chr18.11186_chr18_10491416_10494803_+_1.R 

fitVsDatCorrelation=0.668097228866112
cont.fitVsDatCorrelation=0.289895954405856

fstatistic=10951.0506825664,53,715
cont.fstatistic=6613.95298732014,53,715

residuals=-0.372628103739416,-0.0884594350476813,-0.00267116982056279,0.0733163711465321,1.00853823757710
cont.residuals=-0.374500532050278,-0.121707238542134,-0.0163239023075463,0.0886502465499078,1.22671766918199

predictedValues:
Include	Exclude	Both
chr18.11186_chr18_10491416_10494803_+_1.R.tl.Lung	51.1633061668179	56.1467587408206	50.5752738191647
chr18.11186_chr18_10491416_10494803_+_1.R.tl.cerebhem	62.9978857430132	52.414238969234	63.7191081688607
chr18.11186_chr18_10491416_10494803_+_1.R.tl.cortex	60.252621394497	60.2944370714891	51.8323758250035
chr18.11186_chr18_10491416_10494803_+_1.R.tl.heart	52.8960111449243	60.5132398252663	50.8819416503073
chr18.11186_chr18_10491416_10494803_+_1.R.tl.kidney	52.4062561562319	56.7366159170139	47.7854790794856
chr18.11186_chr18_10491416_10494803_+_1.R.tl.liver	50.6511770625579	60.1615840287926	53.8452648442298
chr18.11186_chr18_10491416_10494803_+_1.R.tl.stomach	51.9428544075827	80.2330719349577	48.4043941462332
chr18.11186_chr18_10491416_10494803_+_1.R.tl.testicle	58.9368440942948	58.2948888391211	52.0048594908289


diffExp=-4.98345257400272,10.5836467737792,-0.0418156769920301,-7.61722868034196,-4.33035976078195,-9.51040696623468,-28.2902175273750,0.641955255173677
diffExpScore=1.48153143233628
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,0,0,0,-1,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	54.0425202389688	55.3949055208606	55.6802483950528
cerebhem	54.6070208857957	48.401758299381	53.3638858474151
cortex	55.149891233324	52.6680039395541	57.714150938827
heart	52.4340066565572	57.9447635284992	53.6308996764395
kidney	52.1714240547347	52.7001923569807	56.50894304708
liver	52.1161114495724	53.0940721733631	52.310232017752
stomach	55.3546721618668	51.7308974845866	56.3210813619126
testicle	53.6814093774959	55.6217336140214	55.2950209539648
cont.diffExp=-1.35238528189178,6.2052625864147,2.48188729376998,-5.51075687194198,-0.528768302246043,-0.977960723790652,3.62377467728015,-1.94032423652548
cont.diffExpScore=7.5385411046473

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=-0.379773338684984
cont.tran.correlation=-0.445598368787275

tran.covariance=-0.00422302861656734
cont.tran.covariance=-0.000593637061700443

tran.mean=57.8776119685384
cont.tran.mean=53.5695864359726

weightedLogRatios:
wLogRatio
Lung	-0.370065523186503
cerebhem	0.745097268803056
cortex	-0.00284366940524696
heart	-0.542925749616496
kidney	-0.317473841382124
liver	-0.690178643994207
stomach	-1.81201153218105
testicle	0.0445855359170971

cont.weightedLogRatios:
wLogRatio
Lung	-0.098918562497661
cerebhem	0.475249524897257
cortex	0.183589394898558
heart	-0.400691021791508
kidney	-0.0399291367285693
liver	-0.0736724264959863
stomach	0.269462998791418
testicle	-0.142058208450793

varWeightedLogRatios=0.537810806917611
cont.varWeightedLogRatios=0.0751393750015498

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04552084206051	0.0681231763639015	59.3853818625556	1.21975996418496e-278	***
df.mm.trans1	-0.115755888057474	0.0578717961202499	-2.00021246648279	0.0458550790604706	*  
df.mm.trans2	0.0195885331839378	0.0522751246218012	0.374719971031278	0.707979851759605	   
df.mm.exp2	-0.0917337882196253	0.067592261715245	-1.35716405830722	0.175157339406819	   
df.mm.exp3	0.210242199337508	0.067592261715245	3.11044776431989	0.00194227739199238	** 
df.mm.exp4	0.102153277814221	0.067592261715245	1.5113161658146	0.131149736213076	   
df.mm.exp5	0.0911951977401958	0.067592261715245	1.34919583138653	0.177701158973411	   
df.mm.exp6	-0.00364674431348509	0.067592261715245	-0.0539520977837407	0.956988399849537	   
df.mm.exp7	0.415960604948751	0.067592261715245	6.15396784177935	1.25838455112644e-09	***
df.mm.exp8	0.151114929018972	0.0675922617152449	2.2356838665289	0.0256803580026284	*  
df.mm.trans1:exp2	0.299812355732506	0.0602441044326528	4.97662565583771	8.11423791280898e-07	***
df.mm.trans2:exp2	0.0229431241764979	0.0471201605637936	0.486906748660935	0.626473641703969	   
df.mm.trans1:exp3	-0.0467187179716618	0.0602441044326528	-0.775490289242973	0.43830640983437	   
df.mm.trans2:exp3	-0.138971308574864	0.0471201605637936	-2.94929615926748	0.00328892974775007	** 
df.mm.trans1:exp4	-0.068847944161404	0.0602441044326528	-1.14281629397229	0.253497390571895	   
df.mm.trans2:exp4	-0.0272600511430305	0.0471201605637936	-0.578522034238922	0.563093903438256	   
df.mm.trans1:exp5	-0.0671918199261945	0.0602441044326528	-1.11532606483193	0.265085440506358	   
df.mm.trans2:exp5	-0.080744366576642	0.0471201605637936	-1.71358428346878	0.0870385497881346	.  
df.mm.trans1:exp6	-0.00641338480034378	0.0602441044326528	-0.106456637719851	0.91524991886121	   
df.mm.trans2:exp6	0.0727117993636161	0.0471201605637936	1.54311442265090	0.123245467920395	   
df.mm.trans1:exp7	-0.400839043040846	0.0602441044326528	-6.65358123945466	5.69136326883665e-11	***
df.mm.trans2:exp7	-0.0589937613210555	0.0471201605637936	-1.25198557507432	0.210984517109667	   
df.mm.trans1:exp8	-0.00967109620289645	0.0602441044326528	-0.160531827868863	0.872507494893988	   
df.mm.trans2:exp8	-0.113569464014339	0.0471201605637936	-2.41020961421774	0.0161948182563353	*  
df.mm.trans1:probe2	-0.0522248912927451	0.0419678833653588	-1.24440136373074	0.213759639002135	   
df.mm.trans1:probe3	0.112543906158784	0.0419678833653588	2.68166743552476	0.0074946498572248	** 
df.mm.trans1:probe4	-0.112595112136077	0.0419678833653588	-2.6828875584661	0.00746763918116236	** 
df.mm.trans1:probe5	-0.119406202163617	0.0419678833653588	-2.84518047107845	0.00456559249262552	** 
df.mm.trans1:probe6	-0.103118587794001	0.0419678833653588	-2.45708335815472	0.0142434762950645	*  
df.mm.trans1:probe7	0.114437274712256	0.0419678833653588	2.72678213756938	0.00655226144019032	** 
df.mm.trans1:probe8	-0.0208969703272893	0.0419678833653588	-0.497927668769164	0.618688133183632	   
df.mm.trans1:probe9	-0.130871668928875	0.0419678833653588	-3.11837668317815	0.00189146016989085	** 
df.mm.trans1:probe10	0.105543992288576	0.0419678833653588	2.51487527664296	0.0121257398080917	*  
df.mm.trans1:probe11	-0.00967242692410666	0.0419678833653588	-0.230472116973392	0.817790853632092	   
df.mm.trans1:probe12	0.0113532779087362	0.0419678833653588	0.270523004696192	0.786835990540976	   
df.mm.trans1:probe13	0.133491074384432	0.0419678833653588	3.18079120698801	0.00153218461672768	** 
df.mm.trans1:probe14	0.0361942144171651	0.0419678833653588	0.862426491754898	0.388742030254764	   
df.mm.trans1:probe15	0.161405591963132	0.0419678833653588	3.84593119834011	0.000130811629799537	***
df.mm.trans2:probe2	-0.0468391315421054	0.0419678833653588	-1.11607085671534	0.264766742574372	   
df.mm.trans2:probe3	-0.107724634548655	0.0419678833653588	-2.56683506315625	0.0104655138436779	*  
df.mm.trans2:probe4	-0.170037370299122	0.0419678833653588	-4.05160700669204	5.64375467365985e-05	***
df.mm.trans2:probe5	-0.124708968051099	0.0419678833653588	-2.97153342153149	0.00306258486609378	** 
df.mm.trans2:probe6	-0.182077048033584	0.0419678833653588	-4.33848537102718	1.64121135948913e-05	***
df.mm.trans3:probe2	-0.109974698601586	0.0419678833653588	-2.62044901440898	0.00896786464287705	** 
df.mm.trans3:probe3	0.032715104720753	0.0419678833653588	0.779527154990064	0.435927190192814	   
df.mm.trans3:probe4	0.0126687796523337	0.0419678833653588	0.301868444068131	0.762840132508158	   
df.mm.trans3:probe5	0.103084021349761	0.0419678833653588	2.45625971775477	0.0142758853345391	*  
df.mm.trans3:probe6	-0.108153663873320	0.0419678833653588	-2.57705786426658	0.0101638219995673	*  
df.mm.trans3:probe7	-0.0227757107594482	0.0419678833653588	-0.542693815677342	0.587509749111076	   
df.mm.trans3:probe8	0.0250127760375291	0.0419678833653588	0.595998035444771	0.551365159971486	   
df.mm.trans3:probe9	-0.0219552290401823	0.0419678833653588	-0.52314358694355	0.601036415785796	   
df.mm.trans3:probe10	-0.172963304301153	0.0419678833653588	-4.12132541437439	4.20840795422091e-05	***
df.mm.trans3:probe11	-0.0224556654807944	0.0419678833653588	-0.535067858564672	0.592769298127098	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.88599071650257	0.0876227952815895	44.3490840940914	1.95584869923683e-207	***
df.mm.trans1	0.098100657017042	0.07443705379994	1.31790085728945	0.187958920668285	   
df.mm.trans2	0.121997550341832	0.067238387690373	1.8144032677229	0.0700346304814505	.  
df.mm.exp2	-0.0820689213191991	0.0869399113050302	-0.943972912869198	0.345502351879266	   
df.mm.exp3	-0.066072787724881	0.0869399113050302	-0.759982230635864	0.447515716920598	   
df.mm.exp4	0.0522868753306007	0.0869399113050302	0.60141394838961	0.547754936937697	   
df.mm.exp5	-0.099878193810603	0.0869399113050302	-1.14881867615644	0.251014926299269	   
df.mm.exp6	-0.0162858669529172	0.0869399113050302	-0.18732325244476	0.85146033585953	   
df.mm.exp7	-0.0558859147231248	0.0869399113050302	-0.642810809031632	0.520552944674228	   
df.mm.exp8	0.00432459439453281	0.0869399113050302	0.0497423373180115	0.960341609745515	   
df.mm.trans1:exp2	0.0924602349043379	0.0774884130685111	1.19321368502655	0.233181475540801	   
df.mm.trans2:exp2	-0.0528825685360935	0.0606078636242917	-0.872536423060755	0.383208721009610	   
df.mm.trans1:exp3	0.0863564126742687	0.0774884130685111	1.11444291158624	0.265463686647536	   
df.mm.trans2:exp3	0.0155932916011198	0.0606078636242917	0.257281657340418	0.797035374054421	   
df.mm.trans1:exp4	-0.0825026610569575	0.0774884130685111	-1.06470964870597	0.287366762427052	   
df.mm.trans2:exp4	-0.0072843029349173	0.0606078636242917	-0.120187422874245	0.904368444191613	   
df.mm.trans1:exp5	0.0646419583510382	0.0774884130685111	0.834214507579155	0.404438734320071	   
df.mm.trans2:exp5	0.0500096679712214	0.0606078636242917	0.825134973924036	0.409570168106302	   
df.mm.trans1:exp6	-0.0200111397691276	0.0774884130685111	-0.258246865262744	0.796290707621	   
df.mm.trans2:exp6	-0.0261364776017167	0.0606078636242917	-0.431239051152451	0.666424547134924	   
df.mm.trans1:exp7	0.0798758329995428	0.0774884130685111	1.03081002483456	0.302978472067518	   
df.mm.trans2:exp7	-0.0125464834271626	0.0606078636242917	-0.207010818017581	0.836060349432273	   
df.mm.trans1:exp8	-0.0110289954572932	0.0774884130685111	-0.142330898524686	0.886858738034163	   
df.mm.trans2:exp8	-0.000238208595611051	0.0606078636242917	-0.0039303249011994	0.996865158818974	   
df.mm.trans1:probe2	-0.0372297541309759	0.053980795506081	-0.689685170104281	0.490615967723752	   
df.mm.trans1:probe3	0.0233385264836529	0.053980795506081	0.43234869484322	0.665618363128542	   
df.mm.trans1:probe4	-0.0062980104631569	0.053980795506081	-0.116671316235927	0.907153283855264	   
df.mm.trans1:probe5	-0.0151930079960675	0.053980795506081	-0.281452095205895	0.778445099769147	   
df.mm.trans1:probe6	0.0258235532042522	0.053980795506081	0.47838408015575	0.632523101454666	   
df.mm.trans1:probe7	-0.0243208563631121	0.053980795506081	-0.450546460738473	0.652453047660894	   
df.mm.trans1:probe8	0.0117893454455943	0.053980795506081	0.218398883066965	0.827180655273423	   
df.mm.trans1:probe9	0.0462449872476093	0.053980795506081	0.856693326099645	0.391901420993232	   
df.mm.trans1:probe10	0.047867465452684	0.053980795506081	0.886749908072245	0.375511721245859	   
df.mm.trans1:probe11	0.0178490071442402	0.053980795506081	0.330654762993063	0.741002145159975	   
df.mm.trans1:probe12	-0.00733464381452166	0.053980795506081	-0.135875059745931	0.89195829685766	   
df.mm.trans1:probe13	0.102247054927470	0.053980795506081	1.89413760891967	0.0586100811036227	.  
df.mm.trans1:probe14	-0.0337516461789367	0.053980795506081	-0.62525284895319	0.532004619658658	   
df.mm.trans1:probe15	-0.0147174202059821	0.053980795506081	-0.272641780618519	0.785207311667881	   
df.mm.trans2:probe2	0.0978998656851467	0.053980795506081	1.81360546407876	0.0701575948071986	.  
df.mm.trans2:probe3	0.0223018981213286	0.053980795506081	0.413145043755724	0.679624219348688	   
df.mm.trans2:probe4	-0.0490849602509803	0.053980795506081	-0.909304129196295	0.363496016589917	   
df.mm.trans2:probe5	0.087022304842639	0.053980795506081	1.61209748813049	0.107382013341590	   
df.mm.trans2:probe6	-0.0476499106533349	0.053980795506081	-0.882719682187104	0.377684420830832	   
df.mm.trans3:probe2	-0.0861450304367906	0.053980795506081	-1.59584588610011	0.110965076540704	   
df.mm.trans3:probe3	-0.149869429264315	0.053980795506081	-2.7763471779039	0.00564117023489016	** 
df.mm.trans3:probe4	-0.0766860717907214	0.053980795506081	-1.42061766729767	0.155863866570458	   
df.mm.trans3:probe5	-0.0672941882240933	0.053980795506081	-1.24663202150314	0.212940698391768	   
df.mm.trans3:probe6	-0.0738924039043207	0.053980795506081	-1.36886467143665	0.171471463105489	   
df.mm.trans3:probe7	-0.123595715466198	0.053980795506081	-2.28962382468548	0.0223336489308843	*  
df.mm.trans3:probe8	-0.0482470907483814	0.053980795506081	-0.893782507205665	0.371739011493608	   
df.mm.trans3:probe9	-0.0707194761602603	0.053980795506081	-1.31008584622088	0.190587524083328	   
df.mm.trans3:probe10	-0.120327560300957	0.053980795506081	-2.2290809013254	0.0261185010489229	*  
df.mm.trans3:probe11	-0.134320947702961	0.053980795506081	-2.48830989694899	0.0130617717086929	*