chr13.6464_chr13_23248781_23254865_+_2.R 

fitVsDatCorrelation=0.890922347571943
cont.fitVsDatCorrelation=0.245300089459279

fstatistic=3474.75561654439,56,784
cont.fstatistic=751.652505177384,56,784

residuals=-1.01375076144549,-0.143999906554984,0.000748117683735374,0.145264521264386,1.08918144381179
cont.residuals=-0.9408235984056,-0.366165985041649,-0.163324300483583,0.147029135714693,2.52089408474584

predictedValues:
Include	Exclude	Both
chr13.6464_chr13_23248781_23254865_+_2.R.tl.Lung	57.1811470168899	51.4997836482486	56.5192821220639
chr13.6464_chr13_23248781_23254865_+_2.R.tl.cerebhem	58.3397214262955	62.2871293906898	57.1074694127109
chr13.6464_chr13_23248781_23254865_+_2.R.tl.cortex	67.3871405988642	49.9538560796923	69.1606186370996
chr13.6464_chr13_23248781_23254865_+_2.R.tl.heart	75.1608425515133	53.5834849038234	72.919133839634
chr13.6464_chr13_23248781_23254865_+_2.R.tl.kidney	234.091090169624	60.7480114361231	277.624121267731
chr13.6464_chr13_23248781_23254865_+_2.R.tl.liver	90.2310231101801	54.9068276237744	97.6967231485128
chr13.6464_chr13_23248781_23254865_+_2.R.tl.stomach	58.2465060926559	51.465985760547	58.2235513902959
chr13.6464_chr13_23248781_23254865_+_2.R.tl.testicle	57.7514324887316	54.5582562237292	59.0742098357924


diffExp=5.6813633686413,-3.94740796439429,17.4332845191719,21.5773576476898,173.343078733501,35.3241954864057,6.78052033210885,3.19317626500241
diffExpScore=1.02647925525009
diffExp1.5=0,0,0,0,1,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,1,1,1,0,0
diffExp1.4Score=0.75
diffExp1.3=0,0,1,1,1,1,0,0
diffExp1.3Score=0.8
diffExp1.2=0,0,1,1,1,1,0,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	70.7982218862144	74.7223341979728	71.2288236797008
cerebhem	65.551740252568	80.5616120023212	72.0468877363604
cortex	61.2243668664168	68.961584300137	68.6493119387071
heart	69.350450167429	72.4061393234442	75.1016166497885
kidney	65.8072826512955	81.306790515272	66.3870055252621
liver	76.4640119356679	87.5512302969605	70.6374638629809
stomach	65.5874443027773	74.6766202846036	60.5101588049831
testicle	66.0492799875989	71.2125401621854	61.6085504997665
cont.diffExp=-3.92411231175835,-15.0098717497532,-7.73721743372013,-3.05568915601518,-15.4995078639765,-11.0872183612926,-9.08917598182632,-5.16326017458651
cont.diffExpScore=0.98602689462922

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,-1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.516316242211497
cont.tran.correlation=0.644724207820507

tran.covariance=0.0184953964268359
cont.tran.covariance=0.00335908163034319

tran.mean=71.0870149075864
cont.tran.mean=72.014478070804

weightedLogRatios:
wLogRatio
Lung	0.417948311632773
cerebhem	-0.268369431599714
cortex	1.21561188114303
heart	1.40446376533773
kidney	6.4497540112391
liver	2.11311597421887
stomach	0.49539882234663
testicle	0.229092447574896

cont.weightedLogRatios:
wLogRatio
Lung	-0.231252404244532
cerebhem	-0.883683969408504
cortex	-0.496729753777611
heart	-0.183716175649317
kidney	-0.907855346097074
liver	-0.596389535538793
stomach	-0.551353034733041
testicle	-0.318234890307018

varWeightedLogRatios=4.55542417569222
cont.varWeightedLogRatios=0.0753168866671127

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96349469038415	0.134988584434337	29.3617027468876	2.06376069925692e-128	***
df.mm.trans1	0.173690994675834	0.117654652309983	1.47627816890924	0.140270754849902	   
df.mm.trans2	0.00650834344627944	0.104994080682156	0.061987717821749	0.950588412011874	   
df.mm.exp2	0.199883085418659	0.137364861684947	1.45512529890723	0.146034841816969	   
df.mm.exp3	-0.0680976941453513	0.137364861684947	-0.495743185775826	0.620214546293717	   
df.mm.exp4	0.0583002195302036	0.137364861684947	0.424418725539272	0.671376904322283	   
df.mm.exp5	-0.0170434517482783	0.137364861684947	-0.124074319583768	0.901288233998796	   
df.mm.exp6	-0.0270770048606873	0.137364861684947	-0.197117403450598	0.843786754567387	   
df.mm.exp7	-0.0119047044262742	0.137364861684947	-0.0866648448536878	0.930960049690517	   
df.mm.exp8	0.0234027623587347	0.137364861684947	0.17036935116937	0.864763628947388	   
df.mm.trans1:exp2	-0.179824142443693	0.128269011505798	-1.40192974384592	0.161332011881251	   
df.mm.trans2:exp2	-0.00970587846371352	0.100049138982840	-0.097011114362296	0.92274236173296	   
df.mm.trans1:exp3	0.232327655109026	0.128269011505798	1.81125318096432	0.0704842561186487	.  
df.mm.trans2:exp3	0.0376197883968932	0.100049138982840	0.376013114948902	0.7070088692307	   
df.mm.trans1:exp4	0.215105918607811	0.128269011505798	1.67699053795302	0.0939429566005015	.  
df.mm.trans2:exp4	-0.0186369230533518	0.100049138982840	-0.186277695568658	0.852275185045366	   
df.mm.trans1:exp5	1.42652951913788	0.128269011505798	11.1213885753957	8.81614588797888e-27	***
df.mm.trans2:exp5	0.182200193215437	0.100049138982840	1.82110705866932	0.068971596423601	.  
df.mm.trans1:exp6	0.483226063266775	0.128269011505798	3.76728609345315	0.000177393984255294	***
df.mm.trans2:exp6	0.0911371037193102	0.100049138982840	0.91092341869071	0.362615708000591	   
df.mm.trans1:exp7	0.0303645673814753	0.128269011505798	0.236725667602909	0.812931457974054	   
df.mm.trans2:exp7	0.0112482165641532	0.100049138982840	0.112426920196510	0.910513690336547	   
df.mm.trans1:exp8	-0.0134788547144912	0.128269011505798	-0.105082705138660	0.916337060614092	   
df.mm.trans2:exp8	0.0342886833009579	0.100049138982840	0.342718424661696	0.731902165504882	   
df.mm.trans1:probe2	-0.126859201735127	0.0815135342190963	-1.55629617768932	0.120041240631000	   
df.mm.trans1:probe3	-0.0798595212161956	0.0815135342190963	-0.97970873157757	0.327532131411170	   
df.mm.trans1:probe4	-0.321512272611622	0.0815135342190963	-3.94428085705921	8.72094708336395e-05	***
df.mm.trans1:probe5	-0.064636283714323	0.0815135342190964	-0.792951555021405	0.428045942284921	   
df.mm.trans1:probe6	0.00160608949728891	0.0815135342190963	0.0197033475811756	0.984285033561149	   
df.mm.trans1:probe7	-0.195997092286033	0.0815135342190963	-2.40447300149227	0.0164267632068715	*  
df.mm.trans1:probe8	0.79264143367053	0.0815135342190963	9.72404694832673	3.53408012815686e-21	***
df.mm.trans1:probe9	0.135476095278601	0.0815135342190963	1.66200737799519	0.096911041539397	.  
df.mm.trans1:probe10	-0.242510058362544	0.0815135342190964	-2.97508948281782	0.00301898259599562	** 
df.mm.trans1:probe11	-0.515116237855137	0.0815135342190964	-6.31939521197278	4.40130569771778e-10	***
df.mm.trans1:probe12	-0.626408772376284	0.0815135342190964	-7.68472105126235	4.58444683631029e-14	***
df.mm.trans1:probe13	-0.625307694064657	0.0815135342190963	-7.67121313110927	5.05668390542647e-14	***
df.mm.trans1:probe14	-0.544440607626605	0.0815135342190963	-6.67914368874289	4.55312813586847e-11	***
df.mm.trans1:probe15	-0.52437423132792	0.0815135342190963	-6.43297136299451	2.17552912163155e-10	***
df.mm.trans1:probe16	-0.585654956918952	0.0815135342190963	-7.18475726184069	1.56856042698000e-12	***
df.mm.trans1:probe17	0.289889337735138	0.0815135342190963	3.55633381023522	0.000398632823503051	***
df.mm.trans1:probe18	-0.00128773531220936	0.0815135342190963	-0.0157978098305505	0.98739971497735	   
df.mm.trans1:probe19	0.383404067817078	0.0815135342190963	4.70356329767943	3.0209079699081e-06	***
df.mm.trans1:probe20	-0.0797243098896363	0.0815135342190963	-0.978049972356114	0.328351303622289	   
df.mm.trans1:probe21	-0.121075381311688	0.0815135342190963	-1.48534083905937	0.137855528126470	   
df.mm.trans1:probe22	0.413865612912094	0.0815135342190963	5.07726252918546	4.78717043238883e-07	***
df.mm.trans2:probe2	-0.148218034702307	0.0815135342190963	-1.81832423440160	0.0693960526744664	.  
df.mm.trans2:probe3	0.0500406734460567	0.0815135342190963	0.613894047478726	0.539463425001788	   
df.mm.trans2:probe4	-0.0925521750855505	0.0815135342190963	-1.13542095766310	0.256545968072077	   
df.mm.trans2:probe5	-0.0459743171481056	0.0815135342190963	-0.564008389386398	0.572909722099916	   
df.mm.trans2:probe6	-0.132826699777915	0.0815135342190963	-1.62950485524154	0.103607879674777	   
df.mm.trans3:probe2	-0.0913061318928163	0.0815135342190963	-1.12013462264314	0.262999419924874	   
df.mm.trans3:probe3	0.119028378455798	0.0815135342190963	1.46022840987201	0.144627905883139	   
df.mm.trans3:probe4	-0.159379946837836	0.0815135342190963	-1.95525747183831	0.0509076101479345	.  
df.mm.trans3:probe5	-0.00268567698117669	0.0815135342190963	-0.0329476203786967	0.973724743805733	   
df.mm.trans3:probe6	-0.308184571638013	0.0815135342190963	-3.78077793571873	0.000168213454857310	***
df.mm.trans3:probe7	-0.321257255657155	0.0815135342190963	-3.94115233420824	8.833239938061e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09690122048008	0.288148566601808	14.2180170069754	5.52791673141316e-41	***
df.mm.trans1	-0.0172271199748054	0.251147306709084	-0.0685936879058808	0.94533053358409	   
df.mm.trans2	0.247952420905083	0.224121869097415	1.10632854305400	0.268923692736380	   
df.mm.exp2	-0.0131704641456197	0.293221002070933	-0.0449165102519963	0.964185294523297	   
df.mm.exp3	-0.188631602472415	0.293221002070933	-0.643308634579946	0.520211968591723	   
df.mm.exp4	-0.105093709128171	0.293221002070933	-0.358411261082683	0.720132050601045	   
df.mm.exp5	0.0817433556049645	0.293221002070933	0.278777287532733	0.78048926073598	   
df.mm.exp6	0.243768300677097	0.293221002070933	0.831346659875773	0.406030942440978	   
df.mm.exp7	0.086024721398259	0.293221002070933	0.293378444213381	0.7693105400905	   
df.mm.exp8	0.027554035928355	0.293221002070933	0.093970199043551	0.925156839281917	   
df.mm.trans1:exp2	-0.063823663304398	0.273804869942949	-0.233099080077343	0.815745312466602	   
df.mm.trans2:exp2	0.088413689616147	0.213566325689367	0.413987033446204	0.678996831238454	   
df.mm.trans1:exp3	0.0433429783786904	0.273804869942949	0.158298785509993	0.874262129238987	   
df.mm.trans2:exp3	0.108402170054989	0.213566325689367	0.507580816896479	0.611890102084093	   
df.mm.trans1:exp4	0.0844324613242007	0.273804869942949	0.308367273897624	0.757884795226192	   
df.mm.trans2:exp4	0.0736057695110147	0.213566325689367	0.344650633817967	0.730449466488383	   
df.mm.trans1:exp5	-0.154846730589806	0.273804869942949	-0.565536802256549	0.571870508191321	   
df.mm.trans2:exp5	0.00270714893505641	0.213566325689367	0.0126759166095968	0.989889577532112	   
df.mm.trans1:exp6	-0.166781988528854	0.273804869942949	-0.609127180840886	0.542616606395504	   
df.mm.trans2:exp6	-0.0853232222361658	0.213566325689367	-0.399516271868949	0.68962164755585	   
df.mm.trans1:exp7	-0.162474327373438	0.273804869942949	-0.593394585740174	0.553088257449324	   
df.mm.trans2:exp7	-0.0866366924117657	0.213566325689367	-0.405666446393702	0.685098382791126	   
df.mm.trans1:exp8	-0.0969867918226991	0.273804869942949	-0.354218651563457	0.723270272826698	   
df.mm.trans2:exp8	-0.0756641398442574	0.213566325689367	-0.354288718504766	0.723217788320107	   
df.mm.trans1:probe2	0.250357774952837	0.173999958161687	1.43883813305400	0.150595482638429	   
df.mm.trans1:probe3	0.371966463033357	0.173999958161687	2.13773880731461	0.0328466166539172	*  
df.mm.trans1:probe4	0.148526687721918	0.173999958161687	0.853601858822872	0.393586350883533	   
df.mm.trans1:probe5	0.353672004135788	0.173999958161687	2.03259821365671	0.0424296655927307	*  
df.mm.trans1:probe6	0.074123137249709	0.173999958161687	0.425995144095558	0.670228308832619	   
df.mm.trans1:probe7	0.319597085537070	0.173999958161687	1.83676530105880	0.0666229565483067	.  
df.mm.trans1:probe8	0.119942325476084	0.173999958161687	0.689323875380645	0.490823435755154	   
df.mm.trans1:probe9	0.450541374519511	0.173999958161687	2.58931886696692	0.00979494795611002	** 
df.mm.trans1:probe10	0.357759188051203	0.173999958161687	2.05608778203705	0.040104615787036	*  
df.mm.trans1:probe11	0.163636536014822	0.173999958161687	0.940440088283038	0.347281528978864	   
df.mm.trans1:probe12	-0.036728550376316	0.173999958161687	-0.211083673607476	0.832876833192181	   
df.mm.trans1:probe13	0.300666503129241	0.173999958161687	1.72796882427898	0.084387586664862	.  
df.mm.trans1:probe14	0.162068433004479	0.173999958161687	0.931427999849743	0.351918939269928	   
df.mm.trans1:probe15	0.510053904961041	0.173999958161687	2.93134498622742	0.00347312679875671	** 
df.mm.trans1:probe16	0.235122487082077	0.173999958161687	1.35127898630638	0.176995872516318	   
df.mm.trans1:probe17	0.142593697224369	0.173999958161687	0.819504204086452	0.412747857480722	   
df.mm.trans1:probe18	0.142719329692634	0.173999958161687	0.820226229939745	0.412336453063401	   
df.mm.trans1:probe19	0.316614625190969	0.173999958161687	1.81962472023562	0.0691974265228842	.  
df.mm.trans1:probe20	0.26046877166986	0.173999958161687	1.49694732356098	0.134809426061322	   
df.mm.trans1:probe21	0.177661151375927	0.173999958161687	1.02104134537112	0.307549808675081	   
df.mm.trans1:probe22	0.403270843401917	0.173999958161687	2.31764908257727	0.0207250190797968	*  
df.mm.trans2:probe2	-0.0145668160762479	0.173999958161687	-0.083717353901384	0.9333025299793	   
df.mm.trans2:probe3	-0.222217087281989	0.173999958161687	-1.27711000410412	0.201941506242341	   
df.mm.trans2:probe4	0.097176745430647	0.173999958161687	0.558487176993151	0.576671221355261	   
df.mm.trans2:probe5	-0.052928549044648	0.173999958161687	-0.304187136616808	0.761066042419141	   
df.mm.trans2:probe6	-0.211434205949387	0.173999958161687	-1.21513940683201	0.224678738725652	   
df.mm.trans3:probe2	0.121893034190988	0.173999958161687	0.700534847702207	0.48380119001256	   
df.mm.trans3:probe3	-0.101583535992289	0.173999958161687	-0.583813565621058	0.559513673114192	   
df.mm.trans3:probe4	-0.218961859759482	0.173999958161687	-1.25840179545339	0.208621099916850	   
df.mm.trans3:probe5	-0.128972576529262	0.173999958161687	-0.741221882418017	0.458780873067565	   
df.mm.trans3:probe6	-0.136392499141913	0.173999958161687	-0.783865126077632	0.433356004066551	   
df.mm.trans3:probe7	0.0319147315773254	0.173999958161687	0.183418041673717	0.85451744497947