chr2.14418_chr2_95102149_95142386_-_0.R 

fitVsDatCorrelation=0.939439666443801
cont.fitVsDatCorrelation=0.275507368234925

fstatistic=5056.47865128847,53,715
cont.fstatistic=630.905543985964,53,715

residuals=-1.19904548033444,-0.137665969770195,0.00264827136131353,0.126665285309137,0.991268270476736
cont.residuals=-1.29630109680079,-0.457592043692058,-0.119277094572889,0.233371482626367,2.90438387285379

predictedValues:
Include	Exclude	Both
chr2.14418_chr2_95102149_95142386_-_0.R.tl.Lung	71.2509885703413	90.780357821309	73.0571859823928
chr2.14418_chr2_95102149_95142386_-_0.R.tl.cerebhem	109.022277172157	100.834947773145	79.2446227446573
chr2.14418_chr2_95102149_95142386_-_0.R.tl.cortex	94.4205232286912	95.4267197379452	91.7255067591561
chr2.14418_chr2_95102149_95142386_-_0.R.tl.heart	75.574287064624	99.20712864838	69.1011679210228
chr2.14418_chr2_95102149_95142386_-_0.R.tl.kidney	80.6830529054262	86.1455879447328	86.8390238765564
chr2.14418_chr2_95102149_95142386_-_0.R.tl.liver	106.404635476390	96.9093829252195	100.779082767385
chr2.14418_chr2_95102149_95142386_-_0.R.tl.stomach	97.2387779154424	141.483056712678	84.6622550652877
chr2.14418_chr2_95102149_95142386_-_0.R.tl.testicle	703.982149446754	205.672549402235	542.957427100446


diffExp=-19.5293692509677,8.18732939901192,-1.00619650925404,-23.6328415837560,-5.46253503930663,9.49525255117067,-44.244278797236,498.30960004452
diffExpScore=1.44136836774798
diffExp1.5=0,0,0,0,0,0,0,1
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,1
diffExp1.4Score=2
diffExp1.3=0,0,0,-1,0,0,-1,1
diffExp1.3Score=1.5
diffExp1.2=-1,0,0,-1,0,0,-1,1
diffExp1.2Score=1.33333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	108.278679832825	114.56391348217	126.680410770540
cerebhem	101.046089140683	137.137338629653	129.634204877749
cortex	75.7986831774536	122.668762856100	106.449849393264
heart	88.2722890389473	152.030657228675	105.305122676966
kidney	85.4950336016005	122.715029481061	95.2142787157226
liver	109.104128014665	134.349547878171	93.9116505515462
stomach	112.781408907231	108.83030515895	129.784938640380
testicle	69.2521909127513	86.4342034815014	105.193089827992
cont.diffExp=-6.28523364934496,-36.0912494889698,-46.8700796786467,-63.7583681897277,-37.2199958794606,-25.2454198635052,3.95110374828072,-17.1820125687501
cont.diffExpScore=1.03004862763780

cont.diffExp1.5=0,0,-1,-1,0,0,0,0
cont.diffExp1.5Score=0.666666666666667
cont.diffExp1.4=0,0,-1,-1,-1,0,0,0
cont.diffExp1.4Score=0.75
cont.diffExp1.3=0,-1,-1,-1,-1,0,0,0
cont.diffExp1.3Score=0.8
cont.diffExp1.2=0,-1,-1,-1,-1,-1,0,-1
cont.diffExp1.2Score=0.857142857142857

tran.correlation=0.915375009823628
cont.tran.correlation=0.275619929673007

tran.covariance=0.193741480589965
cont.tran.covariance=0.0116528553370629

tran.mean=140.939776421592
cont.tran.mean=108.047391301402

weightedLogRatios:
wLogRatio
Lung	-1.06276055633787
cerebhem	0.363209284987448
cortex	-0.0482631660431176
heart	-1.21385463847100
kidney	-0.289770178743490
liver	0.431892250866176
stomach	-1.78680253489035
testicle	7.31084719976592

cont.weightedLogRatios:
wLogRatio
Lung	-0.265924615942221
cerebhem	-1.45626200311752
cortex	-2.19944392452933
heart	-2.58359133458479
kidney	-1.67300929125500
liver	-0.998328000248604
stomach	0.167881639074317
testicle	-0.963767673727971

varWeightedLogRatios=8.27619832657681
cont.varWeightedLogRatios=0.860765146224624

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.92754799825502	0.124075764626815	39.7140248385789	4.77998707700904e-183	***
df.mm.trans1	-0.685740010457106	0.0976002653503256	-7.02600559533026	4.961733977516e-12	***
df.mm.trans2	-0.449428457682164	0.0976002653503256	-4.60478725205295	4.88677230022407e-06	***
df.mm.exp2	0.449088551788448	0.128894290445525	3.48416171295228	0.000523769400228345	***
df.mm.exp3	0.103907453683255	0.128894290445525	0.806144735535587	0.420427434136845	   
df.mm.exp4	0.203345219416517	0.128894290445525	1.5776123109383	0.115097002860041	   
df.mm.exp5	-0.100897932673701	0.128894290445525	-0.782795982079158	0.434006111322644	   
df.mm.exp6	0.144685537533540	0.128894290445525	1.12251316201387	0.262021110248183	   
df.mm.exp7	0.607270551277387	0.128894290445525	4.71138441569715	2.95691300196808e-06	***
df.mm.exp8	1.10259829600768	0.128894290445525	8.55428345349148	7.18940981378432e-17	***
df.mm.trans1:exp2	-0.0237450064393403	0.0969039406527925	-0.245036541129104	0.80649844687271	   
df.mm.trans2:exp2	-0.344046490792563	0.0969039406527925	-3.55038699638939	0.000409859349933627	***
df.mm.trans1:exp3	0.177642309198333	0.0969039406527925	1.83317941460014	0.0671914914658377	.  
df.mm.trans2:exp3	-0.0539917719859361	0.0969039406527925	-0.557167970902123	0.577587038724321	   
df.mm.trans1:exp4	-0.144437806001110	0.0969039406527925	-1.49052561772107	0.136527088535845	   
df.mm.trans2:exp4	-0.114578284984863	0.0969039406527925	-1.18239035701755	0.237443897084086	   
df.mm.trans1:exp5	0.225217791000388	0.0969039406527925	2.32413449322298	0.0203976183600650	*  
df.mm.trans2:exp5	0.0484937420420063	0.0969039406527925	0.500431063126316	0.61692557931532	   
df.mm.trans1:exp6	0.256354911208473	0.0969039406527926	2.64545393594461	0.00833737679288335	** 
df.mm.trans2:exp6	-0.0793521309630881	0.0969039406527925	-0.818874138951762	0.413131068850376	   
df.mm.trans1:exp7	-0.296309663346899	0.0969039406527925	-3.05776691175624	0.00231309794817316	** 
df.mm.trans2:exp7	-0.163533520562076	0.0969039406527925	-1.68758380165382	0.0919272788525019	.  
df.mm.trans1:exp8	1.18794601020544	0.0969039406527926	12.2590062096840	1.70704628757631e-31	***
df.mm.trans2:exp8	-0.284755896579174	0.0969039406527925	-2.93853784129849	0.00340382652574044	** 
df.mm.trans1:probe2	0.043416026501223	0.0736038266434667	0.589860996107282	0.55547019846906	   
df.mm.trans1:probe3	0.27616507706056	0.0736038266434667	3.75204781672956	0.00018961110901439	***
df.mm.trans1:probe4	-0.120622583400706	0.0736038266434667	-1.63880859055054	0.101693045502247	   
df.mm.trans1:probe5	0.104594705961630	0.0736038266434667	1.42104983845856	0.155738240599892	   
df.mm.trans1:probe6	0.330865127954618	0.0736038266434667	4.49521639081773	8.1055748511141e-06	***
df.mm.trans2:probe2	0.128692090368065	0.0736038266434667	1.74844293071124	0.080816517842803	.  
df.mm.trans2:probe3	0.186823602635172	0.0736038266434667	2.53823219735756	0.0113524832413377	*  
df.mm.trans2:probe4	0.171908460663523	0.0736038266434667	2.33559134766510	0.0197881633522061	*  
df.mm.trans2:probe5	0.0322961758774657	0.0736038266434667	0.438783924019424	0.660950665305985	   
df.mm.trans2:probe6	0.268688020515296	0.0736038266434667	3.65046265619867	0.000280846429120461	***
df.mm.trans3:probe2	1.0626956434394	0.0736038266434667	14.438048833888	1.18491354389437e-41	***
df.mm.trans3:probe3	0.0227048298331724	0.0736038266434667	0.308473497487481	0.757811914627288	   
df.mm.trans3:probe4	0.557959993225922	0.0736038266434667	7.58058403578189	1.06984435037551e-13	***
df.mm.trans3:probe5	0.875936490347772	0.0736038266434667	11.9006922641504	6.36654411463502e-30	***
df.mm.trans3:probe6	0.314721917480208	0.0736038266434667	4.27589069525836	2.16221262452017e-05	***
df.mm.trans3:probe7	0.207783736858507	0.0736038266434667	2.82300182387257	0.00488988759011835	** 
df.mm.trans3:probe8	-0.000240354095249269	0.0736038266434667	-0.00326551086010151	0.997395414795542	   
df.mm.trans3:probe9	0.862674335586288	0.0736038266434667	11.7205093121726	3.82549679512823e-29	***
df.mm.trans3:probe10	0.589476253522674	0.0736038266434667	8.00877183163408	4.70058575109386e-15	***
df.mm.trans3:probe11	1.11762387452480	0.0736038266434667	15.1843175211327	2.42221014276114e-45	***
df.mm.trans3:probe12	0.625279224182616	0.0736038266434667	8.49519994675599	1.14263733351264e-16	***
df.mm.trans3:probe13	0.654245026860313	0.0736038266434667	8.88873658742559	4.97834392285222e-18	***
df.mm.trans3:probe14	0.224751411775148	0.0736038266434667	3.05352889957520	0.00234558868484577	** 
df.mm.trans3:probe15	0.636537225222416	0.0736038266434667	8.64815396495309	3.42569386126543e-17	***
df.mm.trans3:probe16	0.151387893643337	0.0736038266434667	2.05679379112519	0.0400683536050728	*  
df.mm.trans3:probe17	0.184565272048587	0.0736038266434667	2.50754995311062	0.0123777077846786	*  
df.mm.trans3:probe18	0.0323349580096539	0.0736038266434667	0.439310827768273	0.660569064981597	   
df.mm.trans3:probe19	1.07311184805959	0.0736038266434667	14.5795659953618	2.40747746657372e-42	***
df.mm.trans3:probe20	0.783709899087431	0.0736038266434667	10.6476787257772	1.12558958504501e-24	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.66886845677623	0.348027183155649	13.4152407706847	9.19758598802045e-37	***
df.mm.trans1	-0.0231031008918344	0.273764546422764	-0.084390404797549	0.932769658452342	   
df.mm.trans2	0.0700061851993434	0.273764546422764	0.25571676871276	0.7982430926754	   
df.mm.exp2	0.087669288824191	0.36154294082752	0.242486517987403	0.808472741173845	   
df.mm.exp3	-0.114278990375817	0.36154294082752	-0.316086908277753	0.752028792151732	   
df.mm.exp4	0.263472685506196	0.36154294082752	0.728745207701042	0.466396152945073	   
df.mm.exp5	0.118019532665036	0.36154294082752	0.326432960895063	0.744192328759187	   
df.mm.exp6	0.466219574484441	0.36154294082752	1.28952752726227	0.197631723811405	   
df.mm.exp7	-0.034811087753651	0.36154294082752	-0.0962847944810467	0.92332136770647	   
df.mm.exp8	-0.542833030576292	0.36154294082752	-1.50143446123944	0.133684696995794	   
df.mm.trans1:exp2	-0.156800820454315	0.271811385595804	-0.576873629155053	0.564206398547703	   
df.mm.trans2:exp2	0.0921807430024877	0.271811385595804	0.33913495860532	0.734607643266386	   
df.mm.trans1:exp3	-0.242348361966808	0.271811385595804	-0.891604895194462	0.372904688129609	   
df.mm.trans2:exp3	0.182633864619957	0.271811385595804	0.671913960556246	0.501855516548058	   
df.mm.trans1:exp4	-0.467754727079845	0.271811385595804	-1.72087981544459	0.0857052502342346	.  
df.mm.trans2:exp4	0.0194766436853458	0.271811385595804	0.0716549957708851	0.942896510175126	   
df.mm.trans1:exp5	-0.354269517451341	0.271811385595804	-1.30336526071118	0.192869621559088	   
df.mm.trans2:exp5	-0.0492875624027084	0.271811385595804	-0.181330014173877	0.856159938305413	   
df.mm.trans1:exp6	-0.458625117890808	0.271811385595804	-1.68729178465248	0.0919834154550642	.  
df.mm.trans2:exp6	-0.306907468293756	0.271811385595804	-1.12911925164953	0.259226211715646	   
df.mm.trans1:exp7	0.0755543260710295	0.271811385595804	0.277966009059614	0.78111880982948	   
df.mm.trans2:exp7	-0.0165319401572014	0.271811385595804	-0.0608213674381734	0.95151846936264	   
df.mm.trans1:exp8	0.0958795403615948	0.271811385595804	0.352742914545059	0.724385142280925	   
df.mm.trans2:exp8	0.261083638066007	0.271811385595804	0.960532383489815	0.337112044661353	   
df.mm.trans1:probe2	0.234620794677719	0.206455567960822	1.13642270341792	0.256160437386295	   
df.mm.trans1:probe3	0.239840886099437	0.206455567960822	1.16170703686205	0.245742137365604	   
df.mm.trans1:probe4	-0.0152572028173304	0.206455567960822	-0.0739006604085664	0.941110099643043	   
df.mm.trans1:probe5	0.211217470310332	0.206455567960822	1.02306502264164	0.306623113952467	   
df.mm.trans1:probe6	0.342093884020159	0.206455567960822	1.65698550733723	0.0979611016960461	.  
df.mm.trans2:probe2	0.193031662377998	0.206455567960822	0.934979202956778	0.350114675351995	   
df.mm.trans2:probe3	0.106453507117825	0.206455567960822	0.515624297127344	0.606276229579887	   
df.mm.trans2:probe4	-0.0570630167794578	0.206455567960822	-0.276393692565784	0.782325574452384	   
df.mm.trans2:probe5	0.0636547883498473	0.206455567960822	0.308321974449857	0.757927150229597	   
df.mm.trans2:probe6	-0.247363178532260	0.206455567960822	-1.19814244282916	0.231258568069687	   
df.mm.trans3:probe2	0.168632694644644	0.206455567960822	0.816798967013788	0.414315382547583	   
df.mm.trans3:probe3	0.248804154506345	0.206455567960822	1.20512203649339	0.228554898698334	   
df.mm.trans3:probe4	-0.000837928119749708	0.206455567960822	-0.00405863657747762	0.99676281751691	   
df.mm.trans3:probe5	-0.0120701110446790	0.206455567960822	-0.0584634803696331	0.953395786272067	   
df.mm.trans3:probe6	0.175436289927725	0.206455567960822	0.84975325035078	0.395746724397217	   
df.mm.trans3:probe7	0.221165726845288	0.206455567960822	1.07125096711975	0.284418062313867	   
df.mm.trans3:probe8	0.318448173410402	0.206455567960822	1.54245379069085	0.123405805151856	   
df.mm.trans3:probe9	0.26050098524089	0.206455567960822	1.26177747499803	0.207440329204618	   
df.mm.trans3:probe10	0.310120378941421	0.206455567960822	1.50211680897980	0.133508441697601	   
df.mm.trans3:probe11	-0.028249881528566	0.206455567960822	-0.136832742306698	0.891201521653944	   
df.mm.trans3:probe12	0.0649595291262086	0.206455567960822	0.314641691516577	0.753125513164073	   
df.mm.trans3:probe13	0.323586600980466	0.206455567960822	1.56734257243124	0.117477075746955	   
df.mm.trans3:probe14	0.0721328520425817	0.206455567960822	0.349386808769769	0.726901810155917	   
df.mm.trans3:probe15	-0.065685209735117	0.206455567960822	-0.318156639629026	0.750459024276339	   
df.mm.trans3:probe16	0.191607804947849	0.206455567960822	0.92808252565128	0.353677950938518	   
df.mm.trans3:probe17	0.136175550118193	0.206455567960822	0.659587684959091	0.509730849162747	   
df.mm.trans3:probe18	-0.0631761495179079	0.206455567960822	-0.306003612021239	0.759690971317439	   
df.mm.trans3:probe19	0.159807843810723	0.206455567960822	0.774054414657632	0.439154475948206	   
df.mm.trans3:probe20	0.0365591587119262	0.206455567960822	0.177080032633772	0.85949567512528