chr7.21267_chr7_14548455_14554297_+_2.R 

fitVsDatCorrelation=0.90293102696791
cont.fitVsDatCorrelation=0.230797681291805

fstatistic=7987.21219560616,59,853
cont.fstatistic=1546.73630606639,59,853

residuals=-0.832195858816507,-0.126085507230608,-0.00435521509943949,0.108130490615991,0.739529528920961
cont.residuals=-1.11691328530427,-0.317499272260733,-0.0207337164860296,0.307687854236146,1.61652482407929

predictedValues:
Include	Exclude	Both
chr7.21267_chr7_14548455_14554297_+_2.R.tl.Lung	150.625073459016	217.019554531781	100.461813997952
chr7.21267_chr7_14548455_14554297_+_2.R.tl.cerebhem	103.245133736381	138.311642884243	73.548108819544
chr7.21267_chr7_14548455_14554297_+_2.R.tl.cortex	116.120609294549	154.367859807044	84.1493907991726
chr7.21267_chr7_14548455_14554297_+_2.R.tl.heart	128.307664123579	159.317374817746	93.4850108838756
chr7.21267_chr7_14548455_14554297_+_2.R.tl.kidney	145.570400763405	212.446405663761	101.366066319726
chr7.21267_chr7_14548455_14554297_+_2.R.tl.liver	132.044158747647	207.372307769617	97.5450436314198
chr7.21267_chr7_14548455_14554297_+_2.R.tl.stomach	136.845247243255	162.803327821932	110.680395808126
chr7.21267_chr7_14548455_14554297_+_2.R.tl.testicle	138.908734278862	170.611437275955	181.498839962465


diffExp=-66.3944810727653,-35.0665091478612,-38.2472505124946,-31.0097106941667,-66.8760049003569,-75.3281490219695,-25.9580805786771,-31.7027029970933
diffExpScore=0.997308810416723
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,0,0,0,-1,-1,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,-1,-1,0,-1,-1,0,0
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,-1,-1,-1,-1,-1,0,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	122.048334845887	122.451026872761	132.385415367451
cerebhem	126.161004287502	97.8797610486964	117.734939435064
cortex	118.114206968983	95.3709670653656	128.193990186756
heart	135.916223861413	97.5331572424712	116.908301333167
kidney	114.213180872218	137.258422943109	132.276250259374
liver	138.428206064410	120.412294169765	113.000235790870
stomach	120.254443819037	122.267366312248	125.399579862810
testicle	119.959936387350	121.077370669232	114.129063343434
cont.diffExp=-0.402692026873908,28.2812432388054,22.7432399036177,38.3830666189418,-23.0452420708917,18.0159118946443,-2.01292249321111,-1.1174342818815
cont.diffExpScore=1.63725912288589

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,1,1,-1,0,0,0
cont.diffExp1.2Score=1.33333333333333

tran.correlation=0.810622883160114
cont.tran.correlation=-0.360665633341423

tran.covariance=0.0171954567708621
cont.tran.covariance=-0.00324887389976827

tran.mean=154.619808263673
cont.tran.mean=119.334118964403

weightedLogRatios:
wLogRatio
Lung	-1.89805416065357
cerebhem	-1.39865507445791
cortex	-1.39421572692183
heart	-1.07425449188465
kidney	-1.95429176780549
liver	-2.30601866561725
stomach	-0.86945043625491
testicle	-1.03538223872344

cont.weightedLogRatios:
wLogRatio
Lung	-0.0158312517042600
cerebhem	1.19565280554853
cortex	0.997678616247632
heart	1.57498088495277
kidney	-0.887741670337868
liver	0.677719477447252
stomach	-0.0796467977423843
testicle	-0.0444292469475851

varWeightedLogRatios=0.261438586677802
cont.varWeightedLogRatios=0.671543223875696

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.6491825847721	0.0973848177363153	58.008863353507	2.66586284792213e-298	***
df.mm.trans1	-0.615205231175799	0.0808670815963353	-7.60761015522635	7.38027837956462e-14	***
df.mm.trans2	-0.279118483073309	0.073856185984324	-3.77921604471361	0.000168265729448088	***
df.mm.exp2	-0.516327772566194	0.0944771505549064	-5.46510737817102	6.07528239344492e-08	***
df.mm.exp3	-0.423629400236223	0.0944771505549064	-4.48393498055413	8.32926468456911e-06	***
df.mm.exp4	-0.397475384877789	0.0944771505549064	-4.20710597793477	2.86047257910434e-05	***
df.mm.exp5	-0.0643923713132894	0.0944771505549064	-0.681565552465166	0.495698702835792	   
df.mm.exp6	-0.147665648067511	0.0944771505549064	-1.56297736754554	0.118428818848731	   
df.mm.exp7	-0.480256690724555	0.0944771505549064	-5.08331049257723	4.56000861801489e-07	***
df.mm.exp8	-0.913047017838388	0.0944771505549064	-9.66420994362823	4.88845835673043e-21	***
df.mm.trans1:exp2	0.138640080474625	0.0802901390446852	1.72673857741691	0.0845766690484723	.  
df.mm.trans2:exp2	0.0658497308772569	0.0629847670366043	1.04548661486654	0.296094296748163	   
df.mm.trans1:exp3	0.163464994550545	0.0802901390446852	2.03592865195524	0.0420660441924409	*  
df.mm.trans2:exp3	0.0829803917565255	0.0629847670366043	1.317467630043	0.188035680043926	   
df.mm.trans1:exp4	0.237112598735053	0.0802901390446852	2.95319701218962	0.00323134839928783	** 
df.mm.trans2:exp4	0.0883862031071569	0.0629847670366042	1.40329491186004	0.160892820159551	   
df.mm.trans1:exp5	0.0302584030097184	0.0802901390446852	0.376863253317798	0.706368961019738	   
df.mm.trans2:exp5	0.0430946566964842	0.0629847670366042	0.684207606442989	0.494029918458174	   
df.mm.trans1:exp6	0.0160082587367135	0.0802901390446852	0.199380134686330	0.842012915324808	   
df.mm.trans2:exp6	0.102193951538646	0.0629847670366042	1.62251852863495	0.105061875062214	   
df.mm.trans1:exp7	0.384313603988369	0.0802901390446851	4.78656044890496	1.99936657547520e-06	***
df.mm.trans2:exp7	0.192812122720529	0.0629847670366043	3.06125007350545	0.00227323586491248	** 
df.mm.trans1:exp8	0.832070355459459	0.0802901390446852	10.3632944887089	8.75892140051362e-24	***
df.mm.trans2:exp8	0.672448229595457	0.0629847670366043	10.6763628927079	4.62593697215056e-25	***
df.mm.trans1:probe2	0.0732564590245073	0.0609847384493805	1.20122609175921	0.229996937999829	   
df.mm.trans1:probe3	0.0717576572854559	0.0609847384493805	1.17664942262591	0.239663707773221	   
df.mm.trans1:probe4	0.138284663757773	0.0609847384493805	2.26752901256687	0.0236073512919114	*  
df.mm.trans1:probe5	0.0465036446004019	0.0609847384493805	0.762545610308742	0.445945241741035	   
df.mm.trans1:probe6	0.07788454725413	0.0609847384493805	1.27711537729685	0.201909035647996	   
df.mm.trans1:probe7	-0.189151154718121	0.0609847384493805	-3.10161459288906	0.00198801318989120	** 
df.mm.trans1:probe8	-0.224871103324623	0.0609847384493805	-3.68733406164026	0.000240950786574183	***
df.mm.trans1:probe9	-0.227249239351784	0.0609847384493805	-3.72632965443327	0.000207086501714007	***
df.mm.trans1:probe10	-0.240629302235501	0.0609847384493805	-3.94572983919955	8.61006004947157e-05	***
df.mm.trans1:probe11	-0.139041070686315	0.0609847384493805	-2.27993222930233	0.0228577748302059	*  
df.mm.trans1:probe12	-0.000619074623119018	0.0609847384493805	-0.0101513040616362	0.991902943944933	   
df.mm.trans2:probe2	-0.00129067134481569	0.0609847384493805	-0.0211638416041908	0.983119907511064	   
df.mm.trans2:probe3	-0.0852530405630773	0.0609847384493805	-1.39794057875382	0.162494311736282	   
df.mm.trans2:probe4	0.135475861527657	0.0609847384493805	2.22147155128175	0.0265808326801346	*  
df.mm.trans2:probe5	0.0643342225734373	0.0609847384493805	1.05492331703344	0.291759098137687	   
df.mm.trans2:probe6	0.144741009423646	0.0609847384493805	2.37339723189576	0.0178459553737074	*  
df.mm.trans3:probe2	-0.511050782447219	0.0609847384493805	-8.37997826081372	2.16803438168055e-16	***
df.mm.trans3:probe3	-0.762287952522559	0.0609847384493805	-12.4996510914823	4.83726502274947e-33	***
df.mm.trans3:probe4	-0.114920278573659	0.0609847384493805	-1.88441045244535	0.0598492904397223	.  
df.mm.trans3:probe5	-0.212941207079869	0.0609847384493805	-3.49171304975945	0.000504579510682261	***
df.mm.trans3:probe6	0.40302577091704	0.0609847384493805	6.60863326078811	6.82717547713508e-11	***
df.mm.trans3:probe7	-0.486283675658194	0.0609847384493805	-7.97385850989303	4.92942565989039e-15	***
df.mm.trans3:probe8	0.0473305432906277	0.0609847384493805	0.776104718886574	0.437902289609001	   
df.mm.trans3:probe9	-0.116357830424955	0.0609847384493805	-1.90798277378095	0.0567289471252884	.  
df.mm.trans3:probe10	0.0478095112701456	0.0609847384493805	0.783958617938965	0.433282025414426	   
df.mm.trans3:probe11	-6.29903177852184e-05	0.0609847384493805	-0.00103288657763946	0.99917611740065	   
df.mm.trans3:probe12	-0.421458073801612	0.0609847384493805	-6.91087777889606	9.42766342958597e-12	***
df.mm.trans3:probe13	-0.101480690104419	0.0609847384493805	-1.66403419420503	0.0964728777089784	.  
df.mm.trans3:probe14	1.09078099325817	0.0609847384493805	17.8861305466376	5.30766989429653e-61	***
df.mm.trans3:probe15	-0.196536148960789	0.0609847384493805	-3.22271036915114	0.00131803234197558	** 
df.mm.trans3:probe16	-0.549580910188339	0.0609847384493805	-9.01177776870373	1.30714336586506e-18	***
df.mm.trans3:probe17	0.401006388006801	0.0609847384493805	6.57552033841467	8.4417059471198e-11	***
df.mm.trans3:probe18	-0.653633099044835	0.0609847384493805	-10.7179782297070	3.11367213274774e-25	***
df.mm.trans3:probe19	-0.155920975715882	0.0609847384493805	-2.55672123354767	0.0107384775172932	*  
df.mm.trans3:probe20	-0.609062100830995	0.0609847384493805	-9.98712327571164	2.7467178231683e-22	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.69076223506674	0.220471847678949	21.2760145317847	6.4921621454178e-81	***
df.mm.trans1	0.165548121437525	0.183076944747415	0.90425433779183	0.366115860902089	   
df.mm.trans2	0.0957151831972154	0.167204808357021	0.572442767272824	0.56717288974855	   
df.mm.exp2	-0.073547962828916	0.213889109518917	-0.343860250736194	0.731036129076276	   
df.mm.exp3	-0.250529290576448	0.213889109518917	-1.17130456590307	0.241803457046044	   
df.mm.exp4	0.00443036981381014	0.213889109518917	0.0207133959450998	0.983479126979176	   
df.mm.exp5	0.0486287755438961	0.213889109518917	0.227355079710570	0.820202126343324	   
df.mm.exp6	0.267472715460151	0.213889109518917	1.25052049663377	0.211452394275513	   
df.mm.exp7	0.0379039179586545	0.213889109518917	0.177212940125417	0.859383201625172	   
df.mm.exp8	0.119846807908879	0.213889109518917	0.560322160293436	0.575406841584407	   
df.mm.trans1:exp2	0.106689712346087	0.181770790530324	0.586946406707124	0.557395010605435	   
df.mm.trans2:exp2	-0.150423409288189	0.142592739679278	-1.05491632762316	0.291762293161675	   
df.mm.trans1:exp3	0.217764149107980	0.181770790530324	1.19801508522158	0.231243910830702	   
df.mm.trans2:exp3	0.000592324837421374	0.142592739679278	0.00415396210742315	0.996686598554437	   
df.mm.trans1:exp4	0.103191171563464	0.181770790530324	0.567699415634378	0.570388473390825	   
df.mm.trans2:exp4	-0.231949144761084	0.142592739679278	-1.62665466196097	0.104179792844013	   
df.mm.trans1:exp5	-0.114979219319145	0.181770790530324	-0.632550581882205	0.527196685518595	   
df.mm.trans2:exp5	0.0655255028907453	0.142592739679278	0.45952902678023	0.645971401941653	   
df.mm.trans1:exp6	-0.141538045530947	0.181770790530324	-0.77866221034746	0.436394668946917	   
df.mm.trans2:exp6	-0.284262246174523	0.142592739679278	-1.99352538434910	0.0465220219372243	*  
df.mm.trans1:exp7	-0.0527112083527095	0.181770790530324	-0.289987231715956	0.771896482802566	   
df.mm.trans2:exp7	-0.0394049133894022	0.142592739679278	-0.276345860792298	0.782349376207204	   
df.mm.trans1:exp8	-0.137106137843563	0.181770790530324	-0.754280362887513	0.45088906402824	   
df.mm.trans2:exp8	-0.131128209060723	0.142592739679278	-0.919599478596587	0.358042072729123	   
df.mm.trans1:probe2	-0.18202083230479	0.138064826516983	-1.31837222337291	0.187732959368865	   
df.mm.trans1:probe3	-0.0369393455562448	0.138064826516983	-0.267550733145643	0.78910983519203	   
df.mm.trans1:probe4	-0.115738918013824	0.138064826516983	-0.83829401690218	0.40210043221139	   
df.mm.trans1:probe5	-0.165797823841898	0.138064826516983	-1.20086938885556	0.230135224178685	   
df.mm.trans1:probe6	-0.0872193709183292	0.138064826516983	-0.631727668216788	0.527734105303314	   
df.mm.trans1:probe7	-0.129594488917198	0.138064826516983	-0.93864956185098	0.348176400122936	   
df.mm.trans1:probe8	-0.072934980198542	0.138064826516983	-0.528266192327923	0.59745199634806	   
df.mm.trans1:probe9	-0.164641228443599	0.138064826516983	-1.19249219802805	0.233399926758732	   
df.mm.trans1:probe10	-0.337789689850014	0.138064826516983	-2.44660206637396	0.0146219001518339	*  
df.mm.trans1:probe11	-0.267858571827376	0.138064826516983	-1.94009277080015	0.0526976930092824	.  
df.mm.trans1:probe12	-0.100047245203117	0.138064826516983	-0.724639632896001	0.468871852152369	   
df.mm.trans2:probe2	0.0314931755400721	0.138064826516983	0.228104263298358	0.819619853235905	   
df.mm.trans2:probe3	0.158380998997064	0.138064826516983	1.14714951659018	0.251641485982141	   
df.mm.trans2:probe4	0.159272225537218	0.138064826516983	1.15360464757927	0.248985421881854	   
df.mm.trans2:probe5	0.166845813677164	0.138064826516983	1.20845995237347	0.227205288080096	   
df.mm.trans2:probe6	0.0360853163231794	0.138064826516983	0.261365021298459	0.793874120925516	   
df.mm.trans3:probe2	-0.154869167756162	0.138064826516983	-1.12171341291702	0.262299975243373	   
df.mm.trans3:probe3	0.0295266855079800	0.138064826516983	0.213861026395075	0.830706541331034	   
df.mm.trans3:probe4	0.0244519302964928	0.138064826516983	0.177104704459140	0.859468190110108	   
df.mm.trans3:probe5	-0.141628648359478	0.138064826516983	-1.02581267026802	0.305270700743062	   
df.mm.trans3:probe6	-0.0420020880135814	0.138064826516983	-0.304220047011139	0.761034451337143	   
df.mm.trans3:probe7	-0.0369653485226735	0.138064826516983	-0.267739071965056	0.788964897961687	   
df.mm.trans3:probe8	-0.0559541376485463	0.138064826516983	-0.405274384940204	0.685377454652835	   
df.mm.trans3:probe9	0.022835315632267	0.138064826516983	0.165395605878359	0.868671801631536	   
df.mm.trans3:probe10	-0.0775300403433857	0.138064826516983	-0.56154809519026	0.574571454632954	   
df.mm.trans3:probe11	-0.101258858510024	0.138064826516983	-0.7334153170255	0.463506674014934	   
df.mm.trans3:probe12	-0.135063855037023	0.138064826516983	-0.978264040482529	0.328221109332814	   
df.mm.trans3:probe13	-0.0355338789887119	0.138064826516983	-0.257370974817695	0.796954478951608	   
df.mm.trans3:probe14	-0.0991124786245226	0.138064826516983	-0.71786914252437	0.473034530359753	   
df.mm.trans3:probe15	-0.0363850587341122	0.138064826516983	-0.263536047898749	0.792201090848484	   
df.mm.trans3:probe16	-0.134494624857433	0.138064826516983	-0.974141120880552	0.330262638309659	   
df.mm.trans3:probe17	-0.0293393937938530	0.138064826516983	-0.212504477309752	0.83176425541236	   
df.mm.trans3:probe18	-0.0625018922100109	0.138064826516983	-0.452699603416534	0.650880179226312	   
df.mm.trans3:probe19	-0.138963006124594	0.138064826516983	-1.00650549187848	0.31445789672814	   
df.mm.trans3:probe20	-0.121372615935363	0.138064826516983	-0.879098746561881	0.379595309483718