chr5.18387_chr5_125866652_125871176_-_0.R 

fitVsDatCorrelation=0.841356985413093
cont.fitVsDatCorrelation=0.244546352305436

fstatistic=7709.18285048515,51,669
cont.fstatistic=2386.19446338062,51,669

residuals=-0.624786437101905,-0.099398055908186,-0.00662853973827585,0.0843994576221051,1.68173515685176
cont.residuals=-0.747719226094906,-0.234877603719122,-0.0110208102015671,0.187138112882074,1.93249570834853

predictedValues:
Include	Exclude	Both
chr5.18387_chr5_125866652_125871176_-_0.R.tl.Lung	82.6429945980055	133.686476047438	73.8573128170191
chr5.18387_chr5_125866652_125871176_-_0.R.tl.cerebhem	90.4332450184774	156.521852298518	65.977397723206
chr5.18387_chr5_125866652_125871176_-_0.R.tl.cortex	78.4439696510898	104.172874619615	66.8827676955941
chr5.18387_chr5_125866652_125871176_-_0.R.tl.heart	89.1745493512066	113.637287883758	66.9065757698094
chr5.18387_chr5_125866652_125871176_-_0.R.tl.kidney	71.6985147073181	110.984263600064	64.091106200356
chr5.18387_chr5_125866652_125871176_-_0.R.tl.liver	81.8320301555098	112.394822584030	69.0884871763603
chr5.18387_chr5_125866652_125871176_-_0.R.tl.stomach	79.2061985739347	111.012282922336	64.2892346983434
chr5.18387_chr5_125866652_125871176_-_0.R.tl.testicle	80.4250462318006	111.580951219027	69.4120605561292


diffExp=-51.043481449432,-66.0886072800409,-25.7289049685250,-24.4627385325514,-39.2857488927456,-30.56279242852,-31.8060843484018,-31.1559049872260
diffExpScore=0.996679222117034
diffExp1.5=-1,-1,0,0,-1,0,0,0
diffExp1.5Score=0.75
diffExp1.4=-1,-1,0,0,-1,0,-1,0
diffExp1.4Score=0.8
diffExp1.3=-1,-1,-1,0,-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	77.1167480436396	84.452205073247	71.8102801981647
cerebhem	66.6438454445338	86.149207442574	75.065328666916
cortex	73.6382346521953	90.2856488488828	70.7752353634702
heart	68.8501256123066	79.676156281662	79.7472793136895
kidney	66.9195056450275	89.8403062992358	75.1220642287586
liver	66.1853125809844	79.5033526894774	72.909980224914
stomach	74.4775072329046	84.6668869470934	76.538317828212
testicle	65.2332688861477	79.802540013238	77.275162697714
cont.diffExp=-7.33545702960747,-19.5053619980402,-16.6474141966875,-10.8260306693554,-22.9208006542083,-13.318040108493,-10.1893797141888,-14.5692711270902
cont.diffExpScore=0.991402416757264

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,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,-1,0,-1,-1,0,-1
cont.diffExp1.2Score=0.833333333333333

tran.correlation=0.630905576440777
cont.tran.correlation=0.333238508691158

tran.covariance=0.00608969482991733
cont.tran.covariance=0.00114842047984066

tran.mean=100.490459966383
cont.tran.mean=77.0900532308219

weightedLogRatios:
wLogRatio
Lung	-2.23890921290719
cerebhem	-2.62162861531116
cortex	-1.27769881634905
heart	-1.11797506253748
kidney	-1.96216956691150
liver	-1.44817307444881
stomach	-1.53292793776673
testicle	-1.49012224838340

cont.weightedLogRatios:
wLogRatio
Lung	-0.398966976235959
cerebhem	-1.11100433701393
cortex	-0.897000579457375
heart	-0.628687855766562
kidney	-1.28148752739839
liver	-0.785454841260973
stomach	-0.56094505081236
testicle	-0.862537480415182

varWeightedLogRatios=0.265717366732792
cont.varWeightedLogRatios=0.0839172836456348

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.43799649784301	0.0904917789179378	60.0938180558307	8.014658690756e-272	***
df.mm.trans1	-1.04745470832236	0.0713313593608116	-14.6843508620672	1.61004899241755e-42	***
df.mm.trans2	-0.451291104415251	0.0713313593608116	-6.32668588485058	4.58512579670752e-10	***
df.mm.exp2	0.360602999634471	0.0943625187744434	3.82146433051907	0.000145026250919148	***
df.mm.exp3	-0.202397330001459	0.0943625187744434	-2.14489113506236	0.0323210190185461	*  
df.mm.exp4	0.0124177520281517	0.0943625187744433	0.131596233223003	0.895343230071155	   
df.mm.exp5	-0.186339513256542	0.0943625187744434	-1.97471957803451	0.0487107623266962	*  
df.mm.exp6	-0.116593859888512	0.0943625187744433	-1.23559503712707	0.217042831998281	   
df.mm.exp7	-0.0895891431413935	0.0943625187744434	-0.949414495341527	0.342752679797878	   
df.mm.exp8	-0.145877001984392	0.0943625187744433	-1.54592102753302	0.122596529931390	   
df.mm.trans1:exp2	-0.270521106032297	0.0713313593608116	-3.79245690053282	0.000162659454138461	***
df.mm.trans2:exp2	-0.202904695615456	0.0713313593608116	-2.84453706523542	0.00458352346176471	** 
df.mm.trans1:exp3	0.150251876713689	0.0713313593608116	2.10639300947116	0.0355420846109276	*  
df.mm.trans2:exp3	-0.0470482224317709	0.0713313593608116	-0.659572772106997	0.509755015609837	   
df.mm.trans1:exp4	0.0636478648511954	0.0713313593608116	0.892284479386532	0.372561297029506	   
df.mm.trans2:exp4	-0.174903388700268	0.0713313593608116	-2.45198451659338	0.0144615807328395	*  
df.mm.trans1:exp5	0.0442794844484733	0.0713313593608116	0.620757614116069	0.534970534083799	   
df.mm.trans2:exp5	0.000230607635767805	0.0713313593608116	0.00323290678649954	0.99742148183857	   
df.mm.trans1:exp6	0.106732532728284	0.0713313593608116	1.49629186496228	0.135049206882139	   
df.mm.trans2:exp6	-0.0568855936465548	0.0713313593608116	-0.797483661552185	0.425453142228631	   
df.mm.trans1:exp7	0.0471136429016778	0.0713313593608116	0.660489906877638	0.509166846748423	   
df.mm.trans2:exp7	-0.0962673322244026	0.0713313593608116	-1.34957938678077	0.177607441473782	   
df.mm.trans1:exp8	0.118672589132995	0.0713313593608116	1.66368046531569	0.0966446176922673	.  
df.mm.trans2:exp8	-0.034869978152893	0.0713313593608116	-0.488844997002119	0.625111650515453	   
df.mm.trans1:probe2	0.178851921861416	0.0534985195206087	3.34311908935197	0.000874595711559955	***
df.mm.trans1:probe3	0.395447707028284	0.0534985195206087	7.39175047406592	4.34139924566173e-13	***
df.mm.trans1:probe4	0.0531257064781806	0.0534985195206087	0.993031339076878	0.321053709626294	   
df.mm.trans1:probe5	-0.0841937320310671	0.0534985195206087	-1.57375816724487	0.116016138488123	   
df.mm.trans1:probe6	0.0324869078467645	0.0534985195206087	0.607248726467093	0.543891903884227	   
df.mm.trans2:probe2	-0.332372576481686	0.0534985195206087	-6.21274344523963	9.15130874634462e-10	***
df.mm.trans2:probe3	-0.520001117391224	0.0534985195206087	-9.71991602853438	5.59969284115956e-21	***
df.mm.trans2:probe4	-0.55651094858562	0.0534985195206087	-10.4023616648166	1.35956330651711e-23	***
df.mm.trans2:probe5	-0.3548627253169	0.0534985195206087	-6.63313169218075	6.78357325709274e-11	***
df.mm.trans2:probe6	-0.42524621415278	0.0534985195206087	-7.94874732914743	8.0227289946971e-15	***
df.mm.trans3:probe2	0.379512192830939	0.0534985195206087	7.09388215284617	3.32942911763307e-12	***
df.mm.trans3:probe3	0.103418351035011	0.0534985195206087	1.9331067842947	0.0536446990799921	.  
df.mm.trans3:probe4	0.112170492978217	0.0534985195206087	2.09670274959678	0.0363948122084137	*  
df.mm.trans3:probe5	0.812776323759988	0.0534985195206087	15.1925012326162	5.29548522495048e-45	***
df.mm.trans3:probe6	0.200233291522999	0.0534985195206087	3.74278191840179	0.000197635777598914	***
df.mm.trans3:probe7	0.0824220396889361	0.0534985195206087	1.54064150611094	0.123876902828655	   
df.mm.trans3:probe8	0.424197190593372	0.0534985195206087	7.92913886953382	9.2695110338627e-15	***
df.mm.trans3:probe9	0.470138701282482	0.0534985195206087	8.78788245909077	1.27615480640531e-17	***
df.mm.trans3:probe10	0.24588371309545	0.0534985195206087	4.59608443932231	5.14683570330194e-06	***
df.mm.trans3:probe11	0.391187592534803	0.0534985195206087	7.31211996220026	7.53474251129629e-13	***
df.mm.trans3:probe12	0.0888865760168696	0.0534985195206087	1.66147730466875	0.097086120802647	.  
df.mm.trans3:probe13	0.444891261332733	0.0534985195206087	8.31595463424651	5.09132928336385e-16	***
df.mm.trans3:probe14	0.591307448515972	0.0534985195206087	11.0527815314252	3.35846885951682e-26	***
df.mm.trans3:probe15	0.168743311498095	0.0534985195206087	3.15416787249770	0.00168163908807160	** 
df.mm.trans3:probe16	0.799454659726403	0.0534985195206087	14.9434912758369	8.8268042365698e-44	***
df.mm.trans3:probe17	0.295728419850032	0.0534985195206087	5.52778698364001	4.65136977111797e-08	***
df.mm.trans3:probe18	0.92096658150474	0.0534985195206087	17.2148050031546	2.96551317269099e-55	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.55937336608933	0.162345068744427	28.0844586247763	2.99406289252424e-115	***
df.mm.trans1	-0.231393017760253	0.127970679519583	-1.80817214246989	0.0710286403305698	.  
df.mm.trans2	-0.100476944745429	0.127970679519583	-0.78515598356304	0.432640076560958	   
df.mm.exp2	-0.170393910929618	0.169289296558382	-1.00652501010809	0.314526992807903	   
df.mm.exp3	0.0351551554277196	0.169289296558382	0.207663190422649	0.835555158570737	   
df.mm.exp4	-0.276438795392307	0.169289296558382	-1.63293723237235	0.102952726141042	   
df.mm.exp5	-0.125068696738704	0.169289296558382	-0.738786794447884	0.46029557902321	   
df.mm.exp6	-0.228446357416047	0.169289296558382	-1.34944359779570	0.177651005177936	   
df.mm.exp7	-0.0960483503081446	0.169289296558382	-0.567362215218496	0.570658445023364	   
df.mm.exp8	-0.297326253761402	0.169289296558382	-1.75632045147559	0.0794912242826603	.  
df.mm.trans1:exp2	0.0244361299439665	0.127970679519583	0.190951005618650	0.848621911907273	   
df.mm.trans2:exp2	0.190288920218671	0.127970679519583	1.48697280449738	0.137493127133252	   
df.mm.trans1:exp3	-0.0813112539547228	0.127970679519583	-0.6353897178633	0.525391539006867	   
df.mm.trans2:exp3	0.0316376109839306	0.127970679519583	0.247225466823352	0.804809587069392	   
df.mm.trans1:exp4	0.163050363070389	0.127970679519583	1.27412282002799	0.203062318470203	   
df.mm.trans2:exp4	0.218223414163519	0.127970679519583	1.70526104090995	0.0886099693875286	.  
df.mm.trans1:exp5	-0.0167612963522748	0.127970679519583	-0.130977630307181	0.895832379299426	   
df.mm.trans2:exp5	0.186916662644454	0.127970679519583	1.46062100589104	0.144588942693661	   
df.mm.trans1:exp6	0.0755844494786039	0.127970679519583	0.590638806970132	0.554961927558951	   
df.mm.trans2:exp6	0.168059796477196	0.127970679519583	1.31326798535502	0.189542902613916	   
df.mm.trans1:exp7	0.061225031841604	0.127970679519583	0.47843015346523	0.632500385545317	   
df.mm.trans2:exp7	0.0985871764879114	0.127970679519583	0.770388786384654	0.441341264070633	   
df.mm.trans1:exp8	0.129975369616864	0.127970679519583	1.01566522976049	0.310156003062954	   
df.mm.trans2:exp8	0.240695833545834	0.127970679519583	1.88086704274318	0.0604241135051817	.  
df.mm.trans1:probe2	0.0963198443429336	0.0959780096396875	1.00356159399981	0.315952814347164	   
df.mm.trans1:probe3	0.113030161732582	0.0959780096396875	1.17766728187957	0.239348044564338	   
df.mm.trans1:probe4	0.0458512900901557	0.0959780096396875	0.47772703624806	0.633000545376256	   
df.mm.trans1:probe5	0.0876621028076018	0.0959780096396875	0.913356123310906	0.36138431110329	   
df.mm.trans1:probe6	0.0732998074317243	0.0959780096396876	0.763714601989562	0.445306585018059	   
df.mm.trans2:probe2	-0.0833826959494084	0.0959780096396875	-0.868768755076675	0.385285181885302	   
df.mm.trans2:probe3	-0.188527420882375	0.0959780096396875	-1.9642772504882	0.0499115331867104	*  
df.mm.trans2:probe4	-0.121311659616253	0.0959780096396876	-1.26395264990044	0.206687243515803	   
df.mm.trans2:probe5	-0.181921599013332	0.0959780096396875	-1.89545084021107	0.0584634712790793	.  
df.mm.trans2:probe6	0.0300873569180082	0.0959780096396875	0.313481775991809	0.754012376298519	   
df.mm.trans3:probe2	0.0980510881819878	0.0959780096396875	1.02159951586913	0.307339762625562	   
df.mm.trans3:probe3	0.169355096266456	0.0959780096396876	1.76451977804327	0.078100783929317	.  
df.mm.trans3:probe4	0.0485890814336447	0.0959780096396876	0.506252230235381	0.612846378197187	   
df.mm.trans3:probe5	0.111431797646379	0.0959780096396876	1.16101384124037	0.246050367899247	   
df.mm.trans3:probe6	0.0431167949803351	0.0959780096396875	0.449236185895087	0.65340678072834	   
df.mm.trans3:probe7	-0.00285725828717137	0.0959780096396875	-0.0297699264435451	0.976259421915318	   
df.mm.trans3:probe8	0.0893977405036027	0.0959780096396875	0.931439825010042	0.351962099660607	   
df.mm.trans3:probe9	0.00953000350173696	0.0959780096396876	0.0992936146260345	0.920934893111954	   
df.mm.trans3:probe10	-0.0094989598613189	0.0959780096396875	-0.0989701692812666	0.921191602388838	   
df.mm.trans3:probe11	0.0736973554198035	0.0959780096396875	0.767856675674686	0.442843270684412	   
df.mm.trans3:probe12	-0.0902301928837492	0.0959780096396875	-0.940113190745293	0.347498815204676	   
df.mm.trans3:probe13	0.0503846792377131	0.0959780096396875	0.524960659497555	0.599784457416771	   
df.mm.trans3:probe14	0.159835384398526	0.0959780096396875	1.66533339249862	0.0963144367731107	.  
df.mm.trans3:probe15	0.0279799729515614	0.0959780096396875	0.291524830079322	0.770740324050289	   
df.mm.trans3:probe16	0.0147218457004185	0.0959780096396875	0.153387695324022	0.878138802108525	   
df.mm.trans3:probe17	0.0542115718680757	0.0959780096396875	0.564833257863881	0.572376525158638	   
df.mm.trans3:probe18	-0.0102796726175125	0.0959780096396875	-0.107104457115787	0.914738221380751