chr15.8543_chr15_92710165_92711322_+_0.R 

fitVsDatCorrelation=0.865360541426076
cont.fitVsDatCorrelation=0.213364031734775

fstatistic=16812.4792659057,53,715
cont.fstatistic=4413.92544317565,53,715

residuals=-0.404144866233725,-0.0703246260962724,-0.00498361762063768,0.0643030354800463,0.629587699793976
cont.residuals=-0.397177302744777,-0.155800979819005,-0.0469941903701404,0.116233286466162,1.12197799553619

predictedValues:
Include	Exclude	Both
chr15.8543_chr15_92710165_92711322_+_0.R.tl.Lung	48.4281041065255	44.4211074382707	57.7307621144228
chr15.8543_chr15_92710165_92711322_+_0.R.tl.cerebhem	50.1947346211359	49.1535526908674	60.9302102264317
chr15.8543_chr15_92710165_92711322_+_0.R.tl.cortex	49.9857795396424	45.7750866127827	54.4969045353892
chr15.8543_chr15_92710165_92711322_+_0.R.tl.heart	50.7505017432309	48.2297630277716	56.7971495184347
chr15.8543_chr15_92710165_92711322_+_0.R.tl.kidney	48.4373173921583	46.1063899275817	58.3068171738567
chr15.8543_chr15_92710165_92711322_+_0.R.tl.liver	50.5942104115259	50.8961291326756	59.3061102794349
chr15.8543_chr15_92710165_92711322_+_0.R.tl.stomach	50.0033578822262	47.2577206768071	58.3053664295552
chr15.8543_chr15_92710165_92711322_+_0.R.tl.testicle	45.5977055626445	47.7140494391751	57.2565983858047


diffExp=4.00699666825485,1.0411819302685,4.21069292685964,2.5207387154593,2.33092746457654,-0.301918721149775,2.74563720541911,-2.11634387653062
diffExpScore=1.24851321328538
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	59.6256486795904	55.8300265196357	57.3836063324905
cerebhem	53.5877448893182	51.9193743645068	54.3608986370652
cortex	55.4545179305399	54.9966848403674	54.3304805023559
heart	57.9935101901772	53.6328587358247	56.2973195301095
kidney	60.0121002609786	54.1493907594933	54.2978230539014
liver	57.6251842888367	54.7439406605589	52.7585058223721
stomach	59.5437615520598	54.247838089193	56.864125778991
testicle	59.5622914554827	52.5125255969178	53.6734314032114
cont.diffExp=3.79562215995465,1.66837052481137,0.457833090172556,4.36065145435251,5.8627095014853,2.8812436282778,5.29592346286676,7.04976585856488
cont.diffExpScore=0.969109220839721

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=0.351133864001139
cont.tran.correlation=0.368179103532520

tran.covariance=0.000515563170700692
cont.tran.covariance=0.000370096274869135

tran.mean=48.3465943878138
cont.tran.mean=55.9648374258426

weightedLogRatios:
wLogRatio
Lung	0.331375801183057
cerebhem	0.0818617361250157
cortex	0.340355536694118
heart	0.198760031735989
kidney	0.190154829399116
liver	-0.0233634366272447
stomach	0.219337184763253
testicle	-0.174330325236696

cont.weightedLogRatios:
wLogRatio
Lung	0.266726624183324
cerebhem	0.125422535293651
cortex	0.0332557798098601
heart	0.314337601464452
kidney	0.415633396894757
liver	0.206624307670559
stomach	0.3763322871667
testicle	0.506911814111269

varWeightedLogRatios=0.0310938901720972
cont.varWeightedLogRatios=0.0243825728268364

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75762129993390	0.0573564545223001	65.5134863413314	2.10324741409287e-304	***
df.mm.trans1	0.107428377521741	0.0451176359683744	2.38107283805925	0.0175231653663904	*  
df.mm.trans2	0.0198246535282611	0.0451176359683744	0.439399208375133	0.660505065613856	   
df.mm.exp2	0.0831252577800949	0.0595839125421364	1.39509565977747	0.163420181046582	   
df.mm.exp3	0.119329748483709	0.0595839125421364	2.00271756909756	0.0455847039998601	*  
df.mm.exp4	0.145406797751230	0.0595839125421364	2.44037008560661	0.0149140072070108	*  
df.mm.exp5	0.0274981810560711	0.0595839125421364	0.461503447539217	0.644577815335393	   
df.mm.exp6	0.152906794927091	0.0595839125421364	2.56624294047422	0.0104832305005183	*  
df.mm.exp7	0.084007176876382	0.0595839125421364	1.40989695527252	0.159004944919174	   
df.mm.exp8	0.0195355117957659	0.0595839125421364	0.327865542262784	0.743109309511907	   
df.mm.trans1:exp2	-0.0472954331695416	0.0447957462265144	-1.05580188195520	0.291415348616004	   
df.mm.trans2:exp2	0.0181091195940514	0.0447957462265144	0.404259804100165	0.686142572846831	   
df.mm.trans1:exp3	-0.0876715012755001	0.0447957462265144	-1.95713898440669	0.0507198589155576	.  
df.mm.trans2:exp3	-0.0893045150119906	0.0447957462265144	-1.99359364526295	0.0465759675971901	*  
df.mm.trans1:exp4	-0.0985656018728941	0.0447957462265144	-2.20033396417791	0.0281023014754317	*  
df.mm.trans2:exp4	-0.0631452262892614	0.0447957462265144	-1.40962550260824	0.159085096856576	   
df.mm.trans1:exp5	-0.0273079524787728	0.0447957462265144	-0.609610393377246	0.542313593904	   
df.mm.trans2:exp5	0.00973862025511715	0.0447957462265144	0.217400558657351	0.827958218283686	   
df.mm.trans1:exp6	-0.109149952485359	0.0447957462265144	-2.43661422523092	0.0150684720130528	*  
df.mm.trans2:exp6	-0.0168346719650894	0.0447957462265144	-0.375809611027868	0.707169913331668	   
df.mm.trans1:exp7	-0.0519973246195024	0.0447957462265144	-1.16076478236510	0.246124968080435	   
df.mm.trans2:exp7	-0.0221058849463917	0.0447957462265144	-0.493481788083427	0.62182374423604	   
df.mm.trans1:exp8	-0.079758421710838	0.0447957462265144	-1.78049097134204	0.0754201815198181	.  
df.mm.trans2:exp8	0.0519756310110974	0.0447957462265144	1.16028050405226	0.246321889519304	   
df.mm.trans1:probe2	-0.0245492491224775	0.0340248117611106	-0.721510211278717	0.470831526285128	   
df.mm.trans1:probe3	0.155634632855884	0.0340248117611106	4.57415117969205	5.63544765811421e-06	***
df.mm.trans1:probe4	0.0540232744040883	0.0340248117611106	1.58776115451829	0.112782449519620	   
df.mm.trans1:probe5	0.106817572097378	0.0340248117611106	3.13940229404788	0.00176257160395513	** 
df.mm.trans1:probe6	0.098870178499547	0.0340248117611106	2.90582587770707	0.00377604079106176	** 
df.mm.trans2:probe2	0.00190572568856574	0.0340248117611106	0.056009881904591	0.955349582276381	   
df.mm.trans2:probe3	0.100934110469409	0.0340248117611106	2.96648549235396	0.00311267947622852	** 
df.mm.trans2:probe4	0.0495835744019132	0.0340248117611106	1.45727696452933	0.145478978521235	   
df.mm.trans2:probe5	0.257530854301872	0.0340248117611106	7.56891341853719	1.16267684352592e-13	***
df.mm.trans2:probe6	0.0130344250678036	0.0340248117611106	0.383085883305357	0.701769950532846	   
df.mm.trans3:probe2	0.56599647695007	0.0340248117611106	16.6348158198185	9.03714097214879e-53	***
df.mm.trans3:probe3	0.189982291300312	0.0340248117611106	5.58363974602371	3.34696107287921e-08	***
df.mm.trans3:probe4	0.0017360710214394	0.0340248117611106	0.0510236774747915	0.959320903832274	   
df.mm.trans3:probe5	0.17531302045366	0.0340248117611106	5.15250522720123	3.32837512839911e-07	***
df.mm.trans3:probe6	-0.0728440326120991	0.0340248117611106	-2.14090920248258	0.0326188877534478	*  
df.mm.trans3:probe7	-0.0695024524924626	0.0340248117611106	-2.04269910383169	0.0414484751539844	*  
df.mm.trans3:probe8	0.10558309697759	0.0340248117611106	3.10312067907657	0.00199035115879137	** 
df.mm.trans3:probe9	0.539519723030209	0.0340248117611106	15.8566556317254	9.56594737781016e-49	***
df.mm.trans3:probe10	0.40762831418019	0.0340248117611106	11.9803253297083	2.86570092073885e-30	***
df.mm.trans3:probe11	0.00821771192304792	0.0340248117611106	0.241521157581849	0.809220468060383	   
df.mm.trans3:probe12	0.101365112592731	0.0340248117611106	2.97915278134143	0.00298836608837379	** 
df.mm.trans3:probe13	0.259755403184089	0.0340248117611106	7.63429361513712	7.28483684077227e-14	***
df.mm.trans3:probe14	0.109091818443228	0.0340248117611106	3.20624311485293	0.00140465927156884	** 
df.mm.trans3:probe15	-0.0419988614993318	0.0340248117611106	-1.23435984875412	0.217474376120972	   
df.mm.trans3:probe16	-0.045610484861267	0.0340248117611106	-1.3405066038719	0.180506462772262	   
df.mm.trans3:probe17	-0.121311993709485	0.0340248117611106	-3.56539793845799	0.000387487553242844	***
df.mm.trans3:probe18	0.295725386063487	0.0340248117611106	8.69146281072135	2.42819254359297e-17	***
df.mm.trans3:probe19	0.583294783226633	0.0340248117611106	17.143218523058	1.91098649191681e-55	***
df.mm.trans3:probe20	0.426375403032728	0.0340248117611106	12.5313082119697	1.04221858261529e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10194441573634	0.111814257717964	36.6853431704832	1.63061259849527e-166	***
df.mm.trans1	-0.00472419166232327	0.0879551397974168	-0.0537113768814909	0.957180121814735	   
df.mm.trans2	-0.0836523146683394	0.0879551397974168	-0.951079321356456	0.341885488948188	   
df.mm.exp2	-0.125271700453660	0.116156603617135	-1.07847248070862	0.281186650993835	   
df.mm.exp3	-0.0328883130793748	0.116156603617135	-0.283137695621494	0.777153241100135	   
df.mm.exp4	-0.0487929003898002	0.116156603617135	-0.420061355707567	0.674566831649805	   
df.mm.exp5	0.0311697960312161	0.116156603617135	0.268342866962219	0.788512813479995	   
df.mm.exp6	0.0302623776821056	0.116156603617135	0.260530841465146	0.794529341149203	   
df.mm.exp7	-0.0210289969090474	0.116156603617135	-0.181040046404605	0.856387447848025	   
df.mm.exp8	0.00451728048047742	0.116156603617135	0.0388895709740866	0.96898928716523	   
df.mm.trans1:exp2	0.0185062737920207	0.0873276278137548	0.211917743047932	0.832231601399766	   
df.mm.trans2:exp2	0.0526518884575883	0.0873276278137548	0.602923608206557	0.546750695862472	   
df.mm.trans1:exp3	-0.0396343273578846	0.0873276278137548	-0.453857826556488	0.650068885844565	   
df.mm.trans2:exp3	0.0178493865364474	0.0873276278137548	0.204395641829583	0.838102501776691	   
df.mm.trans1:exp4	0.0210381829312964	0.0873276278137548	0.240910963208172	0.809693188622894	   
df.mm.trans2:exp4	0.00864298242374437	0.0873276278137548	0.0989719134725315	0.921188310987256	   
df.mm.trans1:exp5	-0.0247094115972677	0.0873276278137548	-0.282950679136342	0.777296541766154	   
df.mm.trans2:exp5	-0.0617349079554614	0.0873276278137548	-0.706934443325594	0.479837541157152	   
df.mm.trans1:exp6	-0.0643885067710841	0.0873276278137549	-0.737321147763301	0.461168958680299	   
df.mm.trans2:exp6	-0.0499075223057582	0.0873276278137548	-0.571497515221608	0.567842045034673	   
df.mm.trans1:exp7	0.0196546989199946	0.0873276278137548	0.225068508237880	0.821990288086448	   
df.mm.trans2:exp7	-0.0077196966839684	0.0873276278137548	-0.0883992486367812	0.929584111573628	   
df.mm.trans1:exp8	-0.00558042881584654	0.0873276278137548	-0.063902214631869	0.949065949494144	   
df.mm.trans2:exp8	-0.0657773908698808	0.0873276278137548	-0.753225439836352	0.451562393365885	   
df.mm.trans1:probe2	0.0422582214913839	0.066330094890765	0.637089718640933	0.524270334461885	   
df.mm.trans1:probe3	-0.0320015061799372	0.066330094890765	-0.482458320505022	0.629628064839561	   
df.mm.trans1:probe4	-0.0942513652861485	0.066330094890765	-1.42094422511177	0.155768933776184	   
df.mm.trans1:probe5	-0.0988630958379426	0.066330094890765	-1.49047119562778	0.136541384930886	   
df.mm.trans1:probe6	-0.0546365398503898	0.066330094890765	-0.823706643875112	0.410380927822378	   
df.mm.trans2:probe2	-0.0289369002962479	0.066330094890765	-0.436255976173444	0.662782711972762	   
df.mm.trans2:probe3	0.0177597552377584	0.066330094890765	0.267748075244063	0.788970460253896	   
df.mm.trans2:probe4	0.101589487899287	0.066330094890765	1.53157459018547	0.126069755070953	   
df.mm.trans2:probe5	-0.00475710616657236	0.066330094890765	-0.0717186696989738	0.942845854002199	   
df.mm.trans2:probe6	0.0188578281133646	0.066330094890765	0.284302745901696	0.776260697053688	   
df.mm.trans3:probe2	0.00236563455118274	0.066330094890765	0.0356645736008453	0.971559773042809	   
df.mm.trans3:probe3	0.0669736667649227	0.066330094890765	1.00970256224143	0.312979382276456	   
df.mm.trans3:probe4	0.0301753700317936	0.066330094890765	0.454927285743939	0.649299646958288	   
df.mm.trans3:probe5	0.0308819010929950	0.066330094890765	0.465579027798053	0.641658636812412	   
df.mm.trans3:probe6	0.103669593032248	0.066330094890765	1.56293449003767	0.118510475309551	   
df.mm.trans3:probe7	0.0226317731568694	0.066330094890765	0.341199167499162	0.733053890965864	   
df.mm.trans3:probe8	-0.00284513751343084	0.066330094890765	-0.0428936144010697	0.965798314416901	   
df.mm.trans3:probe9	0.0054778759791521	0.066330094890765	0.0825850767765866	0.934204581478889	   
df.mm.trans3:probe10	0.0513388863985099	0.066330094890765	0.773990848091756	0.439192041887385	   
df.mm.trans3:probe11	0.0165555657099004	0.066330094890765	0.249593577955296	0.802973353426901	   
df.mm.trans3:probe12	0.0476645521450216	0.066330094890765	0.718596170011778	0.472624531145047	   
df.mm.trans3:probe13	0.0478887395711673	0.066330094890765	0.721976044961678	0.470545248740046	   
df.mm.trans3:probe14	0.0279899167356672	0.066330094890765	0.421979145088849	0.673167086691758	   
df.mm.trans3:probe15	0.0591412939270624	0.066330094890765	0.891620824973318	0.372896152682987	   
df.mm.trans3:probe16	-0.0221908197385742	0.066330094890765	-0.334551303976256	0.738061696421444	   
df.mm.trans3:probe17	0.0729808752821206	0.066330094890765	1.10026791612930	0.271585698757391	   
df.mm.trans3:probe18	0.0170432013242108	0.066330094890765	0.256945227536281	0.797294976175154	   
df.mm.trans3:probe19	0.0648818059312784	0.066330094890765	0.978165432118381	0.328323349221687	   
df.mm.trans3:probe20	0.0811902888915009	0.066330094890765	1.22403396264106	0.221342595668886