chr4.17034_chr4_123507156_123509530_-_1.R 

fitVsDatCorrelation=0.908073251422598
cont.fitVsDatCorrelation=0.260322853741203

fstatistic=7189.24991658432,49,623
cont.fstatistic=1342.36244976100,49,623

residuals=-0.715606363719439,-0.107137299265461,-0.00201721996553516,0.118521819651985,0.737066548852132
cont.residuals=-0.95648812995408,-0.370281512232756,-0.0369331864291986,0.336039027068394,1.41476703719743

predictedValues:
Include	Exclude	Both
chr4.17034_chr4_123507156_123509530_-_1.R.tl.Lung	126.639715524991	51.8819167910744	101.238793693751
chr4.17034_chr4_123507156_123509530_-_1.R.tl.cerebhem	141.489911025626	64.0994144502644	102.147634068267
chr4.17034_chr4_123507156_123509530_-_1.R.tl.cortex	165.113975663397	52.3635026852998	132.859299348209
chr4.17034_chr4_123507156_123509530_-_1.R.tl.heart	135.302011076761	54.4406431766721	124.261744200607
chr4.17034_chr4_123507156_123509530_-_1.R.tl.kidney	86.9610635763612	49.6881581088846	83.0813334306651
chr4.17034_chr4_123507156_123509530_-_1.R.tl.liver	84.3295690184992	54.933707714815	82.7138645386062
chr4.17034_chr4_123507156_123509530_-_1.R.tl.stomach	118.104376004984	53.9913492150732	110.304038913674
chr4.17034_chr4_123507156_123509530_-_1.R.tl.testicle	133.153660802671	54.6556095729143	126.463562196408


diffExp=74.7577987339168,77.390496575361,112.750472978098,80.8613679000886,37.2729054674766,29.3958613036842,64.1130267899107,78.4980512297562
diffExpScore=0.998201568170978
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	109.597036008894	95.113717284548	104.210980051843
cerebhem	90.1545328264294	82.9735175833104	114.922719241751
cortex	96.3855026284465	87.2299352079289	92.8346178961326
heart	99.6060090210292	90.8887492781932	91.5539497977244
kidney	91.1115927730479	106.564338167440	100.023477614577
liver	107.531515003226	104.228771715373	91.2344359715826
stomach	104.6882827454	102.414974816790	133.473381153176
testicle	116.354041186332	85.9267012817927	103.255442970391
cont.diffExp=14.4833187243463,7.18101524311903,9.1555674205176,8.71725974283596,-15.4527453943916,3.30274328785299,2.27330792861041,30.4273399045394
cont.diffExpScore=1.48954926240155

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=0,0,0,0,0,0,0,1
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,0,0,1
cont.diffExp1.2Score=0.5

tran.correlation=0.292685876300051
cont.tran.correlation=0.0192426143166858

tran.covariance=0.0055381685652526
cont.tran.covariance=0.000427755265859865

tran.mean=89.196786525518
cont.tran.mean=98.1730760955114

weightedLogRatios:
wLogRatio
Lung	3.92213312877526
cerebhem	3.60767248692452
cortex	5.2051530541037
heart	4.05337610845679
kidney	2.34266210718024
liver	1.80889910543633
stomach	3.42857650220611
testicle	3.95919676826751

cont.weightedLogRatios:
wLogRatio
Lung	0.655667712328751
cerebhem	0.370198609142728
cortex	0.450978696399551
heart	0.417214788231487
kidney	-0.719152478270166
liver	0.145440420184943
stomach	0.101867980992633
testicle	1.39599355950135

varWeightedLogRatios=1.11505101812154
cont.varWeightedLogRatios=0.349873925805994

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.3227293267436	0.0971677816612298	44.4872698834945	1.48745427480989e-195	***
df.mm.trans1	0.48017235778224	0.0829817013037198	5.78648485435084	1.13883121330444e-08	***
df.mm.trans2	-0.414877065660094	0.0758296369754958	-5.47117304272683	6.48265386272143e-08	***
df.mm.exp2	0.313410029322958	0.0994883793306548	3.15021745686823	0.00170964469679274	** 
df.mm.exp3	0.00272070908419964	0.0994883793306548	0.0273470037657083	0.978191724717371	   
df.mm.exp4	-0.0906042445544055	0.0994883793306548	-0.910701784107645	0.362804741909805	   
df.mm.exp5	-0.221427392519029	0.0994883793306548	-2.22566086621135	0.0263939946910552	*  
df.mm.exp6	-0.147361984013612	0.0994883793306548	-1.48119795502797	0.139059396830693	   
df.mm.exp7	-0.11568238754902	0.0994883793306548	-1.16277286178865	0.245366868161745	   
df.mm.exp8	-0.120233014644949	0.0994883793306548	-1.20851314951416	0.22730843681591	   
df.mm.trans1:exp2	-0.202527784195891	0.0891672717535422	-2.27132422258782	0.0234676522060725	*  
df.mm.trans2:exp2	-0.101945105773327	0.0731752826584434	-1.39316312926553	0.164067337401913	   
df.mm.trans1:exp3	0.262569118704758	0.0891672717535422	2.94468041402564	0.003353421895956	** 
df.mm.trans2:exp3	0.00651882053116392	0.0731752826584434	0.0890850064985944	0.929042982910962	   
df.mm.trans1:exp4	0.156767474212843	0.0891672717535422	1.75812796702076	0.0792167485521234	.  
df.mm.trans2:exp4	0.138744931287405	0.0731752826584434	1.89606279944305	0.0584141800412583	.  
df.mm.trans1:exp5	-0.154458303345872	0.0891672717535422	-1.73223089939091	0.083727394103197	.  
df.mm.trans2:exp5	0.178223724410551	0.0731752826584434	2.43557274991936	0.0151476041179749	*  
df.mm.trans1:exp6	-0.259251622309468	0.0891672717535422	-2.90747509945171	0.00377323120562455	** 
df.mm.trans2:exp6	0.204518822600527	0.0731752826584434	2.79491674197077	0.00535115520786009	** 
df.mm.trans1:exp7	0.0459049942107099	0.0891672717535422	0.514818871408234	0.606862191688455	   
df.mm.trans2:exp7	0.155535916165082	0.0731752826584434	2.12552532104412	0.0339355883917462	*  
df.mm.trans1:exp8	0.170390651108989	0.0891672717535422	1.91091022253039	0.0564747862712305	.  
df.mm.trans2:exp8	0.172314564038832	0.0731752826584434	2.35481924741085	0.0188409775979879	*  
df.mm.trans1:probe2	0.320345226988604	0.0583736817604721	5.48783659566128	5.9256696675312e-08	***
df.mm.trans1:probe3	0.225220067109696	0.0583736817604721	3.85824673581244	0.000126098125821189	***
df.mm.trans1:probe4	0.0895950757823421	0.0583736817604721	1.53485394582412	0.125327372725142	   
df.mm.trans1:probe5	0.551652540495968	0.0583736817604721	9.45036399724782	6.72088037570177e-20	***
df.mm.trans1:probe6	0.166208730373856	0.0583736817604721	2.84732306342899	0.00455414477699568	** 
df.mm.trans1:probe7	0.424110400039898	0.0583736817604721	7.26543858892048	1.11599384703744e-12	***
df.mm.trans1:probe8	0.386668158130621	0.0583736817604721	6.62401524915384	7.56350700295099e-11	***
df.mm.trans1:probe9	-0.302108056815452	0.0583736817604721	-5.17541549041069	3.07159968546685e-07	***
df.mm.trans1:probe10	-0.311767188780300	0.0583736817604721	-5.34088615584658	1.29812171066177e-07	***
df.mm.trans1:probe11	-0.0997250630311907	0.0583736817604721	-1.70839083682263	0.0880619550999662	.  
df.mm.trans1:probe12	-0.486320022276279	0.0583736817604721	-8.3311521152944	5.10728793798811e-16	***
df.mm.trans1:probe13	-0.0882905606578742	0.0583736817604721	-1.51250628699697	0.130912200470594	   
df.mm.trans1:probe14	-0.0298106436046424	0.0583736817604721	-0.510686369363611	0.609751592149208	   
df.mm.trans2:probe2	0.0347065717735391	0.0583736817604721	0.594558553218426	0.552354448251106	   
df.mm.trans2:probe3	0.1424903125061	0.0583736817604721	2.44100266093868	0.0149239248359053	*  
df.mm.trans2:probe4	0.122485657847332	0.0583736817604721	2.09830276510456	0.0362804978906305	*  
df.mm.trans2:probe5	0.0680845116471926	0.0583736817604721	1.16635630294089	0.243916705799914	   
df.mm.trans2:probe6	0.207885566572707	0.0583736817604721	3.56128927117763	0.000397192202121175	***
df.mm.trans3:probe2	0.460181020818952	0.0583736817604721	7.88336467634913	1.42985994138178e-14	***
df.mm.trans3:probe3	0.104443511349284	0.0583736817604721	1.78922261196155	0.0740646364783493	.  
df.mm.trans3:probe4	-0.0938362793466496	0.0583736817604721	-1.60751003734342	0.10844917934687	   
df.mm.trans3:probe5	0.377655049227603	0.0583736817604721	6.46961160985622	1.98894816332949e-10	***
df.mm.trans3:probe6	0.429682390663711	0.0583736817604721	7.3608924039921	5.80011634853495e-13	***
df.mm.trans3:probe7	0.332708137034499	0.0583736817604721	5.69962570460637	1.85395298104353e-08	***
df.mm.trans3:probe8	0.224825381673183	0.0583736817604721	3.85148537650446	0.000129547719974239	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.62758015582848	0.224009183198750	20.6579930775548	1.29836654500610e-72	***
df.mm.trans1	0.0852523843034453	0.191304800950353	0.445636407868143	0.656014723494119	   
df.mm.trans2	-0.0953434607207391	0.174816536414932	-0.545391544049579	0.585679458658742	   
df.mm.exp2	-0.429679206601792	0.229359055137508	-1.87339107385224	0.061482358321665	.  
df.mm.exp3	-0.0993822712718723	0.229359055137508	-0.433304328064525	0.664943676240463	   
df.mm.exp4	-0.0115357279635026	0.229359055137508	-0.0502954982814461	0.959903031696674	   
df.mm.exp5	-0.0300369998171176	0.229359055137508	-0.130960601486213	0.895848751493551	   
df.mm.exp6	0.205473733844472	0.229359055137508	0.895860569888045	0.370673244859188	   
df.mm.exp7	-0.219347958201383	0.229359055137508	-0.956351856567758	0.339265449609965	   
df.mm.exp8	-0.0325397342326774	0.229359055137508	-0.141872463736689	0.887226610462299	   
df.mm.trans1:exp2	0.234394105489859	0.205564924629145	1.14024367684674	0.254622958455644	   
df.mm.trans2:exp2	0.293127498438579	0.168697226780427	1.73759524108898	0.082776320933788	.  
df.mm.trans1:exp3	-0.0290722565264644	0.205564924629145	-0.141426153216134	0.887579012999966	   
df.mm.trans2:exp3	0.0128566371575847	0.168697226780427	0.0762113130307629	0.939275449201954	   
df.mm.trans1:exp4	-0.084052108163141	0.205564924629145	-0.4088835112059	0.682765666657025	   
df.mm.trans2:exp4	-0.0339012486164901	0.168697226780427	-0.200959134085916	0.84079611793684	   
df.mm.trans1:exp5	-0.154688281175083	0.205564924629145	-0.752503285539363	0.45203268010135	   
df.mm.trans2:exp5	0.143712717024960	0.168697226780427	0.851897329717302	0.394598425326339	   
df.mm.trans1:exp6	-0.224500096853626	0.205564924629145	-1.09211285562769	0.275205646561695	   
df.mm.trans2:exp6	-0.113958722316538	0.168697226780427	-0.675522203247975	0.499594702118592	   
df.mm.trans1:exp7	0.173524826730041	0.205564924629145	0.844136357615938	0.398917229375541	   
df.mm.trans2:exp7	0.293307698773860	0.168697226780427	1.73866342898229	0.0825879879564845	.  
df.mm.trans1:exp8	0.092367025959358	0.205564924629145	0.44933261900587	0.6533480031127	   
df.mm.trans2:exp8	-0.0690388434002446	0.168697226780427	-0.409247055911026	0.682499021337088	   
df.mm.trans1:probe2	-0.0484379765735986	0.134573832477278	-0.359936071388744	0.719016923151798	   
df.mm.trans1:probe3	-0.0622310236091768	0.134573832477278	-0.462430343727365	0.643934201886693	   
df.mm.trans1:probe4	0.0187741289881134	0.134573832477278	0.139508020560262	0.889093805052315	   
df.mm.trans1:probe5	-0.148231075902266	0.134573832477278	-1.10148513402331	0.271110937129431	   
df.mm.trans1:probe6	-0.0660378705187381	0.134573832477278	-0.490718509706469	0.623798347946513	   
df.mm.trans1:probe7	-0.0582400742482742	0.134573832477278	-0.432774137261102	0.665328638939764	   
df.mm.trans1:probe8	0.0869550374979783	0.134573832477278	0.646151156560544	0.518419407388248	   
df.mm.trans1:probe9	0.0509541508232827	0.134573832477278	0.378633422897321	0.705089092093402	   
df.mm.trans1:probe10	-0.0584962861708274	0.134573832477278	-0.434678013503881	0.663946678271028	   
df.mm.trans1:probe11	-0.0769003363258511	0.134573832477278	-0.571436028165691	0.567910202844677	   
df.mm.trans1:probe12	0.02968492309051	0.134573832477278	0.220584660063999	0.825488114139207	   
df.mm.trans1:probe13	-0.0339754227984818	0.134573832477278	-0.252466784760835	0.8007635566618	   
df.mm.trans1:probe14	0.0136932123877060	0.134573832477278	0.101752414534363	0.918985936868686	   
df.mm.trans2:probe2	0.141525047192954	0.134573832477278	1.05165353908494	0.293366270104775	   
df.mm.trans2:probe3	0.0574410964657707	0.134573832477278	0.426837041112500	0.669645486248885	   
df.mm.trans2:probe4	0.0123415731639299	0.134573832477278	0.091708565749687	0.926959071432585	   
df.mm.trans2:probe5	0.0691636465193863	0.134573832477278	0.513945729613251	0.607472171895392	   
df.mm.trans2:probe6	0.0392397021583868	0.134573832477278	0.291584934723562	0.770701055441309	   
df.mm.trans3:probe2	0.0551492152255841	0.134573832477278	0.40980638070849	0.68208885682583	   
df.mm.trans3:probe3	0.202273156261958	0.134573832477278	1.50306454485577	0.133329115386866	   
df.mm.trans3:probe4	0.138028688725067	0.134573832477278	1.02567257084227	0.305444019908012	   
df.mm.trans3:probe5	-0.00949104750141513	0.134573832477278	-0.0705266939842678	0.943797087225833	   
df.mm.trans3:probe6	-0.083157385224245	0.134573832477278	-0.617931314680254	0.536846430981612	   
df.mm.trans3:probe7	-0.0541911367617366	0.134573832477278	-0.402687028853743	0.68731661526144	   
df.mm.trans3:probe8	-0.0171841376507232	0.134573832477278	-0.127693009364391	0.898433127949473