chr9.25235_chr9_112300015_112309992_+_2.R 

fitVsDatCorrelation=0.82732499674426
cont.fitVsDatCorrelation=0.218411624217392

fstatistic=9522.90858909078,54,738
cont.fstatistic=3146.17690146661,54,738

residuals=-0.524875301148999,-0.101464526154484,-0.00593048664557265,0.0811012296998458,1.18733866768236
cont.residuals=-0.653724749116918,-0.216156224411948,-0.0301676723265493,0.184712705878599,1.37964667870085

predictedValues:
Include	Exclude	Both
chr9.25235_chr9_112300015_112309992_+_2.R.tl.Lung	68.227251996079	86.281023087045	78.5617474706657
chr9.25235_chr9_112300015_112309992_+_2.R.tl.cerebhem	67.9387768359156	98.3095749466154	60.5468807121789
chr9.25235_chr9_112300015_112309992_+_2.R.tl.cortex	66.5923965512559	75.8098505381182	73.6860832379459
chr9.25235_chr9_112300015_112309992_+_2.R.tl.heart	72.3894749567308	72.1434568947086	95.2706804360844
chr9.25235_chr9_112300015_112309992_+_2.R.tl.kidney	67.2896551573205	94.2401223097339	67.3532272747151
chr9.25235_chr9_112300015_112309992_+_2.R.tl.liver	66.9731288301221	81.2422120116117	63.5399427125119
chr9.25235_chr9_112300015_112309992_+_2.R.tl.stomach	64.904776682685	75.7438128112418	67.1579284575403
chr9.25235_chr9_112300015_112309992_+_2.R.tl.testicle	67.4012499967443	77.5181143067665	68.9000037209474


diffExp=-18.0537710909660,-30.3707981106998,-9.21745398686231,0.246018062022216,-26.9504671524134,-14.2690831814896,-10.8390361285568,-10.1168643100223
diffExpScore=0.995787030419695
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,-1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,-1,0,0,-1,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,0,0,-1,-1,0,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	75.0737881607635	70.6355040333196	72.9158487512595
cerebhem	71.7696329117131	64.6771626251547	74.7320491527508
cortex	71.2596328430072	70.2092915167893	65.7341185973297
heart	75.1703888686991	71.7663548799146	72.0637000067321
kidney	69.7496491654023	67.4637469914963	72.4121467795216
liver	75.6614457991213	73.9769773209886	80.103128415413
stomach	67.9463785824125	77.9346061794642	69.4522953959704
testicle	72.2384774041152	68.8762108469171	69.2673923614867
cont.diffExp=4.43828412744384,7.09247028655842,1.05034132621796,3.40403398878451,2.28590217390601,1.68446847813269,-9.9882275970516,3.36226655719811
cont.diffExpScore=2.32428927003646

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=-0.133491212300611
cont.tran.correlation=-0.070236950616864

tran.covariance=-0.000485024631428091
cont.tran.covariance=-0.000139955840690274

tran.mean=75.1878048695434
cont.tran.mean=71.5255780080799

weightedLogRatios:
wLogRatio
Lung	-1.01893598763831
cerebhem	-1.62710678079858
cortex	-0.552699147387727
heart	0.0145717182797234
kidney	-1.47449009759643
liver	-0.830683722732269
stomach	-0.656372511700311
testicle	-0.598631950454516

cont.weightedLogRatios:
wLogRatio
Lung	0.261304695640201
cerebhem	0.439254141191683
cortex	0.063241966315298
heart	0.199110770221876
kidney	0.140893872068901
liver	0.0971516199357117
stomach	-0.588007790676241
testicle	0.202856165568959

varWeightedLogRatios=0.279851104908176
cont.varWeightedLogRatios=0.0911242830487716

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22233415878700	0.0831877082691707	50.7567072905143	6.50507904758953e-243	***
df.mm.trans1	-0.00739592022341872	0.073280963952367	-0.100925531332067	0.919636987468962	   
df.mm.trans2	0.136631101162559	0.0661236515606733	2.06629697449769	0.0391488180021487	*  
df.mm.exp2	0.386741587918486	0.0880570217729485	4.39194490265278	1.28778009645006e-05	***
df.mm.exp3	-0.0895641538756119	0.0880570217729485	-1.01711541081357	0.309431851906142	   
df.mm.exp4	-0.312573445058984	0.0880570217729485	-3.54967086968876	0.000410153984517031	***
df.mm.exp5	0.228332864494118	0.0880570217729485	2.59301143619035	0.00970240593804678	** 
df.mm.exp6	0.133488867859099	0.0880570217729485	1.51593666435023	0.129963555281093	   
df.mm.exp7	-0.0233378492506135	0.0880570217729485	-0.265031098948466	0.791059511532372	   
df.mm.exp8	0.0119501276507942	0.0880570217729485	0.135708969145097	0.892088363985375	   
df.mm.trans1:exp2	-0.390978703360924	0.0830591005525807	-4.70723497798311	2.99902555655971e-06	***
df.mm.trans2:exp2	-0.256229839356732	0.0679574459066569	-3.7704454006214	0.000175986182667009	***
df.mm.trans1:exp3	0.0653104845873126	0.0830591005525807	0.786313410003371	0.431936300540357	   
df.mm.trans2:exp3	-0.0398172867956572	0.0679574459066569	-0.585914998193846	0.558111774900257	   
df.mm.trans1:exp4	0.371790285936921	0.0830591005525807	4.47621372568992	8.79867756604895e-06	***
df.mm.trans2:exp4	0.133620359431476	0.0679574459066569	1.96623574722056	0.0496458075737414	*  
df.mm.trans1:exp5	-0.242170426451208	0.0830591005525807	-2.91563988581722	0.00365716738391539	** 
df.mm.trans2:exp5	-0.140096525942971	0.0679574459066569	-2.06153312670698	0.0396018926211638	*  
df.mm.trans1:exp6	-0.152041465572248	0.0830591005525807	-1.83052145473208	0.0675751182134712	.  
df.mm.trans2:exp6	-0.193663582613144	0.067957445906657	-2.84977723970307	0.00449692195305595	** 
df.mm.trans1:exp7	-0.0265850035101943	0.0830591005525807	-0.320073337338449	0.749003332193214	   
df.mm.trans2:exp7	-0.106915067960337	0.0679574459066569	-1.57326495329431	0.116086064539051	   
df.mm.trans1:exp8	-0.0241306383789086	0.0830591005525807	-0.290523714058674	0.771497230238284	   
df.mm.trans2:exp8	-0.119048164817257	0.0679574459066569	-1.75180457754071	0.0802227592321441	.  
df.mm.trans1:probe2	0.31984353807354	0.0484960665623924	6.59524701167356	8.09307847228537e-11	***
df.mm.trans1:probe3	-0.266514163503208	0.0484960665623924	-5.49558309353451	5.36601562822949e-08	***
df.mm.trans1:probe4	-0.199506118823808	0.0484960665623924	-4.11386186479958	4.32895775121804e-05	***
df.mm.trans1:probe5	0.205085433319200	0.0484960665623924	4.22890860757435	2.64334346684819e-05	***
df.mm.trans1:probe6	-0.252393570989373	0.0484960665623924	-5.20441324173491	2.52489250822790e-07	***
df.mm.trans1:probe7	0.524803278723222	0.0484960665623924	10.8215638076139	1.96449368276441e-25	***
df.mm.trans1:probe8	-0.00939222130292898	0.0484960665623924	-0.193669754449992	0.84648776719314	   
df.mm.trans1:probe9	-0.178433533829917	0.0484960665623924	-3.67934033578485	0.000250846390381261	***
df.mm.trans1:probe10	-0.263635229385627	0.0484960665623924	-5.43621881264222	7.40200620505265e-08	***
df.mm.trans1:probe11	0.0621539557427924	0.0484960665623924	1.28162880308712	0.200375401356122	   
df.mm.trans1:probe12	0.0542975345859976	0.0484960665623924	1.11962759940832	0.263236742147867	   
df.mm.trans1:probe13	0.287719496433767	0.0484960665623924	5.93284191540778	4.57618937721688e-09	***
df.mm.trans1:probe14	0.355468518187083	0.0484960665623924	7.3298422611194	6.06424545921372e-13	***
df.mm.trans1:probe15	0.0990875746752586	0.0484960665623924	2.0432084847083	0.041386485435053	*  
df.mm.trans1:probe16	0.122065114158851	0.0484960665623924	2.51701061160926	0.0120463480405766	*  
df.mm.trans1:probe17	-0.229316483162986	0.0484960665623924	-4.72855840520509	2.70900007320398e-06	***
df.mm.trans1:probe18	0.0722101876278777	0.0484960665623924	1.4889906078254	0.136917033952960	   
df.mm.trans1:probe19	-0.0468721697294888	0.0484960665623924	-0.96651487537006	0.3341032214358	   
df.mm.trans1:probe20	0.0338464676659554	0.0484960665623924	0.69792191542814	0.48544585783305	   
df.mm.trans1:probe21	-0.245957135034523	0.0484960665623924	-5.07169245815201	4.99206374165793e-07	***
df.mm.trans1:probe22	-0.231102905303907	0.0484960665623924	-4.76539483891096	2.27036268117610e-06	***
df.mm.trans2:probe2	0.282299485902491	0.0484960665623924	5.82108005685987	8.71913113943363e-09	***
df.mm.trans2:probe3	0.056276826782846	0.0484960665623924	1.16044105784215	0.246244525149030	   
df.mm.trans2:probe4	0.32093726424741	0.0484960665623924	6.61779989588454	7.00938809207607e-11	***
df.mm.trans2:probe5	0.178042363438744	0.0484960665623924	3.67127431272564	0.000258750426934758	***
df.mm.trans2:probe6	0.247532671438406	0.0484960665623924	5.10418037965912	4.23085100167799e-07	***
df.mm.trans3:probe2	-0.131066060678241	0.0484960665623924	-2.70261219040556	0.0070378409986563	** 
df.mm.trans3:probe3	-0.198479697967954	0.0484960665623924	-4.09269683166161	4.73412263033332e-05	***
df.mm.trans3:probe4	0.0749624813451489	0.0484960665623924	1.54574353465773	0.122595203265439	   
df.mm.trans3:probe5	0.31616111652049	0.0484960665623924	6.51931463583205	1.30915163973173e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30708056435699	0.144518215007839	29.8030290792296	9.68703676991382e-129	***
df.mm.trans1	0.0377316117863788	0.127307679521383	0.296381270385509	0.767022311987423	   
df.mm.trans2	-0.0636089532240956	0.114873606836580	-0.553729920873698	0.579931418111547	   
df.mm.exp2	-0.157737786541053	0.152977451480643	-1.03111788707640	0.302823335012365	   
df.mm.exp3	0.0454942075073196	0.152977451480643	0.297391589851895	0.766251253784475	   
df.mm.exp4	0.0289243486863993	0.152977451480643	0.189075895868609	0.850085317809727	   
df.mm.exp5	-0.112569656449972	0.152977451480643	-0.735857836304821	0.462051027089255	   
df.mm.exp6	-0.0399906147133046	0.152977451480643	-0.261415093049611	0.79384535572188	   
df.mm.exp7	0.0472504561461383	0.152977451480643	0.308872031066076	0.757506045833238	   
df.mm.exp8	-0.0123889478432466	0.152977451480643	-0.0809854506225334	0.935475480145	   
df.mm.trans1:exp2	0.112727761158277	0.144294790682004	0.781232368995952	0.434916372407076	   
df.mm.trans2:exp2	0.0696130443192968	0.118059374194367	0.589644361528544	0.555609573316617	   
df.mm.trans1:exp3	-0.0976356717247078	0.144294790682004	-0.676640308795879	0.498846211325879	   
df.mm.trans2:exp3	-0.0515464555278182	0.118059374194367	-0.436614676975628	0.662518524742618	   
df.mm.trans1:exp4	-0.027638432344656	0.144294790682004	-0.191541442445870	0.848154100797548	   
df.mm.trans2:exp4	-0.0130414855271901	0.118059374194367	-0.110465480748011	0.912070257315288	   
df.mm.trans1:exp5	0.0390105752277484	0.144294790682004	0.270353316591447	0.78696403738624	   
df.mm.trans2:exp5	0.0666271203582381	0.118059374194367	0.564352647241266	0.572685631290806	   
df.mm.trans1:exp6	0.0477878708722677	0.144294790682004	0.331182232195634	0.740600854147921	   
df.mm.trans2:exp6	0.0862116341437485	0.118059374194367	0.730239633506901	0.465475456034426	   
df.mm.trans1:exp7	-0.147003084322309	0.144294790682004	-1.01876917127434	0.308646418615249	   
df.mm.trans2:exp7	0.0510867285071202	0.118059374194367	0.432720644639482	0.665344152927412	   
df.mm.trans1:exp8	-0.0261096922031928	0.144294790682004	-0.180946880201193	0.856458951047628	   
df.mm.trans2:exp8	-0.0128331125664606	0.118059374194367	-0.108700496288696	0.913469595428308	   
df.mm.trans1:probe2	-0.0579782104834802	0.084250006645461	-0.688168616145787	0.491562849343832	   
df.mm.trans1:probe3	-0.0608462755626192	0.084250006645461	-0.722210928939996	0.470393580389108	   
df.mm.trans1:probe4	-0.0321861544855499	0.084250006645461	-0.382031477113053	0.702547996598931	   
df.mm.trans1:probe5	0.0221478800805689	0.084250006645461	0.262882828885357	0.792714262317466	   
df.mm.trans1:probe6	-0.0903855940087702	0.084250006645461	-1.07282595702489	0.283699886696753	   
df.mm.trans1:probe7	-0.0184960951744337	0.084250006645461	-0.219538204338293	0.826291529306906	   
df.mm.trans1:probe8	-0.0264064830437735	0.084250006645461	-0.313430041078770	0.75404252607919	   
df.mm.trans1:probe9	0.00479426529548783	0.084250006645461	0.0569052215706398	0.954636097756732	   
df.mm.trans1:probe10	-0.0807386835956912	0.084250006645461	-0.958322578364343	0.338213958533961	   
df.mm.trans1:probe11	-0.0697327894507156	0.084250006645461	-0.82768883027112	0.408114305915230	   
df.mm.trans1:probe12	-0.0360663381608491	0.084250006645461	-0.428087066065438	0.668712631019527	   
df.mm.trans1:probe13	0.0232394420581036	0.084250006645461	0.275839053116035	0.782748906298616	   
df.mm.trans1:probe14	0.0301531816272983	0.084250006645461	0.357901237375426	0.720519562285257	   
df.mm.trans1:probe15	-0.0562673503945876	0.084250006645461	-0.667861673072273	0.504430692443419	   
df.mm.trans1:probe16	-0.0163803390413672	0.084250006645461	-0.194425373879181	0.845896330020123	   
df.mm.trans1:probe17	0.0879338342055305	0.084250006645461	1.04372495275367	0.296954421019889	   
df.mm.trans1:probe18	-0.0350484061511514	0.084250006645461	-0.416004787971606	0.677527441811272	   
df.mm.trans1:probe19	-0.0297357833870155	0.084250006645461	-0.352946955982436	0.724228990749973	   
df.mm.trans1:probe20	-0.173076487195393	0.084250006645461	-2.05432016075358	0.0402963792491648	*  
df.mm.trans1:probe21	-0.0132030656341651	0.084250006645461	-0.156712932851459	0.87551395462258	   
df.mm.trans1:probe22	-0.0829195587110409	0.084250006645461	-0.984208334368224	0.325335636527714	   
df.mm.trans2:probe2	0.0484624533514993	0.084250006645461	0.575221952865095	0.565316525311467	   
df.mm.trans2:probe3	0.0270340662866850	0.084250006645461	0.320879099754249	0.748392855393672	   
df.mm.trans2:probe4	0.00070652036010877	0.084250006645461	0.0083859976781003	0.993311286662769	   
df.mm.trans2:probe5	-0.0188823135679675	0.084250006645461	-0.224122398558704	0.822724095380737	   
df.mm.trans2:probe6	0.097353540018288	0.084250006645461	1.15553154111867	0.248246792719386	   
df.mm.trans3:probe2	0.0123695287280237	0.084250006645461	0.146819320502571	0.88331473153279	   
df.mm.trans3:probe3	0.0388207929878958	0.084250006645461	0.460780889326937	0.645091564862459	   
df.mm.trans3:probe4	0.0148378976215632	0.084250006645461	0.176117465295923	0.860249975006243	   
df.mm.trans3:probe5	-0.0255142370282511	0.084250006645461	-0.302839584756587	0.762097454326048