chr8.23637_chr8_122047542_122050397_+_1.R 

fitVsDatCorrelation=0.78145562400614
cont.fitVsDatCorrelation=0.248416038922785

fstatistic=7758.61162454859,59,853
cont.fstatistic=3210.84401097304,59,853

residuals=-0.461365767821145,-0.0871464360789509,-0.00099631811769644,0.0753908570003386,1.63805239074323
cont.residuals=-0.529929728203797,-0.183394182603069,-0.0475807404653178,0.143360213871261,2.22994429842895

predictedValues:
Include	Exclude	Both
chr8.23637_chr8_122047542_122050397_+_1.R.tl.Lung	59.9896692702561	49.7328664037584	68.5636442249755
chr8.23637_chr8_122047542_122050397_+_1.R.tl.cerebhem	80.6006012717814	54.5667966116685	71.5282435393156
chr8.23637_chr8_122047542_122050397_+_1.R.tl.cortex	59.3783145229627	48.5865403143717	65.5157618093972
chr8.23637_chr8_122047542_122050397_+_1.R.tl.heart	56.351590752591	48.7368844433682	61.3139845824382
chr8.23637_chr8_122047542_122050397_+_1.R.tl.kidney	54.7588556935374	50.1019714741396	58.6182402793219
chr8.23637_chr8_122047542_122050397_+_1.R.tl.liver	58.3705596656523	51.7089348997195	55.9849821027012
chr8.23637_chr8_122047542_122050397_+_1.R.tl.stomach	59.6980144623615	52.6378663693208	63.8682432008444
chr8.23637_chr8_122047542_122050397_+_1.R.tl.testicle	59.3166117791994	49.3333070863344	60.5963765368993


diffExp=10.2568028664976,26.0338046601129,10.7917742085910,7.61470630922282,4.65688421939779,6.66162476593272,7.06014809304077,9.98330469286496
diffExpScore=0.988103600954413
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,1,1,0,0,0,0,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	54.6126766269208	55.5822318921524	55.4437457822258
cerebhem	55.4627179790807	57.5296370730507	54.4147022139764
cortex	58.3649030755093	53.4475945959533	52.6317208452533
heart	57.4091368875275	57.131744661062	58.7809005862853
kidney	60.9226750496602	53.4329471308387	52.9528280825516
liver	57.2262504835398	55.2236662683154	55.9838415117069
stomach	51.6597237552897	59.7115411198748	56.9119083654866
testicle	55.9357550862736	56.5817076196885	57.7174228236835
cont.diffExp=-0.969555265231534,-2.06691909396994,4.91730847955603,0.277392226465516,7.4897279188215,2.00258421522444,-8.05181736458506,-0.645952533414928
cont.diffExpScore=6.68424081586678

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=0.752968399690443
cont.tran.correlation=-0.850507053369918

tran.covariance=0.00362716539644752
cont.tran.covariance=-0.00157056376688111

tran.mean=55.8668365638139
cont.tran.mean=56.2646818315461

weightedLogRatios:
wLogRatio
Lung	0.750104033394763
cerebhem	1.63617946897111
cortex	0.79904819145859
heart	0.574748616997169
kidney	0.351826489524113
liver	0.485478116804078
stomach	0.506770692570042
testicle	0.73545407027113

cont.weightedLogRatios:
wLogRatio
Lung	-0.0705497241212373
cerebhem	-0.147601021057963
cortex	0.354051021152595
heart	0.0196056701373642
kidney	0.530485569554623
liver	0.143524691568018
stomach	-0.581864674060401
testicle	-0.04627160215654

varWeightedLogRatios=0.157586179104758
cont.varWeightedLogRatios=0.112878919282226

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95804416093299	0.0827062689503657	47.8566402663904	8.35747798307119e-244	***
df.mm.trans1	0.259110524169201	0.0699284332444713	3.70536721827222	0.000224690345575594	***
df.mm.trans2	-0.0177309629996228	0.0624554424796389	-0.283897804509244	0.776557594073988	   
df.mm.exp2	0.345763348021130	0.0795709889122512	4.34534436165459	1.55805052425276e-05	***
df.mm.exp3	0.011908924344519	0.0795709889122511	0.149664149048743	0.881064973049728	   
df.mm.exp4	0.0289626914509896	0.0795709889122511	0.363985566183285	0.715958988034476	   
df.mm.exp5	0.0728775935491249	0.0795709889122511	0.915881460635012	0.359987918501988	   
df.mm.exp6	0.214282805728317	0.0795709889122511	2.69297653149219	0.00722066858873893	** 
df.mm.exp7	0.122836301341113	0.0795709889122511	1.54373224488354	0.123024101333920	   
df.mm.exp8	0.10417780057483	0.0795709889122511	1.30924350694843	0.190804437475089	   
df.mm.trans1:exp2	-0.0504296071572725	0.070532143149271	-0.714987591551636	0.474812346430853	   
df.mm.trans2:exp2	-0.253003781309154	0.052091439973269	-4.85691663426822	1.41826413619086e-06	***
df.mm.trans1:exp3	-0.0221522085717147	0.070532143149271	-0.314072528957936	0.753542747548671	   
df.mm.trans2:exp3	-0.0352283904523826	0.052091439973269	-0.676279835429012	0.499046315130926	   
df.mm.trans1:exp4	-0.0915245900725734	0.070532143149271	-1.29762950600941	0.194765480785491	   
df.mm.trans2:exp4	-0.0491925776563121	0.052091439973269	-0.94435050521843	0.345257967413498	   
df.mm.trans1:exp5	-0.164110858673676	0.070532143149271	-2.32675275904149	0.0202117746803115	*  
df.mm.trans2:exp5	-0.0654832458443995	0.052091439973269	-1.25708265845603	0.209067796968985	   
df.mm.trans1:exp6	-0.241643526885714	0.070532143149271	-3.42600573435449	0.000641794371172963	***
df.mm.trans2:exp6	-0.17531822749418	0.052091439973269	-3.36558612286675	0.000797933074322203	***
df.mm.trans1:exp7	-0.127709908651103	0.070532143149271	-1.81066252844215	0.0705448269389835	.  
df.mm.trans2:exp7	-0.0660665580363229	0.052091439973269	-1.26828050962357	0.205043828532503	   
df.mm.trans1:exp8	-0.115460771185323	0.070532143149271	-1.63699507813008	0.102000343376002	   
df.mm.trans2:exp8	-0.112244357905830	0.052091439973269	-2.15475628939091	0.0314602222030635	*  
df.mm.trans1:probe2	-0.112298105336946	0.052091439973269	-2.15578807947279	0.0313792623881805	*  
df.mm.trans1:probe3	-0.442928866090913	0.052091439973269	-8.50291077225365	8.2122693613527e-17	***
df.mm.trans1:probe4	0.24383558171915	0.052091439973269	4.68091459641499	3.32086313838653e-06	***
df.mm.trans1:probe5	-0.0266720441152061	0.052091439973269	-0.512023551832951	0.608767061123116	   
df.mm.trans1:probe6	0.092439709303665	0.052091439973269	1.77456621185940	0.07632614601058	.  
df.mm.trans1:probe7	-0.417752584969337	0.052091439973269	-8.01960140060842	3.48958970295597e-15	***
df.mm.trans1:probe8	-0.305003392676816	0.052091439973269	-5.85515379942138	6.79652187197333e-09	***
df.mm.trans1:probe9	-0.438230645893778	0.052091439973269	-8.41271898259404	1.67599288188066e-16	***
df.mm.trans1:probe10	-0.152012799800952	0.052091439973269	-2.91819154699809	0.00361318931830272	** 
df.mm.trans1:probe11	-0.307526842440325	0.052091439973269	-5.90359649489693	5.13043667102875e-09	***
df.mm.trans1:probe12	-0.370807090505319	0.052091439973269	-7.11838817847234	2.31767368833143e-12	***
df.mm.trans1:probe13	-0.289402522902434	0.052091439973269	-5.55566371463224	3.69672935936163e-08	***
df.mm.trans1:probe14	-0.334261543699303	0.052091439973269	-6.41682287667285	2.30518439284098e-10	***
df.mm.trans1:probe15	-0.314874789445959	0.052091439973269	-6.04465512198433	2.236335178001e-09	***
df.mm.trans1:probe16	-0.390991242620745	0.052091439973269	-7.5058635895146	1.53578154634081e-13	***
df.mm.trans2:probe2	-0.138694993964898	0.052091439973269	-2.66252946810589	0.00790163591718174	** 
df.mm.trans2:probe3	-0.198682132647270	0.052091439973269	-3.81410329123605	0.000146529486788031	***
df.mm.trans2:probe4	-0.185872711099207	0.052091439973269	-3.56820067163796	0.000379488673730504	***
df.mm.trans2:probe5	-0.0917645832127745	0.052091439973269	-1.76160580816856	0.078494120510433	.  
df.mm.trans2:probe6	-0.125223704961604	0.052091439973269	-2.40392097100527	0.0164326648214680	*  
df.mm.trans3:probe2	0.0856260612217394	0.052091439973269	1.64376452763984	0.100593345216648	   
df.mm.trans3:probe3	0.204737814640611	0.052091439973269	3.93035429133219	9.16914762931978e-05	***
df.mm.trans3:probe4	-0.226951762013827	0.052091439973269	-4.35679570636344	1.48044199248155e-05	***
df.mm.trans3:probe5	0.0406156932048537	0.052091439973269	0.779699951195358	0.435783784655065	   
df.mm.trans3:probe6	-0.0962312047239389	0.052091439973269	-1.84735159506668	0.0650423063480197	.  
df.mm.trans3:probe7	-0.140627282388909	0.052091439973269	-2.69962363223348	0.0070792102078291	** 
df.mm.trans3:probe8	0.143407259689793	0.052091439973269	2.75299088993093	0.00603082564735732	** 
df.mm.trans3:probe9	-0.147992884680426	0.052091439973269	-2.84102118805641	0.00460406170019646	** 
df.mm.trans3:probe10	-0.136501233693086	0.0520914399732689	-2.6204158257697	0.00893844979274216	** 
df.mm.trans3:probe11	-0.0501155274104074	0.052091439973269	-0.96206838275395	0.336287893534973	   
df.mm.trans3:probe12	0.249793712670462	0.052091439973269	4.79529290798345	1.91639762820997e-06	***
df.mm.trans3:probe13	0.358158376334739	0.052091439973269	6.87557066033364	1.19274468598894e-11	***
df.mm.trans3:probe14	-0.0301759117807242	0.052091439973269	-0.579287341571074	0.562548251641412	   
df.mm.trans3:probe15	0.0482609578806758	0.052091439973269	0.926466189175058	0.354465807233076	   
df.mm.trans3:probe16	0.151415204342387	0.052091439973269	2.90671949978896	0.00374700018785601	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97505278114371	0.128395419105205	30.9594595262516	1.17960208137960e-141	***
df.mm.trans1	-0.00717071845853865	0.108558765952584	-0.0660537948789012	0.947350474574735	   
df.mm.trans2	0.041310178287618	0.0969574956571512	0.42606482363874	0.670168099106337	   
df.mm.exp2	0.0686162195017779	0.123528126702649	0.555470412555901	0.578718588203893	   
df.mm.exp3	0.0793366810049958	0.123528126702649	0.642256003735664	0.520879544649968	   
df.mm.exp4	0.0189857072733636	0.123528126702649	0.153695419659890	0.877886249211983	   
df.mm.exp5	0.115870808902582	0.123528126702649	0.938011544378885	0.348503989305758	   
df.mm.exp6	0.0305805199901629	0.123528126702649	0.247559165725673	0.804535083859755	   
df.mm.exp7	-0.0100615356400883	0.123528126702649	-0.0814513739393779	0.935102101478003	   
df.mm.exp8	0.00156977317579645	0.123528126702649	0.0127078198115571	0.98986387114753	   
df.mm.trans1:exp2	-0.0531712010551125	0.109495981320034	-0.485599566432525	0.62737580702968	   
df.mm.trans2:exp2	-0.0341795570261676	0.080868141581569	-0.422657876856139	0.672651414114376	   
df.mm.trans1:exp3	-0.0128879750201319	0.109495981320034	-0.117702721732436	0.90633091910455	   
df.mm.trans2:exp3	-0.118498627326717	0.080868141581569	-1.46533140256712	0.143199010568528	   
df.mm.trans1:exp4	0.0309517341525177	0.109495981320034	0.282674613071435	0.77749485664921	   
df.mm.trans2:exp4	0.00851062353841015	0.080868141581569	0.105240745885396	0.91620952136903	   
df.mm.trans1:exp5	-0.00653139943573162	0.109495981320034	-0.0596496725906468	0.95244862733125	   
df.mm.trans2:exp5	-0.155306845624102	0.080868141581569	-1.92049480285694	0.0551285543916987	.  
df.mm.trans1:exp6	0.0161661692021513	0.109495981320034	0.147641666911053	0.88266046047006	   
df.mm.trans2:exp6	-0.0370525017416494	0.080868141581569	-0.458184162724646	0.646936836789399	   
df.mm.trans1:exp7	-0.0455260525254959	0.109495981320034	-0.415778295939763	0.677676817967351	   
df.mm.trans2:exp7	0.0817232760478379	0.080868141581569	1.01057442955340	0.312506728624621	   
df.mm.trans1:exp8	0.0223679999271422	0.109495981320034	0.204281469123192	0.838182279205845	   
df.mm.trans2:exp8	0.0162523929678492	0.080868141581569	0.200973988643673	0.840766823674403	   
df.mm.trans1:probe2	0.0374828957962478	0.080868141581569	0.463506333435894	0.643119719675659	   
df.mm.trans1:probe3	0.0265307748617588	0.080868141581569	0.328074496864728	0.742935847812322	   
df.mm.trans1:probe4	0.00605927196411675	0.080868141581569	0.074927800313118	0.940289725273214	   
df.mm.trans1:probe5	0.0984722837237719	0.080868141581569	1.21768946086694	0.223678738202169	   
df.mm.trans1:probe6	0.034433067419857	0.080868141581569	0.425792737986015	0.670366289495738	   
df.mm.trans1:probe7	0.07020855767713	0.080868141581569	0.868185620493244	0.385536911520407	   
df.mm.trans1:probe8	-0.00474603670616513	0.080868141581569	-0.058688583827266	0.953213898128499	   
df.mm.trans1:probe9	0.0890297111157873	0.080868141581569	1.10092441070859	0.2712401245046	   
df.mm.trans1:probe10	0.102424521485863	0.080868141581569	1.26656207849850	0.205657659074183	   
df.mm.trans1:probe11	0.0126333980200066	0.080868141581569	0.156222188032647	0.875894852125323	   
df.mm.trans1:probe12	0.0566245742178565	0.080868141581569	0.700208674397954	0.483987922344523	   
df.mm.trans1:probe13	0.00762671347648435	0.080868141581569	0.094310482809742	0.924884671296146	   
df.mm.trans1:probe14	0.130101331329442	0.080868141581569	1.60880822515519	0.108028298371940	   
df.mm.trans1:probe15	0.119085114510391	0.080868141581569	1.47258379111227	0.141232167250086	   
df.mm.trans1:probe16	0.15316882849597	0.080868141581569	1.8940564912262	0.0585555898879398	.  
df.mm.trans2:probe2	0.0435254647061343	0.080868141581569	0.538227586969235	0.590560345807619	   
df.mm.trans2:probe3	0.0182242580221494	0.080868141581569	0.225357695450034	0.821754998143288	   
df.mm.trans2:probe4	0.0668035902224013	0.080868141581569	0.826080443001386	0.408989450085314	   
df.mm.trans2:probe5	-0.0620090402435318	0.080868141581569	-0.766791953305683	0.443417385128957	   
df.mm.trans2:probe6	-0.0335306224393853	0.080868141581569	-0.414633275646183	0.678514637651342	   
df.mm.trans3:probe2	0.111158871938730	0.080868141581569	1.37456938869564	0.169625976883040	   
df.mm.trans3:probe3	-0.0212654009395255	0.080868141581569	-0.262963888171905	0.792641914213076	   
df.mm.trans3:probe4	0.0340171360576891	0.080868141581569	0.420649410168245	0.67411706022417	   
df.mm.trans3:probe5	-0.00114711795732469	0.080868141581569	-0.0141850416602888	0.988685670784026	   
df.mm.trans3:probe6	0.0891485279230878	0.080868141581569	1.10239367666396	0.270601492844638	   
df.mm.trans3:probe7	0.0387786306406704	0.080868141581569	0.479529143149107	0.631685160792377	   
df.mm.trans3:probe8	-0.032480272645798	0.080868141581569	-0.401644850624349	0.688046022611153	   
df.mm.trans3:probe9	0.0548081511634041	0.080868141581569	0.677747133685778	0.498115823661208	   
df.mm.trans3:probe10	-0.0607585016741241	0.080868141581569	-0.751328032100738	0.452662493614011	   
df.mm.trans3:probe11	-0.0177435210345351	0.080868141581569	-0.219412993640244	0.82638084122288	   
df.mm.trans3:probe12	-0.0366755172593874	0.080868141581569	-0.453522444588318	0.650287936857509	   
df.mm.trans3:probe13	-0.0846711758650055	0.080868141581569	-1.04702759589944	0.295383438678427	   
df.mm.trans3:probe14	0.02139280273681	0.080868141581569	0.264539314474437	0.791428279731253	   
df.mm.trans3:probe15	0.0921562435432013	0.080868141581569	1.1395865138096	0.254778508367737	   
df.mm.trans3:probe16	-0.087892036772856	0.080868141581569	-1.08685614698097	0.27740741154615