chr11.4955_chr11_119418595_119419315_-_0.R 

fitVsDatCorrelation=0.853700309479595
cont.fitVsDatCorrelation=0.370199535052664

fstatistic=5740.61423943051,36,324
cont.fstatistic=1797.90302332511,36,324

residuals=-0.61485622504018,-0.104414256486847,-0.00577112142340853,0.100916259236294,0.704130604620652
cont.residuals=-0.69284482789195,-0.222395066490938,-0.0386765768988721,0.17772468153567,1.01765440705383

predictedValues:
Include	Exclude	Both
chr11.4955_chr11_119418595_119419315_-_0.R.tl.Lung	98.2870586660358	70.1034898712931	71.3983885113672
chr11.4955_chr11_119418595_119419315_-_0.R.tl.cerebhem	145.864935349914	124.044773631738	132.232773766711
chr11.4955_chr11_119418595_119419315_-_0.R.tl.cortex	138.465722595619	95.7672407837905	142.888778955192
chr11.4955_chr11_119418595_119419315_-_0.R.tl.heart	83.7001162460527	65.7544000631175	76.7832980606375
chr11.4955_chr11_119418595_119419315_-_0.R.tl.kidney	87.242658560166	69.3222684272726	74.0749383030561
chr11.4955_chr11_119418595_119419315_-_0.R.tl.liver	88.5165270724744	68.8003171968267	73.941701595558
chr11.4955_chr11_119418595_119419315_-_0.R.tl.stomach	94.453335778449	65.9274940854677	82.8461646379893
chr11.4955_chr11_119418595_119419315_-_0.R.tl.testicle	85.460849239435	69.6937950525938	72.741675666041


diffExp=28.1835687947426,21.8201617181752,42.6984818118288,17.9457161829352,17.9203901328933,19.7162098756477,28.5258416929813,15.7670541868412
diffExpScore=0.994834108351633
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,1,0,0,0,1,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,0,0,0,1,0
diffExp1.3Score=0.75
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	81.435291146209	97.207035678964	90.0622978213025
cerebhem	80.4681211790075	88.4033907141801	96.2728938466465
cortex	73.1987920191671	76.04245890649	115.598296210167
heart	87.3725324808298	81.0023596983253	75.542564696915
kidney	81.329869231666	98.2050645333479	85.9188392855001
liver	82.3690325659002	88.8859362743305	77.1334636234887
stomach	85.3995874055228	96.9921561465527	73.3700216926376
testicle	85.060613719752	82.5794882685368	113.786904170391
cont.diffExp=-15.7717445327549,-7.93526953517262,-2.84366688732290,6.3701727825045,-16.8751953016819,-6.5169037084303,-11.5925687410299,2.48112545121528
cont.diffExpScore=1.31112772453602

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

tran.correlation=0.939897326311793
cont.tran.correlation=0.272834196954129

tran.covariance=0.0476330355243154
cont.tran.covariance=0.00161787633887345

tran.mean=90.7128114137654
cont.tran.mean=85.3719831230488

weightedLogRatios:
wLogRatio
Lung	1.49324473335825
cerebhem	0.794258005314376
cortex	1.74996051119176
heart	1.03923792251343
kidney	1.00104101891426
liver	1.09793099217446
stomach	1.57063483493594
testicle	0.886371298413076

cont.weightedLogRatios:
wLogRatio
Lung	-0.79458791046651
cerebhem	-0.41709758449082
cortex	-0.164351950214378
heart	0.335539201255232
kidney	-0.847089753792582
liver	-0.338787821649706
stomach	-0.574198141612776
testicle	0.131097773489648

varWeightedLogRatios=0.123503208420533
cont.varWeightedLogRatios=0.176200563321187

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.86648906858237	0.105722428259241	46.0308105736024	3.5192848170755e-144	***
df.mm.trans1	-0.233087714731514	0.0895457923442817	-2.60300019274327	0.00966612788709733	** 
df.mm.trans2	-0.653156390489767	0.0895457923442817	-7.29410476349956	2.32713193849654e-12	***
df.mm.exp2	0.349170219285125	0.124703373055906	2.80000621257124	0.005416773668924	** 
df.mm.exp3	-0.0391127259627972	0.124703373055906	-0.313646094763311	0.753991674515223	   
df.mm.exp4	-0.297409831903009	0.124703373055906	-2.38493815054768	0.0176566610906234	*  
df.mm.exp5	-0.167207302125272	0.124703373055906	-1.34084025177339	0.180911603836962	   
df.mm.exp6	-0.158468974765283	0.124703373055906	-1.27076734880491	0.204723083369713	   
df.mm.exp7	-0.249913619852279	0.124703373055906	-2.00406463536668	0.0458951827917639	*  
df.mm.exp8	-0.164334462897634	0.124703373055906	-1.31780286988678	0.188500647039506	   
df.mm.trans1:exp2	0.0456185068381068	0.107996289004022	0.422408096230119	0.673007345082792	   
df.mm.trans2:exp2	0.221499781837088	0.107996289004022	2.05099438026837	0.0410713995380422	*  
df.mm.trans1:exp3	0.381843163602441	0.107996289004022	3.53570633883743	0.000465951733350139	***
df.mm.trans2:exp3	0.351060821234468	0.107996289004022	3.25067485625727	0.00127233274704706	** 
df.mm.trans1:exp4	0.136757831166566	0.107996289004022	1.26631972661090	0.206308298498937	   
df.mm.trans2:exp4	0.233363844848541	0.107996289004022	2.16085059033695	0.0314393693974113	*  
df.mm.trans1:exp5	0.0480083500180901	0.107996289004022	0.444537034196629	0.6569511502754	   
df.mm.trans2:exp5	0.156000913446105	0.107996289004022	1.44450253693712	0.149564013931039	   
df.mm.trans1:exp6	0.0537658888531877	0.107996289004022	0.497849410836563	0.618927915607547	   
df.mm.trans2:exp6	0.139704753127667	0.107996289004022	1.29360697868483	0.196722662266784	   
df.mm.trans1:exp7	0.210127164033078	0.107996289004022	1.94568874514986	0.0525566407020307	.  
df.mm.trans2:exp7	0.188496606555582	0.107996289004022	1.74539892337006	0.0818634324965084	.  
df.mm.trans1:exp8	0.0245004632654879	0.107996289004022	0.226863936635595	0.820672614357574	   
df.mm.trans2:exp8	0.158473176080325	0.107996289004022	1.46739464422174	0.143238747807181	   
df.mm.trans1:probe2	0.0563752525307557	0.0539981445020111	1.04402203169511	0.297253512914856	   
df.mm.trans1:probe3	-0.0356575939758446	0.0539981445020112	-0.660348504651238	0.509499398736891	   
df.mm.trans1:probe4	0.0736156996667507	0.0539981445020112	1.36330054200305	0.173734384560889	   
df.mm.trans1:probe5	-0.0703584698395196	0.0539981445020111	-1.30297939842913	0.193507047699571	   
df.mm.trans1:probe6	-0.4335557693861	0.0539981445020112	-8.02908643221903	1.83209611932425e-14	***
df.mm.trans2:probe2	0.0776089323041973	0.0539981445020112	1.43725183559422	0.151611457770606	   
df.mm.trans2:probe3	0.17579894622077	0.0539981445020111	3.25564790868365	0.00125095781439701	** 
df.mm.trans2:probe4	0.0108518436869115	0.0539981445020112	0.200966973717168	0.84085049737605	   
df.mm.trans2:probe5	0.102278272689422	0.0539981445020111	1.89410717039755	0.0591012416804925	.  
df.mm.trans2:probe6	-0.0367789048672286	0.0539981445020111	-0.681114234691135	0.496285711569571	   
df.mm.trans3:probe2	0.288928837428502	0.0539981445020112	5.35071788286615	1.66019970072982e-07	***
df.mm.trans3:probe3	0.575162170154319	0.0539981445020112	10.6515172967267	6.49792497227935e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40808245684923	0.188590173905645	23.3738713187394	1.68079582146206e-71	***
df.mm.trans1	-0.118320979403438	0.159733907258707	-0.740738027598638	0.459388775583066	   
df.mm.trans2	0.188369063097617	0.159733907258707	1.17926786072122	0.239156751306095	   
df.mm.exp2	-0.173565576540464	0.222448833217924	-0.78024943547548	0.435813700660423	   
df.mm.exp3	-0.601800639697625	0.222448833217924	-2.70534410539283	0.00718428200526655	** 
df.mm.exp4	0.0638127829969487	0.222448833217924	0.286864993058578	0.774399057535993	   
df.mm.exp5	0.0560178158659482	0.222448833217924	0.251823374641259	0.801337245792738	   
df.mm.exp6	0.0768760708184787	0.222448833217924	0.345589903558479	0.729875206606388	   
df.mm.exp7	0.250305757138491	0.222448833217924	1.12522845598959	0.261325088152867	   
df.mm.exp8	-0.35335220360625	0.222448833217924	-1.58846507978797	0.113156539514918	   
df.mm.trans1:exp2	0.161617941116930	0.19264634060893	0.838935951786455	0.40212378251776	   
df.mm.trans2:exp2	0.0786328095684533	0.19264634060893	0.408171830930737	0.68341713435357	   
df.mm.trans1:exp3	0.495170826841688	0.19264634060893	2.57036196626688	0.0106055987877123	*  
df.mm.trans2:exp3	0.356249401433038	0.19264634060893	1.84924042837762	0.0653337867085941	.  
df.mm.trans1:exp4	0.00655944537886383	0.19264634060893	0.0340491563874521	0.972858918706784	   
df.mm.trans2:exp4	-0.246177589046019	0.19264634060893	-1.27787316524094	0.202208934823855	   
df.mm.trans1:exp5	-0.0573132027784789	0.19264634060893	-0.297504757148874	0.76627183750713	   
df.mm.trans2:exp5	-0.0458031205513242	0.19264634060893	-0.237757542689607	0.812219430138765	   
df.mm.trans1:exp6	-0.0654752541321146	0.19264634060893	-0.339872815259069	0.73417253054368	   
df.mm.trans2:exp6	-0.166365230327515	0.19264634060893	-0.863578460933417	0.388458325058262	   
df.mm.trans1:exp7	-0.202773218889341	0.19264634060893	-1.05256719773862	0.293323750279570	   
df.mm.trans2:exp7	-0.252518738748613	0.19264634060893	-1.31078917954234	0.190857320532645	   
df.mm.trans1:exp8	0.396907577328406	0.19264634060893	2.06029128855411	0.0401688420810346	*  
df.mm.trans2:exp8	0.190270434873126	0.19264634060893	0.987667008216747	0.324052749390891	   
df.mm.trans1:probe2	0.239099533682247	0.096323170304465	2.48226395504306	0.0135612480672874	*  
df.mm.trans1:probe3	0.225637487587463	0.096323170304465	2.34250478752155	0.0197596996314594	*  
df.mm.trans1:probe4	0.0442300631536487	0.096323170304465	0.459184046931213	0.646409917231839	   
df.mm.trans1:probe5	0.322097948912683	0.096323170304465	3.34393010419583	0.000922825830991999	***
df.mm.trans1:probe6	0.159360250601560	0.096323170304465	1.65443319709934	0.0990079038345077	.  
df.mm.trans2:probe2	0.0396116077409297	0.096323170304465	0.411236544807678	0.681170967975033	   
df.mm.trans2:probe3	-0.0358859869082507	0.096323170304465	-0.372558199598495	0.70972087224418	   
df.mm.trans2:probe4	-0.101856582828716	0.096323170304465	-1.05744632892336	0.291095707084109	   
df.mm.trans2:probe5	-0.0169344308140204	0.096323170304465	-0.175808486789760	0.86055422958	   
df.mm.trans2:probe6	-0.061410455334196	0.096323170304465	-0.637546035290216	0.524219505445848	   
df.mm.trans3:probe2	0.00202289425824178	0.096323170304465	0.0210011179225899	0.983257691113695	   
df.mm.trans3:probe3	0.0650903707700803	0.096323170304465	0.675749879954512	0.499681483089786