fitVsDatCorrelation=0.939877164429618
cont.fitVsDatCorrelation=0.274441284218233

fstatistic=6726.3190529854,58,830
cont.fstatistic=835.890908511734,58,830

residuals=-0.92625722582234,-0.127838078846109,0.0141332269298894,0.128792710392034,0.83436129646515
cont.residuals=-1.18054291714080,-0.479232820889535,-0.0465843926920878,0.41015886928517,1.91532129658496

predictedValues:
Include	Exclude	Both
Lung	71.3942608576601	43.0130076777320	86.3330239226594
cerebhem	78.3995423891835	44.7612035004946	103.042965264429
cortex	108.063410949864	43.6780743872952	129.836598286566
heart	214.84168287974	43.1849861776868	283.564547057608
kidney	125.408742745790	43.4984735189572	158.375314723040
liver	105.595566132881	46.1380466917327	136.512731552142
stomach	103.490624091713	42.5476447554288	121.183510577462
testicle	121.457018253003	45.330084224038	170.601991868999


diffExp=28.3812531799280,33.6383388886888,64.385336562569,171.656696702053,81.9102692268331,59.4575194411483,60.9429793362841,76.1269340289654
diffExpScore=0.998268396251542
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	89.9965758387656	101.315712070331	80.240943444242
cerebhem	93.3227101939325	125.927456127908	96.9533072061858
cortex	99.1370405810225	86.0725169590647	105.428424821699
heart	84.1943767020123	119.931901072470	91.1870889203111
kidney	94.5251431729869	91.7703014901591	87.3390276810368
liver	117.097993796704	111.822243617932	92.72744501158
stomach	102.422886773383	83.5921213619878	86.9097812844731
testicle	105.387098739962	93.1848510837924	91.2011009574698
cont.diffExp=-11.3191362315658,-32.604745933976,13.0645236219578,-35.737524370458,2.75484168282777,5.27575017877187,18.8307654113954,12.2022476561694
cont.diffExpScore=4.61880107700802

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,-1,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,-1,0,-1,0,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,-1,0,-1,0,0,1,0
cont.diffExp1.2Score=1.5

tran.correlation=-0.165585818291018
cont.tran.correlation=-0.260097356823934

tran.covariance=-0.000975984943363251
cont.tran.covariance=-0.00452292209767604

tran.mean=80.050148077075
cont.tran.mean=99.981308098901

weightedLogRatios:
wLogRatio
Lung	2.03438944635862
cerebhem	2.28762889741606
cortex	3.8316410560212
heart	7.32845215076992
kidney	4.55534449578074
liver	3.51528785312284
stomach	3.72879999392631
testicle	4.24470311257337

cont.weightedLogRatios:
wLogRatio
Lung	-0.540104961421335
cerebhem	-1.40409025387029
cortex	0.6395608766066
heart	-1.63100859849307
kidney	0.134105004234193
liver	0.218515218403302
stomach	0.919817095400252
testicle	0.56557492040663

varWeightedLogRatios=2.65021504129086
cont.varWeightedLogRatios=0.916211766025326

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48538879402889	0.102993934444753	43.5500286323648	1.42905638229040e-216	***
df.mm.trans1	-0.0316694771670010	0.089166825713834	-0.355171073024837	0.722551668092246	   
df.mm.trans2	-0.75247251902793	0.0789965321304949	-9.52538673197597	1.74873638092690e-20	***
df.mm.exp2	-0.043494049269097	0.102100476203901	-0.425992619096476	0.670223668987664	   
df.mm.exp3	0.0217798446498662	0.102100476203900	0.213317757758255	0.831131508501485	   
df.mm.exp4	-0.0835533565589151	0.102100476203900	-0.818344435456445	0.413395416751736	   
df.mm.exp5	-0.0321713061798237	0.102100476203900	-0.315094575225837	0.752769046822659	   
df.mm.exp6	0.00332854782873178	0.102100476203901	0.0326007081699058	0.97400084289072	   
df.mm.exp7	0.0212916372321924	0.102100476203900	0.208536120729465	0.834861545686215	   
df.mm.exp8	-0.0973098176624946	0.102100476203901	-0.953078979457073	0.340827526191062	   
df.mm.trans1:exp2	0.137094653765699	0.0946496746443892	1.44844294796344	0.147870864761887	   
df.mm.trans2:exp2	0.0833332461269757	0.0710589241045065	1.17273441973900	0.241238786557809	   
df.mm.trans1:exp3	0.392720862630631	0.0946496746443892	4.14920457049782	3.68028040210379e-05	***
df.mm.trans2:exp3	-0.00643617280662874	0.0710589241045065	-0.0905751513654085	0.92785203014476	   
df.mm.trans1:exp4	1.18523726877240	0.0946496746443892	12.5223596723970	4.46232899011398e-33	***
df.mm.trans2:exp4	0.0875436751373074	0.0710589241045065	1.23198706201288	0.218302818844407	   
df.mm.trans1:exp5	0.595532164844283	0.0946496746443892	6.29196209159485	5.06919669310707e-10	***
df.mm.trans2:exp5	0.0433945780588001	0.0710589241045065	0.610684422902036	0.54157570517801	   
df.mm.trans1:exp6	0.38807034921385	0.0946496746443892	4.10007060955972	4.53645728761714e-05	***
df.mm.trans2:exp6	0.066806795559597	0.0710589241045065	0.94016052735817	0.347408752559984	   
df.mm.trans1:exp7	0.349971896913916	0.0946496746443892	3.69754992004784	0.000232002124853963	***
df.mm.trans2:exp7	-0.0321697102253795	0.0710589241045065	-0.452718791211466	0.650869555726767	   
df.mm.trans1:exp8	0.628652772650933	0.0946496746443892	6.64189047678041	5.59696623982311e-11	***
df.mm.trans2:exp8	0.149778166551620	0.0710589241045065	2.10780234065100	0.035347389790237	*  
df.mm.trans1:probe2	0.217484926602154	0.0634929319659278	3.4253407405861	0.000644170426058866	***
df.mm.trans1:probe3	0.132142480406391	0.0634929319659278	2.08121559856934	0.0377202024534443	*  
df.mm.trans1:probe4	0.562328397851705	0.0634929319659278	8.85655112215431	4.93808045775061e-18	***
df.mm.trans1:probe5	-0.0395765483364864	0.0634929319659277	-0.623322110211014	0.533244113001334	   
df.mm.trans1:probe6	-0.717586707649353	0.0634929319659277	-11.3018360537269	1.19579921004307e-27	***
df.mm.trans1:probe7	-0.387650491585837	0.0634929319659277	-6.10541172982627	1.57328195671987e-09	***
df.mm.trans1:probe8	0.227663474450875	0.0634929319659278	3.58565067640988	0.000355884394857270	***
df.mm.trans1:probe9	-0.099663900804355	0.0634929319659277	-1.56968496679028	0.116869545905806	   
df.mm.trans1:probe10	0.0321090693700028	0.0634929319659277	0.505710925229182	0.613193870881859	   
df.mm.trans1:probe11	-0.815501731236126	0.0634929319659278	-12.8439765811059	1.43404185618983e-34	***
df.mm.trans1:probe12	-0.692790730312623	0.0634929319659278	-10.9113047525415	5.37848752381383e-26	***
df.mm.trans1:probe13	-0.76360972489717	0.0634929319659277	-12.0266886605102	7.951042540208e-31	***
df.mm.trans1:probe14	-0.819520183809543	0.0634929319659278	-12.9072663434305	7.24140154976793e-35	***
df.mm.trans1:probe15	-0.566725729220338	0.0634929319659278	-8.92580814388064	2.79497477251825e-18	***
df.mm.trans1:probe16	-0.713545984843511	0.0634929319659277	-11.2381955400394	2.23818729042564e-27	***
df.mm.trans1:probe17	-0.191420395977668	0.0634929319659278	-3.01482999840659	0.00264952664529759	** 
df.mm.trans1:probe18	-0.29822050587904	0.0634929319659277	-4.69690872110103	3.09010982475274e-06	***
df.mm.trans1:probe19	-0.0527139874074718	0.0634929319659278	-0.830233945343077	0.406645261159683	   
df.mm.trans1:probe20	-0.231654336986164	0.0634929319659277	-3.64850590157777	0.000280242870044084	***
df.mm.trans1:probe21	-0.0214803195227282	0.0634929319659278	-0.33831040491649	0.735214840313588	   
df.mm.trans1:probe22	-0.510623827859206	0.0634929319659278	-8.04221528363539	3.03478858338470e-15	***
df.mm.trans2:probe2	0.115550441402580	0.0634929319659278	1.81989455872960	0.0691351418753073	.  
df.mm.trans2:probe3	0.0331272938135643	0.0634929319659278	0.521747740856281	0.601985177040087	   
df.mm.trans2:probe4	0.0772885898286157	0.0634929319659278	1.21727863929943	0.223844198342963	   
df.mm.trans2:probe5	0.0721436055504453	0.0634929319659278	1.13624624531688	0.256181481344103	   
df.mm.trans2:probe6	0.130684555616137	0.0634929319659278	2.05825359720773	0.0398774944257710	*  
df.mm.trans3:probe2	0.670974041235226	0.0634929319659277	10.5676965996040	1.41152488857193e-24	***
df.mm.trans3:probe3	1.24490154789252	0.0634929319659277	19.6069311866173	1.24020581372922e-70	***
df.mm.trans3:probe4	0.986698269735309	0.0634929319659278	15.5402851811096	5.42761589113702e-48	***
df.mm.trans3:probe5	0.139676394831023	0.0634929319659277	2.19987312770463	0.0280908474006613	*  
df.mm.trans3:probe6	0.924245660909234	0.0634929319659277	14.5566700464425	6.40151257046127e-43	***
df.mm.trans3:probe7	1.03578662921048	0.0634929319659277	16.3134162675983	4.19853044888943e-52	***
df.mm.trans3:probe8	0.600992029253958	0.0634929319659277	9.46549498732346	2.93710335243657e-20	***
df.mm.trans3:probe9	1.20941394848490	0.0634929319659278	19.0480091411420	2.17439203335501e-67	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.76683715988748	0.290002006977485	16.4372557609836	9.01540077140682e-53	***
df.mm.trans1	-0.262919056558717	0.251068750332033	-1.04719944720724	0.295312451732713	   
df.mm.trans2	-0.161368958626748	0.222432058602379	-0.725475273846259	0.468365001786957	   
df.mm.exp2	0.0645607656225266	0.287486279382509	0.224569902122621	0.822369153092543	   
df.mm.exp3	-0.339318207908010	0.287486279382509	-1.18029357309445	0.238221619678956	   
df.mm.exp4	-0.0258403191409144	0.287486279382509	-0.0898836605225709	0.928401350487812	   
df.mm.exp5	-0.134622044463453	0.287486279382509	-0.468272937242804	0.639712363624398	   
df.mm.exp6	0.217277920983485	0.287486279382509	0.75578535939237	0.449992329654281	   
df.mm.exp7	-0.142790344604975	0.287486279382509	-0.496685771966837	0.619542178089207	   
df.mm.exp8	-0.0538208022602916	0.287486279382509	-0.187211724941772	0.851540385006244	   
df.mm.trans1:exp2	-0.0282689003205745	0.266506913777166	-0.106071921061721	0.915550919400974	   
df.mm.trans2:exp2	0.152903726933538	0.200081982643566	0.76420537678261	0.44496205681079	   
df.mm.trans1:exp3	0.436049725776759	0.266506913777166	1.63616665547879	0.102183820431520	   
df.mm.trans2:exp3	0.176266865731106	0.200081982643566	0.880973206093798	0.378587348969199	   
df.mm.trans1:exp4	-0.0408031702233044	0.266506913777166	-0.153103608626908	0.878353783050162	   
df.mm.trans2:exp4	0.194522906202207	0.200081982643566	0.972216006819252	0.331226338028807	   
df.mm.trans1:exp5	0.183716285526111	0.266506913777166	0.689349041352533	0.490796309565287	   
df.mm.trans2:exp5	0.0356692729951178	0.200081982643566	0.178273288398288	0.858551857387734	   
df.mm.trans1:exp6	0.0459615937092303	0.266506913777166	0.172459292173036	0.863118504764702	   
df.mm.trans2:exp6	-0.118608924610719	0.200081982643566	-0.592801625831614	0.553475393513974	   
df.mm.trans1:exp7	0.272128912512934	0.266506913777166	1.02109513279070	0.307506892186783	   
df.mm.trans2:exp7	-0.0495018854009264	0.200081982643566	-0.247408011190648	0.804653661547718	   
df.mm.trans1:exp8	0.211689404658501	0.266506913777166	0.794311118080414	0.42724137904113	   
df.mm.trans2:exp8	-0.0298355348529509	0.200081982643566	-0.149116549420150	0.881497890300176	   
df.mm.trans1:probe2	0.0761698456755055	0.178778272703825	0.426057621675834	0.67017632280064	   
df.mm.trans1:probe3	-0.201320624797259	0.178778272703825	-1.12609111696016	0.260452441303001	   
df.mm.trans1:probe4	-0.0393381668340692	0.178778272703825	-0.220038857290221	0.825894999546222	   
df.mm.trans1:probe5	0.0474359607504511	0.178778272703825	0.265334036586405	0.790818031258445	   
df.mm.trans1:probe6	0.0267906981046257	0.178778272703825	0.149854329049307	0.880915950890157	   
df.mm.trans1:probe7	-0.0657980145897984	0.178778272703825	-0.36804256800715	0.712935332729697	   
df.mm.trans1:probe8	0.0314435503006933	0.178778272703825	0.175880154926793	0.860430997187646	   
df.mm.trans1:probe9	0.135324218291798	0.178778272703825	0.756938839631727	0.449301316696623	   
df.mm.trans1:probe10	0.0242595377499393	0.178778272703825	0.135696230772568	0.892094330083927	   
df.mm.trans1:probe11	0.0924669107531537	0.178778272703825	0.517215595355594	0.605143468025628	   
df.mm.trans1:probe12	-0.0755538778627552	0.178778272703825	-0.422612192858146	0.672687688373802	   
df.mm.trans1:probe13	-0.162933643375784	0.178778272703825	-0.911372735129336	0.362363624585016	   
df.mm.trans1:probe14	-0.206692174132565	0.178778272703825	-1.15613699028731	0.247957798731462	   
df.mm.trans1:probe15	-0.197286451706198	0.178778272703825	-1.10352588557019	0.27011869273968	   
df.mm.trans1:probe16	0.149559528366297	0.178778272703825	0.836564343666452	0.403078278590241	   
df.mm.trans1:probe17	-0.0463020493786778	0.178778272703825	-0.258991479660308	0.79570603131511	   
df.mm.trans1:probe18	0.111510341961030	0.178778272703825	0.623735425309569	0.532972728948007	   
df.mm.trans1:probe19	0.189263293295597	0.178778272703825	1.05864818153346	0.290068027706294	   
df.mm.trans1:probe20	-0.0361037501576744	0.178778272703825	-0.201947080098967	0.84000758095919	   
df.mm.trans1:probe21	0.106004661296516	0.178778272703825	0.59293928559277	0.553383303141993	   
df.mm.trans1:probe22	-0.0874406724154033	0.178778272703825	-0.489101226300931	0.624899251300792	   
df.mm.trans2:probe2	0.153657395852251	0.178778272703825	0.859485850983746	0.390320688981777	   
df.mm.trans2:probe3	-0.166417540941817	0.178778272703825	-0.930859988884188	0.352196658688038	   
df.mm.trans2:probe4	0.328149583424099	0.178778272703825	1.83551154433476	0.0667875621604699	.  
df.mm.trans2:probe5	0.0268611084391547	0.178778272703825	0.150248170725167	0.880605326222213	   
df.mm.trans2:probe6	-0.150651012074536	0.178778272703825	-0.842669580570977	0.399656020175598	   
df.mm.trans3:probe2	0.0626900189303142	0.178778272703825	0.350657929412766	0.725933968145061	   
df.mm.trans3:probe3	0.019213333340796	0.178778272703825	0.107470181080818	0.914441952208358	   
df.mm.trans3:probe4	0.249609343342348	0.178778272703825	1.39619507207045	0.163029054285154	   
df.mm.trans3:probe5	-0.161250564558992	0.178778272703825	-0.90195839863678	0.367340626572054	   
df.mm.trans3:probe6	0.0108452608452418	0.178778272703825	0.0606631929105206	0.951642048997605	   
df.mm.trans3:probe7	-0.182812686391062	0.178778272703825	-1.0225665771697	0.306810761390072	   
df.mm.trans3:probe8	0.214376969998573	0.178778272703825	1.19912205636824	0.230822708314552	   
df.mm.trans3:probe9	0.16969113445286	0.178778272703825	0.949170902517782	0.342809979515347