chr7.20792_chr7_16997986_16999443_-_1.R 

fitVsDatCorrelation=0.84942503799833
cont.fitVsDatCorrelation=0.2780533796646

fstatistic=7869.35809225538,39,393
cont.fstatistic=2368.02515534081,39,393

residuals=-0.467653025181168,-0.080004670759705,-0.00311270939683602,0.0612016133052059,1.19253848058598
cont.residuals=-0.696329621236683,-0.192085723830711,-0.0380888767526818,0.144846323325983,1.27518846500888

predictedValues:
Include	Exclude	Both
chr7.20792_chr7_16997986_16999443_-_1.R.tl.Lung	81.6412710541273	58.8285303322398	67.9491458696434
chr7.20792_chr7_16997986_16999443_-_1.R.tl.cerebhem	77.730730372079	63.7521054235554	61.7469052959222
chr7.20792_chr7_16997986_16999443_-_1.R.tl.cortex	66.9873899402488	53.6570902219049	64.4425877169632
chr7.20792_chr7_16997986_16999443_-_1.R.tl.heart	79.8107184123944	55.0736316259073	66.4707885926478
chr7.20792_chr7_16997986_16999443_-_1.R.tl.kidney	89.2719354257685	57.8251408962208	69.8091736872271
chr7.20792_chr7_16997986_16999443_-_1.R.tl.liver	79.3086836149903	57.8971366608695	83.883814023518
chr7.20792_chr7_16997986_16999443_-_1.R.tl.stomach	73.692829856728	61.7268332744892	74.6576965407161
chr7.20792_chr7_16997986_16999443_-_1.R.tl.testicle	73.2433592673168	57.2084909931869	70.563208499355


diffExp=22.8127407218876,13.9786249485237,13.3302997183439,24.7370867864870,31.4467945295477,21.4115469541208,11.9659965822388,16.0348682741299
diffExpScore=0.99361911034655
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,1,1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,0,1,1,1,0,0
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,1,0,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	73.0484635878703	69.6597768238065	74.141068406757
cerebhem	79.1797601344468	61.5216731348681	69.3596887564706
cortex	65.0616747190153	63.1722382339551	71.7136392698932
heart	70.2783204592404	62.0374420008009	73.5784132397143
kidney	71.1124083684915	73.2682131276633	71.1410793472529
liver	69.9546689065635	63.3362784709808	67.8778268143515
stomach	67.2788698603561	69.6727353012317	68.2321418811977
testicle	60.512023398115	65.2111120746565	65.9811196785197
cont.diffExp=3.38868676406375,17.6580869995787,1.8894364850602,8.24087845843952,-2.15580475917176,6.6183904355827,-2.39386544087552,-4.69908867654149
cont.diffExpScore=1.59219830815644

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

tran.correlation=0.198432986191136
cont.tran.correlation=-0.0681563111009817

tran.covariance=0.00114656071081930
cont.tran.covariance=-0.000324907315348711

tran.mean=67.9784923357517
cont.tran.mean=67.7691036626289

weightedLogRatios:
wLogRatio
Lung	1.38898393750926
cerebhem	0.843374057651858
cortex	0.90832297801499
heart	1.55597949194461
kidney	1.85628348020919
liver	1.32669204514529
stomach	0.746188795322619
testicle	1.03040603482924

cont.weightedLogRatios:
wLogRatio
Lung	0.202700411357515
cerebhem	1.07128597856319
cortex	0.122615910013097
heart	0.522611400227824
kidney	-0.127798219905172
liver	0.417250889483986
stomach	-0.147764464199636
testicle	-0.309639164837308

varWeightedLogRatios=0.150823462899215
cont.varWeightedLogRatios=0.20023924748035

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.63778112255416	0.0828314359669008	43.9178806945687	1.14498732637136e-153	***
df.mm.trans1	0.777392042862961	0.0676315842603084	11.4945117930528	1.47098409852644e-26	***
df.mm.trans2	0.374887558086089	0.0676315842603084	5.54308408099945	5.4592833640122e-08	***
df.mm.exp2	0.127006748368498	0.0918932275689148	1.38211217223	0.167722288430590	   
df.mm.exp3	-0.236858988604745	0.0918932275689148	-2.57754564586508	0.0103139887122675	*  
df.mm.exp4	-0.0666360070167971	0.0918932275689148	-0.72514600672638	0.468794003180000	   
df.mm.exp5	0.0451430976783015	0.0918932275689148	0.491255981235905	0.623519635218661	   
df.mm.exp6	-0.255619402093384	0.0918932275689148	-2.78170011932254	0.00566799399965496	** 
df.mm.exp7	-0.148491661919201	0.0918932275689148	-1.61591518600041	0.106915089361335	   
df.mm.exp8	-0.174221249225384	0.0918932275689148	-1.89590956629234	0.0587053130178481	.  
df.mm.trans1:exp2	-0.176090975732659	0.0750305061204324	-2.34692506871822	0.0194247299805651	*  
df.mm.trans2:exp2	-0.0466314855838402	0.0750305061204324	-0.621500346925442	0.534630800481425	   
df.mm.trans1:exp3	0.0390284736026863	0.0750305061204324	0.520168070571738	0.603239326049447	   
df.mm.trans2:exp3	0.144845658967773	0.0750305061204324	1.93049022933791	0.0542652586978426	.  
df.mm.trans1:exp4	0.0439589115654757	0.0750305061204324	0.585880514985687	0.558292116262115	   
df.mm.trans2:exp4	0.000680106787646371	0.0750305061204324	0.0090644035714583	0.992772350825788	   
df.mm.trans1:exp5	0.044209161030042	0.0750305061204324	0.589215817884546	0.556055048342914	   
df.mm.trans2:exp5	-0.0623463999271515	0.0750305061204324	-0.830947345964567	0.406507914550898	   
df.mm.trans1:exp6	0.226632121248454	0.0750305061204324	3.02053302005832	0.00268817877027658	** 
df.mm.trans2:exp6	0.239660385308966	0.0750305061204324	3.19417257994080	0.00151542144889956	** 
df.mm.trans1:exp7	0.0460622613096053	0.0750305061204324	0.613913775760358	0.539627497530536	   
df.mm.trans2:exp7	0.196583450429280	0.0750305061204324	2.62004697281051	0.00913243898965448	** 
df.mm.trans1:exp8	0.0656739277138726	0.0750305061204324	0.875296344242414	0.381947311601754	   
df.mm.trans2:exp8	0.146296633570889	0.0750305061204324	1.94982869149338	0.0519073102982228	.  
df.mm.trans1:probe2	-0.0625398074352964	0.0459466137844574	-1.36114073016741	0.174249157367832	   
df.mm.trans1:probe3	-0.0364727095303456	0.0459466137844574	-0.793806257441402	0.42778725834113	   
df.mm.trans1:probe4	-0.148883162149812	0.0459466137844574	-3.24035113552101	0.00129550809328363	** 
df.mm.trans1:probe5	0.147848108243326	0.0459466137844574	3.21782381911546	0.00139880563304006	** 
df.mm.trans1:probe6	-0.054011532145703	0.0459466137844574	-1.17552802474366	0.240495191593145	   
df.mm.trans2:probe2	0.190695588211477	0.0459466137844574	4.15037306353105	4.07225589509644e-05	***
df.mm.trans2:probe3	0.255500567049774	0.0459466137844574	5.56081386646613	4.96920402074697e-08	***
df.mm.trans2:probe4	-0.0380444900634239	0.0459466137844574	-0.82801510121935	0.408164481403545	   
df.mm.trans2:probe5	0.0997014148074573	0.0459466137844574	2.16994042858462	0.0306098406513822	*  
df.mm.trans2:probe6	0.235646115836243	0.0459466137844574	5.12869385634587	4.59094674912778e-07	***
df.mm.trans3:probe2	-0.9740384250573	0.0459466137844574	-21.1993517003595	4.70394581770466e-67	***
df.mm.trans3:probe3	-0.236227553639327	0.0459466137844574	-5.14134849517979	4.31043636310918e-07	***
df.mm.trans3:probe4	-0.874854693933695	0.0459466137844574	-19.0406783411237	9.60367926178265e-58	***
df.mm.trans3:probe5	-0.679146435395961	0.0459466137844574	-14.7812075680254	1.25731833297253e-39	***
df.mm.trans3:probe6	-0.663539809783087	0.0459466137844574	-14.4415388889343	3.18086043446513e-38	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20960436468098	0.150774356350456	27.9198961055172	2.56166907230981e-95	***
df.mm.trans1	0.0974805755902727	0.123106746451726	0.791837802556968	0.42893292876012	   
df.mm.trans2	-0.00954020573144567	0.123106746451726	-0.0774953932779522	0.938268894399873	   
df.mm.exp2	0.0230278301369678	0.167269130106662	0.137669336369979	0.89057226010711	   
df.mm.exp3	-0.180256916869448	0.167269130106662	-1.07764604714872	0.281852767290709	   
df.mm.exp4	-0.146926771643420	0.167269130106662	-0.878385459108505	0.380271277476205	   
df.mm.exp5	0.0649472048469884	0.167269130106662	0.388279683200205	0.698019314542786	   
df.mm.exp6	-0.0501802534293925	0.167269130106662	-0.299997096878511	0.764337983144045	   
df.mm.exp7	0.000962986152612154	0.167269130106662	0.00575710623949615	0.995409440384814	   
df.mm.exp8	-0.137673229971443	0.167269130106662	-0.823064183353219	0.410970638967203	   
df.mm.trans1:exp2	0.0575697765262337	0.136574672826844	0.421526007235788	0.673601484934521	   
df.mm.trans2:exp2	-0.147261369848922	0.136574672826844	-1.07824801481038	0.281584449412265	   
df.mm.trans1:exp3	0.0644694730904926	0.136574672826844	0.472045597884976	0.637156231188025	   
df.mm.trans2:exp3	0.0824987919254248	0.136574672826844	0.604056302811142	0.546154737946591	   
df.mm.trans1:exp4	0.108267031188995	0.136574672826844	0.79273139703041	0.428412621497463	   
df.mm.trans2:exp4	0.0310418165109463	0.136574672826844	0.227288236306468	0.820317809108561	   
df.mm.trans1:exp5	-0.091808468953706	0.136574672826844	-0.672221774751056	0.501837525941001	   
df.mm.trans2:exp5	-0.0144434056154951	0.136574672826844	-0.105754641885960	0.915830982571274	   
df.mm.trans1:exp6	0.00690459301486361	0.136574672826844	0.0505554424693192	0.95970545359425	   
df.mm.trans2:exp6	-0.0449845231261517	0.136574672826844	-0.329376759212048	0.742046402128007	   
df.mm.trans1:exp7	-0.0832398738258648	0.136574672826844	-0.609482505818632	0.54255685789148	   
df.mm.trans2:exp7	-0.000776978202782888	0.136574672826844	-0.00568903579778645	0.995463717309226	   
df.mm.trans1:exp8	-0.0506077965233094	0.136574672826844	-0.370550377136706	0.71117208548337	   
df.mm.trans2:exp8	0.0716800538351886	0.136574672826844	0.524841483062295	0.599989322999086	   
df.mm.trans1:probe2	-0.152178093239603	0.0836345650533308	-1.81955980930330	0.0695867657918749	.  
df.mm.trans1:probe3	0.0146163104142648	0.0836345650533308	0.174763991478218	0.861355081201881	   
df.mm.trans1:probe4	-0.0146685109140966	0.0836345650533308	-0.175388141311467	0.860864986449501	   
df.mm.trans1:probe5	-0.041885045021569	0.0836345650533308	-0.500810221167054	0.616785052758941	   
df.mm.trans1:probe6	0.00257332318281700	0.0836345650533308	0.0307686562508705	0.97546965660971	   
df.mm.trans2:probe2	0.072742110653383	0.0836345650533308	0.869761331418391	0.384961749523542	   
df.mm.trans2:probe3	0.0762104219603419	0.0836345650533308	0.911231162758426	0.362732165316207	   
df.mm.trans2:probe4	0.199354788734991	0.0836345650533308	2.3836411250285	0.0176163823495954	*  
df.mm.trans2:probe5	0.142270730205017	0.0836345650533308	1.70109966034134	0.0897152719020046	.  
df.mm.trans2:probe6	0.0321287747332217	0.0836345650533308	0.384156654760317	0.701070109007055	   
df.mm.trans3:probe2	0.0118061611136714	0.0836345650533308	0.14116365770711	0.887812984390995	   
df.mm.trans3:probe3	-0.0273345576419224	0.0836345650533308	-0.326833261158136	0.743967999054387	   
df.mm.trans3:probe4	0.00927604372590846	0.0836345650533308	0.110911603593484	0.9117430631919	   
df.mm.trans3:probe5	0.0422575820622246	0.0836345650533308	0.505264564182027	0.613656260579202	   
df.mm.trans3:probe6	0.0145440132081906	0.0836345650533308	0.173899549772470	0.86203394634503