chr3.15706_chr3_136073438_136074801_-_2.R 

fitVsDatCorrelation=0.904838151161599
cont.fitVsDatCorrelation=0.248922541020567

fstatistic=10341.144798453,60,876
cont.fstatistic=1986.56113112714,60,876

residuals=-0.586070783555757,-0.100250446146307,-0.00340715274084034,0.0985771510437352,0.555387815588303
cont.residuals=-0.82769102771386,-0.300714819113912,-0.0524608120677653,0.258959004803791,1.13996078248114

predictedValues:
Include	Exclude	Both
chr3.15706_chr3_136073438_136074801_-_2.R.tl.Lung	78.145325545105	113.151533590436	93.2109441620992
chr3.15706_chr3_136073438_136074801_-_2.R.tl.cerebhem	59.828629067318	74.5857608636399	65.1864334986359
chr3.15706_chr3_136073438_136074801_-_2.R.tl.cortex	57.7701955975807	92.0786510465095	61.389422658132
chr3.15706_chr3_136073438_136074801_-_2.R.tl.heart	65.555969381909	91.5333005957312	79.3100980696294
chr3.15706_chr3_136073438_136074801_-_2.R.tl.kidney	64.9096051394745	127.728707129956	62.7343600643179
chr3.15706_chr3_136073438_136074801_-_2.R.tl.liver	62.4056842823034	109.268031778607	64.6416259745411
chr3.15706_chr3_136073438_136074801_-_2.R.tl.stomach	64.558920289509	93.7906033867186	72.2569142993923
chr3.15706_chr3_136073438_136074801_-_2.R.tl.testicle	62.122820174218	87.3649139628473	73.5153437385202


diffExp=-35.0062080453312,-14.7571317963219,-34.3084554489288,-25.9773312138222,-62.8191019904819,-46.8623474963037,-29.2316830972096,-25.2420937886293
diffExpScore=0.996366336543932
diffExp1.5=0,0,-1,0,-1,-1,0,0
diffExp1.5Score=0.75
diffExp1.4=-1,0,-1,0,-1,-1,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,0,-1,-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	77.9506721616713	93.2038349514681	72.6591166758446
cerebhem	71.5165773623389	80.9483170669734	75.4376952340473
cortex	72.1431492355033	87.6637244534779	80.2899815563102
heart	67.140056265827	79.3143891164482	76.3846317003487
kidney	70.3307347033612	83.3777755198965	75.9467431676927
liver	72.4679279818164	93.9016469001731	75.3770214857893
stomach	79.2787663840656	86.2750466024256	80.3383435312255
testicle	70.5263263770366	90.5877739905746	81.6774560721965
cont.diffExp=-15.2531627897968,-9.43173970463454,-15.5205752179746,-12.1743328506213,-13.0470408165352,-21.4337189183567,-6.99628021835997,-20.0614476135380
cont.diffExpScore=0.991298165598743

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,-1,0,-1
cont.diffExp1.2Score=0.75

tran.correlation=0.500296588977559
cont.tran.correlation=0.496187798609889

tran.covariance=0.00800059317396253
cont.tran.covariance=0.00178015929390265

tran.mean=81.5499157394915
cont.tran.mean=79.7891699420661

weightedLogRatios:
wLogRatio
Lung	-1.68186662342017
cerebhem	-0.92633288349294
cortex	-1.99966378864121
heart	-1.45195822910381
kidney	-3.05386023068126
liver	-2.47233991553083
stomach	-1.62627974419284
testicle	-1.46608039277753

cont.weightedLogRatios:
wLogRatio
Lung	-0.794455029971
cerebhem	-0.536639066396785
cortex	-0.852704849010823
heart	-0.714896866582453
kidney	-0.738260284717833
liver	-1.14334645227455
stomach	-0.373398497541395
testicle	-1.09674794407892

varWeightedLogRatios=0.442064120787047
cont.varWeightedLogRatios=0.0669087353375456

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36499289015198	0.0778210463839833	56.0901336203359	3.54866072903471e-292	***
df.mm.trans1	-0.191045345786119	0.0670618572162916	-2.84879294604009	0.00449134910212879	** 
df.mm.trans2	0.301155005782172	0.0591093279455279	5.09488123532212	4.27485791632742e-07	***
df.mm.exp2	-0.326250488278931	0.0757205404526147	-4.30861277968685	1.82884495309800e-05	***
df.mm.exp3	-0.0905544661679602	0.0757205404526147	-1.19590359005201	0.232057790578549	   
df.mm.exp4	-0.226191366832721	0.0757205404526147	-2.98718637612299	0.00289406613692285	** 
df.mm.exp5	0.331561820629215	0.0757205404526147	4.37875665766944	1.33775766962752e-05	***
df.mm.exp6	0.106168642890316	0.0757205404526147	1.40211153084355	0.161236190317389	   
df.mm.exp7	-0.124018073857072	0.0757205404526147	-1.63783925888223	0.101814350563687	   
df.mm.exp8	-0.250720012087165	0.0757205404526147	-3.31112285502061	0.000967221990171523	***
df.mm.trans1:exp2	0.0591645404215288	0.0698108889033126	0.847497308098614	0.396949473195592	   
df.mm.trans2:exp2	-0.0905278214644731	0.0507948827235207	-1.78222325971734	0.0750591173410284	.  
df.mm.trans1:exp3	-0.211542779442424	0.0698108889033126	-3.03022612611923	0.00251551819363448	** 
df.mm.trans2:exp3	-0.115530344917673	0.0507948827235207	-2.27444850195856	0.023180075935914	*  
df.mm.trans1:exp4	0.050525397785383	0.0698108889033126	0.72374666157539	0.469414501019694	   
df.mm.trans2:exp4	0.0141662882477442	0.0507948827235207	0.278892035736199	0.780393505092527	   
df.mm.trans1:exp5	-0.517136449998899	0.0698108889033126	-7.40767605344674	3.02282722480655e-13	***
df.mm.trans2:exp5	-0.210381207116818	0.0507948827235207	-4.14177956196757	3.77983877481749e-05	***
df.mm.trans1:exp6	-0.331082518592433	0.0698108889033125	-4.74256271182823	2.46268566607192e-06	***
df.mm.trans2:exp6	-0.141092697521837	0.0507948827235207	-2.77769511330131	0.00559157539258548	** 
df.mm.trans1:exp7	-0.0669738676576291	0.0698108889033125	-0.959361336171887	0.337641453631984	   
df.mm.trans2:exp7	-0.0636451778556525	0.0507948827235207	-1.25298404963501	0.210545862333601	   
df.mm.trans1:exp8	0.0212631669530723	0.0698108889033126	0.304582383738468	0.760756581913209	   
df.mm.trans2:exp8	-0.00791415351643839	0.0507948827235207	-0.155806118492596	0.876221743398651	   
df.mm.trans1:probe2	0.204646697919092	0.0486323976548028	4.20803225396562	2.84158671355968e-05	***
df.mm.trans1:probe3	0.0157087903578626	0.0486323976548028	0.323010814094854	0.746764137375425	   
df.mm.trans1:probe4	0.385654181359849	0.0486323976548028	7.92998494742655	6.66187417529019e-15	***
df.mm.trans1:probe5	-0.128603895603929	0.0486323976548029	-2.64440788045803	0.00832981194832428	** 
df.mm.trans1:probe6	0.206098749731898	0.0486323976548028	4.23788995958631	2.49550804465727e-05	***
df.mm.trans1:probe7	0.443498909077837	0.0486323976548028	9.11941278786688	5.06131760970113e-19	***
df.mm.trans1:probe8	-0.0728217943496828	0.0486323976548028	-1.49739264073671	0.134651340354663	   
df.mm.trans1:probe9	0.317338537404161	0.0486323976548028	6.52524968348586	1.14729293553383e-10	***
df.mm.trans1:probe10	0.390845472000683	0.0486323976548028	8.03673046874925	2.97408599849356e-15	***
df.mm.trans1:probe11	0.590343183775452	0.0486323976548029	12.1388870844033	1.88030097243795e-31	***
df.mm.trans1:probe12	0.66299359991597	0.0486323976548028	13.6327557736709	1.61241620412821e-38	***
df.mm.trans1:probe13	0.802905629922626	0.0486323976548028	16.509686312851	1.61881959685716e-53	***
df.mm.trans1:probe14	1.01221816988086	0.0486323976548028	20.8136595909928	1.72453991749378e-78	***
df.mm.trans1:probe15	0.60742167223843	0.0486323976548028	12.4900622122307	4.60141345716817e-33	***
df.mm.trans1:probe16	0.954940553186639	0.0486323976548028	19.6358929281031	2.01713849775427e-71	***
df.mm.trans1:probe17	-0.077070372279151	0.0486323976548028	-1.58475370320426	0.113383214601540	   
df.mm.trans1:probe18	-0.0879210082241503	0.0486323976548028	-1.80786908447783	0.0709697535596757	.  
df.mm.trans1:probe19	-0.0877603036471325	0.0486323976548028	-1.80456460876272	0.071486172875974	.  
df.mm.trans1:probe20	-0.0332041474829004	0.0486323976548028	-0.682757772269146	0.494940429139467	   
df.mm.trans1:probe21	0.0760522332474558	0.0486323976548028	1.56381829633985	0.118221405747630	   
df.mm.trans1:probe22	-0.0907358630256595	0.0486323976548028	-1.86574932352114	0.0624104941070269	.  
df.mm.trans2:probe2	0.188067745981675	0.0486323976548028	3.86712880817838	0.000118273226769338	***
df.mm.trans2:probe3	0.0983439581203906	0.0486323976548028	2.02219020370834	0.0434601951243591	*  
df.mm.trans2:probe4	0.308464329010083	0.0486323976548028	6.34277444430338	3.61221793287694e-10	***
df.mm.trans2:probe5	0.241888209382655	0.0486323976548028	4.9738080178485	7.90034490925244e-07	***
df.mm.trans2:probe6	0.227096261705048	0.0486323976548028	4.66964971204993	3.48993046923124e-06	***
df.mm.trans3:probe2	-0.0301543809501765	0.0486323976548028	-0.620047178512871	0.535387980436778	   
df.mm.trans3:probe3	-0.158047764409385	0.0486323976548028	-3.24984520671224	0.00119869865541451	** 
df.mm.trans3:probe4	0.146168428329684	0.0486323976548028	3.00557725669215	0.00272635507236107	** 
df.mm.trans3:probe5	0.246734361600952	0.0486323976548028	5.07345665645142	4.77012391552478e-07	***
df.mm.trans3:probe6	-0.100321633034777	0.0486323976548028	-2.06285599461637	0.0394205640876259	*  
df.mm.trans3:probe7	0.0958804161211537	0.0486323976548028	1.97153380760130	0.0489771758009413	*  
df.mm.trans3:probe8	0.295516197615537	0.0486323976548028	6.07652947142639	1.83013000840841e-09	***
df.mm.trans3:probe9	0.068088126240352	0.0486323976548028	1.40005694812021	0.161850323530145	   
df.mm.trans3:probe10	-0.241036317073050	0.0486323976548028	-4.95629104663825	8.62521125717804e-07	***
df.mm.trans3:probe11	0.334561069656337	0.0486323976548028	6.87938670083845	1.14356745786522e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.61822501585013	0.177029933519243	26.0872549858815	1.81794194674391e-111	***
df.mm.trans1	-0.30837414236308	0.152554568157547	-2.02140221749776	0.0435418090590265	*  
df.mm.trans2	-0.0163382973427217	0.134463887120356	-0.121506953968225	0.903317372971776	   
df.mm.exp2	-0.26465313978429	0.172251631984306	-1.53643327924117	0.124793242058566	   
df.mm.exp3	-0.238570556960654	0.172251631984306	-1.38501188181712	0.166401438112595	   
df.mm.exp4	-0.360667343834014	0.172251631984306	-2.09383992290346	0.0365611784693419	*  
df.mm.exp5	-0.258527873876440	0.172251631984306	-1.50087329158074	0.133748670837815	   
df.mm.exp6	-0.102196674560165	0.172251631984306	-0.593298730368348	0.553134394628342	   
df.mm.exp7	-0.160822488759329	0.172251631984306	-0.933648563480555	0.350742476531702	   
df.mm.exp8	-0.245558922470205	0.172251631984306	-1.42558255989457	0.154345093160392	   
df.mm.trans1:exp2	0.178506195380946	0.158808157892056	1.12403668520782	0.261305385235767	   
df.mm.trans2:exp2	0.123675161514728	0.115549907505656	1.07031813511988	0.284770945985649	   
df.mm.trans1:exp3	0.161146667529974	0.158808157892056	1.01472537474748	0.310516933369752	   
df.mm.trans2:exp3	0.177289870146993	0.115549907505656	1.53431425410977	0.125313428883389	   
df.mm.trans1:exp4	0.211371954971404	0.158808157892056	1.33098927521769	0.183538845244484	   
df.mm.trans2:exp4	0.199298039279113	0.115549907505656	1.72477887331375	0.0849200614984928	.  
df.mm.trans1:exp5	0.155660552204764	0.158808157892056	0.980179823700044	0.327267980561585	   
df.mm.trans2:exp5	0.147120798759152	0.115549907505656	1.27322299026463	0.203276689917578	   
df.mm.trans1:exp6	0.0292645474290520	0.158808157892056	0.184276096502193	0.853839514172207	   
df.mm.trans2:exp6	0.109655731100611	0.115549907505656	0.948990210963556	0.342887316249877	   
df.mm.trans1:exp7	0.177716599967161	0.158808157892056	1.11906467731939	0.263419303020117	   
df.mm.trans2:exp7	0.0835740295117098	0.115549907505656	0.723272145480681	0.469705775292182	   
df.mm.trans1:exp8	0.145468767919384	0.158808157892056	0.916003118796081	0.359917347575318	   
df.mm.trans2:exp8	0.217089313103909	0.115549907505656	1.87874934554390	0.0606105895278708	.  
df.mm.trans1:probe2	0.00420667644092022	0.110630613744652	0.0380245241215935	0.969676792199035	   
df.mm.trans1:probe3	0.179685226721697	0.110630613744652	1.62419081517916	0.104694827985117	   
df.mm.trans1:probe4	0.130313716477146	0.110630613744652	1.17791732384243	0.239149464416755	   
df.mm.trans1:probe5	0.0404144456109656	0.110630613744652	0.365309784001078	0.714968360330026	   
df.mm.trans1:probe6	0.122833814349646	0.110630613744652	1.1103058203506	0.267171942064685	   
df.mm.trans1:probe7	0.0537999382886065	0.110630613744652	0.48630244800759	0.626874374775229	   
df.mm.trans1:probe8	0.176963622425121	0.110630613744652	1.59958999082815	0.110050145953714	   
df.mm.trans1:probe9	-0.0205326359814892	0.110630613744652	-0.185596330766824	0.85280429569398	   
df.mm.trans1:probe10	-0.00553862034523112	0.110630613744652	-0.0500640840519503	0.960082733161001	   
df.mm.trans1:probe11	0.110625499918140	0.110630613744652	0.999953775665353	0.317609008931713	   
df.mm.trans1:probe12	0.122668602665697	0.110630613744652	1.10881245718143	0.267815414056066	   
df.mm.trans1:probe13	0.0783223244000608	0.110630613744652	0.707962486593787	0.479156873668231	   
df.mm.trans1:probe14	0.0457385414938681	0.110630613744652	0.413434762275095	0.6793892972704	   
df.mm.trans1:probe15	0.121499923409691	0.110630613744652	1.09824866099113	0.272397688027572	   
df.mm.trans1:probe16	0.00543457332875551	0.110630613744652	0.0491235937757618	0.96083199863309	   
df.mm.trans1:probe17	0.0235258076017956	0.110630613744652	0.212651876415472	0.831647978351343	   
df.mm.trans1:probe18	0.058638113425729	0.110630613744652	0.53003514525439	0.59622187865162	   
df.mm.trans1:probe19	0.0395530648085525	0.110630613744652	0.357523685983026	0.720785916029174	   
df.mm.trans1:probe20	0.0653006067307553	0.110630613744652	0.59025801738275	0.55516991063796	   
df.mm.trans1:probe21	0.0942170604816112	0.110630613744652	0.851636425872812	0.394648664675537	   
df.mm.trans1:probe22	0.077766081574216	0.110630613744652	0.702934558003165	0.482283294023709	   
df.mm.trans2:probe2	-0.253963074568862	0.110630613744652	-2.29559491692812	0.0219345128028384	*  
df.mm.trans2:probe3	-0.242171497836806	0.110630613744652	-2.18900980153437	0.0288594101883534	*  
df.mm.trans2:probe4	-0.232052195819362	0.110630613744652	-2.09754052666620	0.0362318012512930	*  
df.mm.trans2:probe5	-0.286645849983190	0.110630613744652	-2.59101744337061	0.00972840535232334	** 
df.mm.trans2:probe6	-0.125830833737658	0.110630613744652	-1.13739614631525	0.255683724066410	   
df.mm.trans3:probe2	-0.285690988439889	0.110630613744652	-2.58238636458527	0.00997320978472796	** 
df.mm.trans3:probe3	-0.00467718555294545	0.110630613744652	-0.0422774980146175	0.96628711731428	   
df.mm.trans3:probe4	-0.0211219721448867	0.110630613744652	-0.190923392991732	0.848629851307525	   
df.mm.trans3:probe5	-0.054404108262738	0.110630613744652	-0.49176359437279	0.623009674131442	   
df.mm.trans3:probe6	0.120208170234797	0.110630613744652	1.08657238865411	0.27752472625115	   
df.mm.trans3:probe7	0.103940347871151	0.110630613744652	0.939526089144336	0.347719687083811	   
df.mm.trans3:probe8	0.0911344987792857	0.110630613744652	0.823772875287796	0.410292839216629	   
df.mm.trans3:probe9	-0.000569584744541898	0.110630613744652	-0.00514852738552607	0.995893259842525	   
df.mm.trans3:probe10	-0.011794782011828	0.110630613744652	-0.106614088203937	0.915119551554636	   
df.mm.trans3:probe11	-0.0220951658807966	0.110630613744652	-0.199720178103637	0.841745782855253