fitVsDatCorrelation=0.868025447700229
cont.fitVsDatCorrelation=0.286340670694658

fstatistic=8584.4459130137,53,715
cont.fstatistic=2295.48995591737,53,715

residuals=-0.505224110611311,-0.124688739248364,0.00537545599942066,0.113806396546661,0.794238797974316
cont.residuals=-0.781354989883534,-0.238796580379816,-0.0605438674729708,0.188257710680194,1.20427857175854

predictedValues:
Include	Exclude	Both
Lung	107.378129834406	71.1072932552451	93.6582591285437
cerebhem	56.5146150033842	82.8153711723572	69.4255972929245
cortex	62.801092560597	89.9524833798101	74.6166716425429
heart	146.007520923163	81.4131711469402	127.462375833866
kidney	83.3245075873987	80.7082032440636	85.6306349697743
liver	61.819915523538	72.0295271801932	71.345213215487
stomach	110.639447106772	86.6893235873223	104.382599464626
testicle	80.7513869550627	77.95196848424	79.9604019442178


diffExp=36.2708365791611,-26.3007561689731,-27.1513908192132,64.5943497762223,2.61630434333513,-10.2096116566551,23.95012351945,2.79941847082273
diffExpScore=2.86954083903792
diffExp1.5=1,0,0,1,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,-1,-1,1,0,0,0,0
diffExp1.4Score=4
diffExp1.3=1,-1,-1,1,0,0,0,0
diffExp1.3Score=4
diffExp1.2=1,-1,-1,1,0,0,1,0
diffExp1.2Score=2.5

cont.predictedValues:
Include	Exclude	Both
Lung	89.0276761159726	88.4798339977092	104.286485310010
cerebhem	85.2778410267461	79.9179356993717	109.753728085880
cortex	93.4678087183022	80.7353050428885	86.6338709113655
heart	89.161145041227	103.423593857140	75.4760560780854
kidney	87.8311809242842	103.357487368046	84.790833386765
liver	88.466931353158	88.3421694490125	104.475606340422
stomach	89.2172073596327	77.9184054626251	82.9753598355768
testicle	86.6588052413844	83.5358045870066	89.9179066861761
cont.diffExp=0.54784211826339,5.35990532737442,12.7325036754137,-14.2624488159127,-15.5263064437622,0.124761904145558,11.2988018970075,3.12300065437788
cont.diffExpScore=14.3189420559238

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=-0.0424348666171142
cont.tran.correlation=-0.0540543337437603

tran.covariance=-0.00169343086195745
cont.tran.covariance=-0.000139564090017583

tran.mean=84.4939973090308
cont.tran.mean=88.4261957027817

weightedLogRatios:
wLogRatio
Lung	1.84249740096321
cerebhem	-1.61464601780042
cortex	-1.55208128672604
heart	2.74046099138346
kidney	0.140587635812076
liver	-0.642071853898709
stomach	1.11832235132343
testicle	0.154315349024251

cont.weightedLogRatios:
wLogRatio
Lung	0.0276895482375751
cerebhem	0.286496676345999
cortex	0.653771229828013
heart	-0.677336616218029
kidney	-0.741743786758273
liver	0.00632516496320848
stomach	0.598975936080653
testicle	0.163095739073240

varWeightedLogRatios=2.43276557877172
cont.varWeightedLogRatios=0.270294596284383

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.2139625378206	0.089660520569538	46.9990862316306	7.36875141047708e-221	***
df.mm.trans1	0.218577075367268	0.0782908017482364	2.79186150207220	0.00538049070639067	** 
df.mm.trans2	0.0610705879620387	0.0705286050451143	0.865898140520067	0.386836475543524	   
df.mm.exp2	-0.190036493960851	0.0931425183197243	-2.04027652879778	0.0416897058181818	*  
df.mm.exp3	-0.0740037445043585	0.0931425183197244	-0.79452161955006	0.427155460927867	   
df.mm.exp4	0.134480150576944	0.0931425183197243	1.44381054971397	0.149230281513758	   
df.mm.exp5	-0.0373539894820611	0.0931425183197243	-0.401041223234253	0.688509591665866	   
df.mm.exp6	-0.267122369052833	0.0931425183197244	-2.86788862778972	0.00425389730855977	** 
df.mm.exp7	0.119650613496746	0.0931425183197244	1.28459714913472	0.199349123843716	   
df.mm.exp8	-0.0349573712627133	0.0931425183197244	-0.375310565929916	0.707540816526406	   
df.mm.trans1:exp2	-0.451820757222506	0.0868558461847544	-5.20196137703178	2.57819284261899e-07	***
df.mm.trans2:exp2	0.342460271322817	0.0700254218888064	4.890513503319	1.24304694595318e-06	***
df.mm.trans1:exp3	-0.462380313223113	0.0868558461847544	-5.32353702754293	1.36385324045928e-07	***
df.mm.trans2:exp3	0.309095403988725	0.0700254218888064	4.41404558018284	1.17119882934634e-05	***
df.mm.trans1:exp4	0.172821454464953	0.0868558461847544	1.98975039742676	0.046998924556133	*  
df.mm.trans2:exp4	0.000867008065964754	0.0700254218888063	0.0123813329870612	0.990124831774194	   
df.mm.trans1:exp5	-0.216259824395794	0.0868558461847544	-2.4898706753231	0.0130050563726221	*  
df.mm.trans2:exp5	0.164004301717381	0.0700254218888064	2.34206802749156	0.0194507305354957	*  
df.mm.trans1:exp6	-0.285008589246967	0.0868558461847544	-3.28139787666928	0.00108309553376037	** 
df.mm.trans2:exp6	0.280008594747787	0.0700254218888064	3.99867058555411	7.03217772474034e-05	***
df.mm.trans1:exp7	-0.0897304518920492	0.0868558461847545	-1.03309628348079	0.301908145245875	   
df.mm.trans2:exp7	0.0784902117143152	0.0700254218888064	1.12088166835967	0.262714561880137	   
df.mm.trans1:exp8	-0.250024019379963	0.0868558461847545	-2.87860898675867	0.00411357781370835	** 
df.mm.trans2:exp8	0.126860310634907	0.0700254218888064	1.81163222174297	0.0704624905181131	.  
df.mm.trans1:probe2	0.152277566763447	0.0531881260825774	2.8629992815883	0.00431932723041238	** 
df.mm.trans1:probe3	0.237970166345857	0.0531881260825774	4.4741220244608	8.9241375198912e-06	***
df.mm.trans1:probe4	0.56554221736232	0.0531881260825774	10.6328659987811	1.29117359505050e-24	***
df.mm.trans1:probe5	0.382926766393927	0.0531881260825774	7.19947842868938	1.53347081040624e-12	***
df.mm.trans1:probe6	0.230671367988903	0.0531881260825774	4.33689593859301	1.65281189145485e-05	***
df.mm.trans1:probe7	0.0810618654057747	0.0531881260825774	1.5240594353695	0.127936026686245	   
df.mm.trans1:probe8	0.319232202910403	0.0531881260825774	6.00194491557717	3.09876597799235e-09	***
df.mm.trans1:probe9	0.474635566780038	0.0531881260825774	8.9237128986861	3.74812462296113e-18	***
df.mm.trans1:probe10	0.324876091522842	0.0531881260825774	6.10805673090371	1.65534996479237e-09	***
df.mm.trans1:probe11	0.320037872789689	0.0531881260825774	6.01709246708209	2.83509377920285e-09	***
df.mm.trans1:probe12	0.457964461113757	0.0531881260825774	8.61027629367395	4.62369482905244e-17	***
df.mm.trans1:probe13	0.319427085601747	0.0531881260825774	6.00560894184954	3.0328737394078e-09	***
df.mm.trans1:probe14	0.297681250482791	0.0531881260825774	5.59676139032658	3.11320667657633e-08	***
df.mm.trans1:probe15	0.261797193303673	0.0531881260825774	4.92209845665965	1.06382111075634e-06	***
df.mm.trans1:probe16	0.243773975875599	0.0531881260825774	4.58324054314542	5.40255050737446e-06	***
df.mm.trans1:probe17	0.299841560275817	0.0531881260825774	5.63737778259563	2.48587117794074e-08	***
df.mm.trans1:probe18	0.443451079367232	0.0531881260825774	8.33740746343932	3.88925559057001e-16	***
df.mm.trans1:probe19	0.532513144537875	0.0531881260825774	10.0118801649661	3.59603275186631e-22	***
df.mm.trans1:probe20	0.393558363606356	0.0531881260825774	7.39936509504652	3.84864100315974e-13	***
df.mm.trans2:probe2	0.0579891976112915	0.0531881260825774	1.09026585221785	0.275963343949790	   
df.mm.trans2:probe3	-0.00808387918569538	0.0531881260825774	-0.151986538746350	0.879240427797223	   
df.mm.trans2:probe4	-0.129687498809329	0.0531881260825774	-2.43827914914661	0.0149998263915995	*  
df.mm.trans2:probe5	-0.0367886622679286	0.0531881260825774	-0.691670584724347	0.489368750601643	   
df.mm.trans2:probe6	-0.0135477590028585	0.0531881260825774	-0.254713974728587	0.799017263705887	   
df.mm.trans3:probe2	0.200637928255096	0.0531881260825774	3.77223156806832	0.000175182790547798	***
df.mm.trans3:probe3	-0.148152560133774	0.0531881260825774	-2.78544425317333	0.00548696537304918	** 
df.mm.trans3:probe4	0.50510775299142	0.0531881260825774	9.49662622456775	3.17590811953743e-20	***
df.mm.trans3:probe5	0.00838152988209522	0.0531881260825774	0.157582725683594	0.874830106738018	   
df.mm.trans3:probe6	-0.289720159233497	0.0531881260825774	-5.44708341075395	7.048489956173e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34233899528531	0.173016784932197	25.0977903501502	3.51967927174699e-100	***
df.mm.trans1	0.224147478133873	0.151076780752553	1.48366596784321	0.138338222591965	   
df.mm.trans2	0.14176832857648	0.136098166875960	1.04166229296599	0.297920360525151	   
df.mm.exp2	-0.195904244204019	0.179735952432581	-1.08995580212313	0.276099811241736	   
df.mm.exp3	0.142522019549159	0.179735952432581	0.792952203608895	0.428068708455671	   
df.mm.exp4	0.480882827383806	0.179735952432581	2.67549603112479	0.0076326200506112	** 
df.mm.exp5	0.34884269136586	0.179735952432581	1.94086206262328	0.0526676862681081	.  
df.mm.exp6	-0.00968739519213534	0.179735952432581	-0.0538979267142953	0.95703154413634	   
df.mm.exp7	0.103612257519516	0.179735952432581	0.576469293523122	0.564479443233976	   
df.mm.exp8	0.0637766885800346	0.179735952432581	0.354835455660754	0.722817500293393	   
df.mm.trans1:exp2	0.152871599023654	0.167604639857034	0.912096461971772	0.362025359244919	   
df.mm.trans2:exp2	0.0941298870071108	0.135127180633816	0.696602168161823	0.486278196953804	   
df.mm.trans1:exp3	-0.0938522233063415	0.167604639857034	-0.559961963979021	0.575680767605967	   
df.mm.trans2:exp3	-0.234120716516122	0.135127180633816	-1.73259528851246	0.083598832914237	.  
df.mm.trans1:exp4	-0.479384765391494	0.167604639857034	-2.86021178053547	0.00435703792857457	** 
df.mm.trans2:exp4	-0.324824372518294	0.135127180633816	-2.40384185472308	0.0164773023465017	*  
df.mm.trans1:exp5	-0.362373406945629	0.167604639857034	-2.16207264461611	0.0309436116268369	*  
df.mm.trans2:exp5	-0.193423622762900	0.135127180633816	-1.43141906650937	0.152747092108981	   
df.mm.trans1:exp6	0.00336893136543906	0.167604639857034	0.0201004660032845	0.98396883619509	   
df.mm.trans2:exp6	0.00813029738951093	0.135127180633816	0.0601677423548366	0.95203885000559	   
df.mm.trans1:exp7	-0.101485617905571	0.167604639857034	-0.605506016970279	0.545034972123352	   
df.mm.trans2:exp7	-0.230724723786143	0.135127180633816	-1.70746346296818	0.0881700625326024	.  
df.mm.trans1:exp8	-0.0907453480870971	0.167604639857034	-0.54142503551514	0.588383312505086	   
df.mm.trans2:exp8	-0.121276012797574	0.135127180633816	-0.897495324247326	0.369756767703653	   
df.mm.trans1:probe2	-0.0157934072586905	0.102636461543169	-0.153877160428488	0.87775001322201	   
df.mm.trans1:probe3	-0.0770331262099443	0.102636461543169	-0.75054347209295	0.453174369786664	   
df.mm.trans1:probe4	-0.173703756264021	0.102636461543169	-1.69241762286360	0.0910020385295876	.  
df.mm.trans1:probe5	-0.262267838820703	0.102636461543169	-2.55530865812627	0.0108152314160022	*  
df.mm.trans1:probe6	-0.194833581219116	0.102636461543169	-1.89828817449216	0.0580605604767494	.  
df.mm.trans1:probe7	-0.0650350199044801	0.102636461543169	-0.63364440790982	0.52651555081172	   
df.mm.trans1:probe8	-0.0615384673626635	0.102636461543169	-0.599577055146047	0.54897808818984	   
df.mm.trans1:probe9	-0.137311752937219	0.102636461543169	-1.33784574090628	0.181372076469428	   
df.mm.trans1:probe10	-0.0407273571572908	0.102636461543169	-0.396811781553488	0.691624671450682	   
df.mm.trans1:probe11	-0.0833838119824314	0.102636461543169	-0.812419005183264	0.416821650763524	   
df.mm.trans1:probe12	-0.0689967412439728	0.102636461543169	-0.672243958984819	0.501645569076813	   
df.mm.trans1:probe13	-0.103969543758065	0.102636461543169	-1.01298838828670	0.311408373496672	   
df.mm.trans1:probe14	-0.196818792187634	0.102636461543169	-1.9176303355397	0.0555560042850547	.  
df.mm.trans1:probe15	-0.0633446895446708	0.102636461543169	-0.617175305853936	0.537315586403989	   
df.mm.trans1:probe16	0.00497631083108098	0.102636461543169	0.0484848245570894	0.961343398288314	   
df.mm.trans1:probe17	-0.0712548225323561	0.102636461543169	-0.694244730001595	0.487754249252423	   
df.mm.trans1:probe18	-0.143189787784259	0.102636461543169	-1.39511617637007	0.163413997477605	   
df.mm.trans1:probe19	-0.0467625593041717	0.102636461543169	-0.455613517857915	0.648806252271263	   
df.mm.trans1:probe20	-0.215030050597811	0.102636461543169	-2.09506492492797	0.0365164732471047	*  
df.mm.trans2:probe2	-0.0115001330811932	0.102636461543169	-0.112047248202885	0.910817391600116	   
df.mm.trans2:probe3	0.120824870931095	0.102636461543169	1.17721196848038	0.239502632850517	   
df.mm.trans2:probe4	-0.0418271852680419	0.102636461543169	-0.407527545661241	0.683742552602355	   
df.mm.trans2:probe5	-0.00073592272129664	0.102636461543169	-0.00717018796470408	0.994281066788584	   
df.mm.trans2:probe6	-0.0827535768079278	0.102636461543169	-0.80627854432727	0.420350343984220	   
df.mm.trans3:probe2	-0.0749535483787733	0.102636461543169	-0.730281882791215	0.465457104347508	   
df.mm.trans3:probe3	-0.0368110680055243	0.102636461543169	-0.358654882018138	0.719959158042549	   
df.mm.trans3:probe4	-0.179254625071883	0.102636461543169	-1.74650043831148	0.081153448835856	.  
df.mm.trans3:probe5	-0.0106297340617279	0.102636461543169	-0.103566840690986	0.91754214513743	   
df.mm.trans3:probe6	-0.0650291657953318	0.102636461543169	-0.633587370585459	0.526552761869974