chr11.4110_chr11_103245207_103245976_-_2.R 

fitVsDatCorrelation=0.900977895970366
cont.fitVsDatCorrelation=0.244310635231461

fstatistic=6854.69932471361,58,830
cont.fstatistic=1360.77996458776,58,830

residuals=-0.613666066701688,-0.127475790361349,-0.0105362651982836,0.123427548487852,0.760313015470561
cont.residuals=-1.08680969646414,-0.370002408541478,-0.0835747530474275,0.339425549641313,1.56994790052905

predictedValues:
Include	Exclude	Both
chr11.4110_chr11_103245207_103245976_-_2.R.tl.Lung	109.519782463235	152.274829909291	80.7138311712196
chr11.4110_chr11_103245207_103245976_-_2.R.tl.cerebhem	85.4901864422463	96.9128753736539	73.4988996939659
chr11.4110_chr11_103245207_103245976_-_2.R.tl.cortex	89.191915855829	127.040824236642	65.7510750899564
chr11.4110_chr11_103245207_103245976_-_2.R.tl.heart	96.4361820126294	128.811893346052	71.2380771586758
chr11.4110_chr11_103245207_103245976_-_2.R.tl.kidney	106.221432213201	155.600726712372	80.1881631480619
chr11.4110_chr11_103245207_103245976_-_2.R.tl.liver	105.539915461452	152.520717053887	75.3543055423039
chr11.4110_chr11_103245207_103245976_-_2.R.tl.stomach	103.303176406874	129.342980945858	72.3151318102833
chr11.4110_chr11_103245207_103245976_-_2.R.tl.testicle	100.397737635525	114.045311499865	79.1456703636136


diffExp=-42.7550474460558,-11.4226889314076,-37.8489083808133,-32.3757113334229,-49.3792944991714,-46.9808015924347,-26.0398045389840,-13.6475738643397
diffExpScore=0.996175174419673
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,-1,0,-1,-1,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,0,-1,-1,-1,-1,0,0
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,0,-1,-1,-1,-1,-1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	94.0054443027311	108.376166103008	93.7338405446084
cerebhem	99.0858755769097	111.571154517151	97.133122974937
cortex	103.903657084003	95.0068058517773	119.182753904917
heart	105.586786635338	90.0943947877653	104.827601938566
kidney	97.3985326081717	99.1168451730152	116.22631959185
liver	100.274130447802	92.1821374223207	103.521626829868
stomach	90.761462624149	86.2925313632968	98.7294360724004
testicle	92.0236059370662	82.7354015456875	100.781101047271
cont.diffExp=-14.3707218002772,-12.4852789402408,8.89685123222561,15.4923918475723,-1.71831256484359,8.09199302548149,4.4689312608522,9.28820439137864
cont.diffExpScore=4.00838248844312

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=0.820851829788797
cont.tran.correlation=0.144560255413225

tran.covariance=0.0117811664048954
cont.tran.covariance=0.00107252607250003

tran.mean=115.790655473038
cont.tran.mean=96.775933248762

weightedLogRatios:
wLogRatio
Lung	-1.60206280922619
cerebhem	-0.565741499846337
cortex	-1.65103199739602
heart	-1.36445874770052
kidney	-1.85401878372073
liver	-1.78331862626174
stomach	-1.06781297138199
testicle	-0.595585804813432

cont.weightedLogRatios:
wLogRatio
Lung	-0.656435205783769
cerebhem	-0.552473642300567
cortex	0.411655798530443
heart	0.726763842272931
kidney	-0.0802283757034185
liver	0.384175764351508
stomach	0.226353836450332
testicle	0.475474295069233

varWeightedLogRatios=0.263931014970421
cont.varWeightedLogRatios=0.250824985367797

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.01090865458425	0.100837438582941	49.6929387041369	5.92324431931313e-251	***
df.mm.trans1	-0.415895754284629	0.086311653136016	-4.8185353793332	1.71925805969461e-06	***
df.mm.trans2	-0.0386086322443325	0.076576217334207	-0.50418568046826	0.614264709262994	   
df.mm.exp2	-0.605938686052291	0.0980778448412813	-6.17814030307127	1.01524475772870e-09	***
df.mm.exp3	-0.181459354416334	0.0980778448412813	-1.85015642125893	0.064646239834017	.  
df.mm.exp4	-0.169675078554218	0.0980778448412813	-1.73000414954878	0.084001269594423	.  
df.mm.exp5	-0.00243895568482951	0.0980778448412813	-0.0248675497384394	0.98016658806423	   
df.mm.exp6	0.0333063754467259	0.0980778448412813	0.339591224711610	0.734250297768746	   
df.mm.exp7	-0.111779854999972	0.0980778448412813	-1.13970545724026	0.254737825097212	   
df.mm.exp8	-0.356436794816887	0.0980778448412813	-3.63422335996174	0.000295971904650622	***
df.mm.trans1:exp2	0.358235082250304	0.0891422920478457	4.01868825695021	6.38420369954705e-05	***
df.mm.trans2:exp2	0.154064089190343	0.065739623979871	2.34354989979128	0.0193363338492973	*  
df.mm.trans1:exp3	-0.0238554342276444	0.0891422920478457	-0.267610734249915	0.789065452603674	   
df.mm.trans2:exp3	0.000280860177960683	0.065739623979871	0.00427231190197681	0.996592225266998	   
df.mm.trans1:exp4	0.0424513471285798	0.0891422920478457	0.476220053953680	0.63404294342161	   
df.mm.trans2:exp4	0.00234124787970619	0.0657396239798711	0.0356139530159628	0.971598745594618	   
df.mm.trans1:exp5	-0.0281403406549581	0.0891422920478457	-0.315678899526773	0.752325602508527	   
df.mm.trans2:exp5	0.0240452580812254	0.065739623979871	0.365765068698717	0.714633586391061	   
df.mm.trans1:exp6	-0.0703223431829625	0.0891422920478457	-0.788877440409745	0.430408912813901	   
df.mm.trans2:exp6	-0.0316929188172721	0.0657396239798711	-0.482097658894067	0.629863673091091	   
df.mm.trans1:exp7	0.0533427852894249	0.0891422920478457	0.598400423233385	0.549736041362014	   
df.mm.trans2:exp7	-0.0514394816097141	0.065739623979871	-0.782473012402146	0.434159815892625	   
df.mm.trans1:exp8	0.269471273606035	0.0891422920478457	3.02293409127736	0.002580354023948	** 
df.mm.trans2:exp8	0.0673456538134586	0.065739623979871	1.02443016458505	0.305930612386386	   
df.mm.trans1:probe2	-0.362803663294253	0.0630331191975433	-5.75576249300372	1.21344248214595e-08	***
df.mm.trans1:probe3	-0.266809391258499	0.0630331191975433	-4.23284448961393	2.56504786301911e-05	***
df.mm.trans1:probe4	-0.393431760453757	0.0630331191975433	-6.24166732445457	6.89936459855538e-10	***
df.mm.trans1:probe5	0.297109721896682	0.0630331191975433	4.71354941146973	2.85411533335461e-06	***
df.mm.trans1:probe6	-0.307884196342943	0.0630331191975433	-4.88448295534997	1.24422908420100e-06	***
df.mm.trans1:probe7	0.531901538428242	0.0630331191975433	8.4384454585102	1.42128125954171e-16	***
df.mm.trans1:probe8	0.779122869266428	0.0630331191975433	12.3605317202325	2.46154222636506e-32	***
df.mm.trans1:probe9	0.680832773815821	0.0630331191975433	10.8011912226986	1.54545086084792e-25	***
df.mm.trans1:probe10	0.772867045871382	0.0630331191975433	12.2612851102806	6.96334834649128e-32	***
df.mm.trans1:probe11	0.652499920389778	0.0630331191975433	10.3516996889345	1.05769683353247e-23	***
df.mm.trans1:probe12	0.53582203614669	0.0630331191975434	8.5006428837425	8.694565663837e-17	***
df.mm.trans1:probe13	0.933305210621834	0.0630331191975434	14.8065845781309	3.43175021614836e-44	***
df.mm.trans1:probe14	-0.159176712559354	0.0630331191975433	-2.52528693781598	0.0117455606340263	*  
df.mm.trans1:probe15	-0.194080995526104	0.0630331191975433	-3.07903207070972	0.00214510783531837	** 
df.mm.trans1:probe16	-0.211921549509012	0.0630331191975433	-3.36206667553383	0.000809003910072413	***
df.mm.trans1:probe17	-0.22779730477935	0.0630331191975433	-3.61393038579357	0.000319749398881626	***
df.mm.trans1:probe18	0.244314845485151	0.0630331191975433	3.87597581391265	0.000114577805463003	***
df.mm.trans1:probe19	-0.170009261666319	0.0630331191975433	-2.69714181735980	0.00713557281529438	** 
df.mm.trans2:probe2	0.0608366347902432	0.0630331191975433	0.965153487003929	0.334749060822681	   
df.mm.trans2:probe3	0.0334656077952675	0.0630331191975433	0.530921017733353	0.595615544426049	   
df.mm.trans2:probe4	0.311963804657844	0.0630331191975433	4.94920461860949	9.0250452026114e-07	***
df.mm.trans2:probe5	0.138521745555707	0.0630331191975433	2.19760258288322	0.0282530048726454	*  
df.mm.trans2:probe6	0.416177440132179	0.0630331191975433	6.60252015813933	7.20850662537788e-11	***
df.mm.trans3:probe2	-0.360997346924544	0.0630331191975433	-5.72710586942703	1.42795401188967e-08	***
df.mm.trans3:probe3	0.27379309371117	0.0630331191975433	4.34363866482814	1.57481341505303e-05	***
df.mm.trans3:probe4	0.113883168315448	0.0630331191975433	1.80671954307930	0.0711680676558766	.  
df.mm.trans3:probe5	-0.00395777159339690	0.0630331191975433	-0.0627887631737437	0.949949831087595	   
df.mm.trans3:probe6	-0.558787193826808	0.0630331191975433	-8.86497766476685	4.60856987290189e-18	***
df.mm.trans3:probe7	-0.345309872231	0.0630331191975433	-5.47822916947537	5.69809003073017e-08	***
df.mm.trans3:probe8	-0.436970827028339	0.0630331191975433	-6.93240049978947	8.31381453313025e-12	***
df.mm.trans3:probe9	-0.47485843797578	0.0630331191975433	-7.5334751638673	1.29152383171501e-13	***
df.mm.trans3:probe10	-0.335182070252692	0.0630331191975433	-5.3175548746405	1.35314175401299e-07	***
df.mm.trans3:probe11	0.242593833465702	0.0630331191975433	3.84867251619616	0.000127876207576965	***
df.mm.trans3:probe12	-0.100138385504134	0.0630331191975433	-1.58866301999596	0.112517320841274	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6735846780966	0.225373712407126	20.7370443880962	2.72123393124835e-77	***
df.mm.trans1	-0.108827485990658	0.192908288475219	-0.56414105817251	0.572810540864769	   
df.mm.trans2	-0.0134406609957990	0.171149392777464	-0.0785317480692154	0.93742400522614	   
df.mm.exp2	0.0460653980793073	0.219205964643663	0.210146645207352	0.833604795087803	   
df.mm.exp3	-0.271747048149014	0.219205964643663	-1.23968820187334	0.215441129003661	   
df.mm.exp4	-0.180427537105023	0.219205964643663	-0.823095928974027	0.410689863824476	   
df.mm.exp5	-0.268930379085974	0.219205964643663	-1.22683878389506	0.220231082303190	   
df.mm.exp6	-0.196608042473461	0.219205964643663	-0.896910094545389	0.370026963471769	   
df.mm.exp7	-0.314906924259536	0.219205964643663	-1.43658008928472	0.151214158614504	   
df.mm.exp8	-0.363759746881742	0.219205964643663	-1.65944274131894	0.0974043716183804	.  
df.mm.trans1:exp2	0.0065688074180603	0.199234823629306	0.0329701770925455	0.973706296657756	   
df.mm.trans2:exp2	-0.0170110485424709	0.146929387703612	-0.115777032820594	0.907857257903495	   
df.mm.trans1:exp3	0.37185844504138	0.199234823629306	1.86643297726534	0.0623332601667706	.  
df.mm.trans2:exp3	0.140087382796350	0.146929387703612	0.95343338038634	0.340648113437916	   
df.mm.trans1:exp4	0.296608075281925	0.199234823629306	1.48873610485781	0.136936715305136	   
df.mm.trans2:exp4	-0.00432270616063118	0.146929387703612	-0.0294202965668856	0.976536457963136	   
df.mm.trans1:exp5	0.304388825289479	0.199234823629306	1.52778926768255	0.126945846924460	   
df.mm.trans2:exp5	0.179621592608749	0.146929387703612	1.22250283225222	0.221864563915738	   
df.mm.trans1:exp6	0.26116308371013	0.199234823629306	1.31083050117808	0.190277600688842	   
df.mm.trans2:exp6	0.0347662220493236	0.146929387703612	0.236618572994087	0.8130110928319	   
df.mm.trans1:exp7	0.279789000629074	0.199234823629306	1.40431775696826	0.160598332608367	   
df.mm.trans2:exp7	0.0870417809520935	0.146929387703612	0.592405524262277	0.553740415882958	   
df.mm.trans1:exp8	0.342452178583637	0.199234823629306	1.71883696005272	0.0860169431772505	.  
df.mm.trans2:exp8	0.0937991342772347	0.146929387703612	0.638396006021938	0.523392032316307	   
df.mm.trans1:probe2	-0.0455331605161573	0.140880294836788	-0.323204608344326	0.746621700665848	   
df.mm.trans1:probe3	0.0302288261595396	0.140880294836788	0.214571002953679	0.83015450917592	   
df.mm.trans1:probe4	-0.194942803211685	0.140880294836788	-1.38374783668311	0.166807685149543	   
df.mm.trans1:probe5	0.0432504772838433	0.140880294836788	0.307001609656976	0.758919152672483	   
df.mm.trans1:probe6	0.072772424307485	0.140880294836788	0.516555025610877	0.605604416148404	   
df.mm.trans1:probe7	0.068455744663854	0.140880294836788	0.485914263191747	0.627156208511014	   
df.mm.trans1:probe8	0.0265138423749397	0.140880294836788	0.188201212991897	0.85076492424651	   
df.mm.trans1:probe9	-0.0937362369966909	0.140880294836788	-0.665360880350837	0.506004545284592	   
df.mm.trans1:probe10	-0.247990681677967	0.140880294836788	-1.7602936022051	0.078726324114439	.  
df.mm.trans1:probe11	-0.0400966905062859	0.140880294836788	-0.284615322197746	0.776009866258849	   
df.mm.trans1:probe12	-0.103211645634853	0.140880294836788	-0.732619460758692	0.463997394207092	   
df.mm.trans1:probe13	-0.0667924692542492	0.140880294836788	-0.47410796046125	0.635547616832051	   
df.mm.trans1:probe14	-0.0824179091132449	0.140880294836788	-0.585020844886272	0.558692701847581	   
df.mm.trans1:probe15	0.0503140150978984	0.140880294836788	0.357140188811984	0.721077642839482	   
df.mm.trans1:probe16	-0.0672370987463506	0.140880294836788	-0.477264040540558	0.633299757312863	   
df.mm.trans1:probe17	7.8248760439688e-05	0.140880294836788	0.000555427290454923	0.9995569666198	   
df.mm.trans1:probe18	0.0382492756074824	0.140880294836788	0.271501955981813	0.786072508526143	   
df.mm.trans1:probe19	-0.0514434539659905	0.140880294836788	-0.365157199774379	0.715087093390106	   
df.mm.trans2:probe2	0.117071573553397	0.140880294836788	0.831000344576411	0.406212417721116	   
df.mm.trans2:probe3	0.186901448617462	0.140880294836788	1.3266684942276	0.184983252322242	   
df.mm.trans2:probe4	0.0695393482949489	0.140880294836788	0.493605925339035	0.621715099792112	   
df.mm.trans2:probe5	-0.00512805185906379	0.140880294836788	-0.0364000647855309	0.970972114898472	   
df.mm.trans2:probe6	0.0899708823578532	0.140880294836788	0.638633546743253	0.52323753133384	   
df.mm.trans3:probe2	-0.00584408004544189	0.140880294836788	-0.0414825938021519	0.966921145476326	   
df.mm.trans3:probe3	0.145587203174017	0.140880294836788	1.03341069340238	0.30171275300298	   
df.mm.trans3:probe4	-0.0194523614110015	0.140880294836788	-0.138077233821361	0.890212868547515	   
df.mm.trans3:probe5	-0.0547287869530862	0.140880294836788	-0.388477231798033	0.697762524484215	   
df.mm.trans3:probe6	0.119009399912438	0.140880294836788	0.844755471659911	0.398490809366296	   
df.mm.trans3:probe7	-0.191005775930779	0.140880294836788	-1.35580193207334	0.175530960809470	   
df.mm.trans3:probe8	-0.128919189320608	0.140880294836788	-0.915097384413926	0.360406281247693	   
df.mm.trans3:probe9	0.103851369653216	0.140880294836788	0.737160365638995	0.46123315805249	   
df.mm.trans3:probe10	-0.0414939239293935	0.140880294836788	-0.294533199106836	0.76842414618639	   
df.mm.trans3:probe11	0.0964664028923437	0.140880294836788	0.684740211568278	0.49369903067528	   
df.mm.trans3:probe12	-0.153757241731191	0.140880294836788	-1.09140346355266	0.275412102670568