chr10.2132_chr10_80042665_80043623_-_2.R 

fitVsDatCorrelation=0.966542294401929
cont.fitVsDatCorrelation=0.269165230277580

fstatistic=9630.30529324914,53,715
cont.fstatistic=670.594466805808,53,715

residuals=-0.827607716243521,-0.0848660989902859,-0.00676021627137295,0.086104738797152,0.604079832729456
cont.residuals=-0.843944441898436,-0.379272772787337,-0.213256226165923,0.0750729809650308,1.95080028419644

predictedValues:
Include	Exclude	Both
chr10.2132_chr10_80042665_80043623_-_2.R.tl.Lung	78.1248899052735	50.5137412702192	59.0049962309485
chr10.2132_chr10_80042665_80043623_-_2.R.tl.cerebhem	73.137211874734	52.7336423169003	61.3992386782806
chr10.2132_chr10_80042665_80043623_-_2.R.tl.cortex	70.2740583285905	47.6641969330216	54.9400826508607
chr10.2132_chr10_80042665_80043623_-_2.R.tl.heart	71.541539427832	50.617465812013	57.2965611381407
chr10.2132_chr10_80042665_80043623_-_2.R.tl.kidney	78.3155410043351	49.5583889590361	69.6608040080652
chr10.2132_chr10_80042665_80043623_-_2.R.tl.liver	81.0747813148277	55.0059268877103	61.2002413375453
chr10.2132_chr10_80042665_80043623_-_2.R.tl.stomach	75.5985022262712	53.6453923609151	52.705999924431
chr10.2132_chr10_80042665_80043623_-_2.R.tl.testicle	77.9654790393185	51.6279087395325	57.3999938080277


diffExp=27.6111486350543,20.4035695578337,22.6098613955689,20.9240736158190,28.7571520452990,26.0688544271173,21.9531098653560,26.337570299786
diffExpScore=0.994889232805318
diffExp1.5=1,0,0,0,1,0,0,1
diffExp1.5Score=0.75
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
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	70.5363793324034	86.4337205375316	71.1178482025202
cerebhem	72.9292148375248	96.0827070032552	70.6715180364803
cortex	68.2874934347297	73.0681248958114	53.7938018983598
heart	66.956233492874	70.7694511167949	50.6443463739232
kidney	76.1774671507494	76.5363272629866	91.9988197964876
liver	72.8371984188564	59.0789269804057	97.1153513531913
stomach	71.1748081456812	64.9326547197138	63.149091965914
testicle	66.9592036192552	71.6299974437632	91.4049151336958
cont.diffExp=-15.8973412051282,-23.1534921657304,-4.78063146108171,-3.81321762392091,-0.358860112237195,13.7582714384507,6.24215342596739,-4.670793824508
cont.diffExpScore=2.15819184522679

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,-1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,-1,0,0,0,1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.527755959217897
cont.tran.correlation=0.179664486477030

tran.covariance=0.00121876663992561
cont.tran.covariance=0.00104578748859966

tran.mean=63.5874166500332
cont.tran.mean=72.774369274521

weightedLogRatios:
wLogRatio
Lung	1.8054228540635
cerebhem	1.35046155316873
cortex	1.57551885817705
heart	1.41758591579321
kidney	1.89075710682772
liver	1.62985586294326
stomach	1.42496304693649
testicle	1.71071331366985

cont.weightedLogRatios:
wLogRatio
Lung	-0.885710243423723
cerebhem	-1.22070893865569
cortex	-0.288090254409627
heart	-0.234388309127828
kidney	-0.0203755083118799
liver	0.875836751151062
stomach	0.387277248220721
testicle	-0.28575651582048

varWeightedLogRatios=0.0380950239270544
cont.varWeightedLogRatios=0.437468522683672

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01590288275841	0.0852394118510672	47.1132167098373	1.98531634679729e-221	***
df.mm.trans1	0.00957740116731029	0.075694247386462	0.126527464080754	0.899349982133473	   
df.mm.trans2	-0.125061115530711	0.068853761311505	-1.81632946622793	0.0697384787277131	.  
df.mm.exp2	-0.0627384983568902	0.0928251021163178	-0.675878581617648	0.49933628117462	   
df.mm.exp3	-0.0925919207689248	0.0928251021163179	-0.997487949465427	0.318865171707869	   
df.mm.exp4	-0.05659764424389	0.0928251021163179	-0.60972347946322	0.542238709181839	   
df.mm.exp5	-0.182672174117328	0.0928251021163178	-1.96791783636741	0.0494635449702566	*  
df.mm.exp6	0.0857297826866203	0.0928251021163178	0.923562492602416	0.356025712067069	   
df.mm.exp7	0.140170773359312	0.0928251021163178	1.51005245524713	0.131471818460720	   
df.mm.exp8	0.0473523698995838	0.0928251021163178	0.510124619526377	0.610121591447065	   
df.mm.trans1:exp2	-0.00323290888074891	0.0881417893723894	-0.0366785029413259	0.970751586947965	   
df.mm.trans2:exp2	0.105746720736433	0.074222028868781	1.42473497892903	0.154670148951128	   
df.mm.trans1:exp3	-0.0133140611779430	0.0881417893723894	-0.151052767055733	0.879976697101662	   
df.mm.trans2:exp3	0.0345270447960395	0.0742220288687811	0.465185947113904	0.641939944325012	   
df.mm.trans1:exp4	-0.0314328025789194	0.0881417893723894	-0.356616342857748	0.721484253446978	   
df.mm.trans2:exp4	0.0586489315004612	0.074222028868781	0.790182273299859	0.429683314369001	   
df.mm.trans1:exp5	0.185109538836119	0.0881417893723894	2.10013366139019	0.0360668446192967	*  
df.mm.trans2:exp5	0.163578319841676	0.0742220288687811	2.20390526013335	0.0278489734600082	*  
df.mm.trans1:exp6	-0.0486665266593742	0.0881417893723894	-0.552139081880485	0.581025606655267	   
df.mm.trans2:exp6	-0.000534245293691455	0.0742220288687811	-0.00719793438462805	0.9942589366601	   
df.mm.trans1:exp7	-0.173043001034966	0.0881417893723894	-1.963234491461	0.0500061547967512	.  
df.mm.trans2:exp7	-0.080020594899942	0.0742220288687811	-1.07812459615477	0.281341744091856	   
df.mm.trans1:exp8	-0.0493949164379078	0.0881417893723894	-0.560402923399022	0.575380184376635	   
df.mm.trans2:exp8	-0.0255353802111358	0.074222028868781	-0.344040449989320	0.730917019560361	   
df.mm.trans1:probe2	0.21666853831771	0.0482772462981268	4.48800532200439	8.37695487570796e-06	***
df.mm.trans1:probe3	0.0884638748598808	0.0482772462981268	1.83241343786655	0.0673055861897155	.  
df.mm.trans1:probe4	0.105007448448969	0.0482772462981268	2.17509192219696	0.0299502269181424	*  
df.mm.trans1:probe5	-0.143491004330399	0.0482772462981268	-2.97222843747753	0.00305574567477023	** 
df.mm.trans1:probe6	-0.0287483256364493	0.0482772462981268	-0.595483956539683	0.551708450801926	   
df.mm.trans1:probe7	-0.118511223473308	0.0482772462981268	-2.45480495597169	0.0143332873582759	*  
df.mm.trans1:probe8	0.0698821171578576	0.0482772462981268	1.44751663602174	0.148190584524646	   
df.mm.trans1:probe9	-0.136145466475138	0.0482772462981268	-2.82007523035589	0.00493420496770719	** 
df.mm.trans1:probe10	0.193811042146478	0.0482772462981268	4.01454219135938	6.58511633659561e-05	***
df.mm.trans1:probe11	-0.0584534030992146	0.0482772462981268	-1.21078577552346	0.226377585438603	   
df.mm.trans1:probe12	-0.0628188441289664	0.0482772462981268	-1.301210175515	0.193605663820765	   
df.mm.trans1:probe13	-0.158097628066846	0.0482772462981268	-3.27478553955925	0.00110837880438750	** 
df.mm.trans1:probe14	-0.104274322468790	0.0482772462981268	-2.1599061766047	0.0311116365940313	*  
df.mm.trans1:probe15	0.00606576873495767	0.0482772462981268	0.125644464008980	0.900048684171991	   
df.mm.trans1:probe16	-0.120659350017861	0.0482772462981268	-2.49930058712861	0.0126670141767338	*  
df.mm.trans1:probe17	1.22809482932451	0.0482772462981268	25.4383777761608	3.7069709270818e-102	***
df.mm.trans1:probe18	1.50908939705576	0.0482772462981268	31.2588126451262	6.9873836063115e-136	***
df.mm.trans1:probe19	1.80130375368513	0.0482772462981268	37.311650763209	5.71387303470107e-170	***
df.mm.trans1:probe20	0.985019955956451	0.0482772462981268	20.4033997687783	2.82626930184493e-73	***
df.mm.trans1:probe21	1.56468414511527	0.0482772462981268	32.4103851212404	1.78669838276317e-142	***
df.mm.trans1:probe22	1.81664748474534	0.0482772462981268	37.6294760792068	1.02461166694578e-171	***
df.mm.trans2:probe2	0.210266468089480	0.0482772462981268	4.35539481251728	1.52251592702909e-05	***
df.mm.trans2:probe3	-0.0106018180035299	0.0482772462981268	-0.219602790475256	0.826243196068944	   
df.mm.trans2:probe4	0.0701657875356013	0.0482772462981268	1.45339249679458	0.146553561398097	   
df.mm.trans2:probe5	0.0348523008923592	0.0482772462981268	0.721919818647807	0.470579797444245	   
df.mm.trans2:probe6	0.0093536254932584	0.0482772462981268	0.193748115530387	0.846428143181184	   
df.mm.trans3:probe2	0.466813059099919	0.0482772462981268	9.66942182694525	7.20773116173033e-21	***
df.mm.trans3:probe3	0.133960703732060	0.0482772462981268	2.77482072827458	0.00566742563102058	** 
df.mm.trans3:probe4	0.108038052391573	0.0482772462981268	2.23786691818346	0.025536908668149	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41737375796837	0.320043789797604	13.8024042296272	1.36955116116847e-38	***
df.mm.trans1	-0.190445369977917	0.284205079239263	-0.670098403897939	0.503011420274724	   
df.mm.trans2	0.0750637592431454	0.258521477722723	0.290357922693196	0.771626622747408	   
df.mm.exp2	0.145487883287897	0.348525369010791	0.41743843124198	0.676483064245106	   
df.mm.exp3	0.078792462705734	0.348525369010791	0.226073823347116	0.821208612800455	   
df.mm.exp4	0.0874708532385441	0.348525369010791	0.250974135647026	0.801906212513819	   
df.mm.exp5	-0.302112686501942	0.348525369010791	-0.866831264993476	0.38632526786133	   
df.mm.exp6	-0.659966518254708	0.348525369010791	-1.89359678501416	0.0586820020454499	.  
df.mm.exp7	-0.158177024917198	0.348525369010791	-0.453846517302734	0.650077022345438	   
df.mm.exp8	-0.490869919840138	0.348525369010791	-1.40841948244215	0.159441569381447	   
df.mm.trans1:exp2	-0.112127167949184	0.330941189030840	-0.338812972412251	0.734850104116632	   
df.mm.trans2:exp2	-0.0396564151094418	0.278677420336218	-0.142302218319651	0.886881382718578	   
df.mm.trans1:exp3	-0.111194420751108	0.330941189030841	-0.335994504270502	0.736973584675408	   
df.mm.trans2:exp3	-0.246778122651694	0.278677420336218	-0.885533253300399	0.376166804631295	   
df.mm.trans1:exp4	-0.139560274371436	0.330941189030841	-0.421707176372145	0.673365520753076	   
df.mm.trans2:exp4	-0.287421310723187	0.278677420336218	-1.03137638627636	0.302713091004198	   
df.mm.trans1:exp5	0.379049803156027	0.330941189030840	1.14536907378037	0.252439526590156	   
df.mm.trans2:exp5	0.180500297135769	0.278677420336218	0.647703344311136	0.517384755407917	   
df.mm.trans1:exp6	0.692064714624403	0.330941189030841	2.09120151121446	0.0368623885683287	*  
df.mm.trans2:exp6	0.279462929938578	0.278677420336218	1.00281870558946	0.316287596115873	   
df.mm.trans1:exp7	0.167187366922478	0.330941189030841	0.505187545291915	0.613582796534154	   
df.mm.trans2:exp7	-0.127850207193751	0.278677420336218	-0.458774905550304	0.646535240170736	   
df.mm.trans1:exp8	0.43882485694474	0.330941189030840	1.32599045235148	0.185266299706451	   
df.mm.trans2:exp8	0.30300598111159	0.278677420336218	1.08730007887263	0.277270606421949	   
df.mm.trans1:probe2	0.097883910369759	0.181263954439773	0.540007585469963	0.58935994666795	   
df.mm.trans1:probe3	0.0933757982325812	0.181263954439773	0.515137157418722	0.606616397797057	   
df.mm.trans1:probe4	-0.0678395407458897	0.181263954439773	-0.374258307204869	0.708323110230799	   
df.mm.trans1:probe5	0.0194915869694596	0.181263954439773	0.107531511323924	0.91439749364701	   
df.mm.trans1:probe6	0.16293948436134	0.181263954439773	0.898907258560769	0.369004677964716	   
df.mm.trans1:probe7	-0.0764814780440539	0.181263954439773	-0.421934290689139	0.673199811829881	   
df.mm.trans1:probe8	0.139743300891203	0.181263954439773	0.770938167619169	0.440998260519434	   
df.mm.trans1:probe9	-0.119503905412346	0.181263954439773	-0.659281133867419	0.509927529282283	   
df.mm.trans1:probe10	0.0799281027160411	0.181263954439773	0.440948687029767	0.659383440602159	   
df.mm.trans1:probe11	-0.197073876835867	0.181263954439773	-1.08722044294442	0.277305766771118	   
df.mm.trans1:probe12	0.268492993649692	0.181263954439773	1.48122661496333	0.138986730785892	   
df.mm.trans1:probe13	0.181257941203966	0.181263954439773	0.999966826080647	0.317664853380700	   
df.mm.trans1:probe14	0.0223069088382503	0.181263954439773	0.123063125855296	0.90209169371884	   
df.mm.trans1:probe15	0.00882758062404049	0.181263954439773	0.0487001436734824	0.961171861471436	   
df.mm.trans1:probe16	-0.0699588687463074	0.181263954439773	-0.385950251182191	0.699648322822717	   
df.mm.trans1:probe17	-0.00103626769063007	0.181263954439773	-0.00571689883867331	0.995440194177105	   
df.mm.trans1:probe18	0.157892946531223	0.181263954439773	0.871066434687572	0.384010249488027	   
df.mm.trans1:probe19	0.265313511587661	0.181263954439773	1.46368599541844	0.143719241824692	   
df.mm.trans1:probe20	-0.171408568413897	0.181263954439773	-0.945629642383478	0.344656969063932	   
df.mm.trans1:probe21	-0.00205828810019254	0.181263954439773	-0.0113551980400849	0.990943225027915	   
df.mm.trans1:probe22	-0.0328878734723626	0.181263954439773	-0.181436367611025	0.856076496095208	   
df.mm.trans2:probe2	-0.153906103356701	0.181263954439773	-0.84907175192318	0.396125551435738	   
df.mm.trans2:probe3	-0.36535952492992	0.181263954439773	-2.01562150654346	0.044213220916783	*  
df.mm.trans2:probe4	0.126001375193359	0.181263954439773	0.695126483270147	0.487201877522233	   
df.mm.trans2:probe5	-0.0628616778752694	0.181263954439773	-0.346796350490941	0.72884635918081	   
df.mm.trans2:probe6	0.125529595535674	0.181263954439773	0.69252376140444	0.488833319766163	   
df.mm.trans3:probe2	-0.094609458051542	0.181263954439773	-0.521943032435482	0.601871645814973	   
df.mm.trans3:probe3	0.0479180039139696	0.181263954439773	0.26435484132556	0.791582685758901	   
df.mm.trans3:probe4	-0.103923795285395	0.181263954439773	-0.573328523073378	0.566602548132167