chr9.25166_chr9_59120747_59166292_+_2.R 

fitVsDatCorrelation=0.943398774427743
cont.fitVsDatCorrelation=0.251652071755

fstatistic=4734.98976747595,59,853
cont.fstatistic=543.297611469257,59,853

residuals=-1.08352964712280,-0.111247107493474,0.0061080366839985,0.141421905937597,1.17122952674453
cont.residuals=-1.27635880393893,-0.572460038620603,-0.194964604055458,0.415101093459917,2.45619953059896

predictedValues:
Include	Exclude	Both
chr9.25166_chr9_59120747_59166292_+_2.R.tl.Lung	139.866441632520	46.8401555364063	100.957896727357
chr9.25166_chr9_59120747_59166292_+_2.R.tl.cerebhem	318.345966462032	44.8712537883191	233.877105069773
chr9.25166_chr9_59120747_59166292_+_2.R.tl.cortex	423.730307517945	45.7328575924162	342.843894628429
chr9.25166_chr9_59120747_59166292_+_2.R.tl.heart	154.265878105108	45.2369196448805	106.288475547804
chr9.25166_chr9_59120747_59166292_+_2.R.tl.kidney	128.306171600697	45.3308311891248	84.6472733098893
chr9.25166_chr9_59120747_59166292_+_2.R.tl.liver	95.2109580772985	44.0693533200786	66.1010375304899
chr9.25166_chr9_59120747_59166292_+_2.R.tl.stomach	138.986039638757	43.3265098777658	96.3504298905158
chr9.25166_chr9_59120747_59166292_+_2.R.tl.testicle	136.29057058159	45.6332490477305	93.6693107479713


diffExp=93.026286096114,273.474712673713,377.997449925529,109.028958460228,82.975340411572,51.1416047572199,95.6595297609907,90.6573215338595
diffExpScore=0.999148908068692
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	113.155942600592	122.110223228258	108.441179789023
cerebhem	112.774704046992	90.731407142096	89.9503258966634
cortex	117.306855703357	81.6360097609842	88.7154326132432
heart	103.404584689343	114.956649125578	87.1479401446274
kidney	122.632937246069	98.8393968256665	112.730530863257
liver	121.748009568090	128.845238072633	131.602633020745
stomach	123.320857284101	100.947874874898	113.819066354778
testicle	123.735390772332	163.66530279633	95.6768921495653
cont.diffExp=-8.954280627666,22.0432969048958,35.6708459423732,-11.5520644362355,23.7935404204024,-7.09722850454338,22.3729824092028,-39.9299120239975
cont.diffExpScore=4.58974816523753

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,1,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,1,0,0,0,0,-1
cont.diffExp1.3Score=2
cont.diffExp1.2=0,1,1,0,1,0,1,-1
cont.diffExp1.2Score=1.25

tran.correlation=0.197932539137213
cont.tran.correlation=0.218659418953211

tran.covariance=0.00275973711355893
cont.tran.covariance=0.00233415751936240

tran.mean=118.502716475792
cont.tran.mean=114.988211483582

weightedLogRatios:
wLogRatio
Lung	4.80649123323435
cerebhem	9.37244685995356
cortex	10.9888233811404
heart	5.42879345952658
kidney	4.50944377799013
liver	3.21299418988136
stomach	5.07222726275984
testicle	4.77894421717485

cont.weightedLogRatios:
wLogRatio
Lung	-0.363029680167686
cerebhem	1.00406774766489
cortex	1.66163451399132
heart	-0.496867849790584
kidney	1.01407733853635
liver	-0.273676890772984
stomach	0.943812836264058
testicle	-1.38663998927387

varWeightedLogRatios=7.1928251233423
cont.varWeightedLogRatios=1.07308994819753

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.44194119482191	0.126086335041077	43.1604360063999	1.02033561476052e-216	***
df.mm.trans1	0.0535442816684039	0.108885074765918	0.491750423862166	0.623022303273089	   
df.mm.trans2	-1.60396053939628	0.096199471403929	-16.6732780958999	3.07438705411533e-54	***
df.mm.exp2	-0.0605849592602191	0.123743279058720	-0.489602018962739	0.624541429591634	   
df.mm.exp3	-0.138086288518291	0.123743279058720	-1.11590940185741	0.264775175385247	   
df.mm.exp4	0.0117090172361519	0.123743279058720	0.0946234601605767	0.924636137416306	   
df.mm.exp5	0.0571886054195385	0.123743279058720	0.462155244749903	0.644087846614042	   
df.mm.exp6	-0.0220500696670575	0.123743279058720	-0.178192058872095	0.858614455216218	   
df.mm.exp7	-0.0375789501504563	0.123743279058720	-0.303684777357677	0.76144211440304	   
df.mm.exp8	0.022929870266043	0.123743279058720	0.185301944804308	0.85303626328398	   
df.mm.trans1:exp2	0.883035716572772	0.114378486073415	7.72029554584229	3.24637826331382e-14	***
df.mm.trans2:exp2	0.0176414622303048	0.0844741103204131	0.208838686354791	0.834624029005504	   
df.mm.trans1:exp3	1.24649549526537	0.114378486073415	10.897989106669	5.54450841216505e-26	***
df.mm.trans2:exp3	0.114162453276293	0.084474110320413	1.35144901607453	0.176909932808409	   
df.mm.trans1:exp4	0.0862805987903116	0.114378486073415	0.75434289919637	0.450851541882626	   
df.mm.trans2:exp4	-0.0465363168134081	0.084474110320413	-0.550894429510939	0.581850298293471	   
df.mm.trans1:exp5	-0.143457210999232	0.114378486073415	-1.25423246909521	0.210101107313663	   
df.mm.trans2:exp5	-0.0899420635457806	0.0844741103204131	-1.06472933783650	0.287299725077752	   
df.mm.trans1:exp6	-0.362542868239492	0.114378486073415	-3.16967710174788	0.00158052227488935	** 
df.mm.trans2:exp6	-0.0389261848261466	0.084474110320413	-0.460806094062410	0.645055188829356	   
df.mm.trans1:exp7	0.0312644650500658	0.114378486073415	0.273342182812232	0.784656359010481	   
df.mm.trans2:exp7	-0.0403972241335122	0.0844741103204131	-0.478220178706638	0.632616038253679	   
df.mm.trans1:exp8	-0.0488286942433416	0.114378486073415	-0.426904533532646	0.669556589596707	   
df.mm.trans2:exp8	-0.0490341329731565	0.084474110320413	-0.580463443618031	0.561755440721706	   
df.mm.trans1:probe2	-1.29403748595856	0.078309596144625	-16.5246349064128	1.99801361774520e-53	***
df.mm.trans1:probe3	-1.32537236028946	0.078309596144625	-16.9247758326031	1.27118862274625e-55	***
df.mm.trans1:probe4	-0.568062796123448	0.078309596144625	-7.2540636664034	9.08551787325401e-13	***
df.mm.trans1:probe5	-0.536356682945204	0.078309596144625	-6.8491820843341	1.42100909055139e-11	***
df.mm.trans1:probe6	-1.41812244374321	0.078309596144625	-18.1091783582202	2.86725560853938e-62	***
df.mm.trans1:probe7	-1.46622202363142	0.078309596144625	-18.7234016751095	8.55739078485922e-66	***
df.mm.trans1:probe8	-0.0914370911545873	0.078309596144625	-1.16763584102411	0.243279959732133	   
df.mm.trans1:probe9	-1.20015690163721	0.078309596144625	-15.3257960802239	5.19211483325041e-47	***
df.mm.trans1:probe10	-1.36842751975333	0.078309596144625	-17.4745827730496	1.10857698164239e-58	***
df.mm.trans1:probe11	-0.519192846929946	0.078309596144625	-6.63000286671231	5.95024095354901e-11	***
df.mm.trans1:probe12	-0.640740586467177	0.078309596144625	-8.18214648028365	1.00907771221024e-15	***
df.mm.trans1:probe13	-0.186633795504721	0.078309596144625	-2.38328129237239	0.0173770524630305	*  
df.mm.trans1:probe14	-0.0198858110540717	0.078309596144625	-0.253938368132379	0.799604369483585	   
df.mm.trans1:probe15	-0.7301954446367	0.078309596144625	-9.32446954889345	9.3291755916543e-20	***
df.mm.trans1:probe16	0.00896172682557707	0.078309596144625	0.114439701732419	0.908916174148123	   
df.mm.trans1:probe17	-0.982447932947798	0.078309596144625	-12.5456902003859	2.96325471778244e-33	***
df.mm.trans1:probe18	-1.05143559975580	0.078309596144625	-13.4266507748804	1.99428536960254e-37	***
df.mm.trans1:probe19	-1.06573554574523	0.078309596144625	-13.6092586121500	2.58295135997226e-38	***
df.mm.trans1:probe20	-1.29015023269555	0.078309596144625	-16.4749953545011	3.72584824898005e-53	***
df.mm.trans1:probe21	-0.828301134403501	0.078309596144625	-10.5772622409362	1.18195513299825e-24	***
df.mm.trans1:probe22	-1.17956741326111	0.078309596144625	-15.0628718743823	1.22070047149960e-45	***
df.mm.trans2:probe2	-0.00568594305643589	0.078309596144625	-0.0726085095105699	0.942134676312054	   
df.mm.trans2:probe3	0.00236888043332554	0.078309596144625	0.0302501934622496	0.97587459433269	   
df.mm.trans2:probe4	0.0206016546742180	0.078309596144625	0.263079567364518	0.792552783232328	   
df.mm.trans2:probe5	0.0130874411225860	0.078309596144625	0.167124359809181	0.867311809177918	   
df.mm.trans2:probe6	0.109791229030267	0.078309596144625	1.40201500755413	0.161274551310538	   
df.mm.trans3:probe2	0.689765702158053	0.078309596144625	8.8081887293119	7.01265320154996e-18	***
df.mm.trans3:probe3	0.30525918565427	0.078309596144625	3.89810700965060	0.000104552340278958	***
df.mm.trans3:probe4	1.40426337330547	0.078309596144625	17.9322004255011	2.90864555840457e-61	***
df.mm.trans3:probe5	1.14771589492123	0.078309596144625	14.6561334935451	1.51702338916810e-43	***
df.mm.trans3:probe6	0.0273875680249376	0.078309596144625	0.349734507305558	0.726624282419902	   
df.mm.trans3:probe7	1.03856547736774	0.078309596144625	13.2623015377283	1.23630254577760e-36	***
df.mm.trans3:probe8	0.286551092107736	0.078309596144625	3.65920789041643	0.000268535648011642	***
df.mm.trans3:probe9	0.81824084129745	0.078309596144625	10.4487940377868	3.94890849038521e-24	***
df.mm.trans3:probe10	1.51403715265978	0.078309596144625	19.3339926037110	2.40164603681236e-69	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.93951410408914	0.367932145660562	13.4250680793901	2.02977291369950e-37	***
df.mm.trans1	-0.0981188754852408	0.317737201069277	-0.308805123086131	0.75754517624473	   
df.mm.trans2	-0.135022945130122	0.280719380998173	-0.48098903841271	0.630647637728386	   
df.mm.exp2	-0.113445044372442	0.361094881220204	-0.314169627631084	0.753469029160703	   
df.mm.exp3	-0.165853292324041	0.361094881220204	-0.459306683505435	0.64613097390493	   
df.mm.exp4	0.068114165590869	0.361094881220204	0.188632321124849	0.850425867272935	   
df.mm.exp5	-0.169791458131110	0.361094881220204	-0.470212863603479	0.638323140283331	   
df.mm.exp6	-0.066704725944154	0.361094881220204	-0.184729082059393	0.853485444592017	   
df.mm.exp7	-0.152699294433797	0.361094881220204	-0.422878590573753	0.672490427673274	   
df.mm.exp8	0.507508945940782	0.361094881220204	1.40547255675799	0.160244910200679	   
df.mm.trans1:exp2	0.110070213015127	0.333767507673913	0.329781091581461	0.741646342436241	   
df.mm.trans2:exp2	-0.183575489385068	0.246503640960227	-0.744717151722264	0.456647849217809	   
df.mm.trans1:exp3	0.201879601799672	0.333767507673913	0.604850973081855	0.545438882456172	   
df.mm.trans2:exp3	-0.236800352892685	0.246503640960227	-0.960636329630897	0.337007273397525	   
df.mm.trans1:exp4	-0.158231755513982	0.333767507673913	-0.474077769333293	0.635565774819788	   
df.mm.trans2:exp4	-0.128483178356658	0.246503640960227	-0.521222233700753	0.602347254329499	   
df.mm.trans1:exp5	0.250220211348743	0.333767507673913	0.749684153177682	0.453651660076054	   
df.mm.trans2:exp5	-0.0416363692346135	0.246503640960227	-0.168907725145250	0.865909266701324	   
df.mm.trans1:exp6	0.139891248886670	0.333767507673913	0.419127823021474	0.675228239653115	   
df.mm.trans2:exp6	0.120392599285747	0.246503640960227	0.488400896703882	0.625391434844489	   
df.mm.trans1:exp7	0.238721958761457	0.333767507673913	0.715234267185423	0.47466001251279	   
df.mm.trans2:exp7	-0.0376205182277494	0.246503640960227	-0.152616480962321	0.878736819205796	   
df.mm.trans1:exp8	-0.418130496195767	0.333767507673913	-1.25275974018500	0.210636480609312	   
df.mm.trans2:exp8	-0.214609546003871	0.246503640960227	-0.870614101957616	0.384209835449431	   
df.mm.trans1:probe2	-0.0626442396808944	0.228514991144101	-0.274136236608613	0.784046298652905	   
df.mm.trans1:probe3	-0.206664684402045	0.228514991144101	-0.904381298431848	0.366048597648739	   
df.mm.trans1:probe4	-0.227705319170475	0.228514991144101	-0.996456810253137	0.319310822702339	   
df.mm.trans1:probe5	-0.164920223305374	0.228514991144101	-0.721704175641482	0.470674151915866	   
df.mm.trans1:probe6	0.133504511998121	0.228514991144101	0.58422649354297	0.559222408542084	   
df.mm.trans1:probe7	-0.199114092048005	0.228514991144101	-0.871339298358959	0.383814085740626	   
df.mm.trans1:probe8	-0.66482896066747	0.228514991144101	-2.90934506020321	0.00371598332013513	** 
df.mm.trans1:probe9	0.0128215916796097	0.228514991144101	0.0561083175130703	0.95526865737157	   
df.mm.trans1:probe10	-0.196836467225127	0.228514991144101	-0.86137222875239	0.389275131225193	   
df.mm.trans1:probe11	0.032624155376581	0.228514991144101	0.142765930643072	0.886508760176145	   
df.mm.trans1:probe12	0.00833898976775016	0.228514991144101	0.0364920906326518	0.97089852324407	   
df.mm.trans1:probe13	-0.275301074019209	0.228514991144101	-1.20473966561609	0.228637962439604	   
df.mm.trans1:probe14	-0.299125531841171	0.228514991144101	-1.30899741125755	0.190887749585874	   
df.mm.trans1:probe15	-0.543338628746407	0.228514991144101	-2.37769358599226	0.0176407874613311	*  
df.mm.trans1:probe16	-0.09803947527314	0.228514991144101	-0.429028637387367	0.668010714444158	   
df.mm.trans1:probe17	-0.244215075950661	0.228514991144101	-1.06870483519683	0.285505029444426	   
df.mm.trans1:probe18	-0.101847716620480	0.228514991144101	-0.445693808141692	0.6559315339214	   
df.mm.trans1:probe19	-0.0679400779340038	0.228514991144101	-0.297311251195598	0.766301280466045	   
df.mm.trans1:probe20	-0.0179364613331855	0.228514991144101	-0.0784913989379138	0.93745558028248	   
df.mm.trans1:probe21	-0.0255508527060315	0.228514991144101	-0.111812588653841	0.910998310832758	   
df.mm.trans1:probe22	-0.395387191388506	0.228514991144101	-1.73024618388898	0.0839479876435878	.  
df.mm.trans2:probe2	0.0556391171269211	0.228514991144101	0.243481256299002	0.807691145768942	   
df.mm.trans2:probe3	0.153199258735437	0.228514991144101	0.670412290976699	0.502776463754923	   
df.mm.trans2:probe4	-0.145664336192712	0.228514991144101	-0.637438863259768	0.524010074594252	   
df.mm.trans2:probe5	0.0736093802265929	0.228514991144101	0.32212057448859	0.747440278862434	   
df.mm.trans2:probe6	-0.12985626828373	0.228514991144101	-0.568261485312544	0.570006984429465	   
df.mm.trans3:probe2	-0.0314228865535948	0.228514991144101	-0.137509081554215	0.89066086582256	   
df.mm.trans3:probe3	-0.00591308387203244	0.228514991144101	-0.0258761311125696	0.979362190465262	   
df.mm.trans3:probe4	0.103030942676889	0.228514991144101	0.450871700631309	0.652196607460985	   
df.mm.trans3:probe5	-0.0225363359523031	0.228514991144101	-0.0986208206274385	0.921462508194021	   
df.mm.trans3:probe6	0.0468821330340416	0.228514991144101	0.205159988845011	0.837496075486676	   
df.mm.trans3:probe7	0.116774449324702	0.228514991144101	0.511014392272691	0.609473212451925	   
df.mm.trans3:probe8	-0.206298501799926	0.228514991144101	-0.902778853881994	0.366898132994656	   
df.mm.trans3:probe9	-0.0857134395984294	0.228514991144101	-0.375088912851142	0.707687584313504	   
df.mm.trans3:probe10	-0.116447069804831	0.228514991144101	-0.509581753134959	0.610476316245529