chr18.11312_chr18_68650185_68654633_+_2.R 

fitVsDatCorrelation=0.86341966240683
cont.fitVsDatCorrelation=0.280293831971623

fstatistic=13157.5064440404,55,761
cont.fstatistic=3624.17482920001,55,761

residuals=-0.516633328020218,-0.0755389115519126,-0.0046917884152829,0.0663078310225291,1.10652908777126
cont.residuals=-0.542060571096273,-0.153340695562368,-0.0407678381848504,0.100306801283515,1.58799970855892

predictedValues:
Include	Exclude	Both
chr18.11312_chr18_68650185_68654633_+_2.R.tl.Lung	47.8925454696371	43.4676993573769	68.2398302868552
chr18.11312_chr18_68650185_68654633_+_2.R.tl.cerebhem	56.2612150404728	51.1410176807656	66.8108671079844
chr18.11312_chr18_68650185_68654633_+_2.R.tl.cortex	57.665584618122	41.3598667625936	78.1860098519695
chr18.11312_chr18_68650185_68654633_+_2.R.tl.heart	48.0259311095838	42.1282441560975	63.6770794002378
chr18.11312_chr18_68650185_68654633_+_2.R.tl.kidney	49.1444964176383	42.529761877231	70.9778708590623
chr18.11312_chr18_68650185_68654633_+_2.R.tl.liver	49.7119497779105	46.7220813207699	65.5137250640031
chr18.11312_chr18_68650185_68654633_+_2.R.tl.stomach	47.9917208613878	44.3280223960861	62.9404030119206
chr18.11312_chr18_68650185_68654633_+_2.R.tl.testicle	52.29428237486	43.7964850897232	68.5193884809409


diffExp=4.42484611226015,5.12019735970713,16.3057178555284,5.89768695348638,6.61473454040726,2.98986845714058,3.66369846530179,8.49779728513678
diffExpScore=0.981656272417184
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=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.9450283095809	49.3897400968998	49.7888032067108
cerebhem	53.1917225290616	51.0544255282373	55.4379010904596
cortex	55.5477673715591	58.2069645382142	52.0857004676027
heart	54.3792532336446	52.9064158053531	51.087363819708
kidney	56.7540550628414	55.7656388509552	48.4469904632434
liver	52.125874259919	53.5180469393031	54.4863342140691
stomach	54.5701467125832	55.8760815307556	56.5247789353597
testicle	52.2018669325303	59.1624933172015	51.7880060900298
cont.diffExp=7.55528821268111,2.13729700082423,-2.65919716665504,1.47283742829151,0.988416211886204,-1.39217267938410,-1.30593481817245,-6.96062638467113
cont.diffExpScore=21.0221922314054

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=0.286809569461593
cont.tran.correlation=-0.209306993129107

tran.covariance=0.00138584350924565
cont.tran.covariance=-0.000456856739910045

tran.mean=47.778806519391
cont.tran.mean=54.474720063665

weightedLogRatios:
wLogRatio
Lung	0.370364875768276
cerebhem	0.379984843772573
cortex	1.29233643156843
heart	0.498702491360087
kidney	0.552581242692928
liver	0.240374602836861
stomach	0.304251001132264
testicle	0.685964953895317

cont.weightedLogRatios:
wLogRatio
Lung	0.56523456846134
cerebhem	0.162131052805730
cortex	-0.188946411690764
heart	0.109345093192770
kidney	0.0708029469579138
liver	-0.104555873771241
stomach	-0.0948652722553208
testicle	-0.502893970807169

varWeightedLogRatios=0.112701562985883
cont.varWeightedLogRatios=0.0958924898397209

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.61928829502794	0.0649702975204417	55.7068142390636	9.93512715469316e-271	***
df.mm.trans1	0.140347345058890	0.0568909854035538	2.46695226077281	0.0138460336452481	*  
df.mm.trans2	0.130527839001913	0.0510181090378802	2.55846093599820	0.0107061389629770	*  
df.mm.exp2	0.344776952747878	0.0672806897783106	5.12445627242996	3.78644944257516e-07	***
df.mm.exp3	-6.87467586276084e-05	0.0672806897783106	-0.00102179033618901	0.999184997201033	   
df.mm.exp4	0.0406852500506276	0.0672806897783106	0.604709169669413	0.545552508742738	   
df.mm.exp5	-0.0353487429621529	0.0672806897783106	-0.525392101041573	0.599463685621423	   
df.mm.exp6	0.150252997128125	0.0672806897783106	2.23322616969605	0.0258240036512306	*  
df.mm.exp7	0.102507688094040	0.0672806897783106	1.52358259750014	0.128028444346493	   
df.mm.exp8	0.0913742898563692	0.0672806897783106	1.35810572331299	0.174832639548528	   
df.mm.trans1:exp2	-0.183731418406364	0.0631148815335958	-2.91106334895938	0.0037072342138115	** 
df.mm.trans2:exp2	-0.182208201949274	0.0503482579792524	-3.61895742300277	0.000315355361049694	***
df.mm.trans1:exp3	0.185769423197598	0.0631148815335958	2.94335374928516	0.00334554148225749	** 
df.mm.trans2:exp3	-0.0496383632177817	0.0503482579792524	-0.98590031135212	0.324495382038078	   
df.mm.trans1:exp4	-0.0379040189326066	0.0631148815335958	-0.600555970503254	0.548314578603964	   
df.mm.trans2:exp4	-0.0719849704998484	0.0503482579792524	-1.42974103551929	0.153201755699039	   
df.mm.trans1:exp5	0.0611537426924991	0.0631148815335958	0.96892747330829	0.332889214922172	   
df.mm.trans2:exp5	0.0135347345114469	0.0503482579792524	0.268822300009352	0.788139292918393	   
df.mm.trans1:exp6	-0.112967520105894	0.0631148815335958	-1.78987137994963	0.0738720793071461	.  
df.mm.trans2:exp6	-0.078054229546656	0.0503482579792524	-1.55028659737981	0.121488493572631	   
df.mm.trans1:exp7	-0.100439039484367	0.0631148815335958	-1.59136858128939	0.111941936933846	   
df.mm.trans2:exp7	-0.0829087700575477	0.0503482579792524	-1.64670583223978	0.100031381426165	   
df.mm.trans1:exp8	-0.00344711374525534	0.0631148815335958	-0.0546164971159845	0.956458330438288	   
df.mm.trans2:exp8	-0.0838388435512592	0.0503482579792524	-1.66517863608722	0.0962887162313076	.  
df.mm.trans1:probe2	0.157084557936426	0.0386498137333796	4.0643031042802	5.31833848742802e-05	***
df.mm.trans1:probe3	-0.0119336777516182	0.0386498137333796	-0.308764172421141	0.75758543120158	   
df.mm.trans1:probe4	0.0812562075319386	0.0386498137333796	2.10236996463873	0.0358488568640376	*  
df.mm.trans1:probe5	0.68255428413415	0.0386498137333796	17.6599630943284	9.3549119011674e-59	***
df.mm.trans1:probe6	0.124717557376061	0.0386498137333796	3.22686050278038	0.00130513463829347	** 
df.mm.trans1:probe7	0.272340459364034	0.0386498137333796	7.04635890984462	4.11861386421002e-12	***
df.mm.trans1:probe8	0.141731609205384	0.0386498137333796	3.66707095105555	0.000262417203068085	***
df.mm.trans1:probe9	-0.0419992691532592	0.0386498137333796	-1.08666161868167	0.277530453027563	   
df.mm.trans1:probe10	0.00404289297718381	0.0386498137333796	0.104603168467335	0.916718262917411	   
df.mm.trans1:probe11	0.188791339512590	0.0386498137333796	4.8846636316268	1.26341057552612e-06	***
df.mm.trans1:probe12	0.168096773989418	0.0386498137333796	4.34922597943189	1.55224855006901e-05	***
df.mm.trans1:probe13	0.238513615922470	0.0386498137333796	6.17114528850834	1.10144498995855e-09	***
df.mm.trans1:probe14	0.129285603082162	0.0386498137333796	3.34505112945747	0.00086299039653429	***
df.mm.trans1:probe15	0.269797857296978	0.0386498137333796	6.9805732870575	6.40675657401101e-12	***
df.mm.trans1:probe16	0.198346263861904	0.0386498137333796	5.13188149444052	3.64485543357483e-07	***
df.mm.trans1:probe17	0.0912317869065556	0.0386498137333796	2.36047158043	0.0185030494552547	*  
df.mm.trans1:probe18	0.0359612952406681	0.0386498137333796	0.930439031058264	0.35243887442319	   
df.mm.trans1:probe19	0.169102486504750	0.0386498137333796	4.375247127225	1.38216165956447e-05	***
df.mm.trans1:probe20	0.0796803714226234	0.0386498137333796	2.06159781188824	0.0395851340237254	*  
df.mm.trans1:probe21	0.0320646452612769	0.0386498137333796	0.829619658259427	0.407014016296119	   
df.mm.trans1:probe22	0.0504116472247236	0.0386498137333796	1.30431798643278	0.192519526741204	   
df.mm.trans2:probe2	0.0713784054481	0.0386498137333796	1.8467981745137	0.0651644543632681	.  
df.mm.trans2:probe3	0.126653714346035	0.0386498137333796	3.27695536179652	0.00109691795674843	** 
df.mm.trans2:probe4	0.0406824710439924	0.0386498137333796	1.05259164570973	0.292862290487881	   
df.mm.trans2:probe5	0.00878929517463546	0.0386498137333796	0.227408474340063	0.820167210914404	   
df.mm.trans2:probe6	0.0189199307980034	0.0386498137333796	0.489521914090451	0.624613314727908	   
df.mm.trans3:probe2	0.252382705988407	0.0386498137333796	6.52998505321226	1.20198274000042e-10	***
df.mm.trans3:probe3	0.915370004810991	0.0386498137333796	23.6836847681995	2.48408809048912e-93	***
df.mm.trans3:probe4	0.218541257264234	0.0386498137333796	5.65439354434695	2.21276124865221e-08	***
df.mm.trans3:probe5	0.428977543488259	0.0386498137333796	11.099084369397	1.23584852299649e-26	***
df.mm.trans3:probe6	0.181918110974364	0.0386498137333796	4.70683021215318	2.98902643903188e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04980967448525	0.123622598112093	32.7594609426762	1.59985239286232e-147	***
df.mm.trans1	0.0418774904559283	0.108249641654044	0.386860314879978	0.698967775528708	   
df.mm.trans2	-0.147915268676482	0.0970749932004606	-1.5237216485922	0.127993693372295	   
df.mm.exp2	-0.142507447034301	0.128018709942825	-1.11317671532503	0.265984031111972	   
df.mm.exp3	0.094318913638847	0.128018709942825	0.736758819714487	0.461496095531037	   
df.mm.exp4	-0.00306883884235777	0.128018709942825	-0.0239717994637530	0.980881387198698	   
df.mm.exp5	0.145375801505771	0.128018709942825	1.13558245955375	0.256488864580742	   
df.mm.exp6	-0.0983085221189015	0.128018709942825	-0.76792308064038	0.442771094589034	   
df.mm.exp7	-0.0460947041189786	0.128018709942825	-0.360062245116867	0.718900490045369	   
df.mm.exp8	0.0542084861264399	0.128018709942825	0.423441902755075	0.672092523561542	   
df.mm.trans1:exp2	0.0743238528520816	0.120092194933618	0.618889952783065	0.536174068765853	   
df.mm.trans2:exp2	0.175656965683725	0.0958004303405683	1.83357178103761	0.0671079872251857	.  
df.mm.trans1:exp3	-0.119161976557171	0.120092194933618	-0.992254131278376	0.321388961210700	   
df.mm.trans2:exp3	0.0699433872671501	0.0958004303405683	0.730094708536308	0.465556977441483	   
df.mm.trans1:exp4	-0.0430348412841838	0.120092194933618	-0.358348361506522	0.720182012019042	   
df.mm.trans2:exp4	0.0718507398401886	0.0958004303405683	0.7500043536836	0.453483886244718	   
df.mm.trans1:exp5	-0.148735079710404	0.120092194933618	-1.23850746330865	0.215909855183018	   
df.mm.trans2:exp5	-0.0239606252743907	0.0958004303405684	-0.250109787494808	0.802569949519307	   
df.mm.trans1:exp6	0.0098835876045556	0.120092194933618	0.0822999996795698	0.934429796026312	   
df.mm.trans2:exp6	0.178584732830439	0.0958004303405683	1.86413288745755	0.0626878941425053	.  
df.mm.trans1:exp7	0.00349528696916146	0.120092194933618	0.0291050302735620	0.976788454820562	   
df.mm.trans2:exp7	0.169488400843425	0.0958004303405683	1.76918204063278	0.077263973458814	.  
df.mm.trans1:exp8	-0.141176613751599	0.120092194933618	-1.17556860235285	0.240134959217959	   
df.mm.trans2:exp8	0.126336583872166	0.0958004303405684	1.31874756118571	0.187650233267862	   
df.mm.trans1:probe2	0.00293996893920172	0.0735411499195539	0.0399771956573664	0.968121792329356	   
df.mm.trans1:probe3	-0.0209481481238842	0.0735411499195539	-0.284849341447601	0.775837067564204	   
df.mm.trans1:probe4	-0.146113036518930	0.0735411499195539	-1.98682012286674	0.0473005201831039	*  
df.mm.trans1:probe5	7.72170779905849e-06	0.0735411499195539	0.000104998464227242	0.9999162509057	   
df.mm.trans1:probe6	-0.0261042811282936	0.0735411499195539	-0.354961557670079	0.722716741040141	   
df.mm.trans1:probe7	0.0334369510382777	0.0735411499195539	0.454669951107021	0.649476367615196	   
df.mm.trans1:probe8	-0.0434814275893162	0.0735411499195539	-0.591253028228145	0.554526471107793	   
df.mm.trans1:probe9	0.00864172445657464	0.0735411499195539	0.117508693650123	0.906487961590111	   
df.mm.trans1:probe10	-0.105858277453422	0.0735411499195539	-1.43944278229562	0.150436309945854	   
df.mm.trans1:probe11	-0.0688516577588714	0.0735411499195539	-0.936233086295057	0.349450195768894	   
df.mm.trans1:probe12	-0.058635073788662	0.0735411499195539	-0.797309721874113	0.425519869884548	   
df.mm.trans1:probe13	-0.111378002979735	0.0735411499195539	-1.51449906755022	0.130314520396692	   
df.mm.trans1:probe14	-0.0348528694012298	0.0735411499195539	-0.473923367248881	0.635690493185655	   
df.mm.trans1:probe15	-0.147326949522420	0.0735411499195539	-2.00332670462155	0.0454963654310148	*  
df.mm.trans1:probe16	-0.136871556013209	0.0735411499195539	-1.86115604886423	0.0631075537811596	.  
df.mm.trans1:probe17	-0.108754368210275	0.0735411499195539	-1.47882332992128	0.139601328439440	   
df.mm.trans1:probe18	-0.131135655236777	0.0735411499195539	-1.78316024946883	0.0749587045224843	.  
df.mm.trans1:probe19	-0.102223070011783	0.0735411499195539	-1.39001185218894	0.164931722630537	   
df.mm.trans1:probe20	-0.00329303761837572	0.0735411499195539	-0.0447781632729152	0.964295877751865	   
df.mm.trans1:probe21	0.00919997838873175	0.0735411499195539	0.125099735301876	0.90047763172695	   
df.mm.trans1:probe22	-0.197220719302768	0.0735411499195539	-2.68177366710346	0.00748203104749657	** 
df.mm.trans2:probe2	-0.100882441848878	0.0735411499195539	-1.37178221933206	0.170535517865286	   
df.mm.trans2:probe3	0.0350611778187837	0.0735411499195539	0.476755909543662	0.633672811909274	   
df.mm.trans2:probe4	0.000545369069482417	0.0735411499195539	0.00741583548909681	0.99408501714293	   
df.mm.trans2:probe5	0.0202892452868205	0.0735411499195539	0.275889693171982	0.782707691147277	   
df.mm.trans2:probe6	0.0191663272936755	0.0735411499195539	0.260620446031119	0.794455725707469	   
df.mm.trans3:probe2	-0.0428332437484074	0.0735411499195539	-0.582439135032052	0.560443449004977	   
df.mm.trans3:probe3	-0.0678878794107661	0.0735411499195539	-0.923127792875528	0.356233200743592	   
df.mm.trans3:probe4	0.0261408225229917	0.0735411499195539	0.355458441316011	0.722344675034383	   
df.mm.trans3:probe5	-0.104069369238180	0.0735411499195539	-1.41511751382757	0.157443111247421	   
df.mm.trans3:probe6	-0.0272409664451829	0.0735411499195539	-0.370418010528548	0.71117413130008