chr8.23495_chr8_106063998_106079040_+_2.R 

fitVsDatCorrelation=0.803207958041009
cont.fitVsDatCorrelation=0.236652719744456

fstatistic=10788.5114997771,55,761
cont.fstatistic=4046.87000377887,55,761

residuals=-0.734879234541001,-0.0839121351261052,-0.000144304436138117,0.083412447612746,1.04899781888698
cont.residuals=-0.581219166948973,-0.172623462099437,-0.0253573269323814,0.150508268366694,1.387700281159

predictedValues:
Include	Exclude	Both
chr8.23495_chr8_106063998_106079040_+_2.R.tl.Lung	52.8478132005977	54.6249625686316	65.4146343932039
chr8.23495_chr8_106063998_106079040_+_2.R.tl.cerebhem	60.1744801482923	76.5577173798744	65.0595160421338
chr8.23495_chr8_106063998_106079040_+_2.R.tl.cortex	56.2240667891769	50.2231802779254	73.8443673312522
chr8.23495_chr8_106063998_106079040_+_2.R.tl.heart	55.6255196382498	50.6307646263182	74.6298021622646
chr8.23495_chr8_106063998_106079040_+_2.R.tl.kidney	52.4655875822192	54.4428767633244	64.7374527263676
chr8.23495_chr8_106063998_106079040_+_2.R.tl.liver	56.0620820178461	52.6871932931382	64.8155271617646
chr8.23495_chr8_106063998_106079040_+_2.R.tl.stomach	52.3271794115836	51.8924001189741	76.9447084642741
chr8.23495_chr8_106063998_106079040_+_2.R.tl.testicle	56.6428293813521	55.1650210660779	72.8432771860465


diffExp=-1.77714936803391,-16.3832372315821,6.00088651125147,4.99475501193167,-1.97728918110518,3.37488872470795,0.434779292609498,1.47780831527421
diffExpScore=7.5023914019726
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	61.571810745234	57.3907592926319	61.0832652844544
cerebhem	60.515287440482	61.239658820725	64.6780303670241
cortex	59.3647519400808	60.8591559445095	61.8989111988992
heart	61.3885313462601	58.3022603469557	64.890503152836
kidney	58.5240003880027	64.9145945381944	64.0626987916483
liver	62.2857215003992	61.5517979044151	60.9548968375211
stomach	60.852129589134	59.7330857005677	60.2658219838214
testicle	60.9562654077767	65.1288545112597	56.5185409350959
cont.diffExp=4.18105145260215,-0.724371380242943,-1.49440400442872,3.08627099930442,-6.39059415019169,0.733923595984102,1.11904388856628,-4.17258910348295
cont.diffExpScore=4.69837090008701

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=0.680733724911788
cont.tran.correlation=-0.488287460753807

tran.covariance=0.00421921230600894
cont.tran.covariance=-0.000450047330181029

tran.mean=55.5371046414739
cont.tran.mean=60.9111665885393

weightedLogRatios:
wLogRatio
Lung	-0.13176780961963
cerebhem	-1.01559491684509
cortex	0.448415374057497
heart	0.373658897246148
kidney	-0.147188498919944
liver	0.248064473599815
stomach	0.0329850138437971
testicle	0.106367805756022

cont.weightedLogRatios:
wLogRatio
Lung	0.287263729054838
cerebhem	-0.0488910767228565
cortex	-0.101836492607423
heart	0.211044931110179
kidney	-0.427108467509552
liver	0.0489037825672985
stomach	0.0760836033234356
testicle	-0.274329689391984

varWeightedLogRatios=0.212027325033356
cont.varWeightedLogRatios=0.057018924488405

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.67408916704939	0.0730180801566601	50.3175262779662	2.80907747395889e-244	***
df.mm.trans1	0.104561359111714	0.0635540811802833	1.64523437629609	0.100334434451774	   
df.mm.trans2	0.277855092931609	0.0568444983066614	4.88798566631115	1.24292138810981e-06	***
df.mm.exp2	0.472829492918238	0.0744269189520185	6.35293653930592	3.63397570215305e-10	***
df.mm.exp3	-0.143299457007764	0.0744269189520185	-1.92537134447478	0.0545552741935367	.  
df.mm.exp4	-0.156499751115511	0.0744269189520185	-2.10273048137870	0.0358172125332205	*  
df.mm.exp5	-0.000191703497409711	0.0744269189520185	-0.00257572797731017	0.997945543715002	   
df.mm.exp6	0.0321256060464284	0.0744269189520185	0.431639606996752	0.666125624755583	   
df.mm.exp7	-0.223560128080593	0.0744269189520185	-3.00375363146119	0.00275414205798955	** 
df.mm.exp8	-0.0283770686777124	0.0744269189520185	-0.381274263092988	0.70310617106045	   
df.mm.trans1:exp2	-0.342997482272508	0.0692876907976082	-4.95033790741296	9.1282560155544e-07	***
df.mm.trans2:exp2	-0.135275529178516	0.0543537365275057	-2.48879907474364	0.0130302429046197	*  
df.mm.trans1:exp3	0.205228022935928	0.0692876907976081	2.96196944325085	0.00315195402011748	** 
df.mm.trans2:exp3	0.0592851674791207	0.0543537365275057	1.09072846259832	0.275737611241235	   
df.mm.trans1:exp4	0.207725499381474	0.0692876907976082	2.99801446678671	0.00280591450408027	** 
df.mm.trans2:exp4	0.0805681710483772	0.0543537365275057	1.48229314478878	0.138676278014319	   
df.mm.trans1:exp5	-0.00706715043486058	0.0692876907976082	-0.101997199697476	0.918785766558546	   
df.mm.trans2:exp5	-0.00314724546479449	0.0543537365275057	-0.0579030194769006	0.953841078210888	   
df.mm.trans1:exp6	0.0269177441107531	0.0692876907976082	0.388492440733531	0.697760307531291	   
df.mm.trans2:exp6	-0.0682441597196755	0.0543537365275057	-1.25555599448330	0.209662366698115	   
df.mm.trans1:exp7	0.213659712996944	0.0692876907976082	3.08366046749994	0.00211859131845159	** 
df.mm.trans2:exp7	0.172241506196555	0.0543537365275057	3.16889908956659	0.00159137579113064	** 
df.mm.trans1:exp8	0.0977261365942993	0.0692876907976082	1.41044008638938	0.158818386798546	   
df.mm.trans2:exp8	0.0382151765908082	0.0543537365275057	0.70308278753696	0.482219187209403	   
df.mm.trans1:probe2	0.454516429899332	0.044031559053043	10.3225150250027	1.80913338695033e-23	***
df.mm.trans1:probe3	0.166453814338972	0.0440315590530430	3.78032978887828	0.000168879679546379	***
df.mm.trans1:probe4	0.222481851536673	0.044031559053043	5.05278160304653	5.45674649200885e-07	***
df.mm.trans1:probe5	0.296446360138165	0.0440315590530430	6.73258831877946	3.28317595451458e-11	***
df.mm.trans1:probe6	0.382897747850519	0.0440315590530431	8.69598433680845	2.10056832038893e-17	***
df.mm.trans1:probe7	0.259081957762506	0.0440315590530431	5.88400600238572	5.99922547004292e-09	***
df.mm.trans1:probe8	0.190117407443025	0.0440315590530430	4.31775325543205	1.78474037801559e-05	***
df.mm.trans1:probe9	0.0474600742436154	0.044031559053043	1.07786495105572	0.281435571061951	   
df.mm.trans1:probe10	0.0630624407212133	0.0440315590530430	1.4322100347445	0.152494328655426	   
df.mm.trans1:probe11	0.024037672961349	0.0440315590530430	0.54591918792591	0.585281480549109	   
df.mm.trans1:probe12	0.025423032134188	0.044031559053043	0.577382056891556	0.563852167403793	   
df.mm.trans1:probe13	0.133361466239033	0.0440315590530431	3.02877002557139	0.00253852787095817	** 
df.mm.trans1:probe14	0.211393324529173	0.0440315590530430	4.80095025194351	1.90149730093289e-06	***
df.mm.trans1:probe15	0.0586814959996663	0.0440315590530431	1.33271447256671	0.183024403363754	   
df.mm.trans1:probe16	0.45827654415703	0.0440315590530431	10.4079109169167	8.27070191952278e-24	***
df.mm.trans1:probe17	0.582439839952353	0.0440315590530430	13.2277814476365	4.20498810064012e-36	***
df.mm.trans1:probe18	0.525031812029859	0.0440315590530431	11.9239886872362	3.58126643686336e-30	***
df.mm.trans1:probe19	0.321776310249385	0.044031559053043	7.30785639140676	6.87221447154045e-13	***
df.mm.trans1:probe20	0.439406788515523	0.044031559053043	9.97936021266445	4.00387548609954e-22	***
df.mm.trans1:probe21	0.423096248452684	0.0440315590530431	9.60893181054519	1.03741256269121e-20	***
df.mm.trans2:probe2	0.169592566550051	0.0440315590530431	3.85161393776108	0.000127218563590405	***
df.mm.trans2:probe3	0.346343068889002	0.0440315590530431	7.86579163530812	1.25742385014227e-14	***
df.mm.trans2:probe4	-0.0338320737402128	0.044031559053043	-0.768359659930657	0.442511905895231	   
df.mm.trans2:probe5	0.110261591759254	0.0440315590530431	2.50414916324962	0.0124828160524092	*  
df.mm.trans2:probe6	0.0387420530389355	0.0440315590530431	0.879870117527851	0.379207435140451	   
df.mm.trans3:probe2	0.203682578766397	0.0440315590530431	4.62583163410201	4.38399616640145e-06	***
df.mm.trans3:probe3	0.106679335096138	0.0440315590530430	2.42279259218657	0.0156341376194704	*  
df.mm.trans3:probe4	0.384268856954285	0.0440315590530430	8.72712357269411	1.63619648875023e-17	***
df.mm.trans3:probe5	-0.0809292839485957	0.0440315590530431	-1.83798361196122	0.0664544509866344	.  
df.mm.trans3:probe6	0.366050196763815	0.0440315590530431	8.31335988632264	4.26776290937267e-16	***
df.mm.trans3:probe7	-0.110069010903146	0.0440315590530431	-2.49977546265283	0.0126366768143234	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02285587030759	0.119093674746811	33.7789213311291	1.57616044151184e-153	***
df.mm.trans1	0.102098500126580	0.103657738695375	0.98495781802285	0.324957835373502	   
df.mm.trans2	0.0316726959707352	0.0927143000467079	0.341616082468174	0.732734141095113	   
df.mm.exp2	-0.00958007700373981	0.121391513705396	-0.0789188363446031	0.937117927401613	   
df.mm.exp3	0.00891082186790727	0.121391513705396	0.0734056409374122	0.94150261820891	   
df.mm.exp4	-0.0476868995544709	0.121391513705396	-0.392835529427549	0.694550973485887	   
df.mm.exp5	0.0247976240834189	0.121391513705396	0.204278069582360	0.8381908247248	   
df.mm.exp6	0.08362756582434	0.121391513705396	0.688907842662666	0.491091237614657	   
df.mm.exp7	0.0417182265711165	0.121391513705396	0.343666746526961	0.731191849319603	   
df.mm.exp8	0.194106076321654	0.121391513705396	1.59900861597894	0.110233761104100	   
df.mm.trans1:exp2	-0.0077280529406212	0.113009349110575	-0.0683841912323523	0.945497769405748	   
df.mm.trans2:exp2	0.0744917741619144	0.0886518271281907	0.84027342216195	0.401019016074505	   
df.mm.trans1:exp3	-0.0454143213422417	0.113009349110575	-0.401863400680291	0.687897393634457	   
df.mm.trans2:exp3	0.0497681512671157	0.0886518271281907	0.561388894953635	0.574697727981142	   
df.mm.trans1:exp4	0.0447057833410483	0.113009349110575	0.395593671611237	0.69251567912511	   
df.mm.trans2:exp4	0.0634444606868012	0.0886518271281907	0.7156588052614	0.474421574023191	   
df.mm.trans1:exp5	-0.0755648389660305	0.113009349110575	-0.668660067160405	0.503915118486589	   
df.mm.trans2:exp5	0.0983915492351272	0.0886518271281907	1.10986487726703	0.267407837952321	   
df.mm.trans1:exp6	-0.07209950372686	0.113009349110575	-0.637995920641166	0.523668260185981	   
df.mm.trans2:exp6	-0.0136318055830425	0.0886518271281907	-0.153767903320603	0.877833508727333	   
df.mm.trans1:exp7	-0.0534755584219536	0.113009349110575	-0.473195880188905	0.636209136382069	   
df.mm.trans2:exp7	-0.00171546277713662	0.0886518271281907	-0.0193505631266467	0.984566520153558	   
df.mm.trans1:exp8	-0.204153577844037	0.113009349110575	-1.80651936720989	0.0712321636662562	.  
df.mm.trans2:exp8	-0.0676216940785806	0.0886518271281907	-0.762778346133797	0.445831968698704	   
df.mm.trans1:probe2	-0.00830106375600375	0.0718161880072355	-0.115587640980992	0.908009851127655	   
df.mm.trans1:probe3	0.0270693617330905	0.0718161880072355	0.376925627552986	0.706333954199969	   
df.mm.trans1:probe4	0.0991003121273345	0.0718161880072355	1.37991607292426	0.16801775689734	   
df.mm.trans1:probe5	-0.0293334954882878	0.0718161880072355	-0.408452415844356	0.683056462637595	   
df.mm.trans1:probe6	-0.0388329729772539	0.0718161880072355	-0.540727293592101	0.588853791099042	   
df.mm.trans1:probe7	0.02201411381538	0.0718161880072355	0.306534145381847	0.759281834686178	   
df.mm.trans1:probe8	0.0260158391417674	0.0718161880072355	0.362255918389129	0.717261370321011	   
df.mm.trans1:probe9	-0.00225991478193510	0.0718161880072355	-0.0314680414631227	0.974904529769129	   
df.mm.trans1:probe10	-0.0394584226981188	0.0718161880072355	-0.549436328953347	0.582867238779981	   
df.mm.trans1:probe11	-0.038951120171542	0.0718161880072355	-0.542372426779568	0.587720757238289	   
df.mm.trans1:probe12	-0.105114400848086	0.0718161880072355	-1.46365887364414	0.143700096719531	   
df.mm.trans1:probe13	-0.116627071242123	0.0718161880072355	-1.62396632957423	0.104797097198451	   
df.mm.trans1:probe14	0.0123532711055753	0.0718161880072355	0.172012347749935	0.863473553636312	   
df.mm.trans1:probe15	0.0451646105192868	0.0718161880072355	0.628891782932512	0.529608626633167	   
df.mm.trans1:probe16	-0.0128343539251342	0.0718161880072355	-0.178711155259886	0.858212075092378	   
df.mm.trans1:probe17	-0.0187642831000681	0.0718161880072355	-0.261282081669074	0.793945678070472	   
df.mm.trans1:probe18	0.0124436026499599	0.0718161880072355	0.173270163667087	0.862485150771159	   
df.mm.trans1:probe19	0.0395867584659621	0.0718161880072355	0.551223332293462	0.581642386978161	   
df.mm.trans1:probe20	0.0108319315227708	0.0718161880072355	0.150828550266125	0.880150934178766	   
df.mm.trans1:probe21	-0.0171089294559208	0.0718161880072355	-0.238232213803900	0.811765124759108	   
df.mm.trans2:probe2	0.0549050481575004	0.0718161880072355	0.764521895146659	0.444793288694407	   
df.mm.trans2:probe3	-0.0558075669117681	0.0718161880072355	-0.777088960864165	0.437347770855498	   
df.mm.trans2:probe4	-0.0683720610644918	0.0718161880072355	-0.952042470669751	0.341377720462550	   
df.mm.trans2:probe5	0.0810745341285228	0.0718161880072355	1.12891725916105	0.259288484088786	   
df.mm.trans2:probe6	-0.0721883843076572	0.0718161880072355	-1.00518262401207	0.315128422863425	   
df.mm.trans3:probe2	-0.0538915866364276	0.0718161880072355	-0.750410013839749	0.453239750393864	   
df.mm.trans3:probe3	-0.140666461265408	0.0718161880072355	-1.95870130632994	0.0505126356047287	.  
df.mm.trans3:probe4	-0.0250853571632439	0.0718161880072355	-0.349299480511505	0.726961119585566	   
df.mm.trans3:probe5	0.0148895158681284	0.0718161880072355	0.207328128675227	0.835809073490895	   
df.mm.trans3:probe6	-0.0604653994708059	0.0718161880072355	-0.841946657830322	0.400082317706848	   
df.mm.trans3:probe7	-0.0455046097147684	0.0718161880072355	-0.633626080378756	0.526515261956689