chr2.14126_chr2_71867353_71871347_+_2.R 

fitVsDatCorrelation=0.845409759517361
cont.fitVsDatCorrelation=0.274034434157045

fstatistic=9186.0875774399,55,761
cont.fstatistic=2823.83393794608,55,761

residuals=-0.631602702585892,-0.0927857656486464,-0.0073682283787959,0.0852640386276685,1.37825321069473
cont.residuals=-0.60795251227039,-0.188700805635210,-0.046663635837513,0.113119402582463,1.65550731527023

predictedValues:
Include	Exclude	Both
chr2.14126_chr2_71867353_71871347_+_2.R.tl.Lung	61.9472345294974	69.32707703712	54.5191642799333
chr2.14126_chr2_71867353_71871347_+_2.R.tl.cerebhem	64.1878647538818	54.0999403756617	54.7666841981387
chr2.14126_chr2_71867353_71871347_+_2.R.tl.cortex	57.9706909138983	72.1923293350597	53.9415896026754
chr2.14126_chr2_71867353_71871347_+_2.R.tl.heart	59.6038023944041	72.4524983538285	49.7394164890228
chr2.14126_chr2_71867353_71871347_+_2.R.tl.kidney	60.809122890932	64.0808314003341	51.9946587194066
chr2.14126_chr2_71867353_71871347_+_2.R.tl.liver	61.7933961690433	63.7954374767474	52.4839993279667
chr2.14126_chr2_71867353_71871347_+_2.R.tl.stomach	61.3943959945496	63.9553505569274	54.6036487037222
chr2.14126_chr2_71867353_71871347_+_2.R.tl.testicle	62.6610582897694	63.6775731208542	52.7458050383165


diffExp=-7.37984250762264,10.0879243782201,-14.2216384211614,-12.8486959594243,-3.27170850940211,-2.00204130770408,-2.56095456237777,-1.01651483108484
diffExpScore=1.56047655476362
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=0,0,-1,-1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	61.0275266033463	73.702168906019	63.7168888182068
cerebhem	55.9544165604022	58.5798531866169	60.616231538309
cortex	58.3568450071518	60.1809790867432	61.7885678278967
heart	60.1730602117083	63.6578312299282	59.2377514739689
kidney	59.609578551679	59.9170839268492	65.6759427818491
liver	60.8842444214288	65.7231415406716	55.4252861492067
stomach	60.7643346168168	55.7588883103377	59.9203188954706
testicle	65.462946911807	58.5942848709972	55.3042772222256
cont.diffExp=-12.6746423026727,-2.62543662621469,-1.82413407959133,-3.48477101821998,-0.307505375170237,-4.83889711924281,5.00544630647912,6.86866204080981
cont.diffExpScore=2.52864669478416

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

tran.correlation=-0.85289309342114
cont.tran.correlation=0.0922248939813743

tran.covariance=-0.00245412924609861
cont.tran.covariance=0.00038669421956812

tran.mean=63.3717877245318
cont.tran.mean=61.1466989964064

weightedLogRatios:
wLogRatio
Lung	-0.470757857408255
cerebhem	0.696974242940004
cortex	-0.914802408693935
heart	-0.817024800025974
kidney	-0.216641127208042
liver	-0.131996302130397
stomach	-0.16909598242152
testicle	-0.0667150523858766

cont.weightedLogRatios:
wLogRatio
Lung	-0.793641876680039
cerebhem	-0.185590257276845
cortex	-0.125641618825274
heart	-0.232248654280987
kidney	-0.0210466743549686
liver	-0.317165000921442
stomach	0.349368292330823
testicle	0.457361958840359

varWeightedLogRatios=0.251876614233106
cont.varWeightedLogRatios=0.153143159180566

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43514748012548	0.0802048460255068	55.297749449142	8.9354162146778e-269	***
df.mm.trans1	-0.333672908035981	0.0702310578629525	-4.75107335969656	2.41887060199749e-06	***
df.mm.trans2	-0.194121051258788	0.06298108114109	-3.08221211420489	0.00212879798401927	** 
df.mm.exp2	-0.217001043057927	0.0830569901955807	-2.61267645922322	0.00916090568181573	** 
df.mm.exp3	-0.0151966756090731	0.0830569901955807	-0.182966846899801	0.854872765118745	   
df.mm.exp4	0.0972865749095932	0.0830569901955807	1.17132314427124	0.241835524595756	   
df.mm.exp5	-0.0498221346315584	0.0830569901955808	-0.599854804685775	0.54878156832763	   
df.mm.exp6	-0.0475964135140332	0.0830569901955807	-0.573057287555861	0.566775189916097	   
df.mm.exp7	-0.0911631943234491	0.0830569901955807	-1.09759809630448	0.272727192892103	   
df.mm.exp8	-0.0404778794153202	0.0830569901955808	-0.487350665127688	0.626150202600424	   
df.mm.trans1:exp2	0.252532246646126	0.0779143631553704	3.24115139261996	0.00124228637363326	** 
df.mm.trans2:exp2	-0.0310014251239513	0.0621541601777011	-0.498782785179898	0.618076555929998	   
df.mm.trans1:exp3	-0.051148737352493	0.0779143631553704	-0.656473790981214	0.511717777316459	   
df.mm.trans2:exp3	0.0556949220468591	0.0621541601777011	0.896077139287623	0.370494923104242	   
df.mm.trans1:exp4	-0.135850171025006	0.0779143631553704	-1.74358315364916	0.0816356254765178	.  
df.mm.trans2:exp4	-0.0531909748229386	0.0621541601777011	-0.855791063234764	0.39238272674404	   
df.mm.trans1:exp5	0.031278993180393	0.0779143631553704	0.401453492188841	0.688198978627774	   
df.mm.trans2:exp5	-0.0288681402005440	0.0621541601777011	-0.464460305118899	0.642450839070343	   
df.mm.trans1:exp6	0.0451099474463458	0.0779143631553704	0.578968313664982	0.562781876041998	   
df.mm.trans2:exp6	-0.0355574635162831	0.0621541601777011	-0.57208501272679	0.567433331249302	   
df.mm.trans1:exp7	0.0821987881485681	0.0779143631553704	1.05498889831974	0.291765221399217	   
df.mm.trans2:exp7	0.0105128347455725	0.0621541601777011	0.169141288620358	0.86573045711291	   
df.mm.trans1:exp8	0.0519350874806858	0.0779143631553704	0.66656628351208	0.505251240797365	   
df.mm.trans2:exp8	-0.0445252422343714	0.0621541601777011	-0.71636785224146	0.473984013977972	   
df.mm.trans1:probe2	-0.0552453822019509	0.0477126083411408	-1.15787805619327	0.247277067609816	   
df.mm.trans1:probe3	0.100711791123375	0.0477126083411408	2.11080036545676	0.0351151008673039	*  
df.mm.trans1:probe4	-0.263080513788793	0.0477126083411408	-5.51385729968463	4.81095324167077e-08	***
df.mm.trans1:probe5	-0.031811464522935	0.0477126083411408	-0.666730778906194	0.505146202447782	   
df.mm.trans1:probe6	-0.0396749111276199	0.0477126083411408	-0.831539345825487	0.40592984645437	   
df.mm.trans1:probe7	-0.112903623122834	0.0477126083411408	-2.36632678548158	0.0182151278705661	*  
df.mm.trans1:probe8	0.9152191487045	0.0477126083411408	19.18191397462	3.37386060031476e-67	***
df.mm.trans1:probe9	0.67404827576085	0.0477126083411408	14.1272569074712	2.05648304316954e-40	***
df.mm.trans1:probe10	0.0821861219528456	0.0477126083411408	1.72252418826534	0.0853808896135815	.  
df.mm.trans1:probe11	-0.216021559976193	0.0477126083411408	-4.52755712770215	6.9233705858111e-06	***
df.mm.trans1:probe12	-0.209146427883079	0.0477126083411408	-4.38346246735665	1.33226860532653e-05	***
df.mm.trans1:probe13	-0.208337362082284	0.0477126083411408	-4.36650540236013	1.43721069617661e-05	***
df.mm.trans1:probe14	-0.099253529525565	0.0477126083411408	-2.08023692219698	0.0378378720206032	*  
df.mm.trans1:probe15	-0.161362044241539	0.0477126083411408	-3.38195814170995	0.000756475885041402	***
df.mm.trans1:probe16	-0.212089032742451	0.0477126083411408	-4.4451359947885	1.00901274739168e-05	***
df.mm.trans1:probe17	0.108623561813106	0.0477126083411408	2.27662174820663	0.0230856950537652	*  
df.mm.trans1:probe18	0.439517361329717	0.0477126083411408	9.21176553977531	3.06121781296478e-19	***
df.mm.trans1:probe19	-0.115449036963248	0.0477126083411408	-2.41967565759134	0.0157677169593131	*  
df.mm.trans1:probe20	0.188978322303434	0.0477126083411408	3.96076276007079	8.17254307942325e-05	***
df.mm.trans1:probe21	-0.0468889030895554	0.0477126083411408	-0.98273610938023	0.326049661048812	   
df.mm.trans1:probe22	-0.0433857421916803	0.0477126083411408	-0.909313988484472	0.363472301618926	   
df.mm.trans2:probe2	0.145781458730064	0.0477126083411408	3.05540744466829	0.00232603580317426	** 
df.mm.trans2:probe3	-0.0586705916006441	0.0477126083411408	-1.22966640559985	0.219202082727759	   
df.mm.trans2:probe4	0.0226462547268085	0.0477126083411408	0.474638790755053	0.635180624927182	   
df.mm.trans2:probe5	-0.0345228485616506	0.0477126083411408	-0.72355819063203	0.469559361544603	   
df.mm.trans2:probe6	-0.101524796651960	0.0477126083411408	-2.12784000250138	0.0336711770239149	*  
df.mm.trans3:probe2	-0.0065816456619939	0.0477126083411408	-0.137943530878374	0.890321536980328	   
df.mm.trans3:probe3	0.308738153188702	0.0477126083411408	6.47078757424562	1.74503940974264e-10	***
df.mm.trans3:probe4	0.144899960350991	0.0477126083411408	3.03693227825587	0.00247159767613601	** 
df.mm.trans3:probe5	0.0773683670988665	0.0477126083411408	1.62154972844263	0.10531400871185	   
df.mm.trans3:probe6	0.0227677081765721	0.0477126083411408	0.477184311823513	0.633367888663862	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22547018916916	0.144414819034494	29.2592561997388	1.09842440169915e-126	***
df.mm.trans1	-0.149553203109628	0.126456268099484	-1.1826476089898	0.237318196729018	   
df.mm.trans2	0.095370011086289	0.113402143215820	0.840989494394184	0.400617988410113	   
df.mm.exp2	-0.266542087680085	0.149550317755488	-1.78229034669038	0.0751005065716858	.  
df.mm.exp3	-0.216692946827260	0.149550317755488	-1.44896346647387	0.147759709168068	   
df.mm.exp4	-0.0877195196145682	0.149550317755488	-0.586555220551173	0.557676410258976	   
df.mm.exp5	-0.260862331469370	0.149550317755488	-1.74431144904603	0.0815085289092029	.  
df.mm.exp6	0.022482016799964	0.149550317755488	0.150330785901249	0.880543480708536	   
df.mm.exp7	-0.221883404618964	0.149550317755488	-1.48367056619525	0.138310372884135	   
df.mm.exp8	-0.0176363884964007	0.149550317755488	-0.117929461876744	0.906154667803035	   
df.mm.trans1:exp2	0.179754438772076	0.140290633457388	1.28130035728062	0.200478476476904	   
df.mm.trans2:exp2	0.0369006952641332	0.111913210224842	0.329726000978767	0.741697742483983	   
df.mm.trans1:exp3	0.171944589961766	0.140290633457388	1.22563128930480	0.220716621149061	   
df.mm.trans2:exp3	0.0140170595920833	0.111913210224842	0.125249374617366	0.900359208672716	   
df.mm.trans1:exp4	0.0736192487996956	0.140290633457388	0.524762394932493	0.599901206370188	   
df.mm.trans2:exp4	-0.0587903547397385	0.111913210224842	-0.525320957388535	0.5995131090818	   
df.mm.trans1:exp5	0.237353588401317	0.140290633457388	1.69187053014063	0.0910800432593681	.  
df.mm.trans2:exp5	0.0537917757775729	0.111913210224842	0.480656176956242	0.630899029914218	   
df.mm.trans1:exp6	-0.0248326060283193	0.140290633457388	-0.177008296393942	0.859548971806552	   
df.mm.trans2:exp6	-0.137063150485564	0.111913210224842	-1.22472718109143	0.221056997218088	   
df.mm.trans1:exp7	0.217561401595306	0.140290633457388	1.55079064249423	0.121367615951979	   
df.mm.trans2:exp7	-0.0571119937823122	0.111913210224842	-0.510323970401437	0.60997249769524	   
df.mm.trans1:exp8	0.0877956568877105	0.140290633457388	0.625812677040747	0.531625454855787	   
df.mm.trans2:exp8	-0.211758675084663	0.111913210224842	-1.89216871412431	0.0588478519355841	.  
df.mm.trans1:probe2	-0.0309144798354155	0.0859101169156068	-0.359846790405188	0.719061548541436	   
df.mm.trans1:probe3	0.0375084780381226	0.0859101169156068	0.436601408364614	0.662524285819553	   
df.mm.trans1:probe4	0.05392232048153	0.0859101169156068	0.627659726438275	0.530415161208326	   
df.mm.trans1:probe5	0.00605357792088422	0.0859101169156068	0.0704640866317399	0.943842796658262	   
df.mm.trans1:probe6	0.0389169228073445	0.0859101169156068	0.452995807764692	0.650680883971666	   
df.mm.trans1:probe7	0.0131885260643843	0.0859101169156068	0.153515401187731	0.878032543912712	   
df.mm.trans1:probe8	0.0236470412824364	0.0859101169156068	0.275253277860928	0.783196373968741	   
df.mm.trans1:probe9	-0.00769301163443047	0.0859101169156068	-0.0895472141190034	0.928670594327747	   
df.mm.trans1:probe10	0.0651980720010162	0.0859101169156069	0.75891029301081	0.448141209874099	   
df.mm.trans1:probe11	0.106642805000633	0.0859101169156068	1.24132999499223	0.214866353917458	   
df.mm.trans1:probe12	0.0929371334678497	0.0859101169156068	1.08179498299538	0.279686312381984	   
df.mm.trans1:probe13	0.139536241464313	0.0859101169156068	1.62421198426939	0.104744664810680	   
df.mm.trans1:probe14	0.0676679715698243	0.0859101169156069	0.787660103364746	0.43114080447159	   
df.mm.trans1:probe15	0.151135636028538	0.0859101169156068	1.75922977938681	0.0789403092106072	.  
df.mm.trans1:probe16	0.0523156391202293	0.0859101169156069	0.608957838709743	0.542734122076387	   
df.mm.trans1:probe17	0.068986195482437	0.0859101169156068	0.80300432544173	0.422222955145022	   
df.mm.trans1:probe18	0.148714722947959	0.0859101169156068	1.73105017531343	0.0838481359707745	.  
df.mm.trans1:probe19	-0.0220610494734598	0.0859101169156068	-0.256792218023999	0.797408586949344	   
df.mm.trans1:probe20	0.0935082274497794	0.0859101169156068	1.08844255841994	0.276744360568797	   
df.mm.trans1:probe21	-0.0903987994312728	0.0859101169156068	-1.05224859046665	0.293019511572827	   
df.mm.trans1:probe22	-0.0173872713628593	0.0859101169156068	-0.202389101389997	0.839666641915325	   
df.mm.trans2:probe2	-0.0850976545669278	0.0859101169156068	-0.990542879257431	0.322223681691257	   
df.mm.trans2:probe3	-0.068116839749304	0.0859101169156068	-0.792884961572315	0.428091968326857	   
df.mm.trans2:probe4	-0.127739331497625	0.0859101169156068	-1.48689509552302	0.137456707974518	   
df.mm.trans2:probe5	0.0685242876742828	0.0859101169156068	0.797627685009405	0.425335387766705	   
df.mm.trans2:probe6	-0.0372661336153829	0.0859101169156068	-0.433780501683999	0.664570783722785	   
df.mm.trans3:probe2	-0.0647358879452221	0.0859101169156068	-0.753530437035896	0.451364292983163	   
df.mm.trans3:probe3	0.0121241351349300	0.0859101169156068	0.141125813468977	0.887807899691617	   
df.mm.trans3:probe4	-0.0739511565704262	0.0859101169156068	-0.860796833079293	0.389621068784604	   
df.mm.trans3:probe5	-0.0766570391550901	0.0859101169156068	-0.892293502875727	0.372517690743341	   
df.mm.trans3:probe6	0.102195913788738	0.0859101169156068	1.18956785833651	0.234587292182796