chr17.10613_chr17_21549158_21551640_+_2.R 

fitVsDatCorrelation=0.962733464354915
cont.fitVsDatCorrelation=0.270025389009769

fstatistic=6691.52547940196,50,646
cont.fstatistic=516.040270834635,50,646

residuals=-1.08127868002185,-0.0903730315420265,3.28804103588686e-05,0.075011685069647,1.28687337794291
cont.residuals=-0.842290071606576,-0.34627071424135,-0.176431671270402,-0.000855094353254969,2.53625483617673

predictedValues:
Include	Exclude	Both
chr17.10613_chr17_21549158_21551640_+_2.R.tl.Lung	67.2452765554442	44.0906983196082	46.2256621709327
chr17.10613_chr17_21549158_21551640_+_2.R.tl.cerebhem	83.663143950131	52.3275222855427	50.0703171005578
chr17.10613_chr17_21549158_21551640_+_2.R.tl.cortex	60.591283307423	45.4328496138641	48.4478700119385
chr17.10613_chr17_21549158_21551640_+_2.R.tl.heart	63.3642506260989	45.507999732935	48.9382393852585
chr17.10613_chr17_21549158_21551640_+_2.R.tl.kidney	66.3497590466598	44.8319906313003	48.71758694344
chr17.10613_chr17_21549158_21551640_+_2.R.tl.liver	66.4796599181953	49.0027031739143	48.3232463188151
chr17.10613_chr17_21549158_21551640_+_2.R.tl.stomach	68.5067426919865	47.3448502614696	48.7802711478711
chr17.10613_chr17_21549158_21551640_+_2.R.tl.testicle	70.395561608375	47.915851953838	48.2647268722597


diffExp=23.1545782358360,31.3356216645882,15.1584336935588,17.8562508931639,21.5177684153595,17.4769567442809,21.1618924305169,22.4797096545371
diffExpScore=0.99415687203637
diffExp1.5=1,1,0,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,1,0,0,1,0,1,1
diffExp1.4Score=0.833333333333333
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	60.0277550428561	70.2163848542668	71.0066834974556
cerebhem	70.3226978484456	74.547145242016	56.8347183740937
cortex	54.42763200627	62.3808900461428	51.528668544674
heart	61.6505936181712	78.6558588270019	57.9545148134649
kidney	67.9381585019997	63.0945863008372	59.5540336705425
liver	63.84660016034	79.2871290593026	68.3652863189964
stomach	70.8426428430432	63.3094810594917	89.200872630821
testicle	58.6916606954601	50.8342804504595	74.17395805448
cont.diffExp=-10.1886298114107,-4.22444739357044,-7.95325803987272,-17.0052652088307,4.84357220116241,-15.4405288989626,7.53316178355142,7.8573802450006
cont.diffExpScore=2.10934318069893

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,-1,0,-1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.819745912826671
cont.tran.correlation=0.246586929826461

tran.covariance=0.00430012790000605
cont.tran.covariance=0.00385833423737226

tran.mean=57.6906339797991
cont.tran.mean=65.6295935347565

weightedLogRatios:
wLogRatio
Lung	1.68725117515646
cerebhem	1.96728062499898
cortex	1.14020156529621
heart	1.31855002658922
kidney	1.56765362246043
liver	1.23362085290871
stomach	1.49348647934428
testicle	1.56250427123449

cont.weightedLogRatios:
wLogRatio
Lung	-0.6542510729619
cerebhem	-0.249814941108190
cortex	-0.554422137535846
heart	-1.03366047728794
kidney	0.309284423469452
liver	-0.923719161600217
stomach	0.472666889202704
testicle	0.574969264564351

varWeightedLogRatios=0.0707384058694529
cont.varWeightedLogRatios=0.405614213463924

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79728290941176	0.0937219929133006	40.5164550109867	1.56609826155964e-179	***
df.mm.trans1	0.0693032264104955	0.0813961458990776	0.851431301138312	0.39484531315914	   
df.mm.trans2	-0.0312834087860280	0.073967193214484	-0.422936269804303	0.672482510034625	   
df.mm.exp2	0.309832142768207	0.0979450954683138	3.1633247309299	0.00163293554580345	** 
df.mm.exp3	-0.121162443215751	0.0979450954683137	-1.23704451597526	0.21652005158344	   
df.mm.exp4	-0.0848316359825924	0.0979450954683138	-0.866114179346901	0.386749104305898	   
df.mm.exp5	-0.0492385172427126	0.0979450954683138	-0.502715495934574	0.615335646864852	   
df.mm.exp6	0.0497982487209763	0.0979450954683138	0.508430243320214	0.611325225760256	   
df.mm.exp7	0.0360037626982883	0.0979450954683137	0.367591276787677	0.71329841851416	   
df.mm.exp8	0.085815088092058	0.0979450954683138	0.87615503034371	0.381271430934055	   
df.mm.trans1:exp2	-0.091380376294082	0.0903009162043915	-1.01195403252883	0.311938902794751	   
df.mm.trans2:exp2	-0.138558509130732	0.0742705459291961	-1.86559163390051	0.062551574748529	.  
df.mm.trans1:exp3	0.0169667072121366	0.0903009162043915	0.187890753774118	0.851021293261535	   
df.mm.trans2:exp3	0.151149008648957	0.0742705459291961	2.03511374203511	0.0422471491029111	*  
df.mm.trans1:exp4	0.0253846892189078	0.0903009162043915	0.281112200029630	0.778714357861869	   
df.mm.trans2:exp4	0.116470927043868	0.0742705459291961	1.56819807349878	0.117324586525361	   
df.mm.trans1:exp5	0.0358318675429278	0.0903009162043915	0.39680513829809	0.691642212903647	   
df.mm.trans2:exp5	0.0659116406761275	0.0742705459291961	0.887453294593561	0.375165182250054	   
df.mm.trans1:exp6	-0.0612489926589221	0.0903009162043915	-0.67827653620134	0.497839257286312	   
df.mm.trans2:exp6	0.0558283769349304	0.0742705459291961	0.75168933035921	0.452511674371339	   
df.mm.trans1:exp7	-0.0174183677361491	0.0903009162043914	-0.192892480700014	0.847103800484434	   
df.mm.trans2:exp7	0.0352054542739708	0.0742705459291961	0.474016365889285	0.635648404312972	   
df.mm.trans1:exp8	-0.040031651172525	0.0903009162043915	-0.443313898188092	0.657687129414783	   
df.mm.trans2:exp8	-0.00261753767743221	0.0742705459291961	-0.0352432804240805	0.97189663788258	   
df.mm.trans1:probe2	0.0108252848255726	0.0552977920016451	0.195763419003249	0.844856904100462	   
df.mm.trans1:probe3	1.78132617121839	0.0552977920016451	32.2133326980831	1.67123337076276e-136	***
df.mm.trans1:probe4	2.25779042008130	0.0552977920016451	40.8296667616336	4.36663565121093e-181	***
df.mm.trans1:probe5	2.42958399217285	0.0552977920016451	43.9363653453029	3.21141500984358e-196	***
df.mm.trans1:probe6	-0.0180829001947242	0.0552977920016451	-0.327009443599236	0.743766672311032	   
df.mm.trans1:probe7	0.205240698993762	0.0552977920016451	3.71155323864751	0.000223755790645454	***
df.mm.trans1:probe8	0.113458112647447	0.0552977920016451	2.05176569516685	0.0405950281778793	*  
df.mm.trans1:probe9	0.141601273497536	0.0552977920016451	2.56070393359148	0.0106721676719511	*  
df.mm.trans1:probe10	-0.0289379948781032	0.0552977920016451	-0.523311941229811	0.600936647232736	   
df.mm.trans1:probe11	0.118791520729961	0.0552977920016451	2.14821453859183	0.0320678742449031	*  
df.mm.trans1:probe12	0.337621297508593	0.0552977920016451	6.10551136469516	1.76842482444429e-09	***
df.mm.trans1:probe13	0.195181307350414	0.0552977920016451	3.52964015895259	0.000445684343414755	***
df.mm.trans1:probe14	0.0510582071907042	0.0552977920016451	0.923331752363372	0.35617908229881	   
df.mm.trans1:probe15	0.163211747728236	0.0552977920016451	2.95150568983623	0.00327693092807249	** 
df.mm.trans1:probe16	0.0621423465531597	0.0552977920016451	1.1237762721396	0.261525375905505	   
df.mm.trans1:probe17	0.0396833058562494	0.0552977920016451	0.71762912079869	0.473245414389817	   
df.mm.trans2:probe2	0.0881413577335965	0.0552977920016451	1.59393991230200	0.111438613453054	   
df.mm.trans2:probe3	0.0087764545453628	0.0552977920016451	0.158712567494588	0.873944939212265	   
df.mm.trans2:probe4	0.0072528516360574	0.0552977920016451	0.131159877700752	0.8956896699256	   
df.mm.trans2:probe5	-0.00134459950242044	0.0552977920016451	-0.0243156092449485	0.980608371343299	   
df.mm.trans2:probe6	0.140165981050625	0.0552977920016451	2.53474824178251	0.0114877181030815	*  
df.mm.trans3:probe2	-0.0349141215354311	0.0552977920016451	-0.631383646102768	0.528013045723745	   
df.mm.trans3:probe3	0.0365862993504249	0.0552977920016451	0.661623150330061	0.508448685960437	   
df.mm.trans3:probe4	0.00452656531333328	0.0552977920016451	0.0818579756891309	0.93478500811271	   
df.mm.trans3:probe5	-0.0296494713466155	0.0552977920016451	-0.536178213873955	0.592019960443804	   
df.mm.trans3:probe6	0.0128449787398918	0.0552977920016451	0.232287371248198	0.816388389001199	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14514239171115	0.333665588545623	12.4230443114585	6.31147052635255e-32	***
df.mm.trans1	-0.00832174027986798	0.28978356181441	-0.0287170888085004	0.977099096252925	   
df.mm.trans2	0.147502377175415	0.263335277983361	0.560131473097709	0.575583946647153	   
df.mm.exp2	0.440764268037429	0.348700522777234	1.26401952175738	0.206678923335248	   
df.mm.exp3	0.104377902636947	0.348700522777234	0.299333943653501	0.764781535257154	   
df.mm.exp4	0.343291782795582	0.348700522777234	0.984488867585932	0.325243774788499	   
df.mm.exp5	0.192734079331918	0.348700522777234	0.552720935996548	0.580645712664622	   
df.mm.exp6	0.221079237232635	0.348700522777234	0.634008906760002	0.526299436797736	   
df.mm.exp7	-0.166009324469776	0.348700522777234	-0.476079941456901	0.634178391089134	   
df.mm.exp8	-0.389159248922796	0.348700522777234	-1.11602714507947	0.264825469190428	   
df.mm.trans1:exp2	-0.282476689399308	0.321485997202598	-0.878659387523162	0.379912680269655	   
df.mm.trans2:exp2	-0.380914207038943	0.264415263149543	-1.44059084374230	0.150184790546698	   
df.mm.trans1:exp3	-0.202312975752517	0.321485997202598	-0.629305716307827	0.529371408417875	   
df.mm.trans2:exp3	-0.222700609706056	0.264415263149543	-0.842238103252403	0.39996638692338	   
df.mm.trans1:exp4	-0.316615963699266	0.321485997202598	-0.984851491058062	0.325065734020323	   
df.mm.trans2:exp4	-0.229791349949074	0.264415263149543	-0.86905478606624	0.385139927236208	   
df.mm.trans1:exp5	-0.0689432610464263	0.321485997202598	-0.214451831950176	0.830262401090668	   
df.mm.trans2:exp5	-0.299680795294807	0.264415263149543	-1.13337177183043	0.257478604441575	   
df.mm.trans1:exp6	-0.159402942775491	0.321485997202598	-0.495831682134	0.620181809011139	   
df.mm.trans2:exp6	-0.0995851149895892	0.264415263149543	-0.376623927845148	0.706576773935837	   
df.mm.trans1:exp7	0.331663404591874	0.321485997202598	1.03165738936636	0.302618722887987	   
df.mm.trans2:exp7	0.0624627358947608	0.264415263149543	0.236229690944257	0.813329320353295	   
df.mm.trans1:exp8	0.366649859836894	0.321485997202598	1.14048469615252	0.254507068112943	   
df.mm.trans2:exp8	0.0661485016608243	0.264415263149543	0.250168998842602	0.802536194929617	   
df.mm.trans1:probe2	-0.0367700007967065	0.196869163149046	-0.186773795390538	0.851896633597519	   
df.mm.trans1:probe3	-0.100352753157228	0.196869163149046	-0.509743382620328	0.610405350292823	   
df.mm.trans1:probe4	-0.284251634031685	0.196869163149046	-1.44386063050658	0.149262963099326	   
df.mm.trans1:probe5	-0.0785387614059614	0.196869163149046	-0.398938869600929	0.690070085330444	   
df.mm.trans1:probe6	-0.22275890857429	0.196869163149046	-1.13150736769091	0.258261463595928	   
df.mm.trans1:probe7	-0.229502328174027	0.196869163149046	-1.16576067324609	0.24414145225323	   
df.mm.trans1:probe8	-0.0371524910582774	0.196869163149046	-0.188716660669451	0.850374163021601	   
df.mm.trans1:probe9	-0.0180562952153842	0.196869163149046	-0.0917172345661572	0.926951137068518	   
df.mm.trans1:probe10	0.0446525648478386	0.196869163149046	0.226813403041861	0.820640594846043	   
df.mm.trans1:probe11	-0.167053711718879	0.196869163149046	-0.84855194712442	0.396444967569854	   
df.mm.trans1:probe12	-0.143203596369588	0.196869163149046	-0.72740491237407	0.467241480293085	   
df.mm.trans1:probe13	0.271428439488406	0.196869163149046	1.37872501282952	0.168456773563091	   
df.mm.trans1:probe14	0.0380468065507619	0.196869163149046	0.193259350231287	0.846816605762176	   
df.mm.trans1:probe15	-0.127520794212795	0.196869163149046	-0.647743873001843	0.517380755707039	   
df.mm.trans1:probe16	0.294781722328471	0.196869163149046	1.49734837906177	0.134791060133769	   
df.mm.trans1:probe17	-0.170061337274272	0.196869163149046	-0.86382922827544	0.388002323875836	   
df.mm.trans2:probe2	-0.177972220318464	0.196869163149046	-0.90401268269589	0.366325666201174	   
df.mm.trans2:probe3	-0.207130276050055	0.196869163149046	-1.05212148381634	0.293137206223357	   
df.mm.trans2:probe4	-0.0579809099237516	0.196869163149046	-0.294514940767312	0.768459062258607	   
df.mm.trans2:probe5	-0.150062662731202	0.196869163149046	-0.762245647468883	0.446191619240647	   
df.mm.trans2:probe6	0.100389077745664	0.196869163149046	0.509927893936651	0.610276146460627	   
df.mm.trans3:probe2	0.00858985905550589	0.196869163149046	0.0436323237123869	0.96521096855515	   
df.mm.trans3:probe3	-0.0597686982893356	0.196869163149046	-0.303596039792609	0.761533421837653	   
df.mm.trans3:probe4	0.209994206112617	0.196869163149046	1.06666886145919	0.286519866587671	   
df.mm.trans3:probe5	-0.138499290880110	0.196869163149046	-0.703509318903612	0.481991877546372	   
df.mm.trans3:probe6	-0.149610209370896	0.196869163149046	-0.759947403533324	0.44756326945882