chrX.25636_chrX_114797881_114802555_+_0.R 

fitVsDatCorrelation=0.84881005105661
cont.fitVsDatCorrelation=0.339625363322982

fstatistic=5484.06211130482,36,324
cont.fstatistic=1726.62532072491,36,324

residuals=-0.557754887049085,-0.0923393870391258,-0.00187875930127969,0.0708368507812354,0.76006235529651
cont.residuals=-0.544464509807391,-0.165573727063108,-0.0677755418144112,0.0791028451383485,1.23892866356394

predictedValues:
Include	Exclude	Both
chrX.25636_chrX_114797881_114802555_+_0.R.tl.Lung	52.4697358044192	46.4221750068306	74.3000200243631
chrX.25636_chrX_114797881_114802555_+_0.R.tl.cerebhem	68.3845129503414	66.9895916334542	53.6947361658711
chrX.25636_chrX_114797881_114802555_+_0.R.tl.cortex	53.0282893194031	45.5041708298761	58.8868653737687
chrX.25636_chrX_114797881_114802555_+_0.R.tl.heart	51.7836497221773	45.3259628967095	62.2788471052922
chrX.25636_chrX_114797881_114802555_+_0.R.tl.kidney	53.4307559715573	45.3102447139019	79.5584585589107
chrX.25636_chrX_114797881_114802555_+_0.R.tl.liver	56.2504408989174	46.6767185567442	76.4063331733372
chrX.25636_chrX_114797881_114802555_+_0.R.tl.stomach	52.8161824437665	45.6202793414777	63.1949976045388
chrX.25636_chrX_114797881_114802555_+_0.R.tl.testicle	51.8480431162426	52.9142582007543	69.331200458293


diffExp=6.04756079758856,1.39492131688716,7.52411848952707,6.45768682546788,8.12051125765544,9.57372234217317,7.19590310228881,-1.06621508451171
diffExpScore=1.02448592480350
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=0,0,0,0,0,1,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	66.452617373968	59.6020140515374	53.0951272906505
cerebhem	56.2424367314609	57.4502921595098	55.3776845516526
cortex	52.6307993540284	53.5142006569501	56.7096882730127
heart	63.4878718396802	52.9734600411132	58.0948697252223
kidney	51.452627082314	55.8852022670568	50.8249214717976
liver	55.9339331363301	50.560533189513	56.5817984406427
stomach	62.0789425517243	55.7069484703038	75.731186629445
testicle	53.8570634681005	51.2382435718494	49.7268355935849
cont.diffExp=6.85060332243053,-1.20785542804882,-0.883401302921698,10.5144117985670,-4.43257518474277,5.37339994681709,6.37199408142055,2.61881989625115
cont.diffExpScore=1.45973979221779

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=0.891882733215309
cont.tran.correlation=0.437607866310248

tran.covariance=0.0110761334969775
cont.tran.covariance=0.00222581325020883

tran.mean=52.1734382129108
cont.tran.mean=56.19169912159

weightedLogRatios:
wLogRatio
Lung	0.477469607978047
cerebhem	0.0868641874472283
cortex	0.595914055661423
heart	0.516858054765893
kidney	0.642262130232473
liver	0.734431931841627
stomach	0.570275051481552
testicle	-0.0805777035402875

cont.weightedLogRatios:
wLogRatio
Lung	0.450658511851122
cerebhem	-0.0858502898277887
cortex	-0.0661099263132262
heart	0.7351528387106
kidney	-0.329063330091506
liver	0.401340532750350
stomach	0.441249874574749
testicle	0.197465849571837

varWeightedLogRatios=0.0816898682512674
cont.varWeightedLogRatios=0.125396371062553

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.67944412447516	0.0973104925804675	37.8113811461038	7.54913424344155e-121	***
df.mm.trans1	0.172395658074492	0.0824209706966192	2.09164799464760	0.0372482915349655	*  
df.mm.trans2	0.135902133933714	0.0824209706966192	1.64887810450512	0.100141833357104	   
df.mm.exp2	0.95646608899649	0.114781195043686	8.33295113047445	2.26610316692901e-15	***
df.mm.exp3	0.223108877219296	0.114781195043686	1.94377552119387	0.0527880388472294	.  
df.mm.exp4	0.139430046400095	0.114781195043686	1.21474642555366	0.225347359938009	   
df.mm.exp5	-0.0744749609687445	0.114781195043686	-0.648842878316429	0.516899569187818	   
df.mm.exp6	0.0470912239908246	0.114781195043686	0.41026950427639	0.681879418377985	   
df.mm.exp7	0.151042247319632	0.114781195043686	1.3159145734817	0.189133002807478	   
df.mm.exp8	0.18819242254647	0.114781195043686	1.63957538928606	0.102064017842371	   
df.mm.trans1:exp2	-0.691556251371187	0.0994034307845685	-6.95706622913205	1.92968985708947e-11	***
df.mm.trans2:exp2	-0.589706085127551	0.0994034307845685	-5.93245203382958	7.6602488305353e-09	***
df.mm.trans1:exp3	-0.21251988783524	0.0994034307845685	-2.13795324927791	0.0332684365995145	*  
df.mm.trans2:exp3	-0.243082143572578	0.0994034307845685	-2.44540999897072	0.0150006615928032	*  
df.mm.trans1:exp4	-0.152592131901147	0.0994034307845685	-1.53507912852476	0.12574029677983	   
df.mm.trans2:exp4	-0.163327300410792	0.0994034307845685	-1.64307508424697	0.101337456035617	   
df.mm.trans1:exp5	0.0926249531483665	0.0994034307845685	0.93180841362616	0.352129354802525	   
df.mm.trans2:exp5	0.0502308658141038	0.0994034307845685	0.505323261155506	0.613675374963833	   
df.mm.trans1:exp6	0.0224861127540372	0.0994034307845685	0.226210630524113	0.821180240935756	   
df.mm.trans2:exp6	-0.0416229702995977	0.0994034307845685	-0.418727703571970	0.675692581012313	   
df.mm.trans1:exp7	-0.144461160399085	0.0994034307845685	-1.45328143363752	0.147113484762150	   
df.mm.trans2:exp7	-0.168467161957907	0.0994034307845685	-1.69478216826355	0.0910775827121514	.  
df.mm.trans1:exp8	-0.200111772344013	0.0994034307845685	-2.01312742190664	0.0449279698927812	*  
df.mm.trans2:exp8	-0.0572968434937844	0.0994034307845685	-0.576407102265521	0.564740246439778	   
df.mm.trans1:probe2	0.405563201939574	0.0497017153922842	8.1599437512077	7.49329566815212e-15	***
df.mm.trans1:probe3	-0.0464610714769255	0.0497017153922842	-0.934798147512997	0.350588502272374	   
df.mm.trans1:probe4	0.0849450010906198	0.0497017153922842	1.70909596218497	0.0883907475190762	.  
df.mm.trans1:probe5	0.421570361872739	0.0497017153922842	8.48200828774984	7.98699532376723e-16	***
df.mm.trans1:probe6	0.10995334623443	0.0497017153922842	2.21226461434165	0.0276455190868509	*  
df.mm.trans2:probe2	-0.0104332135990813	0.0497017153922842	-0.209916569614033	0.833864726810768	   
df.mm.trans2:probe3	0.111590020904017	0.0497017153922842	2.24519455763774	0.0254297139580042	*  
df.mm.trans2:probe4	0.00395079649563139	0.0497017153922842	0.0794901436388796	0.93669184407022	   
df.mm.trans2:probe5	0.0541217578416479	0.0497017153922842	1.08893138626056	0.276993428721258	   
df.mm.trans2:probe6	0.0426496049756087	0.0497017153922842	0.858111327526327	0.391465331293536	   
df.mm.trans3:probe2	0.85333405714753	0.0497017153922842	17.169106748376	1.92982498922197e-47	***
df.mm.trans3:probe3	0.107773857094117	0.0497017153922842	2.16841322766192	0.0308546071944454	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37951670642127	0.173116688943167	25.2980618631115	1.05441334858339e-78	***
df.mm.trans1	-0.201635820433026	0.14662802713368	-1.37515197042920	0.17003444272377	   
df.mm.trans2	-0.251859810143125	0.14662802713368	-1.71767850298842	0.0868107471409913	.  
df.mm.exp2	-0.245678423053886	0.204197306086716	-1.20314233210087	0.229799643972134	   
df.mm.exp3	-0.406789906247594	0.204197306086716	-1.99214139521921	0.0471944643555068	*  
df.mm.exp4	-0.253530812041930	0.204197306086716	-1.24159724190614	0.215283271318628	   
df.mm.exp5	-0.276519045461643	0.204197306086716	-1.35417577616923	0.176624009005091	   
df.mm.exp6	-0.400438206917971	0.204197306086716	-1.96103569920711	0.0507311262568585	.  
df.mm.exp7	-0.490771702902	0.204197306086716	-2.40341908670228	0.0168041032465510	*  
df.mm.exp8	-0.295818356705496	0.204197306086716	-1.44868883128102	0.148391582506928	   
df.mm.trans1:exp2	0.0788608239846667	0.176840054455443	0.445944354787206	0.655935296491665	   
df.mm.trans2:exp2	0.208909146481437	0.176840054455443	1.18134518293804	0.238332019463884	   
df.mm.trans1:exp3	0.173602220674042	0.176840054455443	0.981690608548096	0.326984761907789	   
df.mm.trans2:exp3	0.299047591457309	0.176840054455443	1.69106253884726	0.0917864878757212	.  
df.mm.trans1:exp4	0.207890532041984	0.176840054455443	1.17558509401141	0.240623838465534	   
df.mm.trans2:exp4	0.135632479922296	0.176840054455443	0.766978275029147	0.443652937798437	   
df.mm.trans1:exp5	0.0206913943127935	0.176840054455443	0.117006265218082	0.906927583466484	   
df.mm.trans2:exp5	0.212129306325608	0.176840054455443	1.19955463132397	0.231188818792193	   
df.mm.trans1:exp6	0.228120262753296	0.176840054455442	1.28998073120801	0.19797729490301	   
df.mm.trans2:exp6	0.235920136068257	0.176840054455443	1.33408766919205	0.183112062256022	   
df.mm.trans1:exp7	0.422689371987342	0.176840054455443	2.39023547741468	0.0174084751774307	*  
df.mm.trans2:exp7	0.423187223863823	0.176840054455442	2.39305074388818	0.0172778314016522	*  
df.mm.trans1:exp8	0.0856627480239223	0.176840054455443	0.484408061780519	0.628423587432124	   
df.mm.trans2:exp8	0.144615188377867	0.176840054455443	0.817773941673973	0.414087253344167	   
df.mm.trans1:probe2	-0.0313508438745853	0.0884200272277213	-0.35456722710391	0.723144494978839	   
df.mm.trans1:probe3	0.02811430785489	0.0884200272277213	0.317963121437218	0.750717745648159	   
df.mm.trans1:probe4	0.116642828888832	0.0884200272277213	1.31919014895149	0.188037072040058	   
df.mm.trans1:probe5	-0.0450090238108582	0.0884200272277213	-0.509036529642088	0.611073095961506	   
df.mm.trans1:probe6	0.0990773141022872	0.0884200272277213	1.12053023742142	0.263317753649863	   
df.mm.trans2:probe2	-0.0436213162261646	0.0884200272277213	-0.493342035665971	0.622105151046067	   
df.mm.trans2:probe3	-0.0581294109209784	0.0884200272277213	-0.657423580873472	0.511375349572748	   
df.mm.trans2:probe4	-0.0692989576452378	0.0884200272277213	-0.78374730044769	0.43376096316183	   
df.mm.trans2:probe5	-0.0980222460219692	0.0884200272277213	-1.10859778146774	0.268425960543862	   
df.mm.trans2:probe6	-0.0906358388866318	0.0884200272277213	-1.02506006533117	0.306099543079311	   
df.mm.trans3:probe2	0.113421062454106	0.0884200272277213	1.28275308219478	0.200495486153822	   
df.mm.trans3:probe3	0.0247711878087509	0.0884200272277213	0.280153587206595	0.779538455579886