chr11.3379_chr11_71202208_71202905_-_2.R 

fitVsDatCorrelation=0.907477308956471
cont.fitVsDatCorrelation=0.284340478182799

fstatistic=6930.00719664541,59,853
cont.fstatistic=1318.94035748887,59,853

residuals=-1.04730200429917,-0.100373168532288,-0.00775012988912746,0.0924619268364007,1.07960242102357
cont.residuals=-0.88549276959989,-0.315495275098108,-0.133892732256400,0.194912286643248,1.49970365307111

predictedValues:
Include	Exclude	Both
chr11.3379_chr11_71202208_71202905_-_2.R.tl.Lung	82.7403998325264	68.875764338269	64.3557977607145
chr11.3379_chr11_71202208_71202905_-_2.R.tl.cerebhem	78.719016035989	72.4039439681373	64.4032938839954
chr11.3379_chr11_71202208_71202905_-_2.R.tl.cortex	83.0863999769246	60.8437006683431	59.7206476289821
chr11.3379_chr11_71202208_71202905_-_2.R.tl.heart	77.0446764033411	64.1181815396133	62.9937174498401
chr11.3379_chr11_71202208_71202905_-_2.R.tl.kidney	84.7113933476303	72.4121293263143	67.3434839533069
chr11.3379_chr11_71202208_71202905_-_2.R.tl.liver	82.286954243146	69.7957077999386	65.2199114295418
chr11.3379_chr11_71202208_71202905_-_2.R.tl.stomach	80.8599179917141	61.3687036645283	66.8023374507357
chr11.3379_chr11_71202208_71202905_-_2.R.tl.testicle	81.9056627718273	68.2725389306159	63.5761511583613


diffExp=13.8646354942574,6.3150720678517,22.2426993085815,12.9264948637278,12.2992640213159,12.4912464432075,19.4912143271859,13.6331238412114
diffExpScore=0.991248318064258
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=0,0,1,0,0,0,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,0,0,1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	78.9756597756964	72.5901398837067	86.2469463700195
cerebhem	74.6470834710236	90.567150869076	84.806897825891
cortex	73.700030845411	80.9379129792837	93.9325427670663
heart	71.780101776995	81.2709291684327	89.9249634671335
kidney	73.886932249194	77.7447081346657	68.3951993244736
liver	84.0143615639227	69.931701940338	89.5377870597908
stomach	69.3408553731175	66.5496706880755	94.2716760451804
testicle	71.6932586152377	79.4422885833726	71.3736961390196
cont.diffExp=6.38551989198967,-15.9200673980524,-7.23788213387267,-9.49082739143768,-3.85777588547165,14.0826596235847,2.79118468504197,-7.7490299681349
cont.diffExpScore=3.06938880168102

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,-1,0,0,0,1,0,0
cont.diffExp1.2Score=2

tran.correlation=0.177837019326348
cont.tran.correlation=-0.217528279712022

tran.covariance=0.000362624537384753
cont.tran.covariance=-0.00115156611805793

tran.mean=74.3403181774287
cont.tran.mean=76.0670491198468

weightedLogRatios:
wLogRatio
Lung	0.793038423071436
cerebhem	0.361596176476082
cortex	1.32857564929058
heart	0.78101393332979
kidney	0.684107998779614
liver	0.71254451216652
stomach	1.17355403621444
testicle	0.785506969041305

cont.weightedLogRatios:
wLogRatio
Lung	0.364810661977025
cerebhem	-0.85243183060129
cortex	-0.407208479155841
heart	-0.538411615198292
kidney	-0.220270560175084
liver	0.796117079447652
stomach	0.173319587616704
testicle	-0.443760503027732

varWeightedLogRatios=0.0895406534437555
cont.varWeightedLogRatios=0.295102226558777

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31538427330758	0.09621049662833	44.85357029159	1.28573299123139e-226	***
df.mm.trans1	-0.070948841139233	0.0830850315002725	-0.853930483723778	0.393383248779149	   
df.mm.trans2	-0.120209551307805	0.0734052497928466	-1.63761517939169	0.101870808873843	   
df.mm.exp2	-0.000604584365462595	0.0944226218390668	-0.00640296100327573	0.994892708299023	   
df.mm.exp3	-0.0450738263342989	0.0944226218390668	-0.477362579606425	0.633226241112172	   
df.mm.exp4	-0.121506895728897	0.0944226218390667	-1.28684094301038	0.198498837677321	   
df.mm.exp5	0.0282324440644523	0.0944226218390668	0.299000848679794	0.765012227934937	   
df.mm.exp6	-0.00556504786522362	0.0944226218390667	-0.0589376545242373	0.953015570405826	   
df.mm.exp7	-0.175705159820351	0.0944226218390668	-1.86083754505167	0.0631110084008531	.  
df.mm.exp8	-0.00674799799007313	0.0944226218390667	-0.0714659036006686	0.943043712237357	   
df.mm.trans1:exp2	-0.0492186559300543	0.0872767928827078	-0.56393749477252	0.572944904025069	   
df.mm.trans2:exp2	0.0505609920896384	0.0644581833829615	0.78439989208575	0.433023275040635	   
df.mm.trans1:exp3	0.0492468628628488	0.0872767928827078	0.564260684154976	0.572725065494259	   
df.mm.trans2:exp3	-0.0789222467678996	0.0644581833829615	-1.22439452410571	0.221141477019221	   
df.mm.trans1:exp4	0.050184368935912	0.0872767928827077	0.575002440836195	0.565441273628394	   
df.mm.trans2:exp4	0.0499304979182174	0.0644581833829615	0.774618447149968	0.43877981195041	   
df.mm.trans1:exp5	-0.00469033073155829	0.0872767928827078	-0.0537408694412233	0.957154204102956	   
df.mm.trans2:exp5	0.0218370085448608	0.0644581833829615	0.338777908386308	0.734860420486521	   
df.mm.trans1:exp6	6.96349008163369e-05	0.0872767928827077	0.000797862736660363	0.99936358427057	   
df.mm.trans2:exp6	0.0188331980234012	0.0644581833829615	0.292176990336645	0.770222350933949	   
df.mm.trans1:exp7	0.152715416412975	0.0872767928827077	1.74978263257463	0.080515403968647	.  
df.mm.trans2:exp7	0.0603007878400403	0.0644581833829615	0.935502440113443	0.349794187632284	   
df.mm.trans1:exp8	-0.00339186444655462	0.0872767928827077	-0.0388633029986905	0.969008467986938	   
df.mm.trans2:exp8	-0.00204874660253554	0.0644581833829615	-0.0317841194866359	0.974651646319983	   
df.mm.trans1:probe2	-0.00479382379215062	0.0597543352607067	-0.0802255396405178	0.93607669570524	   
df.mm.trans1:probe3	-0.134723870498689	0.0597543352607067	-2.25462922331731	0.0244095151293713	*  
df.mm.trans1:probe4	-0.236890816502763	0.0597543352607067	-3.96441221325974	7.97412403874165e-05	***
df.mm.trans1:probe5	-0.234355639910894	0.0597543352607067	-3.92198555784123	9.48771621420786e-05	***
df.mm.trans1:probe6	0.099682618857135	0.0597543352607067	1.66820730951524	0.0956415826082844	.  
df.mm.trans1:probe7	0.281553902829607	0.0597543352607067	4.71185733388538	2.86520379520866e-06	***
df.mm.trans1:probe8	0.286670672992828	0.0597543352607067	4.79748744157375	1.89607380503785e-06	***
df.mm.trans1:probe9	0.350449487511259	0.0597543352607067	5.86483785623681	6.42601406221784e-09	***
df.mm.trans1:probe10	0.0987753034112653	0.0597543352607067	1.65302321547568	0.0986941087422606	.  
df.mm.trans1:probe11	-0.130576273523157	0.0597543352607067	-2.18521841057148	0.0291442220861372	*  
df.mm.trans1:probe12	-0.0924172132725403	0.0597543352607067	-1.54661938534378	0.122325958458409	   
df.mm.trans1:probe13	-0.251908576218329	0.0597543352607067	-4.21573723679224	2.75537470217920e-05	***
df.mm.trans1:probe14	-0.241286305591639	0.0597543352607067	-4.03797154698337	5.87698525200666e-05	***
df.mm.trans1:probe15	-0.133579370697803	0.0597543352607067	-2.23547580464245	0.0256441784125190	*  
df.mm.trans1:probe16	-0.222501182740658	0.0597543352607067	-3.72359899528446	0.000209303805188672	***
df.mm.trans1:probe17	1.06422202815838	0.0597543352607067	17.8099551022568	1.43268692600845e-60	***
df.mm.trans1:probe18	1.31379479243814	0.0597543352607067	21.9866020884691	3.10912335056011e-85	***
df.mm.trans1:probe19	0.82437120780945	0.0597543352607067	13.7960066698548	3.13684071887365e-39	***
df.mm.trans1:probe20	0.892495831135791	0.0597543352607067	14.9360850094283	5.53374357132052e-45	***
df.mm.trans1:probe21	1.04823625515740	0.0597543352607067	17.5424301949637	4.61344593250002e-59	***
df.mm.trans1:probe22	0.90350293245455	0.0597543352607067	15.1202909129955	6.14174748523789e-46	***
df.mm.trans2:probe2	0.138799473835037	0.0597543352607067	2.32283520901803	0.0204224195472512	*  
df.mm.trans2:probe3	0.126286212434415	0.0597543352607067	2.11342343418985	0.0348543605463445	*  
df.mm.trans2:probe4	0.382823497457504	0.0597543352607067	6.40662298036209	2.45714731379953e-10	***
df.mm.trans2:probe5	0.0277410614767618	0.0597543352607067	0.46425186316153	0.642585767596797	   
df.mm.trans2:probe6	-0.0815759590908271	0.0597543352607067	-1.36518896469876	0.172553600610819	   
df.mm.trans3:probe2	-0.0465442470537737	0.0597543352607067	-0.778926697965968	0.436238926763393	   
df.mm.trans3:probe3	-0.209835409255775	0.0597543352607067	-3.51163490214171	0.000468730341200978	***
df.mm.trans3:probe4	0.5502676570627	0.0597543352607067	9.20883237445276	2.49723315016666e-19	***
df.mm.trans3:probe5	-0.0281873930452958	0.0597543352607067	-0.47172130561431	0.637246363362365	   
df.mm.trans3:probe6	-0.0776549266680286	0.0597543352607067	-1.29956975220663	0.194099573674185	   
df.mm.trans3:probe7	-0.16578372628673	0.0597543352607067	-2.77442173130066	0.00565084103222575	** 
df.mm.trans3:probe8	-0.070403900563175	0.0597543352607067	-1.17822247132370	0.239036511531854	   
df.mm.trans3:probe9	-0.0700579381491814	0.0597543352607067	-1.17243272548377	0.241350694310657	   
df.mm.trans3:probe10	0.520211348106011	0.0597543352607067	8.7058344107811	1.61276833617087e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.25298204884984	0.219564412705062	19.3700882417717	1.4761809810851e-69	***
df.mm.trans1	0.0824034291687055	0.189610456085799	0.434593275443721	0.663967593074743	   
df.mm.trans2	0.0205858615541645	0.167519980927829	0.122886007031205	0.902226313117263	   
df.mm.exp2	0.181731997841279	0.215484258336758	0.843365539756822	0.399260468706541	   
df.mm.exp3	-0.0456454708053355	0.215484258336758	-0.211827402881564	0.832292291044492	   
df.mm.exp4	-0.0243340502394712	0.215484258336758	-0.112927275650187	0.910114782304614	   
df.mm.exp5	0.233909683104594	0.215484258336758	1.08550705703542	0.278003817976067	   
df.mm.exp6	-0.0129080518868693	0.215484258336758	-0.0599025283169252	0.95224729720979	   
df.mm.exp7	-0.305952060194727	0.215484258336758	-1.41983485269994	0.156021122471135	   
df.mm.exp8	0.182744000962463	0.215484258336758	0.848061952984384	0.39664137253984	   
df.mm.trans1:exp2	-0.238100244884176	0.199176580972251	-1.19542289420736	0.232254074801309	   
df.mm.trans2:exp2	0.0395304779399522	0.147101653920241	0.268728983573398	0.788203226143113	   
df.mm.trans1:exp3	-0.0234910123609446	0.199176580972251	-0.11794063461817	0.90614246187384	   
df.mm.trans2:exp3	0.154498726799779	0.147101653920241	1.05028545011159	0.293884354972137	   
df.mm.trans1:exp4	-0.0711983470283108	0.199176580972251	-0.35746344615801	0.720833310492081	   
df.mm.trans2:exp4	0.137293329710951	0.147101653920241	0.933322814884136	0.350917426943543	   
df.mm.trans1:exp5	-0.30051340186165	0.199176580972251	-1.50877879515121	0.131725609247510	   
df.mm.trans2:exp5	-0.165308295271405	0.147101653920241	-1.12376911384719	0.261427145342779	   
df.mm.trans1:exp6	0.0747561062272963	0.199176580972251	0.375325783093501	0.707511500328346	   
df.mm.trans2:exp6	-0.0244019672554876	0.147101653920241	-0.165885063866912	0.86828671051775	   
df.mm.trans1:exp7	0.175846635413551	0.199176580972251	0.882868028737023	0.377556316904693	   
df.mm.trans2:exp7	0.219071558352001	0.147101653920241	1.48925285687666	0.136790419832661	   
df.mm.trans1:exp8	-0.279486980768017	0.199176580972251	-1.40321206139669	0.160917509571615	   
df.mm.trans2:exp8	-0.0925422710188185	0.147101653920241	-0.629104218427042	0.529449266040597	   
df.mm.trans1:probe2	0.195462308536691	0.136366882906570	1.43335613728598	0.152122506314066	   
df.mm.trans1:probe3	0.241922797634153	0.136366882906570	1.77405827923707	0.0764101805666713	.  
df.mm.trans1:probe4	-0.0373685056410314	0.136366882906570	-0.274029183952484	0.784128537957505	   
df.mm.trans1:probe5	-0.131033523152413	0.136366882906570	-0.960889626275236	0.336879959854391	   
df.mm.trans1:probe6	0.0160274248932146	0.136366882906570	0.117531651025532	0.906466432103834	   
df.mm.trans1:probe7	-0.0186232447208611	0.136366882906570	-0.136567209896706	0.89140512097093	   
df.mm.trans1:probe8	0.130818807978634	0.136366882906570	0.959315085820815	0.337671867554458	   
df.mm.trans1:probe9	-0.0282228589015331	0.136366882906570	-0.206962704580331	0.836088378952936	   
df.mm.trans1:probe10	0.0159851076020222	0.136366882906570	0.117221331611533	0.906712257387331	   
df.mm.trans1:probe11	0.0202916108624018	0.136366882906570	0.148801603658451	0.881745355572433	   
df.mm.trans1:probe12	0.116170408365832	0.136366882906570	0.85189604609078	0.394510895836752	   
df.mm.trans1:probe13	0.0616129958184019	0.136366882906570	0.451817879129899	0.651515048012332	   
df.mm.trans1:probe14	-0.000152494775546623	0.136366882906570	-0.00111826839696191	0.999108012556354	   
df.mm.trans1:probe15	0.178981145258729	0.136366882906570	1.31249715065611	0.189705483382126	   
df.mm.trans1:probe16	0.075009215409098	0.136366882906570	0.550054483979731	0.582426000682382	   
df.mm.trans1:probe17	0.281936363920382	0.136366882906570	2.06748411279259	0.0389896442375716	*  
df.mm.trans1:probe18	-0.0813074511231119	0.136366882906570	-0.596240446287963	0.551172795881342	   
df.mm.trans1:probe19	-0.0444732677175629	0.136366882906570	-0.326129532109589	0.744406347191993	   
df.mm.trans1:probe20	0.084066628857306	0.136366882906570	0.616473934620204	0.537746254112928	   
df.mm.trans1:probe21	-0.0519018876673253	0.136366882906570	-0.380604781462117	0.703591293882235	   
df.mm.trans1:probe22	0.0549335503879953	0.136366882906570	0.402836445455985	0.687169486880384	   
df.mm.trans2:probe2	0.0114951012998521	0.136366882906570	0.0842954026288609	0.932841346502534	   
df.mm.trans2:probe3	0.138953385103582	0.136366882906570	1.01896723120659	0.308507436317289	   
df.mm.trans2:probe4	0.0647702900474855	0.136366882906570	0.474970818918417	0.63492935790356	   
df.mm.trans2:probe5	-0.126455817956450	0.136366882906570	-0.927320587382566	0.35402241264366	   
df.mm.trans2:probe6	0.0914160483717852	0.136366882906570	0.670368394608078	0.502804425333195	   
df.mm.trans3:probe2	-0.0114756864290293	0.136366882906570	-0.0841530302990917	0.93295450734916	   
df.mm.trans3:probe3	0.0461824268773171	0.136366882906570	0.338663067549606	0.734946910895668	   
df.mm.trans3:probe4	0.092997633640102	0.136366882906570	0.681966410450388	0.495445317533605	   
df.mm.trans3:probe5	0.220986856740171	0.136366882906570	1.62053170117247	0.105487697134067	   
df.mm.trans3:probe6	0.0382800494148354	0.136366882906570	0.280713679149375	0.778998086166724	   
df.mm.trans3:probe7	0.0809676145386523	0.136366882906570	0.59374837066655	0.552837839667832	   
df.mm.trans3:probe8	0.308880918703815	0.136366882906570	2.26507281034971	0.0237582980641812	*  
df.mm.trans3:probe9	0.111689565740053	0.136366882906570	0.819037315801779	0.412993888203685	   
df.mm.trans3:probe10	0.123923733838464	0.136366882906570	0.908752412588096	0.363737510718446