chr4.16284_chr4_21259975_21266024_-_1.R 

fitVsDatCorrelation=0.899866979535392
cont.fitVsDatCorrelation=0.277767125133529

fstatistic=13154.12808751,72,1152
cont.fstatistic=2698.94819360139,72,1152

residuals=-0.502519137137966,-0.0855757456616242,6.63325730198988e-05,0.0775879875678517,0.821109457393431
cont.residuals=-0.711580654649446,-0.261339530138614,-0.06201118452513,0.226723717491436,1.00633010968841

predictedValues:
Include	Exclude	Both
chr4.16284_chr4_21259975_21266024_-_1.R.tl.Lung	70.483939114747	110.939909545131	55.9971185947351
chr4.16284_chr4_21259975_21266024_-_1.R.tl.cerebhem	79.0584065181428	78.1849983599266	64.5919839885454
chr4.16284_chr4_21259975_21266024_-_1.R.tl.cortex	66.3973420918808	86.9768487905034	53.2810542912772
chr4.16284_chr4_21259975_21266024_-_1.R.tl.heart	69.0538842284307	88.7994081203166	54.8852162876959
chr4.16284_chr4_21259975_21266024_-_1.R.tl.kidney	73.218968051832	110.667300619523	56.1672617079489
chr4.16284_chr4_21259975_21266024_-_1.R.tl.liver	75.5877939278685	101.252413885900	58.1443404051322
chr4.16284_chr4_21259975_21266024_-_1.R.tl.stomach	71.2244633905663	84.7925907858267	57.0379320687099
chr4.16284_chr4_21259975_21266024_-_1.R.tl.testicle	71.9082871167463	85.1821853711858	57.0789220893983


diffExp=-40.4559704303842,0.873408158216165,-20.5795066986226,-19.7455238918859,-37.4483325676913,-25.6646199580317,-13.5681273952604,-13.2738982544395
diffExpScore=1.00437085964407
diffExp1.5=-1,0,0,0,-1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,0,0,-1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=-1,0,-1,0,-1,-1,0,0
diffExp1.3Score=0.8
diffExp1.2=-1,0,-1,-1,-1,-1,0,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	68.1294732177059	71.977229732685	66.7060527530878
cerebhem	64.3440462761474	70.6090334606662	61.6234155955234
cortex	65.8467475277571	66.6582899541175	61.7826548131761
heart	64.56108367622	62.164930634372	66.7591604929846
kidney	65.9532068630949	66.97081180927	57.6433303638943
liver	65.2882931633914	59.8350900582657	68.6714078179647
stomach	62.426434519818	71.459408827792	62.1014114348015
testicle	61.7445170944027	57.3924288047676	67.1838224225196
cont.diffExp=-3.84775651497918,-6.2649871845188,-0.811542426360418,2.39615304184800,-1.01760494617514,5.45320310512572,-9.03297430797405,4.35208828963508
cont.diffExpScore=3.39454424492219

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=-0.0807506970562232
cont.tran.correlation=0.390870536002907

tran.covariance=-0.000635548409051866
cont.tran.covariance=0.00107534876561426

tran.mean=82.733046244908
cont.tran.mean=65.3350641012796

weightedLogRatios:
wLogRatio
Lung	-2.03313708951859
cerebhem	0.0484871808481101
cortex	-1.16921019122102
heart	-1.09666817861396
kidney	-1.85882900005590
liver	-1.30710367074938
stomach	-0.759044412542968
testicle	-0.7386030724797

cont.weightedLogRatios:
wLogRatio
Lung	-0.233433045656594
cerebhem	-0.391231749317629
cortex	-0.0513672391877488
heart	0.156907143551922
kidney	-0.0642557395199152
liver	0.360674278967253
stomach	-0.567801919961528
testicle	0.298690662860535

varWeightedLogRatios=0.436930432252144
cont.varWeightedLogRatios=0.107200858775018

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.70920271914556	0.0671677609642143	70.1110570241359	0	***
df.mm.trans1	-0.393179671100601	0.0569364969937019	-6.90558239197773	8.24644718759755e-12	***
df.mm.trans2	0.00272332784554057	0.0497357848857651	0.05475590365761	0.956342425691312	   
df.mm.exp2	-0.377898955009859	0.0621492408736737	-6.08050797881807	1.62700971312236e-09	***
df.mm.exp3	-0.253355142876056	0.0621492408736737	-4.07656053902626	4.88436757614506e-05	***
df.mm.exp4	-0.223050245955245	0.0621492408736737	-3.58894562217779	0.000345971285081022	***
df.mm.exp5	0.0325755338167754	0.0621492408736737	0.524150148237358	0.600274940164383	   
df.mm.exp6	-0.0590905194157584	0.0621492408736738	-0.950784250701749	0.34191333625236	   
df.mm.exp7	-0.276745342291218	0.0621492408736737	-4.4529158908592	9.2943537352693e-06	***
df.mm.exp8	-0.263324404547241	0.0621492408736737	-4.2369689612538	2.44643076487297e-05	***
df.mm.trans1:exp2	0.492700987437258	0.0557681593222511	8.83480813110993	3.67140570518307e-18	***
df.mm.trans2:exp2	0.0279880480512040	0.0368020785359669	0.760501829369532	0.447110297330542	   
df.mm.trans1:exp3	0.193627299931080	0.0557681593222511	3.47200449654833	0.000535735333063596	***
df.mm.trans2:exp3	0.0100084209846126	0.0368020785359669	0.271952601123638	0.78570719521016	   
df.mm.trans1:exp4	0.202552506742032	0.0557681593222512	3.63204576237851	0.00029354952908942	***
df.mm.trans2:exp4	0.000441531342107087	0.0368020785359669	0.0119974566565738	0.990429721514224	   
df.mm.trans1:exp5	0.0054941100847351	0.0557681593222512	0.098516970104534	0.921538936032797	   
df.mm.trans2:exp5	-0.0350358243724345	0.0368020785359669	-0.952006673704414	0.341293292403004	   
df.mm.trans1:exp6	0.129000463695792	0.0557681593222512	2.31315620353140	0.0208894936806566	*  
df.mm.trans2:exp6	-0.0322816333106829	0.0368020785359669	-0.877168752279408	0.380577871129994	   
df.mm.trans1:exp7	0.287196818753367	0.0557681593222512	5.14983499982179	3.06426859240947e-07	***
df.mm.trans2:exp7	0.0079648091859029	0.0368020785359669	0.216422808242171	0.828696506054012	   
df.mm.trans1:exp8	0.283331051668524	0.0557681593222512	5.08051646516289	4.38804072204034e-07	***
df.mm.trans2:exp8	-0.000871974645034135	0.0368020785359669	-0.0236936249180043	0.981101094462575	   
df.mm.trans1:probe2	0.191212396149134	0.0433716654909035	4.40869387847781	1.13708421421555e-05	***
df.mm.trans1:probe3	-0.395144458218154	0.0433716654909035	-9.11065908458204	3.53148291235593e-19	***
df.mm.trans1:probe4	-0.426694774261242	0.0433716654909035	-9.83809981543675	5.51021676562971e-22	***
df.mm.trans1:probe5	-0.394465544549409	0.0433716654909035	-9.09500569287895	4.03986707510581e-19	***
df.mm.trans1:probe6	-0.476397737725446	0.0433716654909035	-10.9840775615445	9.19286128643109e-27	***
df.mm.trans1:probe7	-0.455220400608022	0.0433716654909035	-10.4958017049978	1.12504586800075e-24	***
df.mm.trans1:probe8	-0.423717912131403	0.0433716654909035	-9.7694637117468	1.03191609228453e-21	***
df.mm.trans1:probe9	-0.409594535513972	0.0433716654909035	-9.44382768975929	1.92651926764429e-20	***
df.mm.trans1:probe10	-0.433172810880064	0.0433716654909035	-9.98746084516665	1.38929918470337e-22	***
df.mm.trans1:probe11	0.493307573349469	0.0433716654909035	11.3739596523664	1.74757369083529e-28	***
df.mm.trans1:probe12	0.34974810095651	0.0433716654909035	8.06397672300281	1.83801244218755e-15	***
df.mm.trans1:probe13	0.258170225165881	0.0433716654909035	5.95250890745775	3.50074739615541e-09	***
df.mm.trans1:probe14	0.176029976536985	0.0433716654909035	4.05864000251280	5.26892600678413e-05	***
df.mm.trans1:probe15	0.0658299737307862	0.0433716654909035	1.51781060251405	0.129336516130913	   
df.mm.trans1:probe16	0.150915620622966	0.0433716654909035	3.47959016364308	0.00052094922317746	***
df.mm.trans1:probe17	0.0970321374687841	0.0433716654909035	2.23722415015709	0.0254623512155668	*  
df.mm.trans1:probe18	0.256288921366024	0.0433716654909035	5.9091325745786	4.52375466736112e-09	***
df.mm.trans1:probe19	-0.5273645359968	0.0433716654909035	-12.1591949496937	4.31682372493802e-32	***
df.mm.trans1:probe20	-0.461651163526642	0.0433716654909035	-10.6440727673570	2.66234110692875e-25	***
df.mm.trans2:probe2	0.00517780194342028	0.0433716654909035	0.119382133123438	0.904993430471046	   
df.mm.trans2:probe3	0.0854743726750672	0.0433716654909035	1.97074222784905	0.0489924838023403	*  
df.mm.trans2:probe4	-0.0153049522570368	0.0433716654909035	-0.352879053266857	0.724243670012136	   
df.mm.trans2:probe5	-0.127410934522815	0.0433716654909035	-2.9376537211728	0.00337287587286274	** 
df.mm.trans2:probe6	-0.0389940669665492	0.0433716654909035	-0.899067779048688	0.368804440670022	   
df.mm.trans3:probe2	-0.395144458218154	0.0433716654909035	-9.11065908458204	3.53148291235593e-19	***
df.mm.trans3:probe3	-0.455220400608021	0.0433716654909035	-10.4958017049978	1.12504586800075e-24	***
df.mm.trans3:probe4	-0.423717912131403	0.0433716654909035	-9.7694637117468	1.03191609228453e-21	***
df.mm.trans3:probe5	-0.396994793978698	0.0433716654909035	-9.15332140200982	2.44531563104840e-19	***
df.mm.trans3:probe6	-0.333061782561383	0.0433716654909035	-7.67924816332537	3.40350049085261e-14	***
df.mm.trans3:probe7	-0.215628321496311	0.0433716654909035	-4.9716403337459	7.64544667024464e-07	***
df.mm.trans3:probe8	-0.421549789781511	0.0433716654909035	-9.71947433906876	1.62593008331579e-21	***
df.mm.trans3:probe9	-0.50994709757875	0.0433716654909035	-11.7576093010701	3.18571242356099e-30	***
df.mm.trans3:probe10	-0.156542179701495	0.0433716654909035	-3.6093190780125	0.000320184583507569	***
df.mm.trans3:probe11	-0.442457621217797	0.0433716654909035	-10.2015363304551	1.87187747672751e-23	***
df.mm.trans3:probe12	-0.450848925907788	0.0433716654909035	-10.3950106781661	2.96888367853177e-24	***
df.mm.trans3:probe13	-0.0425934553464731	0.0433716654909035	-0.98205717637028	0.326277879013653	   
df.mm.trans3:probe14	-0.349489828328373	0.0433716654909035	-8.05802185303842	1.92476158146010e-15	***
df.mm.trans3:probe15	0.0970321374687841	0.0433716654909035	2.23722415015709	0.0254623512155668	*  
df.mm.trans3:probe16	-0.461651163526642	0.0433716654909035	-10.6440727673570	2.66234110692875e-25	***
df.mm.trans3:probe17	-0.359318827162618	0.0433716654909035	-8.28464443538557	3.25960150065805e-16	***
df.mm.trans3:probe18	-0.149333204212209	0.0433716654909035	-3.44310513608312	0.000595728923343737	***
df.mm.trans3:probe19	-0.430427541751153	0.0433716654909035	-9.9241644718815	2.49621032853115e-22	***
df.mm.trans3:probe20	0.223902968144227	0.0433716654909035	5.16242495209658	2.86946973607611e-07	***
df.mm.trans3:probe21	-0.429293596844697	0.0433716654909035	-9.89801963991292	3.17693825695146e-22	***
df.mm.trans3:probe22	-0.239993231634959	0.0433716654909035	-5.53341055545339	3.88562611445904e-08	***
df.mm.trans3:probe23	-0.304323463892161	0.0433716654909035	-7.01664232737357	3.86970393958434e-12	***
df.mm.trans3:probe24	-0.167386100191108	0.0433716654909035	-3.85934222946117	0.000120002831296422	***
df.mm.trans3:probe25	-0.52216491315591	0.0433716654909035	-12.0393097024467	1.57707191598265e-31	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.38974139291496	0.147936417465342	29.6731627555018	3.33986025456512e-144	***
df.mm.trans1	-0.176169118005405	0.125402146317816	-1.40483335555459	0.160340416825444	   
df.mm.trans2	-0.112002133248928	0.109542639656353	-1.02245238566727	0.306781495602693	   
df.mm.exp2	0.00289654290448883	0.136883170006818	0.0211606942208056	0.983121133162086	   
df.mm.exp3	-0.0341772377827317	0.136883170006818	-0.249681811000061	0.802877939011373	   
df.mm.exp4	-0.201152723048784	0.136883170006818	-1.46952122045949	0.141964501560195	   
df.mm.exp5	0.0414638020344727	0.136883170006819	0.302913806221812	0.76201023817711	   
df.mm.exp6	-0.256391935324619	0.136883170006819	-1.87307128635206	0.0613116141149936	.  
df.mm.exp7	-0.0231143548107990	0.136883170006818	-0.168861919326149	0.865934896483552	   
df.mm.exp8	-0.331978942240815	0.136883170006818	-2.42527216621501	0.0154498168204293	*  
df.mm.trans1:exp2	-0.0600620468429107	0.122828892616590	-0.48898956559345	0.62494216432832	   
df.mm.trans2:exp2	-0.0220882691744835	0.0810562623457072	-0.272505399771290	0.785282275531004	   
df.mm.trans1:exp3	9.73598758272426e-05	0.122828892616590	0.000792646369703514	0.999367697030123	   
df.mm.trans2:exp3	-0.042593158204883	0.0810562623457072	-0.525476465016633	0.59935313392556	   
df.mm.trans1:exp4	0.147354619697787	0.122828892616590	1.19967392491076	0.230512674504893	   
df.mm.trans2:exp4	0.0545939323375637	0.0810562623457072	0.67353133191263	0.500744426037058	   
df.mm.trans1:exp5	-0.0739282111199744	0.122828892616590	-0.601879651807504	0.547372681871253	   
df.mm.trans2:exp5	-0.113556737486557	0.0810562623457072	-1.40096192694199	0.161494771067004	   
df.mm.trans1:exp6	0.213794765052933	0.122828892616590	1.74059018605902	0.0820224411984105	.  
df.mm.trans2:exp6	0.0716343991745176	0.0810562623457072	0.88376144052874	0.377009400761601	   
df.mm.trans1:exp7	-0.0643067421334765	0.122828892616590	-0.523547357332365	0.600694099235372	   
df.mm.trans2:exp7	0.0158941193163542	0.0810562623457072	0.196087493506244	0.844576268187763	   
df.mm.trans1:exp8	0.233574209074142	0.122828892616590	1.90162268907889	0.0574697819393824	.  
df.mm.trans2:exp8	0.105541519225893	0.0810562623457072	1.30207730003335	0.193150239839771	   
df.mm.trans1:probe2	-0.0453056576999967	0.0955257212704756	-0.474277054362315	0.635392233783131	   
df.mm.trans1:probe3	0.00533470123180849	0.0955257212704756	0.05584570480974	0.9554744083455	   
df.mm.trans1:probe4	0.131315754948686	0.0955257212704756	1.37466384134251	0.169503011564161	   
df.mm.trans1:probe5	0.133010120691730	0.0955257212704756	1.39240111378086	0.164069666716706	   
df.mm.trans1:probe6	-0.04356564842091	0.0955257212704756	-0.456061967829129	0.648431377607269	   
df.mm.trans1:probe7	0.0853168702672792	0.0955257212704756	0.89312982024715	0.371974187499932	   
df.mm.trans1:probe8	-0.00908273031815625	0.0955257212704756	-0.0950815151914845	0.924266615956219	   
df.mm.trans1:probe9	0.0700162742511287	0.0955257212704757	0.732957294851316	0.463733588391148	   
df.mm.trans1:probe10	-0.168427441270984	0.0955257212704756	-1.76316325101688	0.0781380470630636	.  
df.mm.trans1:probe11	-0.0455205564763351	0.0955257212704756	-0.476526697426824	0.633789561258269	   
df.mm.trans1:probe12	-0.135270014248262	0.0955257212704756	-1.41605854893525	0.157028676715327	   
df.mm.trans1:probe13	0.0475535736817454	0.0955257212704756	0.497809103655969	0.618713608887752	   
df.mm.trans1:probe14	0.180246111209247	0.0955257212704756	1.88688563469613	0.0594269801060114	.  
df.mm.trans1:probe15	-0.0301143536333027	0.0955257212704756	-0.315248639139145	0.752629973132707	   
df.mm.trans1:probe16	0.0789227955021442	0.0955257212704756	0.826194185738508	0.40886502006629	   
df.mm.trans1:probe17	0.0936430789910091	0.0955257212704756	0.980291776346436	0.327147933544593	   
df.mm.trans1:probe18	-0.109732198767602	0.0955257212704756	-1.14871887181989	0.250910377075105	   
df.mm.trans1:probe19	0.0104710515532849	0.0955257212704756	0.109614995982461	0.912733808224674	   
df.mm.trans1:probe20	0.0568561438573671	0.0955257212704756	0.595191987050086	0.55183204074282	   
df.mm.trans2:probe2	0.0346389232156276	0.0955257212704756	0.362613574176000	0.71696008337756	   
df.mm.trans2:probe3	0.112362913830937	0.0955257212704756	1.17625820916639	0.239734716658682	   
df.mm.trans2:probe4	-0.137274653108421	0.0955257212704756	-1.43704387972885	0.150977060880038	   
df.mm.trans2:probe5	-0.102049972084351	0.0955257212704756	-1.06829836746694	0.28560974979199	   
df.mm.trans2:probe6	0.0492500123217047	0.0955257212704756	0.51556807597669	0.606255036136594	   
df.mm.trans3:probe2	0.099017452654926	0.0955257212704756	1.03655278743789	0.300161852384956	   
df.mm.trans3:probe3	0.0657896800196025	0.0955257212704756	0.688711680420844	0.49114339637615	   
df.mm.trans3:probe4	-0.0172543245810649	0.0955257212704756	-0.180624907633100	0.85669377383535	   
df.mm.trans3:probe5	0.120749624423878	0.0955257212704756	1.26405352210828	0.206466521888631	   
df.mm.trans3:probe6	0.0683901364130032	0.0955257212704756	0.715934258369643	0.474177034342103	   
df.mm.trans3:probe7	0.0333584554025947	0.0955257212704756	0.349209144499858	0.726996120981507	   
df.mm.trans3:probe8	0.166635352340068	0.0955257212704756	1.74440297465276	0.0813555393565918	.  
df.mm.trans3:probe9	0.181463002289792	0.0955257212704756	1.89962451867795	0.0577319681046264	.  
df.mm.trans3:probe10	0.0578184762384972	0.0955257212704756	0.605266052635054	0.545121445431567	   
df.mm.trans3:probe11	0.0867918718142723	0.0955257212704756	0.908570703889542	0.363766807873576	   
df.mm.trans3:probe12	0.195914202688791	0.0955257212704756	2.0509052439821	0.0405019911964538	*  
df.mm.trans3:probe13	0.0484984674113433	0.0955257212704756	0.507700614727866	0.611760502971787	   
df.mm.trans3:probe14	0.114543868327104	0.0955257212704756	1.19908927986819	0.230739813730250	   
df.mm.trans3:probe15	0.182435619335880	0.0955257212704756	1.90980624809232	0.0564063144314344	.  
df.mm.trans3:probe16	0.0860441927691145	0.0955257212704756	0.900743712004909	0.367912862956506	   
df.mm.trans3:probe17	0.129984167526773	0.0955257212704756	1.36072427193437	0.173866989422063	   
df.mm.trans3:probe18	0.0581191178528865	0.0955257212704756	0.608413284714444	0.543033340007921	   
df.mm.trans3:probe19	0.0546195068950324	0.0955257212704756	0.571778010870814	0.567583920787706	   
df.mm.trans3:probe20	0.0816492432371306	0.0955257212704756	0.854735689521207	0.392875202739298	   
df.mm.trans3:probe21	0.0375755031957770	0.0955257212704756	0.393354823141131	0.694130216366328	   
df.mm.trans3:probe22	0.0793456792105136	0.0955257212704756	0.830621095085488	0.40635982189711	   
df.mm.trans3:probe23	0.156732101188095	0.0955257212704756	1.64073193170997	0.101126052537815	   
df.mm.trans3:probe24	0.164497497133616	0.0955257212704756	1.72202308389643	0.0853337627691793	.  
df.mm.trans3:probe25	0.215419932891030	0.0955257212704756	2.25509873179686	0.0243142296866214	*