fitVsDatCorrelation=0.856785396233178
cont.fitVsDatCorrelation=0.217523427932297

fstatistic=8864.87001877244,74,1198
cont.fstatistic=2462.74565938169,74,1198

residuals=-0.697473388862023,-0.104012688338629,-0.000929083268785603,0.0940887990626914,1.01934069682393
cont.residuals=-0.656990155512584,-0.235322579374343,-0.0738814238372964,0.177143200038018,1.51882863067376

predictedValues:
Include	Exclude	Both
Lung	55.6152356600112	46.1778209161305	66.1867878613749
cerebhem	65.9249347459188	76.6747292731665	67.6966058351268
cortex	58.6656920452367	49.488580297789	91.3697676008835
heart	55.8957035050667	46.7848130264963	82.721143206413
kidney	55.8421833324915	43.7535346723507	68.4459982517157
liver	56.0777836013464	48.2990271764732	64.2841737075901
stomach	69.7922029274172	49.1396679357773	63.5226317312136
testicle	58.553225446946	53.2214503394266	79.8647880996359


diffExp=9.43741474388066,-10.7497945272477,9.17711174744762,9.11089047857041,12.0886486601408,7.77875642487325,20.6525349916399,5.33177510751943
diffExpScore=1.32117255421727
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=1,0,0,0,1,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	61.4486760979993	62.323081972998	68.7710145689758
cerebhem	58.9003811849149	57.6490021357721	65.5791441691689
cortex	64.3671068685921	58.0368441874338	62.3047048459945
heart	63.4818201521254	63.9667342802156	63.8230151734022
kidney	61.2770871794541	60.3579398644849	62.3449915222222
liver	65.4569547186159	77.4341510679117	64.0719353996173
stomach	61.4854190437153	56.5875266225102	65.3197067352162
testicle	66.4457957760189	58.3654254011643	62.0946942329652
cont.diffExp=-0.874405874998722,1.25137904914281,6.33026268115828,-0.484914128090217,0.91914731496923,-11.9771963492958,4.89789242120512,8.08037037485459
cont.diffExpScore=3.80808674309356

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=0.544773173140435
cont.tran.correlation=0.411165167780121

tran.covariance=0.0086629220085652
cont.tran.covariance=0.00166672295918882

tran.mean=55.6191615563778
cont.tran.mean=62.3489966596204

weightedLogRatios:
wLogRatio
Lung	0.729972425807606
cerebhem	-0.644107098313374
cortex	0.678206860080871
heart	0.700065263452161
kidney	0.9515673216442
liver	0.590156728940396
stomach	1.42801578998787
testicle	0.384018068727500

cont.weightedLogRatios:
wLogRatio
Lung	-0.0582882207205147
cerebhem	0.0872967174218921
cortex	0.425780511619109
heart	-0.0316145409400784
kidney	0.0620839336681939
liver	-0.716738927662458
stomach	0.338462214560305
testicle	0.535709128334595

varWeightedLogRatios=0.348105636815145
cont.varWeightedLogRatios=0.151540766752415

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.06318722508119	0.082040732847263	37.3373947156712	5.21348086466049e-203	***
df.mm.trans1	0.99863054706915	0.069151299035108	14.4412405985627	1.06127728790646e-43	***
df.mm.trans2	0.759058034312148	0.0607501895720008	12.4947434676318	9.16755792082142e-34	***
df.mm.exp2	0.654576939729036	0.0759145040959739	8.62255437908803	2.04287758135714e-17	***
df.mm.exp3	-0.199793556799977	0.075914504095974	-2.63182324878775	0.00860191641065074	** 
df.mm.exp4	-0.204905010305397	0.0759145040959739	-2.69915496051122	0.00704937467391818	** 
df.mm.exp5	-0.0834190666931392	0.0759145040959739	-1.09885545175501	0.272051902675661	   
df.mm.exp6	0.0823617316080933	0.0759145040959739	1.08492747978658	0.278172050914263	   
df.mm.exp7	0.33031672099848	0.0759145040959739	4.35116747362113	1.46944847360475e-05	***
df.mm.exp8	0.00558669967468014	0.0759145040959739	0.0735919932720265	0.941347327386906	   
df.mm.trans1:exp2	-0.48451738369443	0.0671996497900019	-7.21011768972817	9.87755344748178e-13	***
df.mm.trans2:exp2	-0.147504376639072	0.044959480995908	-3.28082916821253	0.00106482957835407	** 
df.mm.trans1:exp3	0.253191463785484	0.0671996497900019	3.76774975132615	0.000172736167331907	***
df.mm.trans2:exp3	0.269035882635936	0.044959480995908	5.98396326373124	2.87252637665346e-09	***
df.mm.trans1:exp4	0.209935340756807	0.0671996497900019	3.12405408975870	0.00182658659670054	** 
df.mm.trans2:exp4	0.217964036515705	0.044959480995908	4.84801051274465	1.41077080599376e-06	***
df.mm.trans1:exp5	0.0874914377955252	0.0671996497900019	1.30196270470062	0.193179408663118	   
df.mm.trans2:exp5	0.0294918526757598	0.044959480995908	0.655965149563094	0.511972506049262	   
df.mm.trans1:exp6	-0.0740791981312933	0.0671996497900019	-1.10237476479104	0.270520185002031	   
df.mm.trans2:exp6	-0.0374499284945885	0.044959480995908	-0.832970658580269	0.405027305364117	   
df.mm.trans1:exp7	-0.103251609761908	0.0671996497900018	-1.53649029547875	0.124682228296920	   
df.mm.trans2:exp7	-0.268149727510305	0.044959480995908	-5.9642531802071	3.2303044482149e-09	***
df.mm.trans1:exp8	0.0458922912312798	0.0671996497900019	0.682924559498341	0.494786553095526	   
df.mm.trans2:exp8	0.136375201209472	0.044959480995908	3.03329127001899	0.00247108372028251	** 
df.mm.trans1:probe2	0.129030583554068	0.0529739818263378	2.43573503643851	0.0150065851380455	*  
df.mm.trans1:probe3	0.119673631873094	0.0529739818263378	2.25910206760395	0.0240561733566976	*  
df.mm.trans1:probe4	-0.0111370576284686	0.0529739818263378	-0.210236369714074	0.833518944388578	   
df.mm.trans1:probe5	0.00104823439394162	0.0529739818263378	0.0197877214021405	0.984216007893272	   
df.mm.trans1:probe6	-0.256788758142925	0.0529739818263378	-4.84745056516128	1.41468028213568e-06	***
df.mm.trans1:probe7	-0.27442361119217	0.0529739818263378	-5.18034706342824	2.59640476639161e-07	***
df.mm.trans1:probe8	-0.31226727935808	0.0529739818263378	-5.89472923484912	4.87377025984117e-09	***
df.mm.trans1:probe9	-0.223149563693725	0.0529739818263378	-4.21243704929084	2.71565033020699e-05	***
df.mm.trans1:probe10	-0.113113889247564	0.0529739818263378	-2.13527254980341	0.0329411261190191	*  
df.mm.trans1:probe11	-0.292744263212532	0.0529739818263378	-5.52618952021054	4.01236818714588e-08	***
df.mm.trans1:probe12	-0.293682898855877	0.0529739818263378	-5.54390832500839	3.63573886751586e-08	***
df.mm.trans1:probe13	-0.0290370174449737	0.0529739818263378	-0.548137339197277	0.583699722548072	   
df.mm.trans1:probe14	-0.148720426857094	0.0529739818263378	-2.80742397927793	0.00507493650767899	** 
df.mm.trans1:probe15	-0.0588863915216265	0.0529739818263378	-1.11160969010544	0.266529035137592	   
df.mm.trans1:probe16	-0.106583341577439	0.0529739818263378	-2.01199415076719	0.0444440773008911	*  
df.mm.trans1:probe17	-0.122896831363403	0.0529739818263378	-2.31994702165849	0.0205111233868916	*  
df.mm.trans1:probe18	-0.0442686508719299	0.0529739818263378	-0.835667800412923	0.403508447182477	   
df.mm.trans2:probe2	0.214209082367512	0.0529739818263378	4.04366587110147	5.59838359135801e-05	***
df.mm.trans2:probe3	0.0095182742315594	0.0529739818263378	0.179678285516892	0.857435526777725	   
df.mm.trans2:probe4	0.0111111071443119	0.0529739818263378	0.209746497454863	0.833901195754748	   
df.mm.trans2:probe5	0.119108055633493	0.0529739818263378	2.24842557661531	0.0247302518316064	*  
df.mm.trans2:probe6	0.00495596424818797	0.0529739818263378	0.0935546862313443	0.925478550075775	   
df.mm.trans3:probe2	-0.290677768609554	0.0529739818263378	-5.48717990583509	4.97983698831507e-08	***
df.mm.trans3:probe3	-1.10766714205849	0.0529739818263378	-20.9096447703271	5.1599592420946e-83	***
df.mm.trans3:probe4	-0.921122493256256	0.0529739818263378	-17.3882057096619	1.46342189881195e-60	***
df.mm.trans3:probe5	-0.298843126612229	0.0529739818263378	-5.6413189326019	2.10409809400176e-08	***
df.mm.trans3:probe6	-0.327501978157614	0.0529739818263378	-6.18231756168998	8.64733247370038e-10	***
df.mm.trans3:probe7	-0.198953622672602	0.0529739818263378	-3.75568563686269	0.000181171228172897	***
df.mm.trans3:probe8	-1.04686168834293	0.0529739818263378	-19.7618085756666	1.84381124539034e-75	***
df.mm.trans3:probe9	-0.355594473975720	0.0529739818263378	-6.71262498525122	2.94336697953651e-11	***
df.mm.trans3:probe10	-0.775527905932364	0.0529739818263378	-14.6397888018828	9.01832007446577e-45	***
df.mm.trans3:probe11	-0.868223464173676	0.0529739818263378	-16.3896206069601	1.26200458769450e-54	***
df.mm.trans3:probe12	-0.392411471600846	0.0529739818263378	-7.40762650025574	2.41907725245431e-13	***
df.mm.trans3:probe13	-0.754747051722344	0.0529739818263378	-14.2475046372122	1.15021666407532e-42	***
df.mm.trans3:probe14	-0.523337732909033	0.0529739818263378	-9.8791466087006	3.51429345670738e-22	***
df.mm.trans3:probe15	-0.754964333407511	0.0529739818263378	-14.2516063051948	1.09387950151053e-42	***
df.mm.trans3:probe16	-0.946248702325453	0.0529739818263378	-17.8625179701895	1.87576147615728e-63	***
df.mm.trans3:probe17	-0.490203888695444	0.0529739818263378	-9.25367268600757	9.67540143160992e-20	***
df.mm.trans3:probe18	-0.821151016182233	0.0529739818263378	-15.5010249913660	1.57212807966641e-49	***
df.mm.trans3:probe19	-0.837506859634785	0.0529739818263378	-15.8097773805327	2.79750838994231e-51	***
df.mm.trans3:probe20	-0.792466652714845	0.0529739818263378	-14.9595447688405	1.62095019265928e-46	***
df.mm.trans3:probe21	-0.974716668585785	0.0529739818263378	-18.3999132211952	8.76634134598998e-67	***
df.mm.trans3:probe22	-0.87022530101337	0.0529739818263378	-16.4274096643554	7.59235986109963e-55	***
df.mm.trans3:probe23	-0.446759827891977	0.0529739818263378	-8.43357083023452	9.52666395247923e-17	***
df.mm.trans3:probe24	-0.775191460806953	0.0529739818263378	-14.6334376628177	9.7618291853544e-45	***
df.mm.trans3:probe25	-0.344093698324658	0.0529739818263378	-6.4955226407689	1.21011781049222e-10	***
df.mm.trans3:probe26	-0.0905979635761364	0.0529739818263378	-1.71023510887174	0.0874813247259053	.  
df.mm.trans3:probe27	-0.657086730484659	0.0529739818263378	-12.4039520502491	2.50996379403797e-33	***
df.mm.trans3:probe28	-0.806305286254242	0.0529739818263378	-15.2207793044049	5.82038496123461e-48	***
df.mm.trans3:probe29	-0.75446502464876	0.0529739818263378	-14.2421807581331	1.22767168750740e-42	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11392437063603	0.155284946749079	26.4927441890657	4.15799258134542e-122	***
df.mm.trans1	-0.000251429977752095	0.130888101746822	-0.00192095365733425	0.99846762148734	   
df.mm.trans2	0.0109263710647822	0.114986661202153	0.0950229439706315	0.924312493649707	   
df.mm.exp2	-0.0727890106648564	0.143689351824449	-0.506572057989276	0.612548333336514	   
df.mm.exp3	0.073892061612487	0.143689351824449	0.514248694661548	0.607172972334574	   
df.mm.exp4	0.133251085313700	0.143689351824449	0.927355323284482	0.353928867231575	   
df.mm.exp5	0.0632633639339664	0.143689351824449	0.440278720244056	0.659814675232778	   
df.mm.exp6	0.351062426072407	0.143689351824449	2.44320418747044	0.0147010910563870	*  
df.mm.exp7	-0.0444569266581063	0.143689351824449	-0.30939611108011	0.757074031043814	   
df.mm.exp8	0.114697862240388	0.143689351824449	0.798234947712192	0.424892362589355	   
df.mm.trans1:exp2	0.0304342816293539	0.127194061742774	0.239274390740832	0.810933749766649	   
df.mm.trans2:exp2	-0.00516990611159249	0.085098345297737	-0.0607521344099504	0.951566744064465	   
df.mm.trans1:exp3	-0.0274916132984534	0.127194061742774	-0.216139125693226	0.828916098004522	   
df.mm.trans2:exp3	-0.145145862472749	0.085098345297737	-1.70562496796996	0.0883371443844746	.  
df.mm.trans1:exp4	-0.100699808625141	0.127194061742774	-0.791702122295515	0.428691108480416	   
df.mm.trans2:exp4	-0.107219768291644	0.085098345297737	-1.25995127069170	0.207932322234297	   
df.mm.trans1:exp5	-0.0660596640061187	0.127194061742774	-0.519361227253769	0.603604798959673	   
df.mm.trans2:exp5	-0.0953027157590232	0.085098345297737	-1.11991267780336	0.262975444776677	   
df.mm.trans1:exp6	-0.287871970739412	0.127194061742774	-2.26325008255165	0.0237986166526041	*  
df.mm.trans2:exp6	-0.133966368856861	0.085098345297737	-1.57425351090139	0.115692884426323	   
df.mm.trans1:exp7	0.0450546932381223	0.127194061742774	0.354220099749917	0.723236276910938	   
df.mm.trans2:exp7	-0.0520863444488141	0.085098345297737	-0.612072353070761	0.540606033103347	   
df.mm.trans1:exp8	-0.0365136394690207	0.127194061742774	-0.287070315773567	0.774108123788025	   
df.mm.trans2:exp8	-0.180306032862783	0.085098345297737	-2.11879599106115	0.0343130540804844	*  
df.mm.trans1:probe2	0.000449422296659977	0.100268021280407	0.00448220969079599	0.996424472297795	   
df.mm.trans1:probe3	0.0600796848239177	0.100268021280407	0.599190889146008	0.549158888080793	   
df.mm.trans1:probe4	0.130721916489613	0.100268021280407	1.30372490471354	0.192577858378414	   
df.mm.trans1:probe5	0.111530925962090	0.100268021280407	1.11232798391608	0.266220312163228	   
df.mm.trans1:probe6	-0.0140039586069139	0.100268021280407	-0.139665253468509	0.888947956750385	   
df.mm.trans1:probe7	-0.063310719445166	0.100268021280407	-0.631414868237133	0.527889750716861	   
df.mm.trans1:probe8	-0.0208431592403662	0.100268021280407	-0.207874444655458	0.835362335387877	   
df.mm.trans1:probe9	0.0688232297244347	0.100268021280407	0.686392618958398	0.492598381184324	   
df.mm.trans1:probe10	0.044058421500679	0.100268021280407	0.439406512047011	0.660446255098212	   
df.mm.trans1:probe11	0.0552594839126246	0.100268021280407	0.551117726339562	0.581655738890945	   
df.mm.trans1:probe12	-0.0729610014679831	0.100268021280407	-0.727659731749791	0.466964089952205	   
df.mm.trans1:probe13	-0.0424188855350364	0.100268021280407	-0.423054977981553	0.672331078562332	   
df.mm.trans1:probe14	-0.0126871360735261	0.100268021280407	-0.126532227439152	0.899331872689197	   
df.mm.trans1:probe15	-0.0175562508595468	0.100268021280407	-0.175093221501295	0.861035971623956	   
df.mm.trans1:probe16	-0.0778355452049801	0.100268021280407	-0.776274870203207	0.437739935009716	   
df.mm.trans1:probe17	0.000252216171565796	0.100268021280407	0.00251541985515457	0.997993406230116	   
df.mm.trans1:probe18	0.0633208405258912	0.100268021280407	0.631515808502991	0.527823792502737	   
df.mm.trans2:probe2	-0.0508207232371768	0.100268021280407	-0.506848769809199	0.612354207721001	   
df.mm.trans2:probe3	-0.00589628037886505	0.100268021280407	-0.0588051933564709	0.953117079047814	   
df.mm.trans2:probe4	0.0358850978170695	0.100268021280407	0.357891752114208	0.720487368735642	   
df.mm.trans2:probe5	0.0913115038985108	0.100268021280407	0.910674238231466	0.362650233932606	   
df.mm.trans2:probe6	0.191359344102590	0.100268021280407	1.90847831301507	0.0565682209969327	.  
df.mm.trans3:probe2	0.0941457211199416	0.100268021280407	0.938940650445832	0.347950479470206	   
df.mm.trans3:probe3	0.109140849710118	0.100268021280407	1.08849110929294	0.276597280851631	   
df.mm.trans3:probe4	0.071469290323529	0.100268021280407	0.712782494467104	0.476119164696712	   
df.mm.trans3:probe5	0.092359639530581	0.100268021280407	0.921127577378741	0.357169273996275	   
df.mm.trans3:probe6	0.0873825218258758	0.100268021280407	0.871489441100113	0.383661578787794	   
df.mm.trans3:probe7	0.0245514278400471	0.100268021280407	0.244858006835373	0.80660831095896	   
df.mm.trans3:probe8	0.0601335439924422	0.100268021280407	0.599728041149574	0.548800906249879	   
df.mm.trans3:probe9	0.195268938088898	0.100268021280407	1.94746974753610	0.0517118086404334	.  
df.mm.trans3:probe10	0.121885564296168	0.100268021280407	1.21559758275579	0.224377745078265	   
df.mm.trans3:probe11	0.0689903475940025	0.100268021280407	0.688059330512428	0.491548620570036	   
df.mm.trans3:probe12	0.198006197621279	0.100268021280407	1.97476917458598	0.04852367228286	*  
df.mm.trans3:probe13	0.0787855642179452	0.100268021280407	0.78574966586421	0.432169535839981	   
df.mm.trans3:probe14	0.158042590482469	0.100268021280407	1.57620134978521	0.115243421314088	   
df.mm.trans3:probe15	0.0467419396137505	0.100268021280407	0.466169961437988	0.641178596228236	   
df.mm.trans3:probe16	0.069915920126962	0.100268021280407	0.697290314839635	0.48575641919935	   
df.mm.trans3:probe17	0.130798394739381	0.100268021280407	1.30448764291053	0.192317915488227	   
df.mm.trans3:probe18	0.0157361505383392	0.100268021280407	0.156940870452923	0.875317893280986	   
df.mm.trans3:probe19	0.133286559834348	0.100268021280407	1.32930278400131	0.184001177886095	   
df.mm.trans3:probe20	0.170211313251827	0.100268021280407	1.69756330162155	0.0898499315378479	.  
df.mm.trans3:probe21	0.130234011873115	0.100268021280407	1.29885890047542	0.194242292230781	   
df.mm.trans3:probe22	0.0969078937802848	0.100268021280407	0.966488542835357	0.333994839427527	   
df.mm.trans3:probe23	0.153749160429490	0.100268021280407	1.53338181472156	0.125445832840896	   
df.mm.trans3:probe24	0.0866016775565776	0.100268021280407	0.863701870752888	0.387924570864913	   
df.mm.trans3:probe25	0.0665773949458225	0.100268021280407	0.663994303424359	0.506821722720075	   
df.mm.trans3:probe26	0.222019068278991	0.100268021280407	2.21425600549251	0.0269988729243326	*  
df.mm.trans3:probe27	0.139123586227375	0.100268021280407	1.38751702138716	0.165542198128273	   
df.mm.trans3:probe28	0.174078098651893	0.100268021280407	1.73612779457442	0.0827983318380619	.  
df.mm.trans3:probe29	0.0831649261828542	0.100268021280407	0.829426223045507	0.407028500565397