chr6.19857_chr6_84214979_84218987_+_2.R 

fitVsDatCorrelation=0.889274513314856
cont.fitVsDatCorrelation=0.271893761967969

fstatistic=8236.76903930946,51,669
cont.fstatistic=1850.44955323715,51,669

residuals=-0.566152287301225,-0.106990745082140,0.00316156362066709,0.09690427142382,0.721028189299368
cont.residuals=-0.676038678892495,-0.306764830529165,-0.0364198645584136,0.270630042303833,1.04193294754196

predictedValues:
Include	Exclude	Both
chr6.19857_chr6_84214979_84218987_+_2.R.tl.Lung	62.2069382501588	124.597020164959	92.0948253397553
chr6.19857_chr6_84214979_84218987_+_2.R.tl.cerebhem	48.8376651385486	70.5358948470282	63.8736680179781
chr6.19857_chr6_84214979_84218987_+_2.R.tl.cortex	50.1219538539862	80.4793546677678	69.2717117469875
chr6.19857_chr6_84214979_84218987_+_2.R.tl.heart	52.3776741235367	100.578176336190	78.4208471127803
chr6.19857_chr6_84214979_84218987_+_2.R.tl.kidney	63.8843092670912	117.425935606453	72.0411372692794
chr6.19857_chr6_84214979_84218987_+_2.R.tl.liver	62.9096121102173	115.246988740132	71.4157851973824
chr6.19857_chr6_84214979_84218987_+_2.R.tl.stomach	52.8266363186116	97.1251575400544	78.3304878444979
chr6.19857_chr6_84214979_84218987_+_2.R.tl.testicle	59.6402055043111	117.988558927248	89.1242549663602


diffExp=-62.3900819148005,-21.6982297084797,-30.3574008137816,-48.2005022126536,-53.5416263393619,-52.337376629915,-44.2985212214429,-58.3483534229368
diffExpScore=0.997313070966932
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
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	73.62451527098	61.3439101025093	80.6663436920495
cerebhem	71.6728974587752	84.522088929827	80.1179783023795
cortex	75.674956362711	71.9856001692778	75.6836152678963
heart	71.742868686158	68.2479378656558	73.702876381333
kidney	68.3724151687928	63.4054801645726	73.1648196953481
liver	75.8579105270264	67.2474970900893	62.8074676399623
stomach	80.4636410737742	71.0608509505545	76.6982472554567
testicle	79.6381308249916	77.2417060803463	79.4118771492792
cont.diffExp=12.2806051684706,-12.8491914710518,3.68935619343316,3.49493082050215,4.96693500422022,8.6104134369371,9.40279012321979,2.39642474464529
cont.diffExpScore=1.74861133891416

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

tran.correlation=0.924891292357763
cont.tran.correlation=0.245790853132189

tran.covariance=0.0205484510077099
cont.tran.covariance=0.00159121241486663

tran.mean=79.7988800872684
cont.tran.mean=72.6314004203776

weightedLogRatios:
wLogRatio
Lung	-3.11034421400613
cerebhem	-1.49706279087760
cortex	-1.96577994512667
heart	-2.79557849232779
kidney	-2.71583078566652
liver	-2.69053747948579
stomach	-2.60128382669774
testicle	-3.02203248681685

cont.weightedLogRatios:
wLogRatio
Lung	0.767836919308538
cerebhem	-0.71806840900661
cortex	0.214991867749650
heart	0.212156269907629
kidney	0.315799625250668
liver	0.514292803551734
stomach	0.537546173056338
testicle	0.133280814744322

varWeightedLogRatios=0.299568472105241
cont.varWeightedLogRatios=0.196913468325815

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.73194692587501	0.0844706723008514	44.1803862124275	1.53404567187329e-200	***
df.mm.trans1	0.27542827798323	0.0708308120136796	3.88853763147649	0.000110912554411679	***
df.mm.trans2	1.10234780917576	0.0650890243964465	16.9360014134109	8.25915471057324e-54	***
df.mm.exp2	-0.445016101947934	0.0844706723008514	-5.26829122849834	1.85908588367873e-07	***
df.mm.exp3	-0.368309297761557	0.0844706723008514	-4.36020322473323	1.50414429519463e-05	***
df.mm.exp4	-0.225406541132055	0.0844706723008514	-2.66845918225020	0.00780465694237266	** 
df.mm.exp5	0.212911797244767	0.0844706723008514	2.52054105224187	0.0119489351067426	*  
df.mm.exp6	0.187525119768544	0.0844706723008514	2.22000269040897	0.0267530015326371	*  
df.mm.exp7	-0.250653404834028	0.0844706723008514	-2.96734236873714	0.00311108068292735	** 
df.mm.exp8	-0.063846397483673	0.0844706723008514	-0.755841000723626	0.450010576831113	   
df.mm.trans1:exp2	0.203051402757265	0.0731537480872879	2.77568009932973	0.00566259564107627	** 
df.mm.trans2:exp2	-0.123946861897107	0.0597297851953187	-2.07512653011886	0.0383564386620599	*  
df.mm.trans1:exp3	0.152301869594474	0.0731537480872878	2.08194212294831	0.0377272798330911	*  
df.mm.trans2:exp3	-0.068774705319201	0.0597297851953187	-1.15143064878444	0.249966446958495	   
df.mm.trans1:exp4	0.0534204343678872	0.0731537480872878	0.730248767351543	0.465493771632862	   
df.mm.trans2:exp4	0.0112571493775719	0.0597297851953187	0.188467936738105	0.85056700278419	   
df.mm.trans1:exp5	-0.186304558344065	0.0731537480872878	-2.54675342296562	0.0110958860539125	*  
df.mm.trans2:exp5	-0.272188688527960	0.0597297851953187	-4.5570009608756	6.16835640235923e-06	***
df.mm.trans1:exp6	-0.176292692948951	0.0731537480872879	-2.40989282925868	0.0162261172534506	*  
df.mm.trans2:exp6	-0.265532257159782	0.0597297851953187	-4.44555854824324	1.02610902553681e-05	***
df.mm.trans1:exp7	0.0872024031180316	0.0731537480872879	1.19204286038742	0.233667109432178	   
df.mm.trans2:exp7	0.00156914469616096	0.0597297851953187	0.0262707239115258	0.979049239978859	   
df.mm.trans1:exp8	0.0217097921662151	0.0731537480872878	0.296769375921938	0.766734699809253	   
df.mm.trans2:exp8	0.00934936814653778	0.0597297851953187	0.156527737643204	0.875664280336576	   
df.mm.trans1:probe2	-0.0542501965642471	0.0517275113417337	-1.04876873364055	0.294663296599247	   
df.mm.trans1:probe3	-0.0332412896960493	0.0517275113417337	-0.642623022716932	0.520688907121841	   
df.mm.trans1:probe4	-0.0343307254747287	0.0517275113417337	-0.663684074184924	0.507121175821614	   
df.mm.trans1:probe5	-0.00687500992406212	0.0517275113417337	-0.132908190356248	0.894305955549854	   
df.mm.trans1:probe6	-0.0386300517173441	0.0517275113417337	-0.746798960849628	0.455447290205304	   
df.mm.trans1:probe7	0.486365944026914	0.0517275113417337	9.40246169613257	8.32219554852127e-20	***
df.mm.trans1:probe8	0.337537839199437	0.0517275113417337	6.52530598214016	1.34034463448371e-10	***
df.mm.trans1:probe9	0.555605702364329	0.0517275113417337	10.7410097248593	6.15894254766785e-25	***
df.mm.trans1:probe10	0.530044387291648	0.0517275113417337	10.2468565284332	5.49997764614257e-23	***
df.mm.trans1:probe11	0.520581379354811	0.0517275113417337	10.0639169728393	2.79294702908147e-22	***
df.mm.trans1:probe12	0.691384111446768	0.0517275113417337	13.3658877744803	2.76630506829573e-36	***
df.mm.trans2:probe2	-0.186372132537562	0.0517275113417337	-3.60295957998656	0.000337950586856725	***
df.mm.trans2:probe3	-0.0665313583535389	0.0517275113417337	-1.28618904385337	0.198822043866991	   
df.mm.trans2:probe4	0.0378568485007082	0.0517275113417337	0.731851340200961	0.464515594401547	   
df.mm.trans2:probe5	-0.104427756490415	0.0517275113417337	-2.01880496048846	0.0439059418509774	*  
df.mm.trans2:probe6	0.153693603417826	0.0517275113417337	2.97121588553646	0.00307261764905395	** 
df.mm.trans3:probe2	-0.442354420197417	0.0517275113417337	-8.55162772620236	8.24021215173464e-17	***
df.mm.trans3:probe3	-0.770055405224938	0.0517275113417337	-14.8867669302246	1.67021121226765e-43	***
df.mm.trans3:probe4	-0.802772386518372	0.0517275113417337	-15.5192539848848	1.27637107073354e-46	***
df.mm.trans3:probe5	-0.939151770953846	0.0517275113417337	-18.1557501336071	3.40769210851540e-60	***
df.mm.trans3:probe6	-0.819095381906452	0.0517275113417337	-15.8348113153011	3.37633981765316e-48	***
df.mm.trans3:probe7	-0.72236121417695	0.0517275113417337	-13.9647393705978	4.45599033360513e-39	***
df.mm.trans3:probe8	-1.00304778089159	0.0517275113417337	-19.3909924307983	8.2993941052559e-67	***
df.mm.trans3:probe9	-0.511730460863952	0.0517275113417337	-9.89281037479738	1.25266726005766e-21	***
df.mm.trans3:probe10	-0.280863077465347	0.0517275113417337	-5.42966537883193	7.90715624215904e-08	***
df.mm.trans3:probe11	-0.250177284523921	0.0517275113417337	-4.83644540467343	1.64170123453525e-06	***
df.mm.trans3:probe12	-0.501241896324694	0.0517275113417337	-9.69004468460272	7.23891455399401e-21	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02507838298251	0.177728638292594	22.6473258426481	9.9108620299374e-85	***
df.mm.trans1	0.274748753085696	0.149029993789017	1.84358024918568	0.0656864306973222	.  
df.mm.trans2	0.058668412894785	0.136949113327434	0.428395711876671	0.668500935119127	   
df.mm.exp2	0.30047278158393	0.177728638292594	1.69062670186705	0.0913739249654762	.  
df.mm.exp3	0.251199159647616	0.177728638292594	1.41338594646783	0.158007387064718	   
df.mm.exp4	0.171041325149716	0.177728638292594	0.96237346323518	0.336209750024395	   
df.mm.exp5	0.0566525258020307	0.177728638292594	0.318758565565354	0.750009092972142	   
df.mm.exp6	0.372015288078018	0.177728638292594	2.09316456622805	0.0367104921709798	*  
df.mm.exp7	0.286310602243641	0.177728638292594	1.61094241757645	0.107663947638387	   
df.mm.exp8	0.324632084717233	0.177728638292594	1.82656035535923	0.0682113718944089	.  
df.mm.trans1:exp2	-0.327338162812604	0.153917515741402	-2.1267115781843	0.0338094175218861	*  
df.mm.trans2:exp2	0.0200442246747295	0.125673125347744	0.159494916827015	0.873327102585567	   
df.mm.trans1:exp3	-0.223729939575040	0.153917515741402	-1.45357036525285	0.146534383320564	   
df.mm.trans2:exp3	-0.0912289596797433	0.125673125347744	-0.72592258231271	0.468140105498784	   
df.mm.trans1:exp4	-0.196930924777586	0.153917515741402	-1.27945753171102	0.201179540068609	   
df.mm.trans2:exp4	-0.0643900073608042	0.125673125347744	-0.512360993511012	0.6085674915649	   
df.mm.trans1:exp5	-0.130661127715841	0.153917515741402	-0.84890356426598	0.396238605787676	   
df.mm.trans2:exp5	-0.0235981315644467	0.125673125347744	-0.187773889597712	0.851110842247963	   
df.mm.trans1:exp6	-0.342131354194919	0.153917515741402	-2.22282274078368	0.0265612136920468	*  
df.mm.trans2:exp6	-0.28013138967698	0.125673125347744	-2.22904768940728	0.0261420726257665	*  
df.mm.trans1:exp7	-0.197483241723997	0.153917515741402	-1.28304592737704	0.199920288543687	   
df.mm.trans2:exp7	-0.139269938020253	0.125673125347744	-1.10819188776348	0.268177201509844	   
df.mm.trans1:exp8	-0.246117134294930	0.153917515741402	-1.59901966393762	0.110288374765667	   
df.mm.trans2:exp8	-0.094188440756153	0.125673125347744	-0.749471619294329	0.453836452868231	   
df.mm.trans1:probe2	-0.0929672607933394	0.108836119124132	-0.854194926661305	0.393302755771075	   
df.mm.trans1:probe3	-0.0553495574080683	0.108836119124132	-0.508558719784373	0.611229230842123	   
df.mm.trans1:probe4	0.084862543090799	0.108836119124132	0.77972775741856	0.435826891197939	   
df.mm.trans1:probe5	-0.125138536996697	0.108836119124132	-1.14978867313315	0.250641779204033	   
df.mm.trans1:probe6	-0.0175152633351033	0.108836119124132	-0.160932450330449	0.87219518800073	   
df.mm.trans1:probe7	0.0457114873080745	0.108836119124132	0.420002915171373	0.674618177783724	   
df.mm.trans1:probe8	-0.096464076559988	0.108836119124132	-0.886324111299545	0.375761386091364	   
df.mm.trans1:probe9	0.0104974231934979	0.108836119124132	0.0964516493051829	0.923190758803507	   
df.mm.trans1:probe10	0.161863942084701	0.108836119124132	1.48722633062731	0.137426190743917	   
df.mm.trans1:probe11	0.0626375920103987	0.108836119124132	0.575522101619204	0.56513177148272	   
df.mm.trans1:probe12	0.00148383843180817	0.108836119124132	0.013633694804165	0.989126287080476	   
df.mm.trans2:probe2	0.0782890629639347	0.108836119124132	0.719329792296642	0.472188936275097	   
df.mm.trans2:probe3	0.0528294238484561	0.108836119124132	0.48540341454294	0.627549130438006	   
df.mm.trans2:probe4	0.17480535433112	0.108836119124132	1.60613365983536	0.108716421235286	   
df.mm.trans2:probe5	0.237614500975876	0.108836119124132	2.18323202708896	0.0293655298956389	*  
df.mm.trans2:probe6	0.0459455572365618	0.108836119124132	0.422153579219035	0.67304855197485	   
df.mm.trans3:probe2	0.043261353533098	0.108836119124132	0.39749077678667	0.691132377374855	   
df.mm.trans3:probe3	0.0546196985833835	0.108836119124132	0.501852684779098	0.615936267943016	   
df.mm.trans3:probe4	-0.0894094261284876	0.108836119124132	-0.821505092684462	0.411651281896162	   
df.mm.trans3:probe5	0.0170211274223438	0.108836119124132	0.156392267193306	0.875771013353902	   
df.mm.trans3:probe6	-0.00987039419775787	0.108836119124132	-0.0906904277476143	0.927765722786005	   
df.mm.trans3:probe7	0.159577930596102	0.108836119124132	1.46622216852566	0.143057635876851	   
df.mm.trans3:probe8	0.07664802893294	0.108836119124132	0.704251764485644	0.481521065217888	   
df.mm.trans3:probe9	0.050172681350544	0.108836119124132	0.460992929133386	0.64495350543528	   
df.mm.trans3:probe10	0.0673702952953408	0.108836119124132	0.619006776771432	0.536122615365672	   
df.mm.trans3:probe11	0.0386181284805443	0.108836119124132	0.354828238927728	0.722830097782661	   
df.mm.trans3:probe12	-0.0260988074679759	0.108836119124132	-0.239799137253406	0.810559429225541