fitVsDatCorrelation=0.875979299585055
cont.fitVsDatCorrelation=0.244611122809072

fstatistic=7432.89044026927,63,945
cont.fstatistic=1828.10992380486,63,945

residuals=-1.00858547273234,-0.106831820700559,-0.00031382281029307,0.109118288265411,0.907861791000451
cont.residuals=-0.759333267109259,-0.277041814189186,-0.107170986305788,0.239356659486270,1.28613825745305

predictedValues:
Include	Exclude	Both
Lung	81.2063264148323	55.200667509406	109.341529362471
cerebhem	59.50237687669	68.8385825582735	79.6698106598359
cortex	59.7339587545684	55.8280958881591	73.656336377219
heart	72.5982580466996	54.2794440172278	91.6360990718172
kidney	62.8906253942894	54.2839805712307	69.9559729377926
liver	62.8871766802883	53.064997998432	70.1862254188757
stomach	63.7804406727114	52.8127892697202	70.2610198777895
testicle	63.6426847999915	57.3129218579295	69.2923979880114


diffExp=26.0056589054263,-9.33620568158347,3.90586286640928,18.3188140294718,8.6066448230587,9.82217868185625,10.9676514029912,6.32976294206205
diffExpScore=1.23369909242243
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,0,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,0,1,0,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	69.0334047608661	65.8993254410075	68.3582333452104
cerebhem	71.9123419919156	73.9576089839313	70.4812157305205
cortex	73.4581679375778	67.1296832788754	78.8998511611066
heart	81.3466500573216	78.5574574205479	72.0454174155923
kidney	71.5127753888531	83.5635288605398	68.0560074623695
liver	74.8990715412486	79.9419099593393	71.9923404986635
stomach	74.3350915468256	68.8699274930713	81.3815798537338
testicle	69.1759386295686	74.4379827735475	80.2911143235214
cont.diffExp=3.1340793198586,-2.04526699201566,6.32848465870234,2.78919263677371,-12.0507534716867,-5.04283841809062,5.46516405375426,-5.26204414397894
cont.diffExpScore=5.48124940165039

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=-0.319693995214980
cont.tran.correlation=0.314531000883875

tran.covariance=-0.00306545928028392
cont.tran.covariance=0.0014633493644963

tran.mean=61.1164579569031
cont.tran.mean=73.6269291290648

weightedLogRatios:
wLogRatio
Lung	1.62281399649486
cerebhem	-0.606150451626922
cortex	0.274286643632811
heart	1.20375987168254
kidney	0.598651706300283
liver	0.688882134290948
stomach	0.766301903082357
testicle	0.429603777310578

cont.weightedLogRatios:
wLogRatio
Lung	0.19566948013602
cerebhem	-0.120294636511215
cortex	0.383032126406842
heart	0.152859856779266
kidney	-0.677078399886625
liver	-0.283357220073879
stomach	0.32610287603441
testicle	-0.31329017303504

varWeightedLogRatios=0.433169777823512
cont.varWeightedLogRatios=0.135987922188717

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.11129280614473	0.0909595404782267	34.205238832418	1.67818443601071e-167	***
df.mm.trans1	1.10219167932017	0.0777979892442625	14.1673543240253	1.85461577154415e-41	***
df.mm.trans2	0.881981564429478	0.068295921067627	12.9141177195068	2.96577598647929e-35	***
df.mm.exp2	0.226397992349316	0.086535060290209	2.61625740584284	0.00903180213588403	** 
df.mm.exp3	0.099275841575853	0.086535060290209	1.14723259269614	0.251575870286633	   
df.mm.exp4	0.0477693101987116	0.086535060290209	0.552022614169443	0.58106338044076	   
df.mm.exp5	0.174268272101565	0.086535060290209	2.01384585065439	0.0443087426019027	*  
df.mm.exp6	0.148215820555684	0.086535060290209	1.71278346670839	0.087080405677994	.  
df.mm.exp7	0.156490869889782	0.086535060290209	1.80841001745379	0.0708604772445476	.  
df.mm.exp8	0.249983376669074	0.086535060290209	2.88881033688214	0.00395534807874358	** 
df.mm.trans1:exp2	-0.537374888733618	0.0788613368258957	-6.81417422481684	1.68671477084728e-11	***
df.mm.trans2:exp2	-0.00560865736912652	0.0551914387592003	-0.101621872798008	0.919078374775411	   
df.mm.trans1:exp3	-0.406368315181777	0.0788613368258957	-5.15294733183293	3.12181727195342e-07	***
df.mm.trans2:exp3	-0.0879736341386382	0.0551914387592003	-1.59397247320307	0.111276583506630	   
df.mm.trans1:exp4	-0.159821538148333	0.0788613368258957	-2.02661461980001	0.0429822459669357	*  
df.mm.trans2:exp4	-0.064598763922246	0.0551914387592003	-1.17044899307825	0.242115406490878	   
df.mm.trans1:exp5	-0.429864314984528	0.0788613368258957	-5.45088800527882	6.3971340944511e-08	***
df.mm.trans2:exp5	-0.191014151563827	0.0551914387592003	-3.46093807043553	0.000562387434847891	***
df.mm.trans1:exp6	-0.403866701636565	0.0788613368258957	-5.12122565875585	3.67868460740949e-07	***
df.mm.trans2:exp6	-0.187673326832711	0.0551914387592003	-3.40040649513647	0.000701026312598374	***
df.mm.trans1:exp7	-0.398037454715438	0.0788613368258957	-5.04730798051517	5.37395998337641e-07	***
df.mm.trans2:exp7	-0.200712533262551	0.0551914387592002	-3.63666064474707	0.000291121468348178	***
df.mm.trans1:exp8	-0.4936921424313	0.0788613368258957	-6.2602558148518	5.82351510832291e-10	***
df.mm.trans2:exp8	-0.212432311789319	0.0551914387592003	-3.84900840719444	0.000126570910395404	***
df.mm.trans1:probe2	0.313472346391045	0.0577205882709079	5.43085848189528	7.13400730016237e-08	***
df.mm.trans1:probe3	0.0451132169718764	0.0577205882709079	0.781579299922246	0.434657622359573	   
df.mm.trans1:probe4	0.0298850613076240	0.0577205882709079	0.517753928067405	0.604751147076788	   
df.mm.trans1:probe5	-0.0525612433599167	0.0577205882709079	-0.910615171023965	0.362730383040702	   
df.mm.trans1:probe6	-0.0251964969562402	0.0577205882709079	-0.436525297316481	0.662555364271296	   
df.mm.trans1:probe7	-0.0662995002748093	0.0577205882709079	-1.14862828430710	0.250999944076367	   
df.mm.trans1:probe8	-0.0615151473088594	0.0577205882709079	-1.06574013106280	0.286813341642427	   
df.mm.trans1:probe9	0.00533971651635328	0.0577205882709079	0.0925097383154112	0.92631266176188	   
df.mm.trans1:probe10	0.162548311342298	0.0577205882709079	2.81612360877869	0.00496186469662692	** 
df.mm.trans1:probe11	0.350281005703246	0.0577205882709079	6.06856264283421	1.86590792449692e-09	***
df.mm.trans1:probe12	0.233250294788886	0.0577205882709079	4.04102421295744	5.75432162159023e-05	***
df.mm.trans1:probe13	0.257750571210584	0.0577205882709079	4.46548760038357	8.95320662857745e-06	***
df.mm.trans1:probe14	0.198804654703656	0.0577205882709079	3.44425898382358	0.000597789547508804	***
df.mm.trans1:probe15	0.205375158408738	0.0577205882709079	3.55809191418533	0.00039213103135428	***
df.mm.trans1:probe16	1.0739139538991	0.0577205882709079	18.6053882344156	4.48840781818274e-66	***
df.mm.trans1:probe17	0.519121496924438	0.0577205882709079	8.993697266008	1.28116077538587e-18	***
df.mm.trans1:probe18	1.07592947652778	0.0577205882709079	18.6403068429930	2.78551823028131e-66	***
df.mm.trans1:probe19	1.02587943157977	0.0577205882709079	17.7731977845560	3.43109439977989e-61	***
df.mm.trans1:probe20	0.315620805705839	0.0577205882709079	5.46808019738977	5.82401016923806e-08	***
df.mm.trans1:probe21	0.999599012416331	0.0577205882709079	17.3178937076105	1.44772904239321e-58	***
df.mm.trans2:probe2	0.163177768187517	0.0577205882709079	2.82702884838339	0.00479738122526504	** 
df.mm.trans2:probe3	-0.0610635034710111	0.0577205882709078	-1.05791547349472	0.290364326886571	   
df.mm.trans2:probe4	0.105838143915898	0.0577205882709078	1.83362898900395	0.0670234546243275	.  
df.mm.trans2:probe5	0.0467900062548813	0.0577205882709078	0.81062940722772	0.41778262008418	   
df.mm.trans2:probe6	0.116971764258679	0.0577205882709079	2.02651718845414	0.0429922390028846	*  
df.mm.trans3:probe2	-0.528715085796307	0.0577205882709079	-9.15990466546904	3.14647972262802e-19	***
df.mm.trans3:probe3	-0.602066689275712	0.0577205882709079	-10.4307095147740	3.48032185817051e-24	***
df.mm.trans3:probe4	-0.196533239821347	0.0577205882709078	-3.40490708270212	0.000689713147165214	***
df.mm.trans3:probe5	-0.0728480509480735	0.0577205882709079	-1.26208088188855	0.207231075740832	   
df.mm.trans3:probe6	-0.517604116372149	0.0577205882709079	-8.96740888957694	1.5966621645685e-18	***
df.mm.trans3:probe7	-0.51148705246541	0.0577205882709079	-8.86143173151283	3.85758574717785e-18	***
df.mm.trans3:probe8	-0.226288978649164	0.0577205882709079	-3.920420519401	9.4777326650071e-05	***
df.mm.trans3:probe9	-0.24332868306651	0.0577205882709079	-4.21563068492065	2.72989316163759e-05	***
df.mm.trans3:probe10	-0.780006323595614	0.0577205882709079	-13.5134853431276	3.57059790914191e-38	***
df.mm.trans3:probe11	-0.72230323442684	0.0577205882709078	-12.5137885122854	2.3506065307049e-33	***
df.mm.trans3:probe12	-0.637145266516946	0.0577205882709079	-11.0384402793427	9.88558833642107e-27	***
df.mm.trans3:probe13	-0.165384541938942	0.0577205882709079	-2.86526085220616	0.00425902429759589	** 
df.mm.trans3:probe14	-0.769837350284597	0.0577205882709079	-13.3373094998862	2.63105338617574e-37	***
df.mm.trans3:probe15	-0.0412979264451276	0.0577205882709079	-0.715479999117445	0.474489230698706	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18770943515128	0.18284760604282	22.9027304528700	1.08640461183335e-92	***
df.mm.trans1	0.0371752980905121	0.156390148998868	0.237708694112064	0.812158567036778	   
df.mm.trans2	0.013774202171652	0.137289014478852	0.100329966122481	0.920103648640885	   
df.mm.exp2	0.125637046419665	0.173953480081876	0.722245087367785	0.47032249017044	   
df.mm.exp3	-0.0627936262807597	0.173953480081876	-0.360979419619568	0.718195512293293	   
df.mm.exp4	0.287296529305938	0.173953480081876	1.65157103595004	0.0989542554690525	.  
df.mm.exp5	0.277195567179129	0.173953480081876	1.59350400491361	0.111381563873146	   
df.mm.exp6	0.222925303226306	0.173953480081876	1.28152252614538	0.200324543244731	   
df.mm.exp7	-0.0563029085782222	0.173953480081876	-0.323666468481813	0.746262174538172	   
df.mm.exp8	-0.036996236879266	0.173953480081876	-0.212678911981828	0.83162328365492	   
df.mm.trans1:exp2	-0.0847796558743167	0.158527698932287	-0.534793959953517	0.592918145376299	   
df.mm.trans2:exp2	-0.0102731742821745	0.110946277851902	-0.0925959345466984	0.926244199475922	   
df.mm.trans1:exp3	0.124919212354686	0.158527698932287	0.787996124311651	0.430896582566988	   
df.mm.trans2:exp3	0.0812917408376724	0.110946277851902	0.732712646260974	0.46391540664069	   
df.mm.trans1:exp4	-0.123167390284885	0.158527698932287	-0.776945550300926	0.437385329813335	   
df.mm.trans2:exp4	-0.111594435949241	0.110946277851902	-1.00584208961209	0.314749013720501	   
df.mm.trans1:exp5	-0.241909971087465	0.158527698932287	-1.52597919932461	0.127349642997309	   
df.mm.trans2:exp5	-0.0397166052816063	0.110946277851902	-0.35798051138428	0.720437841606071	   
df.mm.trans1:exp6	-0.141374323172633	0.158527698932287	-0.891795718507334	0.372729448518568	   
df.mm.trans2:exp6	-0.0297532631735328	0.110946277851902	-0.268177209272845	0.788621443770781	   
df.mm.trans1:exp7	0.130295529772128	0.158527698932287	0.821910181310219	0.411335335844974	   
df.mm.trans2:exp7	0.100394319969635	0.110946277851902	0.904891285344854	0.365753618238898	   
df.mm.trans1:exp8	0.0390588169586879	0.158527698932287	0.24638480985819	0.80543787136623	   
df.mm.trans2:exp8	0.158834364240523	0.110946277851902	1.43163310492079	0.152579489256718	   
df.mm.trans1:probe2	-0.000336497207221442	0.116030394714287	-0.0029000781049658	0.997686687767473	   
df.mm.trans1:probe3	-0.0801543425084647	0.116030394714287	-0.690804704283191	0.489857921390136	   
df.mm.trans1:probe4	0.0466847646649218	0.116030394714287	0.402349442832444	0.687517862502136	   
df.mm.trans1:probe5	0.0925206302874948	0.116030394714287	0.797382707482102	0.425429102194553	   
df.mm.trans1:probe6	0.0486191491302367	0.116030394714287	0.419020802695333	0.67529615230681	   
df.mm.trans1:probe7	-0.0684956319238654	0.116030394714287	-0.590324906612006	0.555113979538542	   
df.mm.trans1:probe8	-0.0411807662358737	0.116030394714287	-0.354913609811268	0.722733503809776	   
df.mm.trans1:probe9	0.0529488748513635	0.116030394714287	0.456336246909656	0.648253060214746	   
df.mm.trans1:probe10	0.128351835842198	0.116030394714287	1.10619149541163	0.268925164315481	   
df.mm.trans1:probe11	-0.0315855929869509	0.116030394714287	-0.272218267159457	0.785513661575264	   
df.mm.trans1:probe12	-0.050507500555444	0.116030394714287	-0.435295429958795	0.663447399609982	   
df.mm.trans1:probe13	0.148407384002845	0.116030394714287	1.27903886191444	0.201197353895182	   
df.mm.trans1:probe14	-0.0316350800596718	0.116030394714287	-0.2726447681021	0.785185859043148	   
df.mm.trans1:probe15	0.129822719225544	0.116030394714287	1.1188682029844	0.263480690549645	   
df.mm.trans1:probe16	-0.052190989367816	0.116030394714287	-0.449804462842095	0.652954594859395	   
df.mm.trans1:probe17	0.0633272323720833	0.116030394714287	0.545781409500678	0.585345011797519	   
df.mm.trans1:probe18	-0.0840809252453444	0.116030394714287	-0.724645688333521	0.468848796315936	   
df.mm.trans1:probe19	-0.0775637691048264	0.116030394714287	-0.668478025053863	0.503991750875147	   
df.mm.trans1:probe20	-0.0326112536945549	0.116030394714287	-0.281057853632722	0.778727551166772	   
df.mm.trans1:probe21	0.189067880737473	0.116030394714287	1.62946856470698	0.103547129079266	   
df.mm.trans2:probe2	-0.070079134668987	0.116030394714287	-0.603972216431306	0.546007070858102	   
df.mm.trans2:probe3	-0.115711006017706	0.116030394714287	-0.99724737042077	0.318899733262063	   
df.mm.trans2:probe4	-0.0639501878108254	0.116030394714287	-0.551150308229981	0.581660922100592	   
df.mm.trans2:probe5	-0.0351579568633729	0.116030394714287	-0.303006440251677	0.761951641883231	   
df.mm.trans2:probe6	0.00443421405609495	0.116030394714287	0.0382159697639034	0.96952355962889	   
df.mm.trans3:probe2	0.0115180341736786	0.116030394714287	0.0992673876706239	0.92094702444672	   
df.mm.trans3:probe3	-0.0454837356513048	0.116030394714287	-0.39199845663978	0.695147739218427	   
df.mm.trans3:probe4	-0.0721370390589194	0.116030394714287	-0.621708124294068	0.534283759407478	   
df.mm.trans3:probe5	0.0689627019957545	0.116030394714287	0.594350317997008	0.552420090737584	   
df.mm.trans3:probe6	0.0428678319588009	0.116030394714287	0.369453470052901	0.711872518995739	   
df.mm.trans3:probe7	-0.0559284360240283	0.116030394714287	-0.4820153905513	0.629906626859673	   
df.mm.trans3:probe8	0.0245920429489802	0.116030394714287	0.211944835743563	0.832195773780901	   
df.mm.trans3:probe9	-0.0357987733325342	0.116030394714287	-0.308529273046818	0.75774762816377	   
df.mm.trans3:probe10	-0.135617822219448	0.116030394714287	-1.16881290073513	0.242773773902552	   
df.mm.trans3:probe11	-0.0544716326835372	0.116030394714287	-0.469460030862329	0.638849145586351	   
df.mm.trans3:probe12	-0.111665531463611	0.116030394714287	-0.962381725396832	0.336104075856332	   
df.mm.trans3:probe13	-0.0857889108985943	0.116030394714287	-0.739365845560046	0.459868490031941	   
df.mm.trans3:probe14	0.165436798725409	0.116030394714287	1.42580570489983	0.154254666183576	   
df.mm.trans3:probe15	0.0750606580369372	0.116030394714287	0.646905133967412	0.517850436497394