fitVsDatCorrelation=0.868939569846446
cont.fitVsDatCorrelation=0.242732771785958

fstatistic=11272.3951196979,62,922
cont.fstatistic=2922.97280773318,62,922

residuals=-0.526044114804564,-0.0857827453841879,-0.00886831747965998,0.0813847039519111,1.19082515940032
cont.residuals=-0.793372227421756,-0.238826256563892,-0.00812401786527586,0.209066956941177,1.71125388150463

predictedValues:
Include	Exclude	Both
Lung	57.5252447385196	85.363793969173	63.894144777059
cerebhem	63.3662376652138	90.7326854228633	65.0690723749205
cortex	54.6522941034188	71.075292166974	65.8049236868544
heart	57.6793056591208	74.1977869277803	73.5425964145172
kidney	59.5805080265067	88.118232093797	88.665749875155
liver	61.341006873312	89.6712355286354	98.4976784678093
stomach	60.0326695135262	77.3279393826943	73.2668867419458
testicle	57.0457304362237	78.0602187699999	85.0209291832586


diffExp=-27.8385492306534,-27.3664477576496,-16.4229980635553,-16.5184812686596,-28.5377240672902,-28.3302286553233,-17.2952698691681,-21.0144883337762
diffExpScore=0.994574776024022
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=-1,-1,0,0,-1,-1,0,0
diffExp1.4Score=0.8
diffExp1.3=-1,-1,-1,0,-1,-1,0,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	69.1678247391187	63.4418852189206	72.8638446398894
cerebhem	68.5083126799186	65.4339072550024	62.266308563512
cortex	70.0081528531812	76.2757686411759	65.7835034423067
heart	69.1568532792222	67.3717698645781	70.4788610085784
kidney	69.8799781800628	63.1315045257916	73.0615409149708
liver	68.2077476718535	69.2547875679801	74.9551081159922
stomach	67.4879958876864	70.2637189866503	67.6169626459033
testicle	70.2935887601543	69.0168907076393	66.2333885348292
cont.diffExp=5.72593952019811,3.07440542491616,-6.26761578799464,1.78508341464409,6.74847365427127,-1.04703989612659,-2.77572309896385,1.27669805251503
cont.diffExpScore=3.01473862792399

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=0.793794776028427
cont.tran.correlation=0.0460931695271221

tran.covariance=0.00344511864034979
cont.tran.covariance=2.31030533732168e-05

tran.mean=70.3606363298599
cont.tran.mean=68.5562929261835

weightedLogRatios:
wLogRatio
Lung	-1.67729864245892
cerebhem	-1.55384580542565
cortex	-1.08577314691023
heart	-1.05287952801340
kidney	-1.67615782197451
liver	-1.63510817895483
stomach	-1.06873500564164
testicle	-1.31744394308266

cont.weightedLogRatios:
wLogRatio
Lung	0.362351941710197
cerebhem	0.193024489098838
cortex	-0.367967258849164
heart	0.110443878324403
kidney	0.426142561862937
liver	-0.0644429825644977
stomach	-0.170578426667867
testicle	0.0777807990486707

varWeightedLogRatios=0.0808117314694489
cont.varWeightedLogRatios=0.0709858523205767

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01418183674986	0.072839856748371	55.109688787789	5.33319356240362e-294	***
df.mm.trans1	-0.0555306638803094	0.0625569709722682	-0.88768146886342	0.374943618030051	   
df.mm.trans2	0.427180808116191	0.0549290519744162	7.77695577770311	1.98347063729656e-14	***
df.mm.exp2	0.139481276835881	0.0698901994216392	1.99572011512526	0.0462582109417836	*  
df.mm.exp3	-0.263881920463154	0.0698901994216392	-3.77566415100902	0.000169796420857905	***
df.mm.exp4	-0.278150219275526	0.0698901994216392	-3.97981722154603	7.43839200679956e-05	***
df.mm.exp5	-0.260783963937784	0.0698901994216392	-3.73133810027506	0.000202114265974314	***
df.mm.exp6	-0.319352579303052	0.0698901994216392	-4.56934708937424	5.55747642602557e-06	***
df.mm.exp7	-0.193082736841902	0.0698901994216392	-2.76265826166923	0.00584737375933908	** 
df.mm.exp8	-0.383481869926718	0.0698901994216392	-5.48691909738614	5.28541962208181e-08	***
df.mm.trans1:exp2	-0.0427739765611307	0.0641598858075587	-0.66667787859578	0.505144702310484	   
df.mm.trans2:exp2	-0.0784856688253079	0.0454762964398555	-1.72585885328434	0.0847078415250824	.  
df.mm.trans1:exp3	0.212649221924603	0.0641598858075587	3.31436409600888	0.000954314360357552	***
df.mm.trans2:exp3	0.0806996359848695	0.0454762964398555	1.77454283445440	0.076303322826374	.  
df.mm.trans1:exp4	0.280824783985103	0.0641598858075588	4.37695267768103	1.34109575318503e-05	***
df.mm.trans2:exp4	0.137962490540479	0.0454762964398555	3.03372308963068	0.00248326442996371	** 
df.mm.trans1:exp5	0.295888547597898	0.0641598858075588	4.61173744113863	4.5566532941007e-06	***
df.mm.trans2:exp5	0.29254137049296	0.0454762964398555	6.43283190133699	2.00918793835668e-10	***
df.mm.trans1:exp6	0.383577262098247	0.0641598858075588	5.978459239294	3.2173641021864e-09	***
df.mm.trans2:exp6	0.3685805700972	0.0454762964398555	8.10489417458753	1.66965776049075e-15	***
df.mm.trans1:exp7	0.235747752389493	0.0641598858075587	3.67437923902476	0.000252173576540859	***
df.mm.trans2:exp7	0.0942160153533606	0.0454762964398555	2.07176095524766	0.0385653246827012	*  
df.mm.trans1:exp8	0.375111214070111	0.0641598858075587	5.84650688430494	6.96296898683254e-09	***
df.mm.trans2:exp8	0.294040381570581	0.0454762964398555	6.46579437178802	1.63174730197987e-10	***
df.mm.trans1:probe2	0.188683373856440	0.0459609598244332	4.10529663821629	4.39605195662907e-05	***
df.mm.trans1:probe3	0.0408237799382675	0.0459609598244332	0.888227314969286	0.37465014546991	   
df.mm.trans1:probe4	0.0507135084653196	0.0459609598244332	1.1034040337504	0.270139648744246	   
df.mm.trans1:probe5	-0.117020105749740	0.0459609598244332	-2.54607619590076	0.0110558309480838	*  
df.mm.trans1:probe6	-0.119226532413743	0.0459609598244332	-2.59408273606944	0.0096348353353752	** 
df.mm.trans1:probe7	-0.171477500290833	0.0459609598244332	-3.73093819071364	0.000202430590722494	***
df.mm.trans1:probe8	-0.0400934523270038	0.0459609598244332	-0.872337141786361	0.383251591242981	   
df.mm.trans1:probe9	-0.0880923058900505	0.0459609598244332	-1.91667681063571	0.0555878680811851	.  
df.mm.trans1:probe10	0.453804686381393	0.0459609598244332	9.87369907231892	6.32775788187274e-22	***
df.mm.trans1:probe11	0.405346359726265	0.0459609598244332	8.81936237351553	5.66687137869301e-18	***
df.mm.trans1:probe12	0.845511945855999	0.0459609598244332	18.3963074114592	1.27780830147714e-64	***
df.mm.trans1:probe13	0.438614224055409	0.0459609598244332	9.54319112853335	1.19845332374990e-20	***
df.mm.trans1:probe14	0.500232756496572	0.0459609598244332	10.8838622693568	4.87583413493828e-26	***
df.mm.trans1:probe15	0.386721182219142	0.0459609598244332	8.41412328411728	1.49674639973177e-16	***
df.mm.trans1:probe16	0.53651400233077	0.0459609598244332	11.6732549620418	1.83815176703465e-29	***
df.mm.trans1:probe17	-0.118270480340123	0.0459609598244332	-2.57328134120579	0.0102292191748289	*  
df.mm.trans1:probe18	0.185300352625274	0.0459609598244332	4.03169022868768	5.99514224267501e-05	***
df.mm.trans1:probe19	-0.0803977898071237	0.0459609598244332	-1.74926263755666	0.0805783507165504	.  
df.mm.trans1:probe20	0.0708857692353633	0.0459609598244332	1.54230393590866	0.123342882662768	   
df.mm.trans1:probe21	-0.106970712917236	0.0459609598244332	-2.32742556565082	0.0201583111318645	*  
df.mm.trans1:probe22	0.0134420515794796	0.0459609598244332	0.292466728954901	0.769995595894796	   
df.mm.trans2:probe2	0.0582245566635831	0.0459609598244332	1.26682638669853	0.205537290011977	   
df.mm.trans2:probe3	0.058699840461677	0.0459609598244332	1.27716741960797	0.201864673704141	   
df.mm.trans2:probe4	-0.138369339496186	0.0459609598244332	-3.01058420069434	0.0026785029169541	** 
df.mm.trans2:probe5	0.198460799548821	0.0459609598244332	4.31802991728031	1.74479001268225e-05	***
df.mm.trans2:probe6	-0.0713871084766737	0.0459609598244332	-1.55321187262769	0.120715695534607	   
df.mm.trans3:probe2	-0.126845568976268	0.0459609598244332	-2.75985465623014	0.00589736717456525	** 
df.mm.trans3:probe3	-0.089051450079288	0.0459609598244332	-1.93754548250203	0.0529837952364368	.  
df.mm.trans3:probe4	-0.15640151222569	0.0459609598244332	-3.40292093165874	0.000695391945238197	***
df.mm.trans3:probe5	-0.141925612995760	0.0459609598244332	-3.08796016310153	0.00207566909706057	** 
df.mm.trans3:probe6	-0.128454123493119	0.0459609598244332	-2.79485293570462	0.0053001019291309	** 
df.mm.trans3:probe7	-0.270987232038748	0.0459609598244332	-5.89603074160974	5.22062142995535e-09	***
df.mm.trans3:probe8	-0.204015509456144	0.0459609598244332	-4.43888705186892	1.01362053260227e-05	***
df.mm.trans3:probe9	-0.570611449223285	0.0459609598244332	-12.4151334394010	7.74374635803634e-33	***
df.mm.trans3:probe10	-0.303378794275025	0.0459609598244332	-6.60079326963374	6.8922500534494e-11	***
df.mm.trans3:probe11	-0.154354207662136	0.0459609598244332	-3.35837650588142	0.000816126927555224	***
df.mm.trans3:probe12	-0.567980557085537	0.0459609598244332	-12.3578915508982	1.42771864434544e-32	***
df.mm.trans3:probe13	-0.257849295312438	0.0459609598244332	-5.6101808207966	2.67214608253113e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0627081761415	0.142773996302141	28.4555190816675	2.4385948048932e-128	***
df.mm.trans1	0.179722793758604	0.122618428165258	1.46570785849892	0.143068828908142	   
df.mm.trans2	0.075491720094917	0.107666882028167	0.701160084446095	0.483380038714298	   
df.mm.exp2	0.178507658731049	0.136992348958789	1.30304838253959	0.192883567882911	   
df.mm.exp3	0.298530383208062	0.136992348958789	2.17917559248414	0.0295711452023692	*  
df.mm.exp4	0.093222895226028	0.136992348958789	0.680497092973216	0.496360605664151	   
df.mm.exp5	0.00262945521985648	0.136992348958789	0.0191941757320148	0.98469035662814	   
df.mm.exp6	0.0453935321387089	0.136992348958789	0.331358156011797	0.740449265982894	   
df.mm.exp7	0.152278912446320	0.136992348958789	1.11158698718372	0.266605540610806	   
df.mm.exp8	0.195779639699328	0.136992348958789	1.42912827750858	0.153306075600142	   
df.mm.trans1:exp2	-0.188088361912688	0.125760314585449	-1.49560982359733	0.135097553377249	   
df.mm.trans2:exp2	-0.147591368515652	0.089138458936964	-1.65575409622040	0.0981119099157542	.  
df.mm.trans1:exp3	-0.286454472688220	0.125760314585449	-2.27778113972183	0.0229677842538722	*  
df.mm.trans2:exp3	-0.114299368870752	0.089138458936964	-1.28226772409854	0.200071048299194	   
df.mm.trans1:exp4	-0.0933815286684001	0.125760314585449	-0.742535743300411	0.45795193348799	   
df.mm.trans2:exp4	-0.0331211032375704	0.089138458936964	-0.37156917039583	0.71029901743519	   
df.mm.trans1:exp5	0.00761392371862769	0.125760314585449	0.0605431351195795	0.951736182407568	   
df.mm.trans2:exp5	-0.00753382439070378	0.089138458936964	-0.0845182256968507	0.932662760323965	   
df.mm.trans1:exp6	-0.0593711655935972	0.125760314585449	-0.472097782112789	0.636968706373826	   
df.mm.trans2:exp6	0.0422744516440772	0.089138458936964	0.474256029868908	0.635429636879022	   
df.mm.trans1:exp7	-0.176864963162607	0.125760314585449	-1.40636546390343	0.159952569634478	   
df.mm.trans2:exp7	-0.0501476286831492	0.089138458936964	-0.562581284006852	0.573856796799351	   
df.mm.trans1:exp8	-0.179634837411011	0.125760314585449	-1.42839049030015	0.153518150333563	   
df.mm.trans2:exp8	-0.111552665645263	0.089138458936964	-1.25145382784943	0.211086369992442	   
df.mm.trans1:probe2	-0.0139269058734514	0.0900884515833872	-0.154591466815927	0.877177230742315	   
df.mm.trans1:probe3	-0.132833892825678	0.0900884515833872	-1.47448302741362	0.140692942039397	   
df.mm.trans1:probe4	-0.0930384800068553	0.0900884515833872	-1.03274591106428	0.301993635275934	   
df.mm.trans1:probe5	0.0347578491725951	0.0900884515833872	0.3858191428723	0.699719620740698	   
df.mm.trans1:probe6	0.00584434822378688	0.0900884515833872	0.064873445164914	0.948288826377816	   
df.mm.trans1:probe7	0.0490267381622117	0.0900884515833872	0.544206691318607	0.586430826404671	   
df.mm.trans1:probe8	0.00886385611710236	0.0900884515833872	0.0983905923712968	0.921643530424008	   
df.mm.trans1:probe9	-0.0965180189239093	0.0900884515833872	-1.0713694955071	0.284283684107878	   
df.mm.trans1:probe10	-0.0138148043464357	0.0900884515833872	-0.153347117234538	0.87815810052119	   
df.mm.trans1:probe11	0.0641668379885826	0.0900884515833872	0.712264855936489	0.476480977189524	   
df.mm.trans1:probe12	-0.076925256950431	0.0900884515833872	-0.853885882134714	0.393390013345661	   
df.mm.trans1:probe13	0.115304583682545	0.0900884515833872	1.27990415703635	0.200900790065167	   
df.mm.trans1:probe14	-0.0391537970564296	0.0900884515833872	-0.434615051854768	0.663943565051005	   
df.mm.trans1:probe15	0.0545663482248569	0.0900884515833872	0.605697481373065	0.544864725478417	   
df.mm.trans1:probe16	-0.145989232883713	0.0900884515833872	-1.62050995791157	0.105464709325404	   
df.mm.trans1:probe17	0.094743990805468	0.0900884515833872	1.05167742524436	0.293223123594487	   
df.mm.trans1:probe18	-0.0768630267789709	0.0900884515833872	-0.853195114668225	0.393772702848906	   
df.mm.trans1:probe19	0.0825291470702379	0.0900884515833872	0.916090193800785	0.359859129676211	   
df.mm.trans1:probe20	0.0611569798494968	0.0900884515833872	0.678854822950186	0.497400214545532	   
df.mm.trans1:probe21	-0.0548783559677669	0.0900884515833872	-0.609160830308762	0.542567906836276	   
df.mm.trans1:probe22	-0.0333500614340996	0.0900884515833872	-0.370192414765063	0.711324149479639	   
df.mm.trans2:probe2	-0.0929793733927835	0.0900884515833872	-1.03208981571540	0.302300691363922	   
df.mm.trans2:probe3	0.0716460827559812	0.0900884515833872	0.795285982795082	0.426651847779476	   
df.mm.trans2:probe4	0.0773846208567814	0.0900884515833872	0.858984914233463	0.3905721037164	   
df.mm.trans2:probe5	0.122315240026428	0.0900884515833872	1.35772385779338	0.174883515818678	   
df.mm.trans2:probe6	0.0481969768239135	0.0900884515833872	0.534996172947891	0.592781503139472	   
df.mm.trans3:probe2	-0.00668405999098106	0.0900884515833872	-0.0741944153052092	0.940871783026443	   
df.mm.trans3:probe3	-0.0876605576453928	0.0900884515833872	-0.973049887135122	0.330783717063771	   
df.mm.trans3:probe4	-0.0106848251944462	0.0900884515833872	-0.118603716754485	0.905615155087698	   
df.mm.trans3:probe5	0.00659084983348765	0.0900884515833872	0.0731597637393852	0.941694852291496	   
df.mm.trans3:probe6	-0.0144018270365403	0.0900884515833872	-0.159863187605236	0.873023862017715	   
df.mm.trans3:probe7	-0.112251931140184	0.0900884515833872	-1.24601909753419	0.213073895430744	   
df.mm.trans3:probe8	-0.0455892157078346	0.0900884515833872	-0.506049498093955	0.612942872602112	   
df.mm.trans3:probe9	-0.00801462412836294	0.0900884515833872	-0.0889639458498683	0.929129883594216	   
df.mm.trans3:probe10	-0.0288750170438053	0.0900884515833872	-0.320518518592565	0.748647921818623	   
df.mm.trans3:probe11	0.0192566990418496	0.0900884515833872	0.213753246985551	0.830786744514267	   
df.mm.trans3:probe12	-0.0718270489522281	0.0900884515833872	-0.797294744107616	0.42548514485226	   
df.mm.trans3:probe13	-0.0211501620521201	0.0900884515833872	-0.234771068659596	0.814438551274696