fitVsDatCorrelation=0.803865510960865
cont.fitVsDatCorrelation=0.222079689821982

fstatistic=12030.9959456907,64,968
cont.fstatistic=4467.89530582474,64,968

residuals=-0.525041485881977,-0.087984962718129,-0.00455897917713877,0.0778240473583302,0.688366123533617
cont.residuals=-0.55870805303653,-0.169879783649629,-0.0350465788607364,0.140669664148809,0.906110596732304

predictedValues:
Include	Exclude	Both
Lung	58.1316335291259	62.8815540047106	58.1866584375452
cerebhem	59.7850734628489	59.4162496792013	55.9333677084303
cortex	55.2532093881803	57.1019384921735	54.3069229246778
heart	56.5642507515027	61.6062824945672	65.8668308860386
kidney	57.9386716821176	65.4067163905682	68.3096527826665
liver	57.3158322404343	66.4028245379783	64.1347395610175
stomach	60.4067013616313	59.3165401145926	58.3445062429513
testicle	57.8355003602079	64.3035195056598	71.978709239357


diffExp=-4.74992047558469,0.368823783647571,-1.84872910399324,-5.04203174306448,-7.46804470845058,-9.0869922975441,1.09016124703873,-6.46801914545191
diffExpScore=1.05607320399544
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	64.4780368263014	61.7748292554707	60.6599861971888
cerebhem	63.011717996181	58.6660657177141	62.9528801035254
cortex	61.8866158481766	60.5152488824707	57.992830048685
heart	63.3677694230554	59.201376144209	63.1447830070387
kidney	61.161712454921	58.851129562289	58.9509104237034
liver	68.0774762826872	65.1881041456631	59.5602897520272
stomach	63.223771302723	57.6977906292489	61.5822641743062
testicle	61.1126266441239	59.0053413514939	61.0460109715691
cont.diffExp=2.7032075708307,4.34565227846687,1.37136696570590,4.16639327884639,2.31058289263203,2.88937213702403,5.52598067347405,2.10728529263002
cont.diffExpScore=0.962149658788324

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=-0.0130559799867567
cont.tran.correlation=0.82599686797258

tran.covariance=1.64706767385110e-05
cont.tran.covariance=0.00111044200113685

tran.mean=59.9791561247188
cont.tran.mean=61.7012257791706

weightedLogRatios:
wLogRatio
Lung	-0.32218143051061
cerebhem	0.0252955536902113
cortex	-0.132580658114604
heart	-0.348212903573767
kidney	-0.499508929138793
liver	-0.606627952668996
stomach	0.0745228186817027
testicle	-0.435772131049098

cont.weightedLogRatios:
wLogRatio
Lung	0.177520969719328
cerebhem	0.293525362558470
cortex	0.092191021156954
heart	0.279860125493993
kidney	0.157671636029222
liver	0.182106505148805
stomach	0.375078907395162
testicle	0.143701795288382

varWeightedLogRatios=0.0608840397705009
cont.varWeightedLogRatios=0.0088421348620872

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9409444020068	0.0683821498266644	57.6311860916385	0	***
df.mm.trans1	0.0287482384169287	0.0585768907456484	0.490777814441515	0.623694838115116	   
df.mm.trans2	0.246209814448834	0.051283124789676	4.80099088069609	1.82819070460283e-06	***
df.mm.exp2	0.0108559025285543	0.0649038753822213	0.167261237709205	0.867199436467898	   
df.mm.exp3	-0.0781939727453711	0.0649038753822213	-1.20476585234524	0.228588169038786	   
df.mm.exp4	-0.171800714739140	0.0649038753822213	-2.6470024128359	0.00825277971982102	** 
df.mm.exp5	-0.124347821411154	0.0649038753822214	-1.91587668192177	0.0556751120362579	.  
df.mm.exp6	-0.0569764444922688	0.0649038753822213	-0.877858897588663	0.380238125956392	   
df.mm.exp7	-0.0226837165082165	0.0649038753822213	-0.349497104365975	0.726792161624867	   
df.mm.exp8	-0.195459976876227	0.0649038753822213	-3.01153013938159	0.00266686093387957	** 
df.mm.trans1:exp2	0.0171901369981871	0.0593775232005363	0.289505793970737	0.772256341717267	   
df.mm.trans2:exp2	-0.0675410115796852	0.0411602095827395	-1.64092972957087	0.101136856195481	   
df.mm.trans1:exp3	0.0274104173020085	0.0593775232005363	0.461629516095427	0.644450742206409	   
df.mm.trans2:exp3	-0.0182208237250214	0.0411602095827395	-0.442680538066606	0.658095720983235	   
df.mm.trans1:exp4	0.144467905605015	0.0593775232005362	2.43304027884595	0.0151528225765007	*  
df.mm.trans2:exp4	0.151311706995362	0.0411602095827395	3.67616463884125	0.000249780216111459	***
df.mm.trans1:exp5	0.121022905112807	0.0593775232005363	2.03819389205701	0.0418019328155132	*  
df.mm.trans2:exp5	0.163719910073868	0.0411602095827395	3.977625763658	7.47950660197491e-05	***
df.mm.trans1:exp6	0.0428433517905146	0.0593775232005363	0.721541578044933	0.470750609326917	   
df.mm.trans2:exp6	0.111463176661555	0.0411602095827395	2.70803229117417	0.00688767685629709	** 
df.mm.trans1:exp7	0.0610737823624017	0.0593775232005363	1.02856736135889	0.303939959068829	   
df.mm.trans2:exp7	-0.0356809553363328	0.0411602095827395	-0.866879826367442	0.386222683711735	   
df.mm.trans1:exp8	0.190352774470359	0.0593775232005363	3.20580523083589	0.00139096212483017	** 
df.mm.trans2:exp8	0.217821480693244	0.0411602095827395	5.29204012567971	1.49642948471378e-07	***
df.mm.trans1:probe2	0.184139194572212	0.0434598969171799	4.23699105690747	2.48173663039479e-05	***
df.mm.trans1:probe3	0.112366139011712	0.0434598969171799	2.58551324283728	0.00986859527744297	** 
df.mm.trans1:probe4	0.221957615597879	0.0434598969171799	5.10718228395378	3.93730171467243e-07	***
df.mm.trans1:probe5	0.154416955170373	0.0434598969171799	3.55309069104882	0.000399106994381312	***
df.mm.trans1:probe6	0.0570725956996113	0.0434598969171799	1.31322436885602	0.189418554857113	   
df.mm.trans1:probe7	0.107873507626634	0.0434598969171799	2.48213905873281	0.0132282868209045	*  
df.mm.trans1:probe8	0.0655088249224689	0.0434598969171799	1.50733962962008	0.132049894315279	   
df.mm.trans1:probe9	-0.174639922105342	0.0434598969171799	-4.01841547020112	6.3135373271028e-05	***
df.mm.trans1:probe10	-0.075916289892543	0.0434598969171799	-1.74681247029219	0.0809870237571007	.  
df.mm.trans1:probe11	-0.0145148785403374	0.0434598969171799	-0.333983271244245	0.738464521358477	   
df.mm.trans1:probe12	0.087881317417352	0.0434598969171799	2.02212438710622	0.0434381459386524	*  
df.mm.trans1:probe13	0.169124634283287	0.0434598969171799	3.89151024922085	0.000106441816969520	***
df.mm.trans1:probe14	-0.0297232074320099	0.0434598969171799	-0.683922639960524	0.494187702166413	   
df.mm.trans1:probe15	-0.029722869148188	0.0434598969171799	-0.683914856144962	0.494192615435616	   
df.mm.trans1:probe16	0.166021482052389	0.0434598969171799	3.82010758950419	0.000141893142275759	***
df.mm.trans1:probe17	0.396282427068479	0.0434598969171799	9.11834714711038	4.30377740537506e-19	***
df.mm.trans1:probe18	0.31847444761264	0.0434598969171799	7.3280074322207	4.92099184915282e-13	***
df.mm.trans1:probe19	0.699250451592899	0.0434598969171799	16.0895561470253	8.50373952552226e-52	***
df.mm.trans1:probe20	0.275086502214488	0.0434598969171799	6.32966301642895	3.75262969582954e-10	***
df.mm.trans1:probe21	0.41544567521487	0.0434598969171799	9.55928809510458	9.4403717045645e-21	***
df.mm.trans1:probe22	0.335257058619221	0.0434598969171799	7.7141705894542	3.02019951107455e-14	***
df.mm.trans2:probe2	-0.305003286339148	0.0434598969171799	-7.01803980162177	4.22733216471717e-12	***
df.mm.trans2:probe3	-0.177669811810128	0.0434598969171799	-4.08813237980539	4.70938991726933e-05	***
df.mm.trans2:probe4	-0.176616564980624	0.0434598969171799	-4.06389746660458	5.21717804857475e-05	***
df.mm.trans2:probe5	-0.0779549788376954	0.0434598969171799	-1.79372212930582	0.0731695549544223	.  
df.mm.trans2:probe6	-0.226683809350977	0.0434598969171799	-5.21593067243029	2.23683144975898e-07	***
df.mm.trans3:probe2	-0.362493739053169	0.0434598969171799	-8.3408789428093	2.5168737170055e-16	***
df.mm.trans3:probe3	-0.339836732254831	0.0434598969171799	-7.81954759125283	1.38062006730155e-14	***
df.mm.trans3:probe4	0.140249345953492	0.0434598969171799	3.22709798922810	0.00129250737930985	** 
df.mm.trans3:probe5	-0.0794581827451746	0.0434598969171799	-1.82831042826897	0.0678105955949817	.  
df.mm.trans3:probe6	-0.209416458687063	0.0434598969171799	-4.81861379206998	1.67737116534438e-06	***
df.mm.trans3:probe7	0.0995176002332982	0.0434598969171799	2.28987198066635	0.0222431560866168	*  
df.mm.trans3:probe8	-0.414055336037373	0.0434598969171799	-9.52729678182221	1.25163736681096e-20	***
df.mm.trans3:probe9	-0.362783088638651	0.0434598969171799	-8.3475367953586	2.38802779126635e-16	***
df.mm.trans3:probe10	-0.111098508621833	0.0434598969171799	-2.55634542423213	0.0107294787034002	*  
df.mm.trans3:probe11	-0.00612725743234012	0.0434598969171799	-0.14098646952653	0.887909964347736	   
df.mm.trans3:probe12	-0.31302122011657	0.0434598969171799	-7.20253020187978	1.18671317253346e-12	***
df.mm.trans3:probe13	-0.136894683591715	0.0434598969171799	-3.1499081521659	0.00168331539985184	** 
df.mm.trans3:probe14	0.172518058887124	0.0434598969171799	3.96959199456653	7.73200145098623e-05	***
df.mm.trans3:probe15	-0.36079524112767	0.0434598969171799	-8.30179698344028	3.42389026026799e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22512920135235	0.112093648947189	37.6928509423663	4.4707172053961e-192	***
df.mm.trans1	-0.00786201997327326	0.096020634687625	-0.0818784420541485	0.934760303990715	   
df.mm.trans2	-0.100611206348698	0.0840645197856491	-1.19683317772159	0.231664612660937	   
df.mm.exp2	-0.111740704451832	0.106391978620859	-1.05027376969869	0.293854391272831	   
df.mm.exp3	-0.0166564326554834	0.106391978620859	-0.156557222371441	0.875626463156008	   
df.mm.exp4	-0.100066446583674	0.106391978620859	-0.940545028683724	0.347172707068522	   
df.mm.exp5	-0.0727090278323209	0.106391978620859	-0.683407046046475	0.494513209851679	   
df.mm.exp6	0.126397976879789	0.106391978620859	1.18804047559096	0.235108871588743	   
df.mm.exp7	-0.103011033738232	0.106391978620859	-0.968221806507843	0.333175450702636	   
df.mm.exp8	-0.105817751340130	0.106391978620859	-0.994602720165824	0.320178077010025	   
df.mm.trans1:exp2	0.0887367619942496	0.0973330504797785	0.91168171095886	0.362163273173116	   
df.mm.trans2:exp2	0.0601061794250757	0.0674707960374957	0.890847343666626	0.373232434248848	   
df.mm.trans1:exp3	-0.0243642846879294	0.0973330504797785	-0.250318720802768	0.802393996312718	   
df.mm.trans2:exp3	-0.00394417431852794	0.0674707960374956	-0.0584575038411587	0.953396276622879	   
df.mm.trans1:exp4	0.0826971585494894	0.0973330504797785	0.849630810314223	0.395740456502471	   
df.mm.trans2:exp4	0.0575152459863849	0.0674707960374957	0.852446530413275	0.394177160580023	   
df.mm.trans1:exp5	0.0199057565984937	0.0973330504797786	0.204511792247066	0.837996568875773	   
df.mm.trans2:exp5	0.0242240673797737	0.0674707960374957	0.35903040726408	0.719650637988581	   
df.mm.trans1:exp6	-0.0720762146727004	0.0973330504797785	-0.740511206804051	0.459169438071741	   
df.mm.trans2:exp6	-0.0726169642434394	0.0674707960374957	-1.07627252838523	0.282073554129215	   
df.mm.trans1:exp7	0.0833667408875514	0.0973330504797786	0.856510100902172	0.391927664475172	   
df.mm.trans2:exp7	0.0347339279686352	0.0674707960374957	0.514799439291223	0.606810699768941	   
df.mm.trans1:exp8	0.0522116000613884	0.0973330504797785	0.53642210743447	0.59179010580988	   
df.mm.trans2:exp8	0.0599497346331052	0.0674707960374957	0.888528639854631	0.374477190016462	   
df.mm.trans1:probe2	-0.0957966696843607	0.0712404982976394	-1.34469398689671	0.179039066701131	   
df.mm.trans1:probe3	-0.0671207668561115	0.0712404982976394	-0.94217149598932	0.346339915415322	   
df.mm.trans1:probe4	-0.0797279802999733	0.0712404982976393	-1.11913844239092	0.263358706558655	   
df.mm.trans1:probe5	-0.0962502251054297	0.0712404982976394	-1.35106052604097	0.176991739629079	   
df.mm.trans1:probe6	-0.074324246870142	0.0712404982976393	-1.04328645428081	0.297076116150014	   
df.mm.trans1:probe7	-0.120358783895894	0.0712404982976394	-1.68947139298550	0.0914511429960892	.  
df.mm.trans1:probe8	-0.127729281304592	0.0712404982976393	-1.79293076770667	0.073296113172984	.  
df.mm.trans1:probe9	-0.0424979705278639	0.0712404982976393	-0.596542297476773	0.550952485636723	   
df.mm.trans1:probe10	-0.0494299300290267	0.0712404982976393	-0.693845933285178	0.487945298038648	   
df.mm.trans1:probe11	-0.136159981461985	0.0712404982976393	-1.91127216563134	0.0562647026914808	.  
df.mm.trans1:probe12	-0.164080393990729	0.0712404982976393	-2.30318986968914	0.0214793919321910	*  
df.mm.trans1:probe13	-0.069083729632087	0.0712404982976393	-0.969725525268766	0.332425554594066	   
df.mm.trans1:probe14	-0.147786576064131	0.0712404982976393	-2.07447420492043	0.0382991001040239	*  
df.mm.trans1:probe15	-0.106372610913965	0.0712404982976393	-1.49314804719003	0.135724291786135	   
df.mm.trans1:probe16	-0.0569346943684227	0.0712404982976393	-0.799190007494786	0.424376295752451	   
df.mm.trans1:probe17	-0.107896234525661	0.0712404982976393	-1.51453509034813	0.130216619722513	   
df.mm.trans1:probe18	0.0185790110994214	0.0712404982976394	0.260792829126477	0.794307743495195	   
df.mm.trans1:probe19	-0.063482038213011	0.0712404982976393	-0.891094808851366	0.373099738573651	   
df.mm.trans1:probe20	-0.173468669880208	0.0712404982976394	-2.43497271952624	0.0150726426282796	*  
df.mm.trans1:probe21	-0.0498331604649434	0.0712404982976393	-0.69950606264351	0.484403877594196	   
df.mm.trans1:probe22	-0.0751186728085067	0.0712404982976394	-1.05443777912199	0.291945643633077	   
df.mm.trans2:probe2	-0.0376697893534271	0.0712404982976394	-0.528769313151693	0.597086703490268	   
df.mm.trans2:probe3	0.0104021606746729	0.0712404982976394	0.146014709655921	0.883940176471216	   
df.mm.trans2:probe4	0.0288804229980783	0.0712404982976394	0.405393332278745	0.68527799144766	   
df.mm.trans2:probe5	-0.0632296901059843	0.0712404982976394	-0.887552608655455	0.375001923354487	   
df.mm.trans2:probe6	0.0401547465318798	0.0712404982976394	0.563650556795872	0.5731225205367	   
df.mm.trans3:probe2	-0.0162683665101452	0.0712404982976394	-0.228358404262935	0.819415853623138	   
df.mm.trans3:probe3	0.0728439652570753	0.0712404982976394	1.02250780100859	0.30679608621604	   
df.mm.trans3:probe4	-0.0298878508444175	0.0712404982976394	-0.41953455630739	0.674918594328343	   
df.mm.trans3:probe5	0.0257864690459255	0.0712404982976394	0.361963625495583	0.717458212514991	   
df.mm.trans3:probe6	0.00136351854538891	0.0712404982976394	0.0191396548026965	0.984733641487945	   
df.mm.trans3:probe7	0.0197439163028094	0.0712404982976394	0.277144556461695	0.781728281468571	   
df.mm.trans3:probe8	-0.039815369464512	0.0712404982976394	-0.558886734595333	0.576368287748921	   
df.mm.trans3:probe9	-0.0529492561606621	0.0712404982976394	-0.743246572187671	0.457512755556087	   
df.mm.trans3:probe10	-0.113506044296733	0.0712404982976394	-1.59327976374492	0.111423898129229	   
df.mm.trans3:probe11	-0.0352074116312353	0.0712404982976393	-0.494205016423951	0.621273524476876	   
df.mm.trans3:probe12	-0.0178337317517791	0.0712404982976393	-0.250331372996167	0.802384215541731	   
df.mm.trans3:probe13	0.00479411503392823	0.0712404982976394	0.0672947992853538	0.94636091368279	   
df.mm.trans3:probe14	0.0179887251357873	0.0712404982976393	0.252507008873398	0.80070280381392	   
df.mm.trans3:probe15	-0.00762818336903318	0.0712404982976394	-0.107076502148581	0.91475047580362