chr19.12165_chr19_11154538_11174104_-_2.R 

fitVsDatCorrelation=0.711443670515447
cont.fitVsDatCorrelation=0.270480632266179

fstatistic=13845.3656461404,55,761
cont.fstatistic=7370.7566392022,55,761

residuals=-0.351079402232428,-0.0760223884926961,-0.00020266199689498,0.0698697875979036,0.661434958895729
cont.residuals=-0.464879386977683,-0.109507669084364,-0.0223822604939802,0.0748245758609504,0.882612633134554

predictedValues:
Include	Exclude	Both
chr19.12165_chr19_11154538_11174104_-_2.R.tl.Lung	44.2730965604619	43.0044590610165	56.480157777504
chr19.12165_chr19_11154538_11174104_-_2.R.tl.cerebhem	51.4220257573048	44.3772393934788	63.3150235764054
chr19.12165_chr19_11154538_11174104_-_2.R.tl.cortex	45.1237034517725	44.8499937618916	51.8141905698993
chr19.12165_chr19_11154538_11174104_-_2.R.tl.heart	46.4148812748161	45.1919403583041	51.5449680929914
chr19.12165_chr19_11154538_11174104_-_2.R.tl.kidney	43.5206022834212	43.5625792345593	58.1395969523406
chr19.12165_chr19_11154538_11174104_-_2.R.tl.liver	48.7703089665453	49.5786210040143	55.8353980277662
chr19.12165_chr19_11154538_11174104_-_2.R.tl.stomach	45.8258990246973	44.4074153279524	54.2493614232536
chr19.12165_chr19_11154538_11174104_-_2.R.tl.testicle	46.3671834576483	44.7610523654109	56.8806020306248


diffExp=1.26863749944546,7.04478636382603,0.273709689880889,1.22294091651192,-0.0419769511381318,-0.808312037468937,1.41848369674489,1.60613109223742
diffExpScore=1.05395535894181
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	49.5458095258142	49.1427355288244	48.4682269418212
cerebhem	50.0020452888541	46.6958405621402	50.3762129093515
cortex	49.7662787519836	51.3585374152185	51.1164295060572
heart	50.9111965658604	47.801985186769	52.5368308278367
kidney	48.1390223086651	48.5187690900022	47.6702834544978
liver	48.5635020398156	47.5604451542715	47.9089149436356
stomach	49.7672783917767	46.2014926360901	49.4815207995333
testicle	50.1046742911474	52.0705181433138	46.8761253178268
cont.diffExp=0.403073996989747,3.30620472671388,-1.59225866323494,3.10921137909146,-0.379746781337069,1.00305688554405,3.56578575568659,-1.96584385216639
cont.diffExpScore=1.81374188568701

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=0.467754054069844
cont.tran.correlation=0.0899068765119408

tran.covariance=0.00112404340881463
cont.tran.covariance=6.4736040058239e-05

tran.mean=45.7156875802059
cont.tran.mean=49.1343831800342

weightedLogRatios:
wLogRatio
Lung	0.109776453392037
cerebhem	0.569674440863709
cortex	0.0231588205214076
heart	0.102113212949724
kidney	-0.00363811063327285
liver	-0.0640316409098161
stomach	0.119770199549031
testicle	0.134632123608948

cont.weightedLogRatios:
wLogRatio
Lung	0.0318480533316333
cerebhem	0.265279780447464
cortex	-0.123551896653271
heart	0.245671439576357
kidney	-0.0304719132799163
liver	0.0808209723340263
stomach	0.287731226581947
testicle	-0.151373715053031

varWeightedLogRatios=0.0373220389881065
cont.varWeightedLogRatios=0.0306104988505510

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.57239234286681	0.0609985182990156	58.5652314594741	3.83676919226974e-284	***
df.mm.trans1	0.140766431236452	0.0530923954113169	2.65134827965299	0.00818388700741092	** 
df.mm.trans2	0.166966408728169	0.047487282091201	3.5160236883531	0.000464003297719654	***
df.mm.exp2	0.0668790546419469	0.0621754470658998	1.07565056301182	0.282424465769432	   
df.mm.exp3	0.147275458142767	0.0621754470658998	2.3687076666561	0.0180991801115056	*  
df.mm.exp4	0.188292677520321	0.0621754470658998	3.02840890425363	0.00254152706962581	** 
df.mm.exp5	-0.0332056278368647	0.0621754470658998	-0.534063354649787	0.593453671824756	   
df.mm.exp6	0.25048176186046	0.0621754470658998	4.02862823961642	6.1734936655485e-05	***
df.mm.exp7	0.106873044048079	0.0621754470658998	1.7188946616629	0.0860402360968324	.  
df.mm.exp8	0.0791843665701398	0.0621754470658998	1.27356328433332	0.203207084673447	   
df.mm.trans1:exp2	0.0828103512603717	0.057882191177125	1.43067063592952	0.152935110070399	   
df.mm.trans2:exp2	-0.0354561523251272	0.045406526507949	-0.780860265074894	0.435127515016057	   
df.mm.trans1:exp3	-0.128244965724027	0.0578821911771249	-2.21562043723580	0.0270127433513541	*  
df.mm.trans2:exp3	-0.105255818136819	0.0454065265079489	-2.31807685440095	0.0207094041755743	*  
df.mm.trans1:exp4	-0.141049744028031	0.057882191177125	-2.43684181886628	0.0150443485474866	*  
df.mm.trans2:exp4	-0.138677726648948	0.045406526507949	-3.05413642738497	0.00233579215982728	** 
df.mm.trans1:exp5	0.0160628780709833	0.057882191177125	0.27750984792247	0.781464014608457	   
df.mm.trans2:exp5	0.0461003258161657	0.045406526507949	1.01527972654097	0.310295183159184	   
df.mm.trans1:exp6	-0.153737248433154	0.057882191177125	-2.65603712137821	0.0080720133739103	** 
df.mm.trans2:exp6	-0.108225858604374	0.045406526507949	-2.38348684490163	0.0173938247247728	*  
df.mm.trans1:exp7	-0.0724008233142534	0.057882191177125	-1.25083072775701	0.211380668071458	   
df.mm.trans2:exp7	-0.074770386074604	0.0454065265079489	-1.64668808263752	0.100035032674637	   
df.mm.trans1:exp8	-0.0329696020781232	0.057882191177125	-0.569598375728955	0.569118223898928	   
df.mm.trans2:exp8	-0.0391497815218163	0.045406526507949	-0.862206042449925	0.388845756974129	   
df.mm.trans1:probe2	0.102199645316989	0.036783490539175	2.77841074403052	0.00559713947160922	** 
df.mm.trans1:probe3	0.0212367905815418	0.036783490539175	0.577345713260254	0.563876700967104	   
df.mm.trans1:probe4	-0.0190211237369142	0.036783490539175	-0.517110351902477	0.60522938572629	   
df.mm.trans1:probe5	0.119725405587958	0.036783490539175	3.25486798107015	0.00118460845719163	** 
df.mm.trans1:probe6	0.207814338729368	0.036783490539175	5.64966335938245	2.27199166167959e-08	***
df.mm.trans1:probe7	0.0925258257248808	0.036783490539175	2.51541722573444	0.0120940525305163	*  
df.mm.trans1:probe8	0.077074254081053	0.036783490539175	2.09534910774625	0.0364698913263196	*  
df.mm.trans1:probe9	0.312615897736832	0.0367834905391750	8.49881001379928	1.00505420455623e-16	***
df.mm.trans1:probe10	0.0184072345589498	0.036783490539175	0.500421093516011	0.616923287874123	   
df.mm.trans1:probe11	0.07827351651476	0.036783490539175	2.12795238753640	0.0336618250976546	*  
df.mm.trans1:probe12	0.0644805294694227	0.036783490539175	1.75297473198608	0.0800089830268298	.  
df.mm.trans1:probe13	0.0122318833729075	0.0367834905391750	0.332537320238283	0.739575142969892	   
df.mm.trans1:probe14	0.049887879743064	0.036783490539175	1.35625735926101	0.175419548465117	   
df.mm.trans1:probe15	0.136059540047853	0.036783490539175	3.69892954837844	0.000232087882665487	***
df.mm.trans1:probe16	0.152048560546954	0.036783490539175	4.13360880977351	3.96798139635917e-05	***
df.mm.trans1:probe17	0.308355534815538	0.036783490539175	8.38298732109544	2.48724786399154e-16	***
df.mm.trans1:probe18	0.115533033369427	0.036783490539175	3.14089369105367	0.00174950317203194	** 
df.mm.trans1:probe19	0.0636572820464174	0.036783490539175	1.73059383743425	0.0839296045445567	.  
df.mm.trans1:probe20	0.0427556339632045	0.036783490539175	1.16235934481718	0.245453920417587	   
df.mm.trans1:probe21	0.206254023261655	0.036783490539175	5.6072444522901	2.87683181471644e-08	***
df.mm.trans2:probe2	-0.00896591066441658	0.036783490539175	-0.243748228702432	0.807491512610542	   
df.mm.trans2:probe3	0.0728103111373571	0.036783490539175	1.97942908816152	0.0481276854546208	*  
df.mm.trans2:probe4	0.077815738379628	0.036783490539175	2.11550718104778	0.0347110501275380	*  
df.mm.trans2:probe5	0.0739487473554816	0.036783490539175	2.010378739797	0.0447434900491771	*  
df.mm.trans2:probe6	0.0696768653948606	0.036783490539175	1.89424288922917	0.0585717022623027	.  
df.mm.trans3:probe2	0.0979963023026763	0.0367834905391750	2.66413820075908	0.0078819541099478	** 
df.mm.trans3:probe3	0.101374423995300	0.036783490539175	2.75597618685302	0.00599172022606427	** 
df.mm.trans3:probe4	0.381692770292459	0.036783490539175	10.3767414320278	1.10117037479780e-23	***
df.mm.trans3:probe5	0.0724789878177214	0.036783490539175	1.97042169612832	0.0491521752928645	*  
df.mm.trans3:probe6	0.173245790781576	0.036783490539175	4.70987903111223	2.94591134704474e-06	***
df.mm.trans3:probe7	0.249561151835175	0.036783490539175	6.78459678994814	2.34024689269302e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94878052241649	0.0835651344156913	47.2539241398625	1.51819257701440e-228	***
df.mm.trans1	-0.00805831649293168	0.0727341135935318	-0.110791430524126	0.91181095304765	   
df.mm.trans2	-0.0505248943313438	0.0650553689113314	-0.776644498015342	0.437609866334997	   
df.mm.exp2	-0.0805183452278966	0.0851774721141295	-0.945300949058571	0.344805290307609	   
df.mm.exp3	-0.00465541078153379	0.0851774721141294	-0.0546554231533897	0.956427328522458	   
df.mm.exp4	-0.0810826738127996	0.0851774721141295	-0.95192627581336	0.341436611334181	   
df.mm.exp5	-0.0249826294691690	0.0851774721141294	-0.293300902798509	0.769372127240748	   
df.mm.exp6	-0.0411461607996163	0.0851774721141295	-0.48306388741479	0.629189314631029	   
df.mm.exp7	-0.0779477208520969	0.0851774721141295	-0.91512132160549	0.360417814490386	   
df.mm.exp8	0.102486478674045	0.0851774721141295	1.20321108539971	0.229268823202209	   
df.mm.trans1:exp2	0.0896845690160962	0.0792959111282093	1.1310112682997	0.258406650746592	   
df.mm.trans2:exp2	0.0294444051333034	0.0622048304909013	0.473345958841738	0.636102127023514	   
df.mm.trans1:exp3	0.0090953453789978	0.0792959111282092	0.114701316241793	0.908712126592972	   
df.mm.trans2:exp3	0.0487575590288709	0.0622048304909013	0.783822713511014	0.433388031897731	   
df.mm.trans1:exp4	0.108267858422982	0.0792959111282093	1.36536495870423	0.172541828657344	   
df.mm.trans2:exp4	0.053420809940902	0.0622048304909013	0.858788771214736	0.390727480632067	   
df.mm.trans1:exp5	-0.00382193429647171	0.0792959111282093	-0.0481983779755331	0.961570789060798	   
df.mm.trans2:exp5	0.0122043104862600	0.0622048304909013	0.196195542853303	0.844509495403019	   
df.mm.trans1:exp6	0.0211207361587099	0.0792959111282093	0.266353407864384	0.790039195160206	   
df.mm.trans2:exp6	0.0084185588134363	0.0622048304909013	0.135336094431890	0.892381936728917	   
df.mm.trans1:exp7	0.0824077419374114	0.0792959111282093	1.03924326948171	0.299021684859498	   
df.mm.trans2:exp7	0.0162307929475035	0.0622048304909013	0.260924960640759	0.794220967792535	   
df.mm.trans1:exp8	-0.091269862291238	0.0792959111282093	-1.15100338709355	0.250092358396752	   
df.mm.trans2:exp8	-0.0446165942605601	0.0622048304909013	-0.717252887090275	0.473438162021432	   
df.mm.trans1:probe2	-0.0294220484232969	0.0503916720749933	-0.583867278297706	0.559482624952412	   
df.mm.trans1:probe3	-0.0952491701582961	0.0503916720749933	-1.89017681367162	0.0591140664782558	.  
df.mm.trans1:probe4	0.0134194259608536	0.0503916720749933	0.266302454518333	0.790078418827829	   
df.mm.trans1:probe5	-0.0352436366520487	0.0503916720749933	-0.699394070504325	0.484519505059351	   
df.mm.trans1:probe6	0.0108070614682465	0.0503916720749933	0.214461259633602	0.8302448273608	   
df.mm.trans1:probe7	-0.0490987699068441	0.0503916720749933	-0.974342939717795	0.330195895970003	   
df.mm.trans1:probe8	-0.0228446104904074	0.0503916720749933	-0.453340989685953	0.650432456768593	   
df.mm.trans1:probe9	0.00407374426057321	0.0503916720749933	0.0808416171328991	0.935589166039587	   
df.mm.trans1:probe10	-0.08274250146799	0.0503916720749933	-1.64198761543082	0.101005713858059	   
df.mm.trans1:probe11	-0.0890387521968244	0.0503916720749933	-1.76693387082525	0.0776400864745733	.  
df.mm.trans1:probe12	-0.0539246998427867	0.0503916720749933	-1.07011134225781	0.284908481528091	   
df.mm.trans1:probe13	-0.137113845922558	0.0503916720749933	-2.72096241852233	0.00665789240258446	** 
df.mm.trans1:probe14	-0.0467331966360218	0.0503916720749933	-0.927399205298707	0.354013334982064	   
df.mm.trans1:probe15	-0.0420049559759644	0.0503916720749933	-0.833569402369746	0.404785226272515	   
df.mm.trans1:probe16	-0.0551781428365285	0.0503916720749933	-1.09498535302444	0.273869486464197	   
df.mm.trans1:probe17	-0.046005087215103	0.0503916720749933	-0.912950202300053	0.361557865229178	   
df.mm.trans1:probe18	-0.0552711549335148	0.0503916720749933	-1.09683113613019	0.273062169372011	   
df.mm.trans1:probe19	-0.0632812197931664	0.0503916720749933	-1.25578725982720	0.209578529991729	   
df.mm.trans1:probe20	-0.0776152469342217	0.0503916720749933	-1.54023956217833	0.123917658005269	   
df.mm.trans1:probe21	-0.10661973391215	0.0503916720749933	-2.11582052196001	0.0346842935871278	*  
df.mm.trans2:probe2	0.0137273400452755	0.0503916720749933	0.272412870619700	0.785378473636979	   
df.mm.trans2:probe3	-0.0367308222291379	0.0503916720749933	-0.728906597393213	0.466283055855806	   
df.mm.trans2:probe4	-0.0399506873633163	0.0503916720749933	-0.792803368458609	0.428139483113634	   
df.mm.trans2:probe5	-0.0019787326556303	0.0503916720749933	-0.0392670569193563	0.968687769917884	   
df.mm.trans2:probe6	0.0190871740372536	0.0503916720749933	0.37877635830079	0.704959594729895	   
df.mm.trans3:probe2	0.0764088284550133	0.0503916720749933	1.51629873168926	0.129859090791836	   
df.mm.trans3:probe3	-0.0340745425130608	0.0503916720749933	-0.676193924709441	0.499122978119783	   
df.mm.trans3:probe4	-0.0370887298552141	0.0503916720749933	-0.736009112775983	0.461951949369410	   
df.mm.trans3:probe5	0.0148933051639069	0.0503916720749933	0.295550922417152	0.76765368005848	   
df.mm.trans3:probe6	-0.0912693542396237	0.0503916720749933	-1.81119916211147	0.0705041879910778	.  
df.mm.trans3:probe7	0.00610859246683337	0.0503916720749933	0.121222261840062	0.903546991755572