chr7.22050_chr7_133913535_133917333_+_2.R 

fitVsDatCorrelation=0.907743983652383
cont.fitVsDatCorrelation=0.221583535302023

fstatistic=6870.72047269273,61,899
cont.fstatistic=1259.68192137206,61,899

residuals=-0.971045221690765,-0.110238403778446,0.00295302068582443,0.114585067677791,0.950044014406185
cont.residuals=-0.78032414597717,-0.312018087357719,-0.112166559012579,0.170656255516014,2.09899820098445

predictedValues:
Include	Exclude	Both
chr7.22050_chr7_133913535_133917333_+_2.R.tl.Lung	50.3844540988757	52.171907334769	68.8709319457769
chr7.22050_chr7_133913535_133917333_+_2.R.tl.cerebhem	51.5965070223561	48.291937212475	64.960494333495
chr7.22050_chr7_133913535_133917333_+_2.R.tl.cortex	53.2541826000666	48.599053982953	66.5902472195337
chr7.22050_chr7_133913535_133917333_+_2.R.tl.heart	60.5129161350767	50.4615169039999	76.0015854547209
chr7.22050_chr7_133913535_133917333_+_2.R.tl.kidney	158.410314869851	55.1137354689344	201.104925955316
chr7.22050_chr7_133913535_133917333_+_2.R.tl.liver	72.2673195215606	51.468884979437	98.1219094194426
chr7.22050_chr7_133913535_133917333_+_2.R.tl.stomach	65.170821004871	51.5973421938688	88.6146781442365
chr7.22050_chr7_133913535_133917333_+_2.R.tl.testicle	51.5505144851338	49.0813444522194	67.5806678168476


diffExp=-1.78745323589337,3.30456980988117,4.65512861711363,10.0513992310769,103.296579400917,20.7984345421236,13.5734788110022,2.46917003291438
diffExpScore=1.01636302161855
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,1,1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,1,1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,0,0,1,1,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	70.8882935507475	57.4789976016025	73.5672932061009
cerebhem	75.9772041064433	67.620312990156	71.8989132003753
cortex	75.5950451467086	74.7852398082042	72.9442279953772
heart	63.733036286022	62.0465316106224	73.530937180001
kidney	71.2017940054828	75.18733915214	77.0517734389976
liver	76.4025554900131	69.1197471541835	71.1886000034382
stomach	71.1416994574882	91.0449293666248	73.8123644880427
testicle	68.6537470961596	66.9306390753682	73.6292312730519
cont.diffExp=13.409295949145,8.35689111628726,0.809805338504404,1.68650467539963,-3.9855451466572,7.28280833582961,-19.9032299091366,1.72310802079143
cont.diffExpScore=5.50666472167124

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

tran.correlation=0.815848652178922
cont.tran.correlation=0.237544195508182

tran.covariance=0.0137455719618436
cont.tran.covariance=0.00235980201083248

tran.mean=60.620797016653
cont.tran.mean=71.1129444936229

weightedLogRatios:
wLogRatio
Lung	-0.137253676891449
cerebhem	0.258824185143371
cortex	0.359425717970404
heart	0.728769222277513
kidney	4.79042771758944
liver	1.39514066224326
stomach	0.948235483687629
testicle	0.192308903309275

cont.weightedLogRatios:
wLogRatio
Lung	0.871508745350846
cerebhem	0.497814385407835
cortex	0.0465273416165437
heart	0.111063030853695
kidney	-0.233803964019132
liver	0.429345773925787
stomach	-1.08243276702181
testicle	0.107175257275714

varWeightedLogRatios=2.495602325008
cont.varWeightedLogRatios=0.339172527754591

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.7877946584584	0.0942244009728457	40.1997212967158	4.92616396229426e-203	***
df.mm.trans1	-0.0304672867967009	0.0805548100176762	-0.378218095108354	0.705357872994623	   
df.mm.trans2	0.176869633437526	0.0710195951930377	2.49043426615962	0.0129382690697423	*  
df.mm.exp2	0.00494663448335236	0.0903199719010604	0.0547678921863603	0.956335548766256	   
df.mm.exp3	0.0181296326378706	0.0903199719010604	0.200726730271023	0.840957658738611	   
df.mm.exp4	0.0513209830323983	0.0903199719010604	0.568213009284558	0.570032230990983	   
df.mm.exp5	0.128768120491809	0.0903199719010604	1.42568822577654	0.154305473280475	   
df.mm.exp6	-0.0068538665066455	0.0903199719010604	-0.0758842851961188	0.939528042603772	   
df.mm.exp7	-0.00580816199565204	0.0903199719010604	-0.0643065080004067	0.948740467363464	   
df.mm.exp8	-0.0192734118524695	0.0903199719010603	-0.2133903659047	0.83107087632617	   
df.mm.trans1:exp2	0.0188246652700998	0.0821090653646003	0.229264152338235	0.818715760559629	   
df.mm.trans2:exp2	-0.0822261955359033	0.058637601363584	-1.40227761067609	0.161177563698926	   
df.mm.trans1:exp3	0.0372640382696009	0.0821090653646003	0.453835859708452	0.65005650758534	   
df.mm.trans2:exp3	-0.0890697436499531	0.058637601363584	-1.51898682037954	0.129117399192697	   
df.mm.trans1:exp4	0.131853171977091	0.0821090653646003	1.60582965342017	0.108662520734790	   
df.mm.trans2:exp4	-0.0846541551149971	0.058637601363584	-1.44368379924167	0.149176314926730	   
df.mm.trans1:exp5	1.01673779881863	0.0821090653646003	12.3827715527375	1.25179570520213e-32	***
df.mm.trans2:exp5	-0.0739133291986223	0.058637601363584	-1.26051078966073	0.207812218979306	   
df.mm.trans1:exp6	0.36754320425558	0.0821090653646003	4.47628045239009	8.57106638175643e-06	***
df.mm.trans2:exp6	-0.0067128599253246	0.058637601363584	-0.114480465933477	0.908882492797259	   
df.mm.trans1:exp7	0.26313732307884	0.0821090653646003	3.20472924530799	0.00139954465193538	** 
df.mm.trans2:exp7	-0.00526585107190621	0.058637601363584	-0.0898033164633588	0.92846351540793	   
df.mm.trans1:exp8	0.0421529255010341	0.0821090653646003	0.513377241768088	0.607813599837992	   
df.mm.trans2:exp8	-0.0417917519607389	0.058637601363584	-0.712712508508117	0.476208614064143	   
df.mm.trans1:probe2	0.262429968941853	0.0594936700237959	4.4110569886996	1.15322913981469e-05	***
df.mm.trans1:probe3	0.213695679523294	0.0594936700237959	3.59190615468539	0.000346068884800655	***
df.mm.trans1:probe4	0.560618739478179	0.0594936700237959	9.42316618312413	3.59219991099099e-20	***
df.mm.trans1:probe5	0.0731891152608323	0.0594936700237959	1.23020004029939	0.218944013419212	   
df.mm.trans1:probe6	0.138446619671169	0.0594936700237959	2.32708151330712	0.0201822284703227	*  
df.mm.trans1:probe7	0.424875731832109	0.0594936700237959	7.14152836196136	1.90467500404119e-12	***
df.mm.trans1:probe8	0.619110708018609	0.0594936700237959	10.4063290728405	5.03504201510547e-24	***
df.mm.trans1:probe9	0.0678064050174956	0.0594936700237959	1.13972469660007	0.254704583386900	   
df.mm.trans1:probe10	0.0983981834113348	0.0594936700237959	1.65392693663003	0.0984914511444516	.  
df.mm.trans1:probe11	0.102515178551662	0.0594936700237959	1.72312749424700	0.0852093345012263	.  
df.mm.trans1:probe12	0.161432052498189	0.0594936700237959	2.7134324111056	0.00678638268794381	** 
df.mm.trans1:probe13	0.154464066217023	0.0594936700237959	2.59631093787359	0.00957688078890221	** 
df.mm.trans1:probe14	0.149120110175894	0.0594936700237959	2.5064869946038	0.0123693182728821	*  
df.mm.trans1:probe15	0.436742259622368	0.0594936700237959	7.34098702345447	4.74509334146706e-13	***
df.mm.trans1:probe16	0.407930055291228	0.0594936700237959	6.85669677342256	1.30908052552157e-11	***
df.mm.trans1:probe17	0.379271983726	0.0594936700237959	6.37499726566375	2.92098568906018e-10	***
df.mm.trans1:probe18	0.389680754414501	0.0594936700237959	6.54995320104877	9.67310132348253e-11	***
df.mm.trans1:probe19	0.416662490756083	0.0594936700237959	7.0034760099592	4.88906416799283e-12	***
df.mm.trans1:probe20	0.463690281732028	0.0594936700237959	7.79394314633076	1.79146798294864e-14	***
df.mm.trans2:probe2	0.00765724023211666	0.0594936700237959	0.128706805766966	0.897618437149688	   
df.mm.trans2:probe3	-0.0722172912766013	0.0594936700237959	-1.21386512628514	0.225118023995278	   
df.mm.trans2:probe4	-0.0297781026426708	0.0594936700237959	-0.500525562312097	0.616827569279334	   
df.mm.trans2:probe5	-0.0737959586647964	0.0594936700237959	-1.24040017425854	0.215151043404882	   
df.mm.trans2:probe6	-0.0342681984117082	0.0594936700237959	-0.575997385906801	0.564761109263101	   
df.mm.trans3:probe2	0.0406926878371787	0.0594936700237959	0.683983486325566	0.494161857727415	   
df.mm.trans3:probe3	0.443447668261458	0.0594936700237959	7.4536949575323	2.13263795525822e-13	***
df.mm.trans3:probe4	1.07629232226686	0.0594936700237959	18.0908712109435	1.27191128753455e-62	***
df.mm.trans3:probe5	-0.0187959586047919	0.0594936700237959	-0.315932074744658	0.752127397710939	   
df.mm.trans3:probe6	0.412705395228525	0.0594936700237959	6.93696312672346	7.65635673491132e-12	***
df.mm.trans3:probe7	0.156376694040802	0.0594936700237959	2.62845936346263	0.00872356944854346	** 
df.mm.trans3:probe8	0.359641438097529	0.0594936700237959	6.04503702584967	2.18707775115992e-09	***
df.mm.trans3:probe9	0.0470723497752842	0.0594936700237959	0.791216103435147	0.429026586232554	   
df.mm.trans3:probe10	0.606299074118792	0.0594936700237959	10.1909845850204	3.72798588270221e-23	***
df.mm.trans3:probe11	0.258984931956122	0.0594936700237959	4.35315104703635	1.49606173286973e-05	***
df.mm.trans3:probe12	0.29309682245747	0.0594936700237959	4.92652112973093	9.96186466375993e-07	***
df.mm.trans3:probe13	-0.0649826188336436	0.0594936700237959	-1.09226105580060	0.275010990767316	   
df.mm.trans3:probe14	0.561689941897336	0.0594936700237959	9.44117150064326	3.07262493591019e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97288016321990	0.219014859772851	18.1397744762175	6.63361908784648e-63	***
df.mm.trans1	0.280287899907528	0.187241311569966	1.49693407698009	0.134761401458159	   
df.mm.trans2	0.0629750599925897	0.165077692420781	0.381487401895994	0.70293176180712	   
df.mm.exp2	0.254756237493101	0.209939418838007	1.21347500580477	0.225266983249162	   
df.mm.exp3	0.3359917397189	0.209939418838007	1.60042235792868	0.109856248470610	   
df.mm.exp4	-0.0294429557008549	0.209939418838007	-0.140245009078422	0.888497826003695	   
df.mm.exp5	0.226698902434597	0.209939418838007	1.07983009426887	0.280507457419180	   
df.mm.exp6	0.292199528982549	0.209939418838007	1.39182784538437	0.164318778843026	   
df.mm.exp7	0.460176122481558	0.209939418838007	2.19194720566812	0.0286391247660125	*  
df.mm.exp8	0.119366059309096	0.209939418838007	0.568573829392189	0.569787386347328	   
df.mm.trans1:exp2	-0.185428195953768	0.190854017125461	-0.971570830662023	0.331525427443115	   
df.mm.trans2:exp2	-0.0922674335389914	0.136297030360137	-0.676958502288669	0.498606432289791	   
df.mm.trans1:exp3	-0.271706306855088	0.190854017125461	-1.42363420454744	0.154899350127900	   
df.mm.trans2:exp3	-0.0727908249550604	0.136297030360137	-0.5340602415381	0.593431887015745	   
df.mm.trans1:exp4	-0.0769593009313464	0.190854017125461	-0.403236474088759	0.686870150904926	   
df.mm.trans2:exp4	0.105907947125627	0.136297030360137	0.777037818401374	0.437340866955625	   
df.mm.trans1:exp5	-0.222286195414238	0.190854017125461	-1.16469225412277	0.244452550205113	   
df.mm.trans2:exp5	0.0418643300307181	0.136297030360137	0.307155114972793	0.758796442739563	   
df.mm.trans1:exp6	-0.217288692333671	0.190854017125461	-1.13850730315429	0.255212009896042	   
df.mm.trans2:exp6	-0.107778684541085	0.136297030360137	-0.79076326355976	0.429290704703506	   
df.mm.trans1:exp7	-0.456607775116887	0.190854017125461	-2.39244518922926	0.0169409697862010	*  
df.mm.trans2:exp7	-0.000242630482733058	0.136297030360137	-0.00178015971508665	0.998580033732849	   
df.mm.trans1:exp8	-0.151395653761296	0.190854017125461	-0.793253692227887	0.427839335748193	   
df.mm.trans2:exp8	0.0328711641947707	0.136297030360137	0.241173003607749	0.809476026608793	   
df.mm.trans1:probe2	-0.0681489084931709	0.138286873284437	-0.492808224487063	0.622268438845579	   
df.mm.trans1:probe3	-0.0204512162885798	0.138286873284437	-0.147889787387950	0.88246291032019	   
df.mm.trans1:probe4	0.0383002311907975	0.138286873284437	0.276962160479392	0.781872829012703	   
df.mm.trans1:probe5	-0.00175465943689262	0.138286873284437	-0.0126885466076272	0.989879091399786	   
df.mm.trans1:probe6	-0.00840247260829065	0.138286873284437	-0.0607611728338663	0.951562917439095	   
df.mm.trans1:probe7	0.147947039641259	0.138286873284437	1.06985598941811	0.284971292879336	   
df.mm.trans1:probe8	-0.0942277704488249	0.138286873284437	-0.681393455581365	0.495798046506625	   
df.mm.trans1:probe9	-0.0376308262243436	0.138286873284437	-0.272121462656417	0.785591110529243	   
df.mm.trans1:probe10	0.051052174943739	0.138286873284437	0.369175856906763	0.712083582383399	   
df.mm.trans1:probe11	0.0953149657339838	0.138286873284437	0.689255339065582	0.490840451172361	   
df.mm.trans1:probe12	0.0683161522774948	0.138286873284437	0.494017621882143	0.621414413436867	   
df.mm.trans1:probe13	0.0174449424318041	0.138286873284437	0.126150385914954	0.899641090310683	   
df.mm.trans1:probe14	0.0676075126876361	0.138286873284437	0.48889320498683	0.625036550378669	   
df.mm.trans1:probe15	0.0209231563062362	0.138286873284437	0.151302548168836	0.879771003362176	   
df.mm.trans1:probe16	0.0447540633134641	0.138286873284437	0.323632042944605	0.746291895712737	   
df.mm.trans1:probe17	-0.163334428262899	0.138286873284437	-1.18112749521021	0.237864449344432	   
df.mm.trans1:probe18	0.0908312254511615	0.138286873284437	0.656831869098191	0.511457132151769	   
df.mm.trans1:probe19	0.0887598334916793	0.138286873284437	0.641852920552427	0.521132305393972	   
df.mm.trans1:probe20	-0.0674346973027508	0.138286873284437	-0.487643517429503	0.625921260093304	   
df.mm.trans2:probe2	-0.0265345042698992	0.138286873284437	-0.191880137569684	0.847879426985214	   
df.mm.trans2:probe3	-0.0586268259242428	0.138286873284437	-0.423950766488555	0.671703138731905	   
df.mm.trans2:probe4	0.148627902436445	0.138286873284437	1.07477954274617	0.282761813828751	   
df.mm.trans2:probe5	0.0626007697530264	0.138286873284437	0.452687722747663	0.65088284298995	   
df.mm.trans2:probe6	0.185220633302524	0.138286873284437	1.33939418039737	0.180780892313435	   
df.mm.trans3:probe2	-0.148511613189768	0.138286873284437	-1.07393861515908	0.283138360133647	   
df.mm.trans3:probe3	0.129283761146606	0.138286873284437	0.934895395896956	0.350093255078178	   
df.mm.trans3:probe4	-0.0101143806627996	0.138286873284437	-0.0731405694739781	0.941710527793804	   
df.mm.trans3:probe5	0.10154065766752	0.138286873284437	0.734275461262798	0.462972336059086	   
df.mm.trans3:probe6	-0.152300894869224	0.138286873284437	-1.10134021582773	0.271043393904768	   
df.mm.trans3:probe7	-0.0708811350693054	0.138286873284437	-0.512565895705174	0.608380922506105	   
df.mm.trans3:probe8	0.0730901293925557	0.138286873284437	0.528539894326914	0.597255117376439	   
df.mm.trans3:probe9	0.0785286736935239	0.138286873284437	0.56786788093763	0.570266474560539	   
df.mm.trans3:probe10	-0.11800891694948	0.138286873284437	-0.853363114999005	0.393685277860981	   
df.mm.trans3:probe11	0.029758923742491	0.138286873284437	0.215197025109397	0.82966251904012	   
df.mm.trans3:probe12	-0.00112454071507730	0.138286873284437	-0.00813194114790832	0.993513525351648	   
df.mm.trans3:probe13	-0.0746823461516485	0.138286873284437	-0.540053761994005	0.589293706988333	   
df.mm.trans3:probe14	-0.08777446520226	0.138286873284437	-0.634727383138673	0.525767725823475