fitVsDatCorrelation=0.831269220372259
cont.fitVsDatCorrelation=0.253268952284597

fstatistic=9789.84525797902,55,761
cont.fstatistic=3223.04825062869,55,761

residuals=-0.654034862427427,-0.0870707637606404,-0.0073614171501484,0.0838029435079128,0.749337523494434
cont.residuals=-0.545704367892473,-0.198686410036923,-0.0468593108533754,0.150422346428905,1.39977535672442

predictedValues:
Include	Exclude	Both
Lung	73.1105376688785	56.6170225580052	68.2129089394942
cerebhem	67.7910871857769	67.0905441576325	53.3155665729062
cortex	64.8465802647211	54.2190760187428	60.6655168234983
heart	71.0490409903892	53.3803039668888	65.1353618301602
kidney	73.218807594504	58.142636134517	65.4161559667429
liver	73.1906403757073	58.1190018644259	62.1931027647291
stomach	71.7651731128337	59.5633728254859	71.9021477774074
testicle	77.3869814355863	57.6822406065409	74.7572564915847


diffExp=16.4935151108732,0.700543028144367,10.6275042459783,17.6687370235004,15.0761714599869,15.0716385112814,12.2018002873478,19.7047408290454
diffExpScore=0.990787201438035
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=0,0,0,1,0,0,0,1
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,0,1,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	67.1948281633426	69.7849854122748	64.8108411603591
cerebhem	70.0329430195388	63.7571546465435	63.2395703246469
cortex	58.6328135575221	66.8360266867254	65.1536599388799
heart	64.2150666467967	63.1204116385546	69.72074441155
kidney	62.3777036854692	67.8979737680975	63.0579384101653
liver	64.8862450193763	65.978214407272	66.0922345518229
stomach	66.1801814120372	65.828909519021	62.0021002775872
testicle	64.7145879612812	66.7462358115238	64.8338561251197
cont.diffExp=-2.59015724893214,6.27578837299527,-8.20321312920337,1.09465500824214,-5.52027008262824,-1.09196938789565,0.351271893016246,-2.03164785024265
cont.diffExpScore=2.13588788162977

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=-0.0874422008382161
cont.tran.correlation=-0.218238286675655

tran.covariance=-0.000202276876130834
cont.tran.covariance=-0.000375217113932493

tran.mean=64.8233154225397
cont.tran.mean=65.511517584711

weightedLogRatios:
wLogRatio
Lung	1.06461605107966
cerebhem	0.0437447139778275
cortex	0.730737382825869
heart	1.17814161077907
kidney	0.96329132408918
liver	0.963291164458543
stomach	0.779019412232164
testicle	1.23480440282548

cont.weightedLogRatios:
wLogRatio
Lung	-0.159857485362754
cerebhem	0.394504203519330
cortex	-0.5417005470227
heart	0.0714163252888615
kidney	-0.354084353542589
liver	-0.0697761378462649
stomach	0.0222974700222355
testicle	-0.129377279537105

varWeightedLogRatios=0.141954867630437
cont.varWeightedLogRatios=0.0791075230798926

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.49869071142052	0.0764991845615622	58.8070413717969	2.93971670050965e-285	***
df.mm.trans1	-0.0784299193695578	0.0659151609742208	-1.18986160710783	0.234471866986274	   
df.mm.trans2	-0.452138416992651	0.0588688399529012	-7.68043700800611	4.88035885404809e-14	***
df.mm.exp2	0.340597122113638	0.0763094907412772	4.46336515687692	9.2884300826779e-06	***
df.mm.exp3	-0.0459668406610671	0.0763094907412772	-0.602373836000491	0.547104762476602	   
df.mm.exp4	-0.0413037739262058	0.0763094907412772	-0.541266538735579	0.58848229162677	   
df.mm.exp5	0.0699339216358595	0.0763094907412772	0.91645116428527	0.359720634232332	   
df.mm.exp6	0.119667733506526	0.0763094907412772	1.56818938698270	0.117252630699926	   
df.mm.exp7	-0.0205143713706939	0.0763094907412772	-0.268831192180887	0.788132452343802	   
df.mm.exp8	-0.0161267535865232	0.0763094907412772	-0.211333523915131	0.832683605908794	   
df.mm.trans1:exp2	-0.416138903769038	0.0699682700421015	-5.94753741258199	4.14784850836181e-09	***
df.mm.trans2:exp2	-0.170863701611778	0.0537196713465599	-3.18065426181577	0.00152901855426779	** 
df.mm.trans1:exp3	-0.0739814934939877	0.0699682700421015	-1.05735776301846	0.290683865275868	   
df.mm.trans2:exp3	0.00268995134945993	0.0537196713465599	0.0500738608787522	0.96007666907237	   
df.mm.trans1:exp4	0.0127016201824628	0.0699682700421015	0.181534003553610	0.855996785410519	   
df.mm.trans2:exp4	-0.0175640796176267	0.0537196713465599	-0.326958061681282	0.743789514788828	   
df.mm.trans1:exp5	-0.0684541098429695	0.0699682700421015	-0.978359330619137	0.328207545486716	   
df.mm.trans2:exp5	-0.043344378282613	0.0537196713465599	-0.806862313117794	0.419997905734976	   
df.mm.trans1:exp6	-0.118572694957912	0.0699682700421015	-1.69466380813137	0.0905483384169193	.  
df.mm.trans2:exp6	-0.0934847605547185	0.0537196713465599	-1.74023329278438	0.0822222939744175	.  
df.mm.trans1:exp7	0.00194116508570716	0.0699682700421015	0.0277435055138439	0.977873998309983	   
df.mm.trans2:exp7	0.0712455147202787	0.0537196713465599	1.32624628808049	0.185156024240553	   
df.mm.trans1:exp8	0.072972811005068	0.0699682700421015	1.04294147848959	0.297306593923468	   
df.mm.trans2:exp8	0.0347663993752596	0.0537196713465599	0.647181907554354	0.517709384967478	   
df.mm.trans1:probe2	-0.188326610855010	0.0469361424246603	-4.01240070287633	6.6042487439487e-05	***
df.mm.trans1:probe3	-0.514059842610276	0.0469361424246603	-10.9523240738290	5.04435213019439e-26	***
df.mm.trans1:probe4	-0.491711157919142	0.0469361424246603	-10.4761732114737	4.40900623540633e-24	***
df.mm.trans1:probe5	-0.46439792512367	0.0469361424246603	-9.8942499560781	8.52729061701368e-22	***
df.mm.trans1:probe6	-0.275550737578782	0.0469361424246603	-5.87075808415834	6.47633079362952e-09	***
df.mm.trans1:probe7	0.240160611700597	0.0469361424246603	5.11675223600005	3.93897772301703e-07	***
df.mm.trans1:probe8	0.0747143757806689	0.0469361424246603	1.59183034482642	0.111838104283864	   
df.mm.trans1:probe9	-0.252996677895352	0.0469361424246603	-5.39023159607653	9.3940425328167e-08	***
df.mm.trans1:probe10	0.243302246165636	0.0469361424246603	5.18368646413952	2.79010660661889e-07	***
df.mm.trans1:probe11	-0.233543699178112	0.0469361424246603	-4.97577532182125	8.04033329963115e-07	***
df.mm.trans1:probe12	-0.0286247542044552	0.0469361424246603	-0.60986593115108	0.542132676401886	   
df.mm.trans1:probe13	0.0178889954786286	0.0469361424246603	0.381134762136518	0.70320963308333	   
df.mm.trans1:probe14	-0.143245195636953	0.0469361424246603	-3.05191667310289	0.00235292113863517	** 
df.mm.trans1:probe15	-0.412193738147534	0.0469361424246603	-8.78201140643732	1.05156487103034e-17	***
df.mm.trans1:probe16	-0.375224120015303	0.0469361424246603	-7.99435361816526	4.8330071965584e-15	***
df.mm.trans1:probe17	-0.1604037514101	0.0469361424246603	-3.41748902069599	0.000665606016143255	***
df.mm.trans1:probe18	-0.281121037526094	0.0469361424246603	-5.98943634912767	3.24573025039284e-09	***
df.mm.trans1:probe19	-0.346738863792363	0.0469361424246603	-7.38745976725572	3.94159888537865e-13	***
df.mm.trans2:probe2	-0.124438126665187	0.0469361424246603	-2.65122185669453	0.008186922557857	** 
df.mm.trans2:probe3	0.101583060838887	0.0469361424246603	2.16428226929693	0.0307530510872232	*  
df.mm.trans2:probe4	-0.138514515952831	0.0469361424246603	-2.95112697374243	0.00326342395868452	** 
df.mm.trans2:probe5	-0.131794872059311	0.0469361424246603	-2.80796131192209	0.00511327796369754	** 
df.mm.trans2:probe6	0.139525415928938	0.0469361424246603	2.97266474663736	0.00304541476269241	** 
df.mm.trans3:probe2	0.101227124996914	0.0469361424246603	2.15669886291568	0.0313416928803452	*  
df.mm.trans3:probe3	0.163830629859053	0.0469361424246603	3.49050052679609	0.000509884408316304	***
df.mm.trans3:probe4	0.389734917016565	0.0469361424246603	8.30351402742884	4.60500928409569e-16	***
df.mm.trans3:probe5	0.0835395265901076	0.0469361424246603	1.77985497475003	0.0754986619383018	.  
df.mm.trans3:probe6	0.280383922939445	0.0469361424246603	5.97373172261662	3.55885486413972e-09	***
df.mm.trans3:probe7	0.868546353936508	0.0469361424246603	18.5048516786538	2.07241014986868e-63	***
df.mm.trans3:probe8	0.330819525015738	0.0469361424246603	7.04828961065035	4.06533776757952e-12	***
df.mm.trans3:probe9	0.0725210723034602	0.0469361424246603	1.5451008233126	0.122737605124595	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30037294236708	0.133133636921157	32.3011752838527	8.22204880230196e-145	***
df.mm.trans1	-0.0927124027992463	0.114713969293090	-0.808204993433444	0.419225152873266	   
df.mm.trans2	-0.0384438030376747	0.102451062833907	-0.375240646356199	0.707586062565221	   
df.mm.exp2	-0.0244253339695267	0.132803507543428	-0.183920849843061	0.85412454344312	   
df.mm.exp3	-0.184753993515286	0.132803507543428	-1.39118308644724	0.164576490887659	   
df.mm.exp4	-0.218758102992978	0.132803507543428	-1.64723136489028	0.0999233230021793	.  
df.mm.exp5	-0.0743821389845738	0.132803507543428	-0.560091674990209	0.575581739362643	   
df.mm.exp6	-0.110633266167961	0.132803507543428	-0.833059820590823	0.405072365189678	   
df.mm.exp7	-0.0292703894121235	0.132803507543429	-0.220403737473212	0.825615849185356	   
df.mm.exp8	-0.0824856571432942	0.132803507543429	-0.621110531409122	0.534712889702293	   
df.mm.trans1:exp2	0.0657947971670547	0.12176770658634	0.540330429237432	0.589127270159279	   
df.mm.trans2:exp2	-0.0659121364619887	0.0934898229512671	-0.705019374101782	0.481013899719245	   
df.mm.trans1:exp3	0.0484522089872154	0.12176770658634	0.397906886362026	0.690810419277395	   
df.mm.trans2:exp3	0.141577372411331	0.0934898229512671	1.51436132770441	0.130349428444218	   
df.mm.trans1:exp4	0.173399686414696	0.12176770658634	1.42402030288503	0.154850489562512	   
df.mm.trans2:exp4	0.118383423110227	0.0934898229512671	1.26627069528131	0.205803651887938	   
df.mm.trans1:exp5	-6.24493668195772e-06	0.12176770658634	-5.12856557541364e-05	0.999959093589893	   
df.mm.trans2:exp5	0.0469694537690868	0.0934898229512671	0.502401783278275	0.615530267884102	   
df.mm.trans1:exp6	0.0756726435531122	0.12176770658634	0.621450840083418	0.534489138721374	   
df.mm.trans2:exp6	0.0545389910436587	0.0934898229512671	0.583368214014994	0.559818293618207	   
df.mm.trans1:exp7	0.0140551503807180	0.12176770658634	0.115425926748092	0.908137979324274	   
df.mm.trans2:exp7	-0.0290893922745351	0.0934898229512671	-0.311150362213205	0.75577152808555	   
df.mm.trans1:exp8	0.0448760213311138	0.12176770658634	0.368537953035144	0.712574648797468	   
df.mm.trans2:exp8	0.0379646825743583	0.0934898229512671	0.40608358616903	0.684795344709139	   
df.mm.trans1:probe2	0.0342209369929812	0.0816842608173915	0.418941625357711	0.675377012343038	   
df.mm.trans1:probe3	-0.0535306962033616	0.0816842608173915	-0.655336727879948	0.512449028073928	   
df.mm.trans1:probe4	0.0259291675565648	0.0816842608173915	0.317431623878319	0.751003187271966	   
df.mm.trans1:probe5	-0.0379088858609722	0.0816842608173915	-0.464090456124945	0.642715665007159	   
df.mm.trans1:probe6	-0.03566504397856	0.0816842608173915	-0.436620759270757	0.662510255909765	   
df.mm.trans1:probe7	0.0475042484048322	0.0816842608173915	0.581559384016829	0.561035724950701	   
df.mm.trans1:probe8	-0.0439195007585983	0.0816842608173915	-0.537673969490672	0.59095934556749	   
df.mm.trans1:probe9	-0.141225942542412	0.0816842608173915	-1.72892477852163	0.0842281234490968	.  
df.mm.trans1:probe10	-0.0354315896037412	0.0816842608173915	-0.433762749998436	0.664583670087021	   
df.mm.trans1:probe11	0.000785325709621735	0.0816842608173915	0.0096141619176473	0.992331646552088	   
df.mm.trans1:probe12	-0.0140771266493511	0.0816842608173915	-0.172335851588607	0.86321932094384	   
df.mm.trans1:probe13	0.148638247332064	0.0816842608173915	1.81966814469132	0.0692023266142996	.  
df.mm.trans1:probe14	-0.0440135317901795	0.0816842608173915	-0.538825121874745	0.590165110198472	   
df.mm.trans1:probe15	0.095035161802384	0.0816842608173915	1.16344520782087	0.245013578289511	   
df.mm.trans1:probe16	-0.0320702295196934	0.0816842608173915	-0.392612104201907	0.694715940809937	   
df.mm.trans1:probe17	0.0534111222501196	0.0816842608173915	0.653872872394872	0.513391243720187	   
df.mm.trans1:probe18	0.000795557935591965	0.0816842608173915	0.00973942749351026	0.992231736596289	   
df.mm.trans1:probe19	0.0297235869696039	0.0816842608173915	0.363883894794031	0.716045783381761	   
df.mm.trans2:probe2	-0.0257093231621947	0.0816842608173915	-0.314740231532106	0.753045169473212	   
df.mm.trans2:probe3	-0.0798169003725635	0.0816842608173915	-0.977139286979623	0.328810714500462	   
df.mm.trans2:probe4	-0.105401690159523	0.0816842608173915	-1.29035494849066	0.197319369015616	   
df.mm.trans2:probe5	-0.0748943696003228	0.0816842608173915	-0.916876382927088	0.359497889285106	   
df.mm.trans2:probe6	0.0381683621904379	0.0816842608173915	0.467267032944875	0.640442601267084	   
df.mm.trans3:probe2	-0.0632577377422242	0.0816842608173915	-0.774417704331554	0.43892434785641	   
df.mm.trans3:probe3	0.0604763481009956	0.0816842608173915	0.740367207780614	0.45930556708809	   
df.mm.trans3:probe4	-0.00460823544127506	0.0816842608173915	-0.0564152187356749	0.955025841894558	   
df.mm.trans3:probe5	-0.00227886760428382	0.0816842608173915	-0.02789849086568	0.977750426403277	   
df.mm.trans3:probe6	-0.0222856655592494	0.0816842608173915	-0.272826923280482	0.785060278296454	   
df.mm.trans3:probe7	-0.0267468371383242	0.0816842608173915	-0.327441747904384	0.743423848894216	   
df.mm.trans3:probe8	-0.0460424367575158	0.0816842608173915	-0.563663505022657	0.573149213948147	   
df.mm.trans3:probe9	0.125048824370457	0.0816842608173915	1.5308802836572	0.126214550354768