chr8.23480_chr8_13357557_13358746_+_2.R 

fitVsDatCorrelation=0.909339992635908
cont.fitVsDatCorrelation=0.254771853756767

fstatistic=7790.3443898413,55,761
cont.fstatistic=1430.84583456578,55,761

residuals=-0.660463081106404,-0.0987587527703852,-0.00535553489865233,0.0808791779444824,1.24051914175856
cont.residuals=-0.877258233611552,-0.306177643519068,-0.0959129016737662,0.2108285792131,1.91990457978728

predictedValues:
Include	Exclude	Both
chr8.23480_chr8_13357557_13358746_+_2.R.tl.Lung	71.8855112038547	87.0728682084129	61.8526212517033
chr8.23480_chr8_13357557_13358746_+_2.R.tl.cerebhem	76.6500223789632	94.5076786124312	87.4948523365649
chr8.23480_chr8_13357557_13358746_+_2.R.tl.cortex	70.333609793272	95.9271488328716	56.4768096435979
chr8.23480_chr8_13357557_13358746_+_2.R.tl.heart	74.7827837519268	86.2785891729085	54.8175080229766
chr8.23480_chr8_13357557_13358746_+_2.R.tl.kidney	75.5218259140682	82.8065932120259	62.4959726835275
chr8.23480_chr8_13357557_13358746_+_2.R.tl.liver	79.7171844008355	84.8788562875979	63.355968286149
chr8.23480_chr8_13357557_13358746_+_2.R.tl.stomach	81.4890536428662	96.2070640634246	57.7706289568531
chr8.23480_chr8_13357557_13358746_+_2.R.tl.testicle	79.4690459988096	98.9055645019191	65.8970719115127


diffExp=-15.1873570045582,-17.8576562334680,-25.5935390395996,-11.4958054209816,-7.28476729795767,-5.16167188676239,-14.7180104205583,-19.4365185031095
diffExpScore=0.991506372508458
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=-1,-1,-1,0,0,0,0,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	78.8543576298944	73.9760937575869	82.6271240649798
cerebhem	83.7362761866814	75.2987209507454	96.929503675249
cortex	79.3973479100241	85.4772154822199	79.370306448152
heart	73.0140813085261	83.4251668395454	91.279784407624
kidney	71.7290896992522	78.0880876581772	91.82939928289
liver	87.6429853655938	63.0372591984152	90.8127254595446
stomach	87.7393058469127	78.0635169052725	84.729974387941
testicle	85.8640779038885	79.693200981459	83.604285765958
cont.diffExp=4.8782638723075,8.43755523593599,-6.07986757219578,-10.4110855310193,-6.358997958925,24.6057261671786,9.67578894164015,6.1708769224296
cont.diffExpScore=2.40044921045048

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=0,0,0,0,0,1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,1,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.199550309046892
cont.tran.correlation=-0.481493248700998

tran.covariance=0.00064012131191588
cont.tran.covariance=-0.00349872837822705

tran.mean=83.5270874985117
cont.tran.mean=79.0647989765122

weightedLogRatios:
wLogRatio
Lung	-0.837774959158467
cerebhem	-0.930704977163895
cortex	-1.36810562314249
heart	-0.62718279150495
kidney	-0.402458554690223
liver	-0.276673619301569
stomach	-0.744411456958825
testicle	-0.981257562992383

cont.weightedLogRatios:
wLogRatio
Lung	0.276878602897655
cerebhem	0.464619085158309
cortex	-0.325491515911131
heart	-0.580818256203522
kidney	-0.366552183957861
liver	1.41984685921397
stomach	0.515990837960482
testicle	0.329311779619837

varWeightedLogRatios=0.118897087140504
cont.varWeightedLogRatios=0.412156319310411

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27926600448394	0.0935574097361228	45.7394664575852	1.40307640519550e-220	***
df.mm.trans1	-0.383712814590787	0.0819231777416043	-4.68381262993807	3.33468042969205e-06	***
df.mm.trans2	0.177108555431010	0.0734662193861336	2.41074819026876	0.0161558946585089	*  
df.mm.exp2	-0.200714563795877	0.096884381034855	-2.0716916560954	0.0386305871905061	*  
df.mm.exp3	0.16594312122009	0.096884381034855	1.71279539021249	0.0871575192112214	.  
df.mm.exp4	0.151093945765621	0.096884381034855	1.55952842090475	0.119287110207261	   
df.mm.exp5	-0.0112383180240859	0.096884381034855	-0.115997211356935	0.90768535416412	   
df.mm.exp6	0.0538754959945792	0.096884381034855	0.556080303338027	0.578319417562984	   
df.mm.exp7	0.293425402853479	0.096884381034855	3.02861410393814	0.00253982243919803	** 
df.mm.exp8	0.164373493841126	0.0968843810348551	1.69659435386176	0.0901823203150749	.  
df.mm.trans1:exp2	0.264889729815334	0.0908856055373245	2.91453996757001	0.00366664822970371	** 
df.mm.trans2:exp2	0.282650316446376	0.0725016319924173	3.89853729742162	0.000105315374535455	***
df.mm.trans1:exp3	-0.187768076971141	0.0908856055373245	-2.06598257073866	0.0391680430001397	*  
df.mm.trans2:exp3	-0.06909941786749	0.0725016319924173	-0.953073964938015	0.340855215349691	   
df.mm.trans1:exp4	-0.111580982282536	0.0908856055373245	-1.22770797006697	0.219936224502171	   
df.mm.trans2:exp4	-0.160257809812532	0.0725016319924173	-2.21040279244049	0.0273740091467446	*  
df.mm.trans1:exp5	0.0605852861952938	0.0908856055373245	0.666610359661551	0.505223094891004	   
df.mm.trans2:exp5	-0.0389993292964691	0.0725016319924174	-0.537909674923564	0.590796680961218	   
df.mm.trans1:exp6	0.0495349488603124	0.0908856055373245	0.545025238787341	0.585895847656426	   
df.mm.trans2:exp6	-0.0793958099486075	0.0725016319924173	-1.09508996924250	0.273823685295256	   
df.mm.trans1:exp7	-0.168031434018998	0.0908856055373245	-1.84882339756202	0.064871008424171	.  
df.mm.trans2:exp7	-0.193667950571192	0.0725016319924173	-2.67122194699628	0.00771906446076293	** 
df.mm.trans1:exp8	-0.0640806377673968	0.0908856055373245	-0.705069162366756	0.480982934321481	   
df.mm.trans2:exp8	-0.0369533265790565	0.0725016319924173	-0.509689583027887	0.610416725623636	   
df.mm.trans1:probe2	0.068625134852311	0.0556558396325786	1.23302667438586	0.217946558485363	   
df.mm.trans1:probe3	0.201310962541403	0.0556558396325786	3.6170681076845	0.000317625791308065	***
df.mm.trans1:probe4	-0.0596219153452067	0.0556558396325786	-1.07126072913123	0.284391835265225	   
df.mm.trans1:probe5	0.208878681485278	0.0556558396325786	3.75304160110108	0.000187997237477482	***
df.mm.trans1:probe6	1.66638454353259	0.0556558396325786	29.9408751091261	9.1887681778623e-131	***
df.mm.trans1:probe7	1.46867592035176	0.0556558396325786	26.388532273477	1.78294583459277e-109	***
df.mm.trans1:probe8	0.255135060957958	0.0556558396325786	4.58415617556532	5.32694137562277e-06	***
df.mm.trans1:probe9	0.461392666787061	0.0556558396325786	8.29010342549897	5.1070332506362e-16	***
df.mm.trans1:probe10	0.0667015641157914	0.0556558396325786	1.19846478925002	0.231109208467349	   
df.mm.trans1:probe11	0.319131711069713	0.0556558396325786	5.73402024255702	1.41439147017557e-08	***
df.mm.trans1:probe12	0.0692653189123777	0.0556558396325786	1.24452922406066	0.213687995604314	   
df.mm.trans1:probe13	0.180200359658859	0.0556558396325786	3.23776194642794	0.00125693384307008	** 
df.mm.trans1:probe14	0.354025551195923	0.0556558396325786	6.36097763564583	3.45783505522442e-10	***
df.mm.trans1:probe15	0.0147966906244038	0.0556558396325786	0.265860522850551	0.790418638277394	   
df.mm.trans1:probe16	0.509528444342265	0.0556558396325786	9.15498621000066	4.9216619560768e-19	***
df.mm.trans1:probe17	1.16675777977172	0.0556558396325786	20.9637980034849	2.24145584526566e-77	***
df.mm.trans1:probe18	0.774586378621363	0.0556558396325786	13.9174322718860	2.15599104542558e-39	***
df.mm.trans1:probe19	0.810464467594027	0.0556558396325786	14.5620742215811	1.48144469132192e-42	***
df.mm.trans1:probe20	0.535925442472183	0.0556558396325786	9.62927602943709	8.69713366580642e-21	***
df.mm.trans1:probe21	0.819119305807114	0.0556558396325786	14.7175806027664	2.48707040494716e-43	***
df.mm.trans1:probe22	0.735319088959482	0.0556558396325786	13.2118946334080	4.99297938011915e-36	***
df.mm.trans2:probe2	0.0067439369063591	0.0556558396325786	0.121172134871746	0.90358668120224	   
df.mm.trans2:probe3	-0.0325120726592783	0.0556558396325786	-0.584162827726834	0.55928388571624	   
df.mm.trans2:probe4	0.0797374365642382	0.0556558396325786	1.43268769442054	0.152357755815774	   
df.mm.trans2:probe5	-0.0724638722731805	0.0556558396325786	-1.30199944429125	0.193310523861565	   
df.mm.trans2:probe6	0.142943856956564	0.0556558396325786	2.56835325637402	0.0104079601037529	*  
df.mm.trans3:probe2	-0.166814469919780	0.0556558396325786	-2.99725008231003	0.00281287674284106	** 
df.mm.trans3:probe3	-0.00492093458812022	0.0556558396325786	-0.0884172194796917	0.929568337608073	   
df.mm.trans3:probe4	0.12599215719911	0.0556558396325786	2.26377246360613	0.0238688780064298	*  
df.mm.trans3:probe5	0.138588734723971	0.0556558396325786	2.49010230802173	0.0129829530561205	*  
df.mm.trans3:probe6	0.219710913007216	0.0556558396325786	3.94767044137102	8.6229178945857e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29250149091155	0.217448135613264	19.7403462614453	2.33663478252822e-70	***
df.mm.trans1	0.114313381807814	0.190407604418187	0.600361430716552	0.548444126071993	   
df.mm.trans2	-0.0402023355725827	0.170751760668955	-0.235443168580411	0.8139281272192	   
df.mm.exp2	-0.0818550638147028	0.225180753566119	-0.363508259557663	0.716326201035257	   
df.mm.exp3	0.191583941562552	0.225180753566119	0.850800694679697	0.395147693403157	   
df.mm.exp4	-0.0563335572113866	0.225180753566119	-0.250170391204618	0.80252310137567	   
df.mm.exp5	-0.146205157386290	0.225180753566119	-0.649279101658929	0.51635389780627	   
df.mm.exp6	-0.148808433723715	0.225180753566119	-0.66083993133108	0.508914970064672	   
df.mm.exp7	0.135416815672661	0.225180753566119	0.601369404481095	0.547773062716487	   
df.mm.exp8	0.147848491840860	0.225180753566119	0.656576947627309	0.51165146378878	   
df.mm.trans1:exp2	0.141924777977364	0.211238271067088	0.671870571844863	0.501870008778049	   
df.mm.trans2:exp2	0.099576228750407	0.168509845987998	0.590922317723197	0.554747931062521	   
df.mm.trans1:exp3	-0.184721552191545	0.211238271067088	-0.8744701007938	0.382138181970871	   
df.mm.trans2:exp3	-0.0470760704015045	0.168509845987998	-0.279366882839934	0.780039190800947	   
df.mm.trans1:exp4	-0.0206167022919167	0.211238271067088	-0.0975992758687605	0.922276198697695	   
df.mm.trans2:exp4	0.176541598041506	0.168509845987998	1.04766339917063	0.295126339206559	   
df.mm.trans1:exp5	0.0514989601009455	0.211238271067088	0.243795595565113	0.807454839171354	   
df.mm.trans2:exp5	0.200300692134634	0.168509845987998	1.18865868614526	0.234944796451731	   
df.mm.trans1:exp6	0.254477435145148	0.211238271067088	1.20469379842788	0.228696046385697	   
df.mm.trans2:exp6	-0.0112075825343233	0.168509845987998	-0.0665099565465244	0.94698930003254	   
df.mm.trans1:exp7	-0.028649408131235	0.211238271067088	-0.135626030200447	0.892152792865299	   
df.mm.trans2:exp7	-0.0816359847427796	0.168509845987998	-0.48445824790911	0.628200088355505	   
df.mm.trans1:exp8	-0.0626855120553479	0.211238271067088	-0.296752627914851	0.76673634836746	   
df.mm.trans2:exp8	-0.0734062009989909	0.168509845987998	-0.435619655151896	0.663236238039755	   
df.mm.trans1:probe2	-0.175630825580514	0.129356494565521	-1.35772715680351	0.174952725591643	   
df.mm.trans1:probe3	-0.126226648473941	0.129356494565521	-0.975804492058229	0.329471438850897	   
df.mm.trans1:probe4	-0.0482321743074024	0.129356494565521	-0.372862409957871	0.709354676887406	   
df.mm.trans1:probe5	-0.0586141495753032	0.129356494565521	-0.453121041754994	0.650590748654352	   
df.mm.trans1:probe6	-0.0766351961524312	0.129356494565521	-0.592434082338357	0.553735933259718	   
df.mm.trans1:probe7	0.036229641684371	0.129356494565521	0.280075939024616	0.779495357127702	   
df.mm.trans1:probe8	0.0632297077827137	0.129356494565521	0.488801957683592	0.625122744851537	   
df.mm.trans1:probe9	-0.0750611460414663	0.129356494565521	-0.580265770911461	0.561907176738029	   
df.mm.trans1:probe10	0.0201582507971307	0.129356494565521	0.155834856725498	0.87620452163308	   
df.mm.trans1:probe11	-0.0841897968379084	0.129356494565521	-0.650835484686583	0.515349148099003	   
df.mm.trans1:probe12	-0.183091697807154	0.129356494565521	-1.41540398433119	0.157359176843428	   
df.mm.trans1:probe13	0.06718757023545	0.129356494565521	0.519398507675379	0.603633897872682	   
df.mm.trans1:probe14	-0.00877649947481214	0.129356494565521	-0.0678473817978014	0.945924947222904	   
df.mm.trans1:probe15	-0.111143765672544	0.129356494565521	-0.859205145020747	0.390497907910569	   
df.mm.trans1:probe16	0.00352170733962193	0.129356494565521	0.0272248204579951	0.978287556738229	   
df.mm.trans1:probe17	0.00194812487401882	0.129356494565521	0.0150601241983414	0.98798816094716	   
df.mm.trans1:probe18	-0.170776931002981	0.129356494565521	-1.32020376384334	0.187163940299679	   
df.mm.trans1:probe19	-0.0422382736738775	0.129356494565521	-0.326526115412652	0.744116114316978	   
df.mm.trans1:probe20	0.0572478489932678	0.129356494565521	0.442558753509441	0.658210681285247	   
df.mm.trans1:probe21	-0.0668634815567953	0.129356494565521	-0.516893116046276	0.605380958561612	   
df.mm.trans1:probe22	-0.119986554448271	0.129356494565521	-0.927564981188447	0.353927357668201	   
df.mm.trans2:probe2	0.284723735239315	0.129356494565521	2.20107800691134	0.0280300589837321	*  
df.mm.trans2:probe3	0.0902798235369748	0.129356494565521	0.697914888929266	0.485443607471851	   
df.mm.trans2:probe4	0.176974914627681	0.129356494565521	1.36811773712714	0.171679036275997	   
df.mm.trans2:probe5	-0.0234662444096884	0.129356494565521	-0.181407547324981	0.856096000415075	   
df.mm.trans2:probe6	0.0888017110981807	0.129356494565521	0.686488230810871	0.492614395382205	   
df.mm.trans3:probe2	0.174847068076124	0.129356494565521	1.35166826113675	0.176883085264284	   
df.mm.trans3:probe3	0.0155810013098489	0.129356494565521	0.120450089206436	0.904158408036187	   
df.mm.trans3:probe4	-0.00154669395101999	0.129356494565521	-0.0119568325982780	0.990463188988345	   
df.mm.trans3:probe5	0.0625588974717544	0.129356494565521	0.48361620869424	0.628797391517312	   
df.mm.trans3:probe6	0.0349125350116041	0.129356494565521	0.269893947952651	0.787315013043398