chr1.630_chr1_59008861_59009713_-_1.R 

fitVsDatCorrelation=0.927588607434677
cont.fitVsDatCorrelation=0.297461171331184

fstatistic=9226.2828948177,53,715
cont.fstatistic=1401.38377876083,53,715

residuals=-0.81702172353658,-0.0847595919536708,0.00250255318306558,0.0857267787258464,0.904172826669169
cont.residuals=-0.914934134360483,-0.267085455590881,-0.0877633962517996,0.136764619174502,1.69317668169701

predictedValues:
Include	Exclude	Both
chr1.630_chr1_59008861_59009713_-_1.R.tl.Lung	51.5109176315053	48.3958660957799	78.0368495996765
chr1.630_chr1_59008861_59009713_-_1.R.tl.cerebhem	55.6534349417054	65.0784546566681	76.4238723714249
chr1.630_chr1_59008861_59009713_-_1.R.tl.cortex	59.6518991699715	46.1844757047117	84.3736445099025
chr1.630_chr1_59008861_59009713_-_1.R.tl.heart	55.5021419352789	46.1014356221903	73.9905121628902
chr1.630_chr1_59008861_59009713_-_1.R.tl.kidney	51.8845447013862	47.1195899964635	73.066348908753
chr1.630_chr1_59008861_59009713_-_1.R.tl.liver	53.3414338375751	47.963570126275	71.7539837334592
chr1.630_chr1_59008861_59009713_-_1.R.tl.stomach	61.9837343608425	45.2872236704716	67.3607123849853
chr1.630_chr1_59008861_59009713_-_1.R.tl.testicle	58.5587214468888	51.486860970691	79.7299339460844


diffExp=3.11505153572543,-9.42501971496267,13.4674234652598,9.40070631308858,4.76495470492269,5.37786371130012,16.6965106903709,7.07186047619778
diffExpScore=1.34680910133955
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,0,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	61.8369711158328	56.752075835918	66.3230601151619
cerebhem	64.8749003167243	88.8875607139788	69.0339871205193
cortex	65.5230620173592	68.0046666343771	61.1342254820406
heart	63.0787285946681	61.9966334465665	66.5669693000886
kidney	55.0352262146133	55.6948504079563	64.128129252853
liver	76.4903163661249	50.8366330475428	59.2518589668089
stomach	60.4477103857425	53.1231841206665	68.4867134913526
testicle	69.6751621483284	74.8352737346956	67.7595062075393
cont.diffExp=5.08489527991481,-24.0126603972545,-2.48160461701787,1.08209514810162,-0.659624193342921,25.6536833185821,7.32452626507606,-5.16011158636721
cont.diffExpScore=9.12493716724997

cont.diffExp1.5=0,0,0,0,0,1,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,0,1,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,-1,0,0,0,1,0,0
cont.diffExp1.3Score=2
cont.diffExp1.2=0,-1,0,0,0,1,0,0
cont.diffExp1.2Score=2

tran.correlation=-0.0994229764794445
cont.tran.correlation=0.0979560615638122

tran.covariance=-0.000795705409835875
cont.tran.covariance=0.00217276743238099

tran.mean=52.8565190542753
cont.tran.mean=64.1933096938184

weightedLogRatios:
wLogRatio
Lung	0.243941055398423
cerebhem	-0.641032250321616
cortex	1.01344343730738
heart	0.728138063675513
kidney	0.375777613561992
liver	0.416964994756993
stomach	1.24595660689185
testicle	0.515544373336884

cont.weightedLogRatios:
wLogRatio
Lung	0.350238515402737
cerebhem	-1.36354008331277
cortex	-0.156168235062221
heart	0.0715627274979924
kidney	-0.0478229062441627
liver	1.68847980775469
stomach	0.521465459317543
testicle	-0.305755551470719

varWeightedLogRatios=0.322989852297078
cont.varWeightedLogRatios=0.736882063737802

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.63998352445342	0.0810570221147782	44.9064551039037	2.73023337579727e-210	***
df.mm.trans1	0.294791471492748	0.0637609358338766	4.62338683768382	4.47985123852174e-06	***
df.mm.trans2	0.252099483729509	0.0637609358338766	3.95382345683149	8.45609439981377e-05	***
df.mm.exp2	0.394415192674451	0.084204899986194	4.6839933630836	3.36755717753868e-06	***
df.mm.exp3	0.0218875895272061	0.084204899986194	0.259932492417838	0.794990677434577	   
df.mm.exp4	0.0793018024806057	0.084204899986194	0.941771826741767	0.346627551622640	   
df.mm.exp5	0.0463148454084423	0.084204899986194	0.550025537896678	0.582473629506473	   
df.mm.exp6	0.109884760040237	0.084204899986194	1.30496871391396	0.192323320590743	   
df.mm.exp7	0.265807979488088	0.084204899986194	3.15668066266535	0.0016627646000996	** 
df.mm.exp8	0.168684555508229	0.084204899986194	2.00326293999383	0.0455260210393842	*  
df.mm.trans1:exp2	-0.317065170885027	0.0633060363087618	-5.00845084248537	6.91957831723428e-07	***
df.mm.trans2:exp2	-0.098236054723986	0.0633060363087617	-1.55176442013935	0.121161097856767	   
df.mm.trans1:exp3	0.124844619020515	0.0633060363087617	1.97208080461098	0.0489853891418856	*  
df.mm.trans2:exp3	-0.0686582704462902	0.0633060363087617	-1.08454539961125	0.278488603413035	   
df.mm.trans1:exp4	-0.00467396709162241	0.0633060363087618	-0.0738313021024745	0.94116526929437	   
df.mm.trans2:exp4	-0.127872110314328	0.0633060363087617	-2.01990391075282	0.0437658523059115	*  
df.mm.trans1:exp5	-0.0390876675860024	0.0633060363087618	-0.617439818777479	0.537141247847146	   
df.mm.trans2:exp5	-0.073040406197097	0.0633060363087617	-1.15376685156622	0.248981284425659	   
df.mm.trans1:exp6	-0.0749651386004093	0.0633060363087618	-1.18417046732768	0.236739093050921	   
df.mm.trans2:exp6	-0.118857391818257	0.0633060363087617	-1.87750487549963	0.0608558026877344	.  
df.mm.trans1:exp7	-0.0807297559283353	0.0633060363087618	-1.27522998809455	0.202642043703035	   
df.mm.trans2:exp7	-0.332197423789402	0.0633060363087617	-5.24748417621946	2.03428221273369e-07	***
df.mm.trans1:exp8	-0.0404482973563638	0.0633060363087618	-0.638932710288254	0.523071328026016	   
df.mm.trans2:exp8	-0.106772306002233	0.0633060363087617	-1.68660545230590	0.0921154628943859	.  
df.mm.trans1:probe2	0.0599474630648	0.0480843863579333	1.24671369659498	0.21291075618251	   
df.mm.trans1:probe3	0.0685644235273635	0.0480843863579333	1.42591865511145	0.154328261095526	   
df.mm.trans1:probe4	-0.0461686872991474	0.0480843863579332	-0.960159644244482	0.337299447119539	   
df.mm.trans1:probe5	0.00811146298216582	0.0480843863579332	0.168692242878702	0.866086429405085	   
df.mm.trans1:probe6	0.0920336723502664	0.0480843863579332	1.91400326220618	0.0560186672948326	.  
df.mm.trans2:probe2	-0.0594551574755407	0.0480843863579333	-1.23647532970401	0.216687936920819	   
df.mm.trans2:probe3	-0.0816107402529494	0.0480843863579333	-1.69723992410865	0.0900864952724495	.  
df.mm.trans2:probe4	-0.0511836280229587	0.0480843863579333	-1.06445422100129	0.287482322185115	   
df.mm.trans2:probe5	-0.0570354882493191	0.0480843863579333	-1.18615402148954	0.235955486153010	   
df.mm.trans2:probe6	-0.0800988291592355	0.0480843863579333	-1.66579705443242	0.0961918657289014	.  
df.mm.trans3:probe2	-0.0217931288108231	0.0480843863579332	-0.45322672205065	0.650523001140158	   
df.mm.trans3:probe3	0.220772612790581	0.0480843863579333	4.5913576009306	5.20239593104715e-06	***
df.mm.trans3:probe4	-0.188466336375340	0.0480843863579332	-3.91949134948762	9.72634960258837e-05	***
df.mm.trans3:probe5	0.236233805217164	0.0480843863579333	4.91290048829309	1.11326621101668e-06	***
df.mm.trans3:probe6	-0.084132781836108	0.0480843863579332	-1.74969024684719	0.0806007679405535	.  
df.mm.trans3:probe7	0.728246476694853	0.0480843863579333	15.1451756350573	3.802786819271e-45	***
df.mm.trans3:probe8	-0.108039324300462	0.0480843863579333	-2.24686914991974	0.0249526825452821	*  
df.mm.trans3:probe9	-0.0840868803015436	0.0480843863579333	-1.74873564311735	0.080765844842806	.  
df.mm.trans3:probe10	-0.122517527461953	0.0480843863579332	-2.54796903406337	0.0110433085831928	*  
df.mm.trans3:probe11	1.34375635979142	0.0480843863579332	27.9457940835241	9.88496019279776e-117	***
df.mm.trans3:probe12	-0.0294365408990504	0.0480843863579332	-0.612185017396895	0.540609979515035	   
df.mm.trans3:probe13	0.358869030837489	0.0480843863579332	7.46331726407236	2.45675844364307e-13	***
df.mm.trans3:probe14	-0.192474661693135	0.0480843863579332	-4.00285157556927	6.91169666040588e-05	***
df.mm.trans3:probe15	0.947886322924189	0.0480843863579332	19.712975348552	2.03805207083343e-69	***
df.mm.trans3:probe16	0.224197248248718	0.0480843863579332	4.66257896232315	3.72623197760448e-06	***
df.mm.trans3:probe17	0.752172857396814	0.0480843863579332	15.6427671094261	1.17822317139002e-47	***
df.mm.trans3:probe18	-0.0883544857710542	0.0480843863579333	-1.83748805929135	0.0665526736004316	.  
df.mm.trans3:probe19	-0.315601172783034	0.0480843863579332	-6.56348550304343	1.00982496353236e-10	***
df.mm.trans3:probe20	-0.171098549294331	0.0480843863579333	-3.55829744858754	0.000397923423000641	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98690844978186	0.207139236106790	19.2474807029144	7.73726139693095e-67	***
df.mm.trans1	0.140647736259508	0.162939510945533	0.863189876067099	0.388322525473878	   
df.mm.trans2	0.0233108957228607	0.162939510945533	0.143064721304171	0.88627937394583	   
df.mm.exp2	0.45657805632022	0.215183560961448	2.12180732710348	0.0341972271067425	*  
df.mm.exp3	0.320250631215715	0.215183560961448	1.48826717889054	0.137121341850026	   
df.mm.exp4	0.104599186123466	0.215183560961448	0.486092830028988	0.627050289948139	   
df.mm.exp5	-0.10167796092568	0.215183560961448	-0.472517326469453	0.6367017691263	   
df.mm.exp6	0.215328205980584	0.215183560961448	1.00067219363082	0.317323844348708	   
df.mm.exp7	-0.120903623828679	0.215183560961448	-0.561862733790987	0.574385624540748	   
df.mm.exp8	0.374512478866026	0.215183560961448	1.7404325739043	0.0822133031655294	.  
df.mm.trans1:exp2	-0.408618675089926	0.161777026342975	-2.52581398191629	0.0117579607563240	*  
df.mm.trans2:exp2	-0.0078980823668988	0.161777026342975	-0.0488207908467454	0.961075747094232	   
df.mm.trans1:exp3	-0.262349882387054	0.161777026342975	-1.62167576149446	0.105313644289343	   
df.mm.trans2:exp3	-0.139366535717213	0.161777026342975	-0.861472972199092	0.389266408800528	   
df.mm.trans1:exp4	-0.0847170033853812	0.161777026342975	-0.52366522800201	0.600673671796386	   
df.mm.trans2:exp4	-0.0162113361005752	0.161777026342975	-0.100207900139087	0.92020736459879	   
df.mm.trans1:exp5	-0.0148500059409662	0.161777026342975	-0.091793045506248	0.926888180775384	   
df.mm.trans2:exp5	0.0828734170050703	0.161777026342975	0.512269380136674	0.608620688676876	   
df.mm.trans1:exp6	-0.00266548032063159	0.161777026342975	-0.0164762598304944	0.986859037803723	   
df.mm.trans2:exp6	-0.325403222596284	0.161777026342975	-2.01143036160408	0.0446547949100658	*  
df.mm.trans1:exp7	0.098180900294955	0.161777026342975	0.606890252060925	0.544116405779815	   
df.mm.trans2:exp7	0.0548248349320094	0.161777026342975	0.338891350467638	0.734791081724093	   
df.mm.trans1:exp8	-0.255170002001958	0.161777026342975	-1.57729442659544	0.115170100052816	   
df.mm.trans2:exp8	-0.0979153653474259	0.161777026342975	-0.605248888305319	0.545205685398647	   
df.mm.trans1:probe2	-0.00812038299040367	0.122878472450447	-0.0660846674642573	0.94732889097821	   
df.mm.trans1:probe3	0.0571850848650146	0.122878472450447	0.465379197223303	0.641801638780273	   
df.mm.trans1:probe4	-0.00714831422477477	0.122878472450447	-0.0581738532569851	0.953626401435816	   
df.mm.trans1:probe5	-0.0228585156407225	0.122878472450447	-0.186025389027689	0.85247761415351	   
df.mm.trans1:probe6	-0.0984816926032537	0.122878472450447	-0.801456029191513	0.423133949917884	   
df.mm.trans2:probe2	0.206323422555640	0.122878472450447	1.67908518425670	0.093572348844697	.  
df.mm.trans2:probe3	0.124404110740217	0.122878472450447	1.01241583053033	0.311681747428564	   
df.mm.trans2:probe4	0.132462443740255	0.122878472450447	1.07799552760288	0.281399299932641	   
df.mm.trans2:probe5	0.232238097188047	0.122878472450447	1.88998196801073	0.0591646031572068	.  
df.mm.trans2:probe6	0.0448670344273828	0.122878472450447	0.365133399957233	0.715119810132467	   
df.mm.trans3:probe2	-0.0322352976985145	0.122878472450447	-0.26233478538329	0.793138909154063	   
df.mm.trans3:probe3	0.0333214646266167	0.122878472450447	0.271174144356769	0.786335367081086	   
df.mm.trans3:probe4	0.0589078238451138	0.122878472450447	0.479399057217851	0.631801358017724	   
df.mm.trans3:probe5	-0.0397375985428184	0.122878472450447	-0.323389424936442	0.746494896875167	   
df.mm.trans3:probe6	-0.0100073241464096	0.122878472450447	-0.0814408247990324	0.935114174801308	   
df.mm.trans3:probe7	0.0185352905221744	0.122878472450447	0.150842455578613	0.880142539894735	   
df.mm.trans3:probe8	-0.0478101773168303	0.122878472450447	-0.389085055855579	0.697329073811905	   
df.mm.trans3:probe9	0.134263926986338	0.122878472450447	1.09265621804082	0.274912782061885	   
df.mm.trans3:probe10	0.0969785478653551	0.122878472450447	0.789223253930535	0.43024315660214	   
df.mm.trans3:probe11	0.0497738840254167	0.122878472450447	0.405065940622667	0.685550203142355	   
df.mm.trans3:probe12	0.204843734898419	0.122878472450447	1.66704330558005	0.09594371720779	.  
df.mm.trans3:probe13	-0.0385207170537006	0.122878472450447	-0.313486294918216	0.754002659834992	   
df.mm.trans3:probe14	-0.0232100495009901	0.122878472450447	-0.188886214469748	0.850235599440043	   
df.mm.trans3:probe15	0.247853859881961	0.122878472450447	2.01706494993998	0.0440619989565902	*  
df.mm.trans3:probe16	0.0796209008180189	0.122878472450447	0.647964604622894	0.517215856560466	   
df.mm.trans3:probe17	-0.0888394285793931	0.122878472450447	-0.722986108207192	0.469924846473832	   
df.mm.trans3:probe18	0.160331809678502	0.122878472450447	1.30479982767656	0.192380806834350	   
df.mm.trans3:probe19	-0.0188893236191327	0.122878472450447	-0.153723620113769	0.87787103597153	   
df.mm.trans3:probe20	0.0882300665577144	0.122878472450447	0.718027045732481	0.472975151625866