fitVsDatCorrelation=0.909643349316568
cont.fitVsDatCorrelation=0.261948073466742

fstatistic=11224.9791389898,57,807
cont.fstatistic=2068.01578399702,57,807

residuals=-0.655420334401583,-0.0861632493836221,-0.00405388824733712,0.0854970636488767,0.819619221091606
cont.residuals=-0.829383720898335,-0.267106358376254,-0.0335897617720193,0.237376029378417,1.17573841751232

predictedValues:
Include	Exclude	Both
Lung	98.773232216232	43.4379879118708	76.0634295877318
cerebhem	90.7323892925643	49.2906605992334	70.8638050915494
cortex	83.5290244990886	45.7578390734668	70.972252695801
heart	84.9941886737857	45.6017573691488	72.972452317056
kidney	100.636608538492	44.0735708759304	71.3177188598922
liver	90.9682079865938	50.6084968250327	78.0626893035879
stomach	96.802346251848	45.8285621941205	87.474003285453
testicle	93.516095830063	49.4344806140204	80.0289013493272


diffExp=55.3352443043613,41.441728693331,37.7711854256218,39.3924313046369,56.5630376625618,40.3597111615611,50.9737840577275,44.0816152160427
diffExpScore=0.997274600894123
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	72.808836032665	67.337249262156	72.0329674899568
cerebhem	73.4088571328356	70.3609923092654	70.0882720994185
cortex	74.6270589084827	72.731536358245	69.2928462135092
heart	71.503221444229	78.2796801807454	71.9484119788297
kidney	70.5782859973578	82.8117417655748	71.7270923867861
liver	75.1193621115669	75.833958167584	73.1701616779514
stomach	75.9777019174993	87.6195035235196	70.013674471639
testicle	75.2715739053956	90.8219550983914	70.2762560160552
cont.diffExp=5.47158677050911,3.04786482357019,1.89552255023774,-6.77645873651632,-12.2334557682170,-0.714596056017172,-11.6418016060202,-15.5503811929958
cont.diffExpScore=1.52877437897473

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,-1
cont.diffExp1.2Score=0.5

tran.correlation=-0.317695371489037
cont.tran.correlation=0.269849029314387

tran.covariance=-0.00116586545065777
cont.tran.covariance=0.000689044976493273

tran.mean=69.6240905469682
cont.tran.mean=75.9432196322196

weightedLogRatios:
wLogRatio
Lung	3.43554685674324
cerebhem	2.56447850965647
cortex	2.48211893738624
heart	2.57227665383444
kidney	3.46667122336859
liver	2.47299374208788
stomach	3.13970230949130
testicle	2.68980007824804

cont.weightedLogRatios:
wLogRatio
Lung	0.331930302442832
cerebhem	0.18127712346519
cortex	0.110621473512213
heart	-0.390705445154257
kidney	-0.693201301331791
liver	-0.0409371886270891
stomach	-0.627525805044055
testicle	-0.829130688634724

varWeightedLogRatios=0.181256197766280
cont.varWeightedLogRatios=0.198902497392229

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53711885147935	0.0738548789734606	61.432892647617	1.6455660656724e-306	***
df.mm.trans1	0.411898970150627	0.0638478919011592	6.45125403338726	1.91061507973757e-10	***
df.mm.trans2	-0.764340812930975	0.0567037004546053	-13.4795578913386	1.69462458733071e-37	***
df.mm.exp2	0.112295820351079	0.0733534066248613	1.5308875963372	0.126189060317176	   
df.mm.exp3	-0.0463251031792005	0.0733534066248613	-0.631533084974677	0.527870865008326	   
df.mm.exp4	-0.0601462670098097	0.0733534066248613	-0.819951925578664	0.412485620077254	   
df.mm.exp5	0.0976381856867005	0.0733534066248612	1.33106545666018	0.183543390511374	   
df.mm.exp6	0.0445239506357646	0.0733534066248612	0.606978635136413	0.544035819190287	   
df.mm.exp7	-0.106356289130785	0.0733534066248613	-1.44991615283398	0.147470417537398	   
df.mm.exp8	0.0238004991604723	0.0733534066248613	0.324463446969698	0.745671274970168	   
df.mm.trans1:exp2	-0.197208062480329	0.0677738850384719	-2.90979427206192	0.00371609697487525	** 
df.mm.trans2:exp2	0.0141044468082594	0.051232241484708	0.275304113181722	0.783153076932273	   
df.mm.trans1:exp3	-0.121307365631403	0.0677738850384719	-1.78988360431960	0.0738474525060482	.  
df.mm.trans2:exp3	0.098353870325404	0.051232241484708	1.91976512202303	0.055239843641297	.  
df.mm.trans1:exp4	-0.0900974864412362	0.0677738850384719	-1.32938352862747	0.184097144116144	   
df.mm.trans2:exp4	0.108758165819247	0.051232241484708	2.12284613492285	0.0340706002880792	*  
df.mm.trans1:exp5	-0.0789487313046723	0.0677738850384719	-1.16488425091548	0.244410070076168	   
df.mm.trans2:exp5	-0.083112238516797	0.051232241484708	-1.62226434191064	0.105137336554249	   
df.mm.trans1:exp6	-0.126840507005422	0.0677738850384719	-1.8715248053645	0.0616339482686471	.  
df.mm.trans2:exp6	0.108261177238336	0.051232241484708	2.11314543539247	0.0348947497998224	*  
df.mm.trans1:exp7	0.0862008821980757	0.0677738850384719	1.27188934423847	0.203778836621525	   
df.mm.trans2:exp7	0.159929458802521	0.051232241484708	3.12165648364726	0.00186246124245736	** 
df.mm.trans1:exp8	-0.0784935688268989	0.0677738850384719	-1.15816835322841	0.247137973652413	   
df.mm.trans2:exp8	0.105513313925613	0.051232241484708	2.05951000518116	0.0397656807626278	*  
df.mm.trans1:probe2	-0.0381719015003088	0.0454641042135838	-0.83960527014857	0.401378398296516	   
df.mm.trans1:probe3	-0.241825178771627	0.0454641042135838	-5.31903537867076	1.35200935611913e-07	***
df.mm.trans1:probe4	-0.334894958180283	0.0454641042135838	-7.36614003449832	4.34344933050797e-13	***
df.mm.trans1:probe5	-0.145247900416158	0.0454641042135838	-3.19478196983282	0.00145373695109414	** 
df.mm.trans1:probe6	-0.5031740897232	0.0454641042135838	-11.0675025589278	1.32364858082546e-26	***
df.mm.trans1:probe7	-0.181986291601616	0.0454641042135838	-4.00285664370886	6.83473429506451e-05	***
df.mm.trans1:probe8	-0.116008739977739	0.0454641042135838	-2.55165568494975	0.0109045414911412	*  
df.mm.trans1:probe9	0.082193188412696	0.0454641042135838	1.80786996322559	0.0709989367838442	.  
df.mm.trans1:probe10	-0.91259282650026	0.0454641042135838	-20.0728210153010	5.21794215735217e-73	***
df.mm.trans1:probe11	-0.722359136158304	0.0454641042135838	-15.8885597473727	1.15407650898622e-49	***
df.mm.trans1:probe12	-1.00896792345891	0.0454641042135837	-22.1926273685922	1.48320759882402e-85	***
df.mm.trans1:probe13	-0.912808495828294	0.0454641042135838	-20.0775647429465	4.89607385951388e-73	***
df.mm.trans1:probe14	-0.77358047116832	0.0454641042135838	-17.0151921950150	1.03124791963763e-55	***
df.mm.trans1:probe15	-0.903833551450583	0.0454641042135837	-19.8801574799429	6.89768397992432e-72	***
df.mm.trans1:probe16	-0.573262899728249	0.0454641042135838	-12.6091321855841	2.11087424143154e-33	***
df.mm.trans1:probe17	-0.651070817323296	0.0454641042135838	-14.3205464747454	1.29583093728905e-41	***
df.mm.trans1:probe18	-0.745668149661549	0.0454641042135838	-16.4012502293789	2.16201010379952e-52	***
df.mm.trans1:probe19	-0.757960668282054	0.0454641042135838	-16.6716287803949	7.57345668787203e-54	***
df.mm.trans1:probe20	-0.517115278707096	0.0454641042135838	-11.3741442320685	6.59294587910996e-28	***
df.mm.trans1:probe21	-0.727399387061209	0.0454641042135838	-15.9994219537240	2.99363883088691e-50	***
df.mm.trans2:probe2	-0.0097502735913162	0.0454641042135838	-0.21446091944341	0.830241818171981	   
df.mm.trans2:probe3	-0.089765090115747	0.0454641042135838	-1.97441677711374	0.0486749411594692	*  
df.mm.trans2:probe4	0.0857111986963345	0.0454641042135838	1.88524991702631	0.0597551350591668	.  
df.mm.trans2:probe5	-0.0344274704062728	0.0454641042135838	-0.757245105821012	0.449124067418216	   
df.mm.trans2:probe6	0.0265763936728352	0.0454641042135838	0.584557732579161	0.559008515874106	   
df.mm.trans3:probe2	0.0949697946340275	0.0454641042135838	2.08889620232862	0.0370298419042287	*  
df.mm.trans3:probe3	0.187193890554208	0.0454641042135838	4.1173997330905	4.22620732413106e-05	***
df.mm.trans3:probe4	0.381024224165955	0.0454641042135838	8.38077051680064	2.32489121226441e-16	***
df.mm.trans3:probe5	0.246637153476222	0.0454641042135838	5.42487656454324	7.67052205353052e-08	***
df.mm.trans3:probe6	0.0812123283070894	0.0454641042135838	1.78629557783797	0.0744267446303187	.  
df.mm.trans3:probe7	0.113517747766833	0.0454641042135838	2.49686537831127	0.0127279546430735	*  
df.mm.trans3:probe8	0.00039582740082018	0.0454641042135838	0.00870637193159334	0.99305555977166	   
df.mm.trans3:probe9	0.217064283764973	0.0454641042135838	4.77441021921903	2.13996713350984e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30129048045876	0.171588112884406	25.0675318246341	5.17898136606691e-103	***
df.mm.trans1	0.0495559323104595	0.148338734491790	0.334072772565026	0.738411439275885	   
df.mm.trans2	-0.0478470935796398	0.131740530751728	-0.363191899308574	0.716556650548114	   
df.mm.exp2	0.0795011529117587	0.170423034894219	0.466493000556554	0.640988556728955	   
df.mm.exp3	0.140509577559457	0.170423034894219	0.824475269124686	0.40991317909437	   
df.mm.exp4	0.133654208586782	0.170423034894219	0.784249668301827	0.433123759294561	   
df.mm.exp5	0.179996869435441	0.170423034894219	1.05617688094314	0.291203485371535	   
df.mm.exp6	0.134409831730499	0.170423034894219	0.788683477054182	0.430528651467704	   
df.mm.exp7	0.334325954684536	0.170423034894219	1.96174158553186	0.0501359622974443	.  
df.mm.exp8	0.357142631781029	0.170423034894219	2.0956241742949	0.0364265699673706	*  
df.mm.trans1:exp2	-0.0712938770990442	0.157460051363363	-0.45277437979823	0.6508329112396	   
df.mm.trans2:exp2	-0.0355756942273114	0.119028610667119	-0.298883554364960	0.76510584377862	   
df.mm.trans1:exp3	-0.115843738076342	0.157460051363363	-0.735702402439938	0.462125596606005	   
df.mm.trans2:exp3	-0.0634480638831787	0.119028610667119	-0.533048848739575	0.594146523502293	   
df.mm.trans1:exp4	-0.151749026650204	0.157460051363363	-0.963730326113129	0.335469835863081	   
df.mm.trans2:exp4	0.0169202839513068	0.119028610667119	0.142153082830034	0.886994570757991	   
df.mm.trans1:exp5	-0.211111657932957	0.157460051363363	-1.34073154495412	0.180384900962277	   
df.mm.trans2:exp5	0.0268594262640684	0.119028610667119	0.225655211075132	0.821526729921801	   
df.mm.trans1:exp6	-0.103168810371578	0.157460051363363	-0.655206253765918	0.512521687616585	   
df.mm.trans2:exp6	-0.0155772068697848	0.119028610667119	-0.130869433680519	0.895911211218933	   
df.mm.trans1:exp7	-0.29172337518203	0.157460051363363	-1.85268182409539	0.0642928989905684	.  
df.mm.trans2:exp7	-0.0710359029616782	0.119028610667119	-0.596796875671688	0.550810388155751	   
df.mm.trans1:exp8	-0.323877394736865	0.157460051363363	-2.05688612402055	0.040018112806726	*  
df.mm.trans2:exp8	-0.057955143564763	0.119028610667119	-0.486900949611546	0.626460740811246	   
df.mm.trans1:probe2	-0.0678788730877161	0.105627413576726	-0.642625534311789	0.520649636878151	   
df.mm.trans1:probe3	-0.210853468194405	0.105627413576726	-1.99620023869318	0.0462477281394217	*  
df.mm.trans1:probe4	-0.229940022102453	0.105627413576726	-2.17689721177758	0.0297774142841036	*  
df.mm.trans1:probe5	-0.100087901454218	0.105627413576726	-0.94755611318188	0.343639124239341	   
df.mm.trans1:probe6	0.116877566620801	0.105627413576726	1.10650789092646	0.268836479733600	   
df.mm.trans1:probe7	-0.0949645280241478	0.105627413576726	-0.89905191094324	0.368893185231892	   
df.mm.trans1:probe8	-0.115337348130128	0.105627413576726	-1.09192627391514	0.275191385104856	   
df.mm.trans1:probe9	-0.163001857123069	0.105627413576726	-1.54317758623018	0.123179716696881	   
df.mm.trans1:probe10	-0.055600826591555	0.105627413576726	-0.526386330108966	0.598764447780927	   
df.mm.trans1:probe11	-0.0994898240840543	0.105627413576726	-0.941893971604125	0.346528793694105	   
df.mm.trans1:probe12	-0.0777236129087583	0.105627413576726	-0.73582804195334	0.462049165032381	   
df.mm.trans1:probe13	-0.0392840738746973	0.105627413576726	-0.371911727689535	0.710056206666987	   
df.mm.trans1:probe14	-0.203428734538181	0.105627413576726	-1.92590850849920	0.0544668118397908	.  
df.mm.trans1:probe15	-0.159528521949907	0.105627413576726	-1.51029469100867	0.131359679566995	   
df.mm.trans1:probe16	-0.261488240726317	0.105627413576726	-2.47557174668843	0.0135065401579275	*  
df.mm.trans1:probe17	-0.0534310519802098	0.105627413576726	-0.505844554656241	0.613103919456546	   
df.mm.trans1:probe18	-0.0533785916386303	0.105627413576726	-0.505347900049231	0.613452490404152	   
df.mm.trans1:probe19	-0.00517639232218935	0.105627413576726	-0.0490061447772673	0.96092652478552	   
df.mm.trans1:probe20	0.0210405220328652	0.105627413576726	0.199195656888650	0.842159873124242	   
df.mm.trans1:probe21	-0.0375969456253085	0.105627413576726	-0.355939280838288	0.721979063412125	   
df.mm.trans2:probe2	-0.151701484199698	0.105627413576726	-1.43619425168926	0.151334595208026	   
df.mm.trans2:probe3	-0.235722905396968	0.105627413576726	-2.23164515171757	0.0259122897185456	*  
df.mm.trans2:probe4	0.0137503377573224	0.105627413576726	0.130177737878002	0.896458248216334	   
df.mm.trans2:probe5	-0.147721925564798	0.105627413576726	-1.39851881782086	0.162341492392135	   
df.mm.trans2:probe6	-0.134551359770997	0.105627413576726	-1.27382991985561	0.203090441944846	   
df.mm.trans3:probe2	0.063926862924615	0.105627413576726	0.605210908418006	0.54520901555819	   
df.mm.trans3:probe3	-0.088201860201732	0.105627413576726	-0.835028116424182	0.403949011091152	   
df.mm.trans3:probe4	-0.00380996565848082	0.105627413576726	-0.0360698565785984	0.971235577663017	   
df.mm.trans3:probe5	-0.0796354839685577	0.105627413576726	-0.753928182769632	0.451112263946548	   
df.mm.trans3:probe6	-0.138239483939269	0.105627413576726	-1.30874627389086	0.190992881684672	   
df.mm.trans3:probe7	-0.0519481295170451	0.105627413576726	-0.491805372847747	0.622990681273271	   
df.mm.trans3:probe8	-0.0142467199550606	0.105627413576726	-0.134877106923686	0.892742672406925	   
df.mm.trans3:probe9	0.0792759718354892	0.105627413576726	0.750524595377924	0.453157585568117