.mm.trans1:probe3	0.477126422780602	0.0515600541345319	9.25379988034284	1.62018431262886e-19	***
df.mm.trans1:probe4	0.320123249476351	0.0515600541345319	6.20874541056681	8.23780672872096e-10	***
df.mm.trans1:probe5	-0.0393671705900431	0.0515600541345319	-0.763520738114922	0.445358478113076	   
df.mm.trans1:probe6	0.128555934065147	0.0515600541345319	2.49332426474408	0.0128388677924299	*  
df.mm.trans1:probe7	-0.0172297295595299	0.0515600541345319	-0.334168182108065	0.738332635693446	   
df.mm.trans1:probe8	0.16015246607965	0.0515600541345319	3.10613456032796	0.00195655105832066	** 
df.mm.trans1:probe9	0.058075404429491	0.0515600541345319	1.12636430283721	0.260319814708870	   
df.mm.trans1:probe10	-0.100491882199899	0.0515600541345319	-1.94902592494749	0.0516112014139467	.  
df.mm.trans1:probe11	0.838917951449521	0.0515600541345319	16.2706957068081	3.28027005333673e-52	***
df.mm.trans1:probe12	0.132661169237684	0.0515600541345319	2.57294472367196	0.0102472861918234	*  
df.mm.trans1:probe13	0.97362483410155	0.0515600541345319	18.8833167545003	5.4766841627873e-67	***
df.mm.trans1:probe14	0.335029569831828	0.0515600541345319	6.49785139786043	1.36534786667325e-10	***
df.mm.trans1:probe15	-0.111324279597243	0.0515600541345319	-2.15911874930877	0.0311118292157385	*  
df.mm.trans1:probe16	0.278031225879692	0.0515600541345319	5.39237653153437	8.94767461494201e-08	***
df.mm.trans1:probe17	0.238324679911321	0.0515600541345319	4.62227365567688	4.3661002646196e-06	***
df.mm.trans1:probe18	-0.0317015322036667	0.0515600541345319	-0.614846759488463	0.538815597543777	   
df.mm.trans1:probe19	0.645115598188564	0.0515600541345319	12.5119263161615	3.64354096814944e-33	***
df.mm.trans1:probe20	0.942448522114556	0.0515600541345319	18.2786565672622	1.77162268825925e-63	***
df.mm.trans1:probe21	0.83437637488992	0.0515600541345319	16.1826124680327	9.88511259720434e-52	***
df.mm.trans1:probe22	0.463349545320352	0.0515600541345319	8.98659927918943	1.53973540941358e-18	***
df.mm.trans1:probe23	0.500981387021801	0.0515600541345319	9.71646355751735	2.90080075955713e-21	***
df.mm.trans1:probe24	0.74077554800681	0.0515600541345319	14.3672375919924	3.46884419284174e-42	***
df.mm.trans1:probe25	0.647707587164839	0.0515600541345319	12.5621975778928	2.12810684182161e-33	***
df.mm.trans1:probe26	0.743889011890555	0.0515600541345319	14.4276227862286	1.71161189334899e-42	***
df.mm.trans1:probe27	0.496773126966541	0.0515600541345319	9.63484494547555	5.96495300693133e-21	***
df.mm.trans1:probe28	0.494510361416871	0.0515600541345319	9.59095892581067	8.7718960472749e-21	***
df.mm.trans1:probe29	0.695706109833366	0.0515600541345319	13.4931221759021	7.77797966077516e-38	***
df.mm.trans1:probe30	0.518482498705917	0.0515600541345319	10.0558951577723	1.37515056332805e-22	***
df.mm.trans2:probe2	0.315773342099824	0.0515600541345319	6.1243795686463	1.37331138178464e-09	***
df.mm.trans2:probe3	0.205546061054995	0.0515600541345319	3.98653695201092	7.26302936480517e-05	***
df.mm.trans2:probe4	0.114607452994103	0.0515600541345319	2.22279543568877	0.0264841639461431	*  
df.mm.trans2:probe5	0.152655692352002	0.0515600541345319	2.96073568801321	0.00315184852798212	** 
df.mm.trans2:probe6	0.322878851062797	0.0515600541345319	6.26218991586651	5.94072363859812e-10	***
df.mm.trans3:probe2	-0.686784359862643	0.0515600541345319	-13.3200860897211	5.38787904941337e-37	***
df.mm.trans3:probe3	-0.860507076734022	0.0515600541345319	-16.6894137560206	1.65966257136642e-54	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31024769966926	0.200920174916782	21.4525380612201	2.21085814138175e-82	***
df.mm.trans1	0.0855919785350881	0.181589124581905	0.471349695264829	0.637508396319779	   
df.mm.trans2	0.200484635353442	0.165465763057624	1.21163817607164	0.225977641878036	   
df.mm.exp2	0.204739133167862	0.226187106813166	0.905175967156166	0.365621051852331	   
df.mm.exp3	0.358808464590729	0.226187106813166	1.58633473696231	0.113024276585776	   
df.mm.exp4	0.287951383262634	0.226187106813166	1.27306718459636	0.203331942116110	   
df.mm.exp5	0.374666036729142	0.226187106813166	1.65644294234075	0.0979902595150383	.  
df.mm.exp6	0.157752470773732	0.226187106813166	0.697442365289362	0.485711054321678	   
df.mm.exp7	0.468758459292798	0.226187106813166	2.07243669145121	0.0385167483334024	*  
df.mm.exp8	0.174859987213230	0.226187106813166	0.773076722528075	0.4396855215422	   
df.mm.trans1:exp2	-0.164892494940783	0.219004724453557	-0.752917524278117	0.451701761684728	   
df.mm.trans2:exp2	-0.215096752201496	0.187544441428827	-1.14691083650765	0.251731631484267	   
df.mm.trans1:exp3	-0.329754989432981	0.219004724453557	-1.50569806316169	0.132505182359652	   
df.mm.trans2:exp3	-0.349810147770075	0.187544441428827	-1.86521202710680	0.0624858279017887	.  
df.mm.trans1:exp4	-0.237359761183044	0.219004724453557	-1.08381114505765	0.278746749153763	   
df.mm.trans2:exp4	-0.308199646302744	0.187544441428828	-1.64334194047391	0.100671047507174	   
df.mm.trans1:exp5	-0.342329604077755	0.219004724453557	-1.56311515622281	0.118386674704287	   
df.mm.trans2:exp5	-0.441735580851469	0.187544441428828	-2.35536482705677	0.0187241546373616	*  
df.mm.trans1:exp6	-0.152168812631231	0.219004724453557	-0.69481977163237	0.487352501251227	   
df.mm.trans2:exp6	-0.142984950779734	0.187544441428828	-0.76240569803289	0.446023138988976	   
df.mm.trans1:exp7	-0.415115326138815	0.219004724453557	-1.89546288179206	0.0583600541346708	.  
df.mm.trans2:exp7	-0.268739347521125	0.187544441428828	-1.43293688404575	0.152232646466054	   
df.mm.trans1:exp8	-0.149989559796161	0.219004724453557	-0.684869060110019	0.493607716514514	   
df.mm.trans2:exp8	-0.246418974636202	0.187544441428828	-1.31392310408580	0.18921605512485	   
df.mm.trans1:probe2	-0.0919347720576156	0.109502362226779	-0.839568847539739	0.401379234999991	   
df.mm.trans1:probe3	0.123741314086450	0.109502362226779	1.13003328485446	0.258771515050893	   
df.mm.trans1:probe4	0.0127023205761856	0.109502362226779	0.116000425176940	0.907678791006026	   
df.mm.trans1:probe5	-0.0256743220260175	0.109502362226779	-0.234463636253309	0.814679849201636	   
df.mm.trans1:probe6	-0.056348581794208	0.109502362226779	-0.514587819370604	0.606970879893754	   
df.mm.trans1:probe7	-0.101545751728832	0.109502362226779	-0.927338457946063	0.354006257103891	   
df.mm.trans1:probe8	-0.0462624813824558	0.109502362226779	-0.42247930037022	0.67277888207269	   
df.mm.trans1:probe9	0.0760946969570531	0.109502362226779	0.694913748065649	0.487293630853137	   
df.mm.trans1:probe10	0.106782201807377	0.109502362226779	0.975158888227744	0.329750654231700	   
df.mm.trans1:probe11	0.0500093461129025	0.109502362226779	0.456696504951496	0.648002420261771	   
df.mm.trans1:probe12	-0.0728869992894102	0.109502362226779	-0.665620337381049	0.505829024814342	   
df.mm.trans1:probe13	0.0175515178991932	0.109502362226779	0.160284376905442	0.8726940175125	   
df.mm.trans1:probe14	-0.0202440332040783	0.109502362226779	-0.184873027324771	0.85337141907297	   
df.mm.trans1:probe15	-0.093979429632952	0.109502362226779	-0.85824111664661	0.390994095891953	   
df.mm.trans1:probe16	0.0807801420601353	0.109502362226779	0.737702277991412	0.460892944140777	   
df.mm.trans1:probe17	0.11005740488133	0.109502362226779	1.00506877334209	0.315141274458713	   
df.mm.trans1:probe18	-0.0774619292110452	0.109502362226779	-0.707399618015748	0.479506319641864	   
df.mm.trans1:probe19	0.106728098163160	0.109502362226779	0.974664801679132	0.329995621065154	   
df.mm.trans1:probe20	0.0536232049002963	0.109502362226779	0.489699069589413	0.624469465735787	   
df.mm.trans1:probe21	-0.0774319720569957	0.109502362226779	-0.707126042602028	0.479676213889719	   
df.mm.trans1:probe22	-0.0343511863189338	0.109502362226779	-0.313702696639482	0.753821535536539	   
df.mm.trans1:probe23	-0.0610522083868248	0.109502362226779	-0.557542386714783	0.57729934541484	   
df.mm.trans1:probe24	0.178773592378576	0.109502362226779	1.63260032699876	0.102912452745130	   
df.mm.trans1:probe25	0.0161951093416377	0.109502362226779	0.147897351365788	0.882457813130784	   
df.mm.trans1:probe26	-0.077024228338137	0.109502362226779	-0.703402436000607	0.481991894439786	   
df.mm.trans1:probe27	-0.0435415894876753	0.109502362226779	-0.397631508601623	0.690998704131582	   
df.mm.trans1:probe28	0.0461716303685976	0.109502362226779	0.421649628644325	0.673384222826834	   
df.mm.trans1:probe29	0.00607234513169524	0.109502362226779	0.0554540103812506	0.955789412748666	   
df.mm.trans1:probe30	-0.137442856128032	0.109502362226779	-1.25515882336299	0.209755837904288	   
df.mm.trans2:probe2	-0.042482399231593	0.109502362226779	-0.387958746895453	0.698140816307081	   
df.mm.trans2:probe3	-0.106651141426749	0.109502362226779	-0.973962015594467	0.330344263884908	   
df.mm.trans2:probe4	-0.223135551416444	0.109502362226779	-2.03772363334347	0.0418775924840442	*  
df.mm.trans2:probe5	-0.112551176096653	0.109502362226779	-1.02784244839906	0.304307664640199	   
df.mm.trans2:probe6	-0.101853518743290	0.109502362226779	-0.930149054979763	0.352550162728713	   
df.mm.trans3:probe2	0.127876291881902	0.109502362226779	1.16779482452690	0.243207279659912	   
df.mm.trans3:probe3	-0.0372357926437108	0.109502362226779	-0.340045565104758	0.733903732883104