fitVsDatCorrelation=0.910025280656074
cont.fitVsDatCorrelation=0.26799512362693

fstatistic=7342.04152272882,54,738
cont.fstatistic=1348.25632143756,54,738

residuals=-0.823789930648692,-0.0964951714252823,-0.0100515112397599,0.094034310299806,0.836023577485958
cont.residuals=-0.789649304833052,-0.309646977114144,-0.0947182828483955,0.193570244805508,1.74308869462819

predictedValues:
Include	Exclude	Both
Lung	71.6050344593822	66.8330589851983	70.5279551730379
cerebhem	70.6246797563747	91.5353177714515	74.2460373751603
cortex	66.699081537814	59.4155462325929	66.6620102350471
heart	69.3993941234574	59.85833702333	63.0119009351205
kidney	60.0283358170865	63.3045783536295	59.0370661551492
liver	62.378766462305	62.390477051083	60.589494762894
stomach	104.218653821084	69.0736303736863	87.1785228736035
testicle	84.094209436561	67.2885517131323	84.7347958906783


diffExp=4.77197547418382,-20.9106380150768,7.28353530522105,9.54105710012745,-3.27624253654301,-0.0117105887780582,35.145023447398,16.8056577234287
diffExpScore=1.94137925911663
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,1,1
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	68.2037834676581	68.6595258423766	60.9916060049129
cerebhem	70.6556911500208	68.6310495221536	81.6314500492834
cortex	68.6582542794945	65.6428795650599	75.3043431997865
heart	73.1754874988207	70.7874936975876	93.7706267976736
kidney	77.3089584591813	81.1024095266852	71.241461371166
liver	76.943901141671	68.5701062071735	68.4117635359007
stomach	82.3497252563163	76.2149468342062	80.178347517611
testicle	69.6323919612789	59.1308550053305	68.3879144895772
cont.diffExp=-0.455742374718483,2.02464162786721,3.01537471443457,2.38799380123301,-3.79345106750395,8.37379493449747,6.13477842211006,10.5015369559484
cont.diffExpScore=1.25689148768237

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=0.171701261265276
cont.tran.correlation=0.710186374360438

tran.covariance=0.0057055373303875
cont.tran.covariance=0.00452148638622584

tran.mean=70.5467283073855
cont.tran.mean=71.6042162134384

weightedLogRatios:
wLogRatio
Lung	0.292193475407905
cerebhem	-1.13776107704778
cortex	0.479004481865465
heart	0.616130199164142
kidney	-0.219014389235080
liver	-0.000775889882295959
stomach	1.82659457310402
testicle	0.963237048930738

cont.weightedLogRatios:
wLogRatio
Lung	-0.0281433630313834
cerebhem	0.123367524961358
cortex	0.188931606170219
heart	0.141878784160371
kidney	-0.209419737418646
liver	0.493771163741326
stomach	0.338490036547279
testicle	0.680308087835504

varWeightedLogRatios=0.75820090088785
cont.varWeightedLogRatios=0.0804482594768606

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06152647591043	0.095156568099371	42.6825657653923	1.68291329446416e-201	***
df.mm.trans1	0.0577196767520238	0.0838244637556052	0.688577942118528	0.491305300873861	   
df.mm.trans2	0.187310316653838	0.0756373734008024	2.47642545255082	0.0134936637802672	*  
df.mm.exp2	0.249366052055931	0.100726467447002	2.47567554364135	0.0135217965437683	*  
df.mm.exp3	-0.132242212758720	0.100726467447002	-1.31288444944522	0.189629982556257	   
df.mm.exp4	-0.0288188338646885	0.100726467447003	-0.286109843769232	0.774874284512	   
df.mm.exp5	-0.0527451975930338	0.100726467447003	-0.523647844800929	0.600680702478453	   
df.mm.exp6	-0.0548380153025724	0.100726467447002	-0.544425082031449	0.58631344320654	   
df.mm.exp7	0.196352092159670	0.100726467447002	1.94935945969694	0.0516309619458471	.  
df.mm.exp8	-0.0159525852272079	0.100726467447002	-0.158375307221028	0.874204396167159	   
df.mm.trans1:exp2	-0.26315178219585	0.0950094565946015	-2.76974305114384	0.00575099884930798	** 
df.mm.trans2:exp2	0.0651609792146682	0.0777350099410375	0.83824494605575	0.402164550749527	   
df.mm.trans1:exp3	0.0612680103644922	0.0950094565946015	0.644862233302927	0.519216869748926	   
df.mm.trans2:exp3	0.0146002728612898	0.0777350099410375	0.187821071514164	0.851068545447465	   
df.mm.trans1:exp4	-0.00246841404343863	0.0950094565946015	-0.0259807195190177	0.979279740258376	   
df.mm.trans2:exp4	-0.0813982983075244	0.0777350099410375	-1.04712533476571	0.295384594406183	   
df.mm.trans1:exp5	-0.123603473221353	0.0950094565946015	-1.30095969024178	0.193678291246513	   
df.mm.trans2:exp5	-0.00149500112138664	0.0777350099410375	-0.019232018141126	0.984661213765462	   
df.mm.trans1:exp6	-0.0831024334568161	0.0950094565946014	-0.874675389539467	0.382035109565379	   
df.mm.trans2:exp6	-0.0139471854396564	0.0777350099410375	-0.179419613507935	0.8576574798697	   
df.mm.trans1:exp7	0.178973655360509	0.0950094565946014	1.88374570043250	0.0599922997492148	.  
df.mm.trans2:exp7	-0.163376902761664	0.0777350099410375	-2.10171585345633	0.0359165698125895	*  
df.mm.trans1:exp8	0.176724911351573	0.0950094565946014	1.86007706691393	0.0632722573262538	.  
df.mm.trans2:exp8	0.0227448460229636	0.0777350099410375	0.292594624226789	0.769914265635809	   
df.mm.trans1:probe2	-0.239865970909535	0.0554735712331929	-4.32396843356661	1.74300453853178e-05	***
df.mm.trans1:probe3	-0.23405633562682	0.0554735712331929	-4.21924044952727	2.75646790573311e-05	***
df.mm.trans1:probe4	0.179663883175959	0.0554735712331929	3.23872934772327	0.00125438163823325	** 
df.mm.trans1:probe5	0.186234221785215	0.0554735712331929	3.35717022800545	0.000827804864576462	***
df.mm.trans1:probe6	0.175901091073875	0.0554735712331929	3.17089899142141	0.00158252474561964	** 
df.mm.trans1:probe7	-0.202535022764419	0.055473571233193	-3.65101828243629	0.00027964361080529	***
df.mm.trans1:probe8	0.122357500722587	0.0554735712331929	2.20568998899017	0.0277132093830474	*  
df.mm.trans1:probe9	-0.22076781390988	0.0554735712331929	-3.97969355500556	7.58181594246956e-05	***
df.mm.trans1:probe10	0.259164230273314	0.0554735712331929	4.67185047784055	3.54728824458960e-06	***
df.mm.trans1:probe11	-0.195888413177757	0.0554735712331929	-3.53120249558669	0.000439369691107957	***
df.mm.trans1:probe12	-0.0340754815630175	0.0554735712331929	-0.614265150151866	0.53922943988808	   
df.mm.trans1:probe13	-0.191364041907518	0.0554735712331929	-3.44964345459363	0.000593220387325016	***
df.mm.trans1:probe14	-0.131737009522830	0.055473571233193	-2.37477066275488	0.0178145506257495	*  
df.mm.trans1:probe15	-0.267485460862968	0.0554735712331929	-4.82185399130959	1.72788488982532e-06	***
df.mm.trans1:probe16	-0.2214423328836	0.0554735712331929	-3.99185284020616	7.211105965009e-05	***
df.mm.trans1:probe17	0.974432242919721	0.055473571233193	17.5657023922892	5.65398078353245e-58	***
df.mm.trans1:probe18	0.66834670332806	0.0554735712331929	12.0480201377075	1.20820579164810e-30	***
df.mm.trans1:probe19	1.10809146843929	0.0554735712331929	19.9751240781170	2.69449295013484e-71	***
df.mm.trans1:probe20	0.79964707324016	0.0554735712331929	14.4149196719047	1.09484299779682e-41	***
df.mm.trans1:probe21	0.661463508579355	0.0554735712331929	11.9239395242606	4.23146504475091e-30	***
df.mm.trans1:probe22	0.90573523738175	0.0554735712331929	16.3273288026533	2.16009814145515e-51	***
df.mm.trans2:probe2	-0.0448698617207871	0.055473571233193	-0.808851147011406	0.418861447180893	   
df.mm.trans2:probe3	0.0670632307870753	0.055473571233193	1.20892218215343	0.227079914474235	   
df.mm.trans2:probe4	-0.322822609568840	0.0554735712331929	-5.81939475668148	8.80359214126584e-09	***
df.mm.trans2:probe5	-0.199125660925470	0.055473571233193	-3.58955907288555	0.000353145382169068	***
df.mm.trans2:probe6	-0.0132734325638349	0.055473571233193	-0.239274888361482	0.810958870118114	   
df.mm.trans3:probe2	0.0815539313450296	0.055473571233193	1.47014027638861	0.141949953726231	   
df.mm.trans3:probe3	-0.304545970680565	0.0554735712331929	-5.48992905829612	5.53367493375533e-08	***
df.mm.trans3:probe4	0.0156990353534757	0.055473571233193	0.283000264891583	0.777255993773302	   
df.mm.trans3:probe5	-0.0454435501034369	0.055473571233193	-0.819192799259434	0.412940881040059	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41441454766114	0.221143786348408	19.9617390140291	3.20645577371281e-71	***
df.mm.trans1	-0.109818920188033	0.194807985132263	-0.563729048957992	0.573109796917499	   
df.mm.trans2	-0.225159377825900	0.175781193851320	-1.28090709189486	0.200628675323662	   
df.mm.exp2	-0.256574568962512	0.234088227871651	-1.09605925635522	0.273410381349776	   
df.mm.exp3	-0.24909098859112	0.234088227871651	-1.06409019734087	0.287635875418764	   
df.mm.exp4	-0.329232496042272	0.234088227871651	-1.40644618926667	0.160012600225085	   
df.mm.exp5	0.136523867381851	0.234088227871651	0.58321543386926	0.55992645632657	   
df.mm.exp6	0.00446480527190085	0.234088227871651	0.0190731730189733	0.984787887642948	   
df.mm.exp7	0.0193555458435002	0.234088227871651	0.0826848322082762	0.934124566457728	   
df.mm.exp8	-0.243137063243089	0.234088227871651	-1.03865566181483	0.299305080934324	   
df.mm.trans1:exp2	0.291893189613115	0.220801899331779	1.32196865378641	0.186588188386873	   
df.mm.trans2:exp2	0.256159736108097	0.180656099453274	1.41794125348285	0.156630005414580	   
df.mm.trans1:exp3	0.255732311338725	0.220801899331779	1.15819796891538	0.247157921316139	   
df.mm.trans2:exp3	0.204160240783086	0.180656099453274	1.13010433304463	0.258799417834645	   
df.mm.trans1:exp4	0.399592951317246	0.220801899331779	1.80973511788870	0.0707435636444349	.  
df.mm.trans2:exp4	0.35975495632223	0.180656099453274	1.99138007192101	0.0468073298941316	*  
df.mm.trans1:exp5	-0.0112140657814139	0.220801899331779	-0.0507879045214352	0.959508271831091	   
df.mm.trans2:exp5	0.0300289217343665	0.180656099453274	0.166221466229173	0.868028194204555	   
df.mm.trans1:exp6	0.116111755077726	0.220801899331779	0.52586393246217	0.599140724907299	   
df.mm.trans2:exp6	-0.00576801721706454	0.180656099453274	-0.0319281620411406	0.974537972690348	   
df.mm.trans1:exp7	0.169119535106083	0.220801899331779	0.765933334893837	0.443960868879292	   
df.mm.trans2:exp7	0.0850421681664318	0.180656099453274	0.470740641604673	0.637965057251696	   
df.mm.trans1:exp8	0.263866884684131	0.220801899331779	1.19503901679596	0.232455675115049	   
df.mm.trans2:exp8	0.0937300505994621	0.180656099453274	0.518831364582324	0.604033879310704	   
df.mm.trans1:probe2	0.201061798862137	0.128920534123987	1.55957931937181	0.119288027609921	   
df.mm.trans1:probe3	-0.153657158266129	0.128920534123987	-1.19187497407009	0.233693397763007	   
df.mm.trans1:probe4	-0.131402018908655	0.128920534123987	-1.01924817331491	0.30841916939254	   
df.mm.trans1:probe5	-0.0354942230871775	0.128920534123987	-0.275318616451291	0.783148527355225	   
df.mm.trans1:probe6	-0.136835029228653	0.128920534123987	-1.06139049266624	0.288859687771719	   
df.mm.trans1:probe7	-0.064679390913516	0.128920534123987	-0.501699681536472	0.61602841931518	   
df.mm.trans1:probe8	-0.202250476788504	0.128920534123987	-1.56879955674085	0.117123288187091	   
df.mm.trans1:probe9	-0.151894612689137	0.128920534123987	-1.17820340817899	0.239095288331149	   
df.mm.trans1:probe10	-0.172640419339678	0.128920534123987	-1.33912274342305	0.18094300849956	   
df.mm.trans1:probe11	-0.141491141608936	0.128920534123987	-1.09750663515604	0.272777939485945	   
df.mm.trans1:probe12	-0.170969946255273	0.128920534123987	-1.32616535772995	0.185195206995461	   
df.mm.trans1:probe13	0.0735894197581296	0.128920534123987	0.570812246925353	0.568300681912091	   
df.mm.trans1:probe14	-0.102745003308519	0.128920534123987	-0.796963835176993	0.425728350982028	   
df.mm.trans1:probe15	-0.167579843076828	0.128920534123987	-1.29986928936907	0.194051618186793	   
df.mm.trans1:probe16	-0.192664388936905	0.128920534123987	-1.49444299347701	0.135487306509779	   
df.mm.trans1:probe17	-0.0713110979652124	0.128920534123987	-0.553139951286816	0.580335097131816	   
df.mm.trans1:probe18	-0.169791430907500	0.128920534123987	-1.31702394859927	0.188239384287908	   
df.mm.trans1:probe19	-0.118193282597036	0.128920534123987	-0.916791753929331	0.35955126286107	   
df.mm.trans1:probe20	-0.240776896179466	0.128920534123987	-1.86763805948790	0.0622086959621331	.  
df.mm.trans1:probe21	-0.0281313552232879	0.128920534123987	-0.218206939758977	0.827328200544474	   
df.mm.trans1:probe22	-0.0387243819604485	0.128920534123987	-0.300374042223608	0.76397645369135	   
df.mm.trans2:probe2	0.0492352075351963	0.128920534123987	0.381903533597257	0.702642858063714	   
df.mm.trans2:probe3	0.0635006984827237	0.128920534123987	0.492556898823062	0.622472210589797	   
df.mm.trans2:probe4	0.00929365292937865	0.128920534123987	0.0720882285551242	0.942551225655115	   
df.mm.trans2:probe5	0.184960394255112	0.128920534123987	1.43468529285824	0.151800410661344	   
df.mm.trans2:probe6	0.131961882008287	0.128920534123987	1.02359087250892	0.306363957132950	   
df.mm.trans3:probe2	-0.0164344737356192	0.128920534123987	-0.127477549230549	0.89859716733303	   
df.mm.trans3:probe3	-0.0277201662280088	0.128920534123987	-0.215017463403848	0.829813106404674	   
df.mm.trans3:probe4	0.107765177002138	0.128920534123987	0.835903897966298	0.403479520256028	   
df.mm.trans3:probe5	0.092889455864633	0.128920534123987	0.720517150319113	0.471434795033577