chr2.13068_chr2_85895909_85898835_+_2.R 

fitVsDatCorrelation=0.704018798952865
cont.fitVsDatCorrelation=0.249038079088379

fstatistic=15964.8842294096,70,1106
cont.fstatistic=8577.11044053781,70,1106

residuals=-0.487440900174573,-0.0781168004859613,-0.00323067650062012,0.0731046195744653,0.643618966126833
cont.residuals=-0.405108484117925,-0.117071209658416,-0.0210460495841445,0.08669671897756,0.703731934728377

predictedValues:
Include	Exclude	Both
chr2.13068_chr2_85895909_85898835_+_2.R.tl.Lung	50.6343849999068	48.9088250702292	52.1827566791681
chr2.13068_chr2_85895909_85898835_+_2.R.tl.cerebhem	54.3127429146199	58.5265688159435	48.8818953237885
chr2.13068_chr2_85895909_85898835_+_2.R.tl.cortex	48.4184620951114	43.4143887951555	49.484404286858
chr2.13068_chr2_85895909_85898835_+_2.R.tl.heart	50.6745434322444	42.0960484532736	51.5868508898044
chr2.13068_chr2_85895909_85898835_+_2.R.tl.kidney	49.0370844146869	47.2503385261551	51.5239968206492
chr2.13068_chr2_85895909_85898835_+_2.R.tl.liver	51.4390439830977	44.1100106592733	54.7167544604453
chr2.13068_chr2_85895909_85898835_+_2.R.tl.stomach	51.7126750206432	44.264770137193	54.093132928978
chr2.13068_chr2_85895909_85898835_+_2.R.tl.testicle	49.727020395789	48.9535248558115	52.3343833467177


diffExp=1.72555992967762,-4.21382590132368,5.00407329995591,8.57849497897078,1.78674588853176,7.32903332382437,7.4479048834502,0.773495539977496
diffExpScore=1.25237097530516
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=0,0,0,1,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	53.30204974967	52.422290398991	49.6977044952637
cerebhem	52.9974240863467	52.9605428853703	53.3109916090339
cortex	51.7938187571186	48.4687872402517	51.797901879082
heart	52.8189042385804	47.1029184887571	51.159990234189
kidney	51.6856013741896	48.2179106404109	48.4978348861421
liver	51.697216734565	51.1005395418628	51.6534490805132
stomach	52.6593119931499	51.6565405744801	52.4899514613435
testicle	49.9409884671258	49.1370812903049	51.9962518570855
cont.diffExp=0.87975935067896,0.0368812009763673,3.32503151686686,5.71598574982332,3.46769073377872,0.59667719270216,1.00277141866984,0.803907176820871
cont.diffExpScore=0.940577718891628

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=0.631416394547876
cont.tran.correlation=0.427330614202334

tran.covariance=0.00224101243979306
cont.tran.covariance=0.000375999950937604

tran.mean=48.9675270355709
cont.tran.mean=51.1226204038234

weightedLogRatios:
wLogRatio
Lung	0.135477770040828
cerebhem	-0.301287830914676
cortex	0.417306920438525
heart	0.710848049110345
kidney	0.143792008631698
liver	0.593867914839591
stomach	0.601519029068521
testicle	0.0611203857109323

cont.weightedLogRatios:
wLogRatio
Lung	0.0660332462680139
cerebhem	0.00276363380996666
cortex	0.259703301473640
heart	0.447783139357617
kidney	0.271575997776893
liver	0.0457343750413139
stomach	0.0760250877894796
testicle	0.0633338927311129

varWeightedLogRatios=0.118533386316314
cont.varWeightedLogRatios=0.0239911694896693

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95061149146156	0.058295512545397	67.7687066973649	0	***
df.mm.trans1	-0.108717271175166	0.0506079819276127	-2.1482237985832	0.0319126447323431	*  
df.mm.trans2	-0.0708048066116137	0.0438016086785886	-1.61648872604592	0.106273862640878	   
df.mm.exp2	0.314995970891383	0.055537529449699	5.67176779400458	1.80321057285056e-08	***
df.mm.exp3	-0.110822075006118	0.055537529449699	-1.99544481189951	0.0462393479134796	*  
df.mm.exp4	-0.137725888847992	0.055537529449699	-2.47987064265672	0.0132910365823068	*  
df.mm.exp5	-0.0538476361270653	0.055537529449699	-0.96957204723764	0.332471830660572	   
df.mm.exp6	-0.134922343797306	0.055537529449699	-2.42939045244183	0.0152826540718914	*  
df.mm.exp7	-0.114651858869902	0.055537529449699	-2.06440329640057	0.0392122343409015	*  
df.mm.exp8	-0.0200703838710918	0.055537529449699	-0.361384167966452	0.717881303788729	   
df.mm.trans1:exp2	-0.24486798629928	0.052267698984216	-4.68488169669046	3.14930760107279e-06	***
df.mm.trans2:exp2	-0.135473003944224	0.0355407128887388	-3.81176945910808	0.000145585509201751	***
df.mm.trans1:exp3	0.0660723732822603	0.052267698984216	1.26411482744272	0.206455160859573	   
df.mm.trans2:exp3	-0.00834485175715104	0.0355407128887388	-0.234796971666686	0.81440977941817	   
df.mm.trans1:exp4	0.138518680461966	0.052267698984216	2.65017751219155	0.00815975722579945	** 
df.mm.trans2:exp4	-0.0122780878178436	0.0355407128887389	-0.345465434423909	0.729810185637355	   
df.mm.trans1:exp5	0.0217935818200960	0.052267698984216	0.416960804543497	0.676787991626168	   
df.mm.trans2:exp5	0.0193496026342817	0.0355407128887389	0.544434848418944	0.586252015675408	   
df.mm.trans1:exp6	0.150688947506779	0.052267698984216	2.88302241030898	0.00401503913262164	** 
df.mm.trans2:exp6	0.0316512475846248	0.0355407128887389	0.890563103889048	0.373357280441377	   
df.mm.trans1:exp7	0.13572388418599	0.052267698984216	2.59670670076707	0.00953709075267641	** 
df.mm.trans2:exp7	0.0148831110584413	0.0355407128887389	0.418762310847091	0.675471158817937	   
df.mm.trans1:exp8	0.00198794821445193	0.052267698984216	0.0380339722828101	0.96966745939503	   
df.mm.trans2:exp8	0.0209839075852619	0.0355407128887389	0.590418871195764	0.555030477607961	   
df.mm.trans1:probe2	0.300294001241578	0.0369588443887563	8.12509173941959	1.18679595587346e-15	***
df.mm.trans1:probe3	-0.0520403319252181	0.0369588443887564	-1.40806166388281	0.159393826314686	   
df.mm.trans1:probe4	-0.0158773637409504	0.0369588443887563	-0.429595784271346	0.667573381415916	   
df.mm.trans1:probe5	0.187870002827504	0.0369588443887564	5.08322178181139	4.35465962618019e-07	***
df.mm.trans1:probe6	0.0434956707361727	0.0369588443887563	1.17686771476559	0.239501522948165	   
df.mm.trans1:probe7	0.43153935379842	0.0369588443887564	11.6762133918263	8.69824629975761e-30	***
df.mm.trans1:probe8	-0.0317310474936304	0.0369588443887564	-0.8585508561865	0.390774385322137	   
df.mm.trans1:probe9	0.232635450073212	0.0369588443887564	6.29444599582731	4.43946434104067e-10	***
df.mm.trans1:probe10	0.180246130441453	0.0369588443887564	4.87694172862958	1.23505891626732e-06	***
df.mm.trans1:probe11	0.060740470586011	0.0369588443887564	1.64346238608287	0.100571521582348	   
df.mm.trans1:probe12	0.183791411260661	0.0369588443887564	4.97286682796212	7.64236900074522e-07	***
df.mm.trans1:probe13	0.084610210750233	0.0369588443887564	2.28930888261141	0.0222491595335247	*  
df.mm.trans1:probe14	0.0662724862112947	0.0369588443887564	1.79314281350897	0.0732232403521934	.  
df.mm.trans1:probe15	0.0522517925942537	0.0369588443887564	1.41378318122278	0.157706976946121	   
df.mm.trans1:probe16	0.101509541387699	0.0369588443887564	2.7465561509434	0.00612020551012192	** 
df.mm.trans1:probe17	-0.0299272316586246	0.0369588443887563	-0.809744789199337	0.418261045767198	   
df.mm.trans1:probe18	0.142229465627356	0.0369588443887564	3.84832015122813	0.000125735861028618	***
df.mm.trans1:probe19	0.156498235980006	0.0369588443887564	4.23439202627277	2.48202944380497e-05	***
df.mm.trans1:probe20	0.0830361637341547	0.0369588443887564	2.24671969882846	0.0248545749070529	*  
df.mm.trans1:probe21	0.243589650926211	0.0369588443887564	6.59083515609908	6.75534566278517e-11	***
df.mm.trans1:probe22	0.0450525593087964	0.0369588443887563	1.21899264043284	0.223106907884425	   
df.mm.trans1:probe23	-0.0130682492375691	0.0369588443887564	-0.353589227523163	0.723714136756021	   
df.mm.trans1:probe24	-0.0209641158626978	0.0369588443887563	-0.567228662297556	0.570673943684479	   
df.mm.trans1:probe25	0.156257252265912	0.0369588443887563	4.22787170027018	2.55385695847597e-05	***
df.mm.trans1:probe26	0.0656649423633373	0.0369588443887563	1.77670442486329	0.0758917019127951	.  
df.mm.trans1:probe27	0.0255224054105454	0.0369588443887564	0.690562863440337	0.489985184539544	   
df.mm.trans1:probe28	0.459102324339883	0.0369588443887564	12.4219880770826	2.92847321471441e-33	***
df.mm.trans1:probe29	-0.00156159546813527	0.0369588443887564	-0.0422522807182342	0.966305212038045	   
df.mm.trans1:probe30	0.184995812312371	0.0369588443887564	5.00545445540638	6.48022521935568e-07	***
df.mm.trans1:probe31	0.235641440160209	0.0369588443887564	6.37577943946472	2.66846264686218e-10	***
df.mm.trans2:probe2	0.0963267661524884	0.0369588443887564	2.60632516372165	0.00927498106707088	** 
df.mm.trans2:probe3	0.0628013252501259	0.0369588443887564	1.69922318429500	0.0895583508641442	.  
df.mm.trans2:probe4	-0.033459405111535	0.0369588443887564	-0.905315240909266	0.365495571977144	   
df.mm.trans2:probe5	0.0201647404504912	0.0369588443887564	0.545599863415258	0.585451036043338	   
df.mm.trans2:probe6	0.0368875816278517	0.0369588443887564	0.998071834710114	0.318462835059768	   
df.mm.trans3:probe2	0.100241579532580	0.0369588443887564	2.71224875102088	0.00678649552567519	** 
df.mm.trans3:probe3	0.208750695694732	0.0369588443887564	5.64819325785625	2.06074077061271e-08	***
df.mm.trans3:probe4	0.090422903480599	0.0369588443887564	2.44658362500391	0.0145765959470884	*  
df.mm.trans3:probe5	0.165217165544795	0.0369588443887564	4.47030117627427	8.61508667360382e-06	***
df.mm.trans3:probe6	0.316027657957886	0.0369588443887564	8.55079922504364	4.01591966882804e-17	***
df.mm.trans3:probe7	0.236674209771197	0.0369588443887564	6.40372321389999	2.23732205141589e-10	***
df.mm.trans3:probe8	0.233580793257168	0.0369588443887564	6.32002426266955	3.78510700766355e-10	***
df.mm.trans3:probe9	0.265825960615144	0.0369588443887564	7.19248572328235	1.17017385447320e-12	***
df.mm.trans3:probe10	0.209435950752572	0.0369588443887564	5.66673428826921	1.85542493911593e-08	***
df.mm.trans3:probe11	0.204597326208850	0.0369588443887564	5.53581502865098	3.86759362489356e-08	***
df.mm.trans3:probe12	0.173178095090916	0.0369588443887564	4.6857010265072	3.13697894266674e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02483699689614	0.0794991833520765	50.6274005239956	2.82254982342377e-290	***
df.mm.trans1	-0.0381087555924600	0.069015487790913	-0.552176863661574	0.580938744112598	   
df.mm.trans2	-0.0770705388420048	0.0597334506106847	-1.29024086260001	0.197236725903156	   
df.mm.exp2	-0.0656999914340639	0.0757380464440498	-0.867463507691588	0.385876264650483	   
df.mm.exp3	-0.14850673496111	0.0757380464440498	-1.96079436866406	0.0501536877780214	.  
df.mm.exp4	-0.145101594222421	0.0757380464440499	-1.91583492095498	0.0556437024369172	.  
df.mm.exp5	-0.0899573066401694	0.0757380464440499	-1.18774263218716	0.235189830249854	   
df.mm.exp6	-0.094705903052098	0.0757380464440499	-1.25044026745607	0.211403248021446	   
df.mm.exp7	-0.0815097740968728	0.0757380464440499	-1.07620645004472	0.282069633763081	   
df.mm.exp8	-0.175063521659463	0.0757380464440499	-2.31143434348796	0.0209919850264713	*  
df.mm.trans1:exp2	0.059968514386469	0.0712788892918899	0.84132223414559	0.400349300101844	   
df.mm.trans2:exp2	0.0759152636986236	0.0484678412974766	1.56630173051623	0.117564048138844	   
df.mm.trans1:exp3	0.119802760818928	0.07127888929189	1.6807607695503	0.0930917512269605	.  
df.mm.trans2:exp3	0.0700948735045868	0.0484678412974766	1.44621405922273	0.148400488515872	   
df.mm.trans1:exp4	0.135995968402872	0.07127888929189	1.90794174479856	0.0566574899964431	.  
df.mm.trans2:exp4	0.0381046668246598	0.0484678412974767	0.786184525751585	0.431927794904743	   
df.mm.trans1:exp5	0.0591617586904996	0.07127888929189	0.830003936344039	0.406715672868808	   
df.mm.trans2:exp5	0.00635595855591526	0.0484678412974767	0.131137644792242	0.895690271228622	   
df.mm.trans1:exp6	0.0641350609217641	0.07127888929189	0.899776379218375	0.3684351284325	   
df.mm.trans2:exp6	0.0691690685868465	0.0484678412974767	1.42711263252501	0.153829681876100	   
df.mm.trans1:exp7	0.069378075730485	0.07127888929189	0.973332727539833	0.330600785243695	   
df.mm.trans2:exp7	0.0667947040903054	0.0484678412974767	1.37812418094599	0.168443682786766	   
df.mm.trans1:exp8	0.109930812135246	0.07127888929189	1.54226325953362	0.123295686820291	   
df.mm.trans2:exp8	0.110345601018461	0.0484678412974767	2.27667661823853	0.0229959334694675	*  
df.mm.trans1:probe2	0.0183729852285265	0.0504017859737406	0.36453044021295	0.715531631291934	   
df.mm.trans1:probe3	0.00401276213298027	0.0504017859737406	0.0796154750363594	0.936557489585245	   
df.mm.trans1:probe4	-0.0110152913486544	0.0504017859737406	-0.218549623507259	0.827041218213557	   
df.mm.trans1:probe5	-0.0307365425336249	0.0504017859737406	-0.609830424454377	0.542099410570331	   
df.mm.trans1:probe6	-0.0614581495373068	0.0504017859737406	-1.21936451952966	0.222965848546308	   
df.mm.trans1:probe7	-0.0136855050150372	0.0504017859737406	-0.271528176048512	0.78603550453228	   
df.mm.trans1:probe8	-0.00336362422716912	0.0504017859737406	-0.0667362110723929	0.946803767754494	   
df.mm.trans1:probe9	0.0388310193563649	0.0504017859737406	0.770429432333925	0.44120973425695	   
df.mm.trans1:probe10	-0.0594718093643041	0.0504017859737406	-1.17995440469687	0.238272076679583	   
df.mm.trans1:probe11	0.00763045068440293	0.0504017859737406	0.151392466298286	0.879693740558252	   
df.mm.trans1:probe12	0.0277091660449864	0.0504017859737406	0.549765559089968	0.582591178069242	   
df.mm.trans1:probe13	-0.0103979287092937	0.0504017859737406	-0.206300798838975	0.836593924035228	   
df.mm.trans1:probe14	-0.0449078326702984	0.0504017859737406	-0.890996852645172	0.373124641592703	   
df.mm.trans1:probe15	-0.0195067838740786	0.0504017859737406	-0.387025647945127	0.698811676651396	   
df.mm.trans1:probe16	-0.0299822391969878	0.0504017859737406	-0.594864618738087	0.552055646974305	   
df.mm.trans1:probe17	-0.0652776040056885	0.0504017859737406	-1.29514466093916	0.195540608508178	   
df.mm.trans1:probe18	-0.0436074935535253	0.0504017859737406	-0.865197387573625	0.387118081598553	   
df.mm.trans1:probe19	0.00510224889243882	0.0504017859737406	0.101231509833741	0.91938503217625	   
df.mm.trans1:probe20	0.0583811356763474	0.0504017859737406	1.15831482056537	0.24698563801246	   
df.mm.trans1:probe21	-0.0988029019788548	0.0504017859737406	-1.96030557390032	0.0502108357820079	.  
df.mm.trans1:probe22	-0.0105628346714407	0.0504017859737405	-0.209572626591922	0.834039841704768	   
df.mm.trans1:probe23	-0.0866253701205162	0.0504017859737406	-1.71869643995648	0.0859495008695617	.  
df.mm.trans1:probe24	-0.0177367603385855	0.0504017859737406	-0.351907377802571	0.724974762580366	   
df.mm.trans1:probe25	-0.0055840345749747	0.0504017859737406	-0.110790410837505	0.911802650248734	   
df.mm.trans1:probe26	0.0541885276154264	0.0504017859737406	1.07513110038717	0.282550520470915	   
df.mm.trans1:probe27	-0.0108015312244015	0.0504017859737406	-0.214308501488998	0.830345992923665	   
df.mm.trans1:probe28	-0.00411066501090341	0.0504017859737406	-0.0815579236228866	0.935013018129313	   
df.mm.trans1:probe29	0.0112747642972484	0.0504017859737406	0.223697713869159	0.823033836869025	   
df.mm.trans1:probe30	-0.0228355863800364	0.0504017859737406	-0.45307097633274	0.65058650152954	   
df.mm.trans1:probe31	-0.0374310948612167	0.0504017859737406	-0.74265413691329	0.457848803045480	   
df.mm.trans2:probe2	0.0132688197511796	0.0504017859737406	0.263260904248366	0.792398526636187	   
df.mm.trans2:probe3	0.062475347587392	0.0504017859737406	1.23954630536191	0.215406200533529	   
df.mm.trans2:probe4	0.0426423129946373	0.0504017859737406	0.846047658248738	0.397709164449014	   
df.mm.trans2:probe5	0.0532205185855868	0.0504017859737406	1.05592525259551	0.291232959989456	   
df.mm.trans2:probe6	0.0365707791368763	0.0504017859737406	0.725584985340197	0.46824672673509	   
df.mm.trans3:probe2	-0.0435732729465959	0.0504017859737406	-0.864518431336892	0.387490619226859	   
df.mm.trans3:probe3	-0.0647739649710034	0.0504017859737406	-1.28515217704291	0.199008167239135	   
df.mm.trans3:probe4	0.0335786820539579	0.0504017859737406	0.666220083380627	0.505409372958706	   
df.mm.trans3:probe5	0.0317306536293892	0.0504017859737406	0.629554152027885	0.529116350035198	   
df.mm.trans3:probe6	0.0547380415382561	0.0504017859737406	1.08603376806478	0.277700697880659	   
df.mm.trans3:probe7	-0.0341892719697205	0.0504017859737406	-0.678334533374138	0.49770150433302	   
df.mm.trans3:probe8	0.0325354099681469	0.0504017859737406	0.645520973901558	0.518723523711082	   
df.mm.trans3:probe9	-0.0379482212368242	0.0504017859737406	-0.752914217297682	0.451661689367813	   
df.mm.trans3:probe10	0.00868158810212893	0.0504017859737406	0.172247628420391	0.863274367852348	   
df.mm.trans3:probe11	0.0163448600684343	0.0504017859737406	0.324291287553779	0.745778834485429	   
df.mm.trans3:probe12	-0.0415119959616765	0.0504017859737406	-0.823621527683649	0.410332201496344