chr18.11215_chr18_74573597_74578228_-_2.R 

fitVsDatCorrelation=0.867328170757817
cont.fitVsDatCorrelation=0.282560286343906

fstatistic=11314.6167908497,59,853
cont.fstatistic=3035.758369658,59,853

residuals=-0.684630919307153,-0.0729153149169863,-0.00453555029717354,0.0716381836829426,0.998218658632148
cont.residuals=-0.449722174155051,-0.155321492126934,-0.0522066122580122,0.0677888258795082,1.33537974981305

predictedValues:
Include	Exclude	Both
chr18.11215_chr18_74573597_74578228_-_2.R.tl.Lung	69.8306796846732	44.419075635379	45.7669837058326
chr18.11215_chr18_74573597_74578228_-_2.R.tl.cerebhem	75.1830562918566	44.998931365476	45.9421954668048
chr18.11215_chr18_74573597_74578228_-_2.R.tl.cortex	66.6334333354228	43.0932322471943	46.0464813193263
chr18.11215_chr18_74573597_74578228_-_2.R.tl.heart	70.5641255998876	44.9183479903495	49.5788420672947
chr18.11215_chr18_74573597_74578228_-_2.R.tl.kidney	69.8979407379898	44.5029575698315	45.42064491232
chr18.11215_chr18_74573597_74578228_-_2.R.tl.liver	74.0359537826611	45.4571956302177	49.8549674953544
chr18.11215_chr18_74573597_74578228_-_2.R.tl.stomach	71.7286193052402	44.5174366902541	46.648386660879
chr18.11215_chr18_74573597_74578228_-_2.R.tl.testicle	73.6681357830267	44.420484728773	47.9603816655103


diffExp=25.4116040492942,30.1841249263806,23.5402010882285,25.6457776095380,25.3949831681583,28.5787581524435,27.211182614986,29.2476510542537
diffExpScore=0.995374958639724
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	46.224117857576	49.1560620541132	56.1740014550935
cerebhem	50.2179248510168	51.5650328971316	52.1801467552113
cortex	51.2575070420958	50.3187224541786	53.0562525753395
heart	51.2123642882518	48.1956887685432	55.2169722192193
kidney	50.3954801374123	50.7887880307858	50.9435910227906
liver	49.5700405374962	54.0733553443439	45.611978522736
stomach	49.3646693736193	45.6648925666568	47.8155611296304
testicle	51.4985398403244	46.0563473118921	49.814526026213
cont.diffExp=-2.93194419653718,-1.34710804611472,0.93878458791724,3.01667551970866,-0.393307893373553,-4.50331480684763,3.69977680696255,5.4421925284323
cont.diffExpScore=4.52543993919761

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=0.79462091975377
cont.tran.correlation=-0.0886669447139243

tran.covariance=0.00049168804115082
cont.tran.covariance=-0.000169073720155046

tran.mean=57.9918503986396
cont.tran.mean=49.7224708347149

weightedLogRatios:
wLogRatio
Lung	1.81860759896586
cerebhem	2.0856305925898
cortex	1.73520574428516
heart	1.82056137178183
kidney	1.81553685091249
liver	1.98070803379659
stomach	1.92443762684278
testicle	2.04707000308770

cont.weightedLogRatios:
wLogRatio
Lung	-0.237645571937008
cerebhem	-0.104023614004762
cortex	0.0726015093606328
heart	0.237116058758145
kidney	-0.0305040459690393
liver	-0.343199279981072
stomach	0.300736016685878
testicle	0.433986605248344

varWeightedLogRatios=0.0157613376887222
cont.varWeightedLogRatios=0.0732512014828717

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.68579289742027	0.0671145729099766	69.8178159265873	0	***
df.mm.trans1	0.101278030529841	0.0577502465259019	1.75372464400504	0.0798368251694105	.  
df.mm.trans2	-0.894071886844004	0.0510322853385347	-17.5197305179058	6.18708202584065e-59	***
df.mm.exp2	0.0830011389078525	0.065439128879022	1.26837169640961	0.20501129356493	   
df.mm.exp3	-0.0832584432606173	0.065439128879022	-1.27230366123207	0.203611963550687	   
df.mm.exp4	-0.0583754470909336	0.065439128879022	-0.892057215475053	0.372613835318146	   
df.mm.exp5	0.0104455950051598	0.0654391288790221	0.159623075430461	0.87321578867203	   
df.mm.exp6	-0.00397568282289035	0.065439128879022	-0.0607539081126864	0.951569429616023	   
df.mm.exp7	0.00995291937010137	0.065439128879022	0.152094313304528	0.879148515011656	   
df.mm.exp8	0.00671629063429964	0.065439128879022	0.102634169331870	0.918277445322761	   
df.mm.trans1:exp2	-0.00914869883714606	0.060117572219268	-0.152180111395348	0.879080866453305	   
df.mm.trans2:exp2	-0.0700314054538535	0.0440877504787104	-1.58845494935540	0.112554066779545	   
df.mm.trans1:exp3	0.0363914466495451	0.060117572219268	0.60533792876422	0.545115497400939	   
df.mm.trans2:exp3	0.0529553950131697	0.0440877504787104	1.20113624392656	0.230031764527135	   
df.mm.trans1:exp4	0.0688238762364394	0.060117572219268	1.14482128428967	0.252604313908898	   
df.mm.trans2:exp4	0.0695527909780403	0.0440877504787104	1.57759899797171	0.115028519805418	   
df.mm.trans1:exp5	-0.00948285654387214	0.060117572219268	-0.157738514610755	0.874700181555467	   
df.mm.trans2:exp5	-0.00855895439855662	0.0440877504787104	-0.194134522755696	0.846116772842552	   
df.mm.trans1:exp6	0.0624530697322992	0.060117572219268	1.03884883282566	0.299169423203264	   
df.mm.trans2:exp6	0.0270778020114174	0.0440877504787104	0.614179714714477	0.53926034451977	   
df.mm.trans1:exp7	0.0168634518550867	0.060117572219268	0.280507865380529	0.779155908787405	   
df.mm.trans2:exp7	-0.00774097980991206	0.0440877504787104	-0.175581192641029	0.860664650095992	   
df.mm.trans1:exp8	0.0467806145425833	0.060117572219268	0.77815209123814	0.43669514061528	   
df.mm.trans2:exp8	-0.00668456842882464	0.0440877504787104	-0.151619630310976	0.879522800604483	   
df.mm.trans1:probe2	-0.840064331978341	0.0418797371604595	-20.0589685832957	1.27968742596443e-73	***
df.mm.trans1:probe3	-1.01247774982251	0.0418797371604595	-24.1758382088997	9.22035176433291e-99	***
df.mm.trans1:probe4	-0.972633828299291	0.0418797371604595	-23.2244492025513	7.57183543525608e-93	***
df.mm.trans1:probe5	-0.794334253668313	0.0418797371604595	-18.9670305385364	3.31592886936789e-67	***
df.mm.trans1:probe6	-0.9678574391467	0.0418797371604595	-23.110399080071	3.84446014860372e-92	***
df.mm.trans1:probe7	0.0690549870449673	0.0418797371604595	1.64888778504954	0.0995388362163336	.  
df.mm.trans1:probe8	-1.15453324059921	0.0418797371604595	-27.5678244153179	3.93202095064657e-120	***
df.mm.trans1:probe9	-0.883257760891338	0.0418797371604595	-21.0903367780746	8.5942748753024e-80	***
df.mm.trans1:probe10	-0.997260160696115	0.0418797371604595	-23.8124742014301	1.69542942595890e-96	***
df.mm.trans1:probe11	-1.01862598052729	0.0418797371604595	-24.3226450210155	1.11692154845303e-99	***
df.mm.trans1:probe12	-0.859265334522282	0.0418797371604595	-20.5174481212731	2.37553545375953e-76	***
df.mm.trans1:probe13	-0.831755020733493	0.0418797371604595	-19.8605597152312	1.91587667762777e-72	***
df.mm.trans1:probe14	-0.67393974117723	0.0418797371604595	-16.0922629145229	4.40237784477512e-51	***
df.mm.trans1:probe15	-0.877607240682168	0.0418797371604595	-20.9554142453108	5.59068671859682e-79	***
df.mm.trans1:probe16	-0.968645888231422	0.0418797371604595	-23.1292255851587	2.94041421953594e-92	***
df.mm.trans1:probe17	-0.869616787535507	0.0418797371604595	-20.7646190376894	7.84637959082714e-78	***
df.mm.trans1:probe18	-0.856109300917899	0.0418797371604595	-20.4420886797300	6.70123531741073e-76	***
df.mm.trans1:probe19	-0.941471590608068	0.0418797371604595	-22.4803605381017	2.93902054294745e-88	***
df.mm.trans1:probe20	-0.930669473530095	0.0418797371604595	-22.2224287121071	1.12284548227439e-86	***
df.mm.trans1:probe21	-0.930849150523506	0.0418797371604595	-22.2267190206332	1.05692114172023e-86	***
df.mm.trans2:probe2	0.157585773329798	0.0418797371604595	3.76281667494755	0.000179501769942548	***
df.mm.trans2:probe3	-0.0383015037483544	0.0418797371604595	-0.914559315441848	0.360681471020229	   
df.mm.trans2:probe4	-0.0122954169416220	0.0418797371604595	-0.293588684535266	0.769143638941529	   
df.mm.trans2:probe5	-0.0180747598019628	0.0418797371604595	-0.431587231140216	0.666150499213374	   
df.mm.trans2:probe6	-0.0557981264261886	0.0418797371604595	-1.33234184857469	0.183103704425688	   
df.mm.trans3:probe2	-0.0918226798096332	0.0418797371604595	-2.1925323804641	0.0286105509150643	*  
df.mm.trans3:probe3	0.184268212822291	0.0418797371604595	4.39993718480799	1.21993277078197e-05	***
df.mm.trans3:probe4	-0.149584615969297	0.0418797371604595	-3.57176587322344	0.000374434429534764	***
df.mm.trans3:probe5	-0.186662639210848	0.0418797371604595	-4.45711104861195	9.41516580021501e-06	***
df.mm.trans3:probe6	-0.112773627128704	0.0418797371604595	-2.69279691743572	0.0072245260111504	** 
df.mm.trans3:probe7	-0.0813660358524586	0.0418797371604595	-1.94284972564918	0.0523630412708534	.  
df.mm.trans3:probe8	-0.142594114453100	0.0418797371604595	-3.40484740643810	0.000692908428968282	***
df.mm.trans3:probe9	-0.170633784850178	0.0418797371604595	-4.07437573441316	5.04413607992442e-05	***
df.mm.trans3:probe10	-0.138838330553010	0.0418797371604595	-3.31516718982882	0.000954535157379138	***
df.mm.trans3:probe11	0.126210627974383	0.0418797371604595	3.01364422347769	0.00265765630740624	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.66154890101121	0.129344732937039	28.3084499682993	7.8763210759643e-125	***
df.mm.trans1	0.18902473229533	0.111297589928201	1.69837219671397	0.0898022236850586	.  
df.mm.trans2	0.213539675872027	0.0983505821773372	2.17120906805606	0.0301904202214748	*  
df.mm.exp2	0.204465897330581	0.126115779055064	1.62125547542555	0.105332417376662	   
df.mm.exp3	0.183838761821448	0.126115779055064	1.45769834035740	0.145291760714342	   
df.mm.exp4	0.0999323636167278	0.126115779055064	0.792385888312167	0.428356018696855	   
df.mm.exp5	0.216810323262603	0.126115779055064	1.71913716814088	0.085952195003746	.  
df.mm.exp6	0.373509989985504	0.126115779055064	2.96164360069825	0.00314490790172968	** 
df.mm.exp7	0.153165799854745	0.126115779055064	1.21448561791678	0.224898440078752	   
df.mm.exp8	0.163064586368608	0.126115779055064	1.29297529294421	0.196369682600941	   
df.mm.trans1:exp2	-0.121595559047305	0.115859953902329	-1.04950463858989	0.294243174534178	   
df.mm.trans2:exp2	-0.156622288401694	0.0849669164864182	-1.84333261554489	0.0656272141387218	.  
df.mm.trans1:exp3	-0.0804783690856402	0.115859953902329	-0.69461765152681	0.487484095116884	   
df.mm.trans2:exp3	-0.160461714995013	0.0849669164864182	-1.88851992787878	0.0592952640008156	.  
df.mm.trans1:exp4	0.00254693585374459	0.115859953902329	0.0219828833687581	0.98246675044234	   
df.mm.trans2:exp4	-0.119662968009602	0.0849669164864182	-1.40834777767568	0.159392480961497	   
df.mm.trans1:exp5	-0.130410525445894	0.115859953902329	-1.12558758271069	0.260656720105180	   
df.mm.trans2:exp5	-0.184134877906156	0.0849669164864182	-2.16713616923582	0.0305005801349583	*  
df.mm.trans1:exp6	-0.303625053596389	0.115859953902329	-2.62062121872022	0.00893311089075387	** 
df.mm.trans2:exp6	-0.278168609765972	0.0849669164864182	-3.27384611880598	0.00110365595454701	** 
df.mm.trans1:exp7	-0.0874325198266193	0.115859953902329	-0.754639691125079	0.450673489162624	   
df.mm.trans2:exp7	-0.226836189167680	0.0849669164864182	-2.66970014386645	0.0077362673271027	** 
df.mm.trans1:exp8	-0.0550128251418119	0.115859953902329	-0.474821742016126	0.635035576302909	   
df.mm.trans2:exp8	-0.228199174758437	0.0849669164864182	-2.68574151204975	0.00737752253652504	** 
df.mm.trans1:probe2	-0.0822764502023745	0.0807115829487429	-1.01938838511723	0.308307647742242	   
df.mm.trans1:probe3	-0.0519204988061825	0.0807115829487429	-0.643284357824519	0.520212496981245	   
df.mm.trans1:probe4	-0.0231640338219269	0.0807115829487429	-0.286997639937722	0.774183826932336	   
df.mm.trans1:probe5	-0.083958959992684	0.0807115829487429	-1.04023433719548	0.298525795363377	   
df.mm.trans1:probe6	-0.0156591073290970	0.0807115829487429	-0.194013136120023	0.84621178856364	   
df.mm.trans1:probe7	-0.107598476991407	0.0807115829487429	-1.33312311641490	0.182847332565157	   
df.mm.trans1:probe8	-0.0819143363159053	0.0807115829487429	-1.01490186814854	0.310440387751039	   
df.mm.trans1:probe9	-0.0664861969340539	0.0807115829487429	-0.823750377641298	0.410311661988344	   
df.mm.trans1:probe10	-0.00381154060778949	0.0807115829487429	-0.0472242083296776	0.962345587474776	   
df.mm.trans1:probe11	0.0516299217175118	0.0807115829487429	0.639684167144883	0.522549710674179	   
df.mm.trans1:probe12	-0.0200906131567524	0.0807115829487429	-0.248918586685522	0.80348367812961	   
df.mm.trans1:probe13	-0.0502561103916851	0.0807115829487429	-0.622662925885135	0.533672469739727	   
df.mm.trans1:probe14	-0.131671325424523	0.0807115829487429	-1.63138078345140	0.103179113436076	   
df.mm.trans1:probe15	0.0466597988467338	0.0807115829487429	0.578105361610437	0.563345569672457	   
df.mm.trans1:probe16	0.0143243975776867	0.0807115829487429	0.177476355367527	0.859176370320274	   
df.mm.trans1:probe17	-0.0475795734565092	0.0807115829487429	-0.589501180849412	0.555681219573908	   
df.mm.trans1:probe18	0.0597106108637062	0.0807115829487429	0.739802252442828	0.459623544511043	   
df.mm.trans1:probe19	-0.0401149213289586	0.0807115829487429	-0.497015668177815	0.619306075183153	   
df.mm.trans1:probe20	0.0085676544147454	0.0807115829487429	0.106151485347356	0.915487120612692	   
df.mm.trans1:probe21	0.079307685158818	0.0807115829487429	0.982605993605447	0.326080006860901	   
df.mm.trans2:probe2	0.199893078080081	0.0807115829487429	2.47663434140582	0.0134556824459567	*  
df.mm.trans2:probe3	0.044842529013935	0.0807115829487429	0.555589760176218	0.578637015378869	   
df.mm.trans2:probe4	0.0300009844627114	0.0807115829487429	0.371706059609362	0.710204025804603	   
df.mm.trans2:probe5	0.0732354325251403	0.0807115829487429	0.907372025792748	0.364466356469489	   
df.mm.trans2:probe6	-0.00947482527607624	0.0807115829487429	-0.117391146721696	0.90657773408526	   
df.mm.trans3:probe2	-0.119885715174733	0.0807115829487429	-1.48535948366752	0.137818084730063	   
df.mm.trans3:probe3	-0.0830430292066182	0.0807115829487429	-1.02888614214587	0.303824835474177	   
df.mm.trans3:probe4	-0.0102533479507073	0.0807115829487429	-0.127036883382882	0.898941145832017	   
df.mm.trans3:probe5	-0.138001424127381	0.0807115829487429	-1.70980941130867	0.0876647977261904	.  
df.mm.trans3:probe6	0.0326171987513415	0.0807115829487429	0.404120419395758	0.686225468513526	   
df.mm.trans3:probe7	-0.00775984051229126	0.0807115829487429	-0.096142836366117	0.923429712891139	   
df.mm.trans3:probe8	-0.122935268728979	0.0807115829487429	-1.52314282829827	0.128093602343544	   
df.mm.trans3:probe9	0.0651230512375679	0.0807115829487429	0.806861281347006	0.419971317366747	   
df.mm.trans3:probe10	0.0224052392201996	0.0807115829487429	0.277596330063658	0.781389513801103	   
df.mm.trans3:probe11	-0.0305187991380427	0.0807115829487429	-0.378121677497319	0.705434281957285