fitVsDatCorrelation=0.899345148702865
cont.fitVsDatCorrelation=0.255702153815685

fstatistic=8768.31525373882,55,761
cont.fstatistic=1782.57640207108,55,761

residuals=-0.752093946188564,-0.104629702908917,0.00338181422617484,0.110555079310908,0.723819695251299
cont.residuals=-1.01237318506178,-0.316354063684696,-0.0146603469607869,0.272671690023185,1.35705269303429

predictedValues:
Include	Exclude	Both
Lung	96.0153599844599	83.7767865725868	105.084240669485
cerebhem	92.1477204486708	71.7037526914388	110.665948308114
cortex	126.420920784595	68.8509915236137	158.307612938628
heart	106.367003498248	82.3049069002319	125.086184115936
kidney	91.060666991501	83.633650177075	91.7576342617214
liver	82.0578568807572	77.8239576408645	86.2155963173268
stomach	99.7778456692515	76.9096171640512	108.549946208012
testicle	102.516657094108	72.4718977591781	132.538520999691


diffExp=12.2385734118731,20.4439677572319,57.5699292609809,24.0620965980163,7.42701681442588,4.23389923989271,22.8682285052003,30.0447593349298
diffExpScore=0.994441000054803
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,0,0,0,0,1
diffExp1.3Score=0.666666666666667
diffExp1.2=0,1,1,1,0,0,1,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	89.8998088688564	91.2108331282935	111.404561732514
cerebhem	99.8327316828718	98.7354248091263	83.3957217552893
cortex	100.092035678350	92.0503877750767	91.3777009945654
heart	101.285626406005	110.352523158248	95.5920428679154
kidney	100.664894035541	97.0279362571244	107.535904306569
liver	104.129980452386	93.189211691586	97.0658028452788
stomach	103.529323197903	83.3701682046886	93.2536226046094
testicle	94.0115400232158	87.2832542187272	94.0761871119422
cont.diffExp=-1.31102425943710,1.09730687374554,8.04164790327314,-9.06689675224283,3.63695777841663,10.9407687607997,20.1591549932149,6.72828580448865
cont.diffExpScore=1.47920597811952

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

tran.correlation=-0.47117709378113
cont.tran.correlation=0.182312371728955

tran.covariance=-0.00443859719583358
cont.tran.covariance=0.00075886505962527

tran.mean=88.3649744862895
cont.tran.mean=96.66660497425

weightedLogRatios:
wLogRatio
Lung	0.613084856153667
cerebhem	1.103229667106
cortex	2.75626859925755
heart	1.16400678672595
kidney	0.380221331410585
liver	0.232081219335248
stomach	1.16433503412530
testicle	1.54567083145326

cont.weightedLogRatios:
wLogRatio
Lung	-0.0652362874209628
cerebhem	0.0508181722467005
cortex	0.382271101883356
heart	-0.399597011442848
kidney	0.169028687249464
liver	0.509541674318204
stomach	0.981377048361394
testicle	0.334632065066098

varWeightedLogRatios=0.636484153309662
cont.varWeightedLogRatios=0.170619970873853

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1952393713736	0.0920597245732615	45.5708442624658	1.10078292100404e-219	***
df.mm.trans1	0.364686835976119	0.0806117356212623	4.52399186254375	7.03796994900222e-06	***
df.mm.trans2	0.101751623001770	0.0722901578955851	1.40754462244693	0.159674276248580	   
df.mm.exp2	-0.248481974759964	0.0953334370700941	-2.60645144449442	0.0093275727618479	** 
df.mm.exp3	-0.330880267897833	0.0953334370700942	-3.47076826417736	0.000548223532989619	***
df.mm.exp4	-0.089578650480107	0.0953334370700942	-0.939635171385294	0.347702873049916	   
df.mm.exp5	0.0809193999614952	0.0953334370700942	0.84880397107679	0.396257296835501	   
df.mm.exp6	-0.0328790174312016	0.0953334370700942	-0.344884422944147	0.730276556779555	   
df.mm.exp7	-0.079535117202092	0.0953334370700942	-0.834283538351959	0.404383030521637	   
df.mm.exp8	-0.311550981650332	0.0953334370700942	-3.26801373395636	0.00113166679769028	** 
df.mm.trans1:exp2	0.207366742663347	0.089430691134341	2.31874248127910	0.0206730603153188	*  
df.mm.trans2:exp2	0.092869100548594	0.0713410118039733	1.30176315418366	0.193391271034565	   
df.mm.trans1:exp3	0.6059890699439	0.089430691134341	6.77607499458542	2.47411100417312e-11	***
df.mm.trans2:exp3	0.134668934943185	0.0713410118039733	1.88767907179702	0.0594493006190685	.  
df.mm.trans1:exp4	0.191965883286258	0.089430691134341	2.14653248064348	0.0321460087951866	*  
df.mm.trans2:exp4	0.0718534192627882	0.0713410118039733	1.00718250899248	0.314167210771768	   
df.mm.trans1:exp5	-0.133901623841606	0.089430691134341	-1.49726701363027	0.134738428464941	   
df.mm.trans2:exp5	-0.0826294060181038	0.0713410118039734	-1.15823148464936	0.247132936182412	   
df.mm.trans1:exp6	-0.124204590894046	0.089430691134341	-1.38883630796801	0.165288842858183	   
df.mm.trans2:exp6	-0.0408276191993391	0.0713410118039733	-0.572288199549551	0.567295762005803	   
df.mm.trans1:exp7	0.117973110087287	0.089430691134341	1.31915686428130	0.187513453840561	   
df.mm.trans2:exp7	-0.00598991269492997	0.0713410118039733	-0.0839617008991786	0.933108963773589	   
df.mm.trans1:exp8	0.37706809675361	0.089430691134341	4.21631647895001	2.78186870046628e-05	***
df.mm.trans2:exp8	0.166593892013195	0.0713410118039733	2.33517702932153	0.0197932196328148	*  
df.mm.trans1:probe2	0.558920000613235	0.0547648901558947	10.2058088498343	5.23228299081074e-23	***
df.mm.trans1:probe3	-0.288781479347595	0.0547648901558947	-5.27311345874235	1.74942917058680e-07	***
df.mm.trans1:probe4	0.336678660767026	0.0547648901558947	6.14770996177716	1.26815458911728e-09	***
df.mm.trans1:probe5	0.127119497170046	0.0547648901558947	2.32118601549617	0.0205401183118997	*  
df.mm.trans1:probe6	-0.278017371544814	0.0547648901558947	-5.07656220533639	4.83608837141914e-07	***
df.mm.trans1:probe7	-0.652435455273685	0.0547648901558947	-11.9133892794535	3.98649486687090e-30	***
df.mm.trans1:probe8	0.0760651970660181	0.0547648901558947	1.38894092272421	0.165257038176240	   
df.mm.trans1:probe9	0.30016436330044	0.0547648901558947	5.48096348675195	5.75593678056551e-08	***
df.mm.trans1:probe10	0.402139792537667	0.0547648901558947	7.34302198713315	5.37915360046697e-13	***
df.mm.trans1:probe11	0.313801590244298	0.0547648901558947	5.72997753398254	1.44708579456143e-08	***
df.mm.trans1:probe12	0.545860532078753	0.0547648901558947	9.96734459842605	4.45616472834809e-22	***
df.mm.trans1:probe13	0.126981630833568	0.0547648901558947	2.31866859354781	0.0206770919045823	*  
df.mm.trans1:probe14	0.216467368787608	0.0547648901558947	3.95266690340122	8.44833574065128e-05	***
df.mm.trans1:probe15	0.0354297432220118	0.0547648901558947	0.64694265105174	0.517864141650284	   
df.mm.trans1:probe16	0.205417313069378	0.0547648901558947	3.75089427705658	0.000189585075954016	***
df.mm.trans1:probe17	-0.302994231209449	0.0547648901558947	-5.53263651852383	4.34095552263564e-08	***
df.mm.trans1:probe18	-0.303538472401077	0.0547648901558947	-5.54257429416948	4.11060771726546e-08	***
df.mm.trans1:probe19	-0.418119486783423	0.0547648901558947	-7.63480919240771	6.78671459608164e-14	***
df.mm.trans1:probe20	-0.4956270147627	0.0547648901558947	-9.05008689603576	1.17619523536644e-18	***
df.mm.trans1:probe21	-0.0792819797630563	0.0547648901558947	-1.44767896981754	0.148118683817409	   
df.mm.trans1:probe22	-0.297955006190620	0.0547648901558947	-5.44062090405836	7.16285577275247e-08	***
df.mm.trans2:probe2	0.262542866001850	0.0547648901558947	4.79399968217759	1.96660658136308e-06	***
df.mm.trans2:probe3	0.563207445401918	0.0547648901558947	10.2840970519375	2.56876988975725e-23	***
df.mm.trans2:probe4	0.473774672772031	0.0547648901558947	8.65106588223543	3.00820373374730e-17	***
df.mm.trans2:probe5	0.068867500126495	0.0547648901558947	1.25751188271273	0.208954098611197	   
df.mm.trans2:probe6	0.205587094073850	0.0547648901558947	3.75399445682483	0.000187296667781806	***
df.mm.trans3:probe2	0.0113854473656011	0.0547648901558947	0.207896835603815	0.835365143808537	   
df.mm.trans3:probe3	-0.786213183274772	0.0547648901558947	-14.3561537517326	1.54835751014417e-41	***
df.mm.trans3:probe4	-0.361447743831162	0.0547648901558947	-6.5999903003961	7.70570196213996e-11	***
df.mm.trans3:probe5	0.478249992583509	0.0547648901558947	8.7327846586036	1.56342460424705e-17	***
df.mm.trans3:probe6	0.616532428549422	0.0547648901558947	11.2578045312314	2.66012391983708e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.25571803180734	0.203548108144761	20.9076766696388	4.73203197845679e-77	***
df.mm.trans1	0.179403255194809	0.178236100053892	1.00654836556996	0.314471792047384	   
df.mm.trans2	0.235163297567874	0.159836724966764	1.47127199720073	0.141630949088747	   
df.mm.exp2	0.473641674206364	0.210786430749234	2.24702165373179	0.0249244659994685	*  
df.mm.exp3	0.314723548767631	0.210786430749234	1.49309207262041	0.135827550904699	   
df.mm.exp4	0.46283371035043	0.210786430749234	2.19574717739326	0.0284111787040978	*  
df.mm.exp5	0.210270059987403	0.210786430749234	0.997550265640936	0.318814568198878	   
df.mm.exp6	0.306181549090525	0.210786430749234	1.45256764395228	0.146756016763465	   
df.mm.exp7	0.229121318432254	0.210786430749234	1.08698324468919	0.277388377267848	   
df.mm.exp8	0.169769997421286	0.210786430749234	0.805412363679405	0.420833336289775	   
df.mm.trans1:exp2	-0.368841387319665	0.197735199348627	-1.86532993890157	0.062519792981406	.  
df.mm.trans2:exp2	-0.394371552515684	0.157738121128917	-2.50016641312324	0.0126228555773167	*  
df.mm.trans1:exp3	-0.207329244698333	0.197735199348627	-1.04851966357690	0.294732128232988	   
df.mm.trans2:exp3	-0.305561102767872	0.157738121128917	-1.93714176751314	0.0530977776439326	.  
df.mm.trans1:exp4	-0.343585015949368	0.197735199348627	-1.73760168690852	0.082685571503399	.  
df.mm.trans2:exp4	-0.272327387186720	0.157738121128917	-1.72645258633550	0.0846718680168705	.  
df.mm.trans1:exp5	-0.097168755787822	0.197735199348627	-0.491408490283531	0.623279255174489	   
df.mm.trans2:exp5	-0.148444794625509	0.157738121128917	-0.941083826554443	0.34696053176725	   
df.mm.trans1:exp6	-0.159237433705587	0.197735199348627	-0.80530646152097	0.420894393178227	   
df.mm.trans2:exp6	-0.284723262808613	0.157738121128917	-1.80503774719057	0.0714639228650612	.  
df.mm.trans1:exp7	-0.0879622453651079	0.197735199348627	-0.444848694895346	0.656555595110319	   
df.mm.trans2:exp7	-0.319004442770688	0.157738121128917	-2.02236745618372	0.0434877424861338	*  
df.mm.trans1:exp8	-0.125048271915685	0.197735199348627	-0.632402689696196	0.527313732073166	   
df.mm.trans2:exp8	-0.213785046103138	0.157738121128917	-1.35531629623263	0.175718928342951	   
df.mm.trans1:probe2	0.0701597665563931	0.121087585647912	0.579413374054689	0.562481758054638	   
df.mm.trans1:probe3	0.212053227478019	0.121087585647912	1.7512383812376	0.0803077181623817	.  
df.mm.trans1:probe4	0.138161210333803	0.121087585647912	1.14100227198795	0.254227909645897	   
df.mm.trans1:probe5	0.059960156846446	0.121087585647912	0.495180051081312	0.620615974821091	   
df.mm.trans1:probe6	0.180658339196650	0.121087585647912	1.49196416981962	0.136122954019304	   
df.mm.trans1:probe7	-0.0087864550206134	0.121087585647912	-0.0725628062827337	0.942173091631432	   
df.mm.trans1:probe8	0.105789459689650	0.121087585647912	0.873660657478584	0.382578686900219	   
df.mm.trans1:probe9	0.115623885904251	0.121087585647912	0.954878118062837	0.33994255313739	   
df.mm.trans1:probe10	0.100895500688703	0.121087585647912	0.833243970873096	0.404968586194067	   
df.mm.trans1:probe11	-0.062258808699272	0.121087585647912	-0.51416343274283	0.607287001648122	   
df.mm.trans1:probe12	0.103926314333516	0.121087585647912	0.858273899652299	0.391011474854037	   
df.mm.trans1:probe13	-0.0286764024872363	0.121087585647912	-0.236823637483523	0.812857346336781	   
df.mm.trans1:probe14	0.0556542978695634	0.121087585647912	0.459620179655658	0.645920143562826	   
df.mm.trans1:probe15	-0.00499567562485228	0.121087585647912	-0.0412567118100633	0.967102064137656	   
df.mm.trans1:probe16	0.125382134520112	0.121087585647912	1.03546646709669	0.300780040132862	   
df.mm.trans1:probe17	0.0795404951745882	0.121087585647912	0.65688397988105	0.511454116853145	   
df.mm.trans1:probe18	0.0715097440230194	0.121087585647912	0.590562142604355	0.554989171246776	   
df.mm.trans1:probe19	0.0582072822362165	0.121087585647912	0.48070396254713	0.630865078063537	   
df.mm.trans1:probe20	0.0290523374756392	0.121087585647912	0.239928290915924	0.810450459788186	   
df.mm.trans1:probe21	0.241055550185819	0.121087585647912	1.99075362594757	0.0468652091922431	*  
df.mm.trans1:probe22	0.137174435402192	0.121087585647912	1.13285300609639	0.257632778016636	   
df.mm.trans2:probe2	0.148450603060341	0.121087585647912	1.22597706664986	0.220586543481204	   
df.mm.trans2:probe3	-0.0875714681797234	0.121087585647912	-0.723207649332079	0.469774539084938	   
df.mm.trans2:probe4	0.0851954824859124	0.121087585647912	0.703585607311027	0.481906085566364	   
df.mm.trans2:probe5	-0.0889556535854118	0.121087585647912	-0.734638923630611	0.462785733330864	   
df.mm.trans2:probe6	0.21038917564576	0.121087585647912	1.73749583427578	0.0827042504476752	.  
df.mm.trans3:probe2	-0.108819838252399	0.121087585647912	-0.898686993139131	0.369103661333586	   
df.mm.trans3:probe3	0.158945529191173	0.121087585647912	1.31264925583156	0.189696890617181	   
df.mm.trans3:probe4	0.105614038883935	0.121087585647912	0.872211947400043	0.383367863615811	   
df.mm.trans3:probe5	0.0671435837301345	0.121087585647912	0.554504273669876	0.579396701269947	   
df.mm.trans3:probe6	0.0344188337051788	0.121087585647912	0.284247419097601	0.776298102414181