chr8.23185_chr8_3544805_3545953_+_2.R 

fitVsDatCorrelation=0.865515934168462
cont.fitVsDatCorrelation=0.286103102035041

fstatistic=9426.92697818956,53,715
cont.fstatistic=2566.09368131368,53,715

residuals=-0.56570165220711,-0.105639894965479,0.00562357861731038,0.103239549859328,0.586514889917166
cont.residuals=-0.758755794030736,-0.239840269223455,0.0180549611678187,0.231113620309849,0.953862040139078

predictedValues:
Include	Exclude	Both
chr8.23185_chr8_3544805_3545953_+_2.R.tl.Lung	81.2203965297765	80.856994708261	88.0369062315764
chr8.23185_chr8_3544805_3545953_+_2.R.tl.cerebhem	62.839657278197	89.0347954587814	62.4657934196088
chr8.23185_chr8_3544805_3545953_+_2.R.tl.cortex	67.5591157073017	92.0907985411703	82.7754719760042
chr8.23185_chr8_3544805_3545953_+_2.R.tl.heart	71.266753671377	83.7079341919953	64.712778654959
chr8.23185_chr8_3544805_3545953_+_2.R.tl.kidney	85.9276056013914	83.2859837752336	90.9630402052086
chr8.23185_chr8_3544805_3545953_+_2.R.tl.liver	75.7483371698576	87.2857451289235	70.0344295206668
chr8.23185_chr8_3544805_3545953_+_2.R.tl.stomach	101.781158765479	83.5961269275373	101.130911474505
chr8.23185_chr8_3544805_3545953_+_2.R.tl.testicle	98.4597594232757	88.3817829403408	94.0130591823798


diffExp=0.363401821515566,-26.1951381805844,-24.5316828338686,-12.4411805206183,2.64162182615772,-11.5374079590659,18.1850318379416,10.0779764829348
diffExpScore=2.38478162672103
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,-1,0,0,0,1,0
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	79.3669265159911	86.1464877863856	77.2753042080101
cerebhem	83.1244237509554	83.2180919844266	90.1946315243227
cortex	84.428803966907	74.1446156017187	92.6840717611399
heart	82.8472277454466	93.1249775972673	84.2369856435237
kidney	89.0310921652277	72.3433696479639	93.7721867198165
liver	81.9299508855018	78.2243550365332	100.715328321164
stomach	83.6918520380595	85.732378974415	87.2691803916702
testicle	98.5030823082829	90.3119448061491	89.3295545338102
cont.diffExp=-6.77956127039447,-0.0936682334711918,10.2841883651883,-10.2777498518207,16.6877225172638,3.70559584896857,-2.04052693635559,8.1911375021338
cont.diffExpScore=2.80793941065855

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=-0.389411563725028
cont.tran.correlation=0.103823217168584

tran.covariance=-0.0031470087178868
cont.tran.covariance=0.000434624372883382

tran.mean=83.3151841136812
cont.tran.mean=84.135598800702

weightedLogRatios:
wLogRatio
Lung	0.0197081917754934
cerebhem	-1.50345514129513
cortex	-1.35304983951864
heart	-0.699429961718859
kidney	0.138572638019274
liver	-0.623550200892894
stomach	0.89052995378877
testicle	0.489769566024534

cont.weightedLogRatios:
wLogRatio
Lung	-0.361891678609099
cerebhem	-0.00497886053768196
cortex	0.567749509902684
heart	-0.523380094679606
kidney	0.91020121898625
liver	0.202848103557545
stomach	-0.106935315896001
testicle	0.394734055957743

varWeightedLogRatios=0.735692814781481
cont.varWeightedLogRatios=0.231252334208479

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89429125116891	0.0889508163109733	43.780275580096	1.66884106114117e-204	***
df.mm.trans1	0.118224121635289	0.0789900463747305	1.49669644545379	0.134913539888664	   
df.mm.trans2	0.48925483826613	0.071851719078498	6.80922940384514	2.08098803492596e-11	***
df.mm.exp2	0.182902125623902	0.0968667946914341	1.88818187085193	0.0594061561191832	.  
df.mm.exp3	0.00755386275515198	0.0968667946914342	0.0779819625415969	0.937864218567086	   
df.mm.exp4	0.211712622930656	0.0968667946914342	2.18560574451812	0.0291681794386048	*  
df.mm.exp5	0.0532397612243624	0.0968667946914341	0.549618281413727	0.582752841319543	   
df.mm.exp6	0.23552429886787	0.0968667946914342	2.43142451051596	0.0152842289461043	*  
df.mm.exp7	0.120313979947204	0.0968667946914342	1.24205596283495	0.214623158430530	   
df.mm.exp8	0.215787718133425	0.0968667946914341	2.22767480663327	0.0262126349486943	*  
df.mm.trans1:exp2	-0.439482170546455	0.0919795660894831	-4.77804135452107	2.14887883349591e-06	***
df.mm.trans2:exp2	-0.086556969001967	0.0774537260729826	-1.11753137506137	0.264142552337780	   
df.mm.trans1:exp3	-0.191717264314476	0.0919795660894831	-2.08434625716719	0.0374830672973314	*  
df.mm.trans2:exp3	0.122539071336848	0.0774537260729826	1.58209394886158	0.114070341983596	   
df.mm.trans1:exp4	-0.342449096590759	0.0919795660894831	-3.72309971823094	0.000212269034458451	***
df.mm.trans2:exp4	-0.177060953630218	0.0774537260729826	-2.28602241115397	0.0225446285067733	*  
df.mm.trans1:exp5	0.00309898077247173	0.0919795660894831	0.0336920568798602	0.973132116182738	   
df.mm.trans2:exp5	-0.0236415851274036	0.0774537260729826	-0.305234961906504	0.760276041035674	   
df.mm.trans1:exp6	-0.305274210687725	0.0919795660894831	-3.31893510337651	0.000949413669552419	***
df.mm.trans2:exp6	-0.159019232308664	0.0774537260729826	-2.05308692520260	0.0404274816865678	*  
df.mm.trans1:exp7	0.105344621735070	0.0919795660894831	1.14530461725145	0.252466199174276	   
df.mm.trans2:exp7	-0.08699888646393	0.0774537260729826	-1.12323694255785	0.261713880047443	   
df.mm.trans1:exp8	-0.0233061916172513	0.0919795660894831	-0.253384448395610	0.80004398189393	   
df.mm.trans2:exp8	-0.126803941977980	0.0774537260729826	-1.63715741523521	0.102037597702640	   
df.mm.trans1:probe2	0.609714880875437	0.0503792831767471	12.1024921838676	8.36505082380852e-31	***
df.mm.trans1:probe3	0.332790276297712	0.0503792831767471	6.6056969316172	7.72552369083886e-11	***
df.mm.trans1:probe4	-0.0535241330274674	0.0503792831767471	-1.06242347354739	0.288402184160634	   
df.mm.trans1:probe5	0.0153182362463567	0.0503792831767471	0.304058241412750	0.761171985911291	   
df.mm.trans1:probe6	0.737935126506764	0.0503792831767471	14.6475908344675	1.11580898308210e-42	***
df.mm.trans1:probe7	0.604929625221831	0.0503792831767471	12.0075075919508	2.18047339245131e-30	***
df.mm.trans1:probe8	0.643524116878422	0.0503792831767471	12.7735862104415	8.38549065085534e-34	***
df.mm.trans1:probe9	0.540094323556484	0.0503792831767471	10.7205638806264	5.71799091121501e-25	***
df.mm.trans1:probe10	-0.0308471035756622	0.0503792831767471	-0.612297389532923	0.540535684607406	   
df.mm.trans1:probe11	0.630251345606701	0.0503792831767471	12.5101292806325	1.29719935567899e-32	***
df.mm.trans1:probe12	0.295047516463441	0.0503792831767471	5.85652470338486	7.20641129619124e-09	***
df.mm.trans1:probe13	0.519542446779007	0.0503792831767471	10.3126208635459	2.42704256079483e-23	***
df.mm.trans1:probe14	0.631473054782584	0.0503792831767471	12.5343795100690	1.00964082223080e-32	***
df.mm.trans1:probe15	0.778241815803864	0.0503792831767471	15.4476555983049	1.14749237519375e-46	***
df.mm.trans1:probe16	0.132957539086199	0.0503792831767471	2.63913122026250	0.00849293073259166	** 
df.mm.trans1:probe17	0.461583965070134	0.0503792831767471	9.16217810108066	5.28939505991658e-19	***
df.mm.trans1:probe18	0.491861270405458	0.0503792831767471	9.76316532094843	3.19703858565649e-21	***
df.mm.trans1:probe19	0.785588998670517	0.0503792831767471	15.5934929823120	2.09624043237439e-47	***
df.mm.trans1:probe20	0.554602217206085	0.0503792831767471	11.0085372842713	3.81354495349603e-26	***
df.mm.trans1:probe21	0.688247969795746	0.0503792831767471	13.6613291495464	6.3945559138825e-38	***
df.mm.trans1:probe22	0.63159333324059	0.0503792831767471	12.5367669687707	9.8501844330274e-33	***
df.mm.trans2:probe2	-0.340829897492046	0.0503792831767471	-6.76527882098485	2.77020048441404e-11	***
df.mm.trans2:probe3	0.226687684493547	0.0503792831767471	4.49962107833594	7.94397459810743e-06	***
df.mm.trans2:probe4	0.343373961833312	0.0503792831767471	6.81577704527162	1.99389647792162e-11	***
df.mm.trans2:probe5	-0.101832786676861	0.0503792831767471	-2.02132265994333	0.043618487580154	*  
df.mm.trans2:probe6	-0.0360388873462959	0.0503792831767471	-0.715351332408991	0.474625499840961	   
df.mm.trans3:probe2	-0.00839142696304918	0.0503792831767471	-0.166565032964230	0.867759400227915	   
df.mm.trans3:probe3	-0.280721749867774	0.0503792831767471	-5.57216641774972	3.56526263589135e-08	***
df.mm.trans3:probe4	0.19305679476715	0.0503792831767471	3.83206712350081	0.000138250961553096	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.47466916841074	0.170165173774913	26.2960338425631	3.84322389212944e-107	***
df.mm.trans1	-0.0639418395120617	0.151109967567395	-0.423147728382272	0.672314723472246	   
df.mm.trans2	-0.0302391088036005	0.137454165909779	-0.219994123884528	0.825938525756599	   
df.mm.exp2	-0.142923072980974	0.185308641733659	-0.771270414827145	0.440801468920823	   
df.mm.exp3	-0.270027066245991	0.185308641733660	-1.45717470982328	0.145507188140668	   
df.mm.exp4	0.0345501263817594	0.185308641733660	0.186446385114719	0.852147606187694	   
df.mm.exp5	-0.253215337869391	0.185308641733659	-1.36645185837222	0.172226735228886	   
df.mm.exp6	-0.329608844310594	0.185308641733660	-1.77870196029138	0.0757134482503215	.  
df.mm.exp7	-0.0733816797399597	0.185308641733660	-0.395997073063812	0.692225325933411	   
df.mm.exp8	0.118268672308746	0.185308641733659	0.638225347734896	0.523531354378069	   
df.mm.trans1:exp2	0.189179900758275	0.175959249127511	1.07513473543629	0.282677075485143	   
df.mm.trans2:exp2	0.108338655347148	0.148170947758887	0.731173397928476	0.464912790320164	   
df.mm.trans1:exp3	0.331853949998793	0.175959249127511	1.88597048262187	0.0597040216801879	.  
df.mm.trans2:exp3	0.119995323689177	0.148170947758887	0.80984380206868	0.418299387399689	   
df.mm.trans1:exp4	0.00836641694643208	0.175959249127511	0.0475474690186321	0.962090175747912	   
df.mm.trans2:exp4	0.0433431161939943	0.148170947758887	0.292521016093688	0.769973158232324	   
df.mm.trans1:exp5	0.368119257908712	0.175959249127511	2.09207108881188	0.0367842869664391	*  
df.mm.trans2:exp5	0.0785899504525526	0.148170947758887	0.530400538305519	0.595998936913645	   
df.mm.trans1:exp6	0.361391730139745	0.175959249127511	2.05383764668067	0.0403545303434257	*  
df.mm.trans2:exp6	0.233140695295063	0.148170947758887	1.57345754226020	0.116055276708906	   
df.mm.trans1:exp7	0.126441566425949	0.175959249127511	0.718584371397956	0.472631798462794	   
df.mm.trans2:exp7	0.0685630581020073	0.148170947758887	0.4627294293452	0.643699112252748	   
df.mm.trans1:exp8	0.09773742899422	0.175959249127511	0.555454910604862	0.578757287350094	   
df.mm.trans2:exp8	-0.0710481350537404	0.148170947758887	-0.479501117650636	0.631728802961659	   
df.mm.trans1:probe2	-0.052348591260461	0.0963768499488088	-0.543165617970149	0.587185063631263	   
df.mm.trans1:probe3	0.0383777315043044	0.0963768499488088	0.398204875182048	0.6905980455072	   
df.mm.trans1:probe4	-0.0646831109909536	0.0963768499488088	-0.671147801835301	0.502343132103525	   
df.mm.trans1:probe5	0.0525904898294423	0.0963768499488088	0.545675541972746	0.585459182214795	   
df.mm.trans1:probe6	-0.100970806514533	0.0963768499488088	-1.04766659802810	0.295146257714557	   
df.mm.trans1:probe7	0.0772873981282436	0.0963768499488088	0.801929074972831	0.422860425528567	   
df.mm.trans1:probe8	-0.0938179917501075	0.0963768499488088	-0.973449451812749	0.330659178525708	   
df.mm.trans1:probe9	-0.064926168023692	0.0963768499488088	-0.673669745983377	0.500739008551817	   
df.mm.trans1:probe10	-0.143905937271533	0.0963768499488088	-1.49315875490815	0.135836759435104	   
df.mm.trans1:probe11	-0.095443558346379	0.0963768499488088	-0.990316226324832	0.322354589195136	   
df.mm.trans1:probe12	-0.127664356954464	0.0963768499488088	-1.32463716154112	0.185714739945269	   
df.mm.trans1:probe13	-0.120044413621739	0.0963768499488088	-1.24557311932795	0.213329169316847	   
df.mm.trans1:probe14	0.077785050503711	0.0963768499488088	0.807092684031767	0.419881479751745	   
df.mm.trans1:probe15	-0.026458180012996	0.0963768499488088	-0.274528375092665	0.783757900445764	   
df.mm.trans1:probe16	-0.0131829025826995	0.0963768499488088	-0.136784949805909	0.891239285660307	   
df.mm.trans1:probe17	-0.08556965412144	0.0963768499488088	-0.887865230777836	0.374911818831702	   
df.mm.trans1:probe18	0.0605510166808811	0.0963768499488088	0.628273456883507	0.53002544452405	   
df.mm.trans1:probe19	-0.122858626235829	0.0963768499488088	-1.27477320851518	0.202803628896542	   
df.mm.trans1:probe20	-0.0637638707406171	0.0963768499488088	-0.661609824086238	0.50843446243695	   
df.mm.trans1:probe21	0.0687483777580978	0.0963768499488088	0.71332874849732	0.475875105730885	   
df.mm.trans1:probe22	-0.152487237103575	0.0963768499488088	-1.58219777036259	0.114046644290864	   
df.mm.trans2:probe2	0.0636909015741854	0.0963768499488088	0.660852700705774	0.508919648752851	   
df.mm.trans2:probe3	-0.0188983159384549	0.0963768499488088	-0.196087711400537	0.844597230771342	   
df.mm.trans2:probe4	0.0151143054420822	0.0963768499488088	0.156825061724991	0.875426992256936	   
df.mm.trans2:probe5	0.0479029362581263	0.0963768499488088	0.497037787431009	0.619315198644143	   
df.mm.trans2:probe6	0.00838151216953998	0.0963768499488088	0.0869660315105949	0.930722861482628	   
df.mm.trans3:probe2	-0.0690588270268177	0.0963768499488088	-0.716549950153992	0.473885815076063	   
df.mm.trans3:probe3	-0.0110300691281378	0.0963768499488088	-0.114447288264729	0.908915359565137	   
df.mm.trans3:probe4	-0.0523662854085849	0.0963768499488088	-0.543349211313709	0.587058740644436