chr4.16395_chr4_138986257_138987569_+_2.R 

fitVsDatCorrelation=0.800061156041238
cont.fitVsDatCorrelation=0.23145958506

fstatistic=10566.4487429396,58,830
cont.fstatistic=4009.28585337477,58,830

residuals=-0.528827658504816,-0.0902464501206462,-0.00321010110703344,0.0820399391061635,2.29537961065379
cont.residuals=-0.677807974347584,-0.180800872691005,-0.0149211863864107,0.149521457938435,2.40499172760205

predictedValues:
Include	Exclude	Both
chr4.16395_chr4_138986257_138987569_+_2.R.tl.Lung	76.8536957424744	94.0998131298929	63.9338494643369
chr4.16395_chr4_138986257_138987569_+_2.R.tl.cerebhem	72.8604714173963	78.0995771394106	76.5976656875839
chr4.16395_chr4_138986257_138987569_+_2.R.tl.cortex	70.9628267366255	79.4327861282897	63.4110196856252
chr4.16395_chr4_138986257_138987569_+_2.R.tl.heart	83.60435009712	78.5259655200528	73.201662964461
chr4.16395_chr4_138986257_138987569_+_2.R.tl.kidney	81.806053193199	98.6751805563795	68.2958367888364
chr4.16395_chr4_138986257_138987569_+_2.R.tl.liver	86.3901606050688	86.2937407917732	69.9824443909063
chr4.16395_chr4_138986257_138987569_+_2.R.tl.stomach	74.025758098184	86.5634542747029	66.3311094214575
chr4.16395_chr4_138986257_138987569_+_2.R.tl.testicle	75.7723893917508	81.410897328768	64.2654035604875


diffExp=-17.2461173874184,-5.23910572201429,-8.46995939166423,5.07838457706707,-16.8691273631806,0.0964198132955545,-12.5376961765189,-5.6385079370172
diffExpScore=1.15122525633936
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=-1,0,0,0,-1,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	78.2124776006824	75.7494085384973	73.2245338674936
cerebhem	75.4006285909682	79.32774765491	74.2579624877412
cortex	80.2366127302337	87.3144151639054	74.4125727650042
heart	77.0485364066428	75.4931803463836	77.1476987026538
kidney	78.3856213717312	75.875086682794	71.575358937653
liver	80.9728410512642	79.6622289453135	79.2316451010162
stomach	80.9135562036258	77.3704963148402	78.3854181574582
testicle	76.6526751022689	72.976592443471	72.5807996261136
cont.diffExp=2.46306906218508,-3.92711906394177,-7.07780243367168,1.55535606025929,2.51053468893721,1.31061210595063,3.54305988878562,3.67608265879791
cont.diffExpScore=5.15724251688964

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=0.316467148623798
cont.tran.correlation=0.449562868760579

tran.covariance=0.00202395727418839
cont.tran.covariance=0.000647397357301758

tran.mean=81.586070009443
cont.tran.mean=78.2245065717208

weightedLogRatios:
wLogRatio
Lung	-0.899522744760499
cerebhem	-0.300200552513515
cortex	-0.486936411765301
heart	0.275403116699898
kidney	-0.843312296381893
liver	0.00497869207609673
stomach	-0.685728885840118
testicle	-0.313199668474817

cont.weightedLogRatios:
wLogRatio
Lung	0.138983496886306
cerebhem	-0.220768480210500
cortex	-0.374260234525459
heart	0.0883891775429219
kidney	0.141450815677834
liver	0.0715710816671761
stomach	0.195714964897692
testicle	0.212049810580717

varWeightedLogRatios=0.167517825750894
cont.varWeightedLogRatios=0.0451899053020479

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.49770791570957	0.0774484544559544	58.073565796816	1.41236971166343e-294	***
df.mm.trans1	-0.255628559889519	0.0670508693304095	-3.81245705599798	0.000147773699597601	***
df.mm.trans2	0.126565585491855	0.0594031032397235	2.13062245218225	0.033413662191755	*  
df.mm.exp2	-0.420446371189834	0.076776599746762	-5.4762306819607	5.76051287299461e-08	***
df.mm.exp3	-0.240980952203077	0.076776599746762	-3.13872915703382	0.00175681106422308	** 
df.mm.exp4	-0.232103917543214	0.076776599746762	-3.02310753939064	0.00257889180459505	** 
df.mm.exp5	0.0439252090167148	0.076776599746762	0.572117144567441	0.567397531788461	   
df.mm.exp6	-0.0600242349494735	0.076776599746762	-0.781803767651289	0.434552863728654	   
df.mm.exp7	-0.157778846889772	0.076776599746762	-2.05503822011116	0.0401877950597947	*  
df.mm.exp8	-0.164189004245452	0.076776599746762	-2.13852924962826	0.0327651499431613	*  
df.mm.trans1:exp2	0.367089073764148	0.0711738128607857	5.15764238291077	3.12940060942362e-07	***
df.mm.trans2:exp2	0.234074952950557	0.0534342519961608	4.38061625654225	1.33493372773525e-05	***
df.mm.trans1:exp3	0.161233565879213	0.0711738128607857	2.26534956325273	0.0237481871505714	*  
df.mm.trans2:exp3	0.0715360980346814	0.0534342519961607	1.33876858685738	0.181012577856817	   
df.mm.trans1:exp4	0.316295911768368	0.0711738128607857	4.44399279812415	1.00281707179091e-05	***
df.mm.trans2:exp4	0.0511771978728871	0.0534342519961608	0.957760162462164	0.338462601576298	   
df.mm.trans1:exp5	0.018522472604695	0.0711738128607857	0.260242803640779	0.794741034237841	   
df.mm.trans2:exp5	0.00355218161715934	0.0534342519961608	0.0664776147220057	0.947013588250031	   
df.mm.trans1:exp6	0.176994463222381	0.0711738128607858	2.4867919268085	0.0130855919463768	*  
df.mm.trans2:exp6	-0.0265747587982528	0.0534342519961608	-0.497335656540344	0.619084089788019	   
df.mm.trans1:exp7	0.120288402768915	0.0711738128607857	1.69006545994939	0.0913909326753062	.  
df.mm.trans2:exp7	0.0743005066006317	0.0534342519961608	1.39050335365357	0.164748797960648	   
df.mm.trans1:exp8	0.150019415324636	0.0711738128607857	2.10778949861897	0.0353485043774414	*  
df.mm.trans2:exp8	0.019342081389312	0.0534342519961608	0.361979080210606	0.717459800423674	   
df.mm.trans1:probe2	-0.178475517791921	0.0477448451323697	-3.73811072791436	0.000198121449238707	***
df.mm.trans1:probe3	0.209012314464965	0.0477448451323697	4.3776938407799	1.35254294262986e-05	***
df.mm.trans1:probe4	0.0292532858047037	0.0477448451323697	0.612700401972207	0.540242287530116	   
df.mm.trans1:probe5	0.671775500696441	0.0477448451323697	14.0701158174036	1.75246355537836e-40	***
df.mm.trans1:probe6	0.353185457908055	0.0477448451323697	7.39735267606103	3.40271039579241e-13	***
df.mm.trans1:probe7	-0.0816952451075885	0.0477448451323697	-1.71107990571743	0.0874399959719895	.  
df.mm.trans1:probe8	-0.0852516421039761	0.0477448451323697	-1.78556746529643	0.0745343478765118	.  
df.mm.trans1:probe9	-0.202297858159662	0.0477448451323697	-4.23706177282183	2.51836185181598e-05	***
df.mm.trans1:probe10	-0.120903174679537	0.0477448451323697	-2.53227703104576	0.0115157593403013	*  
df.mm.trans1:probe11	0.137315653946768	0.0477448451323697	2.87603098441451	0.00413022414066554	** 
df.mm.trans1:probe12	0.190799687043767	0.0477448451323697	3.99623637933658	7.00795461285099e-05	***
df.mm.trans1:probe13	0.0252905816146441	0.0477448451323697	0.529702872520111	0.596459610781883	   
df.mm.trans1:probe14	0.184647361981607	0.0477448451323697	3.86737796446262	0.000118617632255780	***
df.mm.trans1:probe15	0.256316100350656	0.0477448451323697	5.36845600064332	1.03135143213594e-07	***
df.mm.trans1:probe16	0.0573180607608264	0.0477448451323697	1.20050783706420	0.230284683068314	   
df.mm.trans1:probe17	0.319661366323303	0.0477448451323697	6.6952016586725	3.96510261336552e-11	***
df.mm.trans1:probe18	0.45456274807822	0.0477448451323697	9.52066651002789	1.82185111747866e-20	***
df.mm.trans1:probe19	0.180852543577822	0.0477448451323697	3.78789674731207	0.000162892252768159	***
df.mm.trans1:probe20	0.334402184839091	0.0477448451323697	7.00394323014309	5.14298459412982e-12	***
df.mm.trans1:probe21	0.239462648996086	0.0477448451323697	5.01546603266155	6.47172359845215e-07	***
df.mm.trans1:probe22	0.119318245188991	0.0477448451323697	2.49908120673947	0.0126437722198004	*  
df.mm.trans2:probe2	-0.228459082074138	0.0477448451323697	-4.78499996053499	2.02356533461924e-06	***
df.mm.trans2:probe3	-0.368561548690124	0.0477448451323697	-7.71939981516978	3.35792066578435e-14	***
df.mm.trans2:probe4	-0.270188311495433	0.0477448451323697	-5.65900487783241	2.09644620229948e-08	***
df.mm.trans2:probe5	-0.283145074899454	0.0477448451323697	-5.93038000467802	4.43218689648482e-09	***
df.mm.trans2:probe6	-0.0484075900304203	0.0477448451323697	-1.01388097282991	0.310935010802192	   
df.mm.trans3:probe2	-0.204454960340175	0.0477448451323697	-4.28224156499693	2.06644505401041e-05	***
df.mm.trans3:probe3	-0.385253082664707	0.0477448451323697	-8.06899847714693	2.47698915253517e-15	***
df.mm.trans3:probe4	-0.197844225879223	0.0477448451323697	-4.14378191678519	3.76662203008825e-05	***
df.mm.trans3:probe5	-0.402695132729665	0.0477448451323697	-8.43431645056587	1.46826212021056e-16	***
df.mm.trans3:probe6	0.0095004404251879	0.0477448451323697	0.198983584486419	0.842324317927402	   
df.mm.trans3:probe7	-0.313385334948064	0.0477448451323697	-6.56375225596025	9.2365086933254e-11	***
df.mm.trans3:probe8	-0.395171517174208	0.0477448451323697	-8.27673680956801	5.03024248590633e-16	***
df.mm.trans3:probe9	-0.00786013815222693	0.0477448451323697	-0.164627995555021	0.869276878264308	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45065994816869	0.125592577660130	35.4372848387008	2.95975038180069e-168	***
df.mm.trans1	-0.0642617431025606	0.108731563111409	-0.591012777372806	0.554672764528128	   
df.mm.trans2	-0.105924440369237	0.0963297319397202	-1.09960277306201	0.271824065237807	   
df.mm.exp2	-0.00447072856552735	0.124503079292044	-0.0359085782532371	0.971363889867152	   
df.mm.exp3	0.151541279277990	0.124503079292044	1.21716892577832	0.223885907965797	   
df.mm.exp4	-0.0705731619252596	0.124503079292044	-0.566838686453029	0.570977036657655	   
df.mm.exp5	0.0266487313853183	0.124503079292044	0.214040741296117	0.830567856193159	   
df.mm.exp6	0.00620427822433022	0.124503079292044	0.049832327518398	0.960267994110505	   
df.mm.exp7	-0.0129803206489202	0.124503079292044	-0.104257024988695	0.916990568070127	   
df.mm.exp8	-0.0486064799725942	0.124503079292044	-0.390403837792469	0.696338124272572	   
df.mm.trans1:exp2	-0.0321428546390042	0.115417443535551	-0.278492172884625	0.780703938300757	   
df.mm.trans2:exp2	0.0506280670925815	0.0866504759931055	0.584279157296376	0.559191278350614	   
df.mm.trans1:exp3	-0.125990545678679	0.115417443535551	-1.09160748860176	0.275322429820314	   
df.mm.trans2:exp3	-0.00945634419753804	0.0866504759931055	-0.109132051372579	0.913124131506882	   
df.mm.trans1:exp4	0.0555795332057568	0.115417443535551	0.48155228103486	0.630250967462718	   
df.mm.trans2:exp4	0.0671848512762331	0.0866504759931055	0.775354670660768	0.438350958900692	   
df.mm.trans1:exp5	-0.0244374166536597	0.115417443535551	-0.211730704693112	0.83236911138818	   
df.mm.trans2:exp5	-0.0249909758505646	0.0866504759931055	-0.288411293350000	0.773103932370477	   
df.mm.trans1:exp6	0.0284803296266112	0.115417443535551	0.246759317778847	0.805155514327063	   
df.mm.trans2:exp6	0.0441606435859036	0.0866504759931055	0.509641096367633	0.610438403059037	   
df.mm.trans1:exp7	0.0469325031535214	0.115417443535551	0.406632669342266	0.684382627814655	   
df.mm.trans2:exp7	0.034155207726158	0.0866504759931055	0.394172188146729	0.693555168002438	   
df.mm.trans1:exp8	0.0284617899727815	0.115417443535551	0.246598686480303	0.805279796964088	   
df.mm.trans2:exp8	0.0113145819556603	0.0866504759931055	0.130577262571074	0.896141371034864	   
df.mm.trans1:probe2	-0.0563921938930067	0.0774243748604208	-0.72835194335982	0.466603609375562	   
df.mm.trans1:probe3	-0.0760994338482793	0.0774243748604208	-0.982887288214725	0.325949329916626	   
df.mm.trans1:probe4	-0.0918280007286693	0.0774243748604208	-1.18603477127474	0.235947974833133	   
df.mm.trans1:probe5	-0.0440691520806808	0.0774243748604208	-0.569189640344243	0.569381443709743	   
df.mm.trans1:probe6	-0.087153965159426	0.0774243748604208	-1.12566572628511	0.260632419330067	   
df.mm.trans1:probe7	0.0455711499777164	0.0774243748604208	0.588589188609804	0.556297022388561	   
df.mm.trans1:probe8	-0.063190700540948	0.0774243748604208	-0.816160293897975	0.414642629973346	   
df.mm.trans1:probe9	-0.0766037539770729	0.0774243748604208	-0.989401000849831	0.322755323700177	   
df.mm.trans1:probe10	-0.0713984720138814	0.0774243748604208	-0.922170468183918	0.356707639020001	   
df.mm.trans1:probe11	-0.0262357971403254	0.0774243748604208	-0.338857074243387	0.73480311065774	   
df.mm.trans1:probe12	0.0604857653896685	0.0774243748604208	0.781223813543359	0.434893637564844	   
df.mm.trans1:probe13	-0.0404900593391326	0.0774243748604208	-0.522962689878056	0.601139789299936	   
df.mm.trans1:probe14	-0.0462118941570522	0.0774243748604208	-0.596864930977643	0.55076033095058	   
df.mm.trans1:probe15	-0.0369879883319076	0.0774243748604208	-0.477730538975469	0.632967789308172	   
df.mm.trans1:probe16	-0.0789243480984736	0.0774243748604208	-1.01937339811599	0.308322762556674	   
df.mm.trans1:probe17	0.000327200332191329	0.0774243748604208	0.00422606359794574	0.996629114626968	   
df.mm.trans1:probe18	-0.116746023162163	0.0774243748604208	-1.50787169250809	0.131967888875584	   
df.mm.trans1:probe19	-0.0306429834370293	0.0774243748604208	-0.395779539612323	0.692369382393152	   
df.mm.trans1:probe20	-0.00901580097606811	0.0774243748604208	-0.116446545320148	0.907326817177769	   
df.mm.trans1:probe21	0.0558709075580861	0.0774243748604208	0.721619098104558	0.470731923079858	   
df.mm.trans1:probe22	-0.0463037704716329	0.0774243748604208	-0.598051589762377	0.549968657108314	   
df.mm.trans2:probe2	-0.0793757885311363	0.0774243748604208	-1.02520412562883	0.305565573615159	   
df.mm.trans2:probe3	-0.0173449742518800	0.0774243748604208	-0.224024724554111	0.822793196065078	   
df.mm.trans2:probe4	-0.0801981671817978	0.0774243748604208	-1.03582582780136	0.300585092276372	   
df.mm.trans2:probe5	-0.0395611475981674	0.0774243748604208	-0.510965024509238	0.6095114297094	   
df.mm.trans2:probe6	-0.0430929951335444	0.0774243748604208	-0.556581763962984	0.577963245503466	   
df.mm.trans3:probe2	0.0295134967748292	0.0774243748604208	0.381191282823213	0.703158869892595	   
df.mm.trans3:probe3	0.100956122208650	0.0774243748604208	1.30393202903675	0.192618268054585	   
df.mm.trans3:probe4	-0.129737660710728	0.0774243748604208	-1.67566946384283	0.0941795867842476	.  
df.mm.trans3:probe5	0.0628822062047229	0.0774243748604208	0.812175833748555	0.416923614941401	   
df.mm.trans3:probe6	0.0177152704185025	0.0774243748604208	0.228807406587903	0.81907497325594	   
df.mm.trans3:probe7	-0.0166078230009608	0.0774243748604208	-0.214503804918039	0.830206888462617	   
df.mm.trans3:probe8	0.0882492820314058	0.0774243748604208	1.13981265190064	0.254693179560372	   
df.mm.trans3:probe9	-0.0354600337634887	0.0774243748604208	-0.457995738776262	0.647075379667884