chr11.3588_chr11_118759113_118762633_+_2.R 

fitVsDatCorrelation=0.849644335741086
cont.fitVsDatCorrelation=0.248603198401620

fstatistic=10536.7076828092,53,715
cont.fstatistic=3113.84767813879,53,715

residuals=-0.583575933648848,-0.0900814304034807,-0.00904805416825253,0.0886276836696961,0.905156742235034
cont.residuals=-0.642724381326874,-0.190141437716223,-0.0274297769145067,0.151187157075248,1.34956549158239

predictedValues:
Include	Exclude	Both
chr11.3588_chr11_118759113_118762633_+_2.R.tl.Lung	75.0724664332056	60.7676901358459	59.5584364007386
chr11.3588_chr11_118759113_118762633_+_2.R.tl.cerebhem	74.3408719607874	61.14038365757	58.6625002269759
chr11.3588_chr11_118759113_118762633_+_2.R.tl.cortex	68.4335321596489	62.3007629983894	55.3506466417286
chr11.3588_chr11_118759113_118762633_+_2.R.tl.heart	77.6533135444876	67.0447569558562	59.300694475369
chr11.3588_chr11_118759113_118762633_+_2.R.tl.kidney	76.4460869038463	75.4363248485961	62.8125682391405
chr11.3588_chr11_118759113_118762633_+_2.R.tl.liver	76.753170905619	75.93277056843	61.5706033698396
chr11.3588_chr11_118759113_118762633_+_2.R.tl.stomach	84.605010919419	64.949826145692	58.7940444724719
chr11.3588_chr11_118759113_118762633_+_2.R.tl.testicle	84.7320619563614	61.9890214208344	63.6513640509261


diffExp=14.3047762973597,13.2004883032173,6.13276916125952,10.6085565886314,1.00976205525021,0.82040033718907,19.6551847737269,22.7430405355270
diffExpScore=0.988823691027708
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=0,0,0,0,0,0,1,1
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,0,0,0,0,1,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	70.4196874954776	75.8068769956347	88.9137931338685
cerebhem	69.5555677677722	68.5903048265505	69.979459993215
cortex	71.3903336252619	73.4898529484506	71.5711154624963
heart	67.133714907118	74.4746713325606	72.9613346957105
kidney	71.0227019606913	76.5139669979548	65.8183760517364
liver	70.639354963433	76.9154603413232	66.078243270603
stomach	74.3785197504479	72.1022604536997	72.7850622340767
testicle	69.6618060983718	77.6650105738798	77.585182268629
cont.diffExp=-5.38718950015711,0.965262941221724,-2.0995193231887,-7.34095642544261,-5.49126503726346,-6.27610537789022,2.27625929674821,-8.00320447550804
cont.diffExpScore=1.16945613867991

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=0.0355274301071813
cont.tran.correlation=-0.137351963496715

tran.covariance=0.000399938961655232
cont.tran.covariance=-0.000149925098255908

tran.mean=71.7248782196618
cont.tran.mean=72.4850056899142

weightedLogRatios:
wLogRatio
Lung	0.890558222235745
cerebhem	0.823184966669983
cortex	0.392355580318182
heart	0.628530312632356
kidney	0.0575744194408754
liver	0.0465878049381693
stomach	1.13836177229832
testicle	1.33866551944520

cont.weightedLogRatios:
wLogRatio
Lung	-0.316340344473756
cerebhem	0.0591851030837031
cortex	-0.124132173461046
heart	-0.441923633161704
kidney	-0.320254636255271
liver	-0.366026273766504
stomach	0.133453597920407
testicle	-0.467421989457748

varWeightedLogRatios=0.225836843706438
cont.varWeightedLogRatios=0.0517596529436082

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.70444497184761	0.0785159964428976	59.917025637814	6.04168934703032e-281	***
df.mm.trans1	-0.202772719962862	0.0690767342317817	-2.93547056353987	0.00343724885012327	** 
df.mm.trans2	-0.695894345214787	0.0624569144196232	-11.1419904694515	1.06915757770319e-26	***
df.mm.exp2	0.0114786540078093	0.0832227593254506	0.137926861604299	0.890337053649364	   
df.mm.exp3	0.00559412509934557	0.0832227593254506	0.0672186928754573	0.946426395650106	   
df.mm.exp4	0.13643946830962	0.0832227593254506	1.63944898505540	0.101559663902429	   
df.mm.exp5	0.181165335627048	0.0832227593254506	2.17687249371994	0.0298165261621387	*  
df.mm.exp6	0.211704366072867	0.0832227593254506	2.543827767654	0.0111738785605611	*  
df.mm.exp7	0.199013849988664	0.0832227593254506	2.3913392394309	0.0170445812976171	*  
df.mm.exp8	0.0744764590918145	0.0832227593254506	0.894904947822832	0.371139053973027	   
df.mm.trans1:exp2	-0.0212716260017709	0.0782938856826559	-0.271689491667203	0.785939209203426	   
df.mm.trans2:exp2	-0.00536429798145736	0.0643404592706336	-0.0833736352252888	0.933577787492854	   
df.mm.trans1:exp3	-0.0981850505016952	0.0782938856826559	-1.25405770381180	0.210230870825474	   
df.mm.trans2:exp3	0.0193213123010067	0.0643404592706336	0.30029801652077	0.764037135274812	   
df.mm.trans1:exp4	-0.102639112961692	0.0782938856826559	-1.31094672421437	0.190296642730044	   
df.mm.trans2:exp4	-0.0381372931575749	0.0643404592706336	-0.592742010080454	0.553541239173373	   
df.mm.trans1:exp5	-0.163033455756455	0.0782938856826559	-2.08232679135724	0.037667601819489	*  
df.mm.trans2:exp5	0.0350653498113508	0.0643404592706336	0.544996883902496	0.585925610185916	   
df.mm.trans1:exp6	-0.189563531884296	0.0782938856826559	-2.42117925597209	0.0157181797318874	*  
df.mm.trans2:exp6	0.0110857494589211	0.0643404592706336	0.172298264336152	0.863251805695593	   
df.mm.trans1:exp7	-0.0794742205610633	0.0782938856826559	-1.01507569675609	0.310413107530766	   
df.mm.trans2:exp7	-0.132457019099547	0.0643404592706336	-2.05868936282218	0.0398857574214943	*  
df.mm.trans1:exp8	0.046563740277443	0.0782938856826559	0.594730225373884	0.552211966434604	   
df.mm.trans2:exp8	-0.0545773990668756	0.0643404592706336	-0.848259395185666	0.396577405737022	   
df.mm.trans1:probe2	-0.0643913197309828	0.0457137805036879	-1.40857568596384	0.159395365017658	   
df.mm.trans1:probe3	-0.61398615487952	0.0457137805036879	-13.4310955714981	7.75257702269287e-37	***
df.mm.trans1:probe4	-0.376400339277776	0.0457137805036879	-8.2338484179275	8.60344099187902e-16	***
df.mm.trans1:probe5	-0.0450505051832223	0.0457137805036879	-0.98549069201546	0.324716461218776	   
df.mm.trans1:probe6	-0.365775201029131	0.0457137805036879	-8.00142095007046	4.96546864234706e-15	***
df.mm.trans1:probe7	-0.335043172207063	0.0457137805036879	-7.32915039000185	6.27694697792584e-13	***
df.mm.trans1:probe8	-0.200816119208205	0.0457137805036879	-4.39290115574678	1.28782291184869e-05	***
df.mm.trans1:probe9	-0.3662586528142	0.0457137805036879	-8.01199657474516	4.58883051717965e-15	***
df.mm.trans1:probe10	-0.232862446815100	0.0457137805036879	-5.09392231947026	4.49192076045303e-07	***
df.mm.trans1:probe11	0.530589722800573	0.0457137805036879	11.6067784583637	1.17541822501416e-28	***
df.mm.trans1:probe12	-0.163245828538730	0.0457137805036879	-3.57104196459009	0.00037937573296664	***
df.mm.trans1:probe13	0.160513966538508	0.0457137805036879	3.51128182289711	0.000473947440311676	***
df.mm.trans1:probe14	0.0967983622727054	0.0457137805036879	2.11748757609090	0.0345630900155732	*  
df.mm.trans1:probe15	-0.190899720811313	0.0457137805036879	-4.1759775434874	3.33351646601198e-05	***
df.mm.trans1:probe16	-0.439826436643812	0.0457137805036879	-9.62130963131193	1.09145444777506e-20	***
df.mm.trans1:probe17	-0.474916944082679	0.0457137805036879	-10.3889229648019	1.21352752222989e-23	***
df.mm.trans1:probe18	-0.386260662777960	0.0457137805036879	-8.44954537826507	1.63167743447067e-16	***
df.mm.trans1:probe19	-0.506350571017527	0.0457137805036879	-11.0765411532017	1.99744287577319e-26	***
df.mm.trans1:probe20	-0.357182615479715	0.0457137805036879	-7.81345606388645	1.98944906304814e-14	***
df.mm.trans1:probe21	-0.432313390049365	0.0457137805036879	-9.45695992075057	4.45125039665187e-20	***
df.mm.trans2:probe2	0.214693776339745	0.0457137805036879	4.69647826047607	3.17400966652180e-06	***
df.mm.trans2:probe3	0.266845285570493	0.0457137805036879	5.83730513272612	8.0460955211899e-09	***
df.mm.trans2:probe4	0.186775448927106	0.0457137805036879	4.08575809896182	4.89070170289226e-05	***
df.mm.trans2:probe5	0.1491357674191	0.0457137805036879	3.26238096643678	0.00115729076429327	** 
df.mm.trans2:probe6	0.266133419097877	0.0457137805036879	5.8217328815412	8.79566623176628e-09	***
df.mm.trans3:probe2	0.214816560986452	0.0457137805036879	4.69916420430645	3.13379134931334e-06	***
df.mm.trans3:probe3	0.396704701874941	0.0457137805036879	8.67801125839804	2.70252177970301e-17	***
df.mm.trans3:probe4	0.323182124216959	0.0457137805036879	7.06968709776446	3.69997088225649e-12	***
df.mm.trans3:probe5	0.471196854397529	0.0457137805036879	10.3075451035933	2.54123869647129e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22227361765745	0.144211876845232	29.2782654939634	1.84950104505512e-124	***
df.mm.trans1	0.0382205895342353	0.126874598059127	0.301246980238104	0.763313753529953	   
df.mm.trans2	0.149988027592563	0.11471584464914	1.30747437767906	0.191471917815884	   
df.mm.exp2	0.127080788485889	0.152856880919683	0.831371068945611	0.406041586092203	   
df.mm.exp3	0.199623634885882	0.152856880919683	1.30595125116266	0.1919891311707	   
df.mm.exp4	0.132221215544452	0.152856880919683	0.86500008863799	0.38732885922869	   
df.mm.exp5	0.318579194302264	0.152856880919683	2.08416652482696	0.0374994595451685	*  
df.mm.exp6	0.314460175800845	0.152856880919683	2.05721962864122	0.0400272718622870	*  
df.mm.exp7	0.204747231295942	0.152856880919683	1.33947016362007	0.180843264101361	   
df.mm.exp8	0.149685968502158	0.152856880919683	0.979255677608713	0.327784879578221	   
df.mm.trans1:exp2	-0.139427695140108	0.143803921637973	-0.969568100452212	0.332589679050502	   
df.mm.trans2:exp2	-0.227118606870064	0.118175388568753	-1.92187738598320	0.0550183176947737	.  
df.mm.trans1:exp3	-0.185934034098281	0.143803921637973	-1.29296914841009	0.196439349282421	   
df.mm.trans2:exp3	-0.230665307389791	0.118175388568753	-1.95188956163738	0.0513413320997373	.  
df.mm.trans1:exp4	-0.180007716117306	0.143803921637973	-1.25175804711694	0.211067389617816	   
df.mm.trans2:exp4	-0.149951144148331	0.118175388568753	-1.26888640658957	0.204894500139931	   
df.mm.trans1:exp5	-0.310052498436637	0.143803921637973	-2.15607818552539	0.031410442904862	*  
df.mm.trans2:exp5	-0.309294909067013	0.118175388568753	-2.61725315916410	0.00905144846319213	** 
df.mm.trans1:exp6	-0.311345626861647	0.143803921637973	-2.16507048844927	0.0307123953540562	*  
df.mm.trans2:exp6	-0.299942288814500	0.118175388568753	-2.53811129751435	0.0113563701094315	*  
df.mm.trans1:exp7	-0.150052920167225	0.143803921637973	-1.04345499384213	0.297090276833962	   
df.mm.trans2:exp7	-0.254850849919263	0.118175388568753	-2.15654759426489	0.0313736694603153	*  
df.mm.trans1:exp8	-0.160506652555399	0.143803921637973	-1.11614934229315	0.264733173857345	   
df.mm.trans2:exp8	-0.125470140984092	0.118175388568753	-1.06172818641586	0.288717582874754	   
df.mm.trans1:probe2	0.0340820933025146	0.0839634008710863	0.405916065201345	0.684925719659543	   
df.mm.trans1:probe3	0.0807777194984951	0.0839634008710863	0.962058690577787	0.336345361683274	   
df.mm.trans1:probe4	-0.0746473019218998	0.0839634008710863	-0.889045716913134	0.374277513749537	   
df.mm.trans1:probe5	0.00733670847560558	0.0839634008710863	0.0873798393048661	0.930394059485506	   
df.mm.trans1:probe6	-0.0109175681768079	0.0839634008710863	-0.130027703303374	0.896581080198204	   
df.mm.trans1:probe7	-0.092658827841032	0.0839634008710863	-1.10356211015436	0.270154404926883	   
df.mm.trans1:probe8	0.0591547476745238	0.0839634008710863	0.704530153147886	0.481332088047178	   
df.mm.trans1:probe9	-0.0561891187837905	0.0839634008710863	-0.669209658027797	0.503577768338403	   
df.mm.trans1:probe10	-0.0162060037404384	0.0839634008710863	-0.19301271235214	0.847003824483147	   
df.mm.trans1:probe11	-0.0278810191109483	0.0839634008710863	-0.332061574706289	0.739940082214124	   
df.mm.trans1:probe12	0.0389820448015141	0.0839634008710863	0.464274248030584	0.642592598385517	   
df.mm.trans1:probe13	-0.0453539681006659	0.0839634008710863	-0.540163543045385	0.589252454003392	   
df.mm.trans1:probe14	0.0941990305674268	0.0839634008710863	1.12190584933614	0.262279094794517	   
df.mm.trans1:probe15	0.00249128489337109	0.0839634008710863	0.0296710812988161	0.97633765538984	   
df.mm.trans1:probe16	-0.0351068024485659	0.0839634008710863	-0.418120301040061	0.675984707329335	   
df.mm.trans1:probe17	-0.0461255425231242	0.0839634008710863	-0.549352956700066	0.582934779498044	   
df.mm.trans1:probe18	0.0405061393802847	0.0839634008710863	0.482426139961578	0.629650909147389	   
df.mm.trans1:probe19	-0.0528629084533139	0.0839634008710863	-0.629594655586632	0.529160943238314	   
df.mm.trans1:probe20	-0.0154966244674565	0.0839634008710863	-0.184564039887442	0.853623328500212	   
df.mm.trans1:probe21	-0.0406386941893514	0.0839634008710863	-0.484004861257898	0.628530626158759	   
df.mm.trans2:probe2	-0.215787258774948	0.0839634008710863	-2.57001570370236	0.0103708032529836	*  
df.mm.trans2:probe3	-0.113849667346117	0.0839634008710863	-1.35594397279020	0.175545070759833	   
df.mm.trans2:probe4	-0.0783971033662339	0.0839634008710863	-0.93370566881398	0.350770942762898	   
df.mm.trans2:probe5	-0.0675787434838452	0.0839634008710863	-0.804859531447548	0.421168291802101	   
df.mm.trans2:probe6	-0.00918617001898484	0.0839634008710863	-0.109406835879467	0.91291051005535	   
df.mm.trans3:probe2	0.129906069948959	0.0839634008710863	1.54717494290651	0.122263545699433	   
df.mm.trans3:probe3	0.0652772982637648	0.0839634008710863	0.77744943137771	0.437150811216967	   
df.mm.trans3:probe4	0.0504307031938406	0.0839634008710863	0.600627209839555	0.548278645400897	   
df.mm.trans3:probe5	0.140311173905546	0.0839634008710863	1.67109922239779	0.095139672413069	.