chr16.9506_chr16_30323850_30327643_-_2.R 

fitVsDatCorrelation=0.86572480746391
cont.fitVsDatCorrelation=0.259806298715738

fstatistic=7016.53636668777,43,485
cont.fstatistic=1876.77556344796,43,485

residuals=-0.617416406641604,-0.0941706829325968,-0.008035026752483,0.0871534831663106,0.899317862593593
cont.residuals=-0.662470649379105,-0.252562098208073,-0.0323534209959124,0.200220693038771,1.49893967658882

predictedValues:
Include	Exclude	Both
chr16.9506_chr16_30323850_30327643_-_2.R.tl.Lung	94.7945543900023	71.2332545855606	84.4818099651347
chr16.9506_chr16_30323850_30327643_-_2.R.tl.cerebhem	79.4614620936697	66.1574437215185	80.9765567451012
chr16.9506_chr16_30323850_30327643_-_2.R.tl.cortex	116.772595058077	64.8500852480438	91.4925853950238
chr16.9506_chr16_30323850_30327643_-_2.R.tl.heart	71.9834211798962	65.8994002452961	71.5071966403623
chr16.9506_chr16_30323850_30327643_-_2.R.tl.kidney	79.0815536793048	69.5486024357092	73.3093733954093
chr16.9506_chr16_30323850_30327643_-_2.R.tl.liver	77.6316962234837	69.0770525504683	66.945914936936
chr16.9506_chr16_30323850_30327643_-_2.R.tl.stomach	68.8690875692502	77.6288652025866	75.2690897451107
chr16.9506_chr16_30323850_30327643_-_2.R.tl.testicle	70.1271623082538	67.6974429417909	65.3127559581033


diffExp=23.5612998044417,13.3040183721512,51.9225098100332,6.08402093460012,9.5329512435956,8.55464367301542,-8.75977763333637,2.42971936646296
diffExpScore=1.15348554838475
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,0,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	73.2214688832863	85.5202031469466	80.6696273981001
cerebhem	76.4713917367372	66.6090672029612	93.7862507356154
cortex	74.4912264654524	78.186814838469	78.6666639139587
heart	75.361567754457	79.0016529851861	70.1236898463298
kidney	81.9675723809013	79.5505540147706	66.836318285922
liver	78.3169819195986	74.7768100992462	68.080398228745
stomach	73.8105292268427	80.3820841243843	82.9467710553695
testicle	75.217186860937	79.0165917373035	74.8520286982482
cont.diffExp=-12.2987342636603,9.862324533776,-3.69558837301666,-3.64008523072906,2.41701836613069,3.54017182035243,-6.57155489754167,-3.79940487636655
cont.diffExpScore=3.01760346289391

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=-0.385230212887045
cont.tran.correlation=-0.288628950369717

tran.covariance=-0.00400532948474247
cont.tran.covariance=-0.00076118922952922

tran.mean=75.675854964557
cont.tran.mean=76.9938564610925

weightedLogRatios:
wLogRatio
Lung	1.25983453193386
cerebhem	0.784914363457993
cortex	2.62676890585624
heart	0.373738022490669
kidney	0.553154619025998
liver	0.50129151011993
stomach	-0.51389814929134
testicle	0.149251847256523

cont.weightedLogRatios:
wLogRatio
Lung	-0.678677561281721
cerebhem	0.589291595933905
cortex	-0.209894010591178
heart	-0.20500114050694
kidney	0.131437703911673
liver	0.200644519790447
stomach	-0.370511909027253
testicle	-0.214114266214442

varWeightedLogRatios=0.853593045381946
cont.varWeightedLogRatios=0.151858406376357

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.76069900115336	0.0983667465828524	48.3974429015349	8.5851526381876e-188	***
df.mm.trans1	0.0110524787166711	0.0869542435783942	0.127106835294435	0.898908521583994	   
df.mm.trans2	-0.637512131526958	0.0818377976885615	-7.78994730470446	4.10382367755832e-14	***
df.mm.exp2	-0.207985615275092	0.112649270490431	-1.84631124879551	0.0654561275945449	.  
df.mm.exp3	0.034913213623059	0.112649270490431	0.309928448458302	0.756748491584181	   
df.mm.exp4	-0.186368413479296	0.112649270490431	-1.65441296395369	0.0986906103341738	.  
df.mm.exp5	-0.0633184971740163	0.112649270490431	-0.562085283804786	0.574317585638142	   
df.mm.exp6	0.00217784368059165	0.112649270490431	0.01933295858118	0.984583442394416	   
df.mm.exp7	-0.118058290747638	0.112649270490431	-1.04801646946898	0.295152994599163	   
df.mm.exp8	-0.094964242059328	0.112649270490431	-0.843008051857686	0.399639486763672	   
df.mm.trans1:exp2	0.0315458013980323	0.104870763592256	0.300806443258907	0.76369100174245	   
df.mm.trans2:exp2	0.134063259253489	0.094994205734975	1.41127827972490	0.158803732569405	   
df.mm.trans1:exp3	0.173603233421125	0.104870763592256	1.65540163411135	0.0984899263927547	.  
df.mm.trans2:exp3	-0.128794756485435	0.094994205734975	-1.35581697314002	0.175788357238331	   
df.mm.trans1:exp4	-0.088907719876824	0.104870763592256	-0.847783660873326	0.396976644358620	   
df.mm.trans2:exp4	0.108537985798604	0.094994205734975	1.14257480189281	0.253778892838935	   
df.mm.trans1:exp5	-0.117913822253168	0.104870763592256	-1.1243726870496	0.261411123711804	   
df.mm.trans2:exp5	0.0393845527933591	0.094994205734975	0.414599527293678	0.67861841001347	   
df.mm.trans1:exp6	-0.201914007866490	0.104870763592256	-1.92536032875229	0.0547686864252104	.  
df.mm.trans2:exp6	-0.0329150266689459	0.094994205734975	-0.346495098456592	0.729120940720498	   
df.mm.trans1:exp7	-0.201446252846313	0.104870763592256	-1.92090002919735	0.05533021827254	.  
df.mm.trans2:exp7	0.204037854865786	0.094994205734975	2.14789789847849	0.0322166000601255	*  
df.mm.trans1:exp8	-0.206437523985508	0.104870763592256	-1.96849452520581	0.0495802313708218	*  
df.mm.trans2:exp8	0.0440528826668985	0.094994205734975	0.463742839113808	0.643040056221627	   
df.mm.trans1:probe2	-0.0468386333171679	0.052435381796128	-0.893263893820386	0.372158881545318	   
df.mm.trans1:probe3	0.104066811064911	0.052435381796128	1.98466774723847	0.0477444699944175	*  
df.mm.trans1:probe4	-0.0993362228487561	0.052435381796128	-1.89445026327798	0.0587598733120198	.  
df.mm.trans1:probe5	0.0441570743477496	0.052435381796128	0.842123635514566	0.400133810527739	   
df.mm.trans1:probe6	0.0250184965350977	0.052435381796128	0.4771300537559	0.633484436822278	   
df.mm.trans1:probe7	-0.228165078767126	0.052435381796128	-4.35135725061078	1.65073170021011e-05	***
df.mm.trans1:probe8	-0.480717039426325	0.052435381796128	-9.1677989738948	1.37345987648360e-18	***
df.mm.trans1:probe9	-0.376457579058634	0.052435381796128	-7.1794571940436	2.64540664593737e-12	***
df.mm.trans1:probe10	-0.624126190497422	0.052435381796128	-11.9027681141727	7.72974666304378e-29	***
df.mm.trans1:probe11	-0.584027050217678	0.052435381796128	-11.1380337133505	8.20305702349794e-26	***
df.mm.trans1:probe12	-0.577623565431112	0.052435381796128	-11.0159122646794	2.43549876546019e-25	***
df.mm.trans1:probe13	-0.67658326882206	0.052435381796128	-12.9031818906680	5.91485650289103e-33	***
df.mm.trans2:probe2	0.0934709004063679	0.052435381796128	1.78259215828328	0.075278126732546	.  
df.mm.trans2:probe3	0.23832231973345	0.052435381796128	4.54506692942681	6.94243933666908e-06	***
df.mm.trans2:probe4	-0.0243539066021985	0.052435381796128	-0.464455597880225	0.642529784149541	   
df.mm.trans2:probe5	0.323286712182491	0.052435381796128	6.1654306902818	1.48118240141720e-09	***
df.mm.trans2:probe6	0.65423006113531	0.052435381796128	12.4768818062391	3.51908953649861e-31	***
df.mm.trans3:probe2	0.235957605467921	0.052435381796128	4.49996924567724	8.51755115066405e-06	***
df.mm.trans3:probe3	0.670933083018271	0.052435381796128	12.7954266763404	1.67193271442362e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.35034707883865	0.18978033237603	22.9230659698650	2.48755789435381e-79	***
df.mm.trans1	-0.0457121567154938	0.167762031591788	-0.272482136045683	0.785367293462463	   
df.mm.trans2	0.136139583367945	0.157890801371334	0.862238852330395	0.388982218902655	   
df.mm.exp2	-0.357140016261432	0.217335804408094	-1.64326359954398	0.100976491285070	   
df.mm.exp3	-0.0473162741176061	0.217335804408094	-0.217710442356565	0.827746290174413	   
df.mm.exp4	0.0896263583787556	0.217335804408094	0.412386530709239	0.680238353269267	   
df.mm.exp5	0.228590638915649	0.217335804408094	1.05178545954822	0.293421743054632	   
df.mm.exp6	0.102703769294701	0.217335804408094	0.472557982677592	0.636741136071788	   
df.mm.exp7	-0.0817855492917862	0.217335804408094	-0.376309598478383	0.706851304676685	   
df.mm.exp8	0.0226452211465664	0.217335804408094	0.104194618131328	0.91705797758657	   
df.mm.trans1:exp2	0.400568054204988	0.202328622857350	1.97978935727451	0.0482920631424894	*  
df.mm.trans2:exp2	0.107228086583612	0.183273642409184	0.585070963691604	0.558772094735393	   
df.mm.trans1:exp3	0.0645089582825964	0.202328622857350	0.318832587162312	0.749990728078854	   
df.mm.trans2:exp3	-0.0423353428968016	0.183273642409184	-0.230995261185905	0.817415895930061	   
df.mm.trans1:exp4	-0.0608175935124675	0.202328622857350	-0.300588184971469	0.763857348401406	   
df.mm.trans2:exp4	-0.168910224413511	0.183273642409184	-0.921628566951245	0.357180567130026	   
df.mm.trans1:exp5	-0.115755597356550	0.202328622857349	-0.572116765892102	0.567507948989376	   
df.mm.trans2:exp5	-0.300950561942943	0.183273642409184	-1.64208316038718	0.101220966127333	   
df.mm.trans1:exp6	-0.0354279757813318	0.202328622857350	-0.175101156134049	0.861073225113396	   
df.mm.trans2:exp6	-0.236948599939221	0.183273642409184	-1.29286784954162	0.196672299055542	   
df.mm.trans1:exp7	0.0897982744506915	0.202328622857349	0.443823880094331	0.657367860239041	   
df.mm.trans2:exp7	0.0198242242256433	0.183273642409184	0.108167350007607	0.913907677347468	   
df.mm.trans1:exp8	0.00424586365741075	0.202328622857349	0.0209849876772219	0.983266261526696	   
df.mm.trans2:exp8	-0.101740010885884	0.183273642409184	-0.555126255736954	0.579064239854691	   
df.mm.trans1:probe2	-0.00768314939481517	0.101164311428675	-0.0759472316502854	0.939492397116242	   
df.mm.trans1:probe3	0.00641388459636165	0.101164311428675	0.0634006647777533	0.949473578851483	   
df.mm.trans1:probe4	-0.0521337969192112	0.101164311428675	-0.515337831918797	0.606551772267433	   
df.mm.trans1:probe5	-0.0582894010472295	0.101164311428675	-0.576185417802464	0.564757137479578	   
df.mm.trans1:probe6	-0.152079281742075	0.101164311428675	-1.50328984198442	0.133415165593429	   
df.mm.trans1:probe7	0.0922030296886256	0.101164311428675	0.911418546585302	0.362527657066356	   
df.mm.trans1:probe8	-0.0387948854526424	0.101164311428675	-0.383483907563533	0.701529039438256	   
df.mm.trans1:probe9	0.0817898845217196	0.101164311428675	0.808485555495379	0.419207510696599	   
df.mm.trans1:probe10	-0.126690341966847	0.101164311428675	-1.25232248584195	0.211055921348613	   
df.mm.trans1:probe11	-0.0262124737411542	0.101164311428675	-0.259107914352139	0.795661915178598	   
df.mm.trans1:probe12	0.114316901686169	0.101164311428675	1.13001215618185	0.259029637769072	   
df.mm.trans1:probe13	-0.0111804246598862	0.101164311428675	-0.11051747896064	0.91204470542045	   
df.mm.trans2:probe2	-0.0848214928569725	0.101164311428675	-0.838452727637803	0.402189516363256	   
df.mm.trans2:probe3	-0.0877135986143022	0.101164311428675	-0.867040929509456	0.386348338715663	   
df.mm.trans2:probe4	-0.113454484799215	0.101164311428675	-1.12148724384098	0.262635468515836	   
df.mm.trans2:probe5	-0.0391353491531156	0.101164311428675	-0.386849360218378	0.69903740969827	   
df.mm.trans2:probe6	-0.0144812551218220	0.101164311428675	-0.143145887292792	0.886234416773253	   
df.mm.trans3:probe2	0.0096833429324487	0.101164311428675	0.0957189625046366	0.923783316338253	   
df.mm.trans3:probe3	-0.160920439113968	0.101164311428675	-1.59068387696607	0.112332309295051