fitVsDatCorrelation=0.900139664762003
cont.fitVsDatCorrelation=0.283618161897575

fstatistic=12481.7689989555,55,761
cont.fstatistic=2564.59410234363,55,761

residuals=-0.535428164849571,-0.0900343996996875,-0.00079370961947376,0.0793383686655035,0.645199524729559
cont.residuals=-0.609244027573543,-0.181456668332389,-0.061956169449274,0.110768465302152,1.67896175311109

predictedValues:
Include	Exclude	Both
Lung	47.5097727846432	59.1539326080455	76.6651679365187
cerebhem	48.633709559561	58.9452043374095	68.3311549087867
cortex	48.6080823800516	57.6555349085298	70.8677622600881
heart	52.2971714818406	59.934257843834	74.8678568494994
kidney	60.4253202875245	62.8274977274008	102.573966190003
liver	56.5047450593447	62.0815079751256	101.221843968331
stomach	48.580780710258	58.4905984551169	72.3537028299155
testicle	55.5097948589354	60.4025592523943	86.616360531174


diffExp=-11.6441598234023,-10.3114947778484,-9.04745252847821,-7.63708636199342,-2.40217743987632,-5.5767629157809,-9.90981774485891,-4.89276439345898
diffExpScore=0.983979934158985
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=-1,-1,0,0,0,0,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	54.9775742814931	56.4679413942329	56.4358410522524
cerebhem	55.2559551081594	53.5233750007539	67.4313960428485
cortex	56.0390092210003	57.1056725147382	57.7054333268659
heart	61.7576560801643	68.8932988818775	57.7832176409104
kidney	58.8961167393507	60.651647044755	54.3320294002391
liver	58.609978436725	58.2481371380334	50.5376700808836
stomach	55.9681639199928	75.3963620273275	58.0320013257498
testicle	60.3421300699619	61.0422333473669	54.6755469113653
cont.diffExp=-1.49036711273976,1.73258010740553,-1.06666329373792,-7.13564280171319,-1.75553030540436,0.361841298691623,-19.4281981073347,-0.70010327740502
cont.diffExpScore=1.10461367619462

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

tran.correlation=0.935148553647192
cont.tran.correlation=0.32146054239635

tran.covariance=0.00244091857765410
cont.tran.covariance=0.00176055660759718

tran.mean=56.097529389376
cont.tran.mean=59.5734532003708

weightedLogRatios:
wLogRatio
Lung	-0.870372542559974
cerebhem	-0.765408602246613
cortex	-0.677517691868257
heart	-0.54864452299212
kidney	-0.16065170997697
liver	-0.384154569784830
stomach	-0.738105562830111
testicle	-0.342854517791372

cont.weightedLogRatios:
wLogRatio
Lung	-0.107533783106496
cerebhem	0.127304656061466
cortex	-0.0760905650522097
heart	-0.456814339456322
kidney	-0.120143517784605
liver	0.0251913325958791
stomach	-1.24368358374843
testicle	-0.0473621907801869

varWeightedLogRatios=0.0602471654093982
cont.varWeightedLogRatios=0.193637488065411

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.90193955982487	0.0679141016193474	57.4540407188908	5.60654206346662e-279	***
df.mm.trans1	-0.132355355679645	0.0594687158806086	-2.22562995887395	0.0263312238468132	*  
df.mm.trans2	0.298533796810610	0.0533297394941956	5.59788590085093	3.02998110324821e-08	***
df.mm.exp2	0.134928329681671	0.0703291777475133	1.91852562482776	0.0554182340642589	.  
df.mm.exp3	0.0758294002164299	0.0703291777475134	1.07820683598296	0.281283103189027	   
df.mm.exp4	0.132834860204679	0.0703291777475134	1.88875889721852	0.0593041785262496	.  
df.mm.exp5	0.0095858721356012	0.0703291777475134	0.136300074060515	0.89162011317483	   
df.mm.exp6	-0.0561728335176417	0.0703291777475134	-0.798713070687475	0.424705999929088	   
df.mm.exp7	0.0688963591760389	0.0703291777475134	0.979626968246118	0.32758160903999	   
df.mm.exp8	0.0544712116281889	0.0703291777475134	0.774517964986655	0.438865114937042	   
df.mm.trans1:exp2	-0.111546859589472	0.065974616736472	-1.69075418861520	0.0912932429159768	.  
df.mm.trans2:exp2	-0.138463130923572	0.0526295374849442	-2.63090153439373	0.0086881596383005	** 
df.mm.trans1:exp3	-0.0529750117337254	0.065974616736472	-0.802960507452858	0.422248266351202	   
df.mm.trans2:exp3	-0.101486222846391	0.0526295374849442	-1.92831302907466	0.0541879155412806	.  
df.mm.trans1:exp4	-0.0368280058745399	0.065974616736472	-0.55821477556505	0.576861920734285	   
df.mm.trans2:exp4	-0.119729674863751	0.0526295374849442	-2.27495206276518	0.0231861841396167	*  
df.mm.trans1:exp5	0.230886922306501	0.065974616736472	3.49963264248661	0.000493002114333483	***
df.mm.trans2:exp5	0.0506638938488283	0.0526295374849442	0.962651322241276	0.336028312815252	   
df.mm.trans1:exp6	0.229562018781833	0.065974616736472	3.47955062321608	0.000530838873094939	***
df.mm.trans2:exp6	0.104477926376556	0.0526295374849442	1.98515760102289	0.0474855290341908	*  
df.mm.trans1:exp7	-0.0466037978336787	0.065974616736472	-0.706389822919808	0.480161957667767	   
df.mm.trans2:exp7	-0.080173401579894	0.0526295374849442	-1.52335371753608	0.128085660923409	   
df.mm.trans1:exp8	0.101152844784488	0.065974616736472	1.53320852455318	0.125640087730419	   
df.mm.trans2:exp8	-0.0335828094141557	0.0526295374849442	-0.638098129282682	0.523601764878797	   
df.mm.trans1:probe2	-0.0181902801214241	0.0404010367450099	-0.450242904315342	0.652663536656314	   
df.mm.trans1:probe3	0.18342283351323	0.0404010367450099	4.54005263951266	6.5356762750307e-06	***
df.mm.trans1:probe4	0.227514374634259	0.0404010367450099	5.63139941358957	2.51550132619658e-08	***
df.mm.trans1:probe5	-0.0601564229273299	0.0404010367450099	-1.48898215922046	0.136906350668359	   
df.mm.trans1:probe6	0.179821229276820	0.0404010367450099	4.45090630747318	9.8294533915965e-06	***
df.mm.trans1:probe7	-0.0716992589846834	0.0404010367450099	-1.77468859121640	0.0763490228404097	.  
df.mm.trans1:probe8	0.203261950255278	0.0404010367450099	5.03110728415612	6.08894793598266e-07	***
df.mm.trans1:probe9	0.151008796633822	0.0404010367450099	3.73774558279061	0.000199586677745142	***
df.mm.trans1:probe10	0.273224524994398	0.0404010367450099	6.76280974468173	2.69756683146362e-11	***
df.mm.trans1:probe11	0.386849205687001	0.0404010367450099	9.5752296686986	1.38846137398511e-20	***
df.mm.trans1:probe12	0.176255995333992	0.0404010367450099	4.36266020712357	1.46208282619630e-05	***
df.mm.trans1:probe13	0.0926231487280255	0.0404010367450099	2.29259336369544	0.0221434471981484	*  
df.mm.trans1:probe14	0.360418148053216	0.0404010367450099	8.92101235738048	3.39902664830657e-18	***
df.mm.trans1:probe15	0.109392087877412	0.0404010367450099	2.70765546359211	0.00692808419903146	** 
df.mm.trans1:probe16	0.107680808509625	0.0404010367450099	2.66529815037296	0.0078550717964322	** 
df.mm.trans1:probe17	0.0300073753970523	0.0404010367450099	0.742737756618551	0.457869670272959	   
df.mm.trans1:probe18	0.026384073745651	0.0404010367450099	0.653054373633365	0.513918466949279	   
df.mm.trans1:probe19	0.0452247871762695	0.0404010367450099	1.11939669919128	0.263324123647814	   
df.mm.trans1:probe20	0.0842980129527986	0.0404010367450099	2.08653093446199	0.0372629023180250	*  
df.mm.trans1:probe21	-0.0129164193789094	0.0404010367450099	-0.319705146687970	0.749279605991324	   
df.mm.trans1:probe22	0.0834094285420463	0.0404010367450099	2.06453683524219	0.0393051504349097	*  
df.mm.trans2:probe2	-0.198053564148586	0.0404010367450099	-4.90219014424297	1.15886883207608e-06	***
df.mm.trans2:probe3	-0.323420016157287	0.0404010367450099	-8.00524051396365	4.4545054217598e-15	***
df.mm.trans2:probe4	-0.414219062216264	0.0404010367450099	-10.2526839801314	3.41906388198529e-23	***
df.mm.trans2:probe5	-0.185638412397876	0.0404010367450099	-4.59489229371831	5.06693686139978e-06	***
df.mm.trans2:probe6	-0.322632342016252	0.0404010367450099	-7.98574412960088	5.15464226707384e-15	***
df.mm.trans3:probe2	0.210470358810743	0.0404010367450099	5.20952865984903	2.43976384923200e-07	***
df.mm.trans3:probe3	-0.128261422834069	0.0404010367450099	-3.17470622458497	0.00156028345161812	** 
df.mm.trans3:probe4	0.389957768082063	0.0404010367450099	9.65217230793535	7.12930587368884e-21	***
df.mm.trans3:probe5	1.1417274743694	0.0404010367450099	28.25985584418	1.06947803501597e-120	***
df.mm.trans3:probe6	0.0140826442962617	0.0404010367450099	0.34857135932288	0.727507543202495	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12285395412245	0.149506783633110	27.5763671315406	1.34365753491089e-116	***
df.mm.trans1	-0.0527702845577389	0.130915026866353	-0.403088062698948	0.686996660428917	   
df.mm.trans2	-0.0786949515940677	0.117400622752219	-0.670311193835473	0.502862791360496	   
df.mm.exp2	-0.226510125970810	0.154823356414636	-1.46302296511508	0.143873974068977	   
df.mm.exp3	0.00810616892251204	0.154823356414636	0.0523575325469802	0.958257552108028	   
df.mm.exp4	0.291584492734613	0.154823356414636	1.88333659395494	0.0600358878920454	.  
df.mm.exp5	0.178314043662407	0.154823356414636	1.15172573306614	0.249795495446832	   
df.mm.exp6	0.205404080059642	0.154823356414636	1.32669956792271	0.185006046725576	   
df.mm.exp7	0.279053453549510	0.154823356414636	1.80239894039094	0.0718782257349822	.  
df.mm.exp8	0.202685945747996	0.154823356414636	1.30914320966649	0.19088099401022	   
df.mm.trans1:exp2	0.231560883576091	0.145237182183194	1.59436364776076	0.111269816831192	   
df.mm.trans2:exp2	0.172955532191367	0.115859191034792	1.49280804264747	0.135901892939622	   
df.mm.trans1:exp3	0.0110165107890454	0.145237182183194	0.0758518626115301	0.93955689026766	   
df.mm.trans2:exp3	0.00312421815738143	0.115859191034792	0.0269656479514279	0.978494202588244	   
df.mm.trans1:exp4	-0.175291901555456	0.145237182183194	-1.20693543430465	0.227832034793369	   
df.mm.trans2:exp4	-0.092698646216766	0.115859191034792	-0.80009747512331	0.423904010512708	   
df.mm.trans1:exp5	-0.109464246648013	0.145237182183194	-0.753692993781312	0.451266712757565	   
df.mm.trans2:exp5	-0.106840320254507	0.115859191034792	-0.922156622191707	0.356739145216794	   
df.mm.trans1:exp6	-0.141424479229852	0.145237182183194	-0.973748437583064	0.330490872096714	   
df.mm.trans2:exp6	-0.17436503675155	0.115859191034792	-1.50497371157364	0.132745752715252	   
df.mm.trans1:exp7	-0.261195787648557	0.145237182183194	-1.79840853232129	0.0725084781009624	.  
df.mm.trans2:exp7	0.0100325031625590	0.115859191034792	0.0865922079461634	0.931018437945317	   
df.mm.trans1:exp8	-0.109580773261123	0.145237182183194	-0.754495313210528	0.450785268412644	   
df.mm.trans2:exp8	-0.124793039344900	0.115859191034792	-1.07710953468876	0.28177265769313	   
df.mm.trans1:probe2	-0.0563939181150937	0.0889392470071163	-0.634072358523357	0.526224143801081	   
df.mm.trans1:probe3	-0.06298614412932	0.0889392470071163	-0.708192909754231	0.479042322117968	   
df.mm.trans1:probe4	-0.176510723811829	0.0889392470071163	-1.98462129770117	0.0475453398658075	*  
df.mm.trans1:probe5	-0.108352509689711	0.0889392470071163	-1.21827554578960	0.223496838202103	   
df.mm.trans1:probe6	-0.0985169247642688	0.0889392470071163	-1.10768786648695	0.268346621142308	   
df.mm.trans1:probe7	-0.0656908090602354	0.0889392470071163	-0.738603161942435	0.460375728914129	   
df.mm.trans1:probe8	-0.0629136065808564	0.0889392470071163	-0.70737732438664	0.479548586841343	   
df.mm.trans1:probe9	-0.0924542708288836	0.0889392470071163	-1.03952162785329	0.298892362870896	   
df.mm.trans1:probe10	-0.157154104773153	0.0889392470071163	-1.76698263209467	0.0776319130159432	.  
df.mm.trans1:probe11	-0.132488386357816	0.0889392470071163	-1.48965041661771	0.136730492121963	   
df.mm.trans1:probe12	-0.056624874147281	0.0889392470071163	-0.636669142732345	0.524531834315288	   
df.mm.trans1:probe13	-0.0340346033260372	0.0889392470071163	-0.382672492418493	0.702069467485988	   
df.mm.trans1:probe14	-0.0832912889890289	0.0889392470071163	-0.936496448889032	0.349314732703835	   
df.mm.trans1:probe15	-0.172537055153881	0.0889392470071163	-1.93994283693537	0.0527557725680539	.  
df.mm.trans1:probe16	0.0121250690891245	0.0889392470071163	0.136329792494806	0.891596628579197	   
df.mm.trans1:probe17	-0.071635166689915	0.0889392470071163	-0.805439320665524	0.42081779532607	   
df.mm.trans1:probe18	-0.0688935169573117	0.0889392470071163	-0.774613225045623	0.438808840579571	   
df.mm.trans1:probe19	-0.0285175399524177	0.0889392470071163	-0.320640672279763	0.748570737971876	   
df.mm.trans1:probe20	-0.137482982744833	0.0889392470071163	-1.54580781118860	0.122566721549763	   
df.mm.trans1:probe21	0.0129167668956012	0.0889392470071163	0.145231349828807	0.884566671096272	   
df.mm.trans1:probe22	-0.126996028800727	0.0889392470071163	-1.42789637954283	0.153731923789194	   
df.mm.trans2:probe2	-0.0115500089710312	0.0889392470071163	-0.129864029207567	0.896708321061532	   
df.mm.trans2:probe3	-0.0104189501941435	0.0889392470071163	-0.117146822631743	0.906774615640148	   
df.mm.trans2:probe4	-0.107748184101206	0.0889392470071163	-1.21148073237661	0.226087263593116	   
df.mm.trans2:probe5	-0.0758825574345559	0.0889392470071163	-0.853195411340555	0.393819405357637	   
df.mm.trans2:probe6	0.0797684883209825	0.0889392470071163	0.89688738105237	0.370062650308987	   
df.mm.trans3:probe2	0.0282493714047814	0.0889392470071163	0.317625484309768	0.750856170964873	   
df.mm.trans3:probe3	0.026980785779734	0.0889392470071163	0.303361976716254	0.761696933764208	   
df.mm.trans3:probe4	0.167021386256435	0.0889392470071163	1.87792669577100	0.0607733829024688	.  
df.mm.trans3:probe5	0.0224169624499115	0.0889392470071163	0.252048035083070	0.801071990057579	   
df.mm.trans3:probe6	0.0056258042183926	0.0889392470071163	0.0632544619805749	0.949580487587216