fitVsDatCorrelation=0.91633585834289
cont.fitVsDatCorrelation=0.246820219471058

fstatistic=7872.87788103864,52,692
cont.fstatistic=1333.09660534666,52,692

residuals=-0.680959462567432,-0.0976561888805328,-0.0044636243533891,0.0886191251098049,1.39953986828216
cont.residuals=-0.890354260009906,-0.286933474711399,-0.0924662733368189,0.164817094845900,1.80653050762713

predictedValues:
Include	Exclude	Both
Lung	71.7853206132947	48.5146874311782	139.390664213381
cerebhem	77.3662875577947	68.1713880164448	117.725294715444
cortex	78.5036435602271	49.2461133315653	163.721761111193
heart	82.5387788201705	48.9932106704275	150.988766832889
kidney	71.0606827125644	47.9726547121778	122.975183374847
liver	69.6596622404576	49.3459203253106	108.627261135414
stomach	78.8088616363702	50.2716621109424	143.046967517178
testicle	80.548098270662	51.9910911067321	152.719991211566


diffExp=23.2706331821165,9.19489954134991,29.2575302286618,33.545568149743,23.0880280003867,20.3137419151470,28.5371995254278,28.5570071639298
diffExpScore=0.994917785207133
diffExp1.5=0,0,1,1,0,0,1,1
diffExp1.5Score=0.8
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	74.9034634124106	96.107991187368	66.9309624991612
cerebhem	74.9091569300424	69.4077457027182	66.4985201218629
cortex	72.3420381510341	76.4779983353324	70.0867804504504
heart	79.3698861657279	77.414257426861	81.7231168747625
kidney	74.6218341339611	67.1636433621199	72.1231014213952
liver	70.4022997738239	77.171368991435	67.0566608669899
stomach	73.598166565752	69.8676395938753	89.9342968475861
testicle	74.5649232245587	68.4095479824657	67.2903807360095
cont.diffExp=-21.2045277749574,5.5014112273242,-4.13596018429821,1.95562873886684,7.4581907718412,-6.76906921761113,3.7305269718767,6.15537524209302
cont.diffExpScore=6.84975737740387

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

tran.correlation=0.188140745117669
cont.tran.correlation=0.0559904981355343

tran.covariance=0.0015720917959126
cont.tran.covariance=0.000182019371818833

tran.mean=64.04862894477
cont.tran.mean=74.7957475587179

weightedLogRatios:
wLogRatio
Lung	1.59772630584788
cerebhem	0.542201117477012
cortex	1.92587354564749
heart	2.16587488468883
kidney	1.59796915985679
liver	1.40362583907624
stomach	1.8622815181864
testicle	1.82553543908813

cont.weightedLogRatios:
wLogRatio
Lung	-1.10697764460293
cerebhem	0.326326335374652
cortex	-0.239581824671824
heart	0.108814584459241
kidney	0.448559656172738
liver	-0.394762818238822
stomach	0.222250669805754
testicle	0.367772407056618

varWeightedLogRatios=0.243106225810160
cont.varWeightedLogRatios=0.276267524298121

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04520207439033	0.0943461954591627	42.8761547267826	5.32381524297718e-197	***
df.mm.trans1	0.373309569625317	0.0836796328273886	4.46117599960514	9.51135989391697e-06	***
df.mm.trans2	-0.196702051542369	0.0762591218835105	-2.57939046089255	0.0101027084451881	*  
df.mm.exp2	0.583956103438513	0.102857753546332	5.67731729796598	2.01386940221553e-08	***
df.mm.exp3	-0.0564589902709415	0.102857753546332	-0.548903590875233	0.583248673018297	   
df.mm.exp4	0.0694784500343153	0.102857753546332	0.675480920386023	0.499595919409343	   
df.mm.exp5	0.103916711610449	0.102857753546332	1.01029536449715	0.312706922718121	   
df.mm.exp6	0.236288043450118	0.102857753546332	2.29723122762624	0.0219032303468666	*  
df.mm.exp7	0.103027900452273	0.102857753546332	1.00165419620859	0.316860755787198	   
df.mm.exp8	0.0930546894772334	0.102857753546332	0.904692998523606	0.365942903719851	   
df.mm.trans1:exp2	-0.509084985864972	0.0974403316776978	-5.22458182458641	2.31267262604437e-07	***
df.mm.trans2:exp2	-0.243797742898451	0.0823521108995642	-2.96043101063656	0.00317715831134618	** 
df.mm.trans1:exp3	0.14592402189316	0.0974403316776978	1.49757312378442	0.134700172782571	   
df.mm.trans2:exp3	0.071422851918213	0.0823521108995642	0.867286231500727	0.386085834378655	   
df.mm.trans1:exp4	0.0701097723052835	0.0974403316776978	0.719514918495811	0.472066632432294	   
df.mm.trans2:exp4	-0.0596633050012076	0.0823521108995642	-0.724490293563603	0.469009658938625	   
df.mm.trans1:exp5	-0.114062520381832	0.0974403316776978	-1.17058838386465	0.242167223989676	   
df.mm.trans2:exp5	-0.115152142266982	0.0823521108995642	-1.39829011071034	0.162473926371334	   
df.mm.trans1:exp6	-0.266346634080079	0.0974403316776979	-2.73343316360078	0.0064279317384122	** 
df.mm.trans2:exp6	-0.219299534899088	0.0823521108995642	-2.66294977145812	0.00792598494877347	** 
df.mm.trans1:exp7	-0.00968245946592228	0.0974403316776978	-0.0993680881336574	0.920874797596141	   
df.mm.trans2:exp7	-0.0674529453505057	0.0823521108995642	-0.81907973716388	0.41302291039478	   
df.mm.trans1:exp8	0.0221198036953027	0.0974403316776978	0.227008706912740	0.820484016942076	   
df.mm.trans2:exp8	-0.0238488961752527	0.0823521108995642	-0.289596658965288	0.772211569016273	   
df.mm.trans1:probe2	-0.364232043856344	0.0533702676706603	-6.82462464126962	1.92786053239952e-11	***
df.mm.trans1:probe3	-0.321479683967773	0.0533702676706603	-6.02357263695162	2.77061604408053e-09	***
df.mm.trans1:probe4	-0.0548028481151538	0.0533702676706603	-1.02684229453998	0.304853522362378	   
df.mm.trans1:probe5	-0.135170082769148	0.0533702676706603	-2.53268511979857	0.0115391414869118	*  
df.mm.trans1:probe6	-0.2033237045068	0.0533702676706603	-3.80968118356608	0.000151521300282232	***
df.mm.trans1:probe7	-0.0335879113677364	0.0533702676706603	-0.62933751007213	0.529335824962877	   
df.mm.trans1:probe8	0.205600989530038	0.0533702676706603	3.85235072828883	0.000127856503837227	***
df.mm.trans1:probe9	-0.441501612561271	0.0533702676706603	-8.27242642449742	6.73170286557445e-16	***
df.mm.trans1:probe10	-0.129351387189408	0.0533702676706603	-2.42366007957119	0.0156203544001692	*  
df.mm.trans1:probe11	0.382520461756462	0.0533702676706603	7.16729517110419	1.96636815942488e-12	***
df.mm.trans1:probe12	0.42843730727955	0.0533702676706603	8.02764021952018	4.2676710154235e-15	***
df.mm.trans1:probe13	-0.143780288889599	0.0533702676706603	-2.69401476074367	0.00723085073347213	** 
df.mm.trans1:probe14	-0.130200093870465	0.0533702676706603	-2.43956231724206	0.0149551783250926	*  
df.mm.trans1:probe15	-0.140063160070420	0.0533702676706603	-2.62436682788866	0.00887252956188397	** 
df.mm.trans1:probe16	-0.240188227673635	0.0533702676706603	-4.5004126484019	7.95524973768386e-06	***
df.mm.trans1:probe17	-0.315951744246445	0.0533702676706603	-5.91999549629644	5.06792537871546e-09	***
df.mm.trans1:probe18	-0.433442331355239	0.0533702676706603	-8.12141947703813	2.11438373138170e-15	***
df.mm.trans1:probe19	-0.503028838979424	0.0533702676706603	-9.42526355842053	6.31041030219596e-20	***
df.mm.trans1:probe20	-0.533561425039917	0.0533702676706603	-9.99735336409483	4.51648904500733e-22	***
df.mm.trans1:probe21	-0.513684302774134	0.0533702676706603	-9.62491524202204	1.15356107621526e-20	***
df.mm.trans2:probe2	0.160015027780591	0.0533702676706603	2.99820545716614	0.00281307743466606	** 
df.mm.trans2:probe3	0.0100872910030051	0.0533702676706603	0.189005816220602	0.850143676802948	   
df.mm.trans2:probe4	0.0736782883995792	0.0533702676706603	1.38051187702930	0.167874838762789	   
df.mm.trans2:probe5	0.101322483830720	0.0533702676706603	1.89848183741489	0.0580484043447176	.  
df.mm.trans2:probe6	-0.0114374621822396	0.0533702676706603	-0.214304006358341	0.83037312624864	   
df.mm.trans3:probe2	1.35594005523161	0.0533702676706603	25.4062817072402	4.17506994575869e-101	***
df.mm.trans3:probe3	0.331614566998711	0.0533702676706603	6.21347018615409	8.94799168404132e-10	***
df.mm.trans3:probe4	1.17432910737315	0.0533702676706603	22.0034329717018	9.3337377357442e-82	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.49406960059181	0.228333767937617	19.6820191826363	7.96624102442091e-69	***
df.mm.trans1	-0.203225673520045	0.202518880280491	-1.00349001159089	0.315975253634061	   
df.mm.trans2	0.00574568650718328	0.184559987337410	0.0311318102589543	0.975173397600272	   
df.mm.exp2	-0.318916003996939	0.248933179706209	-1.28113096202493	0.200576826298972	   
df.mm.exp3	-0.309336617892157	0.248933179706209	-1.24264920512901	0.214418037986961	   
df.mm.exp4	-0.358057832816590	0.248933179706209	-1.43836925732106	0.150781403771454	   
df.mm.exp5	-0.436820093842215	0.248933179706209	-1.75476846580979	0.079741451974077	.  
df.mm.exp6	-0.283294416137149	0.248933179706209	-1.13803397550898	0.255500149627154	   
df.mm.exp7	-0.631867584580116	0.248933179706209	-2.53830198660479	0.011357281821098	*  
df.mm.exp8	-0.349845611159278	0.248933179706209	-1.40537959452479	0.160357209833975	   
df.mm.trans1:exp2	0.318992012515066	0.235822101493115	1.35268073049710	0.176599456854473	   
df.mm.trans2:exp2	-0.00655799260338005	0.199306052435926	-0.0329041317272006	0.973760526069038	   
df.mm.trans1:exp3	0.274541888685473	0.235822101493115	1.16419066299216	0.244747966189269	   
df.mm.trans2:exp3	0.0808672464646848	0.199306052435926	0.405744057826255	0.685056088989266	   
df.mm.trans1:exp4	0.4159767317552	0.235822101493115	1.76394294309749	0.078182781128355	.  
df.mm.trans2:exp4	0.141756333495669	0.199306052435926	0.7112495168266	0.477169299686768	   
df.mm.trans1:exp5	0.433053111049321	0.235822101493114	1.83635506726228	0.0667339645947049	.  
df.mm.trans2:exp5	0.0784797061349615	0.199306052435926	0.393764791263385	0.693875999216549	   
df.mm.trans1:exp6	0.221320216069161	0.235822101493115	0.938504977556665	0.348312406213703	   
df.mm.trans2:exp6	0.0638504689582983	0.199306052435926	0.320363923613536	0.748789181549747	   
df.mm.trans1:exp7	0.614287569273016	0.235822101493114	2.6048770042487	0.00938812395316323	** 
df.mm.trans2:exp7	0.312997706312344	0.199306052435926	1.57043753808213	0.116770459469180	   
df.mm.trans1:exp8	0.345315679788767	0.235822101493114	1.46430583733412	0.143564525976163	   
df.mm.trans2:exp8	0.00988554899842907	0.199306052435926	0.0495998434448304	0.960455583660742	   
df.mm.trans1:probe2	-0.072409316270541	0.129165084546052	-0.560595121545597	0.575255029933403	   
df.mm.trans1:probe3	0.153875029002765	0.129165084546051	1.19130513902852	0.233942253376935	   
df.mm.trans1:probe4	-0.124868641435096	0.129165084546052	-0.9667368071948	0.334013355473172	   
df.mm.trans1:probe5	-0.00701927711443808	0.129165084546051	-0.0543434561987646	0.956677212549088	   
df.mm.trans1:probe6	-0.0492151131789473	0.129165084546052	-0.38102489811324	0.703301720104816	   
df.mm.trans1:probe7	-0.00846844089664817	0.129165084546051	-0.0655629261298466	0.947744742006392	   
df.mm.trans1:probe8	-0.0810604673392581	0.129165084546052	-0.627572595366184	0.530490989542322	   
df.mm.trans1:probe9	0.0617850475234097	0.129165084546052	0.478341710846644	0.632558096608351	   
df.mm.trans1:probe10	0.138813068957613	0.129165084546051	1.07469498777838	0.282885882953015	   
df.mm.trans1:probe11	0.0108921507386414	0.129165084546052	0.0843273611976618	0.932820547939242	   
df.mm.trans1:probe12	0.151663634168959	0.129165084546051	1.17418445319010	0.240725076242437	   
df.mm.trans1:probe13	0.146393098129469	0.129165084546051	1.13337980340403	0.257447297772104	   
df.mm.trans1:probe14	0.00386601027250952	0.129165084546051	0.0299307687220315	0.97613089688045	   
df.mm.trans1:probe15	-0.0221491257174032	0.129165084546051	-0.171479202721432	0.863897113042263	   
df.mm.trans1:probe16	0.165681943344139	0.129165084546051	1.28271462776822	0.200021547648981	   
df.mm.trans1:probe17	0.0839299269220299	0.129165084546051	0.649788038439337	0.51604477991768	   
df.mm.trans1:probe18	-0.0290670856044675	0.129165084546052	-0.225038257874589	0.822015948509698	   
df.mm.trans1:probe19	-0.0306537765156351	0.129165084546052	-0.237322467006988	0.812476875783092	   
df.mm.trans1:probe20	0.0464439999394317	0.129165084546051	0.359570855410798	0.71927778061377	   
df.mm.trans1:probe21	0.0954724067852798	0.129165084546051	0.739150267433463	0.460066396614037	   
df.mm.trans2:probe2	0.260071926718107	0.129165084546051	2.01348474033927	0.0444503315433597	*  
df.mm.trans2:probe3	0.117651821275582	0.129165084546052	0.910863966752834	0.362684224272839	   
df.mm.trans2:probe4	0.0832956627204903	0.129165084546051	0.644877545764256	0.519220269844692	   
df.mm.trans2:probe5	0.00953630122101561	0.129165084546052	0.0738303331316724	0.941166726247257	   
df.mm.trans2:probe6	0.186016091419353	0.129165084546051	1.44014221856551	0.150279414851158	   
df.mm.trans3:probe2	-0.0474247555945359	0.129165084546051	-0.367163895422741	0.713609021121506	   
df.mm.trans3:probe3	-0.1693789378096	0.129165084546051	-1.31133687098862	0.190178910047243	   
df.mm.trans3:probe4	-0.154908050140188	0.129165084546051	-1.19930281998893	0.230820703807946