chr19.12186_chr19_60871703_60878446_+_2.R 

fitVsDatCorrelation=0.863802210775138
cont.fitVsDatCorrelation=0.235723750827644

fstatistic=14644.0325616618,68,1060
cont.fstatistic=3924.63528963457,68,1060

residuals=-0.597415307063512,-0.0782114319983353,-0.00297905908237305,0.0713157155707067,1.06646911662794
cont.residuals=-0.447680990036918,-0.169808592925657,-0.0483747190894892,0.0992795376489358,1.57472805858252

predictedValues:
Include	Exclude	Both
chr19.12186_chr19_60871703_60878446_+_2.R.tl.Lung	51.7663796196291	40.6433090250509	52.6454395605419
chr19.12186_chr19_60871703_60878446_+_2.R.tl.cerebhem	56.469794354611	43.2271843714668	54.1776426737216
chr19.12186_chr19_60871703_60878446_+_2.R.tl.cortex	56.4048560078592	43.642361014064	59.1090855085311
chr19.12186_chr19_60871703_60878446_+_2.R.tl.heart	54.4800173536218	46.9283967266756	54.1954450941627
chr19.12186_chr19_60871703_60878446_+_2.R.tl.kidney	52.6506270074815	42.9655627594362	53.4430598475485
chr19.12186_chr19_60871703_60878446_+_2.R.tl.liver	58.2094398677507	49.659407033564	54.4131048546062
chr19.12186_chr19_60871703_60878446_+_2.R.tl.stomach	54.4726678408613	45.1788760944384	54.630584465289
chr19.12186_chr19_60871703_60878446_+_2.R.tl.testicle	53.4621941115339	42.412412331113	49.8222794963829


diffExp=11.1230705945782,13.2426099831443,12.7624949937952,7.55162062694618,9.6850642480453,8.55003283418674,9.29379174642297,11.0497817804209
diffExpScore=0.98813175651197
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=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,1,1,0,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	52.3464136217216	56.4386995316644	52.4229766573997
cerebhem	50.0806364702464	54.0091871175697	55.5943315248844
cortex	50.0994622511007	53.6019082256977	53.2308189719266
heart	49.8891456650282	54.6529181670066	48.6161867014707
kidney	51.64997103426	50.0843784077117	50.9081416032627
liver	50.8860846958725	51.1223914064625	52.1304440796362
stomach	50.8533260127221	54.2759587936779	56.0277274914312
testicle	51.5821743645949	57.9221582546714	55.1679229944597
cont.diffExp=-4.09228590994278,-3.92855064732326,-3.50244597459699,-4.76377250197842,1.56559262654832,-0.236306710589986,-3.42263278095577,-6.33998389007652
cont.diffExpScore=1.08285977009004

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=0.714428337747593
cont.tran.correlation=0.186592305217239

tran.covariance=0.00178023615260385
cont.tran.covariance=0.000136545291819141

tran.mean=49.5358428449473
cont.tran.mean=52.4684258762505

weightedLogRatios:
wLogRatio
Lung	0.925483542598009
cerebhem	1.04224507845632
cortex	1.00155622313055
heart	0.585388899550852
kidney	0.78507217667246
liver	0.632995167274437
stomach	0.7303498147255
testicle	0.894462777930218

cont.weightedLogRatios:
wLogRatio
Lung	-0.300749358365844
cerebhem	-0.298408223214346
cortex	-0.266770022356152
heart	-0.360729407369925
kidney	0.120939579107321
liver	-0.0182168674879780
stomach	-0.25803655573715
testicle	-0.463827352159905

varWeightedLogRatios=0.0283030028181508
cont.varWeightedLogRatios=0.0359421613261186

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81056349827033	0.0601761390272949	63.3234959880347	0	***
df.mm.trans1	0.145641873290976	0.0513578945313414	2.83582250830196	0.0046577283242312	** 
df.mm.trans2	-0.115462043202782	0.0447722428671643	-2.57887556684058	0.0100461342363307	*  
df.mm.exp2	0.119911618179213	0.0562195517564197	2.13291665324458	0.0331607861653722	*  
df.mm.exp3	0.0412033696121711	0.0562195517564197	0.732901069554794	0.463780807781752	   
df.mm.exp4	0.165864577979932	0.0562195517564197	2.95030061247317	0.00324443209580619	** 
df.mm.exp5	0.0574647885615826	0.0562195517564197	1.02214953279169	0.306943349461509	   
df.mm.exp6	0.284634852721106	0.0562195517564197	5.06291572644214	4.86612180151773e-07	***
df.mm.exp7	0.119739319481224	0.0562195517564197	2.12985190632636	0.0334136901948379	*  
df.mm.exp8	0.129958049132903	0.0562195517564197	2.31161660085742	0.0209898476965735	*  
df.mm.trans1:exp2	-0.0329466326304660	0.0511663790307561	-0.643911749366939	0.519771956806437	   
df.mm.trans2:exp2	-0.0582762757931905	0.0342192819755517	-1.70302450632443	0.088856630807865	.  
df.mm.trans1:exp3	0.0446109881612897	0.0511663790307561	0.871880891443852	0.383470794478519	   
df.mm.trans2:exp3	0.0299906691369593	0.0342192819755517	0.876426020814418	0.380997047515424	   
df.mm.trans1:exp4	-0.114771494061092	0.0511663790307561	-2.24310369885863	0.0250960817387443	*  
df.mm.trans2:exp4	-0.0220758346528213	0.0342192819755517	-0.645128517559007	0.518983507556766	   
df.mm.trans1:exp5	-0.0405275375328378	0.0511663790307561	-0.792073590129892	0.428494986850264	   
df.mm.trans2:exp5	-0.00190008260826136	0.0342192819755517	-0.0555266650427936	0.955729350942223	   
df.mm.trans1:exp6	-0.167328210550178	0.0511663790307561	-3.27027657066756	0.00110901078653081	** 
df.mm.trans2:exp6	-0.0842812360527287	0.0342192819755517	-2.46297500084731	0.0139370285199318	*  
df.mm.trans1:exp7	-0.0687811475704447	0.0511663790307561	-1.34426451262264	0.179150479933300	   
df.mm.trans2:exp7	-0.0139439077413033	0.0342192819755517	-0.407486859346309	0.683732722381712	   
df.mm.trans1:exp8	-0.0977241936024357	0.0511663790307561	-1.90992982997084	0.0564119230215474	.  
df.mm.trans2:exp8	-0.0873512088937993	0.0342192819755517	-2.55268970740556	0.0108286930262855	*  
df.mm.trans1:probe2	-0.00577358277234726	0.0386297690334518	-0.149459417356277	0.881219560704739	   
df.mm.trans1:probe3	0.0343356419218378	0.0386297690334518	0.888838913121758	0.374291315270844	   
df.mm.trans1:probe4	0.122421064196865	0.0386297690334518	3.16908610276323	0.00157297111814747	** 
df.mm.trans1:probe5	0.120916416605111	0.0386297690334518	3.13013563452587	0.00179503333201014	** 
df.mm.trans1:probe6	0.108836646485603	0.0386297690334518	2.81742938694132	0.00493090564618255	** 
df.mm.trans1:probe7	0.255962367545409	0.0386297690334518	6.62603929430062	5.47598364396713e-11	***
df.mm.trans1:probe8	0.514295924392189	0.0386297690334518	13.3134610239795	1.65458878488475e-37	***
df.mm.trans1:probe9	-0.0989566172737404	0.0386297690334518	-2.56166732936063	0.0105544898667844	*  
df.mm.trans1:probe10	-0.0208023726842841	0.0386297690334518	-0.538506266145937	0.590340641805613	   
df.mm.trans1:probe11	-0.0824429292468429	0.0386297690334518	-2.13418126252453	0.0330569092770613	*  
df.mm.trans1:probe12	-0.0999637143584174	0.0386297690334518	-2.58773782136396	0.009792946147613	** 
df.mm.trans1:probe13	-0.0678338439228463	0.0386297690334518	-1.75599921045619	0.0793773226881429	.  
df.mm.trans1:probe14	0.0810002601045937	0.0386297690334518	2.09683521624090	0.0362446652016898	*  
df.mm.trans1:probe15	-0.0493357041552104	0.0386297690334518	-1.27714209506372	0.201831760609801	   
df.mm.trans1:probe16	-0.0775651401870278	0.0386297690334518	-2.0079110522214	0.0449061182970765	*  
df.mm.trans1:probe17	-0.222515650132293	0.0386297690334518	-5.76021176672331	1.10001547832474e-08	***
df.mm.trans1:probe18	-0.244729382694599	0.0386297690334518	-6.3352535833873	3.49615021247279e-10	***
df.mm.trans1:probe19	-0.136084433485215	0.0386297690334518	-3.52278662001245	0.000445228272916603	***
df.mm.trans1:probe20	-0.196813651101493	0.0386297690334518	-5.09486999342555	4.12939638898227e-07	***
df.mm.trans1:probe21	-0.167177137400923	0.0386297690334518	-4.32767633832226	1.64941181397708e-05	***
df.mm.trans1:probe22	-0.155817641222054	0.0386297690334518	-4.03361565758062	5.88641311210613e-05	***
df.mm.trans2:probe2	0.0388714466007488	0.0386297690334518	1.00625625193585	0.314521962573463	   
df.mm.trans2:probe3	-0.0099493568865141	0.0386297690334518	-0.257556727245725	0.796799019727014	   
df.mm.trans2:probe4	0.132293023813604	0.0386297690334518	3.42463926457970	0.000639183910322876	***
df.mm.trans2:probe5	0.0277987423373407	0.0386297690334518	0.719619687947607	0.471917813750342	   
df.mm.trans2:probe6	0.0543053378479603	0.0386297690334518	1.40578986638346	0.160079573602680	   
df.mm.trans3:probe2	-0.0770901515974994	0.0386297690334518	-1.99561513118918	0.0462313964253071	*  
df.mm.trans3:probe3	-0.0695076820525649	0.0386297690334518	-1.79932947547199	0.0722508981794022	.  
df.mm.trans3:probe4	-0.0828966084587346	0.0386297690334518	-2.14592555256931	0.0321054675274003	*  
df.mm.trans3:probe5	-0.0155709920851178	0.0386297690334518	-0.403082712496520	0.686968663153863	   
df.mm.trans3:probe6	-0.103437802507466	0.0386297690334518	-2.67767074708352	0.00752818141665585	** 
df.mm.trans3:probe7	-0.0603978145726055	0.0386297690334518	-1.56350441858204	0.118232475816287	   
df.mm.trans3:probe8	-0.0240753183423672	0.0386297690334518	-0.623232262184093	0.533265972910759	   
df.mm.trans3:probe9	-0.060071078090567	0.0386297690334518	-1.55504626596519	0.120233550898405	   
df.mm.trans3:probe10	-0.0611032573162586	0.0386297690334518	-1.58176605361906	0.114001299677162	   
df.mm.trans3:probe11	0.370333646033943	0.0386297690334518	9.58674243465577	6.23416056705887e-21	***
df.mm.trans3:probe12	0.0439132880612212	0.0386297690334518	1.13677324923155	0.255890005545066	   
df.mm.trans3:probe13	0.979838522900129	0.0386297690334518	25.3648558460608	2.61504517762135e-111	***
df.mm.trans3:probe14	0.211340800448541	0.0386297690334518	5.47093098758962	5.58472536337429e-08	***
df.mm.trans3:probe15	0.634798570754846	0.0386297690334518	16.432885482829	3.14540387487503e-54	***
df.mm.trans3:probe16	0.278679138135595	0.0386297690334518	7.2141031413952	1.03207520793290e-12	***
df.mm.trans3:probe17	0.0440178298316346	0.0386297690334518	1.13947949814344	0.25476064074446	   
df.mm.trans3:probe18	0.221609964724018	0.0386297690334518	5.73676649560378	1.25830308361895e-08	***
df.mm.trans3:probe19	0.103508424426981	0.0386297690334518	2.67949892056944	0.00748747639253134	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0619591736525	0.116071409392949	34.9953463552864	5.7473571393401e-179	***
df.mm.trans1	-0.149514434907241	0.0990622412482021	-1.50929792243071	0.131520667206136	   
df.mm.trans2	-0.0235595708767527	0.0863594344083458	-0.272808304479539	0.785053712004998	   
df.mm.exp2	-0.14698617671542	0.108439702401772	-1.35546458962819	0.175558318803852	   
df.mm.exp3	-0.110736120737773	0.108439702401772	-1.02117691477511	0.307403627633156	   
df.mm.exp4	-0.00484399855109811	0.108439702401772	-0.0446699727480899	0.964378781462142	   
df.mm.exp5	-0.103517609010991	0.108439702401772	-0.954609858919164	0.339992671914002	   
df.mm.exp6	-0.121630558512540	0.108439702401772	-1.12164231198179	0.262268575094763	   
df.mm.exp7	-0.134513323549404	0.108439702401772	-1.24044349597188	0.215085892445819	   
df.mm.exp8	-0.0397990457124195	0.108439702401771	-0.367015445735577	0.713680750986836	   
df.mm.trans1:exp2	0.102737185230636	0.0986928344628407	1.04097917330886	0.29812258347887	   
df.mm.trans2:exp2	0.102985255504261	0.0660042394131575	1.56028243670256	0.118991642883885	   
df.mm.trans1:exp3	0.0668629679937405	0.0986928344628407	0.677485537401557	0.498245779800246	   
df.mm.trans2:exp3	0.0591657042950191	0.0660042394131575	0.896392486619955	0.370246703184178	   
df.mm.trans1:exp4	-0.0432359713619196	0.0986928344628407	-0.438086225785708	0.661413036743765	   
df.mm.trans2:exp4	-0.0273084759799783	0.0660042394131575	-0.413738211708482	0.679149533052483	   
df.mm.trans1:exp5	0.090123816502066	0.0986928344628407	0.91317487224464	0.361358225690875	   
df.mm.trans2:exp5	-0.0159283248612962	0.0660042394131575	-0.241322754461148	0.80935164837852	   
df.mm.trans1:exp6	0.0933366322763504	0.0986928344628407	0.945728560582511	0.344502576183398	   
df.mm.trans2:exp6	0.0226980626277673	0.0660042394131575	0.343887950676735	0.730998755911442	   
df.mm.trans1:exp7	0.105575424951779	0.0986928344628407	1.06973748931621	0.284981014173167	   
df.mm.trans2:exp7	0.0954396195216327	0.0660042394131575	1.44596196199191	0.148483466759211	   
df.mm.trans1:exp8	0.0250917731987877	0.0986928344628407	0.254241083816831	0.799358622474113	   
df.mm.trans2:exp8	0.0657439706683833	0.0660042394131575	0.99605678745656	0.319449873106735	   
df.mm.trans1:probe2	0.0572267736334392	0.074511456014203	0.768026511554558	0.442642490991524	   
df.mm.trans1:probe3	0.0817930730005331	0.074511456014203	1.09772479798197	0.27257396859116	   
df.mm.trans1:probe4	0.147876287099378	0.074511456014203	1.98461142768692	0.0474452132162711	*  
df.mm.trans1:probe5	0.116860570487064	0.074511456014203	1.56835709216028	0.117096269291590	   
df.mm.trans1:probe6	0.09456868783627	0.074511456014203	1.26918319537661	0.204654333048903	   
df.mm.trans1:probe7	0.0416446836389898	0.074511456014203	0.558903098485309	0.5763459057477	   
df.mm.trans1:probe8	0.0718335673863283	0.074511456014203	0.964060712659215	0.335235367470437	   
df.mm.trans1:probe9	0.0693878984489626	0.074511456014203	0.93123798890383	0.351942511290764	   
df.mm.trans1:probe10	0.00245503380287663	0.074511456014203	0.0329484073215353	0.97372193314234	   
df.mm.trans1:probe11	0.0566524102594551	0.074511456014203	0.76031812139943	0.447233530601943	   
df.mm.trans1:probe12	0.109739830194389	0.074511456014203	1.47279138087800	0.141104155311423	   
df.mm.trans1:probe13	0.110759082877162	0.074511456014203	1.48647052147323	0.137452072840058	   
df.mm.trans1:probe14	0.0443831702909607	0.074511456014203	0.595655657064339	0.551532441502298	   
df.mm.trans1:probe15	0.0683795328320648	0.074511456014203	0.917704960952992	0.358982104349664	   
df.mm.trans1:probe16	0.223782561282741	0.074511456014203	3.00333094068226	0.00273308973928812	** 
df.mm.trans1:probe17	0.0714896870967957	0.074511456014203	0.959445579524961	0.337553111806255	   
df.mm.trans1:probe18	0.0841795549939078	0.074511456014203	1.12975318825956	0.258835821351449	   
df.mm.trans1:probe19	0.150669664889007	0.074511456014203	2.02210066677917	0.0434167515179567	*  
df.mm.trans1:probe20	0.0799928734161485	0.074511456014203	1.07356476030881	0.283262128434821	   
df.mm.trans1:probe21	0.0793540163749136	0.074511456014203	1.06499081644288	0.28712261271658	   
df.mm.trans1:probe22	0.0999572695204482	0.074511456014203	1.34150203025686	0.18004483824042	   
df.mm.trans2:probe2	0.038281836227555	0.074511456014203	0.513771147087207	0.607519079374611	   
df.mm.trans2:probe3	-0.0514567377854564	0.074511456014203	-0.69058827377696	0.489975504438741	   
df.mm.trans2:probe4	2.11166357828312e-05	0.074511456014203	0.000283401196438921	0.999773931856905	   
df.mm.trans2:probe5	-0.0563157342716022	0.074511456014203	-0.755799675433366	0.449937229448559	   
df.mm.trans2:probe6	-0.0616434224124188	0.074511456014203	-0.827301272983696	0.408252522602524	   
df.mm.trans3:probe2	-0.00434053641181488	0.074511456014203	-0.0582532759927213	0.953557864002875	   
df.mm.trans3:probe3	0.118063236314660	0.074511456014203	1.58449777564615	0.113378761003635	   
df.mm.trans3:probe4	0.075727371134409	0.074511456014203	1.01631849899664	0.309709655181474	   
df.mm.trans3:probe5	-0.0363126786519016	0.074511456014203	-0.487343565598555	0.626115646634988	   
df.mm.trans3:probe6	0.0575422519733485	0.074511456014203	0.772260469079816	0.440132316218283	   
df.mm.trans3:probe7	0.129685830523646	0.074511456014203	1.74048176563516	0.0820646446622453	.  
df.mm.trans3:probe8	0.143903385514572	0.074511456014203	1.93129208865738	0.0537133016123546	.  
df.mm.trans3:probe9	0.0521929760091849	0.074511456014203	0.700469146640164	0.483788082550351	   
df.mm.trans3:probe10	0.049768593908	0.074511456014203	0.66793210829907	0.504322330209528	   
df.mm.trans3:probe11	0.0352319463263122	0.074511456014203	0.472839321776191	0.636425161675432	   
df.mm.trans3:probe12	0.114484826337922	0.074511456014203	1.53647281185995	0.124720883817478	   
df.mm.trans3:probe13	-0.000732889008924063	0.074511456014203	-0.00983592387168442	0.99215404553773	   
df.mm.trans3:probe14	0.128208342744428	0.074511456014203	1.72065276405268	0.0856056837237664	.  
df.mm.trans3:probe15	0.0931964874827916	0.074511456014203	1.25076723054542	0.211295421231852	   
df.mm.trans3:probe16	0.0714976957639064	0.074511456014203	0.959553061884576	0.337499016589241	   
df.mm.trans3:probe17	0.175371276750349	0.074511456014203	2.35361494904784	0.0187736840297388	*  
df.mm.trans3:probe18	0.0477456789704913	0.074511456014203	0.640783062424526	0.521802140799797	   
df.mm.trans3:probe19	0.0875026547368797	0.074511456014203	1.17435169593519	0.240517943606953