chr2.12927_chr2_25644220_25644933_-_1.R 

fitVsDatCorrelation=0.898752951141933
cont.fitVsDatCorrelation=0.289228887068577

fstatistic=8918.25092885723,43,485
cont.fstatistic=1862.074011003,43,485

residuals=-0.643484439125871,-0.0920270777991047,-0.00723763766103016,0.0768456881824124,0.723745623867294
cont.residuals=-0.650557723855352,-0.250082432514708,-0.0682576620994374,0.211304431859425,1.11465489351873

predictedValues:
Include	Exclude	Both
chr2.12927_chr2_25644220_25644933_-_1.R.tl.Lung	46.3059269010145	62.7214584858477	73.0438139272897
chr2.12927_chr2_25644220_25644933_-_1.R.tl.cerebhem	50.973362979058	73.9416417099004	66.7781553450692
chr2.12927_chr2_25644220_25644933_-_1.R.tl.cortex	47.2903330396969	61.463739879354	67.2439249175263
chr2.12927_chr2_25644220_25644933_-_1.R.tl.heart	47.7265346268881	69.387981998028	66.1011261599088
chr2.12927_chr2_25644220_25644933_-_1.R.tl.kidney	47.3196072606656	62.8794107208737	81.0362448257266
chr2.12927_chr2_25644220_25644933_-_1.R.tl.liver	48.1982634388557	65.4319294151654	71.4604311434714
chr2.12927_chr2_25644220_25644933_-_1.R.tl.stomach	48.0138572689599	69.498289452042	70.6583314370745
chr2.12927_chr2_25644220_25644933_-_1.R.tl.testicle	49.3429301689667	67.0065880946252	68.0323453986148


diffExp=-16.4155315848332,-22.9682787308424,-14.1734068396570,-21.6614473711399,-15.5598034602081,-17.2336659763097,-21.4844321830822,-17.6636579256584
diffExpScore=0.993250550164423
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,-1,0,0,-1,0
diffExp1.4Score=0.75
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	60.223303554489	62.7446637822117	65.2023497483648
cerebhem	55.8209741764824	66.3438482184233	66.573201392588
cortex	68.3430923995816	61.1526987285559	65.9553034795224
heart	53.8568944459201	65.1100144084202	61.375048253631
kidney	68.623352656981	69.5928358107162	61.7887458876857
liver	65.5072810512878	64.1735110500654	71.861965386908
stomach	66.2234697919805	57.2194489452162	66.607525365721
testicle	57.2483978524296	73.2775368338685	68.4608909997183
cont.diffExp=-2.52136022772266,-10.5228740419409,7.19039367102566,-11.2531199625001,-0.969483153735183,1.33377000122242,9.00402084676435,-16.0291389814390
cont.diffExpScore=2.37502645559126

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,-1,0,0,0,-1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.800439039744811
cont.tran.correlation=-0.361672238217484

tran.covariance=0.00148376416410325
cont.tran.covariance=-0.00273586509392693

tran.mean=57.3438659649964
cont.tran.mean=63.4663327316643

weightedLogRatios:
wLogRatio
Lung	-1.20978601990249
cerebhem	-1.53152040763946
cortex	-1.04525692200636
heart	-1.51658915276187
kidney	-1.13691225498377
liver	-1.23135965452431
stomach	-1.50010581883723
testicle	-1.23983417927501

cont.weightedLogRatios:
wLogRatio
Lung	-0.168919524808014
cerebhem	-0.709543596761014
cortex	0.46344839303365
heart	-0.774400137587855
kidney	-0.0594207676687274
liver	0.0858187101238883
stomach	0.602096011227242
testicle	-1.02958717509849

varWeightedLogRatios=0.0355000555470366
cont.varWeightedLogRatios=0.351959492443172

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.89228264569214	0.0780033449636504	37.0789566401279	1.16996822701896e-143	***
df.mm.trans1	0.93101988485757	0.0624457568707504	14.9092577544474	1.18532212968085e-41	***
df.mm.trans2	1.15509541103031	0.0624457568707504	18.4975804428332	3.35830989802278e-58	***
df.mm.exp2	0.350289205496058	0.0836191226810209	4.18910405018592	3.3276213321174e-05	***
df.mm.exp3	0.083512484479411	0.0836191226810209	0.99872471513464	0.318426079941386	   
df.mm.exp4	0.231101281724252	0.0836191226810209	2.76373722080088	0.00593174502663194	** 
df.mm.exp5	-0.0796671470769972	0.0836191226810209	-0.952738375178855	0.341197215536067	   
df.mm.exp6	0.10427532139676	0.0836191226810209	1.24702721164076	0.212989383098386	   
df.mm.exp7	0.172021633208103	0.0836191226810209	2.05720447300443	0.0402007018676954	*  
df.mm.exp8	0.200688039726694	0.083619122681021	2.40002565552202	0.0167699414671251	*  
df.mm.trans1:exp2	-0.254255967463855	0.0655962366553899	-3.87607552548463	0.000120761941072904	***
df.mm.trans2:exp2	-0.185716678654034	0.06559623665539	-2.83120935168426	0.0048299161909669	** 
df.mm.trans1:exp3	-0.0624765490716827	0.0655962366553899	-0.952441058469612	0.341347761256725	   
df.mm.trans2:exp3	-0.103768708695898	0.06559623665539	-1.58193082388319	0.114317232687687	   
df.mm.trans1:exp4	-0.200883720731573	0.0655962366553899	-3.06242752594049	0.00231747508807277	** 
df.mm.trans2:exp4	-0.130091228955941	0.0655962366553899	-1.98321177538547	0.0479073502362777	*  
df.mm.trans1:exp5	0.101321922784381	0.06559623665539	1.54463011828981	0.123087973390047	   
df.mm.trans2:exp5	0.0821822939751091	0.0655962366553899	1.25285074518610	0.21086373889324	   
df.mm.trans1:exp6	-0.0642222929492967	0.0655962366553899	-0.979054534586927	0.328041138797338	   
df.mm.trans2:exp6	-0.0619685944072058	0.0655962366553899	-0.944697402882395	0.345283761659803	   
df.mm.trans1:exp7	-0.135801934581266	0.06559623665539	-2.07027020916919	0.0389557117201942	*  
df.mm.trans2:exp7	-0.0694231228326243	0.0655962366553899	-1.05834002638503	0.290427270199915	   
df.mm.trans1:exp8	-0.137163506817805	0.06559623665539	-2.09102707428772	0.0370454503937556	*  
df.mm.trans2:exp8	-0.134600725090807	0.06559623665539	-2.05195803835412	0.0407100583168111	*  
df.mm.trans1:probe2	0.0130015740275047	0.0449106731295054	0.289498533901131	0.772323615708697	   
df.mm.trans1:probe3	0.0448006286419174	0.0449106731295054	0.997549703001097	0.318995189459221	   
df.mm.trans1:probe4	-0.00394465087502802	0.0449106731295054	-0.0878332610079823	0.930045454698566	   
df.mm.trans1:probe5	0.0845083883846768	0.0449106731295054	1.88169943792618	0.060475451347335	.  
df.mm.trans1:probe6	0.0531129905544376	0.0449106731295054	1.18263626112394	0.237532768171386	   
df.mm.trans2:probe2	0.466198383546261	0.0449106731295054	10.3805699416244	6.23802517242745e-23	***
df.mm.trans2:probe3	0.375635679755526	0.0449106731295054	8.36406256197352	6.4857579911908e-16	***
df.mm.trans2:probe4	0.0301082098638571	0.0449106731295054	0.670402106355352	0.502920583245049	   
df.mm.trans2:probe5	0.282011899954522	0.0449106731295054	6.27939597211794	7.55791481237553e-10	***
df.mm.trans2:probe6	0.307254994385522	0.0449106731295054	6.84146936518888	2.37163581920295e-11	***
df.mm.trans3:probe2	-0.716109514615216	0.0449106731295054	-15.9451966473588	2.50899407225715e-46	***
df.mm.trans3:probe3	-0.71562999203799	0.0449106731295054	-15.9345193952979	2.80667722187375e-46	***
df.mm.trans3:probe4	-1.06517006332568	0.0449106731295054	-23.7175261268993	3.94452597277182e-83	***
df.mm.trans3:probe5	-0.436569569399317	0.0449106731295054	-9.72084226260461	1.58878596173253e-20	***
df.mm.trans3:probe6	-0.337834291147798	0.0449106731295054	-7.52236089122092	2.63043279782537e-13	***
df.mm.trans3:probe7	-0.736185421983731	0.0449106731295054	-16.3922152727672	2.25053187679883e-48	***
df.mm.trans3:probe8	-1.08268981172923	0.0449106731295054	-24.1076282381064	5.41446595367296e-85	***
df.mm.trans3:probe9	-0.739265689507968	0.0449106731295054	-16.4608018093206	1.08830115860196e-48	***
df.mm.trans3:probe10	-1.04393054314591	0.0449106731295054	-23.2445980075962	7.19456124881011e-81	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0212818124741	0.170301199746738	23.6127626725726	1.24906073695955e-82	***
df.mm.trans1	0.0516407625899535	0.136335016391125	0.378778423598847	0.705018166868079	   
df.mm.trans2	0.107952245843348	0.136335016391125	0.791815989031377	0.428855066627912	   
df.mm.exp2	-0.0409388030103073	0.182561875019380	-0.224246179581369	0.822660187657353	   
df.mm.exp3	0.0892997900180636	0.182561875019380	0.489148076555327	0.624957997808546	   
df.mm.exp4	-0.0142319851539547	0.182561875019380	-0.0779570496438201	0.93789436925684	   
df.mm.exp5	0.287935872984128	0.182561875019380	1.57719607641827	0.115402411629774	   
df.mm.exp6	0.0093672837614994	0.182561875019380	0.0513101859876551	0.959099476696934	   
df.mm.exp7	-0.01852618617609	0.182561875019380	-0.101478943367137	0.919212210680064	   
df.mm.exp8	0.0557537084018721	0.182561875019380	0.305396230160068	0.760195417877288	   
df.mm.trans1:exp2	-0.0349708965732088	0.143213317409524	-0.244187462491412	0.807188900874	   
df.mm.trans2:exp2	0.096716307291467	0.143213317409524	0.675330402513498	0.499787710115864	   
df.mm.trans1:exp3	0.0371813262146183	0.143213317409524	0.259621988284071	0.795265541197355	   
df.mm.trans2:exp3	-0.114999330999109	0.143213317409524	-0.802993276597764	0.422371951092692	   
df.mm.trans1:exp4	-0.0974969685180282	0.143213317409524	-0.680781440452441	0.496334674358837	   
df.mm.trans2:exp4	0.0512368186276673	0.143213317409524	0.357765741025004	0.720674272431526	   
df.mm.trans1:exp5	-0.157362358085085	0.143213317409524	-1.09879696198295	0.272401790226448	   
df.mm.trans2:exp5	-0.184347779764414	0.143213317409524	-1.28722512053306	0.198629790053974	   
df.mm.trans1:exp6	0.0747346341460626	0.143213317409524	0.521841372701089	0.602019011637386	   
df.mm.trans2:exp6	0.0131497062566105	0.143213317409524	0.0918190186112962	0.926879744943923	   
df.mm.trans1:exp7	0.113501735224340	0.143213317409524	0.792536178041161	0.428435596766195	   
df.mm.trans2:exp7	-0.0736534917744583	0.143213317409524	-0.514292197867629	0.607281971681174	   
df.mm.trans1:exp8	-0.106413431113587	0.143213317409524	-0.743041450602624	0.457816340426393	   
df.mm.trans2:exp8	0.0994268632555917	0.143213317409524	0.694257105791893	0.487853394590605	   
df.mm.trans1:probe2	0.0475545802210435	0.0980514556004293	0.484996167877749	0.627898045039374	   
df.mm.trans1:probe3	0.131498004319802	0.0980514556004293	1.34111221005908	0.180511727912776	   
df.mm.trans1:probe4	0.185017398738950	0.0980514556004293	1.88694188786871	0.0597651225228701	.  
df.mm.trans1:probe5	-0.079556684903324	0.0980514556004293	-0.811376887942658	0.417547270948471	   
df.mm.trans1:probe6	0.117675574903794	0.0980514556004293	1.20014102986227	0.23067046032439	   
df.mm.trans2:probe2	-0.0380956095998732	0.0980514556004293	-0.388526711476034	0.697796785411292	   
df.mm.trans2:probe3	0.101784821869530	0.0980514556004293	1.03807558231786	0.299752120172940	   
df.mm.trans2:probe4	-0.059186373848957	0.0980514556004293	-0.603625652332466	0.546374757275097	   
df.mm.trans2:probe5	0.0683610617272891	0.0980514556004293	0.697195786729246	0.486014328090541	   
df.mm.trans2:probe6	0.0845677278694892	0.0980514556004293	0.86248314572822	0.388847962880307	   
df.mm.trans3:probe2	-0.0131935886342236	0.0980514556004293	-0.134557804914075	0.893017325561567	   
df.mm.trans3:probe3	-0.0526020717747803	0.0980514556004293	-0.536474154847223	0.591876872117393	   
df.mm.trans3:probe4	-0.028463565059395	0.0980514556004293	-0.290292121469234	0.77171684291605	   
df.mm.trans3:probe5	0.0180102546483987	0.0980514556004293	0.183681665285954	0.854339960012412	   
df.mm.trans3:probe6	-0.0511331554573996	0.0980514556004293	-0.521493078754209	0.602261372871622	   
df.mm.trans3:probe7	0.167801716238162	0.0980514556004293	1.71136384677421	0.0876533191662185	.  
df.mm.trans3:probe8	-0.00460601149646613	0.0980514556004293	-0.0469754525137918	0.962552128354342	   
df.mm.trans3:probe9	-0.0549471100319886	0.0980514556004293	-0.560390559176441	0.575471829959371	   
df.mm.trans3:probe10	-0.0146596038206859	0.0980514556004293	-0.149509293165677	0.881213926669268