fitVsDatCorrelation=0.918463769754795
cont.fitVsDatCorrelation=0.291087577496599

fstatistic=11052.7936789091,52,692
cont.fstatistic=1877.94946849263,52,692

residuals=-0.664862471206018,-0.0884112234801298,0.00187282804722867,0.0825203058518103,0.709735519810664
cont.residuals=-0.749562574050519,-0.225401211440015,-0.0661475951388077,0.114782246671562,1.54615070157862

predictedValues:
Include	Exclude	Both
Lung	69.3053751364669	59.1285513852895	108.778346397037
cerebhem	72.400445834717	60.0641507727716	94.8785700977369
cortex	79.9420036309205	54.702440764124	96.900376904544
heart	59.8065901304792	54.8176431350617	82.5629280923981
kidney	62.6189927862816	58.9725131908713	100.569563772623
liver	61.6590616112567	59.642140869856	86.213452663168
stomach	59.8441169396528	55.3784972401341	81.5072823477081
testicle	59.7983138591514	59.3336608374605	92.8454451289779


diffExp=10.1768237511773,12.3362950619453,25.2395628667965,4.98894699541749,3.64647959541024,2.01692074140069,4.46561969951875,0.464653021690872
diffExpScore=0.984456434134022
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	67.4660316428641	70.9549728294107	74.8567530265724
cerebhem	66.8413956219391	65.6899927679022	59.1883350220093
cortex	67.8311315138714	69.4876079430169	69.0782472531162
heart	70.0929776884393	69.3843808868402	62.089145694489
kidney	69.7463671730872	66.8471787417373	73.879682487617
liver	63.1736672518437	74.3571818207033	62.871569694511
stomach	67.803319753099	69.3088296385277	63.7777776753163
testicle	79.2506163671913	66.4188915810999	55.2227062643804
cont.diffExp=-3.4889411865466,1.15140285403693,-1.65647642914548,0.708596801599114,2.89918843134998,-11.1835145688596,-1.50550988542874,12.8317247860914
cont.diffExpScore=28.4877548764305

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=-0.102292849825597
cont.tran.correlation=-0.61036860071154

tran.covariance=-0.000339674566067792
cont.tran.covariance=-0.0016276721570031

tran.mean=61.7134061327809
cont.tran.mean=69.0409089513483

weightedLogRatios:
wLogRatio
Lung	0.660503552781316
cerebhem	0.782467245799903
cortex	1.59026591572096
heart	0.352558043617932
kidney	0.246412596081104
liver	0.136523052493401
stomach	0.314314697232357
testicle	0.0318819925514676

cont.weightedLogRatios:
wLogRatio
Lung	-0.213626527082025
cerebhem	0.0728685390320541
cortex	-0.102035879712664
heart	0.0431301058999693
kidney	0.179319738345898
liver	-0.689032743148035
stomach	-0.0928427856459028
testicle	0.756751310183943

varWeightedLogRatios=0.252050366652002
cont.varWeightedLogRatios=0.164453320244195

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.28173324322149	0.0812428351793184	28.08534731937	2.01487344077149e-116	***
df.mm.trans1	1.69059330627718	0.0729679468636671	23.1689855469810	2.38090626665290e-88	***
df.mm.trans2	1.71719056381797	0.0671039370970799	25.5900121230398	3.71584750123462e-102	***
df.mm.exp2	0.196103678739367	0.0919157754849872	2.13351492390332	0.0332335889119392	*  
df.mm.exp3	0.180602253311720	0.0919157754849873	1.96486677459646	0.049829338157525	*  
df.mm.exp4	0.0526431028748183	0.0919157754849873	0.572731966814734	0.567012225790177	   
df.mm.exp5	-0.0256336611134274	0.0919157754849873	-0.278882063260339	0.780418654377127	   
df.mm.exp6	0.124232275829261	0.0919157754849873	1.35158818139495	0.176948735035181	   
df.mm.exp7	0.0763180574815341	0.0919157754849873	0.830304233183555	0.406653151399238	   
df.mm.exp8	0.0142939305224559	0.0919157754849873	0.155511178000022	0.876463692876135	   
df.mm.trans1:exp2	-0.152413687913337	0.0880026550797723	-1.73192147185988	0.0837331689912032	.  
df.mm.trans2:exp2	-0.180404418857808	0.0765964795708227	-2.35525731559244	0.0187880853323285	*  
df.mm.trans1:exp3	-0.0378233026427594	0.0880026550797723	-0.429797289734878	0.667476903061518	   
df.mm.trans2:exp3	-0.25840783468429	0.0765964795708228	-3.37362547380994	0.000783201734240315	***
df.mm.trans1:exp4	-0.200049711624060	0.0880026550797723	-2.27322359129642	0.0233183749838552	*  
df.mm.trans2:exp4	-0.128344916439239	0.0765964795708228	-1.67559811049238	0.0942686798006233	.  
df.mm.trans1:exp5	-0.0758201741396144	0.0880026550797723	-0.861566893304357	0.389224335854093	   
df.mm.trans2:exp5	0.0229912076220263	0.0765964795708227	0.300160108543477	0.764145177023335	   
df.mm.trans1:exp6	-0.241134538731852	0.0880026550797723	-2.74008254084231	0.00630070705865115	** 
df.mm.trans2:exp6	-0.115583800754828	0.0765964795708228	-1.50899625416801	0.131756161914048	   
df.mm.trans1:exp7	-0.223097393541410	0.0880026550797723	-2.53512116582366	0.0114599529345130	*  
df.mm.trans2:exp7	-0.141840586253789	0.0765964795708227	-1.85178988706185	0.0644817623429963	.  
df.mm.trans1:exp8	-0.161838932783417	0.0880026550797723	-1.83902329579390	0.0663400476500031	.  
df.mm.trans2:exp8	-0.0108310599026007	0.0765964795708227	-0.141404147596445	0.887591831942206	   
df.mm.trans1:probe2	0.147179918276324	0.0440013275398861	3.34489722254195	0.000867548178745454	***
df.mm.trans1:probe3	0.128440533134894	0.0440013275398862	2.91901495513893	0.00362541851343947	** 
df.mm.trans1:probe4	0.331182024686676	0.0440013275398861	7.52663710853877	1.62597679071976e-13	***
df.mm.trans1:probe5	0.0849973978120783	0.0440013275398862	1.93170075914255	0.0538044260459382	.  
df.mm.trans1:probe6	0.87118680164737	0.0440013275398862	19.7991026715651	1.80909029608903e-69	***
df.mm.trans1:probe7	0.297105596170773	0.0440013275398862	6.75219619002298	3.08629415431128e-11	***
df.mm.trans1:probe8	0.0123570793864658	0.0440013275398862	0.280834240177513	0.77892148284393	   
df.mm.trans1:probe9	0.97604500439408	0.0440013275398862	22.1821717426439	9.16998784009992e-83	***
df.mm.trans1:probe10	0.133575019397042	0.0440013275398862	3.03570430405673	0.00248980172023942	** 
df.mm.trans1:probe11	0.639302857414238	0.0440013275398862	14.5291720308831	6.13707847755018e-42	***
df.mm.trans1:probe12	0.0842069204105055	0.0440013275398862	1.91373590567634	0.0560661643196951	.  
df.mm.trans1:probe13	0.332075637178342	0.0440013275398861	7.54694587060637	1.40811796948327e-13	***
df.mm.trans1:probe14	0.198953162523281	0.0440013275398862	4.5215263640156	7.22203989858123e-06	***
df.mm.trans1:probe15	0.0741220924112994	0.0440013275398862	1.68454218441726	0.092527849842352	.  
df.mm.trans1:probe16	0.361226800548223	0.0440013275398861	8.20945232210958	1.08716970152404e-15	***
df.mm.trans1:probe17	0.155188783908486	0.0440013275398862	3.52691140438457	0.000448194463198558	***
df.mm.trans1:probe18	0.398363320582151	0.0440013275398862	9.05343867684548	1.39099241889892e-18	***
df.mm.trans1:probe19	0.432477047458078	0.0440013275398862	9.8287272597866	1.98068553632316e-21	***
df.mm.trans1:probe20	0.263156112308173	0.0440013275398861	5.98064029021915	3.56246113635509e-09	***
df.mm.trans1:probe21	0.258880941810440	0.0440013275398861	5.88348025581208	6.25690857011439e-09	***
df.mm.trans1:probe22	0.474874873193255	0.0440013275398861	10.7922851364608	3.33555830790696e-25	***
df.mm.trans2:probe2	0.126613707897754	0.0440013275398862	2.87749745238893	0.00413193044303995	** 
df.mm.trans2:probe3	0.0577769151935588	0.0440013275398862	1.31307209177235	0.189593903016023	   
df.mm.trans2:probe4	0.183758072447461	0.0440013275398862	4.17619382689963	3.34313517852164e-05	***
df.mm.trans2:probe5	0.0272913211322501	0.0440013275398862	0.620238585017945	0.535304915411581	   
df.mm.trans2:probe6	0.331670916466109	0.0440013275398862	7.53774795011486	1.50297367607638e-13	***
df.mm.trans3:probe2	-1.52190821361476	0.0440013275398862	-34.5877794763167	5.43845353084517e-153	***
df.mm.trans3:probe3	-1.47870624875039	0.0440013275398862	-33.6059462617344	1.46192138540882e-147	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19044522044621	0.19652015602403	21.3232337345275	6.18452673510174e-78	***
df.mm.trans1	0.087484763158536	0.176503839024704	0.495653599615424	0.620296195885223	   
df.mm.trans2	0.0491939436151125	0.162319251402761	0.303069064143527	0.761928314862017	   
df.mm.exp2	0.148451302372766	0.222337175943088	0.667685472495007	0.504556990357071	   
df.mm.exp3	0.064836410821738	0.222337175943088	0.291612999700663	0.770669927838022	   
df.mm.exp4	0.202819811115568	0.222337175943088	0.912217267558908	0.361972033822439	   
df.mm.exp5	-0.0132568210586739	0.222337175943088	-0.0596248513207133	0.95247163948026	   
df.mm.exp6	0.155580351475372	0.222337175943088	0.699749606944707	0.484318846370459	   
df.mm.exp7	0.141685251111633	0.222337175943088	0.637253983777774	0.524170188519442	   
df.mm.exp8	0.399129104878075	0.222337175943088	1.79515235445934	0.0730656759939432	.  
df.mm.trans1:exp2	-0.157752955115342	0.212871639309903	-0.741070795652973	0.458901912132683	   
df.mm.trans2:exp2	-0.225550195922112	0.185280979952573	-1.21734133735609	0.223889336667585	   
df.mm.trans1:exp3	-0.0594393906817415	0.212871639309903	-0.279226443101744	0.780154481718457	   
df.mm.trans2:exp3	-0.0857334674864027	0.185280979952573	-0.462721362486036	0.643709570790167	   
df.mm.trans1:exp4	-0.164621434014240	0.212871639309903	-0.773336619889419	0.439587271159183	   
df.mm.trans2:exp4	-0.225203518559249	0.185280979952573	-1.21547024749596	0.224601298376601	   
df.mm.trans1:exp5	0.0464979205710223	0.212871639309903	0.218431730604234	0.827157145506648	   
df.mm.trans2:exp5	-0.0463795694595062	0.185280979952573	-0.250320186515518	0.802414044426538	   
df.mm.trans1:exp6	-0.221317030848677	0.212871639309903	-1.03967363414945	0.298854671147066	   
df.mm.trans2:exp6	-0.108745578829554	0.185280979952573	-0.586922515508014	0.557447173052152	   
df.mm.trans1:exp7	-0.136698329746725	0.212871639309903	-0.64216318430149	0.52097990678775	   
df.mm.trans2:exp7	-0.165158431579975	0.185280979952573	-0.891394419558075	0.373027439261591	   
df.mm.trans1:exp8	-0.238138150939703	0.212871639309903	-1.11869364896005	0.263659033924146	   
df.mm.trans2:exp8	-0.465193067457303	0.185280979952573	-2.51074377724243	0.0122746085762610	*  
df.mm.trans1:probe2	-0.0306030873612413	0.106435819654951	-0.287526205561735	0.773795521546031	   
df.mm.trans1:probe3	-0.153668721768686	0.106435819654951	-1.44376885776665	0.149256562537371	   
df.mm.trans1:probe4	-0.0933927344590314	0.106435819654951	-0.877455867411896	0.380543606026903	   
df.mm.trans1:probe5	-0.142683198361965	0.106435819654951	-1.34055620395955	0.180504502587386	   
df.mm.trans1:probe6	-0.127832283245302	0.106435819654951	-1.20102690672853	0.230151685662107	   
df.mm.trans1:probe7	0.0730273579754956	0.106435819654951	0.686116367706277	0.492869541743769	   
df.mm.trans1:probe8	-0.114504346820706	0.106435819654951	-1.07580650190802	0.282388733124737	   
df.mm.trans1:probe9	-0.197376692239290	0.106435819654951	-1.85441980791011	0.0641043533597381	.  
df.mm.trans1:probe10	-0.0892575935853	0.106435819654951	-0.83860484068859	0.401980670320864	   
df.mm.trans1:probe11	-0.0851545772448643	0.106435819654951	-0.800055634662489	0.423953123226953	   
df.mm.trans1:probe12	-0.0625108813853122	0.106435819654951	-0.587310565070697	0.557186720309105	   
df.mm.trans1:probe13	0.23132808661031	0.106435819654951	2.17340447379689	0.0300883125056435	*  
df.mm.trans1:probe14	-0.178930299090817	0.106435819654951	-1.68110979622162	0.0931928289247935	.  
df.mm.trans1:probe15	0.00394458591108352	0.106435819654951	0.0370606993385428	0.970447296124435	   
df.mm.trans1:probe16	-0.109025351305130	0.106435819654951	-1.02432951292689	0.306037600335894	   
df.mm.trans1:probe17	-0.175630065525769	0.106435819654951	-1.65010300193238	0.099375651547144	.  
df.mm.trans1:probe18	-0.0254997882930859	0.106435819654951	-0.239579009921212	0.81072758254393	   
df.mm.trans1:probe19	-0.139370946528487	0.106435819654951	-1.30943649403280	0.190821124758649	   
df.mm.trans1:probe20	-0.0623727713330147	0.106435819654951	-0.586012975098211	0.558057874886129	   
df.mm.trans1:probe21	-0.0546332788963561	0.106435819654951	-0.513297864135108	0.607906809827611	   
df.mm.trans1:probe22	-0.123497095851963	0.106435819654951	-1.16029637627935	0.246328300505945	   
df.mm.trans2:probe2	0.0943601742123856	0.106435819654951	0.886545286335811	0.375631750444107	   
df.mm.trans2:probe3	0.00592855736402043	0.106435819654951	0.0557007723832062	0.955596262040233	   
df.mm.trans2:probe4	-0.00319445335728835	0.106435819654951	-0.0300129539815099	0.976065375695647	   
df.mm.trans2:probe5	-0.071254863607005	0.106435819654951	-0.669463192353874	0.503423351899357	   
df.mm.trans2:probe6	0.175817522070365	0.106435819654951	1.65186421864686	0.0990158884212082	.  
df.mm.trans3:probe2	-0.0518756986605916	0.106435819654951	-0.487389478737183	0.626136707599992	   
df.mm.trans3:probe3	0.0172328997766548	0.106435819654951	0.161908837011085	0.87142490550889