chr9.24303_chr9_99845017_99845773_-_2.R 

fitVsDatCorrelation=0.90195266216744
cont.fitVsDatCorrelation=0.24954934107059

fstatistic=11568.4260182723,58,830
cont.fstatistic=2289.09901958629,58,830

residuals=-0.431478535609415,-0.0841638305029467,-0.00043095280033503,0.0774028735235168,0.818774451587868
cont.residuals=-0.576929122905583,-0.236188701046054,-0.094831767635944,0.196201951070925,1.16478590421292

predictedValues:
Include	Exclude	Both
chr9.24303_chr9_99845017_99845773_-_2.R.tl.Lung	55.1623477096734	88.5788266421341	57.461448723239
chr9.24303_chr9_99845017_99845773_-_2.R.tl.cerebhem	48.2140799327377	64.0275399425481	53.4932613986677
chr9.24303_chr9_99845017_99845773_-_2.R.tl.cortex	49.7961130134971	80.0241330476238	52.1632980531683
chr9.24303_chr9_99845017_99845773_-_2.R.tl.heart	50.9149182068673	106.568573148861	53.5224735278959
chr9.24303_chr9_99845017_99845773_-_2.R.tl.kidney	48.8534998619795	113.770451572340	53.1812650890055
chr9.24303_chr9_99845017_99845773_-_2.R.tl.liver	50.8778324453042	112.352853211965	56.4387906384198
chr9.24303_chr9_99845017_99845773_-_2.R.tl.stomach	59.4014481849787	87.9248594512537	63.8868742133433
chr9.24303_chr9_99845017_99845773_-_2.R.tl.testicle	50.8116665029211	113.476568404461	61.0359376205597


diffExp=-33.4164789324607,-15.8134600098104,-30.2280200341268,-55.6536549419939,-64.9169517103601,-61.475020766661,-28.5234112662749,-62.6649019015398
diffExpScore=0.997172680513082
diffExp1.5=-1,0,-1,-1,-1,-1,0,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,0,-1,-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	59.3313319693851	60.2606840314857	69.1796947857937
cerebhem	56.5615767082196	64.0039397037575	55.7403256892969
cortex	56.0887053822902	63.6855119948529	56.7695905770588
heart	57.6856649962883	57.4329506709416	66.8953823047988
kidney	60.3039473357186	53.0910336941688	56.3950424236951
liver	56.3175494170409	66.4410304167896	56.9585321355207
stomach	53.9524524084475	63.1031384604763	55.2437618123619
testicle	58.8816017248005	61.8263425764732	62.9932408249689
cont.diffExp=-0.929352062100563,-7.44236299553787,-7.59680661256267,0.252714325346737,7.21291364154981,-10.1234809997487,-9.15068605202875,-2.94474085167268
cont.diffExpScore=1.43916975796314

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=-0.0699919540492508
cont.tran.correlation=-0.705123571109028

tran.covariance=0.000125128713081218
cont.tran.covariance=-0.00180473191961597

tran.mean=73.7972319549466
cont.tran.mean=59.310466343196

weightedLogRatios:
wLogRatio
Lung	-2.01147225371373
cerebhem	-1.13960787572094
cortex	-1.96641493465402
heart	-3.17573079221983
kidney	-3.64475965017937
liver	-3.42676337289048
stomach	-1.67861890501429
testicle	-3.47891523125695

cont.weightedLogRatios:
wLogRatio
Lung	-0.0635823544705403
cerebhem	-0.506466563942574
cortex	-0.51957919429388
heart	0.0177939190009055
kidney	0.51410618816929
liver	-0.680023434803757
stomach	-0.637077916033282
testicle	-0.200080032010326

varWeightedLogRatios=0.942694387406964
cont.varWeightedLogRatios=0.16647306750579

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54376172712729	0.0732448480921615	62.0352399585842	0	***
df.mm.trans1	-0.669894030165495	0.0647922278923888	-10.3391109081493	1.18828413530803e-23	***
df.mm.trans2	-0.0612372374196573	0.0580778820845919	-1.05439859756703	0.292007296059143	   
df.mm.exp2	-0.387650495187588	0.0771932788892893	-5.0218166758217	6.2674282881351e-07	***
df.mm.exp3	-0.107173163816492	0.0771932788892893	-1.38837429059335	0.165395595941038	   
df.mm.exp4	0.175783794822527	0.0771932788892893	2.27719041543289	0.0230285167849201	*  
df.mm.exp5	0.206243482393073	0.0771932788892893	2.67178030730976	0.0076928838370925	** 
df.mm.exp6	0.174855811522334	0.0771932788892892	2.26516885975413	0.0237593198006063	*  
df.mm.exp7	-0.0393719427451064	0.0771932788892893	-0.510043663277651	0.610156471903232	   
df.mm.exp8	0.105200321626859	0.0771932788892893	1.36281711491667	0.173309778254818	   
df.mm.trans1:exp2	0.253020974540403	0.0734376826025064	3.44538342678814	0.000598868651965911	***
df.mm.trans2:exp2	0.0630709454916768	0.0592232749335038	1.06496889208665	0.287199713688237	   
df.mm.trans1:exp3	0.00482947902946641	0.0734376826025064	0.0657629551793834	0.947582386108223	   
df.mm.trans2:exp3	0.0056085639940975	0.0592232749335038	0.0947020238309148	0.924574367747665	   
df.mm.trans1:exp4	-0.255908439498157	0.0734376826025064	-3.48470200078757	0.00051850065788521	***
df.mm.trans2:exp4	0.00911201035721135	0.0592232749335038	0.153858603183332	0.877758625767926	   
df.mm.trans1:exp5	-0.327698075227932	0.0734376826025064	-4.46226056725744	9.22831943147948e-06	***
df.mm.trans2:exp5	0.0440465012191096	0.0592232749335038	0.743736331173263	0.457246487241734	   
df.mm.trans1:exp6	-0.255709108542387	0.0734376826025064	-3.48198771367085	0.000523707694899569	***
df.mm.trans2:exp6	0.0628957304312539	0.0592232749335038	1.06201034140502	0.288539896460369	   
df.mm.trans1:exp7	0.113409935201397	0.0734376826025064	1.54430166070526	0.122896439619534	   
df.mm.trans2:exp7	0.0319616704962537	0.0592232749335038	0.539680903025717	0.589561863219718	   
df.mm.trans1:exp8	-0.187354951673525	0.0734376826025064	-2.55121001962459	0.0109132826252837	*  
df.mm.trans2:exp8	0.14250319610565	0.0592232749335038	2.40620256589412	0.0163369746522204	*  
df.mm.trans1:probe2	0.0489008854421057	0.0428783687757902	1.14045582512262	0.254425418649203	   
df.mm.trans1:probe3	0.08251861129861	0.0428783687757902	1.92448112310656	0.0546358723443106	.  
df.mm.trans1:probe4	0.136541119220304	0.0428783687757902	3.18438231487476	0.00150475493414861	** 
df.mm.trans1:probe5	0.00184048039514778	0.0428783687757902	0.0429232838770431	0.965773011543502	   
df.mm.trans1:probe6	0.0793529405853314	0.0428783687757902	1.85065203856671	0.0645747770511668	.  
df.mm.trans1:probe7	0.0017408695210001	0.0428783687757902	0.0406001807135682	0.967624403951968	   
df.mm.trans1:probe8	0.111401119194837	0.0428783687757902	2.59807269668655	0.00954105347597537	** 
df.mm.trans1:probe9	0.110342011217087	0.0428783687757902	2.57337241055188	0.0102438172789730	*  
df.mm.trans1:probe10	0.0392187084696417	0.0428783687757902	0.914650197509034	0.360640930994751	   
df.mm.trans1:probe11	0.0655009372608724	0.0428783687757902	1.52759862678954	0.126993198881589	   
df.mm.trans1:probe12	0.0592437759164617	0.0428783687757902	1.38167046946785	0.167444685884040	   
df.mm.trans1:probe13	0.0287683867082451	0.0428783687757902	0.670930064030053	0.502451741181173	   
df.mm.trans1:probe14	0.0523818919719106	0.0428783687757902	1.22163910305950	0.222190992162802	   
df.mm.trans1:probe15	0.00991509267158332	0.0428783687757902	0.231237636940646	0.817187182849849	   
df.mm.trans1:probe16	0.0763477781560618	0.0428783687757902	1.78056629335137	0.0753490306285433	.  
df.mm.trans1:probe17	0.381768622290879	0.0428783687757902	8.9035248585863	3.35796430793373e-18	***
df.mm.trans1:probe18	0.377841718930255	0.0428783687757902	8.81194247164528	7.11129151077083e-18	***
df.mm.trans1:probe19	0.365510390130328	0.0428783687757902	8.5243539007179	7.20355715540165e-17	***
df.mm.trans1:probe20	0.650279109536635	0.0428783687757902	15.1656681003171	4.87639091100127e-46	***
df.mm.trans1:probe21	0.548750654792697	0.0428783687757902	12.7978435388272	2.35644125327121e-34	***
df.mm.trans1:probe22	0.66813474671312	0.0428783687757902	15.5820933908838	3.27328626733442e-48	***
df.mm.trans1:probe23	0.0625753894416138	0.0428783687757902	1.45936963621025	0.144841747282789	   
df.mm.trans1:probe24	0.142526914841947	0.0428783687757902	3.32398173977224	0.00092630867092449	***
df.mm.trans1:probe25	0.0912757243594521	0.0428783687757902	2.12871261117069	0.0335719460612353	*  
df.mm.trans1:probe26	0.0361225439603011	0.0428783687757902	0.842442121555157	0.39978320630165	   
df.mm.trans2:probe2	-0.252359668263202	0.0428783687757902	-5.88547735066097	5.75678925944343e-09	***
df.mm.trans2:probe3	0.0430867509924401	0.0428783687757902	1.00485984477953	0.315257198188771	   
df.mm.trans2:probe4	0.142064221750832	0.0428783687757902	3.31319091203497	0.000962308727044946	***
df.mm.trans2:probe5	0.152085427911628	0.0428783687757902	3.54690330471475	0.000411635631574998	***
df.mm.trans2:probe6	-0.0698247460710945	0.0428783687757902	-1.62843755638668	0.103811621429622	   
df.mm.trans3:probe2	0.608968187386521	0.0428783687757902	14.2022237499472	3.86146283097951e-41	***
df.mm.trans3:probe3	0.097252074853272	0.0428783687757902	2.26809175884933	0.0235798035162454	*  
df.mm.trans3:probe4	0.209671377673719	0.0428783687757902	4.88991031282195	1.21134561026110e-06	***
df.mm.trans3:probe5	0.276527392172624	0.0428783687757902	6.44911175652643	1.90846090332866e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95742917541893	0.164246870820068	24.0943961712018	1.13966647422466e-97	***
df.mm.trans1	0.0846365764314186	0.145292412531121	0.582525783397602	0.560370789297747	   
df.mm.trans2	0.132625502674926	0.130235922999641	1.01834808415564	0.308809304123807	   
df.mm.exp2	0.228460648666185	0.173100973463053	1.31981146088094	0.187261872929508	   
df.mm.exp3	0.196780728915171	0.173100973463053	1.1367973557767	0.255951102221596	   
df.mm.exp4	-0.0426130699725677	0.173100973463053	-0.246174640847199	0.805607910553476	   
df.mm.exp5	0.0939143423048838	0.173100973463053	0.542540809713754	0.587591643278234	   
df.mm.exp6	0.239887472498939	0.173100973463053	1.38582393674488	0.166172897757185	   
df.mm.exp7	0.176008204715522	0.173100973463053	1.01679500232903	0.30954725505164	   
df.mm.exp8	0.111720750478279	0.173100973463053	0.645407984965058	0.518841200267506	   
df.mm.trans1:exp2	-0.276268280931879	0.164679289832944	-1.67761399270141	0.0937989661067476	.  
df.mm.trans2:exp2	-0.168195894303279	0.132804392949319	-1.26649345377804	0.205691777190035	   
df.mm.trans1:exp3	-0.252983796942113	0.164679289832944	-1.53622108280129	0.124865264681338	   
df.mm.trans2:exp3	-0.141503518390197	0.132804392949319	-1.06550329584503	0.286958085650955	   
df.mm.trans1:exp4	0.0144842421459083	0.164679289832944	0.0879542422158947	0.929934260375336	   
df.mm.trans2:exp4	-0.00544862288023646	0.132804392949319	-0.0410274295844699	0.967283895404288	   
df.mm.trans1:exp5	-0.0776543092957395	0.164679289832944	-0.471548725856873	0.637372858743345	   
df.mm.trans2:exp5	-0.220586170271944	0.132804392949319	-1.66098549432868	0.0970940213606064	.  
df.mm.trans1:exp6	-0.292018803569126	0.164679289832944	-1.77325760795641	0.076552687556443	.  
df.mm.trans2:exp6	-0.142252563865092	0.132804392949319	-1.07114351194225	0.284416272948986	   
df.mm.trans1:exp7	-0.271042587185415	0.164679289832944	-1.64588144301794	0.100166755736208	   
df.mm.trans2:exp7	-0.129917583494677	0.132804392949319	-0.978262696055966	0.328229457272824	   
df.mm.trans1:exp8	-0.119329603423146	0.164679289832944	-0.724618156564784	0.46889052924515	   
df.mm.trans2:exp8	-0.0860711065262406	0.132804392949319	-0.648104363227557	0.517096695448665	   
df.mm.trans1:probe2	-0.0467305026571545	0.0961519899451624	-0.486006609783177	0.627090760767343	   
df.mm.trans1:probe3	0.0871850466748297	0.0961519899451624	0.906741989682723	0.36480641375615	   
df.mm.trans1:probe4	0.020717456111435	0.0961519899451624	0.215465703031738	0.829457183669804	   
df.mm.trans1:probe5	0.0781803983397876	0.0961519899451624	0.813091839122368	0.416398573911078	   
df.mm.trans1:probe6	0.0534219318098222	0.0961519899451624	0.555598816418567	0.578634862014299	   
df.mm.trans1:probe7	0.00482167978115686	0.0961519899451624	0.0501464377794653	0.9600177590947	   
df.mm.trans1:probe8	0.185781308040389	0.0961519899451624	1.93216290319466	0.0536794256654125	.  
df.mm.trans1:probe9	0.0823387596247911	0.0961519899451624	0.856339631366452	0.392057102335982	   
df.mm.trans1:probe10	0.0146757730106876	0.0961519899451624	0.152630985786748	0.878726383627459	   
df.mm.trans1:probe11	-0.0655282094999717	0.0961519899451624	-0.681506534990527	0.495741138898526	   
df.mm.trans1:probe12	0.0566292674287753	0.0961519899451624	0.588955750796965	0.556051208053499	   
df.mm.trans1:probe13	-0.0161424231479164	0.0961519899451624	-0.167884441675339	0.86671508763103	   
df.mm.trans1:probe14	0.16983050805557	0.0961519899451624	1.76627138088799	0.0777178766144132	.  
df.mm.trans1:probe15	0.105711014884955	0.0961519899451624	1.09941577855273	0.271905535528176	   
df.mm.trans1:probe16	0.156569696040063	0.0961519899451624	1.62835627353483	0.103828849202704	   
df.mm.trans1:probe17	-0.0304186487113093	0.0961519899451624	-0.316360053792519	0.751808777198846	   
df.mm.trans1:probe18	0.0658889928156112	0.0961519899451624	0.685258753908152	0.493371984725529	   
df.mm.trans1:probe19	0.0707660608608189	0.0961519899451624	0.735981240754126	0.461950052674251	   
df.mm.trans1:probe20	0.0811118780460909	0.0961519899451624	0.843579816625228	0.399147296067052	   
df.mm.trans1:probe21	0.0775354850476796	0.0961519899451624	0.806384611404297	0.420252101963794	   
df.mm.trans1:probe22	-0.0509094277802759	0.0961519899451624	-0.529468270072317	0.596622232073855	   
df.mm.trans1:probe23	0.00443615151166447	0.0961519899451624	0.0461368663736913	0.96321226018957	   
df.mm.trans1:probe24	0.110715305811577	0.0961519899451624	1.15146140890813	0.249874042467955	   
df.mm.trans1:probe25	0.0310039202487144	0.0961519899451624	0.322446995287321	0.747195289281807	   
df.mm.trans1:probe26	0.0256337065538856	0.0961519899451624	0.266595694675743	0.789846688208966	   
df.mm.trans2:probe2	-0.0187592218267562	0.0961519899451624	-0.195099673313625	0.845362665731006	   
df.mm.trans2:probe3	0.0212686416620374	0.0961519899451624	0.221198143420302	0.824992559541665	   
df.mm.trans2:probe4	-0.0305271927398596	0.0961519899451624	-0.317488933481979	0.75095248612537	   
df.mm.trans2:probe5	0.0484622986799154	0.0961519899451624	0.504017636115015	0.614382739700908	   
df.mm.trans2:probe6	0.0744327498992786	0.0961519899451624	0.774115542920425	0.439082907406938	   
df.mm.trans3:probe2	0.0734430168128779	0.0961519899451624	0.763822120111753	0.445190321283961	   
df.mm.trans3:probe3	-0.0173327457065964	0.0961519899451624	-0.180264035268346	0.856989291159666	   
df.mm.trans3:probe4	0.177207511197007	0.0961519899451624	1.84299369465023	0.0656863495549822	.  
df.mm.trans3:probe5	0.0767629031416346	0.0961519899451624	0.798349604469073	0.424896003968642