chrX.25512_chrX_18057857_18060000_+_2.R 

fitVsDatCorrelation=0.8435675252832
cont.fitVsDatCorrelation=0.235640093962884

fstatistic=9735.88920760077,53,715
cont.fstatistic=2963.46987082661,53,715

residuals=-0.488217481804463,-0.0839800953463168,-0.0111881671128512,0.0751701798702064,0.987686698543676
cont.residuals=-0.609980233739966,-0.198006268538550,-0.0493910532712754,0.162016542476607,1.08844179476624

predictedValues:
Include	Exclude	Both
chrX.25512_chrX_18057857_18060000_+_2.R.tl.Lung	52.3892313296926	77.8017585151929	73.2608020670886
chrX.25512_chrX_18057857_18060000_+_2.R.tl.cerebhem	53.440922966767	78.94083287866	55.347322601363
chrX.25512_chrX_18057857_18060000_+_2.R.tl.cortex	51.4009483350435	71.2781027980251	58.3087297721335
chrX.25512_chrX_18057857_18060000_+_2.R.tl.heart	53.4247650002412	78.5629703557644	64.7270940373343
chrX.25512_chrX_18057857_18060000_+_2.R.tl.kidney	49.9998895323706	93.2235795266625	64.6150540198734
chrX.25512_chrX_18057857_18060000_+_2.R.tl.liver	50.358313977985	91.0724666698223	57.5208646527808
chrX.25512_chrX_18057857_18060000_+_2.R.tl.stomach	50.1641183139396	77.419377846485	59.2030027508213
chrX.25512_chrX_18057857_18060000_+_2.R.tl.testicle	49.2175025211747	85.0804019700783	61.962895005853


diffExp=-25.4125271855002,-25.499909911893,-19.8771544629816,-25.1382053555233,-43.2236899942919,-40.7141526918373,-27.2552595325453,-35.8628994489036
diffExpScore=0.995901367198126
diffExp1.5=0,0,0,0,-1,-1,-1,-1
diffExp1.5Score=0.8
diffExp1.4=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
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	56.8408914716693	58.0369452179742	59.8074597192233
cerebhem	56.3181366291516	55.1032020654387	59.5438301393741
cortex	57.6559454715303	63.4630794070525	54.1034222446257
heart	59.8788406182147	60.1822623031352	53.266478590222
kidney	57.3199306288606	54.2095327163069	58.4017937712982
liver	55.7786856198698	51.1620784814813	59.2409801751096
stomach	58.0592796748102	55.1661383692047	57.5585522122925
testicle	59.6420783978193	56.7534020132008	60.2180403483878
cont.diffExp=-1.19605374630497,1.21493456371292,-5.80713393552221,-0.303421684920529,3.11039791255369,4.6166071383885,2.89314130560543,2.88867638461852
cont.diffExpScore=2.61731964717227

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=-0.515240950766466
cont.tran.correlation=0.497629386263938

tran.covariance=-0.00145393432883034
cont.tran.covariance=0.000871527316299952

tran.mean=66.485948908619
cont.tran.mean=57.2231518178575

weightedLogRatios:
wLogRatio
Lung	-1.64371517118144
cerebhem	-1.62822714298808
cortex	-1.34144440718845
heart	-1.60848005985703
kidney	-2.63116226973358
liver	-2.49759562728533
stomach	-1.79314468399839
testicle	-2.2823965687247

cont.weightedLogRatios:
wLogRatio
Lung	-0.0843503275451383
cerebhem	0.0876738142982889
cortex	-0.393693840867703
heart	-0.0206972788349323
kidney	0.224324297657183
liver	0.343689075375550
stomach	0.206296280220529
testicle	0.201737359794350

varWeightedLogRatios=0.225640105610717
cont.varWeightedLogRatios=0.0545749098983459

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0218107045518	0.0797708709938496	50.4170338676869	1.30676843008966e-237	***
df.mm.trans1	-0.0794624580730863	0.0708380772710176	-1.12174781041943	0.262346258058309	   
df.mm.trans2	0.324899073589283	0.0644364430929917	5.04216337826723	5.8397434787453e-07	***
df.mm.exp2	0.31480774562555	0.0868699007314771	3.62389899118971	0.00031075799790021	***
df.mm.exp3	0.121654578393438	0.0868699007314772	1.40042267078771	0.161820582149127	   
df.mm.exp4	0.153155608252343	0.0868699007314772	1.76304573808322	0.0783199069041569	.  
df.mm.exp5	0.259734677308943	0.0868699007314771	2.98992717986183	0.00288621952368366	** 
df.mm.exp6	0.359832120689579	0.0868699007314772	4.14219560123423	3.85125221766154e-05	***
df.mm.exp7	0.164725453124214	0.0868699007314771	1.89623162611173	0.0583323018059552	.  
df.mm.exp8	0.194470889560579	0.0868699007314771	2.23864523756861	0.025485933198173	*  
df.mm.trans1:exp2	-0.294932006693395	0.0824870462677166	-3.57549482055855	0.000373088450090226	***
df.mm.trans2:exp2	-0.300273158617354	0.0694603090427024	-4.32294590616858	1.75804182164873e-05	***
df.mm.trans1:exp3	-0.140699017236199	0.0824870462677167	-1.70571045518531	0.0884963090610858	.  
df.mm.trans2:exp3	-0.209229445726371	0.0694603090427024	-3.01221587709525	0.00268497745124258	** 
df.mm.trans1:exp4	-0.133582266973846	0.0824870462677167	-1.61943326883468	0.105795033768897	   
df.mm.trans2:exp4	-0.143419168845309	0.0694603090427024	-2.06476433551625	0.0393053287203786	*  
df.mm.trans1:exp5	-0.306414942467626	0.0824870462677166	-3.71470377873804	0.000219302883016452	***
df.mm.trans2:exp5	-0.0788980223551297	0.0694603090427024	-1.13587203170411	0.256390709669518	   
df.mm.trans1:exp6	-0.399369452694382	0.0824870462677167	-4.84160205468147	1.57926960510643e-06	***
df.mm.trans2:exp6	-0.202340628001761	0.0694603090427024	-2.91303955871212	0.00369091050071961	** 
df.mm.trans1:exp7	-0.208126517854221	0.0824870462677167	-2.52314184191702	0.0118468758105403	*  
df.mm.trans2:exp7	-0.169652378041549	0.0694603090427024	-2.44243626870781	0.0148296301151414	*  
df.mm.trans1:exp8	-0.256922648255109	0.0824870462677166	-3.11470297313412	0.00191485123988181	** 
df.mm.trans2:exp8	-0.105038208578599	0.0694603090427024	-1.51220474003398	0.130923632426157	   
df.mm.trans1:probe2	0.171965449496256	0.0451800159428007	3.80622817207435	0.000153193488367951	***
df.mm.trans1:probe3	-0.0508537136857274	0.0451800159428007	-1.1255798083407	0.260721092183834	   
df.mm.trans1:probe4	0.197978495451073	0.0451800159428007	4.38199259827	1.35225253430098e-05	***
df.mm.trans1:probe5	-0.0904240893612358	0.0451800159428007	-2.00141782764564	0.0457248162406618	*  
df.mm.trans1:probe6	0.00500237199933662	0.0451800159428007	0.110720899383253	0.911868743663582	   
df.mm.trans1:probe7	-0.000691547782351395	0.0451800159428007	-0.0153064970855016	0.987791929134129	   
df.mm.trans1:probe8	0.108528111145760	0.0451800159428007	2.40212644641736	0.0165541383839901	*  
df.mm.trans1:probe9	0.179131102595484	0.0451800159428007	3.96483044234136	8.08327420529984e-05	***
df.mm.trans1:probe10	-0.113313983143126	0.0451800159428007	-2.50805540411018	0.0123601737600938	*  
df.mm.trans1:probe11	0.0818790801717468	0.0451800159428007	1.81228533153703	0.0703614548289422	.  
df.mm.trans1:probe12	0.125758225151457	0.0451800159428007	2.78349227035844	0.00551972852378132	** 
df.mm.trans1:probe13	0.146732835006108	0.0451800159428007	3.24773756591578	0.00121760067216281	** 
df.mm.trans1:probe14	0.283381204891077	0.0451800159428007	6.27226881127812	6.1586696020257e-10	***
df.mm.trans1:probe15	0.0302538120140376	0.0451800159428007	0.669628183671734	0.503311023317951	   
df.mm.trans1:probe16	0.221165263880752	0.0451800159428007	4.89520110308844	1.21471906331469e-06	***
df.mm.trans1:probe17	-0.123369985484718	0.0451800159428007	-2.73063173861886	0.00647701728333069	** 
df.mm.trans1:probe18	-0.121555461495605	0.0451800159428007	-2.69046964590491	0.00730174165736703	** 
df.mm.trans1:probe19	-0.158002792420579	0.0451800159428007	-3.49718319313157	0.000499266900119114	***
df.mm.trans1:probe20	-0.188796784753552	0.0451800159428007	-4.1787675549423	3.29385692860233e-05	***
df.mm.trans1:probe21	-0.156735288474043	0.0451800159428007	-3.46912866680867	0.000553452213240385	***
df.mm.trans1:probe22	-0.122859121621765	0.0451800159428007	-2.71932444152539	0.00670027441857435	** 
df.mm.trans2:probe2	-0.112978380020565	0.0451800159428007	-2.50062727210187	0.0126200861534342	*  
df.mm.trans2:probe3	-0.0112412938863984	0.0451800159428007	-0.248811197867443	0.803578279736812	   
df.mm.trans2:probe4	0.300259096456757	0.0451800159428007	6.6458386565621	5.98039242454295e-11	***
df.mm.trans2:probe5	-0.0402819693915622	0.0451800159428007	-0.891588206665539	0.372913630257532	   
df.mm.trans2:probe6	-0.0612148950495666	0.0451800159428007	-1.35491087756734	0.175873879738115	   
df.mm.trans3:probe2	0.316838512645550	0.0451800159428007	7.0128021434671	5.4202985702125e-12	***
df.mm.trans3:probe3	-0.186527392764822	0.0451800159428007	-4.12853755963636	4.0815655719112e-05	***
df.mm.trans3:probe4	-0.0231975780829227	0.0451800159428007	-0.513447762220615	0.607796759197289	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97641183948850	0.144359808253004	27.5451449237135	2.10235083678583e-114	***
df.mm.trans1	0.0834181982074632	0.128194303565321	0.650716887470416	0.515438308047285	   
df.mm.trans2	0.0782199879262568	0.116609389536778	0.670786359803268	0.502573256120932	   
df.mm.exp2	-0.0566936119122832	0.157206785588695	-0.360630819464832	0.718481962014733	   
df.mm.exp3	0.203848856789317	0.157206785588695	1.29669248070915	0.195155329946152	   
df.mm.exp4	0.204188262957708	0.157206785588695	1.29885145983413	0.194413620402414	   
df.mm.exp5	-0.0360468221660884	0.157206785588695	-0.229295586899149	0.818704756634977	   
df.mm.exp6	-0.135428497464702	0.157206785588695	-0.861467251286647	0.389269556260193	   
df.mm.exp7	0.00880576902290916	0.157206785588695	0.0560139245258025	0.955346362919535	   
df.mm.exp8	0.0188995756855249	0.157206785588695	0.120221119048719	0.904341761669391	   
df.mm.trans1:exp2	0.0474542508101628	0.149275218312238	0.317897715017258	0.750655346665424	   
df.mm.trans2:exp2	0.00482164581951909	0.125700982948676	0.0383580597893000	0.969412906935622	   
df.mm.trans1:exp3	-0.18961147190764	0.149275218312238	-1.27021399835458	0.204421600507677	   
df.mm.trans2:exp3	-0.114470340918707	0.125700982948676	-0.910655893323006	0.362783606447103	   
df.mm.trans1:exp4	-0.152121052331662	0.149275218312238	-1.01906434337595	0.308517107887295	   
df.mm.trans2:exp4	-0.167890394460370	0.125700982948676	-1.33563310741110	0.182094223145068	   
df.mm.trans1:exp5	0.0444392278974629	0.149275218312238	0.297699969224025	0.766018644809617	   
df.mm.trans2:exp5	-0.0321761987522009	0.125700982948676	-0.255974122058683	0.798044444300414	   
df.mm.trans1:exp6	0.116564328756521	0.149275218312238	0.780868586724856	0.4351382414277	   
df.mm.trans2:exp6	0.00934730615800858	0.125700982948676	0.0743614404497147	0.940743588748951	   
df.mm.trans1:exp7	0.0124027959464875	0.149275218312238	0.0830867714461795	0.933805799433298	   
df.mm.trans2:exp7	-0.0595362335822105	0.125700982948676	-0.473633794944302	0.635905652589019	   
df.mm.trans1:exp8	0.0292057756771216	0.149275218312238	0.195650530659631	0.844939294487501	   
df.mm.trans2:exp8	-0.0412637680250256	0.125700982948676	-0.328269254997582	0.742804198023471	   
df.mm.trans1:probe2	-0.0569542299939946	0.081761404346121	-0.69659065239254	0.48628540138391	   
df.mm.trans1:probe3	-0.0797991512906757	0.081761404346121	-0.976000252550233	0.32939442985575	   
df.mm.trans1:probe4	-0.0535107316812174	0.081761404346121	-0.654474224227977	0.513016790958659	   
df.mm.trans1:probe5	-0.126783395214105	0.081761404346121	-1.55065089974963	0.121427860250770	   
df.mm.trans1:probe6	-0.0108367660382919	0.0817614043461209	-0.132541339339238	0.894593417746028	   
df.mm.trans1:probe7	0.00624030161784499	0.0817614043461209	0.0763233174350565	0.939183226036067	   
df.mm.trans1:probe8	0.0422902279067074	0.081761404346121	0.517239500041854	0.605148950369147	   
df.mm.trans1:probe9	0.0463923243122836	0.0817614043461209	0.567411050278573	0.570613049165974	   
df.mm.trans1:probe10	0.0249532421047717	0.081761404346121	0.305195859884414	0.760305807811007	   
df.mm.trans1:probe11	0.0409718414234118	0.0817614043461209	0.501114697711961	0.616444639357183	   
df.mm.trans1:probe12	-0.0171855293212238	0.081761404346121	-0.210191219911930	0.833578313397757	   
df.mm.trans1:probe13	0.039411645279429	0.081761404346121	0.482032391623161	0.629930451760098	   
df.mm.trans1:probe14	0.0637311028982785	0.0817614043461209	0.779476617457368	0.435956929450267	   
df.mm.trans1:probe15	-0.120212129504007	0.081761404346121	-1.47027965658604	0.141925930237723	   
df.mm.trans1:probe16	-0.0659002292084177	0.081761404346121	-0.806006571626901	0.420507042097794	   
df.mm.trans1:probe17	-0.0201970452728775	0.081761404346121	-0.247024197228527	0.804960407563279	   
df.mm.trans1:probe18	-0.0237276861401122	0.081761404346121	-0.290206440677874	0.771742454368158	   
df.mm.trans1:probe19	-0.154712064620902	0.081761404346121	-1.89223834715411	0.0588629765506815	.  
df.mm.trans1:probe20	0.0841194926249395	0.081761404346121	1.02884109314997	0.303902263977975	   
df.mm.trans1:probe21	-0.0665402360070604	0.081761404346121	-0.813834308977562	0.416010818195723	   
df.mm.trans1:probe22	-0.0606763023680262	0.081761404346121	-0.742114239026093	0.458261832200451	   
df.mm.trans2:probe2	-0.0226155046746704	0.081761404346121	-0.276603672056954	0.782164383180637	   
df.mm.trans2:probe3	0.0342348450802426	0.081761404346121	0.418716451289364	0.675549116625988	   
df.mm.trans2:probe4	0.0186486022725573	0.081761404346121	0.228085640427752	0.819644874282755	   
df.mm.trans2:probe5	0.00946414711380737	0.081761404346121	0.115753235765652	0.90788061508141	   
df.mm.trans2:probe6	0.0247475787947104	0.081761404346121	0.302680451646188	0.762221431116466	   
df.mm.trans3:probe2	-0.0176689338422460	0.0817614043461209	-0.216103600268998	0.828968630051588	   
df.mm.trans3:probe3	-0.0681203549504573	0.081761404346121	-0.83316028504211	0.405032558619511	   
df.mm.trans3:probe4	-0.101889223115118	0.081761404346121	-1.24617750795705	0.213107379380398