chr9.24948_chr9_21476339_21477395_+_0.R 

fitVsDatCorrelation=0.885316725799605
cont.fitVsDatCorrelation=0.239643331827275

fstatistic=8505.93580773657,54,738
cont.fstatistic=1940.626112538,54,738

residuals=-0.827018824962682,-0.097850986388761,-0.005594937365722,0.094495983724207,0.830702672934702
cont.residuals=-0.69639145926845,-0.255447036999835,-0.0817516673893435,0.172951724747274,1.60085284417752

predictedValues:
Include	Exclude	Both
chr9.24948_chr9_21476339_21477395_+_0.R.tl.Lung	74.0040887001069	65.2006933442114	80.8388993815257
chr9.24948_chr9_21476339_21477395_+_0.R.tl.cerebhem	72.2232356923566	61.4163983737003	78.6275008775843
chr9.24948_chr9_21476339_21477395_+_0.R.tl.cortex	86.8370925923827	59.4346807651003	91.5396853248226
chr9.24948_chr9_21476339_21477395_+_0.R.tl.heart	74.8694571783084	61.6688132651473	83.0567901977975
chr9.24948_chr9_21476339_21477395_+_0.R.tl.kidney	72.5214629374388	66.4716397836582	78.9664728075867
chr9.24948_chr9_21476339_21477395_+_0.R.tl.liver	70.5641308692095	64.9245945408373	78.191407667984
chr9.24948_chr9_21476339_21477395_+_0.R.tl.stomach	75.1737004089538	65.5526165330868	87.2305589402648
chr9.24948_chr9_21476339_21477395_+_0.R.tl.testicle	72.3817978821671	60.6314571326615	85.7535822824729


diffExp=8.8033953558955,10.8068373186563,27.4024118272824,13.2006439131611,6.04982315378057,5.63953632837217,9.62108387586699,11.7503407495056
diffExpScore=0.989392629667493
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	77.6254865963766	85.0775975963252	75.8531063795859
cerebhem	79.5851411058196	76.2462789221903	85.2238296998893
cortex	79.0716750579222	76.1809440618392	78.267498264264
heart	79.6605204979014	74.5910296830925	70.283890148628
kidney	75.2368260804838	73.3544726028603	76.9825211761003
liver	77.5169950102336	90.5877218254834	77.0592730848694
stomach	76.1448351419955	76.5663188703552	78.77415954082
testicle	79.517028954387	80.2414663951821	69.0329471431236
cont.diffExp=-7.4521109999486,3.33886218362936,2.89073099608299,5.0694908148089,1.88235347762350,-13.0707268152497,-0.421483728359618,-0.724437440795157
cont.diffExpScore=3.67334409523830

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=-0.52873010866428
cont.tran.correlation=-0.00882281578049838

tran.covariance=-0.00144585369779281
cont.tran.covariance=1.49273194753826e-05

tran.mean=68.9922412499579
cont.tran.mean=78.575271150153

weightedLogRatios:
wLogRatio
Lung	0.537097738774723
cerebhem	0.680549190635457
cortex	1.62068541264688
heart	0.818303515827528
kidney	0.369363062155418
liver	0.351079810377700
stomach	0.582212108723803
testicle	0.742820316191745

cont.weightedLogRatios:
wLogRatio
Lung	-0.403130847940416
cerebhem	0.186667100089138
cortex	0.162073189983586
heart	0.285693749275899
kidney	0.109152558045930
liver	-0.690041081497575
stomach	-0.0239315072313266
testicle	-0.0397277673642759

varWeightedLogRatios=0.162006163687581
cont.varWeightedLogRatios=0.110589046229931

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.982905746396	0.084643171465479	47.0552517992594	2.28674673194287e-224	***
df.mm.trans1	0.277844926437961	0.0721056343798117	3.85330395922226	0.000126675136113278	***
df.mm.trans2	0.137937771379133	0.0648256093515363	2.12782838077346	0.0336821540261792	*  
df.mm.exp2	-0.0564149997798126	0.0836740309699428	-0.67422352103579	0.500380345856549	   
df.mm.exp3	-0.0569929956972666	0.0836740309699428	-0.681131230760706	0.496002118113065	   
df.mm.exp4	-0.0711323912443984	0.0836740309699428	-0.85011311657676	0.395537906604347	   
df.mm.exp5	0.0225024085947764	0.0836740309699428	0.268929419724737	0.788059160184205	   
df.mm.exp6	-0.0185434683107642	0.0836740309699428	-0.221615572906071	0.824674464818239	   
df.mm.exp7	-0.055032318595724	0.0836740309699428	-0.657698905595842	0.510936708140727	   
df.mm.exp8	-0.153841320460978	0.0836740309699428	-1.83857905108265	0.066378792810364	.  
df.mm.trans1:exp2	0.0320564735162757	0.0750614229897742	0.427069888092087	0.669452988412428	   
df.mm.trans2:exp2	-0.00337822922463702	0.0579281752868835	-0.0583175494119515	0.953511475368217	   
df.mm.trans1:exp3	0.216906515714078	0.0750614229897743	2.88972027273754	0.00396893058955614	** 
df.mm.trans2:exp3	-0.0355992000343935	0.0579281752868835	-0.614540331334312	0.539047741732787	   
df.mm.trans1:exp4	0.0827580728892161	0.0750614229897742	1.10253802276691	0.270587247011857	   
df.mm.trans2:exp4	0.0154406337641241	0.0579281752868835	0.266547905016098	0.789891726921514	   
df.mm.trans1:exp5	-0.0427401944110391	0.0750614229897742	-0.569402933073381	0.569255988535001	   
df.mm.trans2:exp5	-0.00319712430532361	0.0579281752868835	-0.0551911792403294	0.95600109370888	   
df.mm.trans1:exp6	-0.0290549220215186	0.0750614229897742	-0.387081950544381	0.698807133844817	   
df.mm.trans2:exp6	0.0142998778112522	0.0579281752868835	0.246855312469856	0.805088852572312	   
df.mm.trans1:exp7	0.0707134153959272	0.0750614229897742	0.94207400525248	0.346463025872545	   
df.mm.trans2:exp7	0.0604153416183901	0.0579281752868835	1.04293534742272	0.297319750008438	   
df.mm.trans1:exp8	0.131675833394491	0.0750614229897742	1.75424110214949	0.0798042217683424	.  
df.mm.trans2:exp8	0.0811850704618896	0.0579281752868835	1.4014781245884	0.16149154159248	   
df.mm.trans1:probe2	0.766067935518933	0.0522900800823472	14.6503492500397	7.56262204319374e-43	***
df.mm.trans1:probe3	-0.115334946080756	0.0522900800823473	-2.2056754531476	0.0277142323086204	*  
df.mm.trans1:probe4	-0.123332026876342	0.0522900800823473	-2.35861231579903	0.0186031757766890	*  
df.mm.trans1:probe5	0.976316585151484	0.0522900800823472	18.6711625534703	5.18393235573303e-64	***
df.mm.trans1:probe6	0.346928054421725	0.0522900800823472	6.63468202525943	6.29255227932872e-11	***
df.mm.trans1:probe7	0.0273740775598749	0.0522900800823472	0.523504219476538	0.600780570718341	   
df.mm.trans1:probe8	0.0689788134791285	0.0522900800823472	1.31915677640003	0.187525844552739	   
df.mm.trans1:probe9	0.583307586837855	0.0522900800823472	11.1552245840751	8.19885052400139e-27	***
df.mm.trans1:probe10	-0.0815610171172156	0.0522900800823473	-1.55977992362551	0.119240597440249	   
df.mm.trans1:probe11	-0.328062874787211	0.0522900800823472	-6.27390270335354	5.99767865117643e-10	***
df.mm.trans1:probe12	-0.247431746944232	0.0522900800823472	-4.73190606238454	2.66599098293271e-06	***
df.mm.trans1:probe13	-0.295243617806862	0.0522900800823472	-5.6462644031508	2.33985014448246e-08	***
df.mm.trans1:probe14	-0.178131028712980	0.0522900800823472	-3.40659315174994	0.00069342352526121	***
df.mm.trans1:probe15	-0.122585446134755	0.0522900800823472	-2.34433464132596	0.0193253165641239	*  
df.mm.trans1:probe16	-0.149678888261178	0.0522900800823472	-2.86247196457648	0.00432258047255431	** 
df.mm.trans2:probe2	0.086306557240532	0.0522900800823473	1.65053404210923	0.0992592275815288	.  
df.mm.trans2:probe3	0.15304477679488	0.0522900800823472	2.92684150710541	0.00352947413773454	** 
df.mm.trans2:probe4	0.439837438953621	0.0522900800823473	8.41148910579134	2.08727149234284e-16	***
df.mm.trans2:probe5	0.224502196926299	0.0522900800823472	4.29339937083189	1.99451039067985e-05	***
df.mm.trans2:probe6	0.0589609787329138	0.0522900800823473	1.12757484096527	0.259865975073787	   
df.mm.trans3:probe2	-0.474122674588309	0.0522900800823472	-9.06716290817787	1.08621730844534e-18	***
df.mm.trans3:probe3	-0.500356660993623	0.0522900800823472	-9.56886392611473	1.58150009881900e-20	***
df.mm.trans3:probe4	-0.172228486510648	0.0522900800823472	-3.29371242574919	0.00103588964879694	** 
df.mm.trans3:probe5	0.174206782746825	0.0522900800823472	3.33154553354061	0.00090665283708571	***
df.mm.trans3:probe6	-0.175101678998802	0.0522900800823472	-3.3486596066223	0.000853256368668927	***
df.mm.trans3:probe7	0.119429097169544	0.0522900800823472	2.28397235157156	0.0226563717586574	*  
df.mm.trans3:probe8	-0.0425589813206329	0.0522900800823472	-0.8139016282555	0.415963831703095	   
df.mm.trans3:probe9	0.161193313059717	0.0522900800823472	3.08267481720945	0.00212786310854170	** 
df.mm.trans3:probe10	0.229023089550028	0.0522900800823472	4.37985731116415	1.35939926669251e-05	***
df.mm.trans3:probe11	0.611984367878387	0.0522900800823472	11.7036418172362	3.83439869859302e-29	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.50853299015271	0.176728576456126	25.5110581466828	2.03617497466390e-103	***
df.mm.trans1	-0.129671377238973	0.150551141902891	-0.86131115048343	0.389346447636896	   
df.mm.trans2	-0.0830518450375001	0.135350997135905	-0.613603496057797	0.539666446323007	   
df.mm.exp2	-0.201145969386185	0.174705083985361	-1.15134582690816	0.249962860347007	   
df.mm.exp3	-0.123327253090912	0.174705083985361	-0.705916795765633	0.480462653928486	   
df.mm.exp4	-0.0294091502809755	0.174705083985361	-0.168335972886970	0.86636509731265	   
df.mm.exp5	-0.194295002378015	0.174705083985361	-1.11213135843428	0.266443820630689	   
df.mm.exp6	0.0455800773799122	0.174705083985361	0.260897258054216	0.794244523022822	   
df.mm.exp7	-0.162451390130828	0.174705083985361	-0.929860691085788	0.352747266308671	   
df.mm.exp8	0.0597668300262997	0.174705083985361	0.342101263815012	0.732372084432696	   
df.mm.trans1:exp2	0.226077566500368	0.156722606231315	1.44253322438174	0.149576266072092	   
df.mm.trans2:exp2	0.091550829725725	0.120949673528327	0.756933252112363	0.449331433712969	   
df.mm.trans1:exp3	0.141786164656776	0.156722606231315	0.9046950409152	0.365922203371576	   
df.mm.trans2:exp3	0.0128748536434767	0.120949673528327	0.106448023114849	0.915255820653267	   
df.mm.trans1:exp4	0.0552874532200151	0.156722606231315	0.352772676192058	0.724359598397036	   
df.mm.trans2:exp4	-0.102134348211053	0.120949673528327	-0.844436741593461	0.398699048336767	   
df.mm.trans1:exp5	0.163040013196445	0.156722606231315	1.04030948129976	0.298536831688505	   
df.mm.trans2:exp5	0.0460347283520977	0.120949673528327	0.380610604470266	0.70360173774893	   
df.mm.trans1:exp6	-0.0469786833929717	0.156722606231315	-0.299756905035343	0.764446994800836	   
df.mm.trans2:exp6	0.0171748528585168	0.120949673528327	0.141999993530321	0.887118765886189	   
df.mm.trans1:exp7	0.143192833592213	0.156722606231315	0.913670573987694	0.361188364232853	   
df.mm.trans2:exp7	0.0570449158122413	0.120949673528327	0.471641751053435	0.637321923755206	   
df.mm.trans1:exp8	-0.0356914394059426	0.156722606231315	-0.227736382543714	0.81991426422616	   
df.mm.trans2:exp8	-0.118290164361891	0.120949673528327	-0.978011439891877	0.328389146324595	   
df.mm.trans1:probe2	-0.0791967403653314	0.109177754752479	-0.725392645643626	0.468441131953439	   
df.mm.trans1:probe3	-0.058460322559334	0.109177754752479	-0.535460018314827	0.592493114387065	   
df.mm.trans1:probe4	-0.00059098435940794	0.109177754752479	-0.00541304738083124	0.995682497004777	   
df.mm.trans1:probe5	0.160317509203226	0.109177754752479	1.46840818962332	0.142419461099962	   
df.mm.trans1:probe6	0.0757423651032688	0.109177754752479	0.693752727146545	0.488055589851305	   
df.mm.trans1:probe7	-0.0540667959444309	0.109177754752479	-0.495218060373269	0.620593614254178	   
df.mm.trans1:probe8	-0.0263306520569395	0.109177754752479	-0.2411723168024	0.809488553722687	   
df.mm.trans1:probe9	-0.0337027822730772	0.109177754752479	-0.308696422173969	0.757639584870146	   
df.mm.trans1:probe10	-0.00122255778339450	0.109177754752479	-0.0111978652259905	0.991068609244055	   
df.mm.trans1:probe11	-0.133182316258672	0.109177754752479	-1.21986678110953	0.222905062080539	   
df.mm.trans1:probe12	-0.177936074979480	0.109177754752479	-1.62978324094397	0.103574028168378	   
df.mm.trans1:probe13	-0.0955042744223328	0.109177754752479	-0.874759465779951	0.381989381662320	   
df.mm.trans1:probe14	-0.0764306788556834	0.109177754752479	-0.700057250938733	0.484112166841769	   
df.mm.trans1:probe15	-0.0800201540354878	0.109177754752479	-0.732934600248052	0.463831048313501	   
df.mm.trans1:probe16	-0.120526447724967	0.109177754752479	-1.10394693496141	0.269975971077352	   
df.mm.trans2:probe2	0.0323333597072567	0.109177754752479	0.296153367327996	0.767196275612704	   
df.mm.trans2:probe3	0.0895498675254006	0.109177754752479	0.820220819968525	0.412355069187641	   
df.mm.trans2:probe4	0.0501026397923076	0.109177754752479	0.458908867524317	0.646434734266941	   
df.mm.trans2:probe5	0.0539356147716957	0.109177754752479	0.494016522816167	0.621441510755277	   
df.mm.trans2:probe6	0.0814828511689695	0.109177754752479	0.746331991839387	0.455704561743495	   
df.mm.trans3:probe2	0.109669794930697	0.109177754752479	1.00450678051893	0.315463640625688	   
df.mm.trans3:probe3	0.171012168821141	0.109177754752479	1.56636458781232	0.117691947590537	   
df.mm.trans3:probe4	0.0477247459605891	0.109177754752479	0.437128846153576	0.662145786349066	   
df.mm.trans3:probe5	0.000661007739954212	0.109177754752479	0.00605441778366673	0.995170939213909	   
df.mm.trans3:probe6	-0.091246218073883	0.109177754752479	-0.83575833081337	0.403561370617241	   
df.mm.trans3:probe7	0.0892698011313548	0.109177754752479	0.817655586833984	0.413817774072544	   
df.mm.trans3:probe8	0.0693344888429519	0.109177754752479	0.635060585374218	0.525585740396497	   
df.mm.trans3:probe9	-0.0755219562604122	0.109177754752479	-0.69173391989633	0.489321995856636	   
df.mm.trans3:probe10	-0.0338170433203467	0.109177754752479	-0.309742981956486	0.75684385208933	   
df.mm.trans3:probe11	0.0757910148145596	0.109177754752479	0.694198328097042	0.487776301420723