chr7.22403_chr7_41509761_41519398_+_2.R 

fitVsDatCorrelation=0.884828438836549
cont.fitVsDatCorrelation=0.245822360565462

fstatistic=9432.80287997597,65,991
cont.fstatistic=2167.63175039656,65,991

residuals=-0.759510129787856,-0.0936150790037037,-0.0103212929910335,0.083112903235938,0.905882770918522
cont.residuals=-0.621924632263662,-0.233060169152975,-0.0733566518392646,0.141391744665939,1.92858235123132

predictedValues:
Include	Exclude	Both
chr7.22403_chr7_41509761_41519398_+_2.R.tl.Lung	52.063327631039	48.3990283128842	62.802452359099
chr7.22403_chr7_41509761_41519398_+_2.R.tl.cerebhem	55.3618160726249	58.0221930307328	67.3800082123966
chr7.22403_chr7_41509761_41519398_+_2.R.tl.cortex	53.9649474427718	48.5063768102304	80.123942269862
chr7.22403_chr7_41509761_41519398_+_2.R.tl.heart	52.8320135030411	51.3325080004729	65.157984415105
chr7.22403_chr7_41509761_41519398_+_2.R.tl.kidney	54.1189506023061	52.7465021702261	169.196489776470
chr7.22403_chr7_41509761_41519398_+_2.R.tl.liver	55.4568957300255	50.2873780160761	77.879007732262
chr7.22403_chr7_41509761_41519398_+_2.R.tl.stomach	54.1105347271899	49.451693940662	60.5857923839956
chr7.22403_chr7_41509761_41519398_+_2.R.tl.testicle	53.1815186985944	51.0953942362681	67.2498741214587


diffExp=3.6642993181548,-2.66037695810785,5.4585706325414,1.49950550256822,1.37244843207997,5.16951771394939,4.65884078652788,2.08612446232633
diffExpScore=1.19420052728693
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	62.8516055522266	57.321932065599	63.6969118681161
cerebhem	61.512156369959	57.1877142198496	58.5104999096805
cortex	64.2423232909205	55.6630290917965	59.3723553055
heart	64.8397363527195	56.5115749208173	66.6760536506387
kidney	65.8483257068954	51.6124967676216	57.7701691631372
liver	60.2874029394616	64.9470726399256	64.5208986880355
stomach	63.9070152553328	63.502882228804	63.9362988571712
testicle	69.5476011830261	57.8302866228742	61.975087322475
cont.diffExp=5.52967348662759,4.32444215010939,8.579294199124,8.32816143190217,14.2358289392739,-4.65966970046392,0.404133026528804,11.7173145601519
cont.diffExpScore=1.16820617975580

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

tran.correlation=0.523466626957089
cont.tran.correlation=-0.449801407231696

tran.covariance=0.000668271468292016
cont.tran.covariance=-0.00145282006039790

tran.mean=52.5581924328216
cont.tran.mean=61.1008222004893

weightedLogRatios:
wLogRatio
Lung	0.285791737457597
cerebhem	-0.189495248755810
cortex	0.419627812623968
heart	0.113811035737153
kidney	0.102191453974426
liver	0.388147397337158
stomach	0.355269698258151
testicle	0.158213667657501

cont.weightedLogRatios:
wLogRatio
Lung	0.377096662199358
cerebhem	0.29761773907578
cortex	0.586427179290572
heart	0.564077352998486
kidney	0.99033020121853
liver	-0.307949016884338
stomach	0.0263539678749984
testicle	0.765626109349087

varWeightedLogRatios=0.040773093220713
cont.varWeightedLogRatios=0.170631285772962

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.77467403783635	0.0777535000280436	48.546676824515	3.29068584229159e-264	***
df.mm.trans1	0.101534356225789	0.0668632993666908	1.51853643459853	0.129198165629324	   
df.mm.trans2	0.123469009100532	0.0583423432839014	2.11628471108395	0.0345694117908910	*  
df.mm.exp2	0.172420900510560	0.0738765094035066	2.33390697398565	0.0197997183311975	*  
df.mm.exp3	-0.205491160866571	0.0738765094035066	-2.78154940624221	0.00551264999243001	** 
df.mm.exp4	0.0366802776256069	0.0738765094035066	0.496507995867301	0.619646159031706	   
df.mm.exp5	-0.866325258453793	0.0738765094035066	-11.7266674542243	7.85124185896539e-30	***
df.mm.exp6	-0.11374269341043	0.0738765094035067	-1.53963275104374	0.123969121811520	   
df.mm.exp7	0.0960183594585062	0.0738765094035066	1.29971435079672	0.194001131335323	   
df.mm.exp8	0.00704372621310695	0.0738765094035066	0.0953445996566347	0.9240603939853	   
df.mm.trans1:exp2	-0.110991601634544	0.0681533854649743	-1.62855595327081	0.103724887740318	   
df.mm.trans2:exp2	0.00892493796836173	0.0469714793934601	0.190007597878732	0.849342088817005	   
df.mm.trans1:exp3	0.241365058554756	0.0681533854649743	3.54149771002645	0.000416378873347291	***
df.mm.trans2:exp3	0.207706693449517	0.0469714793934601	4.42197469893691	1.08608789857207e-05	***
df.mm.trans1:exp4	-0.0220237709788142	0.0681533854649743	-0.323150065525839	0.746649768585123	   
df.mm.trans2:exp4	0.0221642207368163	0.0469714793934601	0.471865502705504	0.637126668490298	   
df.mm.trans1:exp5	0.90504885469169	0.0681533854649743	13.2795876318839	3.70350152685328e-37	***
df.mm.trans2:exp5	0.95234298170124	0.0469714793934601	20.2749198875315	9.97511617788307e-77	***
df.mm.trans1:exp6	0.176887942222770	0.0681533854649743	2.59543881812998	0.00958669254813382	** 
df.mm.trans2:exp6	0.152017067611888	0.0469714793934601	3.23636959224779	0.00125072178881411	** 
df.mm.trans1:exp7	-0.0574502823031877	0.0681533854649743	-0.842955664069144	0.399456643308443	   
df.mm.trans2:exp7	-0.074501783692358	0.0469714793934601	-1.58610681746445	0.113034092627925	   
df.mm.trans1:exp8	0.0142064002206358	0.0681533854649743	0.208447461908358	0.834922434315355	   
df.mm.trans2:exp8	0.0471708972307482	0.0469714793934601	1.00424550897402	0.315505432051815	   
df.mm.trans1:probe2	0.223924615732458	0.0493818192650607	4.53455581558318	6.47791259100636e-06	***
df.mm.trans1:probe3	0.385668389153187	0.0493818192650607	7.80992670770356	1.45226344741273e-14	***
df.mm.trans1:probe4	0.283037824094911	0.0493818192650607	5.73162002346824	1.31947372086556e-08	***
df.mm.trans1:probe5	-0.067515986649607	0.0493818192650607	-1.36722355827374	0.171865269922819	   
df.mm.trans1:probe6	0.375692505751314	0.0493818192650607	7.60791140024137	6.46391360438584e-14	***
df.mm.trans1:probe7	0.302732348011887	0.0493818192650607	6.13044137533589	1.26361301262064e-09	***
df.mm.trans1:probe8	0.0884136487662676	0.0493818192650607	1.79040890113223	0.0736933672127789	.  
df.mm.trans1:probe9	-0.0562430939038914	0.0493818192650607	-1.13894333463096	0.255002026770603	   
df.mm.trans1:probe10	0.422252308776691	0.0493818192650607	8.5507645336074	4.58237214636444e-17	***
df.mm.trans1:probe11	-0.102316268617769	0.0493818192650607	-2.07194206573432	0.0385290517835932	*  
df.mm.trans1:probe12	-0.11756659248212	0.0493818192650607	-2.3807667322071	0.0174648018179304	*  
df.mm.trans1:probe13	-0.0212120749601353	0.0493818192650607	-0.429552318562381	0.667614703530822	   
df.mm.trans1:probe14	0.0575976580813733	0.0493818192650607	1.16637375735822	0.243743974571624	   
df.mm.trans1:probe15	0.00114620713552984	0.0493818192650607	0.0232111160056191	0.981486544370346	   
df.mm.trans1:probe16	-0.0159181837974367	0.0493818192650607	-0.322349075719440	0.747256248198672	   
df.mm.trans1:probe17	0.0104766392335431	0.0493818192650607	0.212155797203602	0.832029117041913	   
df.mm.trans1:probe18	0.377455851136531	0.0493818192650607	7.64361979275223	4.97661445489878e-14	***
df.mm.trans1:probe19	0.0630019667113671	0.0493818192650607	1.27581299451928	0.202320585502057	   
df.mm.trans1:probe20	0.102364965024356	0.0493818192650607	2.07292818587555	0.0384369661518686	*  
df.mm.trans1:probe21	0.154977279767644	0.0493818192650607	3.13834690730594	0.00174924250723142	** 
df.mm.trans1:probe22	0.148825512364699	0.0493818192650607	3.01377135511891	0.00264585394321295	** 
df.mm.trans1:probe23	-0.0229556448048544	0.0493818192650607	-0.464860249105815	0.642133657521488	   
df.mm.trans1:probe24	0.303752184778070	0.0493818192650607	6.15109344489026	1.11485315704651e-09	***
df.mm.trans2:probe2	-0.0695965489729916	0.0493818192650607	-1.40935571043721	0.159043730076435	   
df.mm.trans2:probe3	-0.0958754893616765	0.0493818192650607	-1.9415139172386	0.0524791358769481	.  
df.mm.trans2:probe4	0.0115464287765067	0.0493818192650606	0.233819428857619	0.815173457593136	   
df.mm.trans2:probe5	-0.124616443305433	0.0493818192650607	-2.52352880392164	0.0117737594774058	*  
df.mm.trans2:probe6	-0.0947241390545456	0.0493818192650607	-1.91819865011669	0.0553729299667841	.  
df.mm.trans3:probe2	0.00408393169377427	0.0493818192650607	0.082701118641528	0.9341058961927	   
df.mm.trans3:probe3	0.506558151108146	0.0493818192650607	10.2579888438123	1.55807045873419e-23	***
df.mm.trans3:probe4	0.661999829526568	0.0493818192650607	13.4057399945764	8.82048402261385e-38	***
df.mm.trans3:probe5	-0.157990438436564	0.0493818192650606	-3.19936447842350	0.0014210320849309	** 
df.mm.trans3:probe6	-0.268327635571241	0.0493818192650607	-5.43373329627595	6.94671594441624e-08	***
df.mm.trans3:probe7	-0.211384815815851	0.0493818192650607	-4.28062025583196	2.04467560738379e-05	***
df.mm.trans3:probe8	0.342922405570991	0.0493818192650607	6.94430482057231	6.87365756387329e-12	***
df.mm.trans3:probe9	0.236620876635615	0.0493818192650607	4.79165976784966	1.9068915321545e-06	***
df.mm.trans3:probe10	-0.0414094523721943	0.0493818192650607	-0.83855663862698	0.401920365242511	   
df.mm.trans3:probe11	0.243421413646101	0.0493818192650607	4.92937314317073	9.66603734285003e-07	***
df.mm.trans3:probe12	-0.0999368737876296	0.0493818192650607	-2.02375844541512	0.0432629564642908	*  
df.mm.trans3:probe13	0.437746075889381	0.0493818192650607	8.8645190153839	3.50569204630773e-18	***
df.mm.trans3:probe14	0.310130609857142	0.0493818192650607	6.28025889837093	5.05076826883937e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07425526086955	0.161761920541303	25.1867389261694	1.36407408655617e-108	***
df.mm.trans1	0.154110560051402	0.139105451399396	1.10786858819015	0.268187386750244	   
df.mm.trans2	-0.0319077440713319	0.121378066518935	-0.262878994421570	0.792698488127091	   
df.mm.exp2	0.061043977570455	0.153696052778185	0.397173359152911	0.69132514936658	   
df.mm.exp3	0.0628259841580186	0.153696052778185	0.408767714084949	0.682798451886612	   
df.mm.exp4	-0.0288054664067029	0.153696052778185	-0.187418387694543	0.851370971103693	   
df.mm.exp5	0.0393215851374703	0.153696052778185	0.255839915382989	0.798127544471118	   
df.mm.exp6	0.0703829571228185	0.153696052778185	0.457936009745126	0.647098804630827	   
df.mm.exp7	0.115303469783865	0.153696052778185	0.750204495819231	0.453309621929267	   
df.mm.exp8	0.137467861336070	0.153696052778185	0.894413739658392	0.371317732986968	   
df.mm.trans1:exp2	-0.0825856382280967	0.141789405238731	-0.582452815067863	0.560394206956944	   
df.mm.trans2:exp2	-0.0633881976701215	0.097721603717027	-0.648661045859164	0.516707767594146	   
df.mm.trans1:exp3	-0.0409402298638209	0.141789405238731	-0.288739696699410	0.772840967141604	   
df.mm.trans2:exp3	-0.0921931172501023	0.097721603717027	-0.943426159041213	0.345692906076417	   
df.mm.trans1:exp4	0.059947616655761	0.141789405238731	0.422793343090955	0.672537767978203	   
df.mm.trans2:exp4	0.0145676403828697	0.097721603717027	0.149072874663962	0.881526459363729	   
df.mm.trans1:exp5	0.00725593637595247	0.141789405238731	0.0511740377479944	0.959197152210144	   
df.mm.trans2:exp5	-0.144241065602343	0.097721603717027	-1.47604071275808	0.140250623709564	   
df.mm.trans1:exp6	-0.112036262179496	0.141789405238731	-0.790159617292003	0.429623587817126	   
df.mm.trans2:exp6	0.0545064049499829	0.097721603717027	0.557772313150093	0.577125843086079	   
df.mm.trans1:exp7	-0.0986508100120398	0.141789405238731	-0.695755863041682	0.486744859623347	   
df.mm.trans2:exp7	-0.0129014845345624	0.097721603717027	-0.132022849030612	0.894993018091634	   
df.mm.trans1:exp8	-0.0362329145894654	0.141789405238731	-0.255540352457647	0.7983588085466	   
df.mm.trans2:exp8	-0.128638541926703	0.097721603717027	-1.31637772031661	0.188351668752777	   
df.mm.trans1:probe2	-0.0963985703516802	0.102736184496629	-0.938311762540136	0.348312841299735	   
df.mm.trans1:probe3	-0.114779453925416	0.102736184496629	-1.11722519663150	0.264168833718192	   
df.mm.trans1:probe4	-0.232560236140073	0.102736184496629	-2.26366432897558	0.0238103108738133	*  
df.mm.trans1:probe5	-0.134210147372202	0.102736184496629	-1.30635713239483	0.191734243511236	   
df.mm.trans1:probe6	-0.02791138053462	0.102736184496629	-0.271680135595613	0.785924562114945	   
df.mm.trans1:probe7	0.00161287226088732	0.102736184496629	0.0156991645036248	0.98747755350649	   
df.mm.trans1:probe8	-0.105492948208996	0.102736184496629	-1.02683342510601	0.30474947202658	   
df.mm.trans1:probe9	-0.212983194688880	0.102736184496629	-2.07310789019879	0.0384202052444361	*  
df.mm.trans1:probe10	-0.169099691549798	0.102736184496629	-1.64596040215361	0.100088993563816	   
df.mm.trans1:probe11	-0.0451285069880709	0.102736184496629	-0.439265943242731	0.660564610406752	   
df.mm.trans1:probe12	-0.159432449802937	0.102736184496629	-1.55186267218410	0.121014364982895	   
df.mm.trans1:probe13	-0.153680225883244	0.102736184496629	-1.49587242933173	0.135005281258204	   
df.mm.trans1:probe14	-0.125861124177493	0.102736184496629	-1.22509050529926	0.220832220095162	   
df.mm.trans1:probe15	-0.153182720019295	0.102736184496629	-1.49102987199531	0.136271875230735	   
df.mm.trans1:probe16	-0.145844359320926	0.102736184496629	-1.41960069897001	0.156038436789539	   
df.mm.trans1:probe17	-0.160024417305055	0.102736184496629	-1.55762468782658	0.119641519955597	   
df.mm.trans1:probe18	-0.158399911789895	0.102736184496629	-1.54181228907808	0.123438454676841	   
df.mm.trans1:probe19	-0.305371919753609	0.102736184496629	-2.97238914652929	0.00302635260568174	** 
df.mm.trans1:probe20	-0.205222472825808	0.102736184496629	-1.99756759345625	0.0460365577473748	*  
df.mm.trans1:probe21	-0.114948121896371	0.102736184496629	-1.11886695480835	0.263468028866044	   
df.mm.trans1:probe22	-0.092763285538367	0.102736184496629	-0.902927103949545	0.366783998998953	   
df.mm.trans1:probe23	-0.157719573252040	0.102736184496629	-1.53519009903677	0.125056324502360	   
df.mm.trans1:probe24	-0.258993097341518	0.102736184496629	-2.52095304697653	0.0118597273506701	*  
df.mm.trans2:probe2	0.0366668123343175	0.102736184496629	0.356902609474666	0.721240697152081	   
df.mm.trans2:probe3	0.00180423490177655	0.102736184496629	0.0175618250825322	0.985991946138151	   
df.mm.trans2:probe4	-0.0109471215714324	0.102736184496629	-0.106555656364595	0.915163065103462	   
df.mm.trans2:probe5	-0.0384917004657025	0.102736184496629	-0.374665466255131	0.707989437758959	   
df.mm.trans2:probe6	0.137683619821909	0.102736184496629	1.34016676302036	0.180498313162250	   
df.mm.trans3:probe2	-0.0766682844543933	0.102736184496629	-0.746263693070178	0.455685126962168	   
df.mm.trans3:probe3	-0.182673364125824	0.102736184496629	-1.77808203624516	0.0756969417588837	.  
df.mm.trans3:probe4	0.0430657612239088	0.102736184496629	0.419187859028598	0.675169688813066	   
df.mm.trans3:probe5	0.0392843920314918	0.102736184496629	0.38238126346595	0.70226057529456	   
df.mm.trans3:probe6	-0.066629889138969	0.102736184496629	-0.648553277167458	0.51677741394888	   
df.mm.trans3:probe7	-0.0120729930814745	0.102736184496629	-0.117514516824116	0.906476153226293	   
df.mm.trans3:probe8	-0.0193219726648402	0.102736184496629	-0.18807368367349	0.850857393260323	   
df.mm.trans3:probe9	0.0369751617979200	0.102736184496629	0.359903981047042	0.718995623517068	   
df.mm.trans3:probe10	0.0420613421841613	0.102736184496629	0.409411176697355	0.682326410891812	   
df.mm.trans3:probe11	-0.101503524236493	0.102736184496629	-0.988001693208922	0.323392979922884	   
df.mm.trans3:probe12	-0.0768285289242687	0.102736184496629	-0.747823459676857	0.454744066109944	   
df.mm.trans3:probe13	-0.0441591804642697	0.102736184496629	-0.429830839841233	0.667412140953873	   
df.mm.trans3:probe14	-0.174036846704320	0.102736184496629	-1.69401703554633	0.090576266897169	.