chr2.13575_chr2_179064894_179065311_+_0.R 

fitVsDatCorrelation=0.942452823910463
cont.fitVsDatCorrelation=0.263093540280536

fstatistic=14383.9518930872,59,853
cont.fstatistic=1714.72610482929,59,853

residuals=-0.45083681941887,-0.0885188073862453,-0.00338572920875012,0.0827147524629019,0.761600200767458
cont.residuals=-0.68422235680444,-0.307340343583785,-0.124011775382350,0.308267497835611,1.09277945338939

predictedValues:
Include	Exclude	Both
chr2.13575_chr2_179064894_179065311_+_0.R.tl.Lung	69.9182395575101	115.055698983542	61.8334617972399
chr2.13575_chr2_179064894_179065311_+_0.R.tl.cerebhem	58.7629479863511	97.1273183165957	62.5307019767842
chr2.13575_chr2_179064894_179065311_+_0.R.tl.cortex	56.4012265185559	88.2394043148106	57.645974748528
chr2.13575_chr2_179064894_179065311_+_0.R.tl.heart	59.7954052443389	93.5423360606991	59.4343632836039
chr2.13575_chr2_179064894_179065311_+_0.R.tl.kidney	71.1678070268827	112.648286511387	61.9785203228752
chr2.13575_chr2_179064894_179065311_+_0.R.tl.liver	71.2532609403104	113.355649454973	67.0470988568001
chr2.13575_chr2_179064894_179065311_+_0.R.tl.stomach	62.4543644488246	106.560194718214	66.4330255956391
chr2.13575_chr2_179064894_179065311_+_0.R.tl.testicle	63.6786102989465	101.875424099787	61.4728946560354


diffExp=-45.1374594260322,-38.3643703302446,-31.8381777962547,-33.7469308163602,-41.4804794845045,-42.1023885146623,-44.1058302693898,-38.1968138008408
diffExpScore=0.996835167121017
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	73.1165158257198	84.3149976463588	72.7573283465354
cerebhem	71.499885587394	83.9677558698734	80.3423619356388
cortex	70.5282626122038	82.3540679195386	75.7201790428882
heart	78.1185791473687	89.385229163955	77.887961900033
kidney	71.747718372153	89.1668046986682	83.6268102377663
liver	67.6412001489718	88.5448173367134	81.3736062802933
stomach	77.5727538029588	69.4036902970092	78.1100973844222
testicle	69.922409149105	83.9665942123025	82.8082729084516
cont.diffExp=-11.1984818206391,-12.4678702824794,-11.8258053073349,-11.2666500165863,-17.4190863265152,-20.9036171877416,8.16906350594967,-14.0441850631975
cont.diffExpScore=1.16679739780752

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,-1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,-1,-1,0,-1
cont.diffExp1.2Score=0.75

tran.correlation=0.949906659492073
cont.tran.correlation=-0.400700239090313

tran.covariance=0.00855994977541252
cont.tran.covariance=-0.00171135386118569

tran.mean=83.8647609051081
cont.tran.mean=78.2032051118934

weightedLogRatios:
wLogRatio
Lung	-2.23959674364386
cerebhem	-2.17324372215505
cortex	-1.90494878812335
heart	-1.93075207322724
kidney	-2.06408014894446
liver	-2.08855369720459
stomach	-2.35164567103133
testicle	-2.06230584092857

cont.weightedLogRatios:
wLogRatio
Lung	-0.621795166379426
cerebhem	-0.699216368384536
cortex	-0.671757830443896
heart	-0.596249031192378
kidney	-0.952403423985455
liver	-1.17111214716065
stomach	0.477995674669242
testicle	-0.79416168981144

varWeightedLogRatios=0.0225258249273008
cont.varWeightedLogRatios=0.237010701005266

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32482533613342	0.06580131740759	65.7255129003627	0	***
df.mm.trans1	-0.295279332601055	0.0549260977566968	-5.37593866414902	9.83957454788312e-08	***
df.mm.trans2	0.370168159394384	0.0498072304859254	7.4320165121204	2.60047918894787e-13	***
df.mm.exp2	-0.35442170215571	0.0635984890183823	-5.57280066910503	3.36233185510172e-08	***
df.mm.exp3	-0.410074125027521	0.0635984890183823	-6.44785955384875	1.89717259157068e-10	***
df.mm.exp4	-0.32382784923507	0.0635984890183823	-5.09175381731902	4.36724190325956e-07	***
df.mm.exp5	-0.00577508712341515	0.0635984890183823	-0.0908054139736863	0.927668526493747	   
df.mm.exp6	-0.0769227704505283	0.0635984890183823	-1.20950625773978	0.226803515806098	   
df.mm.exp7	-0.261346383152847	0.0635984890183823	-4.10931748830241	4.35110496077827e-05	***
df.mm.exp8	-0.209295119973652	0.0635984890183823	-3.29088195653765	0.00103973740091257	** 
df.mm.trans1:exp2	0.180606669077355	0.0547212762934521	3.30048349217626	0.00100524195627568	** 
df.mm.trans2:exp2	0.185028029873884	0.0420596013442972	4.39918648679659	1.22406563888297e-05	***
df.mm.trans1:exp3	0.195238477000903	0.0547212762934521	3.56787140624981	0.000379958668358274	***
df.mm.trans2:exp3	0.144711399114902	0.0420596013442972	3.44062697908866	0.000608549526016739	***
df.mm.trans1:exp4	0.167430118783641	0.0547212762934522	3.05968957825048	0.00228498174453249	** 
df.mm.trans2:exp4	0.116825625047037	0.0420596013442972	2.7776208359825	0.00559601298495907	** 
df.mm.trans1:exp5	0.0234891017154723	0.0547212762934521	0.429249887913944	0.667849774226984	   
df.mm.trans2:exp5	-0.0153708069392417	0.0420596013442972	-0.365452986903448	0.714863895486864	   
df.mm.trans1:exp6	0.0958368030582088	0.0547212762934522	1.75136271574273	0.0802428466509938	.  
df.mm.trans2:exp6	0.0620366368683304	0.0420596013442972	1.47496968315278	0.140589681883728	   
df.mm.trans1:exp7	0.148455951296233	0.0547212762934521	2.71294752885720	0.00680314354260658	** 
df.mm.trans2:exp7	0.184640067157520	0.0420596013442971	4.38996236902173	1.27595303681638e-05	***
df.mm.trans1:exp8	0.115817284999636	0.0547212762934522	2.11649458573564	0.0345918142264454	*  
df.mm.trans2:exp8	0.0876295043724051	0.0420596013442972	2.08346017488553	0.0375065196022019	*  
df.mm.trans1:probe2	0.109249906500750	0.041313657610227	2.64440170201017	0.00833392614011395	** 
df.mm.trans1:probe3	0.0175182390776378	0.041313657610227	0.424030214001224	0.67165068883975	   
df.mm.trans1:probe4	0.61064327103876	0.041313657610227	14.7806634987360	3.49382580642793e-44	***
df.mm.trans1:probe5	-0.0121591130184225	0.041313657610227	-0.294312189279812	0.768590963590605	   
df.mm.trans1:probe6	0.0837627243993743	0.041313657610227	2.02748265935765	0.0429235451172797	*  
df.mm.trans1:probe7	0.0487776128059684	0.041313657610227	1.18066556261273	0.238064718552307	   
df.mm.trans1:probe8	0.993427059707204	0.041313657610227	24.0459721354056	5.9543940741626e-98	***
df.mm.trans1:probe9	0.829371207783074	0.041313657610227	20.0749886540611	1.02808337224549e-73	***
df.mm.trans1:probe10	1.03909459346081	0.041313657610227	25.151358014924	7.17456197024255e-105	***
df.mm.trans1:probe11	1.05646578740953	0.041313657610227	25.5718289911957	1.62721922311238e-107	***
df.mm.trans1:probe12	0.91875425124333	0.041313657610227	22.2385115331908	8.94970238398896e-87	***
df.mm.trans1:probe13	1.05629149547806	0.041313657610227	25.5676102426858	1.72984451939109e-107	***
df.mm.trans2:probe2	0.267287513915154	0.041313657610227	6.46971314999203	1.65320775505857e-10	***
df.mm.trans2:probe3	0.0705580890567704	0.041313657610227	1.70786352838690	0.0880255208283081	.  
df.mm.trans2:probe4	0.571484748690462	0.041313657610227	13.8328286999453	2.06553252891917e-39	***
df.mm.trans2:probe5	0.0699094146536765	0.041313657610227	1.69216231865103	0.0909802351443404	.  
df.mm.trans2:probe6	0.28133159789647	0.041313657610227	6.80965119454414	1.84523594590779e-11	***
df.mm.trans3:probe2	-0.432592856573391	0.041313657610227	-10.4709406427937	3.21004069347329e-24	***
df.mm.trans3:probe3	-0.274554411273192	0.041313657610227	-6.64560891372706	5.3805483142915e-11	***
df.mm.trans3:probe4	-0.35580504788537	0.041313657610227	-8.61228631079356	3.42876431294405e-17	***
df.mm.trans3:probe5	-0.355279893759685	0.041313657610227	-8.5995749180953	3.79686477510349e-17	***
df.mm.trans3:probe6	0.0482644112213299	0.041313657610227	1.16824348201458	0.243034972988967	   
df.mm.trans3:probe7	-0.341384410661134	0.041313657610227	-8.26323376840462	5.39229039061522e-16	***
df.mm.trans3:probe8	-0.403556491097339	0.041313657610227	-9.76811336591609	1.95134514659774e-21	***
df.mm.trans3:probe9	-0.459281807548389	0.041313657610227	-11.1169485859005	6.6054528786514e-27	***
df.mm.trans3:probe10	-0.184900386243392	0.041313657610227	-4.47552690657001	8.65603688477576e-06	***
df.mm.trans3:probe11	-0.382092991931864	0.041313657610227	-9.24858785287699	1.78207376457661e-19	***
df.mm.trans3:probe12	-0.446244882479461	0.041313657610227	-10.8013888939476	1.40338120722899e-25	***
df.mm.trans3:probe13	-0.256212990785869	0.041313657610227	-6.20165353557184	8.699188617122e-10	***
df.mm.trans3:probe14	-0.246926529136711	0.041313657610227	-5.97687407554991	3.33998484216308e-09	***
df.mm.trans3:probe15	-0.0856340524335217	0.041313657610227	-2.07277828657619	0.038492729318676	*  
df.mm.trans3:probe16	-0.338868622510665	0.041313657610227	-8.20233893855914	8.63694978936688e-16	***
df.mm.trans3:probe17	-0.153402600493411	0.041313657610227	-3.71312077813795	0.000218021157609423	***
df.mm.trans3:probe18	-0.191680573562815	0.041313657610227	-4.6396418194492	4.0380263895269e-06	***
df.mm.trans3:probe19	-0.369966210201667	0.041313657610227	-8.9550582447119	2.09375603991896e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.60322213266102	0.189876526771227	24.2432395985799	3.49936066952345e-99	***
df.mm.trans1	-0.327992925718180	0.158494952411629	-2.0694219009975	0.0388071323722789	*  
df.mm.trans2	-0.151476232698393	0.143723929935643	-1.05393884488284	0.292209355537115	   
df.mm.exp2	-0.125652710634508	0.183520036960772	-0.684681153706213	0.493731133233721	   
df.mm.exp3	-0.0994877188111355	0.183520036960772	-0.54210821040975	0.587885546893976	   
df.mm.exp4	0.0564275573451081	0.183520036960772	0.307473550461248	0.75855800607889	   
df.mm.exp5	-0.102183684038362	0.183520036960772	-0.556798514922942	0.577811149588759	   
df.mm.exp6	-0.140809270653401	0.183520036960772	-0.767269192973732	0.443133796958803	   
df.mm.exp7	-0.206447440093071	0.183520036960772	-1.12493133453978	0.260934569144918	   
df.mm.exp8	-0.178207151278089	0.183520036960772	-0.971050105641495	0.331798592653314	   
df.mm.trans1:exp2	0.103294284232178	0.157903918833863	0.654159092408961	0.513185635107999	   
df.mm.trans2:exp2	0.121525819885940	0.121367342406983	1.00130576706891	0.316962962702633	   
df.mm.trans1:exp3	0.0634469604868492	0.157903918833863	0.401807383600176	0.687926438787441	   
df.mm.trans2:exp3	0.0759558149374936	0.121367342406983	0.625834045890118	0.531591210350754	   
df.mm.trans1:exp4	0.00974608449554413	0.157903918833863	0.061721612531975	0.950798987144074	   
df.mm.trans2:exp4	0.00196813209673248	0.121367342406983	0.0162163235817812	0.987065604913823	   
df.mm.trans1:exp5	0.0832854625565205	0.157903918833863	0.527443923948135	0.598022503868652	   
df.mm.trans2:exp5	0.15813275252027	0.121367342406983	1.30292671310212	0.192951395922464	   
df.mm.trans1:exp6	0.0629722617857581	0.157903918833863	0.39880113331458	0.690139552961916	   
df.mm.trans2:exp6	0.189758347861759	0.121367342406983	1.56350418570952	0.118304949585688	   
df.mm.trans1:exp7	0.265609418917103	0.157903918833863	1.68209516824317	0.0929163880043092	.  
df.mm.trans2:exp7	0.0118277232933242	0.121367342406983	0.0974539201300307	0.922388819723829	   
df.mm.trans1:exp8	0.133539061900913	0.157903918833863	0.845698212477013	0.397958282870181	   
df.mm.trans2:exp8	0.174066425866508	0.121367342406983	1.43421139834148	0.151878428412112	   
df.mm.trans1:probe2	-0.0631280714100314	0.119214844387610	-0.529531970068926	0.596574257236964	   
df.mm.trans1:probe3	-0.059166018321171	0.119214844387610	-0.496297408473741	0.61981245456744	   
df.mm.trans1:probe4	-0.0165982422116643	0.119214844387610	-0.13922965966971	0.889301536225976	   
df.mm.trans1:probe5	0.00889775352482258	0.119214844387610	0.074636288547195	0.940521599821095	   
df.mm.trans1:probe6	0.227231206151632	0.119214844387610	1.90606469621201	0.056977675888219	.  
df.mm.trans1:probe7	0.0321811990195807	0.119214844387610	0.26994288492252	0.787269485874899	   
df.mm.trans1:probe8	0.187167661302643	0.119214844387610	1.57000298296825	0.116785265198090	   
df.mm.trans1:probe9	0.0994100424068435	0.119214844387610	0.833873020742503	0.404585987511067	   
df.mm.trans1:probe10	-0.0753343914363813	0.119214844387610	-0.63192123282435	0.527607669007776	   
df.mm.trans1:probe11	0.0931045904162909	0.119214844387610	0.780981520334623	0.435030048773675	   
df.mm.trans1:probe12	0.118950783210267	0.119214844387610	0.997784997508493	0.318666584922063	   
df.mm.trans1:probe13	-0.0311393742150341	0.119214844387610	-0.261203832249186	0.793998373953252	   
df.mm.trans2:probe2	-0.0901198233709454	0.119214844387610	-0.755944646271851	0.449891088150542	   
df.mm.trans2:probe3	-0.109305987375879	0.119214844387610	-0.916882355862384	0.359463441115863	   
df.mm.trans2:probe4	-0.0375137398576298	0.119214844387610	-0.314673395333716	0.753086599292866	   
df.mm.trans2:probe5	0.0593012523864789	0.119214844387610	0.497431781177095	0.619012794617787	   
df.mm.trans2:probe6	-0.252015270665373	0.119214844387610	-2.11395881074996	0.0348084700259583	*  
df.mm.trans3:probe2	0.208634953317668	0.119214844387610	1.75007528961176	0.0804648651711866	.  
df.mm.trans3:probe3	0.0719924991185323	0.119214844387610	0.603888714432736	0.546078194328425	   
df.mm.trans3:probe4	0.228762525443782	0.119214844387610	1.91890973493195	0.055329188608491	.  
df.mm.trans3:probe5	0.160664537003117	0.119214844387610	1.34768902168541	0.178116247051884	   
df.mm.trans3:probe6	0.0439781531996263	0.119214844387610	0.368898298072996	0.712295075377176	   
df.mm.trans3:probe7	0.0830094705598345	0.119214844387610	0.696301463011949	0.48642974077824	   
df.mm.trans3:probe8	0.245503116330414	0.119214844387610	2.05933344619564	0.0397653377105463	*  
df.mm.trans3:probe9	0.0846208188652161	0.119214844387610	0.709817802471677	0.478011115807025	   
df.mm.trans3:probe10	0.194297238761741	0.119214844387610	1.62980742674974	0.103511392557907	   
df.mm.trans3:probe11	0.312575365663397	0.119214844387610	2.62195003708685	0.00889863900637173	** 
df.mm.trans3:probe12	0.291386330084327	0.119214844387610	2.44421180584631	0.0147184448299273	*  
df.mm.trans3:probe13	0.0801833453716417	0.119214844387610	0.672595311293091	0.501386939411136	   
df.mm.trans3:probe14	0.0936786332583925	0.119214844387610	0.785796716336852	0.432204808488221	   
df.mm.trans3:probe15	0.120805198087551	0.119214844387610	1.01334023215071	0.311185020654914	   
df.mm.trans3:probe16	0.186923084509199	0.119214844387610	1.56795141971956	0.117263336649473	   
df.mm.trans3:probe17	0.248445653475309	0.119214844387610	2.08401608668401	0.0374557899287729	*  
df.mm.trans3:probe18	0.232497797148123	0.119214844387610	1.95024200503244	0.0514745079691935	.  
df.mm.trans3:probe19	0.216197820174677	0.119214844387610	1.81351425894364	0.07010384830064	.