chr2.13732_chr2_33038990_33042448_-_2.R 

fitVsDatCorrelation=0.927435355373136
cont.fitVsDatCorrelation=0.252243506961067

fstatistic=9533.58532286567,53,715
cont.fstatistic=1412.53163602295,53,715

residuals=-0.904610183004422,-0.0772114707431274,-0.00291908776729441,0.0744233181428724,0.702756731268218
cont.residuals=-0.808219007485237,-0.284554906539888,-0.102815368823074,0.145407127436687,1.75898541247227

predictedValues:
Include	Exclude	Both
chr2.13732_chr2_33038990_33042448_-_2.R.tl.Lung	72.2101395133818	51.5330547163926	60.3648000146584
chr2.13732_chr2_33038990_33042448_-_2.R.tl.cerebhem	66.5441463196945	54.853297877906	62.1271020424356
chr2.13732_chr2_33038990_33042448_-_2.R.tl.cortex	66.325253036852	50.2664792369663	57.9703681596104
chr2.13732_chr2_33038990_33042448_-_2.R.tl.heart	69.6759248913433	49.9630685428256	63.4940411163724
chr2.13732_chr2_33038990_33042448_-_2.R.tl.kidney	83.754388043564	53.6734717539764	72.6639009994117
chr2.13732_chr2_33038990_33042448_-_2.R.tl.liver	103.898100162262	50.5191877530204	86.544093911926
chr2.13732_chr2_33038990_33042448_-_2.R.tl.stomach	71.2150905818964	48.9487805464388	62.893829086913
chr2.13732_chr2_33038990_33042448_-_2.R.tl.testicle	75.62458852576	48.6302160662578	64.6665632679933


diffExp=20.6770847969892,11.6908484417885,16.0587737998857,19.7128563485177,30.0809162895876,53.3789124092418,22.2663100354576,26.9943724595021
diffExpScore=0.995046073365048
diffExp1.5=0,0,0,0,1,1,0,1
diffExp1.5Score=0.75
diffExp1.4=1,0,0,0,1,1,1,1
diffExp1.4Score=0.833333333333333
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	70.7785060076817	73.6927164843779	88.9312529892642
cerebhem	66.1560220220439	81.175532514086	76.6383375718046
cortex	67.571655426243	87.0213013899852	62.5212661741011
heart	71.1663914382276	63.463002127443	62.9378703246785
kidney	69.0888729217562	68.2639932702215	78.9526815120044
liver	72.9162470888471	65.8497685403049	84.5892457495788
stomach	67.2901382356037	70.8678119646534	62.0468418579941
testicle	71.7103861994665	68.7557578686014	59.9780936037706
cont.diffExp=-2.91421047669624,-15.019510492042,-19.4496459637421,7.70338931078466,0.824879651534658,7.06647854854224,-3.57767372904969,2.95462833086506
cont.diffExpScore=2.54191305749935

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,-1,-1,0,0,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.0354312863167328
cont.tran.correlation=-0.710401687165137

tran.covariance=-0.000219282058494116
cont.tran.covariance=-0.00267349302567968

tran.mean=63.6021992230336
cont.tran.mean=70.9855064687214

weightedLogRatios:
wLogRatio
Lung	1.38684168123473
cerebhem	0.792377611792906
cortex	1.12444152241313
heart	1.35608031532428
kidney	1.87127720001322
liver	3.08820366926108
stomach	1.52905553895549
testicle	1.81251281417389

cont.weightedLogRatios:
wLogRatio
Lung	-0.172681103161799
cerebhem	-0.878608049989549
cortex	-1.09778193147423
heart	0.482053647908857
kidney	0.0508002227253402
liver	0.432037976932277
stomach	-0.219379807139644
testicle	0.178886453441143

varWeightedLogRatios=0.474092524278624
cont.varWeightedLogRatios=0.331594175322296

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20973861562596	0.0847844596725433	49.6522432517105	6.45084277042867e-234	***
df.mm.trans1	-0.0808771783573068	0.0752902410471131	-1.07420533169362	0.283093041389707	   
df.mm.trans2	-0.243731785729048	0.0684862650087035	-3.55884184512141	0.000397114080144149	***
df.mm.exp2	-0.0480521869901803	0.0923296624891262	-0.520441488626036	0.602917012688105	   
df.mm.exp3	-0.0694205924836439	0.0923296624891263	-0.751877463992891	0.452372177488044	   
df.mm.exp4	-0.117204869400869	0.0923296624891263	-1.26941728412223	0.204705301429592	   
df.mm.exp5	0.00356496167305111	0.0923296624891262	0.0386112282547438	0.96921112803144	   
df.mm.exp6	-0.0162879675568808	0.0923296624891263	-0.176410994232748	0.860021022361408	   
df.mm.exp7	-0.106366612994359	0.0923296624891263	-1.15203077891556	0.249693471255535	   
df.mm.exp8	-0.0806154882205996	0.0923296624891262	-0.87312664259001	0.382887186002408	   
df.mm.trans1:exp2	-0.0336627036823311	0.0876713462026966	-0.383964717554385	0.701118752256979	   
df.mm.trans2:exp2	0.110491056366493	0.0738258802680925	1.49664394065136	0.134927206279405	   
df.mm.trans1:exp3	-0.0155891649065704	0.0876713462026967	-0.177813682369244	0.858919664950615	   
df.mm.trans2:exp3	0.0445355897425826	0.0738258802680926	0.603251726641868	0.546532549066724	   
df.mm.trans1:exp4	0.081479244533296	0.0876713462026966	0.929371431629618	0.353010278122223	   
df.mm.trans2:exp4	0.0862655318323317	0.0738258802680925	1.16849987455707	0.242994639720929	   
df.mm.trans1:exp5	0.144743129697660	0.0876713462026966	1.65097418902424	0.099182970719802	.  
df.mm.trans2:exp5	0.0371304685413324	0.0738258802680925	0.502946506110001	0.615156767423208	   
df.mm.trans1:exp6	0.380118107730422	0.0876713462026966	4.33571656184663	1.66147018555094e-05	***
df.mm.trans2:exp6	-0.00358225375355386	0.0738258802680926	-0.0485230076572769	0.961312979086559	   
df.mm.trans1:exp7	0.0924908828178437	0.0876713462026966	1.05497276845737	0.291794126467610	   
df.mm.trans2:exp7	0.054917628500262	0.0738258802680926	0.743880442750337	0.457193189354236	   
df.mm.trans1:exp8	0.126816491063182	0.0876713462026966	1.44649873140971	0.148475591246580	   
df.mm.trans2:exp8	0.0226371147592748	0.0738258802680925	0.306628443535925	0.7592154728152	   
df.mm.trans1:probe2	-0.0439100622122468	0.0480195739620618	-0.91442007059326	0.360804420314037	   
df.mm.trans1:probe3	0.233572987725656	0.0480195739620618	4.86412036704431	1.41482427900207e-06	***
df.mm.trans1:probe4	-0.234834002528167	0.0480195739620618	-4.89038079999834	1.24385809022354e-06	***
df.mm.trans1:probe5	0.70013656370163	0.0480195739620618	14.5802327245714	2.38942218066111e-42	***
df.mm.trans1:probe6	-0.136781949681703	0.0480195739620618	-2.84846237473429	0.00451929893639253	** 
df.mm.trans1:probe7	0.212777209391793	0.0480195739620618	4.43105158658632	1.08480026183576e-05	***
df.mm.trans1:probe8	-0.130675349515286	0.0480195739620618	-2.72129339628308	0.00666090671801814	** 
df.mm.trans1:probe9	0.812587186389893	0.0480195739620618	16.9219990796229	2.81383986857594e-54	***
df.mm.trans1:probe10	1.47557388795765	0.0480195739620618	30.7285918263963	7.81382288075929e-133	***
df.mm.trans1:probe11	0.328090448946796	0.0480195739620618	6.83243148316989	1.78816910959105e-11	***
df.mm.trans1:probe12	0.372167883989513	0.0480195739620618	7.75033706637104	3.15162968464973e-14	***
df.mm.trans1:probe13	0.0678360150346282	0.0480195739620618	1.41267423755618	0.158186654314314	   
df.mm.trans1:probe14	0.297387279539488	0.0480195739620618	6.19304285736564	9.9510441530204e-10	***
df.mm.trans1:probe15	0.449179816955102	0.0480195739620618	9.35409833727345	1.06294394378727e-19	***
df.mm.trans1:probe16	0.282009960534884	0.0480195739620618	5.87281263173404	6.56218629288397e-09	***
df.mm.trans1:probe17	-0.130575912092522	0.0480195739620618	-2.71922262774936	0.00670231579253697	** 
df.mm.trans1:probe18	-0.0445885358907252	0.0480195739620618	-0.928549177174138	0.353436126066144	   
df.mm.trans1:probe19	-0.0779675003452442	0.0480195739620618	-1.62366080979483	0.104888974511734	   
df.mm.trans1:probe20	-0.105718095242006	0.0480195739620618	-2.20156254042422	0.0280149293933178	*  
df.mm.trans1:probe21	-0.154062334877295	0.0480195739620618	-3.20832365149705	0.00139468096335134	** 
df.mm.trans1:probe22	-0.253510581456615	0.0480195739620618	-5.2793175894668	1.72183629330427e-07	***
df.mm.trans2:probe2	-0.0718999504767933	0.0480195739620618	-1.49730504759576	0.134755206036025	   
df.mm.trans2:probe3	-0.119137507974894	0.0480195739620618	-2.48101967895466	0.0133295959079764	*  
df.mm.trans2:probe4	0.0418750513626306	0.0480195739620618	0.872041292905145	0.383478581795337	   
df.mm.trans2:probe5	-0.052967046721901	0.0480195739620618	-1.10303033433299	0.270385104594762	   
df.mm.trans2:probe6	-0.0357044358353017	0.0480195739620618	-0.7435392047316	0.457399546287378	   
df.mm.trans3:probe2	0.209298482125782	0.0480195739620618	4.35860764385663	1.50091081641426e-05	***
df.mm.trans3:probe3	0.405681070917069	0.0480195739620618	8.4482438606719	1.64829728715919e-16	***
df.mm.trans3:probe4	0.246126456172816	0.0480195739620618	5.12554435337701	3.82219084535806e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96941526268674	0.219375589355636	18.0941520173049	1.55653732574978e-60	***
df.mm.trans1	0.280338049449451	0.194809768986322	1.43903486415578	0.150578256537129	   
df.mm.trans2	0.368455975212043	0.177204818041862	2.07926612427096	0.0379487554425809	*  
df.mm.exp2	0.177936656284687	0.238898427869776	0.744821378153566	0.456624448830106	   
df.mm.exp3	0.472239069288442	0.238898427869776	1.97673577636878	0.0484553322833473	*  
df.mm.exp4	0.201734012582621	0.238898427869776	0.844434240867363	0.398709244316718	   
df.mm.exp5	0.0183317250194622	0.238898427869776	0.076734389518272	0.938856312683473	   
df.mm.exp6	-0.0327154461573366	0.238898427869776	-0.136942911048246	0.891114471008335	   
df.mm.exp7	0.270344714316799	0.238898427869776	1.13163036160357	0.258169261317821	   
df.mm.exp8	0.337620986361309	0.238898427869776	1.41324072063524	0.158020140432518	   
df.mm.trans1:exp2	-0.24547610115422	0.226845265242010	-1.08213015110690	0.279559516771579	   
df.mm.trans2:exp2	-0.081226746019049	0.191020808012016	-0.425224596547302	0.670800900115473	   
df.mm.trans1:exp3	-0.518605839743596	0.226845265242010	-2.28616559040949	0.0225362076974039	*  
df.mm.trans2:exp3	-0.305990104816832	0.191020808012016	-1.60186792214586	0.109626495547283	   
df.mm.trans1:exp4	-0.196268703113613	0.226845265242010	-0.86520960842724	0.387213949527717	   
df.mm.trans2:exp4	-0.351180887925796	0.191020808012016	-1.83844310774617	0.0664117548849781	.  
df.mm.trans1:exp5	-0.0424934029865971	0.226845265242010	-0.187323296967486	0.851460300966405	   
df.mm.trans2:exp5	-0.0948532501465012	0.191020808012016	-0.496559778663142	0.619652147878702	   
df.mm.trans1:exp6	0.0624715613547495	0.226845265242010	0.27539283788049	0.783094011628233	   
df.mm.trans2:exp6	-0.0798126083898874	0.191020808012016	-0.417821541121671	0.676203043942242	   
df.mm.trans1:exp7	-0.320886389938124	0.226845265242010	-1.41456066802094	0.157632666966544	   
df.mm.trans2:exp7	-0.309432343676947	0.191020808012016	-1.61988815196239	0.105697244159362	   
df.mm.trans1:exp8	-0.324540760053504	0.226845265242010	-1.43067019585914	0.152961636028171	   
df.mm.trans2:exp8	-0.406964470282238	0.191020808012016	-2.1304719340138	0.0334733651627966	*  
df.mm.trans1:probe2	0.165547525540228	0.124248268836291	1.33239301513611	0.183155558931443	   
df.mm.trans1:probe3	0.171963164122733	0.124248268836291	1.38402865273970	0.166781533539984	   
df.mm.trans1:probe4	-0.00766358804670684	0.124248268836291	-0.0616796364125148	0.950835193910286	   
df.mm.trans1:probe5	-0.0795714513335015	0.124248268836291	-0.640423018193874	0.522102801819844	   
df.mm.trans1:probe6	-0.0724694834009869	0.124248268836291	-0.58326352616206	0.559899833680182	   
df.mm.trans1:probe7	0.0714719497423596	0.124248268836291	0.575234974392525	0.565313363254078	   
df.mm.trans1:probe8	0.0517751033353776	0.124248268836291	0.416706838817981	0.67701791978856	   
df.mm.trans1:probe9	0.0288829136427179	0.124248268836291	0.232461296348313	0.816246267280752	   
df.mm.trans1:probe10	-0.0204494096694815	0.124248268836291	-0.164585067148303	0.869317102699588	   
df.mm.trans1:probe11	-0.018815134163866	0.124248268836291	-0.151431761102899	0.879677851647045	   
df.mm.trans1:probe12	-0.0931690564610645	0.124248268836291	-0.74986200881256	0.453584476154791	   
df.mm.trans1:probe13	-0.00963473752379098	0.124248268836291	-0.077544239561886	0.938212293390745	   
df.mm.trans1:probe14	0.054662885422232	0.124248268836291	0.439948869583491	0.660107093332311	   
df.mm.trans1:probe15	0.0729801797158883	0.124248268836291	0.587373815341013	0.557138111530392	   
df.mm.trans1:probe16	-0.0286326081863363	0.124248268836291	-0.230446737443581	0.817810565285405	   
df.mm.trans1:probe17	0.127826430140838	0.124248268836291	1.02879848015639	0.303922278034388	   
df.mm.trans1:probe18	-0.0627555711808727	0.124248268836291	-0.50508205682575	0.613656845338262	   
df.mm.trans1:probe19	0.0620761647923565	0.124248268836291	0.499613921173805	0.617500658417148	   
df.mm.trans1:probe20	-0.0774868804619176	0.124248268836291	-0.623645554080225	0.533059285130283	   
df.mm.trans1:probe21	-0.0659895021480191	0.124248268836291	-0.5311100328888	0.595507471267154	   
df.mm.trans1:probe22	-0.0156954636348242	0.124248268836291	-0.126323398964250	0.899511448256333	   
df.mm.trans2:probe2	0.0181957546128706	0.124248268836291	0.146446745562670	0.883610007007661	   
df.mm.trans2:probe3	-0.21617581623206	0.124248268836291	-1.73986984492228	0.0823121605302937	.  
df.mm.trans2:probe4	-0.111654768726932	0.124248268836291	-0.898642450093589	0.369145659805056	   
df.mm.trans2:probe5	-0.115603350734469	0.124248268836291	-0.930422224930852	0.352466542835776	   
df.mm.trans2:probe6	0.04556547986006	0.124248268836291	0.366729293589569	0.71392945401899	   
df.mm.trans3:probe2	-0.155620237094438	0.124248268836291	-1.25249420818476	0.210799342961303	   
df.mm.trans3:probe3	-0.171857593255022	0.124248268836291	-1.38317897596996	0.167041740536759	   
df.mm.trans3:probe4	-0.193904787343196	0.124248268836291	-1.56062365423122	0.119055057924853