chr12.5392_chr12_97839467_97841846_-_0.R 

fitVsDatCorrelation=0.854464557409796
cont.fitVsDatCorrelation=0.25689764255152

fstatistic=7176.60280854777,48,600
cont.fstatistic=2064.8680699412,48,600

residuals=-0.678375603359911,-0.0904076540369127,-0.00576895447953237,0.077940803565827,0.774937923625968
cont.residuals=-0.654030492697925,-0.193495328270733,-0.0707852950682035,0.0966486994565072,1.69494386177909

predictedValues:
Include	Exclude	Both
chr12.5392_chr12_97839467_97841846_-_0.R.tl.Lung	59.5013227103986	46.9027669486183	57.1471073919202
chr12.5392_chr12_97839467_97841846_-_0.R.tl.cerebhem	63.3713252177874	45.0874722703099	61.8850489612429
chr12.5392_chr12_97839467_97841846_-_0.R.tl.cortex	55.2848375321117	45.8916583848024	53.5689451736324
chr12.5392_chr12_97839467_97841846_-_0.R.tl.heart	56.5365819207156	46.439588669113	52.5516656996721
chr12.5392_chr12_97839467_97841846_-_0.R.tl.kidney	58.3392359336641	45.4722038509914	59.2402586557081
chr12.5392_chr12_97839467_97841846_-_0.R.tl.liver	100.105429037153	50.40647458187	161.658209472333
chr12.5392_chr12_97839467_97841846_-_0.R.tl.stomach	57.6692975004553	45.1240414212848	57.425381591167
chr12.5392_chr12_97839467_97841846_-_0.R.tl.testicle	56.4095960425422	45.1073336770358	54.552028453325


diffExp=12.5985557617802,18.2838529474775,9.39317914730927,10.0969932516026,12.8670320826727,49.6989544552833,12.5452560791705,11.3022623655064
diffExpScore=0.99274237313526
diffExp1.5=0,0,0,0,0,1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,0,0,0,1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,1,0,0,0,1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	55.1033895312841	56.2568286480927	60.5561364178736
cerebhem	65.8799873940941	57.0447583307607	57.3407936508427
cortex	57.3578642587965	63.6205661984043	69.2310463040324
heart	57.7446087028068	56.1712303759434	61.9234960107723
kidney	54.6663281209427	65.347394585172	69.3115849201119
liver	57.8449853475214	59.0191386638891	58.8564605555898
stomach	58.7062660182949	53.7925311813332	53.8658623677957
testicle	55.3749660391371	59.2532835866968	57.9706808539567
cont.diffExp=-1.15343911680858,8.83522906333344,-6.26270193960772,1.57337832686338,-10.6810664642293,-1.17415331636766,4.91373483696177,-3.87831754755973
cont.diffExpScore=4.35828203726189

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=0.903578955082873
cont.tran.correlation=-0.361303987402674

tran.covariance=0.00653965788170692
cont.tran.covariance=-0.00143188700686035

tran.mean=54.8530728561784
cont.tran.mean=58.3240079364481

weightedLogRatios:
wLogRatio
Lung	0.943845014638861
cerebhem	1.35441425520315
cortex	0.729850651071178
heart	0.77445363047089
kidney	0.982164730561353
liver	2.92498044542543
stomach	0.964575130756009
testicle	0.87667850180911

cont.weightedLogRatios:
wLogRatio
Lung	-0.0832702087830462
cerebhem	0.592674215512191
cortex	-0.424986391523237
heart	0.111667379812792
kidney	-0.730026921596087
liver	-0.0817427654533794
stomach	0.352168348240963
testicle	-0.274021889791825

varWeightedLogRatios=0.524894296763929
cont.varWeightedLogRatios=0.17912757122494

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0035861668333	0.0885634168814332	45.2058683800922	2.27235845810283e-195	***
df.mm.trans1	0.114786362585607	0.0772374008035733	1.48614999199062	0.137764697680099	   
df.mm.trans2	-0.190185830757857	0.0708030862220106	-2.68612345740847	0.00742878147903456	** 
df.mm.exp2	-0.0561091470159762	0.0947111210249786	-0.592424061807676	0.553789846256066	   
df.mm.exp3	-0.0306339063807535	0.0947111210249786	-0.323445716292116	0.746470412558619	   
df.mm.exp4	0.022796962979457	0.0947111210249786	0.24069995933682	0.809869892679933	   
df.mm.exp5	-0.0866716917189764	0.0947111210249786	-0.915116311379296	0.360498194113272	   
df.mm.exp6	-0.44758717519071	0.0947111210249786	-4.72581435365617	2.85815731504215e-06	***
df.mm.exp7	-0.0747927322889493	0.0947111210249786	-0.789693242773717	0.430018901665769	   
df.mm.exp8	-0.045917206204487	0.0947111210249785	-0.484813247985704	0.627985768407584	   
df.mm.trans1:exp2	0.119122079785593	0.0875434586795819	1.36071936821222	0.174113292481187	   
df.mm.trans2:exp2	0.0166369077417416	0.0740717558219248	0.224605283851220	0.822362821080178	   
df.mm.trans1:exp3	-0.0428659510941803	0.0875434586795818	-0.489653387480088	0.624558161545306	   
df.mm.trans2:exp3	0.00884060191139765	0.0740717558219247	0.119351861087933	0.905036547816199	   
df.mm.trans1:exp4	-0.0739076094518642	0.0875434586795819	-0.844239084982623	0.398872296068801	   
df.mm.trans2:exp4	-0.032721333927472	0.0740717558219247	-0.44175183326472	0.658827961352702	   
df.mm.trans1:exp5	0.0669480165581606	0.0875434586795818	0.764740365161917	0.444726830692546	   
df.mm.trans2:exp5	0.0556962561397179	0.0740717558219247	0.751922990371887	0.452392225254123	   
df.mm.trans1:exp6	0.967812553445199	0.0875434586795818	11.0552240914709	5.49368739532669e-26	***
df.mm.trans2:exp6	0.519630135459004	0.0740717558219247	7.0152263800556	6.21694949945325e-12	***
df.mm.trans1:exp7	0.0435191157418137	0.0875434586795819	0.497114420634192	0.619290470400205	   
df.mm.trans2:exp7	0.0361312354905133	0.0740717558219248	0.487786945099237	0.625878947635882	   
df.mm.trans1:exp8	-0.00744204989720298	0.0875434586795818	-0.0850097769662227	0.932281975231446	   
df.mm.trans2:exp8	0.00688537825634358	0.0740717558219247	0.0929555156340111	0.925969930896487	   
df.mm.trans1:probe2	0.0378905930469775	0.0511143676127268	0.74129045934911	0.458807375726014	   
df.mm.trans1:probe3	0.0972819355664686	0.0511143676127268	1.90322095547645	0.0574900212452086	.  
df.mm.trans1:probe4	-0.00218151025472228	0.0511143676127268	-0.0426790031180806	0.96597161605267	   
df.mm.trans1:probe5	0.0849069328790648	0.0511143676127268	1.66111676314517	0.097212398728382	.  
df.mm.trans1:probe6	0.0081283439772803	0.0511143676127268	0.159022684949671	0.873704513137565	   
df.mm.trans1:probe7	0.0990607718458725	0.0511143676127268	1.93802205666353	0.0530893181724813	.  
df.mm.trans1:probe8	-0.255392603524302	0.0511143676127268	-4.99649346069016	7.66906482311422e-07	***
df.mm.trans1:probe9	0.0427277295301893	0.0511143676127268	0.835924056694202	0.403530391823163	   
df.mm.trans1:probe10	-0.262503342112582	0.0511143676127268	-5.13560774343264	3.80692488850542e-07	***
df.mm.trans1:probe11	-0.113798433123951	0.0511143676127268	-2.22634923288412	0.0263613403827604	*  
df.mm.trans1:probe12	-0.222936302254025	0.0511143676127268	-4.36151932746434	1.52025926616816e-05	***
df.mm.trans1:probe13	-0.267235774615800	0.0511143676127268	-5.22819291516112	2.36703219286562e-07	***
df.mm.trans1:probe14	-0.0675880357201538	0.0511143676127268	-1.32229036329357	0.186575311013294	   
df.mm.trans1:probe15	0.446538586708844	0.0511143676127268	8.73606791131779	2.40362365608009e-17	***
df.mm.trans1:probe16	-0.272378625700114	0.0511143676127268	-5.32880750406263	1.40083200096144e-07	***
df.mm.trans2:probe2	0.0498333873844065	0.0511143676127268	0.974938940103382	0.329983443272152	   
df.mm.trans2:probe3	0.0219612587282222	0.0511143676127268	0.429649426451168	0.667604926568493	   
df.mm.trans2:probe4	0.214060891962237	0.0511143676127268	4.18788105888527	3.23832603201725e-05	***
df.mm.trans2:probe5	0.0429224164006996	0.0511143676127268	0.839732904961393	0.401392626804075	   
df.mm.trans2:probe6	0.052661724021193	0.0511143676127268	1.03027243573059	0.303297259009521	   
df.mm.trans3:probe2	0.207094755979671	0.0511143676127268	4.05159577731148	5.75556756042518e-05	***
df.mm.trans3:probe3	0.181972184348936	0.0511143676127268	3.56009851726362	0.000400070723404081	***
df.mm.trans3:probe4	0.138747086663787	0.0511143676127268	2.71444396446451	0.0068296333231259	** 
df.mm.trans3:probe5	0.0593963529286839	0.0511143676127268	1.16202851962693	0.245685900147317	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.8632400175304	0.164753627267612	23.4485885476456	8.4463895384331e-87	***
df.mm.trans1	0.124466204124503	0.143683954291728	0.866249851892259	0.386699417902298	   
df.mm.trans2	0.166076652405148	0.131714264055944	1.26088585466043	0.207840042060174	   
df.mm.exp2	0.247090753375407	0.176190139008947	1.40240966245483	0.161310123145520	   
df.mm.exp3	0.0292294869024948	0.176190139008947	0.165897405308310	0.868293573886984	   
df.mm.exp4	0.0229671595830921	0.176190139008947	0.130354398448632	0.896329734297036	   
df.mm.exp5	0.00678560142316474	0.176190139008947	0.0385129466457835	0.96929151972172	   
df.mm.exp6	0.124959081225311	0.176190139008947	0.709228575039412	0.478458136703125	   
df.mm.exp7	0.135616320840970	0.176190139008947	0.76971572645211	0.441771459974009	   
df.mm.exp8	0.100443579858843	0.176190139008947	0.570086273975542	0.568832583301978	   
df.mm.trans1:exp2	-0.0684672696735684	0.162856209356994	-0.420415469228333	0.674332493441455	   
df.mm.trans2:exp2	-0.233181991707366	0.137794937000690	-1.69223918369510	0.0911195585807044	.  
df.mm.trans1:exp3	0.0108692444526325	0.162856209356994	0.0667413572718388	0.946809842239362	   
df.mm.trans2:exp3	0.0937798671662077	0.137794937000690	0.68057556545592	0.496402629276847	   
df.mm.trans1:exp4	0.0238515994042282	0.162856209356994	0.146458028824333	0.88360901440401	   
df.mm.trans2:exp4	-0.0244898806801552	0.137794937000690	-0.177726999360162	0.858997364503591	   
df.mm.trans1:exp5	-0.0147488854569416	0.162856209356994	-0.0905638508668148	0.927869381173189	   
df.mm.trans2:exp5	0.143004537319792	0.137794937000690	1.03780690664328	0.299778011106438	   
df.mm.trans1:exp6	-0.076403545311476	0.162856209356994	-0.469147265634761	0.639134799955305	   
df.mm.trans2:exp6	-0.077024737691717	0.137794937000690	-0.55898089848781	0.576383322469321	   
df.mm.trans1:exp7	-0.0722810834864058	0.162856209356994	-0.443833758453506	0.657322792937883	   
df.mm.trans2:exp7	-0.180409120840858	0.137794937000690	-1.30925797977580	0.190948136931531	   
df.mm.trans1:exp8	-0.0955271950435569	0.162856209356994	-0.586573858133671	0.557710560364214	   
df.mm.trans2:exp8	-0.0485498140991126	0.137794937000690	-0.352333802357843	0.724711662682024	   
df.mm.trans1:probe2	0.00732210064695572	0.095087540275926	0.0770037864656966	0.938646202985328	   
df.mm.trans1:probe3	0.0696341855396372	0.0950875402759259	0.732316614117602	0.464261300648505	   
df.mm.trans1:probe4	-0.00209599131724261	0.095087540275926	-0.0220427546149627	0.982421178966913	   
df.mm.trans1:probe5	-0.00828548701659173	0.095087540275926	-0.0871353596122986	0.930592990917486	   
df.mm.trans1:probe6	0.132758768593940	0.095087540275926	1.39617418022066	0.163178379291709	   
df.mm.trans1:probe7	0.0412630351741501	0.095087540275926	0.433947865876145	0.664482290676574	   
df.mm.trans1:probe8	-0.0616479328132539	0.095087540275926	-0.648328189312326	0.517020738335019	   
df.mm.trans1:probe9	0.0856639089451683	0.095087540275926	0.90089520347869	0.368005502626115	   
df.mm.trans1:probe10	0.132645859421845	0.0950875402759259	1.39498675680254	0.163535998010570	   
df.mm.trans1:probe11	-0.0186565616003517	0.095087540275926	-0.19620406150179	0.844516883032576	   
df.mm.trans1:probe12	-0.0202033144452002	0.095087540275926	-0.212470681085808	0.831812010505471	   
df.mm.trans1:probe13	-0.0555536881498042	0.095087540275926	-0.584237303737146	0.559280257608913	   
df.mm.trans1:probe14	0.0705105946937079	0.095087540275926	0.741533480507536	0.458660180085	   
df.mm.trans1:probe15	0.0454217671023081	0.095087540275926	0.477683689897781	0.633049324103876	   
df.mm.trans1:probe16	0.0113229273488237	0.095087540275926	0.119078980442303	0.905252640750147	   
df.mm.trans2:probe2	0.0107394352903473	0.095087540275926	0.112942613292799	0.910113829155503	   
df.mm.trans2:probe3	-0.0609040216689997	0.095087540275926	-0.640504754800343	0.522089029603587	   
df.mm.trans2:probe4	0.0849570498184986	0.095087540275926	0.893461431139868	0.371968338567927	   
df.mm.trans2:probe5	-0.048815288527491	0.095087540275926	-0.513372082039753	0.60788004846725	   
df.mm.trans2:probe6	0.0207412067481348	0.095087540275926	0.218127492707748	0.827403942304996	   
df.mm.trans3:probe2	-0.00176887329150965	0.0950875402759259	-0.0186025770187842	0.985164331269813	   
df.mm.trans3:probe3	-0.0529700140659126	0.0950875402759259	-0.557065772363064	0.577690226885122	   
df.mm.trans3:probe4	-0.107551028380179	0.0950875402759259	-1.13107383015784	0.25847590635909	   
df.mm.trans3:probe5	-0.0887704070487101	0.095087540275926	-0.933565078990532	0.350903818489383