chr16.9453_chr16_32671629_32678919_+_2.R 

fitVsDatCorrelation=0.856785396233178
cont.fitVsDatCorrelation=0.256024812095762

fstatistic=8864.87001877244,74,1198
cont.fstatistic=2511.11264395488,74,1198

residuals=-0.697473388862023,-0.104012688338629,-0.000929083268785603,0.0940887990626914,1.01934069682393
cont.residuals=-0.640756809398757,-0.225065222256152,-0.0772243641390192,0.167774231356643,1.46885612345161

predictedValues:
Include	Exclude	Both
chr16.9453_chr16_32671629_32678919_+_2.R.tl.Lung	55.6152356600112	46.1778209161305	66.1867878613749
chr16.9453_chr16_32671629_32678919_+_2.R.tl.cerebhem	65.9249347459188	76.6747292731665	67.6966058351268
chr16.9453_chr16_32671629_32678919_+_2.R.tl.cortex	58.6656920452367	49.488580297789	91.3697676008835
chr16.9453_chr16_32671629_32678919_+_2.R.tl.heart	55.8957035050667	46.7848130264963	82.721143206413
chr16.9453_chr16_32671629_32678919_+_2.R.tl.kidney	55.8421833324915	43.7535346723507	68.4459982517157
chr16.9453_chr16_32671629_32678919_+_2.R.tl.liver	56.0777836013464	48.2990271764732	64.2841737075901
chr16.9453_chr16_32671629_32678919_+_2.R.tl.stomach	69.7922029274172	49.1396679357773	63.5226317312136
chr16.9453_chr16_32671629_32678919_+_2.R.tl.testicle	58.553225446946	53.2214503394266	79.8647880996359


diffExp=9.43741474388066,-10.7497945272477,9.17711174744762,9.11089047857041,12.0886486601408,7.77875642487325,20.6525349916399,5.33177510751943
diffExpScore=1.32117255421727
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=1,0,0,0,1,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	64.3277598755427	78.078379943919	63.5959049352082
cerebhem	67.1833910433873	60.1141198525513	69.1718201785255
cortex	68.3396473133858	60.7628600209488	65.1875909801408
heart	71.1831851540954	65.5902218388379	63.3637156569973
kidney	66.6767568267645	63.0142455866655	61.421727925539
liver	68.7068249947529	61.5036026908735	67.8149957920332
stomach	66.5245796786451	68.0832378647837	65.034841500806
testicle	66.2335965283542	63.4848628960883	62.0903581638881
cont.diffExp=-13.7506200683763,7.06927119083601,7.576787292437,5.59296331525751,3.66251124009897,7.20322230387937,-1.55865818613862,2.74873363226586
cont.diffExpScore=2.51546444791075

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

tran.correlation=0.544773173140435
cont.tran.correlation=-0.536963765490212

tran.covariance=0.0086629220085652
cont.tran.covariance=-0.00138135926442580

tran.mean=55.6191615563778
cont.tran.mean=66.2379545068497

weightedLogRatios:
wLogRatio
Lung	0.729972425807606
cerebhem	-0.644107098313374
cortex	0.678206860080871
heart	0.700065263452161
kidney	0.9515673216442
liver	0.590156728940396
stomach	1.42801578998787
testicle	0.384018068727500

cont.weightedLogRatios:
wLogRatio
Lung	-0.825420520408563
cerebhem	0.461606485418482
cortex	0.48952102715428
heart	0.345677873057340
kidney	0.235677521839703
liver	0.46233441928033
stomach	-0.0974820250796276
testicle	0.176835570818165

varWeightedLogRatios=0.348105636815145
cont.varWeightedLogRatios=0.195524572069149

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.06318722508119	0.082040732847263	37.3373947156712	5.21348086466049e-203	***
df.mm.trans1	0.99863054706915	0.069151299035108	14.4412405985627	1.06127728790646e-43	***
df.mm.trans2	0.759058034312148	0.0607501895720008	12.4947434676318	9.16755792082142e-34	***
df.mm.exp2	0.654576939729036	0.0759145040959739	8.62255437908803	2.04287758135714e-17	***
df.mm.exp3	-0.199793556799977	0.075914504095974	-2.63182324878775	0.00860191641065074	** 
df.mm.exp4	-0.204905010305397	0.0759145040959739	-2.69915496051122	0.00704937467391818	** 
df.mm.exp5	-0.0834190666931392	0.0759145040959739	-1.09885545175501	0.272051902675661	   
df.mm.exp6	0.0823617316080933	0.0759145040959739	1.08492747978658	0.278172050914263	   
df.mm.exp7	0.33031672099848	0.0759145040959739	4.35116747362113	1.46944847360475e-05	***
df.mm.exp8	0.00558669967468014	0.0759145040959739	0.0735919932720265	0.941347327386906	   
df.mm.trans1:exp2	-0.48451738369443	0.0671996497900019	-7.21011768972817	9.87755344748178e-13	***
df.mm.trans2:exp2	-0.147504376639072	0.044959480995908	-3.28082916821253	0.00106482957835407	** 
df.mm.trans1:exp3	0.253191463785484	0.0671996497900019	3.76774975132615	0.000172736167331907	***
df.mm.trans2:exp3	0.269035882635936	0.044959480995908	5.98396326373124	2.87252637665346e-09	***
df.mm.trans1:exp4	0.209935340756807	0.0671996497900019	3.12405408975870	0.00182658659670054	** 
df.mm.trans2:exp4	0.217964036515705	0.044959480995908	4.84801051274465	1.41077080599376e-06	***
df.mm.trans1:exp5	0.0874914377955252	0.0671996497900019	1.30196270470062	0.193179408663118	   
df.mm.trans2:exp5	0.0294918526757598	0.044959480995908	0.655965149563094	0.511972506049262	   
df.mm.trans1:exp6	-0.0740791981312933	0.0671996497900019	-1.10237476479104	0.270520185002031	   
df.mm.trans2:exp6	-0.0374499284945885	0.044959480995908	-0.832970658580269	0.405027305364117	   
df.mm.trans1:exp7	-0.103251609761908	0.0671996497900018	-1.53649029547875	0.124682228296920	   
df.mm.trans2:exp7	-0.268149727510305	0.044959480995908	-5.9642531802071	3.2303044482149e-09	***
df.mm.trans1:exp8	0.0458922912312798	0.0671996497900019	0.682924559498341	0.494786553095526	   
df.mm.trans2:exp8	0.136375201209472	0.044959480995908	3.03329127001899	0.00247108372028251	** 
df.mm.trans1:probe2	0.129030583554068	0.0529739818263378	2.43573503643851	0.0150065851380455	*  
df.mm.trans1:probe3	0.119673631873094	0.0529739818263378	2.25910206760395	0.0240561733566976	*  
df.mm.trans1:probe4	-0.0111370576284686	0.0529739818263378	-0.210236369714074	0.833518944388578	   
df.mm.trans1:probe5	0.00104823439394162	0.0529739818263378	0.0197877214021405	0.984216007893272	   
df.mm.trans1:probe6	-0.256788758142925	0.0529739818263378	-4.84745056516128	1.41468028213568e-06	***
df.mm.trans1:probe7	-0.27442361119217	0.0529739818263378	-5.18034706342824	2.59640476639161e-07	***
df.mm.trans1:probe8	-0.31226727935808	0.0529739818263378	-5.89472923484912	4.87377025984117e-09	***
df.mm.trans1:probe9	-0.223149563693725	0.0529739818263378	-4.21243704929084	2.71565033020699e-05	***
df.mm.trans1:probe10	-0.113113889247564	0.0529739818263378	-2.13527254980341	0.0329411261190191	*  
df.mm.trans1:probe11	-0.292744263212532	0.0529739818263378	-5.52618952021054	4.01236818714588e-08	***
df.mm.trans1:probe12	-0.293682898855877	0.0529739818263378	-5.54390832500839	3.63573886751586e-08	***
df.mm.trans1:probe13	-0.0290370174449737	0.0529739818263378	-0.548137339197277	0.583699722548072	   
df.mm.trans1:probe14	-0.148720426857094	0.0529739818263378	-2.80742397927793	0.00507493650767899	** 
df.mm.trans1:probe15	-0.0588863915216265	0.0529739818263378	-1.11160969010544	0.266529035137592	   
df.mm.trans1:probe16	-0.106583341577439	0.0529739818263378	-2.01199415076719	0.0444440773008911	*  
df.mm.trans1:probe17	-0.122896831363403	0.0529739818263378	-2.31994702165849	0.0205111233868916	*  
df.mm.trans1:probe18	-0.0442686508719299	0.0529739818263378	-0.835667800412923	0.403508447182477	   
df.mm.trans2:probe2	0.214209082367512	0.0529739818263378	4.04366587110147	5.59838359135801e-05	***
df.mm.trans2:probe3	0.0095182742315594	0.0529739818263378	0.179678285516892	0.857435526777725	   
df.mm.trans2:probe4	0.0111111071443119	0.0529739818263378	0.209746497454863	0.833901195754748	   
df.mm.trans2:probe5	0.119108055633493	0.0529739818263378	2.24842557661531	0.0247302518316064	*  
df.mm.trans2:probe6	0.00495596424818797	0.0529739818263378	0.0935546862313443	0.925478550075775	   
df.mm.trans3:probe2	-0.290677768609554	0.0529739818263378	-5.48717990583509	4.97983698831507e-08	***
df.mm.trans3:probe3	-1.10766714205849	0.0529739818263378	-20.9096447703271	5.1599592420946e-83	***
df.mm.trans3:probe4	-0.921122493256256	0.0529739818263378	-17.3882057096619	1.46342189881195e-60	***
df.mm.trans3:probe5	-0.298843126612229	0.0529739818263378	-5.6413189326019	2.10409809400176e-08	***
df.mm.trans3:probe6	-0.327501978157614	0.0529739818263378	-6.18231756168998	8.64733247370038e-10	***
df.mm.trans3:probe7	-0.198953622672602	0.0529739818263378	-3.75568563686269	0.000181171228172897	***
df.mm.trans3:probe8	-1.04686168834293	0.0529739818263378	-19.7618085756666	1.84381124539034e-75	***
df.mm.trans3:probe9	-0.355594473975720	0.0529739818263378	-6.71262498525122	2.94336697953651e-11	***
df.mm.trans3:probe10	-0.775527905932364	0.0529739818263378	-14.6397888018828	9.01832007446577e-45	***
df.mm.trans3:probe11	-0.868223464173676	0.0529739818263378	-16.3896206069601	1.26200458769450e-54	***
df.mm.trans3:probe12	-0.392411471600846	0.0529739818263378	-7.40762650025574	2.41907725245431e-13	***
df.mm.trans3:probe13	-0.754747051722344	0.0529739818263378	-14.2475046372122	1.15021666407532e-42	***
df.mm.trans3:probe14	-0.523337732909033	0.0529739818263378	-9.8791466087006	3.51429345670738e-22	***
df.mm.trans3:probe15	-0.754964333407511	0.0529739818263378	-14.2516063051948	1.09387950151053e-42	***
df.mm.trans3:probe16	-0.946248702325453	0.0529739818263378	-17.8625179701895	1.87576147615728e-63	***
df.mm.trans3:probe17	-0.490203888695444	0.0529739818263378	-9.25367268600757	9.67540143160992e-20	***
df.mm.trans3:probe18	-0.821151016182233	0.0529739818263378	-15.5010249913660	1.57212807966641e-49	***
df.mm.trans3:probe19	-0.837506859634785	0.0529739818263378	-15.8097773805327	2.79750838994231e-51	***
df.mm.trans3:probe20	-0.792466652714845	0.0529739818263378	-14.9595447688405	1.62095019265928e-46	***
df.mm.trans3:probe21	-0.974716668585785	0.0529739818263378	-18.3999132211952	8.76634134598998e-67	***
df.mm.trans3:probe22	-0.87022530101337	0.0529739818263378	-16.4274096643554	7.59235986109963e-55	***
df.mm.trans3:probe23	-0.446759827891977	0.0529739818263378	-8.43357083023452	9.52666395247923e-17	***
df.mm.trans3:probe24	-0.775191460806953	0.0529739818263378	-14.6334376628177	9.7618291853544e-45	***
df.mm.trans3:probe25	-0.344093698324658	0.0529739818263378	-6.4955226407689	1.21011781049222e-10	***
df.mm.trans3:probe26	-0.0905979635761364	0.0529739818263378	-1.71023510887174	0.0874813247259053	.  
df.mm.trans3:probe27	-0.657086730484659	0.0529739818263378	-12.4039520502491	2.50996379403797e-33	***
df.mm.trans3:probe28	-0.806305286254242	0.0529739818263378	-15.2207793044049	5.82038496123461e-48	***
df.mm.trans3:probe29	-0.75446502464876	0.0529739818263378	-14.2421807581331	1.22767168750740e-42	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34285021652875	0.153791862912344	28.2384915189174	6.87574973531848e-135	***
df.mm.trans1	-0.140983000269672	0.129629596571463	-1.08758342229315	0.276997809563501	   
df.mm.trans2	0.0256207294299148	0.113881050330815	0.224977986728158	0.82203476732271	   
df.mm.exp2	-0.302078118869832	0.142307761057209	-2.12271008008054	0.0339827939925515	*  
df.mm.exp3	-0.214955670296611	0.142307761057209	-1.51049857505801	0.131179955945237	   
df.mm.exp4	-0.0693635100806112	0.142307761057209	-0.487419024551488	0.62605059135049	   
df.mm.exp5	-0.143701782664200	0.142307761057209	-1.00979582277618	0.312797026679855	   
df.mm.exp6	-0.236994429915683	0.142307761057209	-1.66536545972646	0.096101321822689	.  
df.mm.exp7	-0.125775983122041	0.142307761057209	-0.883830805766651	0.37696488558239	   
df.mm.exp8	-0.153756756477971	0.142307761057209	-1.08045236138709	0.280158247902419	   
df.mm.trans1:exp2	0.34551291633323	0.125971075215795	2.74279564369319	0.0061826629782056	** 
df.mm.trans2:exp2	0.0406096789069044	0.0842801142550193	0.481841763811869	0.630006367824418	   
df.mm.trans1:exp3	0.275454493781497	0.125971075215795	2.18664874702093	0.0289610749152067	*  
df.mm.trans2:exp3	-0.0357787755182622	0.0842801142550193	-0.424522152521067	0.671261278506887	   
df.mm.trans1:exp4	0.170628874616553	0.125971075215795	1.35450836094125	0.175829647670048	   
df.mm.trans2:exp4	-0.104923055634792	0.0842801142550193	-1.24493252723068	0.213400020442218	   
df.mm.trans1:exp5	0.179566939068068	0.125971075215795	1.42546166856526	0.154284279138076	   
df.mm.trans2:exp5	-0.0706505893028856	0.0842801142550193	-0.838283027110136	0.402038984481884	   
df.mm.trans1:exp6	0.30285170668649	0.125971075215795	2.40413687164049	0.0163617860053854	*  
df.mm.trans2:exp6	-0.00162300986871013	0.0842801142550193	-0.0192573287667733	0.984639030992403	   
df.mm.trans1:exp7	0.159356219294330	0.125971075215795	1.26502229993151	0.206109341631202	   
df.mm.trans2:exp7	-0.0112061672582879	0.0842801142550192	-0.132963361017519	0.894244686446579	   
df.mm.trans1:exp8	0.182953328689549	0.125971075215795	1.45234394781612	0.146667839457522	   
df.mm.trans2:exp8	-0.0531549388499833	0.0842801142550193	-0.630693720812294	0.528361098252378	   
df.mm.trans1:probe2	-0.0479840835309076	0.0993039319398153	-0.483204265869241	0.629039000909162	   
df.mm.trans1:probe3	-0.0160947541631018	0.0993039319398153	-0.162075698803711	0.871273573473036	   
df.mm.trans1:probe4	-0.132506089019515	0.0993039319398154	-1.33434886646606	0.182343116427003	   
df.mm.trans1:probe5	-0.101912851302218	0.0993039319398153	-1.02627206507778	0.304970530076447	   
df.mm.trans1:probe6	-0.143123714454781	0.0993039319398153	-1.44126936022557	0.149769923999228	   
df.mm.trans1:probe7	-0.054066873881303	0.0993039319398154	-0.544458540816601	0.586227294309847	   
df.mm.trans1:probe8	0.0372164017239452	0.0993039319398154	0.374772690234469	0.707895857830507	   
df.mm.trans1:probe9	-0.0744241348938173	0.0993039319398154	-0.749458087308397	0.453728288978245	   
df.mm.trans1:probe10	-0.138508487730051	0.0993039319398154	-1.39479358998591	0.163336644445483	   
df.mm.trans1:probe11	-0.214324269784810	0.0993039319398153	-2.15826569601196	0.0311053495797049	*  
df.mm.trans1:probe12	-0.194973086032506	0.0993039319398154	-1.96339744281900	0.0498310407227848	*  
df.mm.trans1:probe13	-0.239725809451397	0.0993039319398153	-2.41406160630867	0.0159249685252468	*  
df.mm.trans1:probe14	-0.0166453937713190	0.0993039319398154	-0.167620691811148	0.866909982101121	   
df.mm.trans1:probe15	0.0395145129032271	0.0993039319398153	0.397914887470674	0.690763820430048	   
df.mm.trans1:probe16	-0.146648342595943	0.0993039319398153	-1.47676269943492	0.140002065501797	   
df.mm.trans1:probe17	-0.148224099370222	0.0993039319398154	-1.49263071939644	0.135797168506039	   
df.mm.trans1:probe18	-0.187738754851295	0.0993039319398154	-1.89054704263952	0.0589259866587638	.  
df.mm.trans2:probe2	-0.18356087943737	0.0993039319398153	-1.84847544152249	0.0647798854231668	.  
df.mm.trans2:probe3	-0.0606005198420715	0.0993039319398153	-0.610252974462273	0.541809983608983	   
df.mm.trans2:probe4	-0.0912981923204777	0.0993039319398153	-0.919381443786237	0.358081165737700	   
df.mm.trans2:probe5	0.0233671536722755	0.0993039319398153	0.235309450651335	0.814008776289698	   
df.mm.trans2:probe6	-0.0644289079076771	0.0993039319398154	-0.648805204880761	0.51658864198309	   
df.mm.trans3:probe2	-0.0426804312423098	0.0993039319398154	-0.429795984998630	0.667421346322587	   
df.mm.trans3:probe3	-0.160243799565976	0.0993039319398154	-1.61367023878867	0.106862322951656	   
df.mm.trans3:probe4	-0.190669675357462	0.0993039319398154	-1.92006168973269	0.0550873351628112	.  
df.mm.trans3:probe5	-0.125894505987240	0.0993039319398154	-1.26776959912866	0.205126584754766	   
df.mm.trans3:probe6	-0.064084904412419	0.0993039319398154	-0.645341057101935	0.518829763379435	   
df.mm.trans3:probe7	-0.00986861127838587	0.0993039319398154	-0.0993778502584056	0.9208548947399	   
df.mm.trans3:probe8	0.0577842310180827	0.0993039319398154	0.581892679265749	0.560748450694574	   
df.mm.trans3:probe9	-0.118288879111402	0.0993039319398154	-1.19118021613779	0.233818789260325	   
df.mm.trans3:probe10	-0.050020399654246	0.0993039319398154	-0.503710162096719	0.614557675417752	   
df.mm.trans3:probe11	-0.00525609910605871	0.0993039319398153	-0.0529294158185423	0.957796971132216	   
df.mm.trans3:probe12	-0.106193541165337	0.0993039319398154	-1.06937901743606	0.285114332774096	   
df.mm.trans3:probe13	-0.138436721990239	0.0993039319398154	-1.39407090218886	0.163554696521808	   
df.mm.trans3:probe14	-0.109245343751903	0.0993039319398154	-1.10011095852794	0.27150478529689	   
df.mm.trans3:probe15	-0.0155350489348138	0.0993039319398154	-0.156439414143531	0.875713029442527	   
df.mm.trans3:probe16	-0.0353483367122545	0.0993039319398154	-0.355961098636838	0.721932370218825	   
df.mm.trans3:probe17	-0.105987743073601	0.0993039319398154	-1.06730661116054	0.286048432320594	   
df.mm.trans3:probe18	-0.0652495720382217	0.0993039319398154	-0.657069370402848	0.511262519074068	   
df.mm.trans3:probe19	0.000699235295649636	0.0993039319398154	0.00704136565381336	0.99438302180475	   
df.mm.trans3:probe20	0.0241212174744205	0.0993039319398154	0.242902944558525	0.8081221652908	   
df.mm.trans3:probe21	-0.0474976279250813	0.0993039319398154	-0.478305611844937	0.632519973423119	   
df.mm.trans3:probe22	-0.102968542250985	0.0993039319398153	-1.03690297291945	0.29999032186451	   
df.mm.trans3:probe23	-0.0961958930659275	0.0993039319398154	-0.96870175416849	0.332889539699601	   
df.mm.trans3:probe24	-0.0539775437011226	0.0993039319398154	-0.543558977441462	0.58684612484131	   
df.mm.trans3:probe25	-0.202268493805143	0.0993039319398154	-2.03686289005888	0.0418832633610354	*  
df.mm.trans3:probe26	-0.281779588602103	0.0993039319398154	-2.83754714539279	0.00462280665733351	** 
df.mm.trans3:probe27	-0.0265572595954435	0.0993039319398153	-0.267434119441906	0.789180964523312	   
df.mm.trans3:probe28	-0.0668501640075121	0.0993039319398154	-0.673187483130352	0.500957862310808	   
df.mm.trans3:probe29	-0.0347330490385412	0.0993039319398154	-0.349765093486849	0.726576473984834