chr5.17654_chr5_108359070_108359640_+_2.R 

fitVsDatCorrelation=0.895874737735916
cont.fitVsDatCorrelation=0.205814195330677

fstatistic=9943.16604844802,52,692
cont.fstatistic=2039.12435961117,52,692

residuals=-0.455193292747232,-0.0892896127911528,-0.00269056434812288,0.0810132450787797,1.2595662200508
cont.residuals=-0.577119437118167,-0.251679781210531,-0.0993877434601916,0.211927574127036,1.31776498836619

predictedValues:
Include	Exclude	Both
chr5.17654_chr5_108359070_108359640_+_2.R.tl.Lung	65.1914226618115	47.9702166158704	57.0328067383953
chr5.17654_chr5_108359070_108359640_+_2.R.tl.cerebhem	68.5196610383632	55.3002273501912	75.9427769718123
chr5.17654_chr5_108359070_108359640_+_2.R.tl.cortex	64.8714340595702	52.3970582331074	58.2337622124571
chr5.17654_chr5_108359070_108359640_+_2.R.tl.heart	64.5784367619168	47.637049038579	61.9191844150864
chr5.17654_chr5_108359070_108359640_+_2.R.tl.kidney	65.0438326490257	45.2375318608807	54.8097611542252
chr5.17654_chr5_108359070_108359640_+_2.R.tl.liver	66.2796201628414	49.197996756029	51.9284971492241
chr5.17654_chr5_108359070_108359640_+_2.R.tl.stomach	68.1430237985532	58.2579117545194	58.28089085928
chr5.17654_chr5_108359070_108359640_+_2.R.tl.testicle	66.655109671604	48.0580369100312	57.9373803803763


diffExp=17.221206045941,13.2194336881720,12.4743758264628,16.9413877233378,19.806300788145,17.0816234068124,9.88511204403387,18.5970727615728
diffExpScore=0.992077734052048
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,0,1,1,1,0,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,0,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	60.8740973758814	67.4676320965985	62.7638947130504
cerebhem	67.4257673137717	65.3543013801656	62.9431916195287
cortex	64.9514845339972	65.0054665924693	61.962237306383
heart	65.6987594581703	70.5737853268139	66.4200070116767
kidney	64.1986491493452	73.3313307719797	53.0344860670163
liver	65.6410137656989	64.163378565783	58.9302766482982
stomach	65.5133807519395	63.6040392388632	57.7459906704092
testicle	66.0861789850455	64.1263753954754	54.5224673032584
cont.diffExp=-6.59353472071714,2.07146593360616,-0.0539820584720303,-4.87502586864358,-9.13268162263454,1.47763519991592,1.90934151307623,1.95980358957011
cont.diffExpScore=1.97187004426100

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=0.761784753392338
cont.tran.correlation=-0.315674167453198

tran.covariance=0.00147106650341924
cont.tran.covariance=-0.000493344106921595

tran.mean=58.3336605826809
cont.tran.mean=65.8759775438749

weightedLogRatios:
wLogRatio
Lung	1.23433816720431
cerebhem	0.883085113328456
cortex	0.868243206426654
heart	1.22186997073474
kidney	1.45017472770197
liver	1.20549011759618
stomach	0.649366046869772
testicle	1.32025606884473

cont.weightedLogRatios:
wLogRatio
Lung	-0.427838683831958
cerebhem	0.130914010405784
cortex	-0.00346767364390522
heart	-0.302124427041823
kidney	-0.562412955531947
liver	0.0950070646207654
stomach	0.123262923536460
testicle	0.125711026399015

varWeightedLogRatios=0.0741231200954556
cont.varWeightedLogRatios=0.0805089568477173

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.41558267568403	0.0844146823427682	40.4619502305886	1.52459720729419e-184	***
df.mm.trans1	0.454764404337922	0.0758167294642674	5.99820656405726	3.21485085348899e-09	***
df.mm.trans2	0.395075744161282	0.0697237795984882	5.66629844848296	2.14154980114657e-08	***
df.mm.exp2	-0.0943639539188473	0.0955043109060475	-0.988059628132158	0.323468791416426	   
df.mm.exp3	0.0625109514372323	0.0955043109060476	0.654535390540931	0.512984413946648	   
df.mm.exp4	-0.0986202772484862	0.0955043109060476	-1.03262644704598	0.302139496489685	   
df.mm.exp5	-0.0211613951700963	0.0955043109060476	-0.221575287747103	0.824709890248874	   
df.mm.exp6	0.135586072481627	0.0955043109060476	1.41968536493615	0.156149616863267	   
df.mm.exp7	0.216932849697022	0.0955043109060476	2.27144563045358	0.0234262728948627	*  
df.mm.exp8	0.00829673396979232	0.0955043109060476	0.0868728740208831	0.93079769214115	   
df.mm.trans1:exp2	0.144156774449928	0.0914384161690392	1.57654496315236	0.115357281365377	   
df.mm.trans2:exp2	0.236560642497023	0.0795869257550397	2.97235557540106	0.00305782450312997	** 
df.mm.trans1:exp3	-0.0674314838002441	0.0914384161690392	-0.737452447509433	0.461097223719159	   
df.mm.trans2:exp3	0.0257591665675750	0.0795869257550397	0.323660781255191	0.74629265660034	   
df.mm.trans1:exp4	0.0891729300476246	0.0914384161690392	0.975223913357965	0.329789983077422	   
df.mm.trans2:exp4	0.0916507457253996	0.0795869257550397	1.1515804242457	0.249891241173340	   
df.mm.trans1:exp5	0.0188948802296468	0.0914384161690393	0.206640502113647	0.836351411646687	   
df.mm.trans2:exp5	-0.0374918427945654	0.0795869257550397	-0.471080424817029	0.637731769554054	   
df.mm.trans1:exp6	-0.119031516747572	0.0914384161690392	-1.30176704425329	0.193429203595401	   
df.mm.trans2:exp6	-0.110313497309865	0.0795869257550397	-1.38607561811595	0.166170276776205	   
df.mm.trans1:exp7	-0.172651968197800	0.0914384161690392	-1.88817758915053	0.0594201845186529	.  
df.mm.trans2:exp7	-0.0226332735192462	0.0795869257550397	-0.284384316953128	0.776200952829016	   
df.mm.trans1:exp8	0.0139070679446029	0.0914384161690392	0.152092178837540	0.87915856404081	   
df.mm.trans2:exp8	-0.00646768230178322	0.0795869257550397	-0.0812656380482654	0.935254198209654	   
df.mm.trans1:probe2	0.6272190017293	0.0457192080845196	13.7189384507663	4.61748042440558e-38	***
df.mm.trans1:probe3	0.754576520782929	0.0457192080845196	16.5045842305048	7.55428177185321e-52	***
df.mm.trans1:probe4	0.100721111784539	0.0457192080845196	2.20303710419348	0.0279209670290198	*  
df.mm.trans1:probe5	-0.0862873440441763	0.0457192080845196	-1.88733242895764	0.0595338850558397	.  
df.mm.trans1:probe6	0.944775957355814	0.0457192080845196	20.6647489521086	2.93180351939848e-74	***
df.mm.trans1:probe7	0.00286571461706309	0.0457192080845196	0.062680757981751	0.95003882019162	   
df.mm.trans1:probe8	0.861128000351041	0.0457192080845196	18.8351468984131	3.34720774716435e-64	***
df.mm.trans1:probe9	-0.0597024606724147	0.0457192080845196	-1.30585071731874	0.192037259473221	   
df.mm.trans1:probe10	0.100138556715416	0.0457192080845196	2.19029508407699	0.0288358178787968	*  
df.mm.trans1:probe11	0.482191208916957	0.0457192080845196	10.5467970491865	3.22400228935277e-24	***
df.mm.trans1:probe12	0.506319817078777	0.0457192080845196	11.0745535255720	2.34927630197481e-26	***
df.mm.trans1:probe13	0.427014646357179	0.0457192080845196	9.33993969378846	1.29394610167678e-19	***
df.mm.trans1:probe14	0.530272284294563	0.0457192080845196	11.5984573336062	1.51033689896747e-28	***
df.mm.trans1:probe15	0.937410431046603	0.0457192080845196	20.5036454112163	2.30401762695326e-73	***
df.mm.trans1:probe16	0.633639969487105	0.0457192080845196	13.8593819979496	1.00279874900975e-38	***
df.mm.trans1:probe17	0.252195071100943	0.0457192080845196	5.51617321618341	4.89800913258567e-08	***
df.mm.trans1:probe18	0.169084778780215	0.0457192080845196	3.69833131115555	0.000234221428971512	***
df.mm.trans1:probe19	0.215871201316699	0.0457192080845196	4.72167411381284	2.83249838599557e-06	***
df.mm.trans1:probe20	0.0591154134397237	0.0457192080845196	1.29301044170404	0.19643895043645	   
df.mm.trans1:probe21	0.109262383042794	0.0457192080845196	2.38985729675817	0.0171215455394383	*  
df.mm.trans1:probe22	0.106708386267347	0.0457192080845196	2.33399463241093	0.0198813333462489	*  
df.mm.trans2:probe2	0.0526884892370282	0.0457192080845196	1.15243661131717	0.249539656012235	   
df.mm.trans2:probe3	0.0786423001986482	0.0457192080845196	1.72011510027174	0.0858586069956113	.  
df.mm.trans2:probe4	0.214571851010588	0.0457192080845196	4.69325388606724	3.24201355707933e-06	***
df.mm.trans2:probe5	0.087726708069314	0.0457192080845196	1.91881512704981	0.0554187958191368	.  
df.mm.trans2:probe6	0.105667853286847	0.0457192080845196	2.31123542410231	0.0211128139111136	*  
df.mm.trans3:probe2	-0.378795780710979	0.0457192080845196	-8.28526557176388	6.10278354449849e-16	***
df.mm.trans3:probe3	-0.286592714601113	0.0457192080845196	-6.2685406551946	6.41153016418211e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30202950498351	0.185924393200676	23.1385964526998	3.54198834449172e-88	***
df.mm.trans1	-0.120355202190291	0.166987294495355	-0.720744668353427	0.471310026544765	   
df.mm.trans2	-0.0872186712359334	0.153567496242757	-0.567950076480114	0.570253094661417	   
df.mm.exp2	0.067542179532711	0.210349438752307	0.321095125964392	0.748235254289608	   
df.mm.exp3	0.0405111144473201	0.210349438752307	0.192589600845207	0.847336895024058	   
df.mm.exp4	0.0646647558000608	0.210349438752307	0.307415870389869	0.758619343489256	   
df.mm.exp5	0.304952007405663	0.210349438752307	1.44974005737545	0.147584066548228	   
df.mm.exp6	0.0882026087071732	0.210349438752307	0.419314685270136	0.675116298044818	   
df.mm.exp7	0.0978017360930528	0.210349438752307	0.464948880649106	0.642114329457688	   
df.mm.exp8	0.172126794614057	0.210349438752307	0.81828977360259	0.413473424760314	   
df.mm.trans1:exp2	0.0346773158925872	0.20139425476279	0.17218622216126	0.863341472678086	   
df.mm.trans2:exp2	-0.099366879343044	0.175291198960256	-0.566867474992705	0.570988041417896	   
df.mm.trans1:exp3	0.0243217311827250	0.20139425476279	0.120766757777535	0.903910836867599	   
df.mm.trans2:exp3	-0.0776877050404148	0.175291198960256	-0.443192273777699	0.657765184516246	   
df.mm.trans1:exp4	0.0116075338051201	0.20139425476279	0.0576358735694415	0.954055312406378	   
df.mm.trans2:exp4	-0.0196539512808461	0.175291198960256	-0.112121723152241	0.91075940817987	   
df.mm.trans1:exp5	-0.251777592010480	0.201394254762790	-1.25017266409627	0.211659077537797	   
df.mm.trans2:exp5	-0.221612016124102	0.175291198960256	-1.26425067224481	0.20656572680296	   
df.mm.trans1:exp6	-0.0128096520683118	0.20139425476279	-0.0636048534919701	0.949303234978538	   
df.mm.trans2:exp6	-0.138417946466082	0.175291198960256	-0.789645728291619	0.430005176738412	   
df.mm.trans1:exp7	-0.0243550818182005	0.20139425476279	-0.12093235652073	0.903779718269352	   
df.mm.trans2:exp7	-0.156772716093643	0.175291198960256	-0.89435588907797	0.371442456351868	   
df.mm.trans1:exp8	-0.0899749159218635	0.20139425476279	-0.44676009267414	0.65518806935308	   
df.mm.trans2:exp8	-0.222919001065767	0.175291198960256	-1.27170675075541	0.203904557881182	   
df.mm.trans1:probe2	-0.0680756379512045	0.100697127381395	-0.676043495196888	0.499238900170565	   
df.mm.trans1:probe3	-0.109567100255626	0.100697127381395	-1.08808565949092	0.276936106040184	   
df.mm.trans1:probe4	-0.0708522003555274	0.100697127381395	-0.70361689750266	0.481908058943338	   
df.mm.trans1:probe5	-0.134287051746156	0.100697127381395	-1.33357380928591	0.182782352759717	   
df.mm.trans1:probe6	-0.106925472478705	0.100697127381395	-1.06185226191925	0.288673193956326	   
df.mm.trans1:probe7	-0.191250794242703	0.100697127381395	-1.89926762774802	0.0579448851526264	.  
df.mm.trans1:probe8	-0.117024490574147	0.100697127381395	-1.16214328667899	0.245577917441124	   
df.mm.trans1:probe9	-0.105119532803320	0.100697127381395	-1.04391789057869	0.296887913334383	   
df.mm.trans1:probe10	-0.00516989619935894	0.100697127381395	-0.0513410494797704	0.95906857445981	   
df.mm.trans1:probe11	-0.0831042379057314	0.100697127381395	-0.82528906302332	0.409491905918136	   
df.mm.trans1:probe12	-0.124391199349643	0.100697127381395	-1.23530037633055	0.217137957804335	   
df.mm.trans1:probe13	-0.120007653044771	0.100697127381395	-1.19176838670121	0.233760630627167	   
df.mm.trans1:probe14	-0.0884037055400812	0.100697127381395	-0.877916856607519	0.380293542938544	   
df.mm.trans1:probe15	-0.0465339224838107	0.100697127381395	-0.462117675984552	0.644142185934422	   
df.mm.trans1:probe16	-0.0683195649014637	0.100697127381395	-0.678465877608407	0.497703169182343	   
df.mm.trans1:probe17	-0.168965928585237	0.100697127381395	-1.67796175500886	0.0938060935190115	.  
df.mm.trans1:probe18	-0.108655622396712	0.100697127381395	-1.07903398261972	0.280948535479576	   
df.mm.trans1:probe19	-0.0826206841357457	0.100697127381395	-0.820487001807072	0.412221073024465	   
df.mm.trans1:probe20	0.0309658651655397	0.100697127381395	0.307514881216571	0.758544023615421	   
df.mm.trans1:probe21	-0.060520988366738	0.100697127381395	-0.601020008619629	0.548023469376031	   
df.mm.trans1:probe22	0.00716609311820059	0.100697127381395	0.071164821723848	0.943287140095274	   
df.mm.trans2:probe2	-0.0266747015662967	0.100697127381395	-0.264900322978083	0.791165133640111	   
df.mm.trans2:probe3	-0.0309457494645631	0.100697127381395	-0.307315116819119	0.758695991466251	   
df.mm.trans2:probe4	-0.0058986248267795	0.100697127381395	-0.0585778857865347	0.953305236100345	   
df.mm.trans2:probe5	0.0728854870931827	0.100697127381395	0.723809000202414	0.469427610083175	   
df.mm.trans2:probe6	-0.0378322895886653	0.100697127381395	-0.375703762088205	0.707252280239739	   
df.mm.trans3:probe2	0.130369360793791	0.100697127381395	1.29466812196153	0.195866563878088	   
df.mm.trans3:probe3	0.00440369410435771	0.100697127381395	0.0437320727897085	0.965130588931463