chr15.8651_chr15_43455180_43461365_-_2.R 

fitVsDatCorrelation=0.894555588581106
cont.fitVsDatCorrelation=0.254941305151724

fstatistic=9668.95433678828,48,600
cont.fstatistic=2056.00997577006,48,600

residuals=-0.65489411177711,-0.091060199863772,0.00132121401424072,0.0967646494897654,0.646547290112097
cont.residuals=-0.868173961871512,-0.26090703852646,-0.0589159824171863,0.237219355386870,1.07079832439272

predictedValues:
Include	Exclude	Both
chr15.8651_chr15_43455180_43461365_-_2.R.tl.Lung	85.2410639006953	104.263266018474	60.7721575728012
chr15.8651_chr15_43455180_43461365_-_2.R.tl.cerebhem	74.5927096720948	74.2733630017524	57.4767104711014
chr15.8651_chr15_43455180_43461365_-_2.R.tl.cortex	90.761880373403	86.9836535411812	58.8586223631504
chr15.8651_chr15_43455180_43461365_-_2.R.tl.heart	94.9278116976267	85.1115898291471	57.9259636838538
chr15.8651_chr15_43455180_43461365_-_2.R.tl.kidney	88.2146197280737	117.431217413871	61.9584659097418
chr15.8651_chr15_43455180_43461365_-_2.R.tl.liver	80.9937519308637	107.270082035804	60.3878888735509
chr15.8651_chr15_43455180_43461365_-_2.R.tl.stomach	80.4323492619339	88.940855604071	56.8332372359418
chr15.8651_chr15_43455180_43461365_-_2.R.tl.testicle	105.592002182549	85.5978013027893	59.7410390342358


diffExp=-19.0222021177788,0.319346670342412,3.77822683222176,9.81622186847954,-29.2165976857977,-26.2763301049405,-8.50850634213715,19.9942008797594
diffExpScore=2.33323634102657
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,-1,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,0,0,-1,-1,0,1
diffExp1.2Score=1.33333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	80.751736398894	81.7561168667683	74.555436861512
cerebhem	67.8647036978471	92.9883912623284	91.6385820851158
cortex	75.1688168756156	80.6889564101815	82.8358618843747
heart	70.6240322313215	76.0901567839933	81.5375667851697
kidney	79.0920494849929	76.2954590106163	87.147662557317
liver	78.2036790101872	84.0109843298292	72.9617796811423
stomach	69.3033055246523	68.7996984638096	85.806628638214
testicle	79.7676494869217	86.61146630412	83.8479689770589
cont.diffExp=-1.00438046787431,-25.1236875644813,-5.52013953456593,-5.46612455267183,2.79659047437664,-5.80730531964201,0.503607060842725,-6.8438168171983
cont.diffExpScore=1.11798935594792

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

tran.correlation=-0.0442638601231098
cont.tran.correlation=0.101625064022290

tran.covariance=0.000312872676811606
cont.tran.covariance=0.000854249248209375

tran.mean=90.6642510933956
cont.tran.mean=78.00107513388

weightedLogRatios:
wLogRatio
Lung	-0.915767748478883
cerebhem	0.0184911649505738
cortex	0.190782643700578
heart	0.491031422664986
kidney	-1.32249460506499
liver	-1.2741951044863
stomach	-0.446233014223369
testicle	0.956119884826727

cont.weightedLogRatios:
wLogRatio
Lung	-0.0543589687521813
cerebhem	-1.37794236532116
cortex	-0.308630115085049
heart	-0.32015899494991
kidney	0.156689432563964
liver	-0.314827109330133
stomach	0.0308857552759735
testicle	-0.36385134308649

varWeightedLogRatios=0.70568334687721
cont.varWeightedLogRatios=0.220318887939317

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.74431818518023	0.0789055112570287	60.1265755661347	3.51292482405903e-256	***
df.mm.trans1	-0.362137691256936	0.0666538089158862	-5.43311323309273	8.05976812947891e-08	***
df.mm.trans2	-0.141898606297003	0.0615280972918494	-2.30624076710722	0.0214371018355427	*  
df.mm.exp2	-0.416855315716762	0.0806700762427274	-5.16740946745223	3.23625616730186e-07	***
df.mm.exp3	-0.0864493922832602	0.0806700762427274	-1.07164138562537	0.284311993725098	   
df.mm.exp4	-0.0473564075571038	0.0806700762427274	-0.587038090984488	0.557398944842432	   
df.mm.exp5	0.133890584903365	0.0806700762427274	1.65973048668633	0.0974912087563244	.  
df.mm.exp6	-0.0163374126048743	0.0806700762427274	-0.202521348259505	0.83957782327118	   
df.mm.exp7	-0.150003906392701	0.0806700762427274	-1.85947396332384	0.0634492476707913	.  
df.mm.exp8	0.0339523798382549	0.0806700762427274	0.420879481210553	0.673993799859354	   
df.mm.trans1:exp2	0.283414804185732	0.0707523372867935	4.00573062394978	6.95920431427835e-05	***
df.mm.trans2:exp2	0.0776885925700467	0.0591956523917165	1.31240368897291	0.189885778992494	   
df.mm.trans1:exp3	0.149205481675223	0.0707523372867935	2.10884173437863	0.0353720525810454	*  
df.mm.trans2:exp3	-0.0947495016058427	0.0591956523917165	-1.60061588609337	0.109988384345564	   
df.mm.trans1:exp4	0.154989845047640	0.0707523372867935	2.190596819712	0.0288649863087911	*  
df.mm.trans2:exp4	-0.155599479988734	0.0591956523917165	-2.62856263428068	0.00879423261205439	** 
df.mm.trans1:exp5	-0.099601167462749	0.0707523372867935	-1.40774384680773	0.159724802416898	   
df.mm.trans2:exp5	-0.0149569109936610	0.0591956523917165	-0.252669079389248	0.800610480376832	   
df.mm.trans1:exp6	-0.034773860744029	0.0707523372867935	-0.491487095374301	0.623261716421407	   
df.mm.trans2:exp6	0.0447680934150747	0.0591956523917165	0.756273334379861	0.449782159168542	   
df.mm.trans1:exp7	0.0919370672689552	0.0707523372867935	1.29942092084237	0.194298567574907	   
df.mm.trans2:exp7	-0.00894359311556496	0.0591956523917165	-0.151085303636530	0.879959206765487	   
df.mm.trans1:exp8	0.180146963267886	0.0707523372867935	2.54616271597733	0.0111402627716908	*  
df.mm.trans2:exp8	-0.231211887294356	0.0591956523917165	-3.90589305046177	0.000104547858294646	***
df.mm.trans1:probe2	0.334118177643071	0.047462110722079	7.03968223409924	5.28778625464834e-12	***
df.mm.trans1:probe3	0.517680387509519	0.047462110722079	10.9072348370866	2.14934545580533e-25	***
df.mm.trans1:probe4	0.0751231018389226	0.047462110722079	1.58280153781648	0.113993643551027	   
df.mm.trans1:probe5	0.543367094338022	0.047462110722079	11.4484393144625	1.38336490906175e-27	***
df.mm.trans1:probe6	0.582846432955703	0.047462110722079	12.2802467924076	4.42320484842549e-31	***
df.mm.trans1:probe7	-0.135413549726795	0.047462110722079	-2.85308739258833	0.00447896313961503	** 
df.mm.trans1:probe8	-0.242914109114647	0.047462110722079	-5.1180637653699	4.16222427929483e-07	***
df.mm.trans1:probe9	-0.136581624780828	0.047462110722079	-2.87769807753812	0.00414842990198542	** 
df.mm.trans1:probe10	-0.0264231681483213	0.047462110722079	-0.556721303505565	0.57792544474424	   
df.mm.trans1:probe11	-0.0338482847702294	0.047462110722079	-0.713164337937535	0.47602134714979	   
df.mm.trans1:probe12	-0.148595773940255	0.047462110722079	-3.13082944857590	0.00182790079090219	** 
df.mm.trans2:probe2	0.264396351099807	0.047462110722079	5.57068253133571	3.83424602895818e-08	***
df.mm.trans2:probe3	0.271879652110003	0.047462110722079	5.72835147813025	1.60518325083164e-08	***
df.mm.trans2:probe4	-0.0363125347216371	0.047462110722079	-0.765084699546344	0.444521932173429	   
df.mm.trans2:probe5	0.0393576993562703	0.047462110722079	0.829244607066356	0.407295781690412	   
df.mm.trans2:probe6	0.128171717289741	0.047462110722079	2.70050605292859	0.00711883864687356	** 
df.mm.trans3:probe2	-0.0043344425197806	0.047462110722079	-0.091324268007412	0.927265415819808	   
df.mm.trans3:probe3	-0.237689254451452	0.047462110722079	-5.00797901389751	7.24268244584908e-07	***
df.mm.trans3:probe4	-0.0955721524098261	0.047462110722079	-2.01365154132024	0.0444920899954552	*  
df.mm.trans3:probe5	-0.112717448445435	0.047462110722079	-2.37489329342026	0.017867422750397	*  
df.mm.trans3:probe6	-0.0898133747752913	0.047462110722079	-1.89231733289752	0.0589298537134271	.  
df.mm.trans3:probe7	-0.196781954786575	0.047462110722079	-4.14608519917833	3.86961222588415e-05	***
df.mm.trans3:probe8	-0.220616331736533	0.047462110722079	-4.64826212699185	4.11873919304123e-06	***
df.mm.trans3:probe9	-0.240826284640357	0.047462110722079	-5.07407447701914	5.19986664960826e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.33318441073061	0.170706016884062	25.3838996997602	4.15274129975257e-97	***
df.mm.trans1	-0.015000113879398	0.144200399299343	-0.104022693087414	0.91718610060395	   
df.mm.trans2	0.0704824863643257	0.133111315646042	0.529500336032638	0.596654265998794	   
df.mm.exp2	-0.251438781575696	0.174523517784100	-1.44071575434748	0.150186626092640	   
df.mm.exp3	-0.19010001665352	0.174523517784100	-1.08925157518707	0.27648025306861	   
df.mm.exp4	-0.295351587735258	0.174523517784100	-1.69233116250058	0.09110201898003	.  
df.mm.exp5	-0.245955300270770	0.174523517784101	-1.40929602722675	0.159265720045453	   
df.mm.exp6	0.0167513820209064	0.174523517784100	0.0959835226426575	0.923565708308637	   
df.mm.exp7	-0.46598142308964	0.174523517784100	-2.67002080296191	0.00779014479964027	** 
df.mm.exp8	-0.0720321550415528	0.174523517784100	-0.412736094001167	0.679947419418391	   
df.mm.trans1:exp2	0.0775753880001472	0.153067250830218	0.506805914259175	0.612477200793301	   
df.mm.trans2:exp2	0.380172810764883	0.128065250141108	2.96858679732394	0.00311110129388342	** 
df.mm.trans1:exp3	0.118457027139576	0.153067250830218	0.773888774359503	0.439301379796314	   
df.mm.trans2:exp3	0.176961104070967	0.128065250141108	1.38180422773533	0.167546078198377	   
df.mm.trans1:exp4	0.161342608925188	0.153067250830218	1.05406354429236	0.292278098709212	   
df.mm.trans2:exp4	0.223529867392262	0.128065250141108	1.74543732312994	0.0814206666736163	.  
df.mm.trans1:exp5	0.225188192529405	0.153067250830218	1.47117160142363	0.141768966786288	   
df.mm.trans2:exp5	0.176828090820642	0.128065250141108	1.38076559117953	0.167865147486923	   
df.mm.trans1:exp6	-0.0488141546736471	0.153067250830218	-0.318906587848707	0.749908336038205	   
df.mm.trans2:exp6	0.0104555430680916	0.128065250141108	0.0816423116854199	0.934958380159933	   
df.mm.trans1:exp7	0.313094561652933	0.153067250830218	2.04547060167832	0.0412440700852084	*  
df.mm.trans2:exp7	0.293440154189359	0.128065250141108	2.2913331591984	0.0222898212764207	*  
df.mm.trans1:exp8	0.0597707171384106	0.153067250830218	0.390486644362014	0.696315326721818	   
df.mm.trans2:exp8	0.129723736167970	0.128065250141108	1.01295032044238	0.311492192117218	   
df.mm.trans1:probe2	0.122758430491320	0.102680633395614	1.19553635804280	0.232349830172652	   
df.mm.trans1:probe3	0.115432977745761	0.102680633395614	1.12419425093547	0.261380184817181	   
df.mm.trans1:probe4	0.140901920188630	0.102680633395614	1.37223462233384	0.170503191187587	   
df.mm.trans1:probe5	0.0234953620114068	0.102680633395614	0.228819800135850	0.81908694583322	   
df.mm.trans1:probe6	0.182827355009557	0.102680633395614	1.78054370102247	0.075492927990327	.  
df.mm.trans1:probe7	0.197779213918001	0.102680633395614	1.92615888096430	0.0545566437137386	.  
df.mm.trans1:probe8	0.087329400967786	0.102680633395614	0.850495347368167	0.395389012103363	   
df.mm.trans1:probe9	0.152945971068557	0.102680633395614	1.48953084930123	0.136873079434044	   
df.mm.trans1:probe10	0.221871072005613	0.102680633395614	2.16078791753042	0.0311067785151562	*  
df.mm.trans1:probe11	0.160299215898812	0.102680633395614	1.56114362171104	0.119017134768730	   
df.mm.trans1:probe12	0.131457617351556	0.102680633395614	1.28025717220762	0.200949432151420	   
df.mm.trans2:probe2	-0.0130016291379198	0.102680633395614	-0.126622019245113	0.899281988578654	   
df.mm.trans2:probe3	0.0178230623710220	0.102680633395614	0.173577643433035	0.862255946809713	   
df.mm.trans2:probe4	0.0135658645615977	0.102680633395614	0.132117071281887	0.894935984893597	   
df.mm.trans2:probe5	-0.0359870792061896	0.102680633395614	-0.350475820182532	0.726104640387106	   
df.mm.trans2:probe6	0.0187057905351342	0.102680633395614	0.182174475522209	0.855507334680752	   
df.mm.trans3:probe2	-0.0928955449672027	0.102680633395614	-0.904703661198597	0.365985516648456	   
df.mm.trans3:probe3	-0.0725017192878844	0.102680633395614	-0.706089521366173	0.480406539151477	   
df.mm.trans3:probe4	-0.0684617540678309	0.102680633395614	-0.666744563252328	0.505191612559919	   
df.mm.trans3:probe5	-0.175090935335786	0.102680633395614	-1.70519921377176	0.0886748859190429	.  
df.mm.trans3:probe6	-0.094519055072421	0.102680633395614	-0.9205149203575	0.357673685902755	   
df.mm.trans3:probe7	-0.104119938009092	0.102680633395614	-1.01401729387403	0.310983225468724	   
df.mm.trans3:probe8	-0.071349420398166	0.102680633395614	-0.694867357540218	0.487407424131113	   
df.mm.trans3:probe9	-0.0151759732867091	0.102680633395614	-0.147797815272898	0.882551986626495