chr9.24590_chr9_83768426_83785124_+_2.R 

fitVsDatCorrelation=0.930756938837162
cont.fitVsDatCorrelation=0.251643324727243

fstatistic=6851.8906888931,53,715
cont.fstatistic=966.403886763297,53,715

residuals=-0.873591499804948,-0.102846520901800,-0.00198514310666074,0.0981120289666845,0.69227538057521
cont.residuals=-0.975311150320797,-0.422752568157869,-0.0760083666892744,0.376742526439055,1.59815249935939

predictedValues:
Include	Exclude	Both
chr9.24590_chr9_83768426_83785124_+_2.R.tl.Lung	66.9443814344863	133.745551496118	76.1665521511187
chr9.24590_chr9_83768426_83785124_+_2.R.tl.cerebhem	66.8336136507586	151.388489818253	95.3484620280944
chr9.24590_chr9_83768426_83785124_+_2.R.tl.cortex	74.7902314094003	118.333879872394	75.7565508693909
chr9.24590_chr9_83768426_83785124_+_2.R.tl.heart	64.4217417155286	121.524343603539	76.2444257232078
chr9.24590_chr9_83768426_83785124_+_2.R.tl.kidney	72.5261324193612	148.843411269781	81.3545187282021
chr9.24590_chr9_83768426_83785124_+_2.R.tl.liver	67.7208707208022	132.690128912217	80.9055770886771
chr9.24590_chr9_83768426_83785124_+_2.R.tl.stomach	64.8691812275275	135.052047708429	73.4526353357725
chr9.24590_chr9_83768426_83785124_+_2.R.tl.testicle	64.9119541607957	116.232856246022	75.1299561269224


diffExp=-66.8011700616321,-84.5548761674941,-43.5436484629939,-57.1026018880103,-76.3172788504201,-64.9692581914152,-70.1828664809016,-51.3209020852265
diffExpScore=0.998061236249303
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	79.020219334993	95.5093097904418	93.915495677115
cerebhem	83.834785794208	83.6455378497455	95.4247048466455
cortex	85.5250246165443	75.5144220428236	65.6330330891005
heart	84.1949122222384	77.301420173904	76.3045236575087
kidney	86.105959845123	77.9254947369795	88.0846641147616
liver	83.8420321739753	82.2821218434834	82.9086595234835
stomach	99.5971844704964	86.3770309519114	95.3683978606144
testicle	81.784245864559	65.9154232729354	54.5325759151048
cont.diffExp=-16.4890904554488,0.189247944462437,10.0106025737207,6.89349204833448,8.18046510814357,1.55991033049189,13.2201535185850,15.8688225916235
cont.diffExpScore=1.79088129714745

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=-1,0,0,0,0,0,0,1
cont.diffExp1.2Score=2

tran.correlation=0.101539096787789
cont.tran.correlation=0.0916913600091797

tran.covariance=0.000568174994817785
cont.tran.covariance=0.00075356497210927

tran.mean=100.051800979088
cont.tran.mean=83.0234453115226

weightedLogRatios:
wLogRatio
Lung	-3.1488814850664
cerebhem	-3.7701752898851
cortex	-2.08493626669985
heart	-2.84505832598777
kidney	-3.33837759143155
liver	-3.06157787034411
stomach	-3.32840446244392
testicle	-2.60074736412433

cont.weightedLogRatios:
wLogRatio
Lung	-0.846105020478364
cerebhem	0.0100063921408510
cortex	0.546063290893789
heart	0.375039481151509
kidney	0.439797601262714
liver	0.0830017195214293
stomach	0.645116541026571
testicle	0.926748812079752

varWeightedLogRatios=0.26546572220861
cont.varWeightedLogRatios=0.290901996008931

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54205959756405	0.104214956383767	43.5835676103737	1.73830175397278e-203	***
df.mm.trans1	-0.684844063118181	0.0925448981706392	-7.40012768564972	3.82816761764167e-13	***
df.mm.trans2	0.239172464270554	0.0841816194658192	2.84114829090057	0.00462305830049513	** 
df.mm.exp2	-0.102361590686433	0.113489332672462	-0.901949005038697	0.367387684753754	   
df.mm.exp3	-0.00620637501950132	0.113489332672462	-0.0546868579923131	0.956403216152262	   
df.mm.exp4	-0.135257379547123	0.113489332672462	-1.19180698627848	0.233732363913772	   
df.mm.exp5	0.121146533457335	0.113489332672462	1.06747066534414	0.286119637416110	   
df.mm.exp6	-0.0567506468261076	0.113489332672462	-0.500052696493457	0.617191832746443	   
df.mm.exp7	0.0145132528254878	0.113489332672462	0.127882087978912	0.898278244786597	   
df.mm.exp8	-0.157470855692570	0.113489332672462	-1.38753882840286	0.165709804456283	   
df.mm.trans1:exp2	0.100705596231283	0.107763445753007	0.934506089032262	0.350358385446746	   
df.mm.trans2:exp2	0.22627177844033	0.0907449421963352	2.49349190118795	0.0128743087605603	*  
df.mm.trans1:exp3	0.117031508539946	0.107763445753007	1.08600377170735	0.27784332221271	   
df.mm.trans2:exp3	-0.116222630924797	0.0907449421963352	-1.28076152909260	0.200692724621507	   
df.mm.trans1:exp4	0.0968464131597656	0.107763445753007	0.898694473650524	0.369117960243958	   
df.mm.trans2:exp4	0.0394328557545394	0.0907449421963352	0.434546045213437	0.66402307597375	   
df.mm.trans1:exp5	-0.0410617361791336	0.107763445753007	-0.381035850257115	0.70328983586241	   
df.mm.trans2:exp5	-0.0141908365833287	0.0907449421963352	-0.156381570585229	0.875776406294792	   
df.mm.trans1:exp6	0.0682829148688146	0.107763445753007	0.63363707787628	0.526520332838386	   
df.mm.trans2:exp6	0.0488280735223497	0.0907449421963352	0.538080385975734	0.590689002710163	   
df.mm.trans1:exp7	-0.0460027542237992	0.107763445753007	-0.42688644467844	0.669590546984656	   
df.mm.trans2:exp7	-0.00479213548789393	0.0907449421963352	-0.0528088439080791	0.957898965470986	   
df.mm.trans1:exp8	0.126640509181275	0.107763445753007	1.17517130504098	0.240317379949411	   
df.mm.trans2:exp8	0.0171272907514148	0.0907449421963352	0.188740995772065	0.850349377543123	   
df.mm.trans1:probe2	-0.0756978881528873	0.0590244701134063	-1.28248314652288	0.200088832696110	   
df.mm.trans1:probe3	-0.0722281006388028	0.0590244701134063	-1.22369756983888	0.221469438889033	   
df.mm.trans1:probe4	0.0740889454469868	0.0590244701134063	1.25522423673836	0.209807455350729	   
df.mm.trans1:probe5	-0.0129876822922808	0.0590244701134063	-0.220038947699606	0.825903630115864	   
df.mm.trans1:probe6	-0.112855054490060	0.0590244701134063	-1.91200453427581	0.0562749927314996	.  
df.mm.trans1:probe7	0.236844575539873	0.0590244701134063	4.01265060211151	6.6369468742681e-05	***
df.mm.trans1:probe8	0.0911888876609955	0.0590244701134064	1.54493360949773	0.122804788591423	   
df.mm.trans1:probe9	-0.092815900336488	0.0590244701134063	-1.57249866298091	0.116277326699446	   
df.mm.trans1:probe10	-0.0959383457749544	0.0590244701134064	-1.62539952651203	0.104518123710773	   
df.mm.trans1:probe11	1.03458258383883	0.0590244701134063	17.5280283220000	1.71706208215234e-57	***
df.mm.trans1:probe12	0.984621055633224	0.0590244701134063	16.6815738242364	5.14596767708139e-53	***
df.mm.trans1:probe13	1.09547332641042	0.0590244701134063	18.5596469448288	4.61280558461494e-63	***
df.mm.trans1:probe14	1.06633378380993	0.0590244701134063	18.0659611473196	2.21068579767468e-60	***
df.mm.trans1:probe15	1.31109075942737	0.0590244701134064	22.2126646272014	1.55606733035709e-83	***
df.mm.trans1:probe16	1.28982128392371	0.0590244701134063	21.8523144967760	1.77797779312042e-81	***
df.mm.trans1:probe17	0.312722600855669	0.0590244701134063	5.29818565511595	1.55915575172736e-07	***
df.mm.trans1:probe18	0.387874697122174	0.0590244701134063	6.57142192682006	9.60338823737418e-11	***
df.mm.trans1:probe19	0.499864846697775	0.0590244701134063	8.46877313319987	1.40459400690628e-16	***
df.mm.trans1:probe20	0.474435026237726	0.0590244701134063	8.03793791500666	3.78019587579377e-15	***
df.mm.trans1:probe21	0.275827610632046	0.0590244701134063	4.67310608806968	3.54555175720241e-06	***
df.mm.trans1:probe22	0.340564909670682	0.0590244701134063	5.7698935545942	1.18142306539605e-08	***
df.mm.trans2:probe2	0.00344579048465094	0.0590244701134063	0.0583790159916792	0.953463040505955	   
df.mm.trans2:probe3	0.140609690358943	0.0590244701134063	2.38222706766843	0.0174687765193661	*  
df.mm.trans2:probe4	0.0914265057519178	0.0590244701134063	1.54895936509478	0.121833976113919	   
df.mm.trans2:probe5	0.0560598182314136	0.0590244701134063	0.949772494758588	0.342548780803363	   
df.mm.trans2:probe6	0.855528830635945	0.0590244701134063	14.4944771040245	6.28266977460136e-42	***
df.mm.trans3:probe2	0.143989578470509	0.0590244701134063	2.43948955736249	0.014950094117412	*  
df.mm.trans3:probe3	0.461398551645914	0.0590244701134063	7.81707232202862	1.93751785722419e-14	***
df.mm.trans3:probe4	0.340749547732335	0.0590244701134063	5.77302171586865	1.16064961956179e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.49139805089415	0.275849836885572	16.2820399011412	6.18622589370865e-51	***
df.mm.trans1	-0.0403481360153071	0.244959993755168	-0.164713165594028	0.869216308107365	   
df.mm.trans2	0.0591446742375503	0.222822969026609	0.265433471674490	0.790752064048216	   
df.mm.exp2	-0.0894334887131598	0.300398474386613	-0.297716188125705	0.766006270010836	   
df.mm.exp3	0.202521462406550	0.300398474386613	0.674176068370787	0.500417282267575	   
df.mm.exp4	0.0595825055222346	0.300398474386613	0.198344900532191	0.842831604809291	   
df.mm.exp5	-0.0534987329194023	0.300398474386613	-0.178092558654440	0.858700730624363	   
df.mm.exp6	0.0348167050479869	0.300398474386613	0.115901737247765	0.907762962434633	   
df.mm.exp7	0.115576334236110	0.300398474386613	0.3847434128023	0.700541938725211	   
df.mm.exp8	0.207126747750204	0.300398474386613	0.689506656693707	0.490728191856922	   
df.mm.trans1:exp2	0.148577754342790	0.285242444699853	0.520882347993936	0.602610003812485	   
df.mm.trans2:exp2	-0.0432021558281036	0.240195633828896	-0.179862369433737	0.85731157930436	   
df.mm.trans1:exp3	-0.123416204383286	0.285242444699853	-0.432671247482648	0.665384093118853	   
df.mm.trans2:exp3	-0.437421531410926	0.240195633828897	-1.82110525673636	0.0690086458402239	.  
df.mm.trans1:exp4	0.00384822831735316	0.285242444699853	0.0134910788659187	0.989239766421458	   
df.mm.trans2:exp4	-0.271093905312861	0.240195633828896	-1.12863793979692	0.259429143066729	   
df.mm.trans1:exp5	0.139373601324762	0.285242444699853	0.488614523940918	0.625264454204524	   
df.mm.trans2:exp5	-0.149971820012889	0.240195633828897	-0.624373630870084	0.532581408648998	   
df.mm.trans1:exp6	0.0244139932782976	0.285242444699853	0.0855903240627015	0.931816051317935	   
df.mm.trans2:exp6	-0.183886579964592	0.240195633828897	-0.765570035696753	0.444184815747829	   
df.mm.trans1:exp7	0.115853800917373	0.285242444699853	0.406159051957645	0.684747266365131	   
df.mm.trans2:exp7	-0.216078266685284	0.240195633828897	-0.899592816242477	0.368639848648027	   
df.mm.trans1:exp8	-0.172745876701784	0.285242444699853	-0.605610700341447	0.544965478190722	   
df.mm.trans2:exp8	-0.577978020516511	0.240195633828896	-2.40628029453789	0.0163686208255254	*  
df.mm.trans1:probe2	-0.0321480853939392	0.156233721320029	-0.205769184285683	0.837029785649974	   
df.mm.trans1:probe3	-0.116683489825201	0.156233721320029	-0.746852144590387	0.455398328170941	   
df.mm.trans1:probe4	-0.203791886814984	0.156233721320029	-1.30440397305481	0.192515599182360	   
df.mm.trans1:probe5	0.146553430853546	0.156233721320029	0.938039685768902	0.348540766158085	   
df.mm.trans1:probe6	-0.106329435917652	0.156233721320029	-0.680579295041223	0.496358054863766	   
df.mm.trans1:probe7	-0.178425293463664	0.156233721320029	-1.14204086004056	0.253819339613517	   
df.mm.trans1:probe8	-0.281072374887785	0.156233721320029	-1.79905063076643	0.0724322446008783	.  
df.mm.trans1:probe9	-0.147177286734553	0.156233721320029	-0.942032779422022	0.346494030048472	   
df.mm.trans1:probe10	-0.144873386663250	0.156233721320029	-0.927286282623273	0.354090816311371	   
df.mm.trans1:probe11	-0.0473919135940889	0.156233721320029	-0.303339850025151	0.761719120833583	   
df.mm.trans1:probe12	-0.210021016726886	0.156233721320029	-1.34427455835017	0.179285962972802	   
df.mm.trans1:probe13	-0.203367077155827	0.156233721320029	-1.30168490795434	0.193443347671693	   
df.mm.trans1:probe14	-0.0203681323838424	0.156233721320029	-0.130369629627655	0.896310662801265	   
df.mm.trans1:probe15	-0.175818705717978	0.156233721320029	-1.12535696028024	0.260815411404125	   
df.mm.trans1:probe16	-0.146952872508458	0.156233721320029	-0.940596378725688	0.347229399634684	   
df.mm.trans1:probe17	0.0387837548674393	0.156233721320029	0.248241893873824	0.804018532470422	   
df.mm.trans1:probe18	-0.0472001953163169	0.156233721320029	-0.302112725201187	0.762653988851785	   
df.mm.trans1:probe19	-0.00740359396822302	0.156233721320029	-0.0473879384403671	0.962217274765467	   
df.mm.trans1:probe20	-0.100260274590042	0.156233721320029	-0.641732615359449	0.52125247977691	   
df.mm.trans1:probe21	-0.0106197069312522	0.156233721320029	-0.0679732060500485	0.945825966939218	   
df.mm.trans1:probe22	-0.120432466583034	0.156233721320029	-0.77084809582395	0.441051619162625	   
df.mm.trans2:probe2	0.129074376225667	0.156233721320029	0.826162080344174	0.408987744537611	   
df.mm.trans2:probe3	0.135323164310805	0.156233721320029	0.866158491057182	0.38669380219282	   
df.mm.trans2:probe4	0.0558612877712982	0.156233721320029	0.357549492512387	0.7207859966247	   
df.mm.trans2:probe5	-0.154973035069311	0.156233721320029	-0.991930767314081	0.321566861313611	   
df.mm.trans2:probe6	-0.0784757700352276	0.156233721320029	-0.502297259338	0.615613091458897	   
df.mm.trans3:probe2	0.0394477842579817	0.156233721320029	0.252492124777447	0.800733267452293	   
df.mm.trans3:probe3	0.0957812343144238	0.156233721320029	0.613063770773439	0.540029127633607	   
df.mm.trans3:probe4	-0.00642579189857902	0.156233721320029	-0.0411293531530009	0.967204255935612