chr13.6322_chr13_95612874_95617184_+_2.R 

fitVsDatCorrelation=0.916342888451622
cont.fitVsDatCorrelation=0.237799708947480

fstatistic=9734.85730742692,59,853
cont.fstatistic=1642.19211834267,59,853

residuals=-1.26600639987099,-0.0880289835807957,-0.00557506529404355,0.082115868278534,0.706914324318908
cont.residuals=-0.795568718787794,-0.281764872164829,-0.108400899662539,0.229076819310662,1.8887740555753

predictedValues:
Include	Exclude	Both
chr13.6322_chr13_95612874_95617184_+_2.R.tl.Lung	53.2676694635996	98.5144673399626	85.0762674165974
chr13.6322_chr13_95612874_95617184_+_2.R.tl.cerebhem	57.9664989440584	84.5522630528642	71.9594993419602
chr13.6322_chr13_95612874_95617184_+_2.R.tl.cortex	62.3794606738225	73.1007856810173	75.8882182136965
chr13.6322_chr13_95612874_95617184_+_2.R.tl.heart	55.8297520627792	89.8125094700485	72.7945888182809
chr13.6322_chr13_95612874_95617184_+_2.R.tl.kidney	53.704569358757	110.387079102425	88.1284912169152
chr13.6322_chr13_95612874_95617184_+_2.R.tl.liver	55.4626933159754	106.265996856583	84.9140758506496
chr13.6322_chr13_95612874_95617184_+_2.R.tl.stomach	54.2960563941781	80.214639848245	71.9765431694169
chr13.6322_chr13_95612874_95617184_+_2.R.tl.testicle	54.5871964127954	92.939165210028	83.7804293183978


diffExp=-45.246797876363,-26.5857641088058,-10.7213250071948,-33.9827574072694,-56.6825097436677,-50.8033035406075,-25.9185834540669,-38.3519687972326
diffExpScore=0.996543297052964
diffExp1.5=-1,0,0,-1,-1,-1,0,-1
diffExp1.5Score=0.833333333333333
diffExp1.4=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	67.4850019032966	62.4055592313235	63.7966922861885
cerebhem	66.1670849855946	71.9912492489598	69.141239980195
cortex	66.4780426333957	77.1872274590467	65.6088034221007
heart	61.0646280756903	72.3057098913435	71.252206613391
kidney	65.4983035709252	93.7293533414491	80.4940552298411
liver	66.7619077705568	71.8434579809114	64.3069881870627
stomach	66.4897286940956	66.8082121942149	60.2247053445286
testicle	65.3367071660151	68.6220005414457	71.3022166020176
cont.diffExp=5.07944267197319,-5.82416426336525,-10.7091848256509,-11.2410818156531,-28.2310497705239,-5.08155021035468,-0.318483500119243,-3.28529337543061
cont.diffExpScore=1.15110838256949

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,-1,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,0,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,-1,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.688176283225974
cont.tran.correlation=-0.165576115488053

tran.covariance=-0.00518202559118546
cont.tran.covariance=-0.000675506823646753

tran.mean=73.9550501991962
cont.tran.mean=69.3858859180165

weightedLogRatios:
wLogRatio
Lung	-2.63336109378181
cerebhem	-1.60387267173578
cortex	-0.668121307510601
heart	-2.02528532841631
kidney	-3.12964698476796
liver	-2.82255720808163
stomach	-1.63500174388245
testicle	-2.27006593376983

cont.weightedLogRatios:
wLogRatio
Lung	0.326524270712436
cerebhem	-0.357217031241383
cortex	-0.638008910409558
heart	-0.709070086690503
kidney	-1.56300450113108
liver	-0.310872611582257
stomach	-0.0200671345581742
testicle	-0.206248899931046

varWeightedLogRatios=0.630552088919055
cont.varWeightedLogRatios=0.31637057560994

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04141449068777	0.0781446916847912	51.7170700089194	3.49306122982613e-265	***
df.mm.trans1	-0.0622751810792066	0.0674838442554941	-0.922816146090203	0.35636397104111	   
df.mm.trans2	0.503111392368928	0.0596216713781935	8.43839799756	1.36880113564392e-16	***
df.mm.exp2	0.0991467619983771	0.0766925328344144	1.29278246961074	0.196436352998489	   
df.mm.exp3	-0.0261711310375782	0.0766925328344144	-0.34124744705047	0.733001346435245	   
df.mm.exp4	0.110404698153576	0.0766925328344143	1.43957558934647	0.150354381045037	   
df.mm.exp5	0.0867104347709366	0.0766925328344144	1.13062421550416	0.258531084339583	   
df.mm.exp6	0.118031217989805	0.0766925328344143	1.53901838454918	0.124170660762253	   
df.mm.exp7	-0.019167505102725	0.0766925328344144	-0.249926614682413	0.802704277189077	   
df.mm.exp8	-0.0184398074447237	0.0766925328344143	-0.240438107377895	0.810048411724872	   
df.mm.trans1:exp2	-0.0146110932042561	0.0708885029188001	-0.206113722291364	0.836751263191754	   
df.mm.trans2:exp2	-0.251980335099653	0.0523546291054073	-4.81295234834597	1.75862110075797e-06	***
df.mm.trans1:exp3	0.184077625899229	0.0708885029188001	2.59672045987601	0.00957387646286224	** 
df.mm.trans2:exp3	-0.272193168174699	0.0523546291054073	-5.19902772354062	2.50903678639267e-07	***
df.mm.trans1:exp4	-0.0634273502347592	0.0708885029188001	-0.894748056781685	0.371174179276511	   
df.mm.trans2:exp4	-0.202883842812487	0.0523546291054073	-3.87518441595708	0.000114711832424888	***
df.mm.trans1:exp5	-0.0785419168582805	0.0708885029188001	-1.10796410735669	0.268189629990341	   
df.mm.trans2:exp5	0.0270792411663795	0.0523546291054073	0.517227256292084	0.605131617351898	   
df.mm.trans1:exp6	-0.07765018622686	0.0708885029188001	-1.09538476663564	0.273657285665193	   
df.mm.trans2:exp6	-0.0422892768084866	0.0523546291054073	-0.807746660249358	0.419461626733667	   
df.mm.trans1:exp7	0.038289532685151	0.0708885029188001	0.540137414511491	0.589243256933508	   
df.mm.trans2:exp7	-0.186329868875416	0.0523546291054073	-3.55899510815503	0.00039283601073816	***
df.mm.trans1:exp8	0.0429095943006752	0.0708885029188001	0.605311052341257	0.545133343424799	   
df.mm.trans2:exp8	-0.0398184649104823	0.0523546291054073	-0.760552898394419	0.447134336179759	   
df.mm.trans1:probe2	-0.116153060836608	0.048534040145497	-2.39322876250154	0.0169161033702037	*  
df.mm.trans1:probe3	-0.0925877076265071	0.048534040145497	-1.90768597357534	0.0567673757123561	.  
df.mm.trans1:probe4	-0.00502659218438118	0.0485340401454971	-0.103568385597248	0.917536221709204	   
df.mm.trans1:probe5	0.197901010298893	0.0485340401454971	4.07757132325309	4.97664112970501e-05	***
df.mm.trans1:probe6	-0.00283460413639223	0.048534040145497	-0.0584044544384632	0.953440145606596	   
df.mm.trans1:probe7	-0.239571213455390	0.0485340401454971	-4.9361481701749	9.58363388886216e-07	***
df.mm.trans1:probe8	-0.230492820799308	0.0485340401454971	-4.74909610055805	2.39623128533801e-06	***
df.mm.trans1:probe9	-0.173277683603266	0.0485340401454971	-3.57022994755451	0.000376604072103508	***
df.mm.trans1:probe10	-0.197494737942756	0.0485340401454971	-4.06920044881282	5.15529172992241e-05	***
df.mm.trans1:probe11	-0.219183185103631	0.0485340401454971	-4.51607128618504	7.18585014771364e-06	***
df.mm.trans1:probe12	-0.162959043269320	0.0485340401454971	-3.35762369629224	0.000820962116332487	***
df.mm.trans1:probe13	0.505236511412049	0.0485340401454971	10.4099413503890	5.67493712381455e-24	***
df.mm.trans1:probe14	0.119251083918314	0.0485340401454971	2.45706072605576	0.0142060122727491	*  
df.mm.trans1:probe15	-0.0674377370417724	0.0485340401454971	-1.38949357687110	0.165045280384441	   
df.mm.trans1:probe16	0.0099475232766647	0.0485340401454971	0.204959719958274	0.83765249280593	   
df.mm.trans1:probe17	0.254729689076454	0.0485340401454971	5.24847485008082	1.93677765179809e-07	***
df.mm.trans1:probe18	0.516264481595309	0.048534040145497	10.6371627016344	6.70951783495519e-25	***
df.mm.trans1:probe19	0.00767382201933601	0.0485340401454971	0.158112162027541	0.874405839209221	   
df.mm.trans1:probe20	-0.113217011941447	0.0485340401454971	-2.33273413056158	0.0198938223923736	*  
df.mm.trans1:probe21	-0.165601393127624	0.048534040145497	-3.41206692521739	0.00067505618818525	***
df.mm.trans1:probe22	0.0529210167757211	0.0485340401454971	1.09038968561184	0.275849443234234	   
df.mm.trans2:probe2	0.453510657789135	0.048534040145497	9.34417692056101	7.88033571664132e-20	***
df.mm.trans2:probe3	-0.0277394797443732	0.0485340401454971	-0.571546890825795	0.567779549826155	   
df.mm.trans2:probe4	0.341633713479052	0.0485340401454971	7.03905367150335	3.97955086322956e-12	***
df.mm.trans2:probe5	-0.0608362783105239	0.048534040145497	-1.25347649048270	0.210375800820066	   
df.mm.trans2:probe6	0.0242718809289241	0.0485340401454971	0.500100153545038	0.617133544885002	   
df.mm.trans3:probe2	0.503293817087694	0.048534040145497	10.3699138909290	8.2364898751166e-24	***
df.mm.trans3:probe3	0.157283831498413	0.048534040145497	3.24069109076645	0.00123861032830675	** 
df.mm.trans3:probe4	-0.561527329293868	0.048534040145497	-11.5697627399347	7.3594399931754e-29	***
df.mm.trans3:probe5	-0.315253669106469	0.0485340401454971	-6.49551671695557	1.40448869349389e-10	***
df.mm.trans3:probe6	0.922812161038836	0.048534040145497	19.0137099296164	1.77531541081296e-67	***
df.mm.trans3:probe7	-0.343549295849538	0.048534040145497	-7.07852251367563	3.04306200612255e-12	***
df.mm.trans3:probe8	-0.353577946677287	0.048534040145497	-7.28515379344722	7.31535210365429e-13	***
df.mm.trans3:probe9	-0.40971972765843	0.048534040145497	-8.44190441245273	1.33142407959867e-16	***
df.mm.trans3:probe10	0.0132123183310126	0.0485340401454971	0.272227869169849	0.785512693976509	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11729831812856	0.18957068651508	21.7190663483778	1.32978903133901e-83	***
df.mm.trans1	0.0904535618669691	0.163708607819366	0.552527830221209	0.580731521625584	   
df.mm.trans2	0.0460278364585161	0.144635815058701	0.318232634426235	0.750386374949379	   
df.mm.exp2	0.0427179415342864	0.186047904042468	0.229607217313964	0.81845204274575	   
df.mm.exp3	0.169537420787232	0.186047904042468	0.911256816677352	0.36241751588821	   
df.mm.exp4	-0.0632483045311248	0.186047904042467	-0.339957092538316	0.733972533180642	   
df.mm.exp5	0.144393921721258	0.186047904042467	0.776111520655984	0.437898276044378	   
df.mm.exp6	0.122095529076742	0.186047904042467	0.656258557198645	0.511834762139439	   
df.mm.exp7	0.110932437263935	0.186047904042467	0.596257387767258	0.551161485139536	   
df.mm.exp8	-0.0486186117361897	0.186047904042467	-0.261323082280421	0.793906449230241	   
df.mm.trans1:exp2	-0.0624401870592143	0.171967946569538	-0.363092008160751	0.716626110901855	   
df.mm.trans2:exp2	0.100172270233861	0.127006745663392	0.788716140317057	0.43049707886337	   
df.mm.trans1:exp3	-0.184571092777285	0.171967946569538	-1.07328776355803	0.283445555277311	   
df.mm.trans2:exp3	0.0430422134812375	0.127006745663392	0.338897066107913	0.734770682450202	   
df.mm.trans1:exp4	-0.0367242946586866	0.171967946569538	-0.213553138193905	0.830946577778784	   
df.mm.trans2:exp4	0.210497043908838	0.127006745663392	1.65736900673545	0.0978126001272815	.  
df.mm.trans1:exp5	-0.174275058260484	0.171967946569538	-1.01341594022008	0.311148893673758	   
df.mm.trans2:exp5	0.262363126197656	0.127006745663392	2.06574166456482	0.0391543810278105	*  
df.mm.trans1:exp6	-0.132868233304729	0.171967946569538	-0.772633714335837	0.439953211791134	   
df.mm.trans2:exp6	0.0187396665842921	0.127006745663392	0.147548592686235	0.88273389601387	   
df.mm.trans1:exp7	-0.125790336424026	0.171967946569538	-0.731475480944704	0.464689670703689	   
df.mm.trans2:exp7	-0.0427607888909789	0.127006745663392	-0.336681242146842	0.73644001836185	   
df.mm.trans1:exp8	0.0162672420969337	0.171967946569538	0.0945946173193143	0.924659041076442	   
df.mm.trans2:exp8	0.143577441000939	0.127006745663392	1.13047098601727	0.25859557452257	   
df.mm.trans1:probe2	0.0399511659217296	0.117738404379974	0.339321448529202	0.734451108254023	   
df.mm.trans1:probe3	0.0834892951131749	0.117738404379974	0.70910843027677	0.478450953691109	   
df.mm.trans1:probe4	-0.0086512465525291	0.117738404379974	-0.0734785442191758	0.941442543514707	   
df.mm.trans1:probe5	-0.0979215980653107	0.117738404379974	-0.831687830160251	0.405817929118070	   
df.mm.trans1:probe6	0.079247267487271	0.117738404379974	0.673079169915694	0.501079232391087	   
df.mm.trans1:probe7	-0.0352587540016276	0.117738404379974	-0.299466891769979	0.76465678203197	   
df.mm.trans1:probe8	0.0541255784633545	0.117738404379974	0.459710480606451	0.64584118772834	   
df.mm.trans1:probe9	-0.0964084969493116	0.117738404379974	-0.818836448965073	0.413108433200549	   
df.mm.trans1:probe10	0.0363586120948023	0.117738404379974	0.308808432442003	0.757542659578572	   
df.mm.trans1:probe11	0.00654946259233578	0.117738404379974	0.0556272409739721	0.955651789344197	   
df.mm.trans1:probe12	-0.0700032548392741	0.117738404379974	-0.59456602293806	0.552291264994625	   
df.mm.trans1:probe13	0.0985508143272735	0.117738404379974	0.837032018959788	0.402809010378832	   
df.mm.trans1:probe14	0.0422324765384733	0.117738404379974	0.358697544449283	0.719910124976203	   
df.mm.trans1:probe15	-0.097673142336671	0.117738404379974	-0.829577594932008	0.407009741023305	   
df.mm.trans1:probe16	0.0690536338456474	0.117738404379974	0.586500506859194	0.557694392710345	   
df.mm.trans1:probe17	-0.0867848594112753	0.117738404379974	-0.737098993894948	0.46126484064368	   
df.mm.trans1:probe18	0.00296308913206744	0.117738404379974	0.0251667172463519	0.97992787048238	   
df.mm.trans1:probe19	0.0999295175864735	0.117738404379974	0.848741904671764	0.396263039671461	   
df.mm.trans1:probe20	0.00738311222229045	0.117738404379974	0.0627077652459357	0.95001390437507	   
df.mm.trans1:probe21	-0.0241961627501664	0.117738404379974	-0.205507819454379	0.837224422364113	   
df.mm.trans1:probe22	0.0299754629884163	0.117738404379974	0.254593759328327	0.799098243526737	   
df.mm.trans2:probe2	-0.159040072091525	0.117738404379974	-1.35079180772876	0.177120343203015	   
df.mm.trans2:probe3	-0.161678335681936	0.117738404379974	-1.37319964996431	0.170051129869213	   
df.mm.trans2:probe4	-0.106510645837575	0.117738404379974	-0.904638094923012	0.365912571479134	   
df.mm.trans2:probe5	-0.0653433121037096	0.117738404379974	-0.554987240126246	0.579048886705647	   
df.mm.trans2:probe6	0.0178236787639722	0.117738404379974	0.151383729530174	0.879708817439173	   
df.mm.trans3:probe2	-0.0543729358591312	0.117738404379974	-0.46181138724842	0.644334335488644	   
df.mm.trans3:probe3	-0.0572047095391593	0.117738404379974	-0.485862789124814	0.627189233276632	   
df.mm.trans3:probe4	-0.0327051276795515	0.117738404379974	-0.277777908166678	0.781250161826932	   
df.mm.trans3:probe5	-0.115935279294905	0.117738404379974	-0.984685327658682	0.325057874309927	   
df.mm.trans3:probe6	-0.153305759482067	0.117738404379974	-1.30208796602431	0.193237802121722	   
df.mm.trans3:probe7	-0.176413681336617	0.117738404379974	-1.49835291437518	0.134411545159351	   
df.mm.trans3:probe8	-0.0158428996873447	0.117738404379974	-0.134560169816939	0.892991377742116	   
df.mm.trans3:probe9	-0.239062849986739	0.117738404379974	-2.03045770193401	0.0426198227157434	*  
df.mm.trans3:probe10	-0.135940482048139	0.117738404379974	-1.15459762482785	0.248578594938821