chr2.13752_chr2_151753119_151757362_+_2.R 

fitVsDatCorrelation=0.92847891571477
cont.fitVsDatCorrelation=0.206940918459986

fstatistic=11825.8851336634,56,784
cont.fstatistic=1692.10171681242,56,784

residuals=-0.493250172442471,-0.0880597593976511,-0.0093083377016783,0.085392318870118,0.768185121467857
cont.residuals=-0.744370854040563,-0.293691729297247,-0.0753289585174557,0.244142264062077,1.2174954618245

predictedValues:
Include	Exclude	Both
chr2.13752_chr2_151753119_151757362_+_2.R.tl.Lung	57.9010951605293	104.751305320451	68.0434011045314
chr2.13752_chr2_151753119_151757362_+_2.R.tl.cerebhem	58.9893858964006	83.749882949285	67.0378454454226
chr2.13752_chr2_151753119_151757362_+_2.R.tl.cortex	53.0468399975132	105.272790304713	74.6711508379457
chr2.13752_chr2_151753119_151757362_+_2.R.tl.heart	54.4599037671206	111.992922827968	67.9468463837121
chr2.13752_chr2_151753119_151757362_+_2.R.tl.kidney	58.5498641147477	112.282251877301	70.2939001617167
chr2.13752_chr2_151753119_151757362_+_2.R.tl.liver	59.0206440972573	134.988286271877	63.1554056780306
chr2.13752_chr2_151753119_151757362_+_2.R.tl.stomach	56.6283185840103	121.213041981020	66.5364294386821
chr2.13752_chr2_151753119_151757362_+_2.R.tl.testicle	57.473437709272	120.329599548675	68.1762418481864


diffExp=-46.8502101599221,-24.7604970528844,-52.2259503071993,-57.5330190608476,-53.7323877625533,-75.9676421746199,-64.58472339701,-62.856161839403
diffExpScore=0.997724741977188
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.540039648354	60.5206194566628	68.0329270982418
cerebhem	68.8764602198364	70.1724104411683	75.7070205098813
cortex	65.4232584166756	64.4608649202431	74.3429800170636
heart	74.5598216880745	64.2681863265887	72.4260726529294
kidney	67.9849553341119	64.5622355605579	74.5538636586151
liver	72.2040409330591	58.517607587737	68.1153531802708
stomach	71.832073664672	63.4537430072183	72.90039661928
testicle	67.4857537624181	66.223787690262	66.5229416158542
cont.diffExp=6.01942019169129,-1.29595022133189,0.96239349643244,10.2916353614858,3.42271977355402,13.6864333453221,8.3783306574536,1.26196607215614
cont.diffExpScore=1.03640547741923

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

tran.correlation=0.0489968065257942
cont.tran.correlation=-0.198893803623571

tran.covariance=5.82109091063969e-06
cont.tran.covariance=-0.000493910823555889

tran.mean=84.4155981505088
cont.tran.mean=66.6928661661025

weightedLogRatios:
wLogRatio
Lung	-2.58197003280045
cerebhem	-1.49043818467877
cortex	-2.95663554097157
heart	-3.14195553974076
kidney	-2.86203852792496
liver	-3.71585234362586
stomach	-3.36153822768300
testicle	-3.26656538335479

cont.weightedLogRatios:
wLogRatio
Lung	0.393539287889266
cerebhem	-0.0790672626146614
cortex	0.061848853037485
heart	0.629400937525712
kidney	0.216620558902435
liver	0.877329124400742
stomach	0.522411649488519
testicle	0.079329301929502

varWeightedLogRatios=0.451469375000766
cont.varWeightedLogRatios=0.106389985590369

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36893305605363	0.0723143418903793	60.4158586228504	3.32540974418813e-297	***
df.mm.trans1	-0.468203042211497	0.0630284315358267	-7.42844190792468	2.87396942881245e-13	***
df.mm.trans2	0.245532778126526	0.0562460735382315	4.36533188329374	1.43936575567306e-05	***
df.mm.exp2	-0.190244582558912	0.0735873304637984	-2.58528990465965	0.00990915845051652	** 
df.mm.exp3	-0.175543102362908	0.0735873304637984	-2.38550714173912	0.0172921381701901	*  
df.mm.exp4	0.00699510579447085	0.0735873304637984	0.095058561716845	0.924292602074715	   
df.mm.exp5	0.0480299840391412	0.0735873304637984	0.652693659851811	0.514145123332279	   
df.mm.exp6	0.347297277250742	0.0735873304637984	4.71952542729616	2.79932896427783e-06	***
df.mm.exp7	0.146129688610138	0.0735873304637984	1.98579956208667	0.0474034970230718	*  
df.mm.exp8	0.129281818539318	0.0735873304637984	1.75684887227861	0.0793339094759105	.  
df.mm.trans1:exp2	0.208865811129973	0.0687146190238277	3.0396124448794	0.00244757408115036	** 
df.mm.trans2:exp2	-0.0335096643750651	0.0535970332051484	-0.625214911556825	0.532011948238589	   
df.mm.trans1:exp3	0.0879821000654263	0.0687146190238277	1.28039857188057	0.200783678485022	   
df.mm.trans2:exp3	0.180509066429029	0.0535970332051484	3.36789287828882	0.000794403744523095	***
df.mm.trans1:exp4	-0.0682666846779279	0.0687146190238276	-0.993481236565622	0.320782010319177	   
df.mm.trans2:exp4	0.0598515544504165	0.0535970332051484	1.11669528836285	0.264466748001623	   
df.mm.trans1:exp5	-0.0368875139290038	0.0687146190238277	-0.536821923093433	0.59154292826178	   
df.mm.trans2:exp5	0.0213968031432859	0.0535970332051484	0.399216185369578	0.689842638172495	   
df.mm.trans1:exp6	-0.328146293681917	0.0687146190238276	-4.77549462317659	2.13934196403294e-06	***
df.mm.trans2:exp6	-0.0936982909510673	0.0535970332051484	-1.74819920707975	0.0808209557915919	.  
df.mm.trans1:exp7	-0.168356799273780	0.0687146190238277	-2.45008706539434	0.0144997880203681	*  
df.mm.trans2:exp7	-0.000169033677723663	0.0535970332051484	-0.00315378795458077	0.99748444774488	   
df.mm.trans1:exp8	-0.136695229441039	0.0687146190238276	-1.98931801388055	0.0470131956402713	*  
df.mm.trans2:exp8	0.00936380194498425	0.0535970332051484	0.174707467652982	0.861354605680842	   
df.mm.trans1:probe2	-0.0540081538035935	0.0436673782965714	-1.23680779360720	0.216528801731820	   
df.mm.trans1:probe3	-0.0366091004432133	0.0436673782965714	-0.83836268334176	0.40208256350743	   
df.mm.trans1:probe4	-0.0300088181194629	0.0436673782965714	-0.68721364300039	0.492151337831857	   
df.mm.trans1:probe5	-0.0810164850241917	0.0436673782965714	-1.8553091159712	0.0639272572068663	.  
df.mm.trans1:probe6	-0.0110856315380524	0.0436673782965714	-0.253865287326461	0.799666194786042	   
df.mm.trans1:probe7	-0.0344929825860408	0.0436673782965714	-0.789902758800362	0.429823399332945	   
df.mm.trans1:probe8	-0.0611366204747423	0.0436673782965714	-1.40005246157731	0.161893201306291	   
df.mm.trans1:probe9	0.00675220504666355	0.0436673782965714	0.154628129969362	0.877154289334796	   
df.mm.trans1:probe10	-0.0481967679449691	0.0436673782965714	-1.10372479010844	0.270051187933183	   
df.mm.trans1:probe11	1.22412758811137	0.0436673782965714	28.0329993661994	2.51162599143240e-120	***
df.mm.trans1:probe12	0.711265671553572	0.0436673782965714	16.2882613818247	1.34777405580169e-51	***
df.mm.trans1:probe13	0.328100518421865	0.0436673782965714	7.513629881637	1.57032586371659e-13	***
df.mm.trans1:probe14	0.709711678674472	0.0436673782965714	16.2526743385965	2.08117131549263e-51	***
df.mm.trans1:probe15	0.46316727519446	0.0436673782965714	10.6067113085840	1.18388497071803e-24	***
df.mm.trans1:probe16	0.309022836534045	0.0436673782965714	7.0767435231693	3.27771200962513e-12	***
df.mm.trans1:probe17	0.259892909264377	0.0436673782965714	5.95164902044928	4.00103992516303e-09	***
df.mm.trans1:probe18	0.159848489427631	0.0436673782965714	3.66059277344301	0.000268502367760729	***
df.mm.trans1:probe19	0.148171495556998	0.0436673782965714	3.39318505797798	0.000725521462626697	***
df.mm.trans1:probe20	0.216528500690517	0.0436673782965714	4.95858714530426	8.7080028923823e-07	***
df.mm.trans1:probe21	0.170925480970884	0.0436673782965714	3.91426020151763	9.85654773464376e-05	***
df.mm.trans1:probe22	0.231222182666737	0.0436673782965714	5.29507819536059	1.54626213646131e-07	***
df.mm.trans2:probe2	0.180630976953356	0.0436673782965714	4.13651984615571	3.90713625049395e-05	***
df.mm.trans2:probe3	0.218098062137321	0.0436673782965714	4.99453071480696	7.27419959359e-07	***
df.mm.trans2:probe4	0.256997468897156	0.0436673782965713	5.88534230637186	5.88476989479516e-09	***
df.mm.trans2:probe5	-0.00562116109230879	0.0436673782965714	-0.128726782133155	0.897606856937285	   
df.mm.trans2:probe6	-0.167503931007447	0.0436673782965714	-3.83590537242304	0.000135153964759240	***
df.mm.trans3:probe2	-0.069582351964807	0.0436673782965714	-1.59346300783691	0.111459412828352	   
df.mm.trans3:probe3	-0.0498888370740014	0.0436673782965714	-1.14247383333106	0.253605894465217	   
df.mm.trans3:probe4	0.112331917896838	0.0436673782965714	2.57244474660064	0.0102812623559049	*  
df.mm.trans3:probe5	0.0659276618161292	0.0436673782965714	1.50976917753969	0.131505231184268	   
df.mm.trans3:probe6	0.314646827257789	0.0436673782965714	7.20553510496631	1.35976531646439e-12	***
df.mm.trans3:probe7	0.143745510109581	0.0436673782965714	3.29182826441557	0.00103993668358960	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06694580577679	0.190500325776754	21.3487603719	4.50019668981696e-80	***
df.mm.trans1	0.149736377460838	0.166038111208618	0.90181932552041	0.367429812979403	   
df.mm.trans2	0.0135527506883320	0.148171096529357	0.0914668987797277	0.92714497157914	   
df.mm.exp2	0.0756026101550339	0.193853806311971	0.389998069129300	0.696643902491365	   
df.mm.exp3	-0.0425493085017590	0.193853806311971	-0.21949173612451	0.826324149068379	   
df.mm.exp4	0.111303983288058	0.193853806311970	0.574164549077441	0.56602114542767	   
df.mm.exp5	-0.00540196053260561	0.193853806311971	-0.0278661566433841	0.977775992574522	   
df.mm.exp6	0.0468248871547394	0.193853806311971	0.241547421975216	0.809194028290005	   
df.mm.exp7	0.0547520081303862	0.193853806311971	0.282439685720039	0.777680934584052	   
df.mm.exp8	0.126613193553806	0.193853806311970	0.653137516165387	0.513859113152689	   
df.mm.trans1:exp2	-0.0410920083403283	0.181017443669859	-0.22700579296254	0.820478415762922	   
df.mm.trans2:exp2	0.0723684855308353	0.141192632323556	0.512551429489581	0.608409504294373	   
df.mm.trans1:exp3	0.0256232705772927	0.181017443669859	0.141551388959092	0.887470730255538	   
df.mm.trans2:exp3	0.105623478299188	0.141192632323556	0.748080665123815	0.454635840668233	   
df.mm.trans1:exp4	0.00249392911621763	0.181017443669859	0.0137772861314188	0.989011168948646	   
df.mm.trans2:exp4	-0.0512233683358924	0.141192632323556	-0.362790660482265	0.716859051868657	   
df.mm.trans1:exp5	0.0268845296962241	0.181017443669859	0.148518999888521	0.881971383515192	   
df.mm.trans2:exp5	0.0700474870994106	0.141192632323556	0.496112905798727	0.619953805722462	   
df.mm.trans1:exp6	0.0348672593321905	0.181017443669859	0.192618228527090	0.847307851244241	   
df.mm.trans2:exp6	-0.0804813182351042	0.141192632323556	-0.570010749928324	0.568833707911592	   
df.mm.trans1:exp7	0.0217752101533109	0.181017443669859	0.120293435327839	0.904281524232853	   
df.mm.trans2:exp7	-0.00742494870161096	0.141192632323556	-0.0525873664894639	0.958074079113125	   
df.mm.trans1:exp8	-0.112500539769818	0.181017443669859	-0.621490048080657	0.534457903728991	   
df.mm.trans2:exp8	-0.0365575889232532	0.141192632323556	-0.258919947320468	0.79576497124805	   
df.mm.trans1:probe2	0.0123503279191329	0.115034577842439	0.107361874583910	0.914529366774582	   
df.mm.trans1:probe3	0.00672411111364155	0.115034577842439	0.0584529559699125	0.953402729075198	   
df.mm.trans1:probe4	-0.0500394994833746	0.115034577842439	-0.434995289433	0.663685559585512	   
df.mm.trans1:probe5	-0.0742310682506993	0.115034577842439	-0.645293525155302	0.518925781916407	   
df.mm.trans1:probe6	0.079627504199461	0.115034577842439	0.69220494996318	0.489013586509665	   
df.mm.trans1:probe7	-0.0495633680264588	0.115034577842439	-0.43085626040498	0.66669131744428	   
df.mm.trans1:probe8	-0.0587726844944965	0.115034577842439	-0.510913201898273	0.609555672642027	   
df.mm.trans1:probe9	0.130481019448420	0.115034577842439	1.13427650968684	0.257025271706831	   
df.mm.trans1:probe10	0.0365403111593109	0.115034577842439	0.317646327257875	0.750837809158036	   
df.mm.trans1:probe11	-0.0544202141596459	0.115034577842439	-0.473077010237604	0.636289919579656	   
df.mm.trans1:probe12	-0.14397240647427	0.115034577842439	-1.25155765487719	0.211104387272639	   
df.mm.trans1:probe13	0.0121744444758653	0.115034577842439	0.105832913061501	0.915741994558465	   
df.mm.trans1:probe14	0.00137814960071543	0.115034577842439	0.0119803073698681	0.990444374222189	   
df.mm.trans1:probe15	-0.114016166378465	0.115034577842439	-0.991146910058917	0.321919644589951	   
df.mm.trans1:probe16	0.0341434894221234	0.115034577842439	0.296810663910892	0.76668967879548	   
df.mm.trans1:probe17	-0.106083764583916	0.115034577842439	-0.922190236827899	0.356713053950295	   
df.mm.trans1:probe18	0.0556776128991823	0.115034577842439	0.484007625737047	0.628515623921419	   
df.mm.trans1:probe19	-0.0931214814319352	0.115034577842439	-0.809508611919125	0.418468262388928	   
df.mm.trans1:probe20	-0.069191336177143	0.115034577842439	-0.601482940824221	0.547692246901099	   
df.mm.trans1:probe21	-0.0596547406429696	0.115034577842439	-0.518580949848643	0.604199326373839	   
df.mm.trans1:probe22	-0.0435014270456274	0.115034577842439	-0.378159574812459	0.705414420219246	   
df.mm.trans2:probe2	0.104614930847023	0.115034577842439	0.909421608781946	0.363407104933025	   
df.mm.trans2:probe3	0.0875697554267703	0.115034577842439	0.761247244691186	0.446738311917751	   
df.mm.trans2:probe4	0.0791737859832535	0.115034577842439	0.688260760096817	0.491492179139438	   
df.mm.trans2:probe5	-0.0200975570068435	0.115034577842439	-0.174708834367792	0.861353532079188	   
df.mm.trans2:probe6	0.0410514683869842	0.115034577842439	0.356861990167963	0.721291152869076	   
df.mm.trans3:probe2	0.0733315404257875	0.115034577842439	0.637473895251118	0.524002325458479	   
df.mm.trans3:probe3	-0.111159261447360	0.115034577842439	-0.966311725850062	0.33418619894842	   
df.mm.trans3:probe4	0.0719328611547538	0.115034577842439	0.625315122669282	0.531946222531083	   
df.mm.trans3:probe5	-0.0289330872901305	0.115034577842439	-0.251516438211819	0.801480766133237	   
df.mm.trans3:probe6	-0.0766886725462928	0.115034577842439	-0.666657573615232	0.505187019740087	   
df.mm.trans3:probe7	-0.000185247698577214	0.115034577842439	-0.00161036535319794	0.998715524554624