chr4.17128_chr4_14394783_14401855_+_2.R 

fitVsDatCorrelation=0.845191894074173
cont.fitVsDatCorrelation=0.232805434815480

fstatistic=8267.9928651361,61,899
cont.fstatistic=2486.80955218279,61,899

residuals=-0.800016796987627,-0.0923893246298574,-0.00347562252300106,0.0969205175150089,1.05672415496680
cont.residuals=-0.589668399978619,-0.254846232499507,-0.0443480908733805,0.206477947929790,1.19002614675054

predictedValues:
Include	Exclude	Both
chr4.17128_chr4_14394783_14401855_+_2.R.tl.Lung	60.9949465978829	54.350117235842	65.5108136688341
chr4.17128_chr4_14394783_14401855_+_2.R.tl.cerebhem	59.9866889982902	54.0017236664322	76.0591371483882
chr4.17128_chr4_14394783_14401855_+_2.R.tl.cortex	78.9630179303917	52.4936831468052	86.3549743238554
chr4.17128_chr4_14394783_14401855_+_2.R.tl.heart	71.8410618712819	56.890654304462	76.0687325736425
chr4.17128_chr4_14394783_14401855_+_2.R.tl.kidney	66.5831754074405	62.4221975463098	67.7125177860366
chr4.17128_chr4_14394783_14401855_+_2.R.tl.liver	57.3697698274323	63.1683255739613	58.3470068199398
chr4.17128_chr4_14394783_14401855_+_2.R.tl.stomach	56.5585536284146	48.4244213210141	67.3266008952511
chr4.17128_chr4_14394783_14401855_+_2.R.tl.testicle	55.7269433880872	52.7699177483066	62.3127579345544


diffExp=6.64482936204087,5.98496533185794,26.4693347835866,14.9504075668198,4.16097786113077,-5.79855574652898,8.13413230740052,2.95702563978065
diffExpScore=1.16428836261710
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	71.589017659011	71.9731302109232	66.982032769983
cerebhem	72.1597014397618	71.972787572872	68.9359893454933
cortex	63.9001017562573	70.8523100345914	68.9119390565706
heart	66.560888813607	78.396643550582	75.1600382016434
kidney	62.7787967840386	69.1610382378746	66.9092286918264
liver	70.5863096131001	78.6231378825224	65.7000970640681
stomach	67.8836316491541	64.6578191339875	63.9562365523907
testicle	67.0922641812793	73.1657195576931	72.289263487098
cont.diffExp=-0.384112551912224,0.186913866889810,-6.95220827833403,-11.8357547369750,-6.38224145383595,-8.03682826942224,3.22581251516665,-6.07345537641378
cont.diffExpScore=1.15638012518699

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=0.0610075941683939
cont.tran.correlation=0.24729038027076

tran.covariance=0.00114157143719516
cont.tran.covariance=0.000813960018047601

tran.mean=59.5340748870222
cont.tran.mean=70.0845811298285

weightedLogRatios:
wLogRatio
Lung	0.467503948758777
cerebhem	0.424796102765747
cortex	1.70044754813905
heart	0.97011772145386
kidney	0.268848102116945
liver	-0.394545230812195
stomach	0.614511483836911
testicle	0.217719242885783

cont.weightedLogRatios:
wLogRatio
Lung	-0.0228688965178233
cerebhem	0.0110945199324228
cortex	-0.434687071038011
heart	-0.700473443240321
kidney	-0.405486028100873
liver	-0.464827518930795
stomach	0.204161969372630
testicle	-0.368245382490359

varWeightedLogRatios=0.372308901541466
cont.varWeightedLogRatios=0.0918937430694922

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94945532510893	0.084714107298387	46.6209873545362	3.85688062910167e-242	***
df.mm.trans1	-0.0766269379908099	0.0728694143744636	-1.05156516830281	0.293281666681273	   
df.mm.trans2	0.0760666800288867	0.0640976440125374	1.18673129411758	0.235647131580351	   
df.mm.exp2	-0.172395070203091	0.0818153842933265	-2.10712290472188	0.0353833962793789	*  
df.mm.exp3	-0.0528165182272111	0.0818153842933265	-0.645557295653005	0.518730828301262	   
df.mm.exp4	0.0599274118006166	0.0818153842933265	0.732471188863007	0.46407194109566	   
df.mm.exp5	0.193079236988886	0.0818153842933265	2.35993803190673	0.0184908047726728	*  
df.mm.exp6	0.204889878506195	0.0818153842933265	2.50429525297612	0.0124456675150292	*  
df.mm.exp7	-0.218297274399172	0.0818153842933265	-2.66816902816868	0.00776388016957537	** 
df.mm.exp8	-0.0697837105759172	0.0818153842933265	-0.85294118188979	0.393919105824982	   
df.mm.trans1:exp2	0.155726739700327	0.0752592075520493	2.06920514798971	0.0388121153926415	*  
df.mm.trans2:exp2	0.165964265388583	0.0540165859666245	3.07246862086264	0.00218693190090914	** 
df.mm.trans1:exp3	0.311005115540957	0.0752592075520492	4.13245270123083	3.92440905011895e-05	***
df.mm.trans2:exp3	0.0180625889542974	0.0540165859666245	0.334389681077917	0.738163518751493	   
df.mm.trans1:exp4	0.103737775059123	0.0752592075520493	1.37840642272745	0.168420764204088	   
df.mm.trans2:exp4	-0.0142431025013147	0.0540165859666245	-0.263680168719200	0.792086804575496	   
df.mm.trans1:exp5	-0.105418330950643	0.0752592075520493	-1.40073665906907	0.161637898959625	   
df.mm.trans2:exp5	-0.0546050655796804	0.0540165859666245	-1.01089442441659	0.312338958193549	   
df.mm.trans1:exp6	-0.266163390056423	0.0752592075520493	-3.53662227804278	0.000425992974442859	***
df.mm.trans2:exp6	-0.054533651230166	0.0540165859666245	-1.00957234253681	0.312971869253311	   
df.mm.trans1:exp7	0.142782705235881	0.0752592075520493	1.89721244589418	0.0581201324298624	.  
df.mm.trans2:exp7	0.102854762997854	0.0540165859666245	1.90413298355075	0.0572118817443363	.  
df.mm.trans1:exp8	-0.020543554226281	0.0752592075520493	-0.272970642324038	0.784938473667652	   
df.mm.trans2:exp8	0.0402782286913362	0.0540165859666245	0.745664095028572	0.456065268272562	   
df.mm.trans1:probe2	0.359961488992219	0.0532162960067799	6.76412144404713	2.41394826273806e-11	***
df.mm.trans1:probe3	0.37479935158974	0.0532162960067799	7.04294322817938	3.74009819094584e-12	***
df.mm.trans1:probe4	0.110258799359901	0.0532162960067799	2.07189916686148	0.0385595060805205	*  
df.mm.trans1:probe5	0.000766590712231675	0.0532162960067799	0.0144051873158179	0.988509917066322	   
df.mm.trans1:probe6	0.248761981899078	0.0532162960067799	4.67454521576221	3.39716568485496e-06	***
df.mm.trans1:probe7	-0.0930260821515977	0.0532162960067799	-1.74807510353118	0.0807923825092988	.  
df.mm.trans1:probe8	0.0488456822432957	0.0532162960067799	0.917870763441947	0.358932707520482	   
df.mm.trans1:probe9	0.0741163848009276	0.0532162960067799	1.39273850986331	0.164043207822593	   
df.mm.trans1:probe10	0.283870933418244	0.0532162960067799	5.33428582444142	1.21438533235635e-07	***
df.mm.trans1:probe11	0.4860726185342	0.0532162960067799	9.13390549526922	4.27703386856999e-19	***
df.mm.trans1:probe12	0.570180154527732	0.0532162960067799	10.7143900893645	2.71665997937084e-25	***
df.mm.trans1:probe13	0.300233311851233	0.0532162960067799	5.64175514607372	2.25501765827438e-08	***
df.mm.trans1:probe14	0.410616772479788	0.0532162960067799	7.71599685230769	3.1858418860949e-14	***
df.mm.trans1:probe15	0.678550450564247	0.0532162960067799	12.7508019437843	2.39023695990895e-34	***
df.mm.trans1:probe16	0.800367035870162	0.0532162960067799	15.0398861989228	9.09811337920291e-46	***
df.mm.trans1:probe17	0.778491140066025	0.0532162960067799	14.6288110688283	1.24741494084595e-43	***
df.mm.trans1:probe18	0.35392771127428	0.0532162960067799	6.65073930040507	5.05784205990535e-11	***
df.mm.trans1:probe19	0.710521550797214	0.0532162960067799	13.3515784470737	3.15667169596588e-37	***
df.mm.trans1:probe20	0.409369206341969	0.0532162960067799	7.69255354205438	3.78446200122041e-14	***
df.mm.trans1:probe21	0.659570216120317	0.0532162960067799	12.3941398709201	1.10905452892196e-32	***
df.mm.trans1:probe22	0.524474153549186	0.0532162960067799	9.855517818872	7.90232880427398e-22	***
df.mm.trans2:probe2	-0.159677160016957	0.0532162960067799	-3.00053126577268	0.00276949439458413	** 
df.mm.trans2:probe3	-0.0425435079220337	0.0532162960067799	-0.799445115770808	0.424243530244343	   
df.mm.trans2:probe4	-0.0707824829159936	0.0532162960067799	-1.33009037132114	0.183825829867778	   
df.mm.trans2:probe5	-0.128733700632015	0.0532162960067799	-2.41906540461994	0.0157580581003210	*  
df.mm.trans2:probe6	-0.139617369867776	0.0532162960067799	-2.62358300641571	0.00884845937513527	** 
df.mm.trans3:probe2	0.669987298879929	0.0532162960067799	12.5898897359292	1.36249919101016e-33	***
df.mm.trans3:probe3	0.352560869805824	0.0532162960067799	6.62505465921392	5.97149074327097e-11	***
df.mm.trans3:probe4	0.204585562299029	0.0532162960067799	3.8444156705864	0.000129350902743850	***
df.mm.trans3:probe5	0.0726796623419155	0.0532162960067799	1.36574071845692	0.172361942229402	   
df.mm.trans3:probe6	0.298916546332742	0.0532162960067799	5.61701149389764	2.59013767443291e-08	***
df.mm.trans3:probe7	-0.0681834341638494	0.0532162960067799	-1.28125103173589	0.200435894838459	   
df.mm.trans3:probe8	0.158438177369148	0.0532162960067799	2.97724924990951	0.00298649802979727	** 
df.mm.trans3:probe9	-0.0809046421847793	0.0532162960067799	-1.52029825928644	0.128787643552634	   
df.mm.trans3:probe10	0.462337787668566	0.0532162960067799	8.68789867693278	1.72377582059462e-17	***
df.mm.trans3:probe11	0.571086463052183	0.0532162960067799	10.7314207471227	2.30737703011864e-25	***
df.mm.trans3:probe12	0.158337581179275	0.0532162960067799	2.97535892312201	0.0030047819220229	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.51865376684575	0.154148090770605	29.3137186731049	4.22916770751757e-133	***
df.mm.trans1	-0.241595336969083	0.132595165783084	-1.82205237681376	0.0687791708862712	.  
df.mm.trans2	-0.205795385544634	0.116633814160663	-1.76445730619039	0.0779945613166337	.  
df.mm.exp2	-0.0208186674963943	0.148873495651175	-0.139841329078310	0.888816680920658	   
df.mm.exp3	-0.15772103953059	0.148873495651175	-1.05942994648386	0.289688574091272	   
df.mm.exp4	-0.102531537821065	0.148873495651175	-0.68871586156146	0.491179783436533	   
df.mm.exp5	-0.170091966310871	0.148873495651175	-1.14252685185423	0.253539278367525	   
df.mm.exp6	0.0935917196214491	0.148873495651175	0.628666098099445	0.529727367229174	   
df.mm.exp7	-0.114105181701113	0.148873495651175	-0.766457328096012	0.443605441958327	   
df.mm.exp8	-0.124689989590753	0.148873495651175	-0.837556672162208	0.402502339262387	   
df.mm.trans1:exp2	0.0287587282153312	0.136943698364133	0.210004027632304	0.833712112534082	   
df.mm.trans2:exp2	0.0208139068466097	0.0982900471036417	0.211760065845349	0.832342221920927	   
df.mm.trans1:exp3	0.0441003157912166	0.136943698364133	0.322032457995650	0.74750297190785	   
df.mm.trans2:exp3	0.142025751869333	0.0982900471036418	1.44496575242836	0.148815962276973	   
df.mm.trans1:exp4	0.0297070102633900	0.136943698364133	0.216928640151072	0.82831317573481	   
df.mm.trans2:exp4	0.188019794558336	0.0982900471036418	1.91290776735593	0.0560773114909487	.  
df.mm.trans1:exp5	0.0387676743962075	0.136943698364133	0.283092065274332	0.777171436035954	   
df.mm.trans2:exp5	0.130236781280481	0.0982900471036418	1.32502511819079	0.185499491961635	   
df.mm.trans1:exp6	-0.107697186306061	0.136943698364133	-0.7864340425486	0.431820493223954	   
df.mm.trans2:exp6	-0.00521854625824806	0.0982900471036418	-0.0530933335777668	0.95766933832982	   
df.mm.trans1:exp7	0.0609584441034938	0.136943698364133	0.445135079829709	0.656329296802263	   
df.mm.trans2:exp7	0.00692136737248754	0.0982900471036417	0.0704177846734504	0.943876797047539	   
df.mm.trans1:exp8	0.0598170614615426	0.136943698364133	0.436800394440124	0.662361004082256	   
df.mm.trans2:exp8	0.141124130964492	0.0982900471036418	1.43579268830428	0.151409186936646	   
df.mm.trans1:probe2	-0.0172325606331803	0.0968338177540434	-0.177960148973479	0.858794363444906	   
df.mm.trans1:probe3	0.0349694817952785	0.0968338177540434	0.361128814358022	0.718088000413788	   
df.mm.trans1:probe4	0.0627519031295482	0.0968338177540434	0.648037065820716	0.517126445665998	   
df.mm.trans1:probe5	0.0350402470008538	0.0968338177540434	0.361859604563517	0.717541983471274	   
df.mm.trans1:probe6	-0.0139752152795766	0.0968338177540434	-0.144321638903812	0.885278837799484	   
df.mm.trans1:probe7	-0.0135952174020961	0.0968338177540434	-0.140397411951967	0.888377452168463	   
df.mm.trans1:probe8	-0.0250178434504504	0.0968338177540434	-0.258358536621941	0.796189348907088	   
df.mm.trans1:probe9	-0.0565813328125084	0.0968338177540434	-0.58431376687248	0.559155824180225	   
df.mm.trans1:probe10	0.0463210609752304	0.0968338177540434	0.478356240098736	0.632512979316911	   
df.mm.trans1:probe11	-0.0315809880403004	0.0968338177540434	-0.326135938588270	0.744397409456298	   
df.mm.trans1:probe12	0.00264958370735041	0.0968338177540434	0.0273621733481615	0.978176940815199	   
df.mm.trans1:probe13	-0.0162986345469148	0.0968338177540434	-0.168315521632258	0.866372922083599	   
df.mm.trans1:probe14	-0.135386609647981	0.0968338177540434	-1.39813355280344	0.162417793920259	   
df.mm.trans1:probe15	0.118129731787115	0.0968338177540433	1.21992228053182	0.222814264238938	   
df.mm.trans1:probe16	-0.0466547352337902	0.0968338177540434	-0.481802084394655	0.630063812889353	   
df.mm.trans1:probe17	0.0284996241377683	0.0968338177540434	0.294314783809898	0.768585318537876	   
df.mm.trans1:probe18	-0.0313035011198508	0.0968338177540434	-0.323270339287472	0.746565693636304	   
df.mm.trans1:probe19	0.00553859396967857	0.0968338177540434	0.057196897717557	0.954401059226305	   
df.mm.trans1:probe20	0.0079533610186039	0.0968338177540434	0.082134126311175	0.934558338533992	   
df.mm.trans1:probe21	-0.0815806333252314	0.0968338177540434	-0.842480811119573	0.399742948155557	   
df.mm.trans1:probe22	-0.0806158950926057	0.0968338177540434	-0.832517987645277	0.405337728107853	   
df.mm.trans2:probe2	-0.122053153448099	0.0968338177540434	-1.26043934112060	0.207837966223825	   
df.mm.trans2:probe3	-0.138206904897833	0.0968338177540434	-1.42725865925143	0.153852585832176	   
df.mm.trans2:probe4	-0.153225140936676	0.0968338177540434	-1.58235154298952	0.113921058660052	   
df.mm.trans2:probe5	-0.134244561686931	0.0968338177540434	-1.38633965695652	0.165986928446055	   
df.mm.trans2:probe6	-0.110449661189922	0.0968338177540434	-1.14061041639877	0.254335845080335	   
df.mm.trans3:probe2	0.112740039163791	0.0968338177540434	1.16426308265723	0.24462628677433	   
df.mm.trans3:probe3	0.195517565974591	0.0968338177540434	2.01910417774917	0.0437727779928115	*  
df.mm.trans3:probe4	0.0701219606985989	0.0968338177540434	0.724147434491407	0.469163625330699	   
df.mm.trans3:probe5	0.164333743956779	0.0968338177540433	1.69706976104345	0.0900296053118506	.  
df.mm.trans3:probe6	0.0978813430594654	0.0968338177540433	1.0108177631505	0.31237563462168	   
df.mm.trans3:probe7	0.166554131939925	0.0968338177540434	1.71999964271749	0.0857766605201304	.  
df.mm.trans3:probe8	0.217077881566404	0.0968338177540434	2.24175692543466	0.0252203167740215	*  
df.mm.trans3:probe9	0.0529431211419968	0.0968338177540434	0.546742061502435	0.584691627330057	   
df.mm.trans3:probe10	0.228196701455604	0.0968338177540434	2.35658065279653	0.0186577452157826	*  
df.mm.trans3:probe11	0.213908537140539	0.0968338177540434	2.20902719836849	0.0274242710153379	*  
df.mm.trans3:probe12	0.0786614317027778	0.0968338177540434	0.812334301458369	0.416814882571886