chr1.1110_chr1_182334032_182339663_+_2.R 

fitVsDatCorrelation=0.910025280656074
cont.fitVsDatCorrelation=0.228535362709406

fstatistic=7342.04152272882,54,738
cont.fstatistic=1320.10172974676,54,738

residuals=-0.823789930648692,-0.0964951714252823,-0.0100515112397599,0.094034310299806,0.836023577485958
cont.residuals=-0.88090477262277,-0.318799251538849,-0.113292058571789,0.205612653827989,2.01841558567645

predictedValues:
Include	Exclude	Both
chr1.1110_chr1_182334032_182339663_+_2.R.tl.Lung	71.6050344593822	66.8330589851983	70.5279551730379
chr1.1110_chr1_182334032_182339663_+_2.R.tl.cerebhem	70.6246797563747	91.5353177714515	74.2460373751603
chr1.1110_chr1_182334032_182339663_+_2.R.tl.cortex	66.699081537814	59.4155462325929	66.6620102350471
chr1.1110_chr1_182334032_182339663_+_2.R.tl.heart	69.3993941234574	59.85833702333	63.0119009351205
chr1.1110_chr1_182334032_182339663_+_2.R.tl.kidney	60.0283358170865	63.3045783536295	59.0370661551492
chr1.1110_chr1_182334032_182339663_+_2.R.tl.liver	62.378766462305	62.390477051083	60.589494762894
chr1.1110_chr1_182334032_182339663_+_2.R.tl.stomach	104.218653821084	69.0736303736863	87.1785228736035
chr1.1110_chr1_182334032_182339663_+_2.R.tl.testicle	84.094209436561	67.2885517131323	84.7347958906783


diffExp=4.77197547418382,-20.9106380150768,7.28353530522105,9.54105710012745,-3.27624253654301,-0.0117105887780582,35.145023447398,16.8056577234287
diffExpScore=1.94137925911663
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,1,1
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	75.1049260933997	65.3097771949122	76.7952639166225
cerebhem	64.0402956496165	66.6483497946327	71.9634838073979
cortex	66.3515782389515	67.9833690705421	71.5162322471193
heart	73.4445054533684	67.0809733816701	72.4916346841283
kidney	72.3974039217185	67.4139645354452	70.5789109265573
liver	76.2822107249555	62.7080531169175	88.7441782877063
stomach	72.6469422297175	71.0133619685523	78.8263974706414
testicle	71.6168886459524	63.5790421965958	67.4870716249656
cont.diffExp=9.79514889848753,-2.60805414501620,-1.63179083159055,6.36353207169834,4.98343938627328,13.5741576080380,1.63358026116516,8.03784644935666
cont.diffExpScore=1.18177591758198

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

tran.correlation=0.171701261265276
cont.tran.correlation=-0.315909450302241

tran.covariance=0.0057055373303875
cont.tran.covariance=-0.00074642254375701

tran.mean=70.5467283073855
cont.tran.mean=68.9763526385592

weightedLogRatios:
wLogRatio
Lung	0.292193475407905
cerebhem	-1.13776107704778
cortex	0.479004481865465
heart	0.616130199164142
kidney	-0.219014389235080
liver	-0.000775889882295959
stomach	1.82659457310402
testicle	0.963237048930738

cont.weightedLogRatios:
wLogRatio
Lung	0.593775896722199
cerebhem	-0.166835229269597
cortex	-0.102214204736215
heart	0.385286133276161
kidney	0.302853780542052
liver	0.830134789086036
stomach	0.097210075890678
testicle	0.501403163732207

varWeightedLogRatios=0.75820090088785
cont.varWeightedLogRatios=0.119401565186675

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06152647591043	0.095156568099371	42.6825657653923	1.68291329446416e-201	***
df.mm.trans1	0.0577196767520238	0.0838244637556052	0.688577942118528	0.491305300873861	   
df.mm.trans2	0.187310316653838	0.0756373734008024	2.47642545255082	0.0134936637802672	*  
df.mm.exp2	0.249366052055931	0.100726467447002	2.47567554364135	0.0135217965437683	*  
df.mm.exp3	-0.132242212758720	0.100726467447002	-1.31288444944522	0.189629982556257	   
df.mm.exp4	-0.0288188338646885	0.100726467447003	-0.286109843769232	0.774874284512	   
df.mm.exp5	-0.0527451975930338	0.100726467447003	-0.523647844800929	0.600680702478453	   
df.mm.exp6	-0.0548380153025724	0.100726467447002	-0.544425082031449	0.58631344320654	   
df.mm.exp7	0.196352092159670	0.100726467447002	1.94935945969694	0.0516309619458471	.  
df.mm.exp8	-0.0159525852272079	0.100726467447002	-0.158375307221028	0.874204396167159	   
df.mm.trans1:exp2	-0.26315178219585	0.0950094565946015	-2.76974305114384	0.00575099884930798	** 
df.mm.trans2:exp2	0.0651609792146682	0.0777350099410375	0.83824494605575	0.402164550749527	   
df.mm.trans1:exp3	0.0612680103644922	0.0950094565946015	0.644862233302927	0.519216869748926	   
df.mm.trans2:exp3	0.0146002728612898	0.0777350099410375	0.187821071514164	0.851068545447465	   
df.mm.trans1:exp4	-0.00246841404343863	0.0950094565946015	-0.0259807195190177	0.979279740258376	   
df.mm.trans2:exp4	-0.0813982983075244	0.0777350099410375	-1.04712533476571	0.295384594406183	   
df.mm.trans1:exp5	-0.123603473221353	0.0950094565946015	-1.30095969024178	0.193678291246513	   
df.mm.trans2:exp5	-0.00149500112138664	0.0777350099410375	-0.019232018141126	0.984661213765462	   
df.mm.trans1:exp6	-0.0831024334568161	0.0950094565946014	-0.874675389539467	0.382035109565379	   
df.mm.trans2:exp6	-0.0139471854396564	0.0777350099410375	-0.179419613507935	0.8576574798697	   
df.mm.trans1:exp7	0.178973655360509	0.0950094565946014	1.88374570043250	0.0599922997492148	.  
df.mm.trans2:exp7	-0.163376902761664	0.0777350099410375	-2.10171585345633	0.0359165698125895	*  
df.mm.trans1:exp8	0.176724911351573	0.0950094565946014	1.86007706691393	0.0632722573262538	.  
df.mm.trans2:exp8	0.0227448460229636	0.0777350099410375	0.292594624226789	0.769914265635809	   
df.mm.trans1:probe2	-0.239865970909535	0.0554735712331929	-4.32396843356661	1.74300453853178e-05	***
df.mm.trans1:probe3	-0.23405633562682	0.0554735712331929	-4.21924044952727	2.75646790573311e-05	***
df.mm.trans1:probe4	0.179663883175959	0.0554735712331929	3.23872934772327	0.00125438163823325	** 
df.mm.trans1:probe5	0.186234221785215	0.0554735712331929	3.35717022800545	0.000827804864576462	***
df.mm.trans1:probe6	0.175901091073875	0.0554735712331929	3.17089899142141	0.00158252474561964	** 
df.mm.trans1:probe7	-0.202535022764419	0.055473571233193	-3.65101828243629	0.00027964361080529	***
df.mm.trans1:probe8	0.122357500722587	0.0554735712331929	2.20568998899017	0.0277132093830474	*  
df.mm.trans1:probe9	-0.22076781390988	0.0554735712331929	-3.97969355500556	7.58181594246956e-05	***
df.mm.trans1:probe10	0.259164230273314	0.0554735712331929	4.67185047784055	3.54728824458960e-06	***
df.mm.trans1:probe11	-0.195888413177757	0.0554735712331929	-3.53120249558669	0.000439369691107957	***
df.mm.trans1:probe12	-0.0340754815630175	0.0554735712331929	-0.614265150151866	0.53922943988808	   
df.mm.trans1:probe13	-0.191364041907518	0.0554735712331929	-3.44964345459363	0.000593220387325016	***
df.mm.trans1:probe14	-0.131737009522830	0.055473571233193	-2.37477066275488	0.0178145506257495	*  
df.mm.trans1:probe15	-0.267485460862968	0.0554735712331929	-4.82185399130959	1.72788488982532e-06	***
df.mm.trans1:probe16	-0.2214423328836	0.0554735712331929	-3.99185284020616	7.211105965009e-05	***
df.mm.trans1:probe17	0.974432242919721	0.055473571233193	17.5657023922892	5.65398078353245e-58	***
df.mm.trans1:probe18	0.66834670332806	0.0554735712331929	12.0480201377075	1.20820579164810e-30	***
df.mm.trans1:probe19	1.10809146843929	0.0554735712331929	19.9751240781170	2.69449295013484e-71	***
df.mm.trans1:probe20	0.79964707324016	0.0554735712331929	14.4149196719047	1.09484299779682e-41	***
df.mm.trans1:probe21	0.661463508579355	0.0554735712331929	11.9239395242606	4.23146504475091e-30	***
df.mm.trans1:probe22	0.90573523738175	0.0554735712331929	16.3273288026533	2.16009814145515e-51	***
df.mm.trans2:probe2	-0.0448698617207871	0.055473571233193	-0.808851147011406	0.418861447180893	   
df.mm.trans2:probe3	0.0670632307870753	0.055473571233193	1.20892218215343	0.227079914474235	   
df.mm.trans2:probe4	-0.322822609568840	0.0554735712331929	-5.81939475668148	8.80359214126584e-09	***
df.mm.trans2:probe5	-0.199125660925470	0.055473571233193	-3.58955907288555	0.000353145382169068	***
df.mm.trans2:probe6	-0.0132734325638349	0.055473571233193	-0.239274888361482	0.810958870118114	   
df.mm.trans3:probe2	0.0815539313450296	0.055473571233193	1.47014027638861	0.141949953726231	   
df.mm.trans3:probe3	-0.304545970680565	0.0554735712331929	-5.48992905829612	5.53367493375533e-08	***
df.mm.trans3:probe4	0.0156990353534757	0.055473571233193	0.283000264891583	0.777255993773302	   
df.mm.trans3:probe5	-0.0454435501034369	0.055473571233193	-0.819192799259434	0.412940881040059	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0400019538729	0.22346566544745	18.0788486937513	9.26987556098021e-61	***
df.mm.trans1	0.234388626925137	0.196853353878425	1.19067631974355	0.234163512462013	   
df.mm.trans2	0.0600715729609931	0.177626792530565	0.338189819819306	0.735316329978219	   
df.mm.exp2	-0.0741009615293703	0.236546015958767	-0.313262352904253	0.75416986088351	   
df.mm.exp3	-0.0125787308463695	0.236546015958767	-0.0531766759857928	0.957605529930614	   
df.mm.exp4	0.0620744369537645	0.236546015958767	0.262420132937622	0.793070786474579	   
df.mm.exp5	0.0794063066228326	0.236546015958767	0.335690737808385	0.737199499383724	   
df.mm.exp6	-0.169713121478794	0.236546015958767	-0.717463453319701	0.473315204471299	   
df.mm.exp7	0.0243464897813789	0.236546015958767	0.102924962327934	0.918050486429598	   
df.mm.exp8	0.0547938315594345	0.236546015958767	0.231641320769426	0.81688082215431	   
df.mm.trans1:exp2	-0.085272684118772	0.223120188819142	-0.382182735547488	0.702435854686574	   
df.mm.trans2:exp2	0.0943894961688476	0.182552881761116	0.51705289589656	0.605274154315004	   
df.mm.trans1:exp3	-0.111339872493684	0.223120188819142	-0.499012989738614	0.61791894266889	   
df.mm.trans2:exp3	0.0527000812093218	0.182552881761116	0.288683918330491	0.772904335644504	   
df.mm.trans1:exp4	-0.084430494163236	0.223120188819142	-0.378408133347691	0.705236251669933	   
df.mm.trans2:exp4	-0.0353157417540845	0.182552881761116	-0.193454857646661	0.846655986702415	   
df.mm.trans1:exp5	-0.116122015714741	0.223120188819142	-0.520446026553283	0.602908832054422	   
df.mm.trans2:exp5	-0.0476958736204429	0.182552881761116	-0.261271545868317	0.793956002041835	   
df.mm.trans1:exp6	0.185266733096723	0.223120188819142	0.830344999604217	0.406612280072212	   
df.mm.trans2:exp6	0.129061247328014	0.182552881761116	0.706980060149914	0.479802032751734	   
df.mm.trans1:exp7	-0.0576213401177254	0.223120188819142	-0.258252471112923	0.796284067459357	   
df.mm.trans2:exp7	0.0593798138849281	0.182552881761116	0.325274590639609	0.745065452648198	   
df.mm.trans1:exp8	-0.102349060869344	0.223120188819142	-0.458717166792585	0.646572344195093	   
df.mm.trans2:exp8	-0.0816516931378767	0.182552881761116	-0.447276933402365	0.654806361586503	   
df.mm.trans1:probe2	0.140441840828398	0.130274123562616	1.07804863304948	0.281364290947910	   
df.mm.trans1:probe3	0.191815943756766	0.130274123562616	1.47240248877644	0.141338543139838	   
df.mm.trans1:probe4	0.131961709678595	0.130274123562616	1.01295411605795	0.311414057394081	   
df.mm.trans1:probe5	0.136645290455858	0.130274123562616	1.04890585113151	0.294564823489893	   
df.mm.trans1:probe6	0.103218418101789	0.130274123562616	0.79231711777494	0.428430402268347	   
df.mm.trans1:probe7	0.164521165005252	0.130274123562616	1.26288445092609	0.207029571917895	   
df.mm.trans1:probe8	0.090527176643548	0.130274123562616	0.69489760643092	0.487338190390779	   
df.mm.trans1:probe9	-0.067345014762912	0.130274123562616	-0.516948515339983	0.605346983122591	   
df.mm.trans1:probe10	0.140180331303615	0.130274123562616	1.07604125416539	0.282260444873203	   
df.mm.trans1:probe11	-0.0543648314408634	0.130274123562616	-0.41731105114465	0.676572274683035	   
df.mm.trans1:probe12	0.0168323790802223	0.130274123562616	0.129207386854012	0.89722875613977	   
df.mm.trans1:probe13	0.0466423054610209	0.130274123562616	0.358032003482274	0.720421742302429	   
df.mm.trans1:probe14	-0.0207900346344098	0.130274123562616	-0.159586831719632	0.873250218203496	   
df.mm.trans1:probe15	-0.0140268057584123	0.130274123562616	-0.107671465175280	0.914285569709293	   
df.mm.trans1:probe16	0.0871119676475104	0.130274123562616	0.668682047249698	0.503907420211516	   
df.mm.trans1:probe17	-0.0214017546507315	0.130274123562616	-0.164282468885271	0.869553764635084	   
df.mm.trans1:probe18	0.0403663042030586	0.130274123562616	0.309856655329225	0.756757438131191	   
df.mm.trans1:probe19	-0.0184311576392073	0.130274123562616	-0.141479805314893	0.887529522972185	   
df.mm.trans1:probe20	-0.0585830452945178	0.130274123562616	-0.449690573173266	0.653065614820696	   
df.mm.trans1:probe21	0.0374956987691215	0.130274123562616	0.28782153925679	0.773564154958243	   
df.mm.trans1:probe22	0.128562492657535	0.130274123562616	0.986861313219588	0.324034061326641	   
df.mm.trans2:probe2	0.185592831810639	0.130274123562616	1.42463312540678	0.154686001180383	   
df.mm.trans2:probe3	0.145119419514644	0.130274123562616	1.11395429534317	0.265661454660894	   
df.mm.trans2:probe4	0.235433894892849	0.130274123562616	1.80721918101938	0.0711352173940582	.  
df.mm.trans2:probe5	0.236265960524306	0.130274123562616	1.81360621789749	0.07014441502052	.  
df.mm.trans2:probe6	0.0673383745771351	0.130274123562616	0.516897544467216	0.60538254814757	   
df.mm.trans3:probe2	-0.0171938285651281	0.130274123562616	-0.131981917014117	0.895034568105903	   
df.mm.trans3:probe3	0.184425217905485	0.130274123562616	1.41567037921266	0.157293901270684	   
df.mm.trans3:probe4	-0.0153953511589524	0.130274123562616	-0.118176585939975	0.905959898346953	   
df.mm.trans3:probe5	-0.118431954477635	0.130274123562616	-0.909098071350375	0.363595146848314