fitVsDatCorrelation=0.918605282144228
cont.fitVsDatCorrelation=0.254660431755403

fstatistic=8748.88013928607,57,807
cont.fstatistic=1449.21893778824,57,807

residuals=-0.94039283137346,-0.103016020794307,-0.0156880944567041,0.0886850999566206,0.842797645317673
cont.residuals=-0.705031389089489,-0.261155373084760,-0.0891128127567658,0.134761040442824,1.87316779513552

predictedValues:
Include	Exclude	Both
Lung	58.3549485633525	80.1151168000081	65.3942315222347
cerebhem	62.1299110075449	75.5483260360969	58.9119808374958
cortex	56.5677960661682	78.5859578830653	63.5010694544503
heart	58.136358282232	79.0672083553832	60.2662630969967
kidney	59.9740976670988	77.7460247671216	65.3299201338258
liver	59.6651860119	69.3847995140278	61.6087394839997
stomach	60.4575412312998	78.4239123007244	59.3348326553607
testicle	59.2029887584486	74.5668140060709	61.6703215557224


diffExp=-21.7601682366556,-13.4184150285519,-22.0181618168972,-20.9308500731513,-17.7719271000228,-9.71961350212776,-17.9663710694245,-15.3638252476223
diffExpScore=0.99285455682298
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=-1,0,-1,-1,0,0,0,0
diffExp1.3Score=0.75
diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	63.326010722579	59.2309977208233	64.934245393857
cerebhem	66.6181389791668	83.0319941903007	60.024419379424
cortex	66.1312694168411	72.8911082587936	69.4772531705079
heart	69.6205477991991	58.3314483958629	55.7028242777714
kidney	64.3719597795783	75.6042706962232	59.0793246828303
liver	63.5836790199393	86.9126997276411	63.9955274360657
stomach	60.3528218318847	75.1315682456899	60.9262820872725
testicle	63.0618881528779	71.686679140831	60.4161603146807
cont.diffExp=4.09501300175580,-16.4138552111339,-6.75983884195247,11.2890994033362,-11.2323109166449,-23.3290207077018,-14.7787464138052,-8.62479098795319
cont.diffExpScore=1.44593618117711

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,-1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,0,0,0,-1,-1,0
cont.diffExp1.2Score=0.75

tran.correlation=-0.347806324558139
cont.tran.correlation=-0.292593982062099

tran.covariance=-0.000448754156542601
cont.tran.covariance=-0.00187648691127601

tran.mean=67.9954367031589
cont.tran.mean=68.7429426298895

weightedLogRatios:
wLogRatio
Lung	-1.33899005945629
cerebhem	-0.826568720007145
cortex	-1.38070301591570
heart	-1.29661691183252
kidney	-1.09619136783279
liver	-0.628458624797112
stomach	-1.10112290354121
testicle	-0.968192942684129

cont.weightedLogRatios:
wLogRatio
Lung	0.275083796342401
cerebhem	-0.949075751794419
cortex	-0.41268744531776
heart	0.735025197708422
kidney	-0.682758403568639
liver	-1.34665102146108
stomach	-0.922069021345407
testicle	-0.539443588632479

varWeightedLogRatios=0.0693922827527873
cont.varWeightedLogRatios=0.466021929279899

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30128188521808	0.0822733277233746	52.2803927377311	2.47280159415667e-261	***
df.mm.trans1	-0.264324922012559	0.0714435544580337	-3.69977283490037	0.000230426537828899	***
df.mm.trans2	-0.0202042618911522	0.0635030567879118	-0.318162036807623	0.750444342648266	   
df.mm.exp2	0.108380911143697	0.0825345596206504	1.31315792610808	0.189502993572389	   
df.mm.exp3	-0.0209985400198481	0.0825345596206504	-0.254421179641143	0.799235004763864	   
df.mm.exp4	0.0647423504917401	0.0825345596206504	0.784427163473243	0.433019697483689	   
df.mm.exp5	-0.00166461376518984	0.0825345596206504	-0.0201686877938263	0.983913791782345	   
df.mm.exp6	-0.0619618896815919	0.0825345596206504	-0.75073872043886	0.453028756756512	   
df.mm.exp7	0.111299002508241	0.0825345596206504	1.34851391974222	0.177871468084632	   
df.mm.exp8	0.00129010680882137	0.0825345596206504	0.0156311103463934	0.987532549754799	   
df.mm.trans1:exp2	-0.0456975428481874	0.0767690555304742	-0.595259932956286	0.551836614249412	   
df.mm.trans2:exp2	-0.167072940067357	0.0586877003106379	-2.84681354326424	0.00452798410523476	** 
df.mm.trans1:exp3	-0.01010577421439	0.0767690555304742	-0.13163864195748	0.89530293066381	   
df.mm.trans2:exp3	0.00172701024432732	0.0586877003106379	0.0294271241705866	0.976531215909405	   
df.mm.trans1:exp4	-0.0684952577875278	0.0767690555304742	-0.892224833485648	0.372538393730777	   
df.mm.trans2:exp4	-0.0779086813851612	0.0586877003106379	-1.32751293665939	0.184714467510516	   
df.mm.trans1:exp5	0.0290332138070471	0.0767690555304742	0.378189019083635	0.70538963996038	   
df.mm.trans2:exp5	-0.0283525252428624	0.0586877003106379	-0.483108472350946	0.629149754499006	   
df.mm.trans1:exp6	0.0841664276830903	0.0767690555304742	1.09635877504940	0.273248854677991	   
df.mm.trans2:exp6	-0.0818348543341409	0.0586877003106379	-1.39441235388307	0.163576865689844	   
df.mm.trans1:exp7	-0.0759018450967958	0.0767690555304742	-0.988703645919753	0.323104501193837	   
df.mm.trans2:exp7	-0.132634678183468	0.0586877003106379	-2.26000810189228	0.0240865572400073	*  
df.mm.trans1:exp8	0.0131377561711582	0.0767690555304742	0.171133487059028	0.864161704629646	   
df.mm.trans2:exp8	-0.0730591114040001	0.0586877003106379	-1.24487943840521	0.213537450333146	   
df.mm.trans1:probe2	-0.117979101651329	0.0502571439998095	-2.347509075561	0.0191401157008919	*  
df.mm.trans1:probe3	-0.164358570327851	0.0502571439998095	-3.27035237673820	0.00111969161174744	** 
df.mm.trans1:probe4	-0.0119079491966541	0.0502571439998095	-0.236940427746933	0.812763138144544	   
df.mm.trans1:probe5	-0.171596817231387	0.0502571439998095	-3.41437661543277	0.000671180741601165	***
df.mm.trans1:probe6	0.617464019422335	0.0502571439998095	12.2860944789198	6.29188357159847e-32	***
df.mm.trans1:probe7	-0.127066746855101	0.0502571439998095	-2.52833202888694	0.0116502274845445	*  
df.mm.trans1:probe8	-0.206642903994365	0.0502571439998095	-4.11171203829546	4.32949835585995e-05	***
df.mm.trans1:probe9	0.139539082344318	0.0502571439998095	2.77650242808956	0.00562202612532848	** 
df.mm.trans1:probe10	-0.209109086121614	0.0502571439998095	-4.16078331316254	3.51195321568476e-05	***
df.mm.trans1:probe11	0.0819864984395374	0.0502571439998095	1.63134018200175	0.103208714814053	   
df.mm.trans1:probe12	0.100106449623897	0.0502571439998095	1.99188496712581	0.0467202755230603	*  
df.mm.trans1:probe13	-0.228688838655868	0.0502571439998095	-4.55037474188217	6.17848214229171e-06	***
df.mm.trans1:probe14	-0.0307371435288612	0.0502571439998095	-0.611597498038841	0.540976343369152	   
df.mm.trans1:probe15	-0.0487113892322643	0.0502571439998095	-0.969243083778278	0.332714313124622	   
df.mm.trans1:probe16	0.134400736973314	0.0502571439998095	2.67426133434530	0.00764090538709478	** 
df.mm.trans1:probe17	-0.144277228129403	0.0502571439998095	-2.87078048306824	0.00420173758426637	** 
df.mm.trans1:probe18	-0.174190331633458	0.0502571439998095	-3.46598150571626	0.000556205403961969	***
df.mm.trans1:probe19	-0.0741754042223034	0.0502571439998095	-1.47591761725626	0.140356073025868	   
df.mm.trans1:probe20	-0.127013009314813	0.0502571439998095	-2.52726277711471	0.0116854741990520	*  
df.mm.trans1:probe21	1.39357620548831	0.0502571439998095	27.7289176140529	2.11392188710416e-119	***
df.mm.trans1:probe22	0.256997534244466	0.0502571439998095	5.1136517874044	3.94958895012856e-07	***
df.mm.trans2:probe2	0.149785266720385	0.0502571439998095	2.98037761001606	0.00296537014666537	** 
df.mm.trans2:probe3	1.36015503984744	0.0502571439998095	27.0639143333054	2.69096065575365e-115	***
df.mm.trans2:probe4	0.0891220520901254	0.0502571439998095	1.77332106437372	0.0765526202195618	.  
df.mm.trans2:probe5	-0.139267359594101	0.0502571439998095	-2.77109577883353	0.0057153232624264	** 
df.mm.trans2:probe6	-0.0263778805568352	0.0502571439998095	-0.524858327742125	0.599825831131201	   
df.mm.trans3:probe2	0.514428729949623	0.0502571439998095	10.2359324268719	3.33479004388436e-23	***
df.mm.trans3:probe3	-0.0789547666865095	0.0502571439998095	-1.57101578806008	0.116570953545428	   
df.mm.trans3:probe4	-0.0340939742703324	0.0502571439998095	-0.678390603940042	0.497718542333118	   
df.mm.trans3:probe5	-0.0528074448195571	0.0502571439998095	-1.05074504074002	0.293690246314134	   
df.mm.trans3:probe6	0.59849785072321	0.0502571439998095	11.9087119380576	3.07867926157488e-30	***
df.mm.trans3:probe7	0.265678944570037	0.0502571439998095	5.28639161371853	1.60711072328781e-07	***
df.mm.trans3:probe8	0.0967014624113903	0.0502571439998095	1.92413365971924	0.0546892094987538	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11041617582254	0.201329921576814	20.4163203543209	5.12602305695639e-75	***
df.mm.trans1	0.0600149682976052	0.174828533307499	0.343279024094124	0.731477966367592	   
df.mm.trans2	0.00349422729840943	0.155397451358539	0.0224857439286273	0.982066042304943	   
df.mm.exp2	0.467085411027907	0.201969178537086	2.31265688364495	0.0209922376904878	*  
df.mm.exp3	0.183242927516795	0.201969178537086	0.907281639921845	0.364528713476886	   
df.mm.exp4	0.232804248381980	0.201969178537086	1.15267215556473	0.249386292754675	   
df.mm.exp5	0.354943853288455	0.201969178537086	1.75741593771586	0.0792260804060337	.  
df.mm.exp6	0.402081761485152	0.201969178537086	1.99080753012679	0.0468388942069326	*  
df.mm.exp7	0.253417858072527	0.201969178537086	1.25473530123803	0.209938124984100	   
df.mm.exp8	0.258798893010769	0.201969178537086	1.28137815326732	0.200428936704266	   
df.mm.trans1:exp2	-0.416404670157508	0.187860493274849	-2.21656327468644	0.0269310678691426	*  
df.mm.trans2:exp2	-0.129304420337364	0.143613859169420	-0.900361713592168	0.368196386923559	   
df.mm.trans1:exp3	-0.139897386873871	0.187860493274849	-0.744687637273444	0.456677374859035	   
df.mm.trans2:exp3	0.0242787172969553	0.143613859169420	0.169055531529961	0.86579532630635	   
df.mm.trans1:exp4	-0.138040654250580	0.187860493274849	-0.734804065741592	0.462672296924108	   
df.mm.trans2:exp4	-0.248107891865479	0.143613859169420	-1.72760409963490	0.0844418165905061	.  
df.mm.trans1:exp5	-0.338561878782814	0.187860493274849	-1.80219839137483	0.0718872411183426	.  
df.mm.trans2:exp5	-0.110876096010764	0.143613859169420	-0.772043148565238	0.440314895753892	   
df.mm.trans1:exp6	-0.398021099892801	0.187860493274849	-2.11870571057470	0.0344202998237521	*  
df.mm.trans2:exp6	-0.0186226130344460	0.143613859169420	-0.129671419890451	0.896858707935537	   
df.mm.trans1:exp7	-0.301506310482434	0.187860493274849	-1.60494793357812	0.108896498538598	   
df.mm.trans2:exp7	-0.0156220531510080	0.143613859169420	-0.108778172534022	0.913405477669989	   
df.mm.trans1:exp8	-0.262978453815902	0.187860493274849	-1.39986033908232	0.161939446344876	   
df.mm.trans2:exp8	-0.0679389637100073	0.143613859169420	-0.473066903869355	0.636293374204355	   
df.mm.trans1:probe2	-0.140950653735532	0.122983561503391	-1.14609344543698	0.252096215034135	   
df.mm.trans1:probe3	-0.103362853478542	0.122983561503391	-0.840460726742668	0.400899051835736	   
df.mm.trans1:probe4	-0.0256019298097593	0.122983561503391	-0.208173592444331	0.835145953348611	   
df.mm.trans1:probe5	-0.202325697790009	0.122983561503391	-1.64514423973996	0.1003294862235	   
df.mm.trans1:probe6	0.0435479349548296	0.122983561503391	0.354095575233677	0.72335975475354	   
df.mm.trans1:probe7	-0.0196373998763130	0.122983561503391	-0.159674997505837	0.873177052391611	   
df.mm.trans1:probe8	0.0321179859616613	0.122983561503391	0.26115673972229	0.794038269057541	   
df.mm.trans1:probe9	-0.00871242894828282	0.122983561503391	-0.0708422234791322	0.94354087829403	   
df.mm.trans1:probe10	-0.0233404062871766	0.122983561503391	-0.189784764742994	0.849525519781118	   
df.mm.trans1:probe11	0.116198158041110	0.122983561503391	0.944826744490615	0.34503012544836	   
df.mm.trans1:probe12	-0.0746603716639082	0.122983561503391	-0.607076025049491	0.54397122049521	   
df.mm.trans1:probe13	-0.139214301441164	0.122983561503391	-1.13197487322178	0.257981238619562	   
df.mm.trans1:probe14	-0.115635257717262	0.122983561503391	-0.940249707389336	0.347370838651421	   
df.mm.trans1:probe15	-0.183688882044566	0.122983561503391	-1.49360515990181	0.135669714920998	   
df.mm.trans1:probe16	-0.000629353058136323	0.122983561503391	-0.00511737544792903	0.995918207673267	   
df.mm.trans1:probe17	-0.0551639372951681	0.122983561503391	-0.448547241768137	0.653878708872605	   
df.mm.trans1:probe18	0.0964361417664007	0.122983561503391	0.78413847011368	0.433188959916066	   
df.mm.trans1:probe19	0.0225190643238707	0.122983561503391	0.183106295252718	0.854760608412254	   
df.mm.trans1:probe20	0.0130571755001610	0.122983561503391	0.106170087616148	0.915473788649011	   
df.mm.trans1:probe21	0.0255033707797093	0.122983561503391	0.207372192412936	0.835771513889677	   
df.mm.trans1:probe22	0.0794940173551475	0.122983561503391	0.646379210224414	0.518217576958063	   
df.mm.trans2:probe2	-0.083738660882838	0.122983561503391	-0.680893119854307	0.496134438658746	   
df.mm.trans2:probe3	-0.125667552059122	0.122983561503391	-1.02182397812294	0.307170432217159	   
df.mm.trans2:probe4	-0.0977834887977052	0.122983561503391	-0.795093975181464	0.426792617161603	   
df.mm.trans2:probe5	-0.0185079554460431	0.122983561503391	-0.150491295095018	0.880414624077236	   
df.mm.trans2:probe6	-0.128817776464979	0.122983561503391	-1.04743898200921	0.295210765907643	   
df.mm.trans3:probe2	-0.0451069341688746	0.122983561503391	-0.366772059757196	0.713885196108624	   
df.mm.trans3:probe3	0.0928271820322971	0.122983561503391	0.754793412205237	0.450593156300579	   
df.mm.trans3:probe4	-0.0377646383948184	0.122983561503391	-0.307070619302053	0.75886883962595	   
df.mm.trans3:probe5	-0.0802505001420834	0.122983561503391	-0.65253029885519	0.514244959772055	   
df.mm.trans3:probe6	0.0449877812436108	0.122983561503391	0.365803207304013	0.714607792119422	   
df.mm.trans3:probe7	-0.0440918235311215	0.122983561503391	-0.358518024621573	0.720049447795986	   
df.mm.trans3:probe8	0.0649971499990227	0.122983561503391	0.528502746257113	0.597295750727315