chr9.24298_chr9_65851835_65852711_+_2.R 

fitVsDatCorrelation=0.761174901345313
cont.fitVsDatCorrelation=0.279143161863329

fstatistic=12446.4262699186,56,784
cont.fstatistic=5669.90979763162,56,784

residuals=-0.563826195701723,-0.090256848634477,0.000110368797799918,0.0781227231025539,0.844684371426882
cont.residuals=-0.657494624088856,-0.152225779892887,-0.0160228484170147,0.136740445220746,0.803938418633012

predictedValues:
Include	Exclude	Both
chr9.24298_chr9_65851835_65852711_+_2.R.tl.Lung	67.0204200539797	80.0547318160357	64.8448816953522
chr9.24298_chr9_65851835_65852711_+_2.R.tl.cerebhem	65.4820328550519	80.785335321195	66.9030447669891
chr9.24298_chr9_65851835_65852711_+_2.R.tl.cortex	60.2520768729843	67.5694409931975	62.5180977205837
chr9.24298_chr9_65851835_65852711_+_2.R.tl.heart	65.9952491600712	75.0900861590936	66.4345107681854
chr9.24298_chr9_65851835_65852711_+_2.R.tl.kidney	69.3096614257598	87.1663573514692	62.8257362932076
chr9.24298_chr9_65851835_65852711_+_2.R.tl.liver	66.5397759454305	83.8755947003604	66.3794705449417
chr9.24298_chr9_65851835_65852711_+_2.R.tl.stomach	65.6664079973374	75.5719803830872	67.4740082659455
chr9.24298_chr9_65851835_65852711_+_2.R.tl.testicle	65.6074577029592	79.7042137474826	73.623802800067


diffExp=-13.034311762056,-15.3033024661431,-7.31736412021318,-9.09483699902243,-17.8566959257094,-17.3358187549299,-9.90557238574975,-14.0967560445234
diffExpScore=0.990471168188165
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,-1,0,0,-1,-1,0,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	78.9376720929727	69.9479274617037	64.6625049007522
cerebhem	67.2045380575513	67.0404447924168	65.8277863224957
cortex	67.2835550648391	66.0011933460842	66.6073901294987
heart	69.3079880435889	69.8293690260588	66.1258616156529
kidney	68.3555295463816	68.4625216126543	65.0079623876642
liver	67.6091436922099	65.0290034069322	69.5287781593637
stomach	70.9123906885463	63.2598784848348	69.6873089617101
testicle	70.3175322861014	70.899042340693	61.8763808499559
cont.diffExp=8.98974463126903,0.164093265134511,1.28236171875494,-0.521380982469921,-0.106992066272625,2.58014028527765,7.65251220371144,-0.581510054591519
cont.diffExpScore=1.06939578463664

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=0.896240514217997
cont.tran.correlation=0.373507778616213

tran.covariance=0.00278794857127712
cont.tran.covariance=0.000779188498449137

tran.mean=72.2306764053435
cont.tran.mean=68.774858121473

weightedLogRatios:
wLogRatio
Lung	-0.763074519503058
cerebhem	-0.90030925140705
cortex	-0.476338055690927
heart	-0.54923352040956
kidney	-0.99790235377401
liver	-0.998740864098725
stomach	-0.597796380703551
testicle	-0.833225368814878

cont.weightedLogRatios:
wLogRatio
Lung	0.520894311217199
cerebhem	0.0102836124480051
cortex	0.0808071521855125
heart	-0.031794031627271
kidney	-0.00660871196271591
liver	0.163199166534874
stomach	0.480110855792923
testicle	-0.0350608015617027

varWeightedLogRatios=0.041383116316162
cont.varWeightedLogRatios=0.0518285573794986

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46885689008100	0.0701733975778713	63.6830628746729	0	***
df.mm.trans1	-0.293021732651802	0.0611624066437315	-4.79087970423795	1.98596211070663e-06	***
df.mm.trans2	-0.095545623673448	0.0545808476909836	-1.75053389083276	0.080417361141676	.  
df.mm.exp2	-0.0453831413185148	0.0714086979476104	-0.635540804172211	0.525261219007341	   
df.mm.exp3	-0.239473127100668	0.0714086979476104	-3.35355683528019	0.00083610000997385	***
df.mm.exp4	-0.103655279383215	0.0714086979476104	-1.45157778201280	0.147019065339473	   
df.mm.exp5	0.150328038194569	0.0714086979476104	2.10517825580377	0.0355933818693679	*  
df.mm.exp6	0.0160368428465978	0.0714086979476104	0.224578283983884	0.822365876593677	   
df.mm.exp7	-0.117779291397826	0.0714086979476104	-1.64936898141226	0.0994726482273282	.  
df.mm.exp8	-0.152666468538629	0.0714086979476104	-2.13792539181479	0.032831415982942	*  
df.mm.trans1:exp2	0.0221615888930768	0.0666802484005255	0.332356123809854	0.73970920544477	   
df.mm.trans2:exp2	0.0544680483720745	0.0520102350623704	1.04725633919471	0.295304116150984	   
df.mm.trans1:exp3	0.13301281991724	0.0666802484005255	1.99478590898878	0.0464120200276242	*  
df.mm.trans2:exp3	0.0699184031757047	0.0520102350623704	1.34432007645916	0.179233745656262	   
df.mm.trans1:exp4	0.0882406865146326	0.0666802484005254	1.32334069880185	0.186107886888748	   
df.mm.trans2:exp4	0.0396332724533945	0.0520102350623704	0.762028327806373	0.446272276004652	   
df.mm.trans1:exp5	-0.116741077083867	0.0666802484005255	-1.75075948101818	0.080378450628185	.  
df.mm.trans2:exp5	-0.0652201403650843	0.0520102350623704	-1.25398664872160	0.210220625314226	   
df.mm.trans1:exp6	-0.0232342893591738	0.0666802484005254	-0.348443353414243	0.727600793751236	   
df.mm.trans2:exp6	0.0305872942853996	0.0520102350623704	0.588101442893298	0.556633568468998	   
df.mm.trans1:exp7	0.097369442452122	0.0666802484005255	1.46024414707122	0.144623583265159	   
df.mm.trans2:exp7	0.0601543275777748	0.0520102350623704	1.15658634316184	0.247793698142450	   
df.mm.trans1:exp8	0.131358492642425	0.0666802484005254	1.96997605427922	0.0491927027565942	*  
df.mm.trans2:exp8	0.14827837459168	0.0520102350623704	2.85094605732632	0.00447342300439803	** 
df.mm.trans1:probe2	0.043744138886673	0.0423745583280525	1.03232082203707	0.302240189651131	   
df.mm.trans1:probe3	-0.137537100001124	0.0423745583280525	-3.2457471045798	0.00122115636081152	** 
df.mm.trans1:probe4	-0.0507173900174712	0.0423745583280525	-1.19688303592053	0.231713935793611	   
df.mm.trans1:probe5	-0.166149433710156	0.0423745583280525	-3.92097145706798	9.59115171296506e-05	***
df.mm.trans1:probe6	0.0847417188492656	0.0423745583280525	1.99982541866791	0.0458636985338473	*  
df.mm.trans1:probe7	-0.0837653032442297	0.0423745583280525	-1.97678292233139	0.048416190967878	*  
df.mm.trans1:probe8	0.124772377872313	0.0423745583280525	2.9445115842001	0.0033302789150291	** 
df.mm.trans1:probe9	-0.0179069852183123	0.0423745583280525	-0.422588126575415	0.67271166389446	   
df.mm.trans1:probe10	0.274465431756685	0.0423745583280525	6.47712784713523	1.6494183256778e-10	***
df.mm.trans1:probe11	-0.00108179215618072	0.0423745583280525	-0.0255292845250627	0.979639286851127	   
df.mm.trans1:probe12	0.0439310842850692	0.0423745583280525	1.03673255883794	0.300180164141555	   
df.mm.trans1:probe13	-0.0526232074012875	0.0423745583280525	-1.24185854620342	0.214660131739049	   
df.mm.trans1:probe14	-0.088707587850814	0.0423745583280525	-2.09341622310405	0.0366327666814223	*  
df.mm.trans1:probe15	-0.0493492054056556	0.0423745583280525	-1.16459515692428	0.244537150911911	   
df.mm.trans1:probe16	-0.0769933468755149	0.0423745583280525	-1.81697107682994	0.069603221249355	.  
df.mm.trans1:probe17	0.171881555236559	0.0423745583280525	4.05624417146482	5.48546507216957e-05	***
df.mm.trans1:probe18	0.0639036812363081	0.0423745583280525	1.50806719309221	0.131940177647922	   
df.mm.trans1:probe19	0.217957304990726	0.0423745583280525	5.14358883232148	3.40823071855205e-07	***
df.mm.trans1:probe20	0.0186801367222925	0.0423745583280525	0.440833779969479	0.659454866694504	   
df.mm.trans1:probe21	0.291910335263312	0.0423745583280525	6.88881127688506	1.15527368702651e-11	***
df.mm.trans1:probe22	0.234547167883463	0.0423745583280525	5.53509410216531	4.24384726768121e-08	***
df.mm.trans2:probe2	0.177369885579681	0.0423745583280525	4.18576364162974	3.16396380784583e-05	***
df.mm.trans2:probe3	0.0178838781417495	0.0423745583280525	0.422042821149834	0.673109466947908	   
df.mm.trans2:probe4	0.107648460432969	0.0423745583280525	2.54040312584696	0.0112643088393382	*  
df.mm.trans2:probe5	0.0866682085310412	0.0423745583280525	2.04528877587535	0.0411597231540309	*  
df.mm.trans2:probe6	-0.267379766107889	0.0423745583280525	-6.30991275561873	4.66572589233913e-10	***
df.mm.trans3:probe2	-0.0875886241979887	0.0423745583280525	-2.06700972597522	0.0390609659972795	*  
df.mm.trans3:probe3	0.0800908614531859	0.0423745583280525	1.89006952787906	0.0591172887907516	.  
df.mm.trans3:probe4	-0.171466108563722	0.0423745583280525	-4.04644001800037	5.71529958903895e-05	***
df.mm.trans3:probe5	0.243034582257006	0.0423745583280525	5.73538915439537	1.38887543146013e-08	***
df.mm.trans3:probe6	0.417677048508389	0.0423745583280525	9.85678824720355	1.09705767988548e-21	***
df.mm.trans3:probe7	0.160211380800846	0.0423745583280525	3.78083895436814	0.000168172970730306	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42810166434729	0.103899956450586	42.6188981749311	2.57281276128338e-206	***
df.mm.trans1	-0.0269023054450137	0.0905581260996355	-0.297072240821489	0.766490046479203	   
df.mm.trans2	-0.188257943075587	0.0808133551156079	-2.32953999751989	0.0200835756610127	*  
df.mm.exp2	-0.221233307904711	0.105728963724701	-2.09245697783214	0.0367186641969808	*  
df.mm.exp3	-0.2474549982418	0.105728963724701	-2.34046555952377	0.0195095790221046	*  
df.mm.exp4	-0.154173213026159	0.105728963724701	-1.45819279405404	0.145187875685520	   
df.mm.exp5	-0.170728975437416	0.105728963724701	-1.61477961594293	0.106760778787180	   
df.mm.exp6	-0.300392269769395	0.105728963724701	-2.84115401482193	0.00461164787660609	** 
df.mm.exp7	-0.282549883074217	0.105728963724701	-2.67239811230843	0.00768756317633211	** 
df.mm.exp8	-0.0580885919394325	0.105728963724701	-0.549410397047727	0.582880294399433	   
df.mm.trans1:exp2	0.0603155035468785	0.0987279388494873	0.610926392769433	0.54142538523241	   
df.mm.trans2:exp2	0.178778326969149	0.0770072612198713	2.32157752576995	0.0205111512725082	*  
df.mm.trans1:exp3	0.0877122721313512	0.0987279388494874	0.88842401809958	0.374585197770220	   
df.mm.trans2:exp3	0.189376749286908	0.0770072612198713	2.45920639543587	0.0141394282279567	*  
df.mm.trans1:exp4	0.0240747999383057	0.0987279388494873	0.243849919474245	0.807410852039353	   
df.mm.trans2:exp4	0.152476822166476	0.0770072612198713	1.98003174961805	0.0480492240677017	*  
df.mm.trans1:exp5	0.0267928555690718	0.0987279388494873	0.271380683941129	0.78616969637948	   
df.mm.trans2:exp5	0.149264369414095	0.0770072612198713	1.93831551790831	0.0529433952497359	.  
df.mm.trans1:exp6	0.145476925165185	0.0987279388494873	1.47351324114005	0.141014071410104	   
df.mm.trans2:exp6	0.227474574585985	0.0770072612198713	2.95393669353464	0.00323133831982505	** 
df.mm.trans1:exp7	0.175336484002872	0.0987279388494873	1.77595608746756	0.0761279566117157	.  
df.mm.trans2:exp7	0.182050108246057	0.0770072612198713	2.3640641851457	0.0183185615270463	*  
df.mm.trans1:exp8	-0.0575488281568086	0.0987279388494873	-0.58290316629159	0.560126106093653	   
df.mm.trans2:exp8	0.071594446383702	0.0770072612198713	0.929710331851505	0.352807248649688	   
df.mm.trans1:probe2	-0.0754838791807134	0.0627405101771192	-1.20311229487327	0.229296049916296	   
df.mm.trans1:probe3	-0.0524544116958611	0.0627405101771192	-0.836053317828944	0.403379641005523	   
df.mm.trans1:probe4	-0.0811797091973138	0.0627405101771192	-1.29389622379767	0.196082304313555	   
df.mm.trans1:probe5	-0.0122523877539900	0.0627405101771192	-0.195286709008278	0.84521910006133	   
df.mm.trans1:probe6	-0.0666749127719194	0.0627405101771192	-1.06270912658653	0.288241105763118	   
df.mm.trans1:probe7	-0.0866875952095664	0.0627405101771192	-1.38168457611906	0.167462160949177	   
df.mm.trans1:probe8	-0.00531572523944075	0.0627405101771192	-0.084725566056671	0.932501200264826	   
df.mm.trans1:probe9	-0.101870145635337	0.0627405101771192	-1.62367416757934	0.104847336205104	   
df.mm.trans1:probe10	-0.00448061656400059	0.0627405101771192	-0.0714150482894005	0.943085646575069	   
df.mm.trans1:probe11	0.0360916771875609	0.0627405101771192	0.575253167143086	0.565285143219214	   
df.mm.trans1:probe12	-0.0491499886532318	0.0627405101771192	-0.78338522454598	0.433637513288356	   
df.mm.trans1:probe13	-0.0494031583933245	0.0627405101771192	-0.787420412327813	0.4312737816321	   
df.mm.trans1:probe14	-0.0205554213718403	0.0627405101771192	-0.327625983815106	0.743281939083742	   
df.mm.trans1:probe15	0.0214248384050865	0.0627405101771192	0.341483331018559	0.732831254948148	   
df.mm.trans1:probe16	-0.049059383677925	0.0627405101771192	-0.781941102159166	0.434485271165629	   
df.mm.trans1:probe17	-0.0995714052758693	0.0627405101771192	-1.58703531410208	0.112907852265915	   
df.mm.trans1:probe18	-0.0240756297859126	0.0627405101771192	-0.383733407936053	0.701280088455812	   
df.mm.trans1:probe19	0.00203158008743688	0.0627405101771192	0.0323806752878108	0.974176714824938	   
df.mm.trans1:probe20	-0.0688578176520312	0.0627405101771192	-1.09750171711455	0.272759091285033	   
df.mm.trans1:probe21	-0.0701329294727034	0.0627405101771192	-1.11782529779747	0.263984028063763	   
df.mm.trans1:probe22	-0.0860255597852096	0.0627405101771192	-1.37113261499398	0.170725957915146	   
df.mm.trans2:probe2	-0.0367220183212484	0.0627405101771192	-0.585299963573464	0.558514461343302	   
df.mm.trans2:probe3	0.0306450240180864	0.0627405101771192	0.488440784615461	0.625374245729073	   
df.mm.trans2:probe4	0.0274841269255676	0.0627405101771192	0.438060303430411	0.661463235809905	   
df.mm.trans2:probe5	0.00974001084177824	0.0627405101771192	0.155242773995330	0.876669887202858	   
df.mm.trans2:probe6	0.0716484136524139	0.0627405101771192	1.14198009308734	0.253810947556245	   
df.mm.trans3:probe2	-0.0457700287911928	0.0627405101771192	-0.729513175171544	0.465905701714421	   
df.mm.trans3:probe3	-0.0159111873657710	0.0627405101771192	-0.253603091859678	0.799868696792154	   
df.mm.trans3:probe4	-0.0235427000587431	0.0627405101771192	-0.375239219322269	0.707584056472843	   
df.mm.trans3:probe5	-0.101902039806273	0.0627405101771192	-1.62418251809873	0.104738806597535	   
df.mm.trans3:probe6	-0.0569557544528347	0.0627405101771192	-0.907798713973573	0.364263525215215	   
df.mm.trans3:probe7	-0.0622376224766049	0.0627405101771192	-0.991984641197615	0.321511073067023