chr6.20500_chr6_53680454_53769259_+_2.R 

fitVsDatCorrelation=0.828233502243828
cont.fitVsDatCorrelation=0.261593730568644

fstatistic=7713.72143372572,44,508
cont.fstatistic=2592.62086868593,44,508

residuals=-0.575247483529408,-0.0831927802773157,-0.0032240754274642,0.0764438568279603,1.07890174645409
cont.residuals=-0.514660950049581,-0.189887525867892,-0.0702082899532736,0.139325340497456,0.99304908026032

predictedValues:
Include	Exclude	Both
chr6.20500_chr6_53680454_53769259_+_2.R.tl.Lung	57.568413210955	43.6956114077168	49.442997158375
chr6.20500_chr6_53680454_53769259_+_2.R.tl.cerebhem	58.858344261703	50.9479368832613	53.8592388007516
chr6.20500_chr6_53680454_53769259_+_2.R.tl.cortex	53.6247545010904	45.9251287318744	51.6877828437523
chr6.20500_chr6_53680454_53769259_+_2.R.tl.heart	55.4255366886742	44.4631899939029	52.1122826993549
chr6.20500_chr6_53680454_53769259_+_2.R.tl.kidney	57.8426216508846	48.6313261337937	48.9002893230377
chr6.20500_chr6_53680454_53769259_+_2.R.tl.liver	56.6079822872515	47.9891869302796	51.5814553584029
chr6.20500_chr6_53680454_53769259_+_2.R.tl.stomach	57.0169422375662	45.5819716911697	53.3106515061628
chr6.20500_chr6_53680454_53769259_+_2.R.tl.testicle	59.0384175454707	47.6974436503641	52.8193953021186


diffExp=13.8728018032382,7.91040737844172,7.69962576921606,10.9623466947713,9.21129551709091,8.61879535697192,11.4349705463965,11.3409738951065
diffExpScore=0.987812490331832
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=1,0,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,0,0,1,0,0,1,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	49.9899630168827	52.2970014543177	55.8831368256059
cerebhem	53.4319025424334	61.8430878260358	52.692762725876
cortex	53.148581943149	57.8430655728235	55.0492278347614
heart	49.5092491876539	54.0044879564113	50.603651697663
kidney	57.2868783119262	54.3350691558998	58.5538443060586
liver	55.9733602131206	54.9131280614103	58.9384618259644
stomach	57.888397658958	46.8485205604765	52.6305455746217
testicle	51.4368254230559	55.9229531205991	56.3215859542249
cont.diffExp=-2.30703843743500,-8.4111852836024,-4.69448362967448,-4.49523876875737,2.95180915602641,1.06023215171032,11.0398770984815,-4.48612769754315
cont.diffExpScore=3.81409780228797

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

tran.correlation=0.512942449519509
cont.tran.correlation=-0.290873293642293

tran.covariance=0.000811548750462033
cont.tran.covariance=-0.00140727486152659

tran.mean=51.9321754878724
cont.tran.mean=54.1670295003221

weightedLogRatios:
wLogRatio
Lung	1.07949928001957
cerebhem	0.577745890713565
cortex	0.605192916641965
heart	0.860546321157287
kidney	0.688801215027702
liver	0.653026945471205
stomach	0.879996336751802
testicle	0.84716999771573

cont.weightedLogRatios:
wLogRatio
Lung	-0.177506626103815
cerebhem	-0.592298882713937
cortex	-0.33987316007694
heart	-0.34290397835298
kidney	0.21275072780901
liver	0.0767865404121601
stomach	0.836385482637105
testicle	-0.332990766773164

varWeightedLogRatios=0.029473423914238
cont.varWeightedLogRatios=0.203230524100513

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93404966064890	0.0818332150750058	48.0739960790135	3.5704074126292e-191	***
df.mm.trans1	-0.0157650182088414	0.0705166565213838	-0.223564459611904	0.82318609525446	   
df.mm.trans2	-0.168440452743974	0.0658590378221148	-2.55759054966657	0.0108292392210478	*  
df.mm.exp2	0.0901626501297044	0.0885438301684102	1.01828269635744	0.309028334858468	   
df.mm.exp3	-0.065599485291422	0.0885438301684102	-0.740870201420606	0.459114454838488	   
df.mm.exp4	-0.0730998527311308	0.0885438301684102	-0.825578163855064	0.409430955193674	   
df.mm.exp5	0.122809209872197	0.0885438301684102	1.38698777361014	0.166053743559879	   
df.mm.exp6	0.0345622290184952	0.0885438301684102	0.390340342774396	0.69644851786494	   
df.mm.exp7	-0.0426766889331427	0.0885438301684102	-0.48198376840003	0.630025060370109	   
df.mm.exp8	0.0467864488456967	0.0885438301684102	0.528398746210876	0.597453363919345	   
df.mm.trans1:exp2	-0.0680030733617424	0.0799259418646365	-0.850826049406001	0.395266843959743	   
df.mm.trans2:exp2	0.0633939441449265	0.0702588251040839	0.902291549154323	0.367329623006226	   
df.mm.trans1:exp3	-0.00536375107044501	0.0799259418646365	-0.0671090129851597	0.946521322357957	   
df.mm.trans2:exp3	0.115364247872079	0.0702588251040839	1.6419894255444	0.101211057198660	   
df.mm.trans1:exp4	0.0351662558068428	0.0799259418646366	0.439985503910617	0.660134737597643	   
df.mm.trans2:exp4	0.0905138368378281	0.0702588251040839	1.28829135277651	0.198231021712877	   
df.mm.trans1:exp5	-0.118057342665876	0.0799259418646366	-1.47708415955634	0.140272725892638	   
df.mm.trans2:exp5	-0.0157889875638636	0.0702588251040839	-0.224726040329784	0.82228276812203	   
df.mm.trans1:exp6	-0.0513862594743535	0.0799259418646365	-0.642923414795434	0.520563873335762	   
df.mm.trans2:exp6	0.0591658126200757	0.0702588251040839	0.84211218352179	0.400121430190368	   
df.mm.trans1:exp7	0.0330511093096207	0.0799259418646365	0.413521674422009	0.679398954133969	   
df.mm.trans2:exp7	0.0849412980233888	0.0702588251040839	1.20897692065806	0.227234103153682	   
df.mm.trans1:exp8	-0.021572107535635	0.0799259418646365	-0.269901198939510	0.78734586897994	   
df.mm.trans2:exp8	0.040843683771066	0.0702588251040839	0.581331721823682	0.561274745862804	   
df.mm.trans1:probe2	-0.0941516205525944	0.0466666960145511	-2.01753345733405	0.0441652089939653	*  
df.mm.trans1:probe3	-0.0470600255722272	0.0466666960145511	-1.00842848522110	0.313728883865817	   
df.mm.trans1:probe4	-0.00699519983958172	0.0466666960145511	-0.149897045151869	0.880905336804684	   
df.mm.trans1:probe5	-0.0888558472301304	0.0466666960145511	-1.90405267179027	0.0574680333962452	.  
df.mm.trans1:probe6	-0.0463140445967523	0.0466666960145511	-0.992443188656663	0.321453855670058	   
df.mm.trans1:probe7	0.500782331248131	0.0466666960145511	10.7310432067439	2.36427645901799e-24	***
df.mm.trans1:probe8	0.657807894128072	0.0466666960145511	14.0958745809423	2.55166465652863e-38	***
df.mm.trans1:probe9	0.196398993815091	0.0466666960145511	4.20854722078142	3.03940794153217e-05	***
df.mm.trans1:probe10	0.558013332042959	0.0466666960145511	11.9574210239560	3.29746935828135e-29	***
df.mm.trans1:probe11	0.374984632564885	0.0466666960145511	8.0353799302175	6.57168382927242e-15	***
df.mm.trans1:probe12	0.285109236543758	0.0466666960145511	6.10947979807396	1.99054506915452e-09	***
df.mm.trans2:probe2	0.0811917138929102	0.0466666960145511	1.73982134641788	0.0824960231270817	.  
df.mm.trans2:probe3	0.0216343998736838	0.0466666960145511	0.46359399146102	0.64313722567924	   
df.mm.trans2:probe4	0.0108121612554168	0.0466666960145511	0.231689024053588	0.816872852900217	   
df.mm.trans2:probe5	0.0451718123048019	0.0466666960145511	0.967966797793375	0.333521558429927	   
df.mm.trans2:probe6	-0.0307869866164207	0.0466666960145511	-0.659720726893136	0.509732037296182	   
df.mm.trans3:probe2	0.104561744164759	0.0466666960145511	2.24060739445011	0.0254829190842511	*  
df.mm.trans3:probe3	0.094985629357547	0.0466666960145511	2.03540506334388	0.0423286183372103	*  
df.mm.trans3:probe4	0.19031079234169	0.0466666960145511	4.0780858426821	5.27034006982111e-05	***
df.mm.trans3:probe5	0.365023052892627	0.0466666960145511	7.82191764291196	3.03259659625025e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.80631206521177	0.140945755220906	27.0055104479457	2.91026211504606e-100	***
df.mm.trans1	0.0459543156977128	0.121454636726052	0.378366087425428	0.705316719642294	   
df.mm.trans2	0.159871896914604	0.113432569103537	1.40940029991456	0.159328697187194	   
df.mm.exp2	0.293031598938925	0.152503809141552	1.92147068711535	0.0552315735256918	.  
df.mm.exp3	0.177098551430960	0.152503809141552	1.16127297034647	0.246076272765216	   
df.mm.exp4	0.121704302555918	0.152503809141552	0.79804106691495	0.425219549650739	   
df.mm.exp5	0.127796096956789	0.152503809141552	0.837986261957372	0.402432510610849	   
df.mm.exp6	0.108635820489752	0.152503809141552	0.712348242980067	0.476576192707373	   
df.mm.exp7	0.096641129443102	0.152503809141552	0.63369649576032	0.526564120180098	   
df.mm.exp8	0.0877527853452383	0.152503809141552	0.575413727953428	0.565266303227751	   
df.mm.trans1:exp2	-0.226445851087208	0.137660755813249	-1.64495574464523	0.100597533224960	   
df.mm.trans2:exp2	-0.125370299260324	0.12101055978495	-1.03602776057827	0.300682100559547	   
df.mm.trans1:exp3	-0.115829372893243	0.137660755813249	-0.841411716861255	0.400513222401497	   
df.mm.trans2:exp3	-0.0763040099294746	0.12101055978495	-0.630556623034187	0.52861402209707	   
df.mm.trans1:exp4	-0.131367043775046	0.137660755813249	-0.954281000412778	0.340395282528519	   
df.mm.trans2:exp4	-0.0895761849989948	0.12101055978495	-0.740234448614917	0.459499717706294	   
df.mm.trans1:exp5	0.00845325513268535	0.137660755813249	0.0614064268552548	0.95105968531896	   
df.mm.trans2:exp5	-0.0895652735316503	0.12101055978495	-0.740144279068029	0.459554374678285	   
df.mm.trans1:exp6	0.00441780096438268	0.137660755813249	0.0320919418049389	0.974411334195312	   
df.mm.trans2:exp6	-0.0598224096166728	0.12101055978495	-0.49435693647715	0.62126782534847	   
df.mm.trans1:exp7	0.0500536035853744	0.137660755813249	0.363601109769275	0.716307163397622	   
df.mm.trans2:exp7	-0.206660735333285	0.12101055978495	-1.70779092089604	0.0882860982683306	.  
df.mm.trans1:exp8	-0.0592206671059335	0.137660755813249	-0.430192808081576	0.667237829178926	   
df.mm.trans2:exp8	-0.0207169149329662	0.12101055978495	-0.171199232280081	0.864135280655984	   
df.mm.trans1:probe2	0.0511580773009864	0.0803765647898185	0.636480016715852	0.524750277487742	   
df.mm.trans1:probe3	0.085455582306461	0.0803765647898185	1.0631902785336	0.288200800787373	   
df.mm.trans1:probe4	0.158424807442040	0.0803765647898185	1.97103232585661	0.0492625634880943	*  
df.mm.trans1:probe5	0.101334160099033	0.0803765647898185	1.26074261028719	0.207980326982483	   
df.mm.trans1:probe6	0.0664048237102967	0.0803765647898185	0.826171457861413	0.409094680001161	   
df.mm.trans1:probe7	0.154185121573784	0.0803765647898185	1.91828453949198	0.0556351474020819	.  
df.mm.trans1:probe8	0.0777287161037247	0.0803765647898185	0.9670569563029	0.333975722038832	   
df.mm.trans1:probe9	0.0295548910198517	0.0803765647898185	0.367705326759567	0.713246116530411	   
df.mm.trans1:probe10	0.117296218416491	0.0803765647898185	1.45933355976603	0.145091384206762	   
df.mm.trans1:probe11	0.102714138529397	0.0803765647898185	1.27791152555462	0.201864252343082	   
df.mm.trans1:probe12	0.0681931634900173	0.0803765647898185	0.848420975297211	0.396603155616592	   
df.mm.trans2:probe2	-0.0398351849747371	0.0803765647898185	-0.495606960547574	0.62038606576673	   
df.mm.trans2:probe3	0.0566180367356164	0.0803765647898185	0.704409760278638	0.481500418970296	   
df.mm.trans2:probe4	-0.0372695889564162	0.0803765647898185	-0.463687258268312	0.643070438529737	   
df.mm.trans2:probe5	-0.0718384688774727	0.0803765647898185	-0.893773814112701	0.371866149646623	   
df.mm.trans2:probe6	-0.0093689828813429	0.0803765647898185	-0.116563614106206	0.907251938994796	   
df.mm.trans3:probe2	0.0349575350430916	0.0803765647898185	0.434921984218959	0.663803756517698	   
df.mm.trans3:probe3	-0.0212479045765272	0.0803765647898185	-0.264354474865773	0.791614001840686	   
df.mm.trans3:probe4	0.0122274216727864	0.0803765647898185	0.152126701418014	0.8791474027802	   
df.mm.trans3:probe5	0.0296848979680613	0.0803765647898185	0.369322800068480	0.712041024673347