chr1.570_chr1_172204672_172205545_-_0.R 

fitVsDatCorrelation=0.874718081845621
cont.fitVsDatCorrelation=0.256543520109233

fstatistic=9950.92922112482,49,623
cont.fstatistic=2492.29567287584,49,623

residuals=-0.574550637814417,-0.0796845239931643,-0.00801428039618624,0.0755232647680635,0.835910166896014
cont.residuals=-0.564248171526372,-0.198532870260014,-0.0677504735360517,0.116845956997059,1.11795428256049

predictedValues:
Include	Exclude	Both
chr1.570_chr1_172204672_172205545_-_0.R.tl.Lung	55.5893593380817	55.4797662434656	73.077694378955
chr1.570_chr1_172204672_172205545_-_0.R.tl.cerebhem	60.7372234537641	68.2475564859795	59.1157906956724
chr1.570_chr1_172204672_172205545_-_0.R.tl.cortex	43.4453169548885	50.4612931559619	58.8178303446313
chr1.570_chr1_172204672_172205545_-_0.R.tl.heart	46.13243954683	51.5097892004803	64.5045204785894
chr1.570_chr1_172204672_172205545_-_0.R.tl.kidney	43.8583688876443	54.3552009725065	67.5237327198599
chr1.570_chr1_172204672_172205545_-_0.R.tl.liver	49.9533739586954	55.6031432622862	66.1159810936268
chr1.570_chr1_172204672_172205545_-_0.R.tl.stomach	45.3857892511632	51.8265754003698	64.0484281216449
chr1.570_chr1_172204672_172205545_-_0.R.tl.testicle	50.2867490008863	56.0547831655176	60.9721171590668


diffExp=0.109593094616102,-7.51033303221536,-7.01597620107334,-5.37734965365026,-10.4968320848622,-5.64976930359076,-6.44078614920656,-5.76803416463132
diffExpScore=0.984113490280985
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,-1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	59.3159728062419	58.4996097631563	56.2597535613219
cerebhem	55.3583136984962	66.194990661568	56.8256164499482
cortex	54.0085988189243	61.0190370964028	57.06018522173
heart	60.4772525311911	69.0642811975029	60.2934116401064
kidney	62.9134289733105	60.6591909021102	60.7523755166919
liver	58.0137195137874	64.0808176957495	55.2907441356084
stomach	64.9747797361334	56.6817942195349	61.97886316532
testicle	69.8339515673113	64.5611360821558	57.0567869247102
cont.diffExp=0.816363043085659,-10.8366769630718,-7.01043827747851,-8.58702866631177,2.25423807120031,-6.0670981819622,8.29298551659848,5.27281548515559
cont.diffExpScore=2.91361461384517

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=0.874930649305853
cont.tran.correlation=-0.13378214563267

tran.covariance=0.0097495024359562
cont.tran.covariance=-0.000806933068987658

tran.mean=52.4329205174075
cont.tran.mean=61.6035547039735

weightedLogRatios:
wLogRatio
Lung	0.00792724638478217
cerebhem	-0.485558461376257
cortex	-0.575812795516104
heart	-0.428523855752466
kidney	-0.834320899298085
liver	-0.424812588762494
stomach	-0.515097398547237
testicle	-0.43131492011133

cont.weightedLogRatios:
wLogRatio
Lung	0.056486715959154
cerebhem	-0.733564472205363
cortex	-0.49429272882507
heart	-0.553473572888995
kidney	0.150460785252134
liver	-0.408844367438765
stomach	0.560620976833709
testicle	0.330271336688629

varWeightedLogRatios=0.0541740405578178
cont.varWeightedLogRatios=0.222283360511828

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.06503498580419	0.074434262906629	41.1777435030021	6.1237164217306e-180	***
df.mm.trans1	0.932347420807703	0.0588242040037953	15.8497243880690	8.76464128867396e-48	***
df.mm.trans2	0.956229297879871	0.0588242040037954	16.2557116424079	8.56822039284634e-50	***
df.mm.exp2	0.507713298345054	0.0779775883361442	6.51101565434948	1.53755891348536e-10	***
df.mm.exp3	-0.124222414533864	0.0779775883361441	-1.59305278842902	0.111655551134888	   
df.mm.exp4	-0.135934059256221	0.0779775883361441	-1.74324523439016	0.0817841124697128	.  
df.mm.exp5	-0.178460280126763	0.0779775883361441	-2.28860989336398	0.0224359182108632	*  
df.mm.exp6	-0.00456771089355289	0.0779775883361441	-0.0585772270086438	0.953307633215176	   
df.mm.exp7	-0.139024390815155	0.0779775883361441	-1.78287625690412	0.0750932597587769	.  
df.mm.exp8	0.0911674180029302	0.0779775883361441	1.16914898175521	0.242790744072170	   
df.mm.trans1:exp2	-0.419148355891465	0.0593320482804559	-7.06445113625941	4.32789737397279e-12	***
df.mm.trans2:exp2	-0.300590049414119	0.059332048280456	-5.06623415381286	5.35373518205927e-07	***
df.mm.trans1:exp3	-0.122266323787846	0.0593320482804559	-2.06071300977014	0.0397449946656446	*  
df.mm.trans2:exp3	0.0294106026760578	0.059332048280456	0.495695050624869	0.620284387536629	   
df.mm.trans1:exp4	-0.0505413645306905	0.0593320482804559	-0.851839199816382	0.394630667549053	   
df.mm.trans2:exp4	0.061687548335441	0.059332048280456	1.03970029896576	0.298882488981012	   
df.mm.trans1:exp5	-0.0585659707690177	0.0593320482804559	-0.987088301623819	0.323982549004523	   
df.mm.trans2:exp5	0.157982201091971	0.059332048280456	2.66267903553922	0.00795236321858566	** 
df.mm.trans1:exp6	-0.102334043702227	0.0593320482804559	-1.72476842900326	0.0850651873621668	.  
df.mm.trans2:exp6	0.00678906195650522	0.059332048280456	0.114424870761483	0.908937865842629	   
df.mm.trans1:exp7	-0.0637683693547614	0.0593320482804559	-1.07477107571503	0.282893303042116	   
df.mm.trans2:exp7	0.0709090650484706	0.059332048280456	1.19512248613585	0.232493921831850	   
df.mm.trans1:exp8	-0.191417619079469	0.0593320482804559	-3.22620952128029	0.00132012790530064	** 
df.mm.trans2:exp8	-0.0808563167399964	0.059332048280456	-1.36277642662525	0.173445361019378	   
df.mm.trans1:probe2	0.276949516604846	0.0438196217141945	6.32021696607056	4.97795058612514e-10	***
df.mm.trans1:probe3	0.106647225067845	0.0438196217141945	2.43377785786086	0.0152221908408931	*  
df.mm.trans1:probe4	-0.0291132984603314	0.0438196217141945	-0.664389543346987	0.506686847710932	   
df.mm.trans1:probe5	0.0554276275886298	0.0438196217141945	1.26490429219463	0.206378574602413	   
df.mm.trans1:probe6	0.0434956745928697	0.0438196217141945	0.992607258834008	0.321286746182992	   
df.mm.trans2:probe2	-0.0981237618132179	0.0438196217141945	-2.23926537872034	0.0254907626221360	*  
df.mm.trans2:probe3	-0.0753394129685111	0.0438196217141945	-1.71930769872681	0.0860550670105762	.  
df.mm.trans2:probe4	0.190084648692264	0.0438196217141945	4.3378888556377	1.67790742536191e-05	***
df.mm.trans2:probe5	0.0207100399602918	0.0438196217141945	0.472620236098095	0.636649663356265	   
df.mm.trans2:probe6	-0.15274134894053	0.0438196217141945	-3.48568387780154	0.00052536907322181	***
df.mm.trans3:probe2	-0.533656167429879	0.0438196217141945	-12.1784749971270	9.33161478688358e-31	***
df.mm.trans3:probe3	-0.936465102493636	0.0438196217141945	-21.3709079599445	1.98405252348315e-76	***
df.mm.trans3:probe4	-0.99208178574514	0.0438196217141945	-22.6401266586876	2.88182871599941e-83	***
df.mm.trans3:probe5	-0.194402706720872	0.0438196217141945	-4.43643051025928	1.08142657883935e-05	***
df.mm.trans3:probe6	-0.188102249006042	0.0438196217141945	-4.29264885564063	2.04701090513443e-05	***
df.mm.trans3:probe7	-0.620271150333993	0.0438196217141944	-14.1551005250479	1.21606570519876e-39	***
df.mm.trans3:probe8	-0.95532876649453	0.0438196217141945	-21.8013923699636	9.63484759436656e-79	***
df.mm.trans3:probe9	-0.672150251280296	0.0438196217141945	-15.3390245051469	2.75689535031701e-45	***
df.mm.trans3:probe10	-0.816031328135677	0.0438196217141945	-18.6225096478033	7.21788507366247e-62	***
df.mm.trans3:probe11	-0.694273921177271	0.0438196217141945	-15.8439049452674	9.36248576075277e-48	***
df.mm.trans3:probe12	-0.583814544853036	0.0438196217141945	-13.3231306436386	8.20692561926011e-36	***
df.mm.trans3:probe13	-0.76074068665441	0.0438196217141945	-17.3607314918464	2.31657357738497e-55	***
df.mm.trans3:probe14	-0.929748662084888	0.0438196217141945	-21.2176332362932	1.3174184871788e-75	***
df.mm.trans3:probe15	-0.936373380414915	0.0438196217141945	-21.3688147862672	2.03604358370788e-76	***
df.mm.trans3:probe16	-0.77997566125584	0.0438196217141945	-17.7996895167897	1.31098549403575e-57	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13566630644833	0.148448992306112	27.8591739977614	1.51293223388739e-111	***
df.mm.trans1	-0.0641407936733447	0.117316857406469	-0.546731263445931	0.584759132929395	   
df.mm.trans2	-0.0444366666902682	0.117316857406469	-0.378774778600727	0.704984164391312	   
df.mm.exp2	0.0445251361563732	0.155515671935680	0.286306425597984	0.77473861720448	   
df.mm.exp3	-0.0656967080179108	0.155515671935680	-0.42244429259247	0.67284650884915	   
df.mm.exp4	0.116162861066294	0.155515671935680	0.746952764441244	0.455373874533679	   
df.mm.exp5	0.0183053309673072	0.155515671935680	0.117707307176592	0.906337514757101	   
df.mm.exp6	0.086299783648031	0.155515671935680	0.554926603691261	0.579144038707708	   
df.mm.exp7	-0.0372604365759729	0.155515671935680	-0.239592808314416	0.810724744985636	   
df.mm.exp8	0.247766604570903	0.155515671935680	1.59319380154418	0.111623918653934	   
df.mm.trans1:exp2	-0.113576911616467	0.118329683599337	-0.959834490904557	0.337510874565298	   
df.mm.trans2:exp2	0.0790595706583705	0.118329683599337	0.668129654821568	0.504298278737362	   
df.mm.trans1:exp3	-0.0280386463642769	0.118329683599337	-0.236953615622057	0.812770667571398	   
df.mm.trans2:exp3	0.107862523550584	0.118329683599337	0.911542398066448	0.362362225014243	   
df.mm.trans1:exp4	-0.0967741836089228	0.118329683599337	-0.817835226675658	0.413763950938033	   
df.mm.trans2:exp4	0.0498547379079737	0.118329683599337	0.421320639010421	0.673666280297396	   
df.mm.trans1:exp5	0.04057568148573	0.118329683599337	0.342903659094693	0.731786607471535	   
df.mm.trans2:exp5	0.0179457494597432	0.118329683599337	0.151658898375046	0.879505073167264	   
df.mm.trans1:exp6	-0.108498883389437	0.118329683599337	-0.916920252713704	0.359539233981377	   
df.mm.trans2:exp6	0.00482519607577833	0.118329683599337	0.0407775625608566	0.967486291266599	   
df.mm.trans1:exp7	0.128381001462050	0.118329683599337	1.08494333422497	0.2783664068184	   
df.mm.trans2:exp7	0.00569342264653181	0.118329683599337	0.0481149148155392	0.961640079720424	   
df.mm.trans1:exp8	-0.0845249265805038	0.118329683599337	-0.714317185759612	0.4752986135016	   
df.mm.trans2:exp8	-0.149174066957079	0.118329683599337	-1.26066480040782	0.207901570385159	   
df.mm.trans1:probe2	-0.00491084834432211	0.0873922630881306	-0.0561931705484016	0.955205938228728	   
df.mm.trans1:probe3	0.121069682387442	0.0873922630881306	1.38535927677430	0.166438425682172	   
df.mm.trans1:probe4	0.044672085529253	0.0873922630881306	0.511167510151367	0.609414868693584	   
df.mm.trans1:probe5	0.0760271386626283	0.0873922630881306	0.86995273924831	0.384661287484308	   
df.mm.trans1:probe6	0.0129104251698144	0.0873922630881306	0.147729612595052	0.882603958386496	   
df.mm.trans2:probe2	0.0054488691263267	0.0873922630881306	0.0623495597182532	0.950304459909868	   
df.mm.trans2:probe3	-0.145309123459501	0.0873922630881306	-1.66272297254695	0.0968708768256195	.  
df.mm.trans2:probe4	-0.11850757308105	0.0873922630881306	-1.35604192972485	0.175577108781364	   
df.mm.trans2:probe5	-0.131267018313670	0.0873922630881306	-1.50204393015082	0.133592434054644	   
df.mm.trans2:probe6	-0.0989753919431759	0.0873922630881306	-1.13254181143432	0.257842404650455	   
df.mm.trans3:probe2	0.0324291498259821	0.0873922630881306	0.371075752933403	0.71070725897792	   
df.mm.trans3:probe3	-0.0198066934622876	0.0873922630881306	-0.22664126963177	0.820777041724368	   
df.mm.trans3:probe4	-0.0320889884571370	0.0873922630881306	-0.36718340186221	0.713606908759377	   
df.mm.trans3:probe5	-0.0170500852963025	0.0873922630881306	-0.195098338157331	0.84537957192471	   
df.mm.trans3:probe6	-0.0993713554309078	0.0873922630881306	-1.13707268720913	0.255945048881157	   
df.mm.trans3:probe7	-0.0392025931045313	0.0873922630881306	-0.448581965030446	0.6538892228534	   
df.mm.trans3:probe8	0.0156477726273434	0.0873922630881306	0.179052150320944	0.85795496697333	   
df.mm.trans3:probe9	0.0498685961484291	0.0873922630881306	0.570629417138897	0.568456624387271	   
df.mm.trans3:probe10	0.0110383636033271	0.0873922630881306	0.126308247587037	0.899528679557106	   
df.mm.trans3:probe11	0.0321694383236596	0.0873922630881306	0.368103962375003	0.712920750707926	   
df.mm.trans3:probe12	0.090489047715515	0.0873922630881306	1.03543545524461	0.300867333712194	   
df.mm.trans3:probe13	0.0350911724108961	0.0873922630881306	0.401536373712036	0.688162960949436	   
df.mm.trans3:probe14	-0.00157630935787627	0.0873922630881306	-0.0180371728820735	0.985614973448438	   
df.mm.trans3:probe15	-0.0124514477019847	0.0873922630881306	-0.142477689236953	0.886748765883939	   
df.mm.trans3:probe16	0.0010631579810405	0.0873922630881306	0.0121653558733038	0.990297584378455