chr5.18829_chr5_21886538_21902480_+_2.R 

fitVsDatCorrelation=0.806367285274788
cont.fitVsDatCorrelation=0.213223218929504

fstatistic=6162.63267517896,52,692
cont.fstatistic=2249.75007185637,52,692

residuals=-0.520224516085356,-0.0989195430027567,-0.00390179093278205,0.0813633623721325,1.45820784758982
cont.residuals=-0.61956446975914,-0.203024638562075,-0.083318354476964,0.124234117426490,1.86605615383593

predictedValues:
Include	Exclude	Both
chr5.18829_chr5_21886538_21902480_+_2.R.tl.Lung	53.2769413444241	64.8833650799639	99.7103789978287
chr5.18829_chr5_21886538_21902480_+_2.R.tl.cerebhem	72.889675273968	104.449084092952	89.9974734665251
chr5.18829_chr5_21886538_21902480_+_2.R.tl.cortex	48.8463153483813	66.8189164780588	109.010841749561
chr5.18829_chr5_21886538_21902480_+_2.R.tl.heart	50.5382521965643	62.7469832534495	84.7069810033751
chr5.18829_chr5_21886538_21902480_+_2.R.tl.kidney	48.3375429974638	54.8708459432792	69.2034783720276
chr5.18829_chr5_21886538_21902480_+_2.R.tl.liver	49.9904957156356	52.3567211668493	65.8669397713213
chr5.18829_chr5_21886538_21902480_+_2.R.tl.stomach	50.8636374178021	62.8674067992167	78.8097163679472
chr5.18829_chr5_21886538_21902480_+_2.R.tl.testicle	54.83444011379	63.514390583837	76.0072715313476


diffExp=-11.6064237355397,-31.5594088189839,-17.9726011296774,-12.2087310568851,-6.53330294581541,-2.36622545121376,-12.0037693814147,-8.6799504700471
diffExpScore=0.990378177366617
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,-1,-1,0,0,-1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	56.9867701142898	69.8215050422612	52.4695401654525
cerebhem	55.104261029751	58.8988992825327	63.777419964584
cortex	56.3138373404268	65.9863844087006	59.3659077901209
heart	58.7170390901595	60.198149538511	57.6533950474012
kidney	56.2207185677873	57.1444877166403	71.1600562510793
liver	59.6103724243173	59.9253107062333	55.7313564894485
stomach	61.2781878848202	57.7709263848629	58.441275779683
testicle	58.4295618996348	60.2788775480922	57.3585067272206
cont.diffExp=-12.8347349279713,-3.79463825278171,-9.67254706827379,-1.48111044835149,-0.923769148852955,-0.314938281916021,3.50726149995726,-1.84931564845736
cont.diffExpScore=1.21204932476065

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

tran.correlation=0.948842754865533
cont.tran.correlation=-0.275621908566548

tran.covariance=0.0256119360264142
cont.tran.covariance=-0.000648792552608247

tran.mean=60.1303133628522
cont.tran.mean=59.5428305611888

weightedLogRatios:
wLogRatio
Lung	-0.802944466831016
cerebhem	-1.60767135322763
cortex	-1.26743212242843
heart	-0.872210509125634
kidney	-0.499690318804549
liver	-0.181981577069031
stomach	-0.854952759795734
testicle	-0.599223604226297

cont.weightedLogRatios:
wLogRatio
Lung	-0.841818687911304
cerebhem	-0.269213376171284
cortex	-0.651499263149379
heart	-0.101768736700384
kidney	-0.0658004298485261
liver	-0.0215541792937195
stomach	0.240819264571325
testicle	-0.127237961781993

varWeightedLogRatios=0.197325763247168
cont.varWeightedLogRatios=0.124658027554490

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.72904540509203	0.100110648142738	37.2492384603797	1.70523927857081e-167	***
df.mm.trans1	0.269990029964235	0.088792369823988	3.04068953784467	0.00244948942119831	** 
df.mm.trans2	0.483596700722081	0.080918473515541	5.97634482846742	3.65288959815408e-09	***
df.mm.exp2	0.892040003908544	0.109142253420133	8.17318660697536	1.43089050844947e-15	***
df.mm.exp3	-0.146607292067330	0.109142253420133	-1.34326795968724	0.179625572636144	   
df.mm.exp4	0.0768178831641977	0.109142253420133	0.703832665690841	0.481773746522752	   
df.mm.exp5	0.100314469560097	0.109142253420133	0.919116716180901	0.358354784583656	   
df.mm.exp6	0.136451458609927	0.109142253420133	1.25021661486747	0.21164303606513	   
df.mm.exp7	0.157314718542340	0.109142253420133	1.44137319518932	0.149931633088108	   
df.mm.exp8	0.278930837390559	0.109142253420133	2.55566317031077	0.0108111879211322	*  
df.mm.trans1:exp2	-0.578596620924222	0.103393832809295	-5.59604577181519	3.16149686447381e-08	***
df.mm.trans2:exp2	-0.415931559476933	0.0873837377114644	-4.75982797680631	2.36025401549308e-06	***
df.mm.trans1:exp3	0.0597826224528037	0.103393832809295	0.578202982019923	0.563315196084361	   
df.mm.trans2:exp3	0.176002238654931	0.0873837377114645	2.01413035496467	0.0443823431751194	*  
df.mm.trans1:exp4	-0.129590981764055	0.103393832809295	-1.25337245213725	0.210493496497002	   
df.mm.trans2:exp4	-0.110298656626360	0.0873837377114644	-1.26223321999064	0.207290105981676	   
df.mm.trans1:exp5	-0.197609540486092	0.103393832809295	-1.9112314063313	0.0563876894309015	.  
df.mm.trans2:exp5	-0.267923576056587	0.0873837377114644	-3.06605763352963	0.00225347283609574	** 
df.mm.trans1:exp6	-0.200122174322674	0.103393832809295	-1.93553298959126	0.0533319664901667	.  
df.mm.trans2:exp6	-0.350962415159131	0.0873837377114644	-4.0163355831492	6.55788839750992e-05	***
df.mm.trans1:exp7	-0.20367006024843	0.103393832809295	-1.96984727922883	0.0492542673407859	*  
df.mm.trans2:exp7	-0.188878138704311	0.0873837377114644	-2.16147928265526	0.0310005766103835	*  
df.mm.trans1:exp8	-0.250115988995959	0.103393832809295	-2.41906100393131	0.0158175308958162	*  
df.mm.trans2:exp8	-0.300255608475741	0.0873837377114644	-3.43605819960649	0.000625516587015389	***
df.mm.trans1:probe2	-0.0867400485371797	0.0566311345365685	-1.53166715177089	0.126061599222868	   
df.mm.trans1:probe3	-0.136590281511140	0.0566311345365685	-2.4119291027613	0.0161276369471954	*  
df.mm.trans1:probe4	-0.0218044185543841	0.0566311345365685	-0.385025282167078	0.70033699341469	   
df.mm.trans1:probe5	-0.016738541181748	0.0566311345365685	-0.295571355204607	0.767646117719672	   
df.mm.trans1:probe6	-0.0659089540389065	0.0566311345365685	-1.16382895342397	0.244894449848689	   
df.mm.trans1:probe7	-0.204747063235076	0.0566311345365685	-3.61545049221756	0.000321597178569957	***
df.mm.trans1:probe8	-0.0680072133574116	0.0566311345365685	-1.20088029162646	0.230208524669759	   
df.mm.trans1:probe9	0.0166536248681262	0.0566311345365685	0.294071891803129	0.768791152070051	   
df.mm.trans1:probe10	-0.174714832663021	0.0566311345365685	-3.08513742648404	0.00211565210697864	** 
df.mm.trans1:probe11	-0.173670248768172	0.0566311345365685	-3.06669202708675	0.00224876091720579	** 
df.mm.trans1:probe12	-0.108949370603259	0.0566311345365685	-1.92384227324472	0.0547842233760391	.  
df.mm.trans1:probe13	-0.189272419873604	0.0566311345365685	-3.34219721046530	0.000875894869123497	***
df.mm.trans1:probe14	-0.159489714998631	0.0566311345365685	-2.81629030221253	0.00499653991866508	** 
df.mm.trans1:probe15	-0.128246682422567	0.0566311345365685	-2.26459673591307	0.0238459757774329	*  
df.mm.trans1:probe16	0.293377573891918	0.0566311345365685	5.18049967200419	2.90586637195331e-07	***
df.mm.trans1:probe17	0.0182154680688875	0.0566311345365685	0.321651123855292	0.747814141478954	   
df.mm.trans1:probe18	0.145057962121218	0.0566311345365685	2.56145251738776	0.0106343563660200	*  
df.mm.trans1:probe19	0.342660147786479	0.0566311345365685	6.0507378245303	2.36131440462415e-09	***
df.mm.trans1:probe20	0.0904530434095755	0.0566311345365685	1.59723170213316	0.110670579049050	   
df.mm.trans1:probe21	0.0401665278180597	0.0566311345365685	0.709265815469808	0.478398429338284	   
df.mm.trans2:probe2	-0.0133258443713376	0.0566311345365685	-0.235309507400609	0.814038133071951	   
df.mm.trans2:probe3	0.00935666947680907	0.0566311345365685	0.165221296613211	0.868818056831019	   
df.mm.trans2:probe4	-0.090301459250408	0.0566311345365685	-1.59455500917251	0.111268374242472	   
df.mm.trans2:probe5	-0.109232751645783	0.0566311345365685	-1.92884625285492	0.0541586173379406	.  
df.mm.trans2:probe6	-0.197004925588947	0.0566311345365685	-3.47873881039298	0.000535335153046006	***
df.mm.trans3:probe2	0.232298883359123	0.0566311345365685	4.10196414499024	4.58401716960036e-05	***
df.mm.trans3:probe3	0.406503507118833	0.0566311345365685	7.17809223575313	1.82714698176969e-12	***
df.mm.trans3:probe4	-0.160251838252671	0.0566311345365685	-2.8297479745738	0.00479347409193619	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34948812716547	0.165380385682285	26.2999031549082	3.23226550822893e-106	***
df.mm.trans1	-0.301479870543823	0.146682861809045	-2.05531762078855	0.0402231073175492	*  
df.mm.trans2	-0.0480299443996469	0.133675374269292	-0.359302860846228	0.719478143335719	   
df.mm.exp2	-0.398878141459123	0.180300380626041	-2.21229783361596	0.0272719014863219	*  
df.mm.exp3	-0.191859792341785	0.180300380626041	-1.06411196513068	0.287649149038002	   
df.mm.exp4	-0.212605944770828	0.180300380626041	-1.17917635022519	0.238733218710626	   
df.mm.exp5	-0.518591755061411	0.180300380626041	-2.87626544803041	0.00414789911574635	** 
df.mm.exp6	-0.168142764762288	0.180300380626041	-0.932570215206765	0.351367173464625	   
df.mm.exp7	-0.224641199117283	0.180300380626041	-1.24592748133577	0.213212668799534	   
df.mm.exp8	-0.211045832200212	0.180300380626041	-1.17052349788401	0.242193301328195	   
df.mm.trans1:exp2	0.365286049654986	0.170804127876493	2.13862541963342	0.0328154944133100	*  
df.mm.trans2:exp2	0.228758486561278	0.144355835400012	1.58468471972321	0.113494840767424	   
df.mm.trans1:exp3	0.179980938345641	0.170804127876493	1.05372710006039	0.292375655420264	   
df.mm.trans2:exp3	0.135366158770127	0.144355835400012	0.937725575104223	0.348712615997409	   
df.mm.trans1:exp4	0.242516765955492	0.170804127876493	1.41985307363797	0.156100793236336	   
df.mm.trans2:exp4	0.0643055005721295	0.144355835400012	0.445465196428936	0.65612292810867	   
df.mm.trans1:exp5	0.505057964176933	0.170804127876493	2.95694237871192	0.00321286614617552	** 
df.mm.trans2:exp5	0.318232630270074	0.144355835400012	2.20450132402509	0.0278174629699108	*  
df.mm.trans1:exp6	0.213153220020147	0.170804127876493	1.24793951217781	0.21247530922871	   
df.mm.trans2:exp6	0.0152996725095716	0.144355835400012	0.105985826393342	0.91562431302983	   
df.mm.trans1:exp7	0.297246015405872	0.170804127876493	1.74027419068471	0.0822554172095388	.  
df.mm.trans2:exp7	0.0351847870466152	0.144355835400012	0.243736506730869	0.807507137173106	   
df.mm.trans1:exp8	0.236048653235164	0.170804127876493	1.38198447642816	0.167422403548609	   
df.mm.trans2:exp8	0.0640855276715498	0.144355835400012	0.443941372331558	0.657223754406341	   
df.mm.trans1:probe2	0.00299210658727798	0.0935532737529523	0.0319829169760461	0.97449489642249	   
df.mm.trans1:probe3	-0.0417259382855103	0.0935532737529523	-0.446012593805072	0.655727665051977	   
df.mm.trans1:probe4	0.0561717055099522	0.0935532737529523	0.60042479815603	0.548419747018397	   
df.mm.trans1:probe5	0.00569699458321591	0.0935532737529523	0.0608957266237423	0.951459835439513	   
df.mm.trans1:probe6	-0.0249082986844156	0.0935532737529523	-0.266247216000066	0.790128131590412	   
df.mm.trans1:probe7	-0.000570566630068798	0.0935532737529523	-0.00609884194513067	0.995135616064455	   
df.mm.trans1:probe8	0.044657382239749	0.0935532737529523	0.477347082023837	0.633265713767713	   
df.mm.trans1:probe9	-0.0164044865620164	0.0935532737529524	-0.175349144973120	0.860856592549887	   
df.mm.trans1:probe10	-0.035688049752712	0.0935532737529523	-0.381473018752438	0.702969387235576	   
df.mm.trans1:probe11	-0.000685543093875577	0.0935532737529523	-0.00732783649758642	0.994155396665566	   
df.mm.trans1:probe12	0.0111075444464055	0.0935532737529523	0.118729617904526	0.905524027384255	   
df.mm.trans1:probe13	0.0171532039641960	0.0935532737529523	0.183352257768047	0.854575341637586	   
df.mm.trans1:probe14	-0.0134554935250859	0.0935532737529523	-0.143827072910544	0.885678897154104	   
df.mm.trans1:probe15	0.0370034368643646	0.0935532737529523	0.395533318930989	0.692571242053048	   
df.mm.trans1:probe16	-0.0190463363524443	0.0935532737529523	-0.203588133139416	0.838735219876555	   
df.mm.trans1:probe17	-0.06163674344631	0.0935532737529523	-0.658841117725876	0.510216956773743	   
df.mm.trans1:probe18	0.0170312209330261	0.0935532737529523	0.182048369338744	0.855598081115161	   
df.mm.trans1:probe19	-0.128061507473674	0.0935532737529523	-1.36886185096898	0.171486627356035	   
df.mm.trans1:probe20	0.0190178538226481	0.0935532737529523	0.203283680621042	0.838973069858228	   
df.mm.trans1:probe21	0.00162354022280397	0.0935532737529523	0.0173541786158256	0.98615906605425	   
df.mm.trans2:probe2	-0.097587441730197	0.0935532737529523	-1.04312161205494	0.29725624044342	   
df.mm.trans2:probe3	-0.0833140359829432	0.0935532737529523	-0.890551796219894	0.373479178504052	   
df.mm.trans2:probe4	-0.0436800586061881	0.0935532737529523	-0.466900375090398	0.640718121410904	   
df.mm.trans2:probe5	-0.103450097079426	0.0935532737529523	-1.10578810264415	0.269202530760581	   
df.mm.trans2:probe6	-0.227129619478518	0.0935532737529523	-2.42781049093272	0.015444277253589	*  
df.mm.trans3:probe2	-0.063976879821037	0.0935532737529523	-0.683855061982992	0.494295619062653	   
df.mm.trans3:probe3	-0.0556808996123127	0.0935532737529523	-0.59517852640144	0.551918717875683	   
df.mm.trans3:probe4	-0.0393242138583723	0.0935532737529523	-0.420340328893421	0.674367342763648