fitVsDatCorrelation=0.897031788346143
cont.fitVsDatCorrelation=0.208785618962508

fstatistic=8469.64114660374,56,784
cont.fstatistic=1718.67301363583,56,784

residuals=-0.82207744814532,-0.0942709954126731,-0.0110675412764512,0.0786022340908064,1.00461591966724
cont.residuals=-0.634593843828651,-0.2245203997156,-0.0758448802715032,0.0971990830671295,2.00188424287368

predictedValues:
Include	Exclude	Both
Lung	82.2340187250057	47.9933528650607	68.9020283741763
cerebhem	86.1159264059767	45.507078218873	78.0216831633304
cortex	76.0142190182586	47.4959899749963	71.194482772172
heart	80.1677635573648	52.0905667861119	76.4314203879396
kidney	84.5973684574397	50.4297702440022	76.7150127602464
liver	85.716798901977	52.4283931321148	74.2402963534245
stomach	87.0577540110462	48.5625696256598	90.9823606636322
testicle	79.4841696027956	45.2134229885942	76.0515312919706


diffExp=34.2406658599449,40.6088481871037,28.5182290432623,28.0771967712530,34.1675982134376,33.2884057698621,38.4951843853864,34.2707466142014
diffExpScore=0.996332521137485
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	65.2577454145463	61.619107812997	62.5227433543845
cerebhem	65.9126960227981	60.7502956042096	60.782726968835
cortex	64.6028806761212	61.8667029295976	66.2407760072799
heart	70.0675624019397	71.0163983944285	68.6836872897363
kidney	64.6577287832242	67.2033584285333	71.1158806120713
liver	64.4364015003134	53.4508963863079	70.6037127982308
stomach	62.0632907600924	61.2460463017557	67.2572791200382
testicle	64.1924896261835	60.0658989723695	64.1292915446648
cont.diffExp=3.63863760154928,5.16240041858851,2.73617774652357,-0.948835992488867,-2.54562964530915,10.9855051140055,0.81724445833676,4.12659065381402
cont.diffExpScore=1.23982498823500

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

tran.correlation=0.188613337157336
cont.tran.correlation=0.620411930171444

tran.covariance=0.000511434745279216
cont.tran.covariance=0.00167636781100509

tran.mean=65.6943226572048
cont.tran.mean=63.6505937509636

weightedLogRatios:
wLogRatio
Lung	2.22958717524832
cerebhem	2.63854858829418
cortex	1.92614480934193
heart	1.79721984214696
kidney	2.16201187533004
liver	2.06730241361666
stomach	2.43685759364773
testicle	2.30939121342499

cont.weightedLogRatios:
wLogRatio
Lung	0.23807722401609
cerebhem	0.338270696037605
cortex	0.179452818933979
heart	-0.0572493534718169
kidney	-0.161738077652244
liver	0.761160446620122
stomach	0.0546324307891904
testicle	0.274324736555751

varWeightedLogRatios=0.0739161269996474
cont.varWeightedLogRatios=0.0799269711258402

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.14172429743527	0.0842130889197138	61.0561180381025	3.63802107431455e-300	***
df.mm.trans1	-0.0350784244460197	0.073399256228353	-0.477912532749474	0.632845681566305	   
df.mm.trans2	-1.28879202328391	0.0655009154261558	-19.6759391055666	2.34837487439277e-70	***
df.mm.exp2	-0.131370500128423	0.0856955375892962	-1.53299114310979	0.125681447636192	   
df.mm.exp3	-0.121795597142517	0.0856955375892962	-1.42125950275536	0.155638961828427	   
df.mm.exp4	-0.0472344891510222	0.0856955375892962	-0.551189600763086	0.581660750802046	   
df.mm.exp5	-0.029558554078584	0.0856955375892962	-0.344925242434981	0.730243085075496	   
df.mm.exp6	0.055244072286139	0.0856955375892962	0.644655180890531	0.519339242204424	   
df.mm.exp7	-0.209186821300428	0.0856955375892962	-2.44104684076988	0.0148650064932929	*  
df.mm.exp8	-0.192405236881122	0.0856955375892962	-2.24521885612343	0.0250320377924298	*  
df.mm.trans1:exp2	0.177495800584571	0.0800210604240832	2.21811357714964	0.0268329294272668	*  
df.mm.trans2:exp2	0.0781758598957894	0.0624159966771197	1.25249718113445	0.210762232553402	   
df.mm.trans1:exp3	0.043146942723948	0.0800210604240832	0.539194838149913	0.589905504025893	   
df.mm.trans2:exp3	0.111378363699910	0.0624159966771197	1.78445221785170	0.074736820696569	.  
df.mm.trans1:exp4	0.0217869030125564	0.0800210604240832	0.272264612554414	0.785490250970931	   
df.mm.trans2:exp4	0.129155842409044	0.0624159966771197	2.06927469374834	0.0388474541410549	*  
df.mm.trans1:exp5	0.0578926449521291	0.0800210604240832	0.723467605219409	0.469608441300261	   
df.mm.trans2:exp5	0.0790777148774827	0.0624159966771197	1.26694628120023	0.205550728445152	   
df.mm.trans1:exp6	-0.0137643155828978	0.0800210604240832	-0.17200866259397	0.86347510839948	   
df.mm.trans2:exp6	0.0331417066170078	0.0624159966771197	0.530980972529383	0.595582348077616	   
df.mm.trans1:exp7	0.26618948936784	0.0800210604240832	3.32649290020815	0.0009204111674741	***
df.mm.trans2:exp7	0.220977363819812	0.0624159966771197	3.54039630197586	0.000423092926838602	***
df.mm.trans1:exp8	0.158394044669539	0.0800210604240832	1.9794044696497	0.0481198946583231	*  
df.mm.trans2:exp8	0.132736729066542	0.0624159966771197	2.12664599034113	0.0337612405646954	*  
df.mm.trans1:probe2	-0.874894276911564	0.0508524964101095	-17.2045492094590	1.60174740714148e-56	***
df.mm.trans1:probe3	-0.880425833130565	0.0508524964101095	-17.3133257024435	4.09057042973993e-57	***
df.mm.trans1:probe4	-1.16789663752413	0.0508524964101095	-22.966357995592	1.15973469782618e-89	***
df.mm.trans1:probe5	-1.17564067171307	0.0508524964101095	-23.1186422438712	1.42579924947191e-90	***
df.mm.trans1:probe6	-0.753110219396262	0.0508524964101095	-14.809700065119	6.15553100189347e-44	***
df.mm.trans1:probe7	-0.944936080291115	0.0508524964101095	-18.5819015190621	3.83089242998147e-64	***
df.mm.trans1:probe8	-1.07931917954390	0.0508524964101095	-21.2245072658680	2.41422336552193e-79	***
df.mm.trans1:probe9	-1.18272358657248	0.0508524964101095	-23.2579257669907	2.09168270389068e-91	***
df.mm.trans1:probe10	-1.05270524741497	0.0508524964101095	-20.7011517964670	2.77237509015686e-76	***
df.mm.trans1:probe11	-1.1813192447205	0.0508524964101095	-23.2303097805372	3.06083655053791e-91	***
df.mm.trans1:probe12	-0.946011942286475	0.0508524964101095	-18.6030580417761	2.91341949370578e-64	***
df.mm.trans1:probe13	-1.00835518979795	0.0508524964101095	-19.8290204214534	3.10246417559791e-71	***
df.mm.trans1:probe14	-1.13437233117264	0.0508524964101095	-22.3071119660336	9.79864898640388e-86	***
df.mm.trans1:probe15	-0.870844412069107	0.0508524964101095	-17.1249097595135	4.34068084179e-56	***
df.mm.trans1:probe16	-1.14462675759878	0.0508524964101095	-22.5087623696529	6.20233736271223e-87	***
df.mm.trans1:probe17	-0.865279880394998	0.0508524964101095	-17.0154848135043	1.70207068388998e-55	***
df.mm.trans1:probe18	-0.749605820821077	0.0508524964101095	-14.7407870554818	1.36999581057777e-43	***
df.mm.trans1:probe19	-0.892020990884926	0.0508524964101095	-17.5413412095063	2.31123831503730e-58	***
df.mm.trans1:probe20	-0.649677811589726	0.0508524964101095	-12.7757309365951	4.33664257729798e-34	***
df.mm.trans1:probe21	-0.893336274943295	0.0508524964101095	-17.5672058995652	1.66663437738362e-58	***
df.mm.trans1:probe22	-0.768124910936853	0.0508524964101095	-15.1049597396786	1.95219933153246e-45	***
df.mm.trans2:probe2	0.092587169150676	0.0508524964101095	1.82070056903381	0.0690334635246456	.  
df.mm.trans2:probe3	-0.0383411016880968	0.0508524964101095	-0.753966951374183	0.451095447258301	   
df.mm.trans2:probe4	0.043380072638722	0.0508524964101095	0.853056893979703	0.393888291491113	   
df.mm.trans2:probe5	0.144777772996263	0.0508524964101095	2.84701407436668	0.00452846870957812	** 
df.mm.trans2:probe6	-0.00671072563904681	0.0508524964101095	-0.131964527069171	0.895046208090896	   
df.mm.trans3:probe2	0.231438052329858	0.0508524964101095	4.55116402670544	6.181390533061e-06	***
df.mm.trans3:probe3	0.124876158560072	0.0508524964101095	2.45565443932162	0.0142788351546614	*  
df.mm.trans3:probe4	1.69115808623072	0.0508524964101095	33.2561468092354	5.91647309614754e-152	***
df.mm.trans3:probe5	0.419038134607797	0.0508524964101095	8.24026673594124	7.19275257529608e-16	***
df.mm.trans3:probe6	0.296108381196648	0.0508524964101095	5.82288780492951	8.43463107847196e-09	***
df.mm.trans3:probe7	0.141203557992381	0.0508524964101095	2.77672814434947	0.00562191516744409	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13622621422463	0.186342722677537	22.1968754925960	4.41992306092788e-85	***
df.mm.trans1	0.0896674361090911	0.162414387401667	0.552090473902011	0.581043709282609	   
df.mm.trans2	-0.0643092295885333	0.144937314079732	-0.443703748733442	0.6573792106334	   
df.mm.exp2	0.0240110014377576	0.189623014670899	0.126624932524304	0.89926972928699	   
df.mm.exp3	-0.0638414777792521	0.189623014670899	-0.336675787430404	0.736451416320591	   
df.mm.exp4	0.119072672034236	0.189623014670899	0.627944198866857	0.530223357627493	   
df.mm.exp5	-0.0512661716493226	0.189623014670899	-0.270358383122944	0.786955707211737	   
df.mm.exp6	-0.276426983147742	0.189623014670899	-1.45777127121144	0.145304038454904	   
df.mm.exp7	-0.12925764490591	0.189623014670899	-0.681655890400452	0.495657874026488	   
df.mm.exp8	-0.0673591031869198	0.189623014670899	-0.355226412278199	0.722515522850621	   
df.mm.trans1:exp2	-0.0140246654296988	0.177066801161793	-0.079205505140876	0.93688937761245	   
df.mm.trans2:exp2	-0.0382110675254079	0.138111152418776	-0.276668950017487	0.782107238332416	   
df.mm.trans1:exp3	0.0537557372539196	0.177066801161793	0.30359015298865	0.761520702069922	   
df.mm.trans2:exp3	0.0678515814001975	0.138111152418776	0.491282421527119	0.623364209139558	   
df.mm.trans1:exp4	-0.0479574611275907	0.177066801161793	-0.270843889497784	0.786582391414208	   
df.mm.trans2:exp4	0.0228661274315869	0.138111152418776	0.165563222311352	0.868543374551891	   
df.mm.trans1:exp5	0.042029074970313	0.177066801161793	0.237362818408344	0.812437343948889	   
df.mm.trans2:exp5	0.138017380335330	0.138111152418776	0.999321039019631	0.317947524947763	   
df.mm.trans1:exp6	0.263760954428514	0.177066801161793	1.48961269248607	0.136728263554966	   
df.mm.trans2:exp6	0.134218376826464	0.138111152418776	0.971814183546102	0.331442723640865	   
df.mm.trans1:exp7	0.0790675851517796	0.177066801161793	0.446540992625334	0.655329821366969	   
df.mm.trans2:exp7	0.123184927862491	0.138111152418776	0.891926000942872	0.372706256977202	   
df.mm.trans1:exp8	0.0509005801945517	0.177066801161793	0.287465407747678	0.773831950356749	   
df.mm.trans2:exp8	0.0418293646978178	0.138111152418776	0.302867393148557	0.762071262630502	   
df.mm.trans1:probe2	-0.117005147186298	0.112523988343945	-1.03982403137592	0.298742226898762	   
df.mm.trans1:probe3	-0.129787980698714	0.112523988343945	-1.15342499505083	0.249087501474040	   
df.mm.trans1:probe4	-0.0454317454943419	0.112523988343945	-0.403751645875488	0.686505456961445	   
df.mm.trans1:probe5	-0.119829331373486	0.112523988343945	-1.06492253906973	0.287238843257091	   
df.mm.trans1:probe6	-0.00390673324153324	0.112523988343945	-0.0347191145553052	0.972312556307348	   
df.mm.trans1:probe7	-0.0798250748147673	0.112523988343945	-0.709404954353122	0.478284127647240	   
df.mm.trans1:probe8	-0.113091843867652	0.112523988343945	-1.00504652858528	0.315184536307473	   
df.mm.trans1:probe9	-0.0510145880174324	0.112523988343945	-0.453366333421274	0.650410427566094	   
df.mm.trans1:probe10	0.083000134811936	0.112523988343945	0.737621693236068	0.460965074621861	   
df.mm.trans1:probe11	0.0519144604312322	0.112523988343945	0.461363494089355	0.644665809352457	   
df.mm.trans1:probe12	-0.0723765604327514	0.112523988343945	-0.643210052344774	0.520275891660381	   
df.mm.trans1:probe13	-0.148445446206153	0.112523988343945	-1.31923377753382	0.187476122811382	   
df.mm.trans1:probe14	-0.0845875118765112	0.112523988343945	-0.751728703553931	0.452439828873036	   
df.mm.trans1:probe15	-0.128008414212320	0.112523988343945	-1.13760999851023	0.255630913949597	   
df.mm.trans1:probe16	-0.0103099468534382	0.112523988343945	-0.0916244349775844	0.927019842053725	   
df.mm.trans1:probe17	-0.049764292113015	0.112523988343945	-0.44225496132348	0.658426691836366	   
df.mm.trans1:probe18	-0.132595019878130	0.112523988343945	-1.17837113516485	0.239006209011941	   
df.mm.trans1:probe19	-0.0377910864478001	0.112523988343945	-0.335849155402194	0.737074477762973	   
df.mm.trans1:probe20	-0.104543317934613	0.112523988343945	-0.929075830613665	0.353135746542993	   
df.mm.trans1:probe21	-0.075338842036089	0.112523988343945	-0.66953583093594	0.503350858858313	   
df.mm.trans1:probe22	-0.0101800301868062	0.112523988343945	-0.0904698663514266	0.927936946831794	   
df.mm.trans2:probe2	0.160749757210416	0.112523988343945	1.42858211458932	0.153522642144546	   
df.mm.trans2:probe3	0.103420876293360	0.112523988343945	0.919100698574959	0.358325586532379	   
df.mm.trans2:probe4	0.0792936464951362	0.112523988343945	0.704682154108905	0.481217336063029	   
df.mm.trans2:probe5	0.0535956983950084	0.112523988343945	0.476304645647521	0.633990064659973	   
df.mm.trans2:probe6	0.240655406058808	0.112523988343945	2.13870312988918	0.0327681201808964	*  
df.mm.trans3:probe2	-0.135066740977629	0.112523988343945	-1.20033730554217	0.230370930558357	   
df.mm.trans3:probe3	0.0144353282754546	0.112523988343945	0.128286674582944	0.897955009734826	   
df.mm.trans3:probe4	0.0517520685108971	0.112523988343945	0.459920318081065	0.645700931578185	   
df.mm.trans3:probe5	-0.0138946566633429	0.112523988343945	-0.123481729254673	0.901757290332286	   
df.mm.trans3:probe6	-0.0819529530608816	0.112523988343945	-0.728315395383795	0.466638014636838	   
df.mm.trans3:probe7	-0.0176513017659813	0.112523988343945	-0.156867011432511	0.875390045767099