fitVsDatCorrelation=0.882615152942663
cont.fitVsDatCorrelation=0.226231498098488

fstatistic=9877.16972883548,56,784
cont.fstatistic=2289.76262525596,56,784

residuals=-0.742077669504821,-0.0916263578361608,-0.00672686174911405,0.0785631351994588,0.906622697585507
cont.residuals=-0.621330900540808,-0.233669993920559,-0.0603947355367047,0.174198142583654,1.45646137152608

predictedValues:
Include	Exclude	Both
Lung	49.9145886910717	77.0870834154174	64.1875449220396
cerebhem	57.4192567903464	97.5678894418032	61.3467095247201
cortex	50.5874793560221	77.8010473799999	68.5543012562211
heart	51.206112001127	96.0814264997988	88.4290225753909
kidney	49.0188280433167	85.1071610883158	72.1059824828517
liver	50.8620586272877	78.7367717069	61.9040353791646
stomach	52.3982898486384	78.393362326434	73.5533142917198
testicle	52.1822354086756	92.0786251673417	75.9279461643664


diffExp=-27.1724947243458,-40.1486326514569,-27.2135680239777,-44.8753144986718,-36.0883330449990,-27.8747130796123,-25.9950724777957,-39.8963897586661
diffExpScore=0.996299921253297
diffExp1.5=-1,-1,-1,-1,-1,-1,0,-1
diffExp1.5Score=0.875
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	57.7161538319327	59.6773957795431	62.9850180666034
cerebhem	59.0920609505957	61.2743874852416	67.1975544070138
cortex	60.896580933047	61.4636924293662	63.3284923411039
heart	61.3907800326972	58.0056683113437	64.0601265590112
kidney	58.8715803280451	59.5997271842482	61.6471361875726
liver	59.0527988817396	69.537260880996	55.7848337512408
stomach	57.667883217491	67.5382361393496	58.2474963009548
testicle	60.0131983038593	58.6728425398213	63.3175342185489
cont.diffExp=-1.96124194761042,-2.18232653464584,-0.567111496319178,3.38511172135346,-0.728146856203139,-10.4844619992563,-9.8703529218586,1.34035576403809
cont.diffExpScore=1.38294672079327

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=0.592833532078867
cont.tran.correlation=-0.436508662109692

tran.covariance=0.00278430116013100
cont.tran.covariance=-0.000670801807006468

tran.mean=68.527638487031
cont.tran.mean=60.6543904518323

weightedLogRatios:
wLogRatio
Lung	-1.79395815140771
cerebhem	-2.28792401137751
cortex	-1.78160547807697
heart	-2.67501473166484
kidney	-2.29954547443039
liver	-1.81247838631448
stomach	-1.67604319114167
testicle	-2.40715648509448

cont.weightedLogRatios:
wLogRatio
Lung	-0.136079049020040
cerebhem	-0.148587454269748
cortex	-0.0381334010599395
heart	0.231918028864938
kidney	-0.0501720175887876
liver	-0.679896193748566
stomach	-0.653097268153576
testicle	0.0922310345207169

varWeightedLogRatios=0.136716655042182
cont.varWeightedLogRatios=0.108139642232032

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26655012058193	0.0761242087352735	56.0472179805391	1.89247910853758e-276	***
df.mm.trans1	-0.423307779769324	0.0660106187188605	-6.4127224980604	2.46868172268516e-10	***
df.mm.trans2	0.0701842865707859	0.0588129953853214	1.19334657435766	0.233094657891508	   
df.mm.exp2	0.42094676636419	0.0765026246980681	5.50238332378183	5.07644267577585e-08	***
df.mm.exp3	-0.0432070218994645	0.0765026246980681	-0.564778294470145	0.572386128239589	   
df.mm.exp4	-0.07458516811048	0.0765026246980681	-0.974936067943345	0.32989268615983	   
df.mm.exp5	-0.0354612316956428	0.0765026246980681	-0.463529608763061	0.643113452212921	   
df.mm.exp6	0.0762022840463288	0.0765026246980681	0.996074113104947	0.319521460939735	   
df.mm.exp7	-0.0708371611728512	0.0765026246980681	-0.925944194103448	0.354759914231844	   
df.mm.exp8	0.0541602715084123	0.0765026246980682	0.707953115624019	0.479184783354713	   
df.mm.trans1:exp2	-0.280880353716072	0.0709311412690962	-3.95990179617268	8.18019101321086e-05	***
df.mm.trans2:exp2	-0.185334064953508	0.0545679836703176	-3.39638836709155	0.000717205386547327	***
df.mm.trans1:exp3	0.0565978054492479	0.0709311412690962	0.797926051048988	0.425155018084513	   
df.mm.trans2:exp3	0.052426179206467	0.0545679836703176	0.960749796496226	0.336974191613796	   
df.mm.trans1:exp4	0.100130749485769	0.0709311412690962	1.41166133371373	0.158446447917288	   
df.mm.trans2:exp4	0.294845456517807	0.0545679836703176	5.40326830287829	8.68655988688078e-08	***
df.mm.trans1:exp5	0.0173523832171844	0.0709311412690962	0.244637022705633	0.806801506917088	   
df.mm.trans2:exp5	0.134436676578938	0.0545679836703176	2.46365483084663	0.0139665344629842	*  
df.mm.trans1:exp6	-0.0573983670865636	0.0709311412690962	-0.809212513144368	0.418638427595164	   
df.mm.trans2:exp6	-0.0550277350301944	0.0545679836703175	-1.00842529499815	0.313561460213992	   
df.mm.trans1:exp7	0.119397796923346	0.0709311412690962	1.68329163731313	0.0927167593383732	.  
df.mm.trans2:exp7	0.087640684486761	0.0545679836703175	1.60608251564247	0.108658453265337	   
df.mm.trans1:exp8	-0.00973147095009344	0.0709311412690962	-0.137196029500985	0.890911073878291	   
df.mm.trans2:exp8	0.123546825689264	0.0545679836703176	2.26408999159095	0.0238410019068878	*  
df.mm.trans1:probe2	-0.0237070397320936	0.0464353319993199	-0.510538822731811	0.609817737939504	   
df.mm.trans1:probe3	-0.0935262202131301	0.0464353319993199	-2.01411761661357	0.0443383003335228	*  
df.mm.trans1:probe4	-0.128137997430357	0.0464353319993199	-2.75949351309114	0.00592413853992554	** 
df.mm.trans1:probe5	-0.0474236102972319	0.0464353319993199	-1.02128289505772	0.307435459035762	   
df.mm.trans1:probe6	-0.106685954683811	0.0464353319993199	-2.29751678496395	0.021851878536607	*  
df.mm.trans1:probe7	-0.0168283334347766	0.0464353319993199	-0.362403642016021	0.717148086205678	   
df.mm.trans1:probe8	-0.0521013211796135	0.0464353319993199	-1.12201892258198	0.262197910167071	   
df.mm.trans1:probe9	-0.0537816554197466	0.0464353319993199	-1.15820546777902	0.247132888339375	   
df.mm.trans1:probe10	0.120058127234506	0.0464353319993199	2.58549087656496	0.00990343337908186	** 
df.mm.trans1:probe11	0.0836289000339422	0.0464353319993199	1.80097560269768	0.0720909000016297	.  
df.mm.trans1:probe12	0.0947445710799363	0.0464353319993199	2.04035519938403	0.0416495396517413	*  
df.mm.trans1:probe13	0.0530452207847893	0.0464353319993199	1.14234610803614	0.253658928412288	   
df.mm.trans1:probe14	0.131930580971892	0.0464353319993199	2.84116803501747	0.00461144722687172	** 
df.mm.trans1:probe15	0.174876321938418	0.0464353319993199	3.76601855546072	0.000178280322129444	***
df.mm.trans1:probe16	0.405986761304158	0.0464353319993199	8.74305714687478	1.36793214531295e-17	***
df.mm.trans1:probe17	0.201754736295038	0.0464353319993199	4.34485396374452	1.57691604303102e-05	***
df.mm.trans1:probe18	0.308898285012631	0.0464353319993199	6.6522251852782	5.41557622636929e-11	***
df.mm.trans1:probe19	0.296175819710268	0.0464353319993199	6.37824275089936	3.05919882852824e-10	***
df.mm.trans1:probe20	0.298644172217094	0.0464353319993199	6.43139952615108	2.19700925935405e-10	***
df.mm.trans1:probe21	0.297506990701595	0.0464353319993199	6.40690995180033	2.55974737249585e-10	***
df.mm.trans2:probe2	0.0142510161666604	0.0464353319993199	0.306900275136809	0.75900077370185	   
df.mm.trans2:probe3	-0.105022377622631	0.0464353319993199	-2.26169111107398	0.0239896202282698	*  
df.mm.trans2:probe4	0.0687891422838072	0.0464353319993199	1.48139658578977	0.138902705692787	   
df.mm.trans2:probe5	0.239007905375352	0.0464353319993199	5.14711309437504	3.34689631731295e-07	***
df.mm.trans2:probe6	-0.102207078776954	0.0464353319993199	-2.20106273340419	0.0280223445922110	*  
df.mm.trans3:probe2	0.246457208405596	0.0464353319993199	5.30753626159506	1.44767158396236e-07	***
df.mm.trans3:probe3	0.199675080508797	0.0464353319993199	4.30006789898092	1.9227965240102e-05	***
df.mm.trans3:probe4	0.473420863699499	0.0464353319993199	10.1952725072889	5.2625442690059e-23	***
df.mm.trans3:probe5	1.02700375167152	0.0464353319993199	22.1168603184868	1.31779012332929e-84	***
df.mm.trans3:probe6	-0.0575650377845879	0.0464353319993199	-1.23968183936816	0.215464033313916	   
df.mm.trans3:probe7	-0.0280481233677076	0.0464353319993199	-0.604025472847236	0.54600144290628	   
df.mm.trans3:probe8	0.126116738160069	0.0464353319993199	2.71596503632009	0.00675380830294962	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.82854052661369	0.157734885339135	24.2719961306100	1.68505639253976e-97	***
df.mm.trans1	0.152785228259973	0.136778792814698	1.11702424853946	0.264326159198193	   
df.mm.trans2	0.259279474139788	0.121864794888254	2.12759947922236	0.0336817754609285	*  
df.mm.exp2	-0.0147718636540767	0.158518990678206	-0.093186712777295	0.925779037831619	   
df.mm.exp3	0.0776947947941539	0.158518990678206	0.490129254934978	0.624179565733198	   
df.mm.exp4	0.0163847513546456	0.158518990678206	0.103361441329807	0.9177025466441	   
df.mm.exp5	0.0399891622351319	0.158518990678206	0.252267328122912	0.800900559178206	   
df.mm.exp6	0.297199115189286	0.158518990678206	1.87484864695234	0.0611851063322363	.  
df.mm.exp7	0.201099665468833	0.158518990678206	1.26861560629708	0.204954735451242	   
df.mm.exp8	0.0167856628377603	0.158518990678206	0.105890548292950	0.915696280158474	   
df.mm.trans1:exp2	0.0383313498084227	0.146974472653803	0.260802771503809	0.794313094638177	   
df.mm.trans2:exp2	0.0411804783157510	0.113068822526059	0.364207191653209	0.715801500385583	   
df.mm.trans1:exp3	-0.0240548607259029	0.146974472653803	-0.163666929988338	0.870035533583514	   
df.mm.trans2:exp3	-0.0482014798607257	0.113068822526059	-0.426302129834391	0.67000472544581	   
df.mm.trans1:exp4	0.0453378134319755	0.146974472653803	0.308474067729899	0.757803574496089	   
df.mm.trans2:exp4	-0.0447973346191688	0.113068822526059	-0.396195287245026	0.692068770763513	   
df.mm.trans1:exp5	-0.0201677919426139	0.146974472653803	-0.137219692498023	0.890892376610545	   
df.mm.trans2:exp5	-0.0412914841614563	0.113068822526059	-0.365188946333459	0.715068863880785	   
df.mm.trans1:exp6	-0.274304271920922	0.146974472653803	-1.86633955521680	0.0623670230242635	.  
df.mm.trans2:exp6	-0.144289697041230	0.113068822526059	-1.27612275265338	0.202290044278799	   
df.mm.trans1:exp7	-0.201936360361679	0.146974472653803	-1.37395533193943	0.169848230035368	   
df.mm.trans2:exp7	-0.0773590851869558	0.113068822526059	-0.684176976983437	0.494065598440689	   
df.mm.trans1:exp8	0.0222417501680334	0.146974472653803	0.151330702307901	0.879753780951145	   
df.mm.trans2:exp8	-0.0337620099847921	0.113068822526059	-0.298596989254142	0.765326686529578	   
df.mm.trans1:probe2	0.0970437958870002	0.0962173780231814	1.00858907071464	0.313482926684475	   
df.mm.trans1:probe3	0.137231875431973	0.0962173780231814	1.42626912363908	0.154188741333447	   
df.mm.trans1:probe4	0.093027754761902	0.0962173780231814	0.96684982144794	0.333917264021654	   
df.mm.trans1:probe5	0.0598718349945739	0.0962173780231814	0.622255939879687	0.533954511951386	   
df.mm.trans1:probe6	0.0062862807778303	0.0962173780231814	0.0653341517611897	0.947924606747544	   
df.mm.trans1:probe7	0.128785396997513	0.0962173780231814	1.33848375047681	0.181126811135425	   
df.mm.trans1:probe8	0.0981514922601763	0.0962173780231814	1.02010150636748	0.307994996891012	   
df.mm.trans1:probe9	0.192195708175339	0.0962173780231814	1.99751554369975	0.0461143409706496	*  
df.mm.trans1:probe10	0.171412274974424	0.0962173780231814	1.78151055969459	0.0752161072010043	.  
df.mm.trans1:probe11	0.103098570325400	0.0962173780231814	1.07151714631593	0.284266700848420	   
df.mm.trans1:probe12	0.0193667009302773	0.0962173780231814	0.201280697189767	0.84053130407435	   
df.mm.trans1:probe13	0.0432005053099208	0.0962173780231814	0.448988594342206	0.653563963641405	   
df.mm.trans1:probe14	0.213254809513927	0.0962173780231814	2.21638558330438	0.0269514466308981	*  
df.mm.trans1:probe15	0.145590147209487	0.0962173780231814	1.51313775329037	0.130647666994187	   
df.mm.trans1:probe16	0.130557573663922	0.0962173780231814	1.35690221814678	0.175202820628449	   
df.mm.trans1:probe17	0.0935080823127904	0.0962173780231814	0.97184192953441	0.331428926817299	   
df.mm.trans1:probe18	0.184692178631241	0.0962173780231814	1.91953036370148	0.0552799041889098	.  
df.mm.trans1:probe19	0.0386609323126325	0.0962173780231814	0.401808208734581	0.687934688434935	   
df.mm.trans1:probe20	0.0765249535989472	0.0962173780231814	0.795334015239017	0.426659949528829	   
df.mm.trans1:probe21	0.119668046549210	0.0962173780231814	1.24372591529545	0.213972203143122	   
df.mm.trans2:probe2	0.0300839091595234	0.0962173780231814	0.312666066958043	0.754617525966091	   
df.mm.trans2:probe3	-0.0162905256107724	0.0962173780231814	-0.169309598177239	0.86559680086427	   
df.mm.trans2:probe4	0.0849129370105766	0.0962173780231814	0.882511441853245	0.377770797818694	   
df.mm.trans2:probe5	-0.0270118805219631	0.0962173780231814	-0.280738064962186	0.77898537758881	   
df.mm.trans2:probe6	-0.0558279910029698	0.0962173780231814	-0.580227731725545	0.561927774307512	   
df.mm.trans3:probe2	-0.163843713079563	0.0962173780231814	-1.70284948983009	0.0889925992674538	.  
df.mm.trans3:probe3	-0.0109378967282226	0.0962173780231814	-0.113679014674328	0.909521351984236	   
df.mm.trans3:probe4	-0.0758954782455893	0.0962173780231814	-0.78879179421522	0.43047216113191	   
df.mm.trans3:probe5	-0.108405519914358	0.0962173780231814	-1.12667297884837	0.260225500982306	   
df.mm.trans3:probe6	-0.262181000553919	0.0962173780231814	-2.72488199055634	0.00657578284584804	** 
df.mm.trans3:probe7	-0.0520605074996916	0.0962173780231814	-0.541071774863256	0.588611812448401	   
df.mm.trans3:probe8	-0.108342598934254	0.0962173780231814	-1.12601903273805	0.260502023235337