chr9.24672_chr9_4233775_4240782_+_2.R 

fitVsDatCorrelation=0.821209847927458
cont.fitVsDatCorrelation=0.25990279939394

fstatistic=11832.9654269814,52,692
cont.fstatistic=4123.4446607056,52,692

residuals=-0.467243199705812,-0.0789162976344982,-0.00907701243956666,0.0739567048615253,0.705228688466056
cont.residuals=-0.546367397369847,-0.153015285740593,-0.0239556976996366,0.112575353990702,1.21595128896506

predictedValues:
Include	Exclude	Both
chr9.24672_chr9_4233775_4240782_+_2.R.tl.Lung	53.8902015565662	41.9034135075663	61.5604596884462
chr9.24672_chr9_4233775_4240782_+_2.R.tl.cerebhem	60.7078185352523	46.5077717598133	57.5433607315251
chr9.24672_chr9_4233775_4240782_+_2.R.tl.cortex	51.8490526410751	45.0169051553635	62.516236581584
chr9.24672_chr9_4233775_4240782_+_2.R.tl.heart	53.8252972066295	41.5930318469559	60.7356003016306
chr9.24672_chr9_4233775_4240782_+_2.R.tl.kidney	53.8534805516626	42.1040714921329	61.156194891554
chr9.24672_chr9_4233775_4240782_+_2.R.tl.liver	53.3037683626453	44.9604399994258	67.3930790464018
chr9.24672_chr9_4233775_4240782_+_2.R.tl.stomach	53.0633204410215	41.7284215957425	59.9162717159939
chr9.24672_chr9_4233775_4240782_+_2.R.tl.testicle	52.7629899397555	44.0256947018295	58.3412196191647


diffExp=11.9867880489999,14.2000467754390,6.83214748571164,12.2322653596737,11.7494090595296,8.3433283632195,11.334898845279,8.73729523792601
diffExpScore=0.98842809286944
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=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,1,0,1,1,0,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	56.7130061569636	51.4256687265412	53.2857967956531
cerebhem	52.9741826753921	51.0808159608303	53.2912775801259
cortex	52.2792741993898	51.633790573155	54.8829982301033
heart	56.0618372851495	51.9470103048588	56.8031242824053
kidney	57.5820987133166	49.0283444481844	53.2355654267907
liver	52.3982274887253	52.4258515095338	52.4667446088558
stomach	57.5873513373571	51.7924207207605	49.8022978251961
testicle	54.8335067720378	50.6515921867983	53.9018270784562
cont.diffExp=5.28733743042241,1.8933667145618,0.645483626234849,4.11482698029069,8.55375426513221,-0.0276240208084531,5.79493061659657,4.18191458523955
cont.diffExpScore=0.96995445067741

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=0.48729727504474
cont.tran.correlation=-0.420312081270845

tran.covariance=0.00096923856694636
cont.tran.covariance=-0.00035766223586893

tran.mean=48.8184799558399
cont.tran.mean=53.1509361911871

weightedLogRatios:
wLogRatio
Lung	0.971395455456728
cerebhem	1.05857697492375
cortex	0.547911696771562
heart	0.994334962974433
kidney	0.950822201389597
liver	0.662323858849636
stomach	0.925488918148866
testicle	0.701569492206377

cont.weightedLogRatios:
wLogRatio
Lung	0.390394894739286
cerebhem	0.143821379826346
cortex	0.0490784037186742
heart	0.304035734471059
kidney	0.638879289153224
liver	-0.00208668212267332
stomach	0.424265508219027
testicle	0.314517667748999

varWeightedLogRatios=0.0347756407296898
cont.varWeightedLogRatios=0.0449914013535305

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.560453863058	0.0658435995341584	54.0744110019517	1.13668358005539e-250	***
df.mm.trans1	0.515596928019034	0.0566837905996776	9.09602061831706	9.80793827286101e-19	***
df.mm.trans2	0.132286741878746	0.0511167161606517	2.58793505950167	0.00985790448927894	** 
df.mm.exp2	0.290856978518023	0.0667692606522878	4.35615095444459	1.52418726832521e-05	***
df.mm.exp3	0.0176522570058215	0.0667692606522878	0.264377002731072	0.791568149263908	   
df.mm.exp4	0.00485000507137387	0.0667692606522878	0.072638292291884	0.942114943768074	   
df.mm.exp5	0.0106841308752108	0.0667692606522878	0.160015713381196	0.872915391901824	   
df.mm.exp6	-0.0310484936028854	0.0667692606522878	-0.465011792845449	0.642069298745327	   
df.mm.exp7	0.0074240917812619	0.0667692606522878	0.111190264932303	0.911497712571523	   
df.mm.exp8	0.0819783738130047	0.0667692606522878	1.22778615506799	0.219944720491475	   
df.mm.trans1:exp2	-0.171733154651529	0.06090907851989	-2.81950012748017	0.00494741055920268	** 
df.mm.trans2:exp2	-0.186604836841275	0.0483547542230537	-3.85907941917134	0.000124459710865247	***
df.mm.trans1:exp3	-0.056264265729533	0.06090907851989	-0.923741864050033	0.355942711103566	   
df.mm.trans2:exp3	0.0540185407665664	0.0483547542230537	1.11712987966781	0.264326507523852	   
df.mm.trans1:exp4	-0.00605511220148819	0.06090907851989	-0.0994123100961193	0.920839700351778	   
df.mm.trans2:exp4	-0.0122846470841781	0.0483547542230537	-0.254052518342058	0.799530452950785	   
df.mm.trans1:exp5	-0.0113657672478858	0.06090907851989	-0.186602186801665	0.852027242806273	   
df.mm.trans2:exp5	-0.00590697640684587	0.0483547542230537	-0.122159165148432	0.902808431753948	   
df.mm.trans1:exp6	0.0201068511769694	0.06090907851989	0.33011254915642	0.741414831280903	   
df.mm.trans2:exp6	0.101464194033764	0.0483547542230537	2.09832922665109	0.0362380189664927	*  
df.mm.trans1:exp7	-0.022886838129427	0.06090907851989	-0.37575413527153	0.70721484451637	   
df.mm.trans2:exp7	-0.0116089136733033	0.0483547542230537	-0.240078020451784	0.810340872433316	   
df.mm.trans1:exp8	-0.103117049076701	0.06090907851989	-1.69296682173624	0.0909118703367565	.  
df.mm.trans2:exp8	-0.0325722314138031	0.0483547542230537	-0.673609698511794	0.500784404355191	   
df.mm.trans1:probe2	-0.161723174545753	0.0398743518324864	-4.05581952090803	5.56421033611178e-05	***
df.mm.trans1:probe3	0.038912039112629	0.0398743518324864	0.975866373354479	0.329471697981721	   
df.mm.trans1:probe4	-0.105279727014004	0.0398743518324864	-2.64028685547763	0.00847034179644246	** 
df.mm.trans1:probe5	-0.0293554724370353	0.0398743518324864	-0.736199363449436	0.461858859516178	   
df.mm.trans1:probe6	-0.0372631089552336	0.0398743518324864	-0.934513220723369	0.350365193351013	   
df.mm.trans1:probe7	-0.0433749319619857	0.0398743518324864	-1.08779027040252	0.277066425310026	   
df.mm.trans1:probe8	0.0269509694657026	0.0398743518324864	0.675897368286377	0.49933162165485	   
df.mm.trans1:probe9	-0.0840356817279424	0.0398743518324864	-2.10751216925053	0.0354323485904099	*  
df.mm.trans1:probe10	-0.166273222610494	0.0398743518324864	-4.16992916421598	3.43404166255973e-05	***
df.mm.trans1:probe11	-0.0200328057305467	0.0398743518324864	-0.502398278841128	0.615547220254056	   
df.mm.trans1:probe12	-0.190862335818208	0.0398743518324864	-4.78659406477697	2.07520750055533e-06	***
df.mm.trans1:probe13	-0.303561588484057	0.0398743518324864	-7.61295355368608	8.80306910397948e-14	***
df.mm.trans1:probe14	-0.360645845716526	0.0398743518324864	-9.04455694305985	1.49591692233635e-18	***
df.mm.trans1:probe15	-0.214819096223188	0.0398743518324864	-5.38740033005806	9.80835157481005e-08	***
df.mm.trans1:probe16	-0.293058074053348	0.0398743518324864	-7.34953825166854	5.62439245183708e-13	***
df.mm.trans1:probe17	-0.283130918069891	0.0398743518324864	-7.10057731494505	3.08922003439321e-12	***
df.mm.trans2:probe2	0.0736332945326902	0.0398743518324864	1.84663301467636	0.0652271392959889	.  
df.mm.trans2:probe3	0.122376086617989	0.0398743518324864	3.06904265509056	0.00223138052928302	** 
df.mm.trans2:probe4	0.0467254665667805	0.0398743518324864	1.17181758246694	0.241673589369104	   
df.mm.trans2:probe5	0.2196044521055	0.0398743518324864	5.50741120578126	5.13728400854103e-08	***
df.mm.trans2:probe6	0.134434313053396	0.0398743518324864	3.37144823364551	0.00078931670518886	***
df.mm.trans3:probe2	0.43578404457321	0.0398743518324864	10.9289311185284	9.2882437735946e-26	***
df.mm.trans3:probe3	-0.300496389326282	0.0398743518324864	-7.53608210582777	1.52081376777028e-13	***
df.mm.trans3:probe4	-0.385631989112423	0.0398743518324864	-9.67117887539532	7.75144303930823e-21	***
df.mm.trans3:probe5	-0.252944795611039	0.0398743518324864	-6.34354626436736	4.05560059195028e-10	***
df.mm.trans3:probe6	4.68629114209358e-05	0.0398743518324864	0.00117526453139122	0.999062613510719	   
df.mm.trans3:probe7	0.0571477243143982	0.0398743518324864	1.43319506620391	0.152253726625304	   
df.mm.trans3:probe8	-0.260447511263616	0.0398743518324864	-6.53170520132253	1.25978737620806e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09474691557667	0.111422955546247	36.7495808696003	8.50245784336549e-165	***
df.mm.trans1	0.0140728858197391	0.0959223907086684	0.146711166347810	0.88340273443284	   
df.mm.trans2	-0.153548980228514	0.086501583035169	-1.77510023332273	0.0763208349421075	.  
df.mm.exp2	-0.0750301796333627	0.112989393261468	-0.664046221221278	0.506881913788355	   
df.mm.exp3	-0.106898484712013	0.112989393261468	-0.946093094461002	0.344431338619248	   
df.mm.exp4	-0.0653830342776494	0.112989393261468	-0.578665239190611	0.563003360998045	   
df.mm.exp5	-0.0315875641361415	0.112989393261468	-0.279562206897110	0.779896942889201	   
df.mm.exp6	-0.0443781398334864	0.112989393261468	-0.392763767929891	0.694614922377951	   
df.mm.exp7	0.0900144548876946	0.112989393261468	0.796662875066447	0.425920081722842	   
df.mm.exp8	-0.0603634553268261	0.112989393261468	-0.534240016557479	0.593347070160333	   
df.mm.trans1:exp2	0.00683128525885082	0.103072577992395	0.0662764567638432	0.94717686952458	   
df.mm.trans2:exp2	0.0683017454709555	0.0818276896822736	0.83470211289311	0.404173515329777	   
df.mm.trans1:exp3	0.0254949205087225	0.103072577992395	0.247349207765072	0.804711347295947	   
df.mm.trans2:exp3	0.110937359570153	0.0818276896822736	1.35574351421761	0.175623057655902	   
df.mm.trans1:exp4	0.0538347831128736	0.103072577992395	0.522299763539879	0.601628787744888	   
df.mm.trans2:exp4	0.0754697613198901	0.0818276896822736	0.922301015865528	0.356693029105076	   
df.mm.trans1:exp5	0.0467957273935509	0.103072577992395	0.454007538231978	0.649965757282928	   
df.mm.trans2:exp5	-0.0161512862430289	0.0818276896822736	-0.197381672460047	0.843586832126163	   
df.mm.trans1:exp6	-0.0347526658759371	0.103072577992395	-0.337166941516698	0.736093293613542	   
df.mm.trans2:exp6	0.0636405195486542	0.0818276896822736	0.777738193461921	0.436989181957859	   
df.mm.trans1:exp7	-0.0747150760511215	0.103072577992395	-0.724878309113741	0.46877171635546	   
df.mm.trans2:exp7	-0.0829080738334337	0.0818276896822736	-1.01320316087812	0.311317256287208	   
df.mm.trans1:exp8	0.0266613299617820	0.103072577992395	0.258665597398264	0.795970183671056	   
df.mm.trans2:exp8	0.0451966812337743	0.0818276896822736	0.552339695881274	0.580893986316601	   
df.mm.trans1:probe2	-0.0720856276090879	0.0674768415320557	-1.06830174578997	0.285756943717819	   
df.mm.trans1:probe3	-0.094537001118024	0.0674768415320557	-1.40102884147464	0.161653729315958	   
df.mm.trans1:probe4	-0.134106088441524	0.0674768415320557	-1.98743873300317	0.0472675580693917	*  
df.mm.trans1:probe5	-0.0589038207337333	0.0674768415320557	-0.872948694638446	0.382993847069102	   
df.mm.trans1:probe6	-0.109832493124827	0.0674768415320557	-1.62770649353304	0.104042322492648	   
df.mm.trans1:probe7	-0.147097181331001	0.0674768415320557	-2.17996542207924	0.0295963378807175	*  
df.mm.trans1:probe8	-0.163410808956159	0.0674768415320557	-2.42173174152688	0.0157027627847732	*  
df.mm.trans1:probe9	-0.132644656791080	0.0674768415320557	-1.96578046303583	0.0497234190798282	*  
df.mm.trans1:probe10	-0.162515902873999	0.0674768415320557	-2.40846932346105	0.0162799934674391	*  
df.mm.trans1:probe11	-0.141053139059762	0.0674768415320557	-2.09039332394883	0.0369468352257114	*  
df.mm.trans1:probe12	-0.130033129032074	0.0674768415320557	-1.92707788449612	0.0543790156800286	.  
df.mm.trans1:probe13	-0.0874289271999159	0.0674768415320557	-1.29568790143181	0.195515049570133	   
df.mm.trans1:probe14	-0.163379162822615	0.0674768415320557	-2.42126274901293	0.0157228632414474	*  
df.mm.trans1:probe15	-0.0486496968760621	0.0674768415320557	-0.720983611139393	0.471163094362484	   
df.mm.trans1:probe16	0.00893451429197955	0.0674768415320557	0.132408602553442	0.89469960106848	   
df.mm.trans1:probe17	-0.133662665401344	0.0674768415320557	-1.98086724817797	0.0480016673055306	*  
df.mm.trans2:probe2	-0.0139083697785714	0.0674768415320557	-0.206120640249056	0.836757302344952	   
df.mm.trans2:probe3	-0.0821493239373008	0.0674768415320557	-1.21744471246887	0.223850048970354	   
df.mm.trans2:probe4	0.0690041587236183	0.0674768415320557	1.02263468705537	0.306837962698111	   
df.mm.trans2:probe5	0.0456753371478882	0.0674768415320557	0.676903899335442	0.498693136350581	   
df.mm.trans2:probe6	-0.0334687457995093	0.0674768415320557	-0.496003444138824	0.620049475050237	   
df.mm.trans3:probe2	0.0327186535516907	0.0674768415320557	0.484887152522504	0.627909847933161	   
df.mm.trans3:probe3	-0.0106134053758860	0.0674768415320557	-0.157289599437519	0.875062497956564	   
df.mm.trans3:probe4	0.0376429529717768	0.0674768415320557	0.557864774300292	0.577117151193154	   
df.mm.trans3:probe5	0.0617350830729264	0.0674768415320557	0.914907717540372	0.360558763572502	   
df.mm.trans3:probe6	-0.0268544959243779	0.0674768415320557	-0.397980926709801	0.690766991295807	   
df.mm.trans3:probe7	0.0154239511147662	0.0674768415320557	0.228581403109078	0.819261811538968	   
df.mm.trans3:probe8	0.0531392434954709	0.0674768415320557	0.787518240168762	0.431248203118117