chr19.11782_chr19_44979308_44981669_+_2.R 

fitVsDatCorrelation=0.904255577148413
cont.fitVsDatCorrelation=0.198159338641211

fstatistic=7219.9290842492,52,692
cont.fstatistic=1359.37055310887,52,692

residuals=-0.819882402861446,-0.093650075469896,-0.00613967445259646,0.0878166218199753,1.34202683986580
cont.residuals=-0.713767007718165,-0.290255796750256,-0.114233363725596,0.189810553753354,1.82967136275558

predictedValues:
Include	Exclude	Both
chr19.11782_chr19_44979308_44981669_+_2.R.tl.Lung	79.8819485009186	73.9463247987836	51.9072258420424
chr19.11782_chr19_44979308_44981669_+_2.R.tl.cerebhem	69.9673273893945	58.0992417305066	62.668360470305
chr19.11782_chr19_44979308_44981669_+_2.R.tl.cortex	74.2297109673365	62.8344222094627	48.3563256758312
chr19.11782_chr19_44979308_44981669_+_2.R.tl.heart	94.2896699374352	67.429292723277	52.5281275688627
chr19.11782_chr19_44979308_44981669_+_2.R.tl.kidney	89.8825757168735	80.6941655697942	51.444286499714
chr19.11782_chr19_44979308_44981669_+_2.R.tl.liver	86.6844810174663	81.2559363428782	52.5773350061622
chr19.11782_chr19_44979308_44981669_+_2.R.tl.stomach	73.3806407151068	69.5162960789704	49.0362624414946
chr19.11782_chr19_44979308_44981669_+_2.R.tl.testicle	82.7570518970683	66.8082823498567	49.3008916449736


diffExp=5.93562370213503,11.8680856588879,11.3952887578738,26.8603772141581,9.18841014707937,5.42854467458812,3.86434463613641,15.9487695472116
diffExpScore=0.989069777314366
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=0,0,0,1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,1,0,0,0,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	76.6108071283099	65.9529830252383	88.2940753725414
cerebhem	77.1054629572311	69.8635169448833	96.0637371878083
cortex	82.4241085767066	67.2314048556665	74.055458596887
heart	75.385205235459	74.3733069515559	81.6178331691284
kidney	82.0979968881227	69.608512893138	87.1485077849269
liver	80.8541569501438	74.4885379902881	73.0470473634015
stomach	73.7927538411799	69.7778485458173	80.1217788588886
testicle	75.0984337175036	73.557729230455	75.8788373533313
cont.diffExp=10.6578241030716,7.24194601234787,15.1927037210402,1.01189828390308,12.4894839949847,6.36561895985565,4.01490529536258,1.54070448704866
cont.diffExpScore=0.983197537189228

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

tran.correlation=0.607103047210192
cont.tran.correlation=-0.20540304812399

tran.covariance=0.0078355534348093
cont.tran.covariance=-0.000406255428880071

tran.mean=75.7285854965706
cont.tran.mean=74.2639228582312

weightedLogRatios:
wLogRatio
Lung	0.335243337242547
cerebhem	0.772330640762844
cortex	0.703950216807036
heart	1.46815212047979
kidney	0.479294418989644
liver	0.286488566010270
stomach	0.230927352589479
testicle	0.922451841852555

cont.weightedLogRatios:
wLogRatio
Lung	0.638706376445459
cerebhem	0.423702933272735
cortex	0.878110510205766
heart	0.0583241877381841
kidney	0.713806728286555
liver	0.356842551176581
stomach	0.239064586768907
testicle	0.0893102967016947

varWeightedLogRatios=0.171064677126830
cont.varWeightedLogRatios=0.0886780191875546

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30826677757343	0.103507915153385	41.6225828835326	1.43101499385626e-190	***
df.mm.trans1	-0.125035961922814	0.0929652447038656	-1.34497534343191	0.1790738160768	   
df.mm.trans2	-0.0344747705266308	0.0854941683432386	-0.403241194045223	0.686895355890553	   
df.mm.exp2	-0.562107092245485	0.117105837938305	-4.79999205967531	1.94527088602839e-06	***
df.mm.exp3	-0.165360893075592	0.117105837938305	-1.41206361686861	0.158380746393612	   
df.mm.exp4	0.0616709994261715	0.117105837938305	0.526626174338652	0.598621967831636	   
df.mm.exp5	0.214239588700505	0.117105837938305	1.82945267693122	0.067761943159873	.  
df.mm.exp6	0.163162235348464	0.117105837938305	1.39328865427208	0.163979880234818	   
df.mm.exp7	-0.0897701066733905	0.117105837938305	-0.766572429298397	0.443597197849771	   
df.mm.exp8	-0.0146372607707521	0.117105837938305	-0.124991725676935	0.900566401689321	   
df.mm.trans1:exp2	0.429585572651468	0.112120303718653	3.83146993366553	0.000138963664427826	***
df.mm.trans2:exp2	0.320920215447689	0.0975881982819205	3.28851460624972	0.00105814452923327	** 
df.mm.trans1:exp3	0.0919754793519705	0.112120303718653	0.820328489144724	0.412311344804004	   
df.mm.trans2:exp3	0.00252445127850828	0.0975881982819205	0.025868407481153	0.979369756578921	   
df.mm.trans1:exp4	0.104150738439963	0.112120303718653	0.928919517568479	0.353254701275054	   
df.mm.trans2:exp4	-0.153930955246354	0.0975881982819205	-1.57735215893284	0.115171519516856	   
df.mm.trans1:exp5	-0.0962853856087737	0.112120303718653	-0.8587685050371	0.39076561086791	   
df.mm.trans2:exp5	-0.126912803270733	0.0975881982819205	-1.30049335375675	0.193864864152099	   
df.mm.trans1:exp6	-0.0814372650229324	0.112120303718653	-0.72633824848785	0.467877038555306	   
df.mm.trans2:exp6	-0.0688978435864426	0.0975881982819205	-0.706005898248117	0.480422080870184	   
df.mm.trans1:exp7	0.00488035592933084	0.112120303718653	0.0435278514904602	0.96529331991352	   
df.mm.trans2:exp7	0.0279918182549005	0.0975881982819205	0.286836100550145	0.774323680610814	   
df.mm.trans1:exp8	0.0499965895960119	0.112120303718653	0.445919141652253	0.655795137866796	   
df.mm.trans2:exp8	-0.0868751685595855	0.0975881982819205	-0.890222076942272	0.373656036820429	   
df.mm.trans1:probe2	0.646208193369906	0.0560601518593267	11.5270503546165	3.03250364678855e-28	***
df.mm.trans1:probe3	0.0331656870948013	0.0560601518593267	0.591608941374703	0.554305697338245	   
df.mm.trans1:probe4	-0.178092415830715	0.0560601518593267	-3.17680937214740	0.00155523039691104	** 
df.mm.trans1:probe5	0.210779930121267	0.0560601518593267	3.75988867547457	0.000184352339021898	***
df.mm.trans1:probe6	0.166002388172819	0.0560601518593267	2.96114767204651	0.00316986796811218	** 
df.mm.trans1:probe7	0.0169468763097752	0.0560601518593267	0.302298080681275	0.762515677519006	   
df.mm.trans1:probe8	-0.179731837222129	0.0560601518593267	-3.20605334200904	0.00140758891674792	** 
df.mm.trans1:probe9	-0.0926989734163976	0.0560601518593267	-1.65356265264871	0.0986699373741294	.  
df.mm.trans1:probe10	-0.128508666233296	0.0560601518593267	-2.29233532145555	0.0221855862937157	*  
df.mm.trans1:probe11	-0.136501343537712	0.0560601518593267	-2.43490855822579	0.0151472032844716	*  
df.mm.trans1:probe12	-0.209124193257780	0.0560601518593267	-3.73035367050986	0.000206880411112864	***
df.mm.trans1:probe13	-0.153020159999919	0.0560601518593267	-2.72957091489686	0.00650288841250317	** 
df.mm.trans1:probe14	-0.219228281679269	0.0560601518593267	-3.91059022154248	0.000101141308790998	***
df.mm.trans1:probe15	0.0597455488331933	0.0560601518593267	1.06574004621169	0.286912864660116	   
df.mm.trans1:probe16	0.0742295183383537	0.0560601518593267	1.32410483875641	0.185905414287493	   
df.mm.trans1:probe17	0.854706817733204	0.0560601518593267	15.2462451382212	1.82404099767366e-45	***
df.mm.trans1:probe18	0.795663067191922	0.0560601518593267	14.1930237575614	2.57527723918746e-40	***
df.mm.trans1:probe19	0.74735831448666	0.0560601518593267	13.3313644308711	2.98296251448622e-36	***
df.mm.trans1:probe20	0.86733247065579	0.0560601518593267	15.4714613123456	1.36775084509130e-46	***
df.mm.trans1:probe21	0.774180288150151	0.0560601518593267	13.8098143239573	1.72071462241760e-38	***
df.mm.trans1:probe22	0.983563906975739	0.0560601518593267	17.5447956231696	2.75377246105126e-57	***
df.mm.trans2:probe2	0.0255989060278083	0.0560601518593267	0.456632834175055	0.648078266547459	   
df.mm.trans2:probe3	0.165673370351895	0.0560601518593267	2.95527865796053	0.00323002387204653	** 
df.mm.trans2:probe4	0.0357716931872725	0.0560601518593267	0.638094832083855	0.523623045464434	   
df.mm.trans2:probe5	-0.0131818691160167	0.0560601518593267	-0.235137948771426	0.814171228779054	   
df.mm.trans2:probe6	0.0520652412719319	0.0560601518593267	0.928738855409822	0.353348275971379	   
df.mm.trans3:probe2	-0.285520033270418	0.0560601518593267	-5.09310131707958	4.54707335009573e-07	***
df.mm.trans3:probe3	-0.312374081928462	0.0560601518593267	-5.57212336335284	3.60647731564796e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91928270068399	0.237604973282653	16.4949523006055	8.47044804218772e-52	***
df.mm.trans1	0.447741863219803	0.213404013126380	2.09809486082459	0.0362587843101132	*  
df.mm.trans2	0.201005213129774	0.196253972992455	1.02420965071368	0.306094158294059	   
df.mm.exp2	-0.0203014387964484	0.268819340562899	-0.075520752167377	0.939822213780243	   
df.mm.exp3	0.268196940453876	0.268819340562899	0.997684689993958	0.318781011747288	   
df.mm.exp4	0.182653103827751	0.268819340562899	0.679464146609694	0.49707102522129	   
df.mm.exp5	0.136179586827158	0.268819340562899	0.506584037227389	0.612608075600962	   
df.mm.exp6	0.365181290374893	0.268819340562899	1.35846360462835	0.174759295637744	   
df.mm.exp7	0.116022177696766	0.268819340562899	0.431599071159908	0.666167264652847	   
df.mm.exp8	0.240725156460799	0.268819340562899	0.895490465666376	0.370836340200746	   
df.mm.trans1:exp2	0.0267374203110724	0.257374923744103	0.103885102410584	0.917290629684247	   
df.mm.trans2:exp2	0.0779029099043747	0.224016117135749	0.347755826234445	0.728129317199736	   
df.mm.trans1:exp3	-0.195057118538116	0.257374923744103	-0.757871496183727	0.448785919935915	   
df.mm.trans2:exp3	-0.248998577688545	0.224016117135749	-1.11152081766356	0.266730259129393	   
df.mm.trans1:exp4	-0.198780217124214	0.257374923744103	-0.772337157919289	0.440178452890737	   
df.mm.trans2:exp4	-0.0624981136564898	0.224016117135749	-0.278989362263686	0.780336342933811	   
df.mm.trans1:exp5	-0.0670041211267919	0.257374923744103	-0.260336633235533	0.794681561539398	   
df.mm.trans2:exp5	-0.0822348250421083	0.224016117135749	-0.367093341736102	0.71366162231358	   
df.mm.trans1:exp6	-0.311272442108258	0.257374923744103	-1.20941246948263	0.226917439991451	   
df.mm.trans2:exp6	-0.243478139064118	0.224016117135749	-1.08687777547976	0.277469262769113	   
df.mm.trans1:exp7	-0.153499789365759	0.257374923744103	-0.596405380651527	0.55109949100657	   
df.mm.trans2:exp7	-0.0596476841325306	0.224016117135749	-0.266265145986730	0.790114329413289	   
df.mm.trans1:exp8	-0.26066360588781	0.257374923744103	-1.01277778773420	0.311520291300587	   
df.mm.trans2:exp8	-0.131596736584081	0.224016117135749	-0.587443163762792	0.557097735776465	   
df.mm.trans1:probe2	0.0286123119813271	0.128687461872051	0.22233954703198	0.82411517233623	   
df.mm.trans1:probe3	-0.0267395184001958	0.128687461872051	-0.207786508578293	0.835456802615634	   
df.mm.trans1:probe4	-0.0781801281354672	0.128687461872051	-0.607519388432717	0.543705581029045	   
df.mm.trans1:probe5	-0.0151829188559923	0.128687461872051	-0.117982891535214	0.906115450688902	   
df.mm.trans1:probe6	-0.0392533545067791	0.128687461872051	-0.305028585813644	0.760436100628267	   
df.mm.trans1:probe7	-0.0457735809861493	0.128687461872051	-0.355695732282451	0.722176841882251	   
df.mm.trans1:probe8	-0.096227976196405	0.128687461872051	-0.747764971012332	0.454855963697276	   
df.mm.trans1:probe9	-0.0557134880494784	0.128687461872051	-0.432936412289117	0.665195866990619	   
df.mm.trans1:probe10	-0.121892012228029	0.128687461872051	-0.947194159048854	0.343870487276064	   
df.mm.trans1:probe11	0.0985726128920353	0.128687461872051	0.765984591335262	0.443946664359714	   
df.mm.trans1:probe12	-0.113738241207967	0.128687461872051	-0.883833122150256	0.377093260316863	   
df.mm.trans1:probe13	0.0419643990150116	0.128687461872051	0.326095475072273	0.74445070984714	   
df.mm.trans1:probe14	-0.0287838346673494	0.128687461872051	-0.223672409484368	0.823078231076702	   
df.mm.trans1:probe15	-0.120955658752291	0.128687461872051	-0.939917976411347	0.347587601864764	   
df.mm.trans1:probe16	-0.000694438547376598	0.128687461872051	-0.00539631862556315	0.995695936829131	   
df.mm.trans1:probe17	0.0729758455232426	0.128687461872051	0.567078132256579	0.57084499690331	   
df.mm.trans1:probe18	-0.0320607608730008	0.128687461872051	-0.249136632323027	0.803329021259604	   
df.mm.trans1:probe19	-0.0302422979475812	0.128687461872051	-0.235005784616763	0.814273765799405	   
df.mm.trans1:probe20	0.0528238432572597	0.128687461872051	0.410481662228915	0.6815797148335	   
df.mm.trans1:probe21	-0.0839182909572113	0.128687461872051	-0.6521093021529	0.514547199621111	   
df.mm.trans1:probe22	-0.112752809428800	0.128687461872051	-0.876175563559604	0.381238636272136	   
df.mm.trans2:probe2	0.145122888557890	0.128687461872051	1.12771583530165	0.259830798004683	   
df.mm.trans2:probe3	0.145602051869366	0.128687461872051	1.13143930069996	0.258262178472281	   
df.mm.trans2:probe4	0.0570876741879576	0.128687461872051	0.443614889574227	0.657459706012782	   
df.mm.trans2:probe5	0.0392411680192186	0.128687461872051	0.304933887484971	0.760508194776005	   
df.mm.trans2:probe6	0.230833980871640	0.128687461872051	1.79375657514443	0.0732885118596595	.  
df.mm.trans3:probe2	-0.147315958535329	0.128687461872051	-1.14475766630474	0.252705337360346	   
df.mm.trans3:probe3	-0.114754346209973	0.128687461872051	-0.891729035141503	0.372848142438522