fitVsDatCorrelation=0.868559468778256
cont.fitVsDatCorrelation=0.237101662190593

fstatistic=7958.1448077495,65,991
cont.fstatistic=2059.70190797203,65,991

residuals=-0.855042459602089,-0.104381891415778,-0.00731042054773874,0.0855703170073775,1.20411434730890
cont.residuals=-0.645525546885299,-0.2586631480847,-0.0904823232291647,0.206204241624036,1.76542174048967

predictedValues:
Include	Exclude	Both
Lung	91.0676019842033	54.6067434549356	74.8456344096829
cerebhem	93.0564080278125	56.1941435112355	76.772419334382
cortex	101.528423993334	54.9456287968704	87.977708226637
heart	91.6483056325711	54.2885204498742	72.1960616398023
kidney	87.0807162911871	56.5999503873479	73.3366921727813
liver	94.3633987598678	53.6989742578159	68.2360968376283
stomach	105.649416625867	56.4186034144014	79.0332948685798
testicle	89.0351315894453	57.1514233987002	77.4564563089594


diffExp=36.4608585292677,36.862264516577,46.5827951964632,37.3597851826969,30.4807659038392,40.6644245020519,49.2308132114652,31.8837081907451
diffExpScore=0.996779651677627
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.010801016189	60.4826334350684	66.8997609810946
cerebhem	69.451321694319	65.988046646484	64.0049182530132
cortex	71.1655699491817	65.086742200215	67.9067497379173
heart	70.1473878998252	73.8014841584478	69.4861053634676
kidney	78.6110963363622	68.0042515763299	72.1667048436281
liver	70.971182939261	65.6199130076903	70.0600625527413
stomach	72.063009968396	76.0974678769298	66.3042036040355
testicle	65.5137823432101	59.7118131285779	68.7464453251556
cont.diffExp=5.52816758112061,3.46327504783498,6.07882774896676,-3.65409625862254,10.6068447600323,5.35126993157077,-4.03445790853374,5.80196921463223
cont.diffExpScore=1.47698240577885

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=-0.0917137343930158
cont.tran.correlation=0.518159268039359

tran.covariance=-0.000160923928856721
cont.tran.covariance=0.00269403825786469

tran.mean=74.8333369109668
cont.tran.mean=68.6704065110305

weightedLogRatios:
wLogRatio
Lung	2.17664725908672
cerebhem	2.15931237135196
cortex	2.6483686672452
heart	2.22870709251783
kidney	1.83162879570843
liver	2.40458796268793
stomach	2.72665451063466
testicle	1.89183816225446

cont.weightedLogRatios:
wLogRatio
Lung	0.362625534349867
cerebhem	0.215610277487544
cortex	0.376829038090257
heart	-0.217135869653262
kidney	0.622099823258187
liver	0.33106689916166
stomach	-0.234498972163036
testicle	0.383524301672368

varWeightedLogRatios=0.103799072964326
cont.varWeightedLogRatios=0.0917959489787603

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.7712266115426	0.0867534477599309	54.9975445903408	2.26140190523226e-303	***
df.mm.trans1	0.171669066894586	0.0742340231423395	2.31253890908514	0.0209521349703291	*  
df.mm.trans2	-0.742942991031226	0.064910521820352	-11.4456481044385	1.41055160263026e-28	***
df.mm.exp2	0.0248412316516211	0.0819648401518942	0.303071800122909	0.761898752293429	   
df.mm.exp3	-0.0467322458593713	0.0819648401518942	-0.570149905407841	0.568705262402393	   
df.mm.exp4	0.0365540784395394	0.0819648401518942	0.445972667936627	0.65571440770088	   
df.mm.exp5	0.0114508050227803	0.0819648401518942	0.139703865725353	0.88892235870735	   
df.mm.exp6	0.111241762332129	0.0819648401518942	1.35718879126684	0.175030244758835	   
df.mm.exp7	0.126724249814822	0.0819648401518942	1.54608060700151	0.122404364625634	   
df.mm.exp8	-0.0113123059301315	0.0819648401518942	-0.138014127876879	0.89025728905019	   
df.mm.trans1:exp2	-0.00323749410083905	0.0748749042970406	-0.043238707698315	0.965519959871982	   
df.mm.trans2:exp2	0.00381393020765016	0.0513902846181272	0.0742150045673196	0.940854284123386	   
df.mm.trans1:exp3	0.155468934814549	0.0748749042970406	2.07638241776950	0.0381158800710052	*  
df.mm.trans2:exp3	0.0529189931820506	0.0513902846181272	1.02974703439148	0.303380024681432	   
df.mm.trans1:exp4	-0.0301977013605292	0.0748749042970406	-0.40330871396817	0.686808130730288	   
df.mm.trans2:exp4	-0.0423986652379599	0.0513902846181272	-0.825032699332519	0.409551572025606	   
df.mm.trans1:exp5	-0.0562174526882159	0.0748749042970406	-0.750818357846474	0.452940218719203	   
df.mm.trans2:exp5	0.0243999220121381	0.0513902846181272	0.47479639767419	0.63503671363257	   
df.mm.trans1:exp6	-0.075690599033003	0.0748749042970406	-1.01089410054838	0.312313868735865	   
df.mm.trans2:exp6	-0.128005243969165	0.0513902846181272	-2.49084520392037	0.0129067200657815	*  
df.mm.trans1:exp7	0.0217998628040505	0.0748749042970406	0.291150459672937	0.77099717438	   
df.mm.trans2:exp7	-0.0940826795507639	0.0513902846181272	-1.83074836517986	0.0674382681260858	.  
df.mm.trans1:exp8	-0.0112587749319621	0.0748749042970406	-0.150367803974704	0.880505040050996	   
df.mm.trans2:exp8	0.0568592205995395	0.0513902846181272	1.10641964764451	0.268813430913	   
df.mm.trans1:probe2	-0.207589221014162	0.0552987816411455	-3.75395650416471	0.000184188411682832	***
df.mm.trans1:probe3	-0.855197167880746	0.0552987816411455	-15.4650273025985	1.71757998770859e-48	***
df.mm.trans1:probe4	-0.96874809324005	0.0552987816411455	-17.5184346651002	4.56793250431697e-60	***
df.mm.trans1:probe5	-0.0233532821526746	0.0552987816411455	-0.422310970686891	0.672889656916066	   
df.mm.trans1:probe6	-1.03679795710442	0.0552987816411455	-18.7490198940112	2.30349875717368e-67	***
df.mm.trans1:probe7	-0.749104826669811	0.0552987816411455	-13.5464978510925	1.75992649433240e-38	***
df.mm.trans1:probe8	-0.42689224352826	0.0552987816411455	-7.71974048720502	2.84008490855738e-14	***
df.mm.trans1:probe9	-0.176896269210910	0.0552987816411455	-3.19891802244137	0.00142321170636274	** 
df.mm.trans1:probe10	-0.244315936857863	0.0552987816411455	-4.41810704697476	1.10531728233954e-05	***
df.mm.trans1:probe11	-1.15980971154758	0.0552987816411454	-20.9735129260897	4.08140064959953e-81	***
df.mm.trans1:probe12	-0.909773163259214	0.0552987816411455	-16.4519567386326	5.92730427572059e-54	***
df.mm.trans1:probe13	-1.11881183315248	0.0552987816411455	-20.2321244690864	1.84412655596534e-76	***
df.mm.trans1:probe14	-0.9720493060735	0.0552987816411455	-17.5781324149507	2.04878886058257e-60	***
df.mm.trans1:probe15	-1.10774469536934	0.0552987816411455	-20.0319909859482	3.24138165133374e-75	***
df.mm.trans1:probe16	-1.06051077193758	0.0552987816411454	-19.1778325030673	5.80800827406716e-70	***
df.mm.trans1:probe17	-0.879188877781363	0.0552987816411455	-15.8988833332125	7.21290501334082e-51	***
df.mm.trans1:probe18	-0.93252276553521	0.0552987816411455	-16.8633510153388	2.75091728592748e-56	***
df.mm.trans1:probe19	-0.9976132370805	0.0552987816411455	-18.0404198333769	3.93058014075088e-63	***
df.mm.trans1:probe20	-0.841350411481674	0.0552987816411455	-15.2146283609196	3.87827352780216e-47	***
df.mm.trans1:probe21	-0.799361098094809	0.0552987816411455	-14.4553112088827	4.06793294106297e-43	***
df.mm.trans1:probe22	-0.9215247443271	0.0552987816411455	-16.6644674073874	3.72835905701625e-55	***
df.mm.trans2:probe2	-0.170466160804266	0.0552987816411454	-3.08263863588324	0.00210847339386300	** 
df.mm.trans2:probe3	-0.141149186165756	0.0552987816411455	-2.55248274874709	0.0108448117728754	*  
df.mm.trans2:probe4	-0.109642815511594	0.0552987816411454	-1.98273474130239	0.0476728446133384	*  
df.mm.trans2:probe5	-0.101033109012617	0.0552987816411454	-1.82704041597622	0.0679943390815446	.  
df.mm.trans2:probe6	-0.0964859840013838	0.0552987816411454	-1.74481211227252	0.0813275615396671	.  
df.mm.trans3:probe2	0.400960839374793	0.0552987816411455	7.25080783834947	8.33612320355487e-13	***
df.mm.trans3:probe3	0.489754341451167	0.0552987816411455	8.85651233022395	3.74693913617671e-18	***
df.mm.trans3:probe4	-0.0586172186741305	0.0552987816411455	-1.06000922505887	0.289398681069481	   
df.mm.trans3:probe5	-0.246622765810994	0.0552987816411455	-4.45982277532662	9.14047122722268e-06	***
df.mm.trans3:probe6	-0.148417380497095	0.0552987816411455	-2.68391773005473	0.00739782971075234	** 
df.mm.trans3:probe7	0.050680941314658	0.0552987816411455	0.916492910161126	0.35963139414533	   
df.mm.trans3:probe8	0.093051792509827	0.0552987816411455	1.68270963207969	0.092746312822872	.  
df.mm.trans3:probe9	0.355241482891382	0.0552987816411455	6.42403814964093	2.05694335349549e-10	***
df.mm.trans3:probe10	0.362677200693225	0.0552987816411455	6.55850255520588	8.73707488041845e-11	***
df.mm.trans3:probe11	-0.0420926128646639	0.0552987816411455	-0.761185176516521	0.446727557054957	   
df.mm.trans3:probe12	-0.0840061965244426	0.0552987816411455	-1.51913286389545	0.129048011003044	   
df.mm.trans3:probe13	-0.124135664376309	0.0552987816411455	-2.24481734845211	0.0249997751368282	*  
df.mm.trans3:probe14	0.354572966083787	0.0552987816411455	6.41194897176477	2.21985018146334e-10	***
df.mm.trans3:probe15	0.0667023950923461	0.0552987816411455	1.20621816815428	0.228021264842973	   
df.mm.trans3:probe16	0.37785143515726	0.0552987816411455	6.83290705407001	1.45097137956151e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21116280380109	0.170060972449441	24.7626644911317	9.16694660605031e-106	***
df.mm.trans1	-0.000800538355087693	0.145519405745755	-0.00550124810491844	0.995611768393028	   
df.mm.trans2	-0.108337170717901	0.127242740755581	-0.851421229019304	0.394741090952679	   
df.mm.exp2	0.18216052677321	0.160673964929520	1.13372771284455	0.257182964638838	   
df.mm.exp3	0.133615313086756	0.160673964929520	0.831592804381012	0.405839151573225	   
df.mm.exp4	0.221871342907985	0.160673964929520	1.38087924204341	0.167627416302305	   
df.mm.exp5	0.216125043633174	0.160673964929520	1.34511551842253	0.178895659500445	   
df.mm.exp6	0.107820952558677	0.160673964929520	0.67105428440739	0.502342321182051	   
df.mm.exp7	0.326323308265926	0.160673964929520	2.03096567890802	0.0425249994112694	*  
df.mm.exp8	-0.0476138764322552	0.160673964929520	-0.296338466864504	0.767033702237957	   
df.mm.trans1:exp2	-0.131352807275595	0.146775711693325	-0.894921957864838	0.371046111408042	   
df.mm.trans2:exp2	-0.0950431857361896	0.100739302036694	-0.943456861568985	0.345677216269115	   
df.mm.trans1:exp3	-0.0584245601524464	0.146775711693325	-0.398053325570101	0.690676612757472	   
df.mm.trans2:exp3	-0.0602507106206106	0.100739302036694	-0.59808544830561	0.549919574430958	   
df.mm.trans1:exp4	-0.161091153375679	0.146775711693325	-1.09753276967421	0.272675165674225	   
df.mm.trans2:exp4	-0.0228487741255422	0.100739302036694	-0.226810923478699	0.820617545923416	   
df.mm.trans1:exp5	-0.0414305597967309	0.146775711693325	-0.282271224024424	0.777794488254854	   
df.mm.trans2:exp5	-0.0989110904000803	0.100739302036694	-0.981852051784634	0.326412378533379	   
df.mm.trans1:exp6	-0.0353654123988370	0.146775711693325	-0.240948669168983	0.809644740299114	   
df.mm.trans2:exp6	-0.0262980238945488	0.100739302036694	-0.261050288843274	0.794107964766263	   
df.mm.trans1:exp7	-0.238600813907689	0.146775711693325	-1.62561510453599	0.104349504222108	   
df.mm.trans2:exp7	-0.0966645907494375	0.100739302036694	-0.959551920602223	0.337514822523974	   
df.mm.trans1:exp8	0.0400560339950267	0.146775711693325	0.272906419821832	0.784982008874946	   
df.mm.trans2:exp8	0.0347874789368743	0.100739302036694	0.345321818134127	0.729925728651626	   
df.mm.trans1:probe2	-0.0620945072860768	0.108401047151304	-0.572822024490283	0.566895144391222	   
df.mm.trans1:probe3	-0.0407881380311203	0.108401047151304	-0.376270701280116	0.70679619785898	   
df.mm.trans1:probe4	0.0051447307113623	0.108401047151304	0.0474601569501572	0.962156047117515	   
df.mm.trans1:probe5	0.0686856343054094	0.108401047151304	0.633625191918479	0.526471663171858	   
df.mm.trans1:probe6	-0.0307128945129854	0.108401047151304	-0.283326548221594	0.776985703119999	   
df.mm.trans1:probe7	-0.116851290742882	0.108401047151304	-1.07795352363878	0.281316812417328	   
df.mm.trans1:probe8	0.0125825038394618	0.108401047151304	0.116073637387463	0.907617698302906	   
df.mm.trans1:probe9	-0.0075001436839161	0.108401047151304	-0.0691888490103562	0.944853257000616	   
df.mm.trans1:probe10	0.0197596609141349	0.108401047151304	0.182282933914418	0.8553979711591	   
df.mm.trans1:probe11	-0.137928267997536	0.108401047151304	-1.27238870492660	0.203533504584983	   
df.mm.trans1:probe12	0.0663213981124194	0.108401047151304	0.61181510562208	0.540800426130256	   
df.mm.trans1:probe13	-0.080802885630613	0.108401047151304	-0.74540687340252	0.456202542423666	   
df.mm.trans1:probe14	0.0899774333370244	0.108401047151304	0.830042104772617	0.406714884346405	   
df.mm.trans1:probe15	-0.0511570580319367	0.108401047151304	-0.471924020812571	0.6370849121861	   
df.mm.trans1:probe16	0.0372743748982991	0.108401047151304	0.343856225357972	0.731027356488216	   
df.mm.trans1:probe17	-0.156589019600998	0.108401047151304	-1.44453419700304	0.148904857378308	   
df.mm.trans1:probe18	-0.0981309424795004	0.108401047151304	-0.905258252187646	0.365548615159671	   
df.mm.trans1:probe19	-0.0730696359649574	0.108401047151304	-0.674067620979418	0.500425518662632	   
df.mm.trans1:probe20	-0.102570583077403	0.108401047151304	-0.946213950629433	0.344270123765095	   
df.mm.trans1:probe21	0.0202459899497961	0.108401047151304	0.186769320793895	0.851879729249211	   
df.mm.trans1:probe22	-0.142463990875760	0.108401047151304	-1.31423076270574	0.189072664331477	   
df.mm.trans2:probe2	-0.0409971751697727	0.108401047151304	-0.378199069539887	0.70536371290986	   
df.mm.trans2:probe3	0.0607608992263223	0.108401047151304	0.560519485955825	0.57525184653925	   
df.mm.trans2:probe4	0.0761894353013423	0.108401047151304	0.702847779643663	0.482315685865702	   
df.mm.trans2:probe5	-0.0458636549650635	0.108401047151304	-0.423092360916475	0.672319671601272	   
df.mm.trans2:probe6	-0.0604154224864169	0.108401047151304	-0.557332461946517	0.577426156934499	   
df.mm.trans3:probe2	0.211083247217654	0.108401047151304	1.94724361770258	0.0517877023492037	.  
df.mm.trans3:probe3	0.0291133950222869	0.108401047151304	0.268571160402638	0.788315605304209	   
df.mm.trans3:probe4	0.0134828872369168	0.108401047151304	0.124379677053282	0.901039887420584	   
df.mm.trans3:probe5	0.122092409309865	0.108401047151304	1.12630285885938	0.260310014121434	   
df.mm.trans3:probe6	0.155664008949363	0.108401047151304	1.43600097084016	0.151317639009551	   
df.mm.trans3:probe7	0.061497651785918	0.108401047151304	0.56731603062912	0.57062797255465	   
df.mm.trans3:probe8	-0.0229098979689812	0.108401047151304	-0.211343880626946	0.832662397151422	   
df.mm.trans3:probe9	0.123165759781132	0.108401047151304	1.13620452032368	0.256145664240551	   
df.mm.trans3:probe10	0.134719722214804	0.108401047151304	1.24278986001643	0.214239250687310	   
df.mm.trans3:probe11	0.162221718930874	0.108401047151304	1.49649586598963	0.134842882600605	   
df.mm.trans3:probe12	0.0963347708550201	0.108401047151304	0.888688563317637	0.374386132649338	   
df.mm.trans3:probe13	0.0200871184309987	0.108401047151304	0.185303730534646	0.853028729716231	   
df.mm.trans3:probe14	0.250123526012991	0.108401047151304	2.30739031204996	0.0212384075578419	*  
df.mm.trans3:probe15	0.144536105225868	0.108401047151304	1.33334602408523	0.182724679211561	   
df.mm.trans3:probe16	0.117512370928822	0.108401047151304	1.08405199042774	0.278605411271666