chr6.19839_chr6_86364011_86368072_+_2.R 

fitVsDatCorrelation=0.899345148702865
cont.fitVsDatCorrelation=0.247988817257355

fstatistic=8768.31525373882,55,761
cont.fstatistic=1775.13974787267,55,761

residuals=-0.752093946188564,-0.104629702908917,0.00338181422617484,0.110555079310908,0.723819695251299
cont.residuals=-0.881813714060153,-0.302612801911282,-0.0146635805678779,0.281850190483624,1.33966895412407

predictedValues:
Include	Exclude	Both
chr6.19839_chr6_86364011_86368072_+_2.R.tl.Lung	96.0153599844599	83.7767865725868	105.084240669485
chr6.19839_chr6_86364011_86368072_+_2.R.tl.cerebhem	92.1477204486708	71.7037526914388	110.665948308114
chr6.19839_chr6_86364011_86368072_+_2.R.tl.cortex	126.420920784595	68.8509915236137	158.307612938628
chr6.19839_chr6_86364011_86368072_+_2.R.tl.heart	106.367003498248	82.3049069002319	125.086184115936
chr6.19839_chr6_86364011_86368072_+_2.R.tl.kidney	91.060666991501	83.633650177075	91.7576342617214
chr6.19839_chr6_86364011_86368072_+_2.R.tl.liver	82.0578568807572	77.8239576408645	86.2155963173268
chr6.19839_chr6_86364011_86368072_+_2.R.tl.stomach	99.7778456692515	76.9096171640512	108.549946208012
chr6.19839_chr6_86364011_86368072_+_2.R.tl.testicle	102.516657094108	72.4718977591781	132.538520999691


diffExp=12.2385734118731,20.4439677572319,57.5699292609809,24.0620965980163,7.42701681442588,4.23389923989271,22.8682285052003,30.0447593349298
diffExpScore=0.994441000054803
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,0,0,0,0,1
diffExp1.3Score=0.666666666666667
diffExp1.2=0,1,1,1,0,0,1,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	99.4362591421704	88.6237237680441	100.265047325755
cerebhem	96.9352851102088	88.701377352004	98.3352569242531
cortex	104.877922350483	99.3815438240416	86.5493266915262
heart	95.2101380659294	96.0875540718834	115.200937355352
kidney	111.870889058748	96.966595122185	94.983030913203
liver	100.627627967699	81.3924582230718	96.4084956067787
stomach	97.5505321348563	89.2490496830508	90.4993358618819
testicle	103.010692540938	94.786843329059	111.961017461266
cont.diffExp=10.8125353741263,8.23390775820474,5.4963785264417,-0.87741600595406,14.904293936563,19.2351697446272,8.30148245180553,8.22384921187914
cont.diffExpScore=1.01002031060466

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=0,0,0,0,0,1,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.47117709378113
cont.tran.correlation=0.435014747143389

tran.covariance=-0.00443859719583358
cont.tran.covariance=0.00141591194777752

tran.mean=88.3649744862895
cont.tran.mean=96.5442807340233

weightedLogRatios:
wLogRatio
Lung	0.613084856153667
cerebhem	1.103229667106
cortex	2.75626859925755
heart	1.16400678672595
kidney	0.380221331410585
liver	0.232081219335248
stomach	1.16433503412530
testicle	1.54567083145326

cont.weightedLogRatios:
wLogRatio
Lung	0.522857705842475
cerebhem	0.40208960595587
cortex	0.249014029833908
heart	-0.0418367373274836
kidney	0.664259336736204
liver	0.955785028915393
stomach	0.403421866232312
testicle	0.382166880714409

varWeightedLogRatios=0.636484153309662
cont.varWeightedLogRatios=0.0854163626131255

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1952393713736	0.0920597245732615	45.5708442624658	1.10078292100404e-219	***
df.mm.trans1	0.364686835976119	0.0806117356212623	4.52399186254375	7.03796994900222e-06	***
df.mm.trans2	0.101751623001770	0.0722901578955851	1.40754462244693	0.159674276248580	   
df.mm.exp2	-0.248481974759964	0.0953334370700941	-2.60645144449442	0.0093275727618479	** 
df.mm.exp3	-0.330880267897833	0.0953334370700942	-3.47076826417736	0.000548223532989619	***
df.mm.exp4	-0.089578650480107	0.0953334370700942	-0.939635171385294	0.347702873049916	   
df.mm.exp5	0.0809193999614952	0.0953334370700942	0.84880397107679	0.396257296835501	   
df.mm.exp6	-0.0328790174312016	0.0953334370700942	-0.344884422944147	0.730276556779555	   
df.mm.exp7	-0.079535117202092	0.0953334370700942	-0.834283538351959	0.404383030521637	   
df.mm.exp8	-0.311550981650332	0.0953334370700942	-3.26801373395636	0.00113166679769028	** 
df.mm.trans1:exp2	0.207366742663347	0.089430691134341	2.31874248127910	0.0206730603153188	*  
df.mm.trans2:exp2	0.092869100548594	0.0713410118039733	1.30176315418366	0.193391271034565	   
df.mm.trans1:exp3	0.6059890699439	0.089430691134341	6.77607499458542	2.47411100417312e-11	***
df.mm.trans2:exp3	0.134668934943185	0.0713410118039733	1.88767907179702	0.0594493006190685	.  
df.mm.trans1:exp4	0.191965883286258	0.089430691134341	2.14653248064348	0.0321460087951866	*  
df.mm.trans2:exp4	0.0718534192627882	0.0713410118039733	1.00718250899248	0.314167210771768	   
df.mm.trans1:exp5	-0.133901623841606	0.089430691134341	-1.49726701363027	0.134738428464941	   
df.mm.trans2:exp5	-0.0826294060181038	0.0713410118039734	-1.15823148464936	0.247132936182412	   
df.mm.trans1:exp6	-0.124204590894046	0.089430691134341	-1.38883630796801	0.165288842858183	   
df.mm.trans2:exp6	-0.0408276191993391	0.0713410118039733	-0.572288199549551	0.567295762005803	   
df.mm.trans1:exp7	0.117973110087287	0.089430691134341	1.31915686428130	0.187513453840561	   
df.mm.trans2:exp7	-0.00598991269492997	0.0713410118039733	-0.0839617008991786	0.933108963773589	   
df.mm.trans1:exp8	0.37706809675361	0.089430691134341	4.21631647895001	2.78186870046628e-05	***
df.mm.trans2:exp8	0.166593892013195	0.0713410118039733	2.33517702932153	0.0197932196328148	*  
df.mm.trans1:probe2	0.558920000613235	0.0547648901558947	10.2058088498343	5.23228299081074e-23	***
df.mm.trans1:probe3	-0.288781479347595	0.0547648901558947	-5.27311345874235	1.74942917058680e-07	***
df.mm.trans1:probe4	0.336678660767026	0.0547648901558947	6.14770996177716	1.26815458911728e-09	***
df.mm.trans1:probe5	0.127119497170046	0.0547648901558947	2.32118601549617	0.0205401183118997	*  
df.mm.trans1:probe6	-0.278017371544814	0.0547648901558947	-5.07656220533639	4.83608837141914e-07	***
df.mm.trans1:probe7	-0.652435455273685	0.0547648901558947	-11.9133892794535	3.98649486687090e-30	***
df.mm.trans1:probe8	0.0760651970660181	0.0547648901558947	1.38894092272421	0.165257038176240	   
df.mm.trans1:probe9	0.30016436330044	0.0547648901558947	5.48096348675195	5.75593678056551e-08	***
df.mm.trans1:probe10	0.402139792537667	0.0547648901558947	7.34302198713315	5.37915360046697e-13	***
df.mm.trans1:probe11	0.313801590244298	0.0547648901558947	5.72997753398254	1.44708579456143e-08	***
df.mm.trans1:probe12	0.545860532078753	0.0547648901558947	9.96734459842605	4.45616472834809e-22	***
df.mm.trans1:probe13	0.126981630833568	0.0547648901558947	2.31866859354781	0.0206770919045823	*  
df.mm.trans1:probe14	0.216467368787608	0.0547648901558947	3.95266690340122	8.44833574065128e-05	***
df.mm.trans1:probe15	0.0354297432220118	0.0547648901558947	0.64694265105174	0.517864141650284	   
df.mm.trans1:probe16	0.205417313069378	0.0547648901558947	3.75089427705658	0.000189585075954016	***
df.mm.trans1:probe17	-0.302994231209449	0.0547648901558947	-5.53263651852383	4.34095552263564e-08	***
df.mm.trans1:probe18	-0.303538472401077	0.0547648901558947	-5.54257429416948	4.11060771726546e-08	***
df.mm.trans1:probe19	-0.418119486783423	0.0547648901558947	-7.63480919240771	6.78671459608164e-14	***
df.mm.trans1:probe20	-0.4956270147627	0.0547648901558947	-9.05008689603576	1.17619523536644e-18	***
df.mm.trans1:probe21	-0.0792819797630563	0.0547648901558947	-1.44767896981754	0.148118683817409	   
df.mm.trans1:probe22	-0.297955006190620	0.0547648901558947	-5.44062090405836	7.16285577275247e-08	***
df.mm.trans2:probe2	0.262542866001850	0.0547648901558947	4.79399968217759	1.96660658136308e-06	***
df.mm.trans2:probe3	0.563207445401918	0.0547648901558947	10.2840970519375	2.56876988975725e-23	***
df.mm.trans2:probe4	0.473774672772031	0.0547648901558947	8.65106588223543	3.00820373374730e-17	***
df.mm.trans2:probe5	0.068867500126495	0.0547648901558947	1.25751188271273	0.208954098611197	   
df.mm.trans2:probe6	0.205587094073850	0.0547648901558947	3.75399445682483	0.000187296667781806	***
df.mm.trans3:probe2	0.0113854473656011	0.0547648901558947	0.207896835603815	0.835365143808537	   
df.mm.trans3:probe3	-0.786213183274772	0.0547648901558947	-14.3561537517326	1.54835751014417e-41	***
df.mm.trans3:probe4	-0.361447743831162	0.0547648901558947	-6.5999903003961	7.70570196213996e-11	***
df.mm.trans3:probe5	0.478249992583509	0.0547648901558947	8.7327846586036	1.56342460424705e-17	***
df.mm.trans3:probe6	0.616532428549422	0.0547648901558947	11.2578045312314	2.66012391983708e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36301414502166	0.203970737334569	21.3903925731518	7.52457213728018e-80	***
df.mm.trans1	0.273464489104155	0.178606173641148	1.53110322856809	0.126159453068320	   
df.mm.trans2	0.0743755888215297	0.160168595727882	0.464358125158878	0.642523999254312	   
df.mm.exp2	-0.00516287868236257	0.211224088948379	-0.0244426604373913	0.98050592806972	   
df.mm.exp3	0.314949689212482	0.211224088948379	1.49106899113885	0.136357759114801	   
df.mm.exp4	-0.101430917867514	0.211224088948379	-0.480205256760761	0.631219448919915	   
df.mm.exp5	0.261914456702901	0.211224088948379	1.23998383899722	0.215363577982377	   
df.mm.exp6	-0.0339841153947849	0.211224088948379	-0.160891286424676	0.872221725845864	   
df.mm.exp7	0.090359524006923	0.211224088948379	0.427789862684677	0.66892514728201	   
df.mm.exp8	-0.00778660092871692	0.211224088948379	-0.0368641709735100	0.970602975436195	   
df.mm.trans1:exp2	-0.0203103565281070	0.198145759131564	-0.102502100560332	0.918385148744143	   
df.mm.trans2:exp2	0.00603871169679092	0.158065634535660	0.0382038240920014	0.969535191465038	   
df.mm.trans1:exp3	-0.261669486980014	0.198145759131564	-1.32059090301434	0.187034813783463	   
df.mm.trans2:exp3	-0.200382852992344	0.158065634535660	-1.26771928370767	0.205285960826585	   
df.mm.trans1:exp4	0.0580005190353474	0.198145759131564	0.292716429004349	0.769818703884974	   
df.mm.trans2:exp4	0.182291130890451	0.158065634535660	1.15326225985779	0.249164848782348	   
df.mm.trans1:exp5	-0.14408585387538	0.198145759131564	-0.727171020499663	0.467344832730152	   
df.mm.trans2:exp5	-0.171947502083682	0.158065634535660	-1.08782343859121	0.277017462473007	   
df.mm.trans1:exp6	0.0458941399929069	0.198145759131564	0.231618078499647	0.81689692903668	   
df.mm.trans2:exp6	-0.0511328510993184	0.158065634535660	-0.323491258865524	0.746412101486336	   
df.mm.trans1:exp7	-0.109505829205477	0.198145759131564	-0.552652904030955	0.580663395646333	   
df.mm.trans2:exp7	-0.0833283356008496	0.158065634535660	-0.527175535945169	0.598225338923382	   
df.mm.trans1:exp8	0.0431025675986664	0.198145759131565	0.217529599359465	0.8278539523377	   
df.mm.trans2:exp8	0.0750176327216041	0.158065634535660	0.474597991789798	0.635209696889835	   
df.mm.trans1:probe2	0.0412514369368414	0.121339001142188	0.339968489509007	0.733974069798659	   
df.mm.trans1:probe3	-0.101041531590688	0.121339001142188	-0.832720977093626	0.405263364091728	   
df.mm.trans1:probe4	-0.1267870362803	0.121339001142188	-1.04489929113334	0.296401306339518	   
df.mm.trans1:probe5	-0.112799364790122	0.121339001142188	-0.929621669276319	0.352861785243239	   
df.mm.trans1:probe6	0.0374668526779441	0.121339001142188	0.30877831797906	0.757574674259883	   
df.mm.trans1:probe7	-0.0355422823063518	0.121339001142188	-0.292917215172246	0.76966528133662	   
df.mm.trans1:probe8	-0.211095673499004	0.121339001142188	-1.73971823990570	0.0823127994025808	.  
df.mm.trans1:probe9	0.0373278746005309	0.121339001142188	0.307632947767463	0.75844581929235	   
df.mm.trans1:probe10	-0.00809158798232697	0.121339001142188	-0.0666857968679421	0.946849356933557	   
df.mm.trans1:probe11	-0.131508887584077	0.121339001142188	-1.08381383022900	0.278790607659795	   
df.mm.trans1:probe12	-0.203832549171929	0.121339001142188	-1.67986012125708	0.0933949678202198	.  
df.mm.trans1:probe13	-0.0929414132021688	0.121339001142188	-0.765964877964155	0.443934711135827	   
df.mm.trans1:probe14	-0.052617452474669	0.121339001142188	-0.433640066090625	0.664672731923423	   
df.mm.trans1:probe15	0.00457667772521155	0.121339001142188	0.0377181094465124	0.969922328978875	   
df.mm.trans1:probe16	-0.0349107642023147	0.121339001142188	-0.287712638753349	0.773645047238053	   
df.mm.trans1:probe17	-0.085417494591714	0.121339001142188	-0.70395745628085	0.48167460962123	   
df.mm.trans1:probe18	0.125418177169878	0.121339001142188	1.03361801225733	0.301643130946721	   
df.mm.trans1:probe19	0.0683110679352353	0.121339001142188	0.562977008976582	0.573616359250147	   
df.mm.trans1:probe20	-0.0108998235752009	0.121339001142188	-0.0898295146045264	0.928446330105732	   
df.mm.trans1:probe21	-0.00249498489521834	0.121339001142188	-0.0205621018117221	0.983600362373523	   
df.mm.trans1:probe22	-0.139301833457052	0.121339001142188	-1.14803840600117	0.251313464719782	   
df.mm.trans2:probe2	0.128556726726338	0.121339001142188	1.05948397066243	0.289715582289847	   
df.mm.trans2:probe3	0.0534737713522317	0.121339001142188	0.440697309594379	0.659557303250236	   
df.mm.trans2:probe4	0.0262272762235244	0.121339001142188	0.216148773079074	0.828929706915569	   
df.mm.trans2:probe5	0.187119876704210	0.121339001142188	1.54212474919699	0.123458987172820	   
df.mm.trans2:probe6	0.168740555531816	0.121339001142188	1.39065390306025	0.164736919017721	   
df.mm.trans3:probe2	-0.0892356106087041	0.121339001142188	-0.735423975545467	0.462307912729489	   
df.mm.trans3:probe3	-0.061053837776147	0.121339001142188	-0.50316746636642	0.614992134093937	   
df.mm.trans3:probe4	-0.282361831627239	0.121339001142188	-2.32704925019417	0.0202241679626277	*  
df.mm.trans3:probe5	-0.094652960019883	0.121339001142188	-0.780070374149249	0.435592002077503	   
df.mm.trans3:probe6	-0.0909181800384049	0.121339001142188	-0.7492906582597	0.453913585401171