chr11.4256_chr11_73923524_73925956_-_2.R 

fitVsDatCorrelation=0.881853514193433
cont.fitVsDatCorrelation=0.249520100437863

fstatistic=8152.35910611965,59,853
cont.fstatistic=1921.8623273404,59,853

residuals=-0.940853766282,-0.0864217931837961,-0.0107981964685081,0.0696937738423144,1.65122885165936
cont.residuals=-0.677613274442303,-0.191130280335613,-0.0793151515946326,0.0806356779557713,2.65168707641223

predictedValues:
Include	Exclude	Both
chr11.4256_chr11_73923524_73925956_-_2.R.tl.Lung	45.5242852336186	41.9377483409125	59.5757873340371
chr11.4256_chr11_73923524_73925956_-_2.R.tl.cerebhem	49.8351189591702	43.9626718729313	67.3752170084549
chr11.4256_chr11_73923524_73925956_-_2.R.tl.cortex	46.5681979187093	43.5246174164233	72.8839193754485
chr11.4256_chr11_73923524_73925956_-_2.R.tl.heart	47.5435260152325	44.0148609280634	61.6114927090564
chr11.4256_chr11_73923524_73925956_-_2.R.tl.kidney	46.2206776698729	49.2907733753566	101.509571940685
chr11.4256_chr11_73923524_73925956_-_2.R.tl.liver	48.6687926289619	48.7480487002263	65.272440912185
chr11.4256_chr11_73923524_73925956_-_2.R.tl.stomach	51.7738015072993	44.469174585801	62.0363254789845
chr11.4256_chr11_73923524_73925956_-_2.R.tl.testicle	48.0903813935957	45.5381067939293	62.1019346795003


diffExp=3.58653689270609,5.87244708623897,3.04358050228596,3.52866508716913,-3.0700957054837,-0.079256071264453,7.30462692149829,2.55227459966638
diffExpScore=1.22320876249250
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	51.7587743471146	44.7207615270715	52.2795261669479
cerebhem	49.5041631284041	47.3350605824853	54.9150732084943
cortex	54.2019035586514	46.43527745697	60.9603297439133
heart	53.0284208635311	47.7139402093904	51.2432091493638
kidney	51.2252681463916	47.5191847523445	53.773743148539
liver	54.6123744009053	47.5054325185993	54.8767564428267
stomach	55.6558098225527	47.5285800875011	48.907937034268
testicle	53.9819547709739	60.0883235974954	51.5258575681035
cont.diffExp=7.03801282004312,2.16910254591880,7.76662610168133,5.3144806541407,3.70608339404711,7.10694188230603,8.12722973505155,-6.10636882652148
cont.diffExpScore=1.31041204898257

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=0.0301068535429038
cont.tran.correlation=0.224913608321682

tran.covariance=0.000124806867030885
cont.tran.covariance=0.000801001212297696

tran.mean=46.6069239587565
cont.tran.mean=50.8009518606489

weightedLogRatios:
wLogRatio
Lung	0.309956801588377
cerebhem	0.482211556470161
cortex	0.257327827845351
heart	0.294829942713965
kidney	-0.248594209266772
liver	-0.00632287794462297
stomach	0.588708412928155
testicle	0.209722716256734

cont.weightedLogRatios:
wLogRatio
Lung	0.566138029204247
cerebhem	0.173829877504176
cortex	0.605540432764123
heart	0.413760802837463
kidney	0.292788808984844
liver	0.547983532515138
stomach	0.62199056632595
testicle	-0.433188775348293

varWeightedLogRatios=0.0698073170384666
cont.varWeightedLogRatios=0.125276639849425

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.2644131602259	0.0805786923879104	40.5121138540054	6.07018108995226e-201	***
df.mm.trans1	0.497652479272252	0.0695857877250468	7.15163966007855	1.84493742455685e-12	***
df.mm.trans2	0.468031666912874	0.0614787289329283	7.61290408953451	7.10256090528261e-14	***
df.mm.exp2	0.0146005700485830	0.0790813026256615	0.184627333690949	0.853565230403061	   
df.mm.exp3	-0.141806496906640	0.0790813026256615	-1.79317350876089	0.0732993531322046	.  
df.mm.exp4	0.0581415081648632	0.0790813026256615	0.735211816629796	0.462412590969398	   
df.mm.exp5	-0.356171944022767	0.0790813026256615	-4.50387047503174	7.60102494462738e-06	***
df.mm.exp6	0.125950254495152	0.0790813026256615	1.59266792924934	0.111605178824868	   
df.mm.exp7	0.146777371195163	0.0790813026256615	1.85603127821436	0.0637937871594445	.  
df.mm.exp8	0.0956715081408262	0.0790813026256615	1.20978670007114	0.226695914670188	   
df.mm.trans1:exp2	0.075873440462854	0.0730964925112782	1.03799016691734	0.299568776619479	   
df.mm.trans2:exp2	0.0325540008735733	0.0539853374914371	0.603015603611571	0.546658599380395	   
df.mm.trans1:exp3	0.164478432002330	0.0730964925112781	2.25015491648868	0.0246932213545628	*  
df.mm.trans2:exp3	0.178946856143941	0.0539853374914371	3.31473071132185	0.000956007569161853	***
df.mm.trans1:exp4	-0.0147418045079160	0.0730964925112781	-0.201675949165981	0.840218150165567	   
df.mm.trans2:exp4	-0.00980051931094118	0.0539853374914371	-0.181540391638668	0.855986560424896	   
df.mm.trans1:exp5	0.371353285627634	0.0730964925112782	5.08031607084754	4.63032823852987e-07	***
df.mm.trans2:exp5	0.517722518521214	0.0539853374914371	9.59005801535152	9.37056783490179e-21	***
df.mm.trans1:exp6	-0.0591581632181383	0.0730964925112781	-0.809316031258418	0.418559074658373	   
df.mm.trans2:exp6	0.0245285790803159	0.0539853374914371	0.454356316364726	0.649687980822838	   
df.mm.trans1:exp7	-0.0181390372438614	0.0730964925112781	-0.248151951217943	0.804076567001347	   
df.mm.trans2:exp7	-0.0881674643969132	0.0539853374914371	-1.63317427460554	0.102801380641390	   
df.mm.trans1:exp8	-0.0408352470189378	0.0730964925112781	-0.558648515353009	0.576548238263435	   
df.mm.trans2:exp8	-0.0133083571521493	0.0539853374914371	-0.246517994895562	0.805340587699056	   
df.mm.trans1:probe2	-0.000249945013593362	0.0500457472786681	-0.00499433073107293	0.996016284934556	   
df.mm.trans1:probe3	0.0863650250069178	0.0500457472786681	1.72572155883724	0.0847596649869843	.  
df.mm.trans1:probe4	0.0867407994634866	0.0500457472786681	1.73323017799076	0.0834161434236223	.  
df.mm.trans1:probe5	-0.104737190514202	0.0500457472786681	-2.09282898566780	0.0366593513467565	*  
df.mm.trans1:probe6	0.215962101100072	0.0500457472786681	4.31529376307517	1.78061809227506e-05	***
df.mm.trans1:probe7	0.0240005423757046	0.0500457472786681	0.47957206517595	0.631654646443451	   
df.mm.trans1:probe8	1.04645705731411	0.0500457472786681	20.9100096255364	1.04899961160956e-78	***
df.mm.trans1:probe9	0.339561301847363	0.0500457472786681	6.78501811465807	2.17002508463047e-11	***
df.mm.trans1:probe10	0.247145072364516	0.0500457472786681	4.93838309553749	9.47749523470152e-07	***
df.mm.trans1:probe11	-0.0674044503536195	0.0500457472786681	-1.34685670649083	0.178384104822488	   
df.mm.trans1:probe12	-0.0511587371321814	0.0500457472786681	-1.02223944918468	0.306957404327753	   
df.mm.trans1:probe13	0.00275308144028395	0.0500457472786681	0.0550112964634948	0.956142345875954	   
df.mm.trans1:probe14	-0.0499692789941691	0.0500457472786681	-0.998472032317287	0.318333673221238	   
df.mm.trans1:probe15	0.0259327189903092	0.0500457472786681	0.518180273059145	0.604466872416937	   
df.mm.trans1:probe16	-0.0785963901767277	0.0500457472786681	-1.57049088984688	0.116671795298334	   
df.mm.trans1:probe17	-0.0435099424268411	0.0500457472786681	-0.869403391752071	0.384871093848801	   
df.mm.trans1:probe18	-0.015732620811949	0.0500457472786681	-0.314364789566345	0.753320866868013	   
df.mm.trans1:probe19	0.0762111464453605	0.0500457472786681	1.52282962268495	0.128171957212869	   
df.mm.trans1:probe20	-0.0124352019556465	0.0500457472786681	-0.24847669645942	0.80382540666305	   
df.mm.trans1:probe21	0.0333047854371316	0.0500457472786681	0.665486824518409	0.505919070244598	   
df.mm.trans1:probe22	0.0371292605596252	0.0500457472786681	0.74190640720938	0.458348267531252	   
df.mm.trans2:probe2	0.0976387464487726	0.0500457472786681	1.9509898794216	0.0513853237139042	.  
df.mm.trans2:probe3	-0.0081472458404464	0.0500457472786681	-0.162795967359232	0.870717641521638	   
df.mm.trans2:probe4	-0.00921509801432657	0.0500457472786681	-0.184133488166626	0.853952499836915	   
df.mm.trans2:probe5	0.0217736400581815	0.0500457472786681	0.435074731463995	0.663618237656097	   
df.mm.trans2:probe6	-0.0421858943881487	0.0500457472786681	-0.842946637468442	0.399494587502046	   
df.mm.trans3:probe2	-0.45945485643567	0.0500457472786681	-9.1806972903672	3.16852580723062e-19	***
df.mm.trans3:probe3	0.861418200071929	0.0500457472786681	17.2126153951768	3.22356387832083e-57	***
df.mm.trans3:probe4	-0.302797465474675	0.0500457472786681	-6.05041351043512	2.16101729622467e-09	***
df.mm.trans3:probe5	-0.397250170773026	0.0500457472786681	-7.93774081463966	6.46764366653548e-15	***
df.mm.trans3:probe6	-0.326765719535977	0.0500457472786681	-6.52934039962393	1.13326692521860e-10	***
df.mm.trans3:probe7	-0.305762866803755	0.0500457472786681	-6.10966732300321	1.51649837282739e-09	***
df.mm.trans3:probe8	-0.113826812418991	0.0500457472786681	-2.27445524562102	0.0231861785971776	*  
df.mm.trans3:probe9	-0.267994083339083	0.0500457472786681	-5.35498214956848	1.10091655521974e-07	***
df.mm.trans3:probe10	-0.116046901964274	0.0500457472786681	-2.31881644844055	0.0206404971344504	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76006114593309	0.165484781146436	22.7214920905980	9.66022759111468e-90	***
df.mm.trans1	0.184295307369162	0.142908609104079	1.28960255455948	0.197538233808852	   
df.mm.trans2	0.000499253653710033	0.126259110208062	0.00395419905056604	0.996845938392501	   
df.mm.exp2	-0.0369068417875438	0.162409585834209	-0.227245464594799	0.820287328661178	   
df.mm.exp3	-0.0698748338456192	0.162409585834209	-0.430238359926296	0.667130935085681	   
df.mm.exp4	0.109041507958550	0.162409585834209	0.671398226887061	0.502148648748049	   
df.mm.exp5	0.0221541428299549	0.162409585834209	0.136409083959923	0.891530079513477	   
df.mm.exp6	0.0655876862992859	0.162409585834209	0.403841226257661	0.686430698503722	   
df.mm.exp7	0.200150990390038	0.162409585834209	1.2323840945839	0.218145211746691	   
df.mm.exp8	0.3519545866915	0.162409585834209	2.16708013190048	0.0305048665473887	*  
df.mm.trans1:exp2	-0.00763035894450874	0.150118557491209	-0.050828885329221	0.959473773192607	   
df.mm.trans2:exp2	0.0937202437516499	0.110869902391558	0.845317274842179	0.398170761283219	   
df.mm.trans1:exp3	0.115996892308509	0.150118557491209	0.77270188474401	0.439912878671483	   
df.mm.trans2:exp3	0.107496436742162	0.110869902391558	0.969572755304843	0.332534332734026	   
df.mm.trans1:exp4	-0.0848074657993469	0.150118557491209	-0.564936588897833	0.572265434100575	   
df.mm.trans2:exp4	-0.0442557626664659	0.110869902391558	-0.399168410107989	0.689869031610461	   
df.mm.trans1:exp5	-0.0325151844147856	0.150118557491209	-0.216596701688195	0.828574445777122	   
df.mm.trans2:exp5	0.0385415187097406	0.110869902391558	0.347628327240914	0.72820508869675	   
df.mm.trans1:exp6	-0.0119211622218878	0.150118557491209	-0.0794116491732605	0.936723828879148	   
df.mm.trans2:exp6	-0.00518147046821317	0.110869902391558	-0.0467346895455346	0.962735621337335	   
df.mm.trans1:exp7	-0.127558489164931	0.150118557491209	-0.849718324614203	0.3957201304023	   
df.mm.trans2:exp7	-0.139257631719789	0.110869902391558	-1.25604540741792	0.209443415234103	   
df.mm.trans1:exp8	-0.309898737256965	0.150118557491209	-2.06435994613866	0.0392854338345022	*  
df.mm.trans2:exp8	-0.0565769044118008	0.110869902391558	-0.510299938859772	0.609973365958889	   
df.mm.trans1:probe2	-0.0537566276418996	0.102779150297589	-0.523030473459367	0.601088876609031	   
df.mm.trans1:probe3	0.0375228639852982	0.102779150297589	0.365082449861218	0.715140360904408	   
df.mm.trans1:probe4	0.0379194170740509	0.102779150297589	0.368940752713543	0.712263441562439	   
df.mm.trans1:probe5	-0.067960542696925	0.102779150297589	-0.66122888251314	0.508644096675654	   
df.mm.trans1:probe6	-0.025726746784167	0.102779150297589	-0.250310950320927	0.802407162993	   
df.mm.trans1:probe7	0.0612713240580572	0.102779150297589	0.596145462193945	0.551236212801286	   
df.mm.trans1:probe8	0.102976082928158	0.102779150297589	1.00191607568265	0.316668257621012	   
df.mm.trans1:probe9	0.0765437189803277	0.102779150297589	0.744739752748502	0.456634190666909	   
df.mm.trans1:probe10	0.029832061755214	0.102779150297589	0.29025402203499	0.771692457026107	   
df.mm.trans1:probe11	-0.0729467633190217	0.102779150297589	-0.709742813671935	0.47805760126009	   
df.mm.trans1:probe12	-4.16186943122967e-05	0.102779150297589	-0.00040493323978446	0.999677004669111	   
df.mm.trans1:probe13	0.0710246471958379	0.102779150297589	0.691041393027588	0.489727539313047	   
df.mm.trans1:probe14	-0.00765364729156497	0.102779150297589	-0.0744669251439073	0.940656317330441	   
df.mm.trans1:probe15	0.113504934728934	0.102779150297589	1.10435759003931	0.269749471178296	   
df.mm.trans1:probe16	-0.099831397154234	0.102779150297589	-0.971319541611113	0.331664523688365	   
df.mm.trans1:probe17	-0.122150977817852	0.102779150297589	-1.18848012913294	0.234975079453743	   
df.mm.trans1:probe18	0.0949536325356568	0.102779150297589	0.92386084396228	0.35582003320387	   
df.mm.trans1:probe19	-0.0322587127731435	0.102779150297589	-0.313864365289467	0.753700795294616	   
df.mm.trans1:probe20	-0.0546012048778395	0.102779150297589	-0.531247871963777	0.595385324684448	   
df.mm.trans1:probe21	-0.0990206051099955	0.102779150297589	-0.963430859501065	0.335604384278777	   
df.mm.trans1:probe22	0.0820007072251044	0.102779150297589	0.797834064473948	0.425188835152888	   
df.mm.trans2:probe2	0.172258066951335	0.102779150297589	1.6760020534571	0.094104244176584	.  
df.mm.trans2:probe3	0.130262394994788	0.102779150297589	1.26740097206119	0.205357836001741	   
df.mm.trans2:probe4	0.212104039922979	0.102779150297589	2.06368742404318	0.0393493558820922	*  
df.mm.trans2:probe5	0.0146616699340612	0.102779150297589	0.142652180832488	0.886598572500002	   
df.mm.trans2:probe6	0.108753154528354	0.102779150297589	1.05812467035841	0.290298160041252	   
df.mm.trans3:probe2	-0.0523515051475344	0.102779150297589	-0.509359193921672	0.610632213364097	   
df.mm.trans3:probe3	-0.0691089198721299	0.102779150297589	-0.672402132845332	0.501509818094854	   
df.mm.trans3:probe4	-0.00736571253604447	0.102779150297589	-0.0716654352046852	0.942884963230081	   
df.mm.trans3:probe5	0.116456489265040	0.102779150297589	1.13307503445834	0.257501117879135	   
df.mm.trans3:probe6	-0.0295594443951748	0.102779150297589	-0.287601564223752	0.773721603926263	   
df.mm.trans3:probe7	1.84550711620053e-05	0.102779150297589	0.000179560456654584	0.999856773447252	   
df.mm.trans3:probe8	-0.00301666976777211	0.102779150297589	-0.0293509895639103	0.976591526183012	   
df.mm.trans3:probe9	0.139119226467693	0.102779150297589	1.35357439777313	0.176230752478609	   
df.mm.trans3:probe10	0.0232992647914829	0.102779150297589	0.226692522014647	0.82071715567529