fitVsDatCorrelation=0.883106712900663
cont.fitVsDatCorrelation=0.213472002150481

fstatistic=14591.4068008795,79,1313
cont.fstatistic=3352.46592676967,79,1313

residuals=-0.480339980473584,-0.094346279439712,-0.00628256204411062,0.0765743369983979,1.04169653288525
cont.residuals=-0.533037032350118,-0.214105150358154,-0.0488176580617417,0.141280180097502,1.13652926158516

predictedValues:
Include	Exclude	Both
Lung	59.581648867321	43.7118734322734	67.6123786979126
cerebhem	57.6532908609144	42.4453292125277	59.0382590269592
cortex	53.3960130026861	46.471500917135	57.9431092519538
heart	55.2590887265665	47.0434869806889	60.3196638695793
kidney	56.3225642287212	44.5947936942061	62.2785396009
liver	57.8270939418757	45.8659578133778	61.6801793901173
stomach	56.4701924027627	48.0316058166475	62.9325265978213
testicle	54.7578559570284	43.6565750708639	59.9963260173782


diffExp=15.8697754350476,15.2079616483867,6.9245120855511,8.21560174587754,11.7277705345150,11.9611361284979,8.4385865861152,11.1012808861646
diffExpScore=0.98894375550834
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=1,1,0,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,0,0,1,1,0,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	60.6562367465492	62.2223769157068	57.6602976350636
cerebhem	61.1806647323511	60.77831809949	59.2345725559336
cortex	60.0787043742515	61.0845254810681	55.4319363667767
heart	60.7603232031432	53.5568889621979	57.5158835900677
kidney	57.8000582793182	67.3072290916854	59.8579214586619
liver	59.364281533111	58.307402418022	63.5213492798567
stomach	60.9281201976332	55.1629115955993	60.3617382907675
testicle	62.7779959424714	59.861475954812	61.0124434580111
cont.diffExp=-1.56614016915753,0.402346632861097,-1.00582110681660,7.2034342409453,-9.50717081236715,1.05687911508890,5.76520860203392,2.91651998765931
cont.diffExpScore=4.69629945920517

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=-0.393383834739824
cont.tran.correlation=-0.498332917772689

tran.covariance=-0.00058127347995752
cont.tran.covariance=-0.00083039331703342

tran.mean=50.8180544328498
cont.tran.mean=60.1142195954631

weightedLogRatios:
wLogRatio
Lung	1.21799973300318
cerebhem	1.19470669376833
cortex	0.542848802398578
heart	0.632824283250074
kidney	0.913916527884542
liver	0.913401421297103
stomach	0.639772983231269
testicle	0.881263251729377

cont.weightedLogRatios:
wLogRatio
Lung	-0.104976204849089
cerebhem	0.0271216557447485
cortex	-0.0681385170542423
heart	0.510302624320524
kidney	-0.629384007603283
liver	0.0731967538987246
stomach	0.403579428709330
testicle	0.195795555425965

varWeightedLogRatios=0.063813499661209
cont.varWeightedLogRatios=0.122677815324759

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.26313449844349	0.0630636974716088	51.743469369403	0	***
df.mm.trans1	0.777284206128709	0.0535342289995969	14.5193873275837	2.07219709877300e-44	***
df.mm.trans2	0.500970673046815	0.0463757738594973	10.8024218542333	4.0478261600534e-26	***
df.mm.exp2	0.0733022059963877	0.0575357295970514	1.27402931204238	0.202878487088798	   
df.mm.exp3	0.105937360218597	0.0575357295970514	1.84124475279142	0.0658111715998693	.  
df.mm.exp4	0.112270799228228	0.0575357295970514	1.95132311720928	0.0512310310276417	.  
df.mm.exp5	0.0459191544933598	0.0575357295970513	0.798098065583809	0.42495790379222	   
df.mm.exp6	0.110041656495438	0.0575357295970514	1.91257949218876	0.0560192816573596	.  
df.mm.exp7	0.112332726428820	0.0575357295970514	1.95239944319011	0.0511030662109726	.  
df.mm.exp8	0.033815114393346	0.0575357295970513	0.587723743666214	0.556818796737153	   
df.mm.trans1:exp2	-0.106202499208271	0.0519358307650217	-2.04487918348266	0.0410658045011056	*  
df.mm.trans2:exp2	-0.102705098074543	0.0325973119937959	-3.15072292138966	0.00166545269137598	** 
df.mm.trans1:exp3	-0.215548901659037	0.0519358307650217	-4.15029274556646	3.53427235124332e-05	***
df.mm.trans2:exp3	-0.0447178874702103	0.0325973119937959	-1.37182745248201	0.170351507058167	   
df.mm.trans1:exp4	-0.187585592269134	0.0519358307650217	-3.61187237223268	0.000315458602284598	***
df.mm.trans2:exp4	-0.0388181388469482	0.0325973119937959	-1.19083864504952	0.233932171729843	   
df.mm.trans1:exp5	-0.102171535798864	0.0519358307650217	-1.96726487848301	0.0493629445753134	*  
df.mm.trans2:exp5	-0.0259218040475522	0.0325973119937959	-0.795212932050527	0.426633374848837	   
df.mm.trans1:exp6	-0.139931859076106	0.0519358307650217	-2.69432214744409	0.00714312272175239	** 
df.mm.trans2:exp6	-0.0619382428865054	0.0325973119937959	-1.90010277222533	0.057638525957424	.  
df.mm.trans1:exp7	-0.165967417330264	0.0519358307650217	-3.19562457913048	0.00142849759449518	** 
df.mm.trans2:exp7	-0.0180932461460835	0.0325973119937959	-0.555053316958378	0.578952736027728	   
df.mm.trans1:exp8	-0.118241889938652	0.0519358307650217	-2.27669199080736	0.0229649656196616	*  
df.mm.trans2:exp8	-0.035080980269213	0.0325973119937958	-1.07619242580155	0.282038908362811	   
df.mm.trans1:probe2	0.0238804375328236	0.0410588793476284	0.581614450083694	0.560926242975409	   
df.mm.trans1:probe3	0.779565673719635	0.0410588793476284	18.9865307116489	3.21655352057343e-71	***
df.mm.trans1:probe4	-0.070963964738541	0.0410588793476284	-1.72834636176304	0.0841612456072356	.  
df.mm.trans1:probe5	-0.0290580566800558	0.0410588793476284	-0.707716750718726	0.479246773639441	   
df.mm.trans1:probe6	0.0219883613131169	0.0410588793476284	0.535532427150547	0.592372435144933	   
df.mm.trans1:probe7	0.24649225169259	0.0410588793476284	6.00338478811471	2.49693269849773e-09	***
df.mm.trans1:probe8	0.200577506706185	0.0410588793476284	4.88511888032742	1.16080632678806e-06	***
df.mm.trans1:probe9	0.350265042631284	0.0410588793476284	8.53079889652457	3.9484962549251e-17	***
df.mm.trans1:probe10	0.404423824010288	0.0410588793476284	9.84985051798906	3.92679980019066e-22	***
df.mm.trans1:probe11	-0.171444729962667	0.0410588793476284	-4.17558230245683	3.16818745662669e-05	***
df.mm.trans1:probe12	-0.120949200776833	0.0410588793476284	-2.94575016899040	0.00327851324374261	** 
df.mm.trans1:probe13	-0.146555072721471	0.0410588793476284	-3.5693880361578	0.00037071769087563	***
df.mm.trans1:probe14	-0.171643295245823	0.0410588793476284	-4.18041841309380	3.10241966131817e-05	***
df.mm.trans1:probe15	-0.0930563036356943	0.0410588793476284	-2.26641119081272	0.0235883676847468	*  
df.mm.trans1:probe16	-0.187317980424658	0.0410588793476284	-4.56217956751121	5.53568567235035e-06	***
df.mm.trans1:probe17	0.100082476993393	0.0410588793476284	2.43753552419286	0.0149197444673065	*  
df.mm.trans1:probe18	0.181738235649149	0.0410588793476284	4.4262833895306	1.03817753715271e-05	***
df.mm.trans1:probe19	0.0230637548469755	0.0410588793476284	0.561723924603598	0.574399998136681	   
df.mm.trans1:probe20	0.145594112741955	0.0410588793476284	3.54598359856026	0.000404906840854893	***
df.mm.trans1:probe21	0.561613657941113	0.0410588793476284	13.6782510108511	6.47703055628901e-40	***
df.mm.trans1:probe22	0.39200696155594	0.0410588793476284	9.54743450830648	6.23773946911268e-21	***
df.mm.trans2:probe2	0.0637287720636496	0.0410588793476284	1.55213130694788	0.120871797561049	   
df.mm.trans2:probe3	0.0510463156168375	0.0410588793476284	1.24324668446622	0.213998890884235	   
df.mm.trans2:probe4	0.253994125115251	0.0410588793476284	6.1860949239454	8.22349692121144e-10	***
df.mm.trans2:probe5	0.119743762258633	0.0410588793476284	2.91639139112427	0.00360123106479074	** 
df.mm.trans2:probe6	-0.00198748441224269	0.0410588793476284	-0.0484057150078425	0.961400266429107	   
df.mm.trans3:probe2	0.0493035568911641	0.0410588793476284	1.20080132907992	0.230044856926269	   
df.mm.trans3:probe3	-0.520667027551684	0.0410588793476284	-12.6809848642827	7.59612964645206e-35	***
df.mm.trans3:probe4	-0.544898319669025	0.0410588793476284	-13.2711444717134	8.23518708855754e-38	***
df.mm.trans3:probe5	0.103293271921770	0.0410588793476284	2.51573529436175	0.0119969712268180	*  
df.mm.trans3:probe6	0.0462842512174405	0.0410588793476284	1.12726533097923	0.259836276104115	   
df.mm.trans3:probe7	-0.313248889879143	0.0410588793476284	-7.62926058519512	4.51424572248383e-14	***
df.mm.trans3:probe8	-0.322657803892089	0.0410588793476284	-7.85841720521107	8.04784555980471e-15	***
df.mm.trans3:probe9	-0.182781777546587	0.0410588793476284	-4.45169913185039	9.24173967554107e-06	***
df.mm.trans3:probe10	-0.587530004388768	0.0410588793476284	-14.3094505676688	2.86085662212012e-43	***
df.mm.trans3:probe11	-0.560282727161358	0.0410588793476284	-13.6458358353543	9.56444686971526e-40	***
df.mm.trans3:probe12	-0.616696335515117	0.0410588793476284	-15.0198043715175	3.56031534020296e-47	***
df.mm.trans3:probe13	-0.572203372937493	0.0410588793476284	-13.9361663549773	2.84384459552965e-41	***
df.mm.trans3:probe14	-0.409918882894856	0.0410588793476284	-9.9836841484212	1.12841728509358e-22	***
df.mm.trans3:probe15	-0.40239452553536	0.0410588793476284	-9.80042641028883	6.20126558292567e-22	***
df.mm.trans3:probe16	-0.479513049440452	0.0410588793476284	-11.6786687084325	4.73384535728478e-30	***
df.mm.trans3:probe17	-0.56637159102593	0.0410588793476284	-13.7941317450654	1.59874110577763e-40	***
df.mm.trans3:probe18	0.474010417763839	0.0410588793476284	11.5446506406225	1.96148743795078e-29	***
df.mm.trans3:probe19	-0.301158173993358	0.0410588793476284	-7.33478796251542	3.873799675034e-13	***
df.mm.trans3:probe20	-0.263328383970705	0.0410588793476284	-6.41343329761178	1.98068066758336e-10	***
df.mm.trans3:probe21	-0.47632731175387	0.0410588793476284	-11.6010792140965	1.07977803732422e-29	***
df.mm.trans3:probe22	-0.509915819308701	0.0410588793476284	-12.4191363088957	1.45505770749850e-33	***
df.mm.trans3:probe23	-0.613678833006037	0.0410588793476284	-14.9463122899744	9.15547284453016e-47	***
df.mm.trans3:probe24	-0.285142069546035	0.0410588793476284	-6.94471145039921	5.94879979094035e-12	***
df.mm.trans3:probe25	-0.408262816090383	0.0410588793476284	-9.94335019798743	1.64562744421635e-22	***
df.mm.trans3:probe26	-0.454036475393248	0.0410588793476284	-11.0581799261765	3.0567674849056e-27	***
df.mm.trans3:probe27	-0.308249871263122	0.0410588793476284	-7.5075081483179	1.10793520997957e-13	***
df.mm.trans3:probe28	-0.231300134288428	0.0410588793476284	-5.6333767010567	2.15950396244696e-08	***
df.mm.trans3:probe29	-0.623017338592549	0.0410588793476284	-15.1737540939128	4.87174531608553e-48	***
df.mm.trans3:probe30	0.0527412018228138	0.0410588793476284	1.28452609181746	0.199184521052951	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.169508903543	0.131316455680344	31.7516101233541	1.19731752812481e-164	***
df.mm.trans1	-0.0532557761182587	0.111473406914839	-0.477744222520658	0.632911785958533	   
df.mm.trans2	-0.039248880357039	0.0965674785466553	-0.406439941766486	0.684485612566123	   
df.mm.exp2	-0.0418093485960645	0.119805662981764	-0.348976396903947	0.727162983364046	   
df.mm.exp3	0.0113898141842600	0.119805662981764	0.0950690802152964	0.924274463568303	   
df.mm.exp4	-0.145748015221145	0.119805662981764	-1.2165369448632	0.223999084919505	   
df.mm.exp5	-0.00708464797637515	0.119805662981764	-0.0591344999898173	0.952853978221868	   
df.mm.exp6	-0.183322577875575	0.119805662981764	-1.53016621512691	0.126216467339509	   
df.mm.exp7	-0.161738084486225	0.119805662981764	-1.35000366811412	0.177247480746724	   
df.mm.exp8	-0.0608083468990398	0.119805662981764	-0.507558202055072	0.61184845234814	   
df.mm.trans1:exp2	0.0504180905819684	0.10814508968408	0.466207857696108	0.641144058229916	   
df.mm.trans2:exp2	0.0183277712267172	0.0678768237092148	0.270015157238848	0.787191024488138	   
df.mm.trans1:exp3	-0.0209568338383066	0.10814508968408	-0.193784423310637	0.846374658394496	   
df.mm.trans2:exp3	-0.0298459380679598	0.0678768237092148	-0.439707346882054	0.660221442971991	   
df.mm.trans1:exp4	0.147462550407038	0.10814508968408	1.36356214450249	0.172939148887699	   
df.mm.trans2:exp4	-0.004222243275187	0.0678768237092148	-0.0622044910833652	0.95040945349097	   
df.mm.trans1:exp5	-0.0411480299213660	0.10814508968408	-0.3804891192154	0.703643916979294	   
df.mm.trans2:exp5	0.0856376022472102	0.0678768237092148	1.26166189823618	0.207294559927021	   
df.mm.trans1:exp6	0.161792840518531	0.10814508968408	1.49607199911868	0.134875210194972	   
df.mm.trans2:exp6	0.118336941786301	0.0678768237092148	1.74340717964733	0.0814965769640236	.  
df.mm.trans1:exp7	0.166210434566443	0.10814508968408	1.53692077053139	0.12455364361149	   
df.mm.trans2:exp7	0.0413142280298577	0.0678768237092148	0.608664721950579	0.542851978389726	   
df.mm.trans1:exp8	0.0951905140247638	0.10814508968408	0.880211152469706	0.378906008667247	   
df.mm.trans2:exp8	0.0221268132637999	0.0678768237092148	0.325984806811106	0.744487796203125	   
df.mm.trans1:probe2	-0.0727600980636588	0.085496200291218	-0.851033119785706	0.394906129551874	   
df.mm.trans1:probe3	-0.00733256807453822	0.085496200291218	-0.0857648415901754	0.931666431618809	   
df.mm.trans1:probe4	-0.034392550196298	0.085496200291218	-0.402269926372748	0.687550861360865	   
df.mm.trans1:probe5	-0.071649430667821	0.085496200291218	-0.838042280519696	0.402159483685939	   
df.mm.trans1:probe6	-0.106091226338996	0.085496200291218	-1.24088820295671	0.214868715444654	   
df.mm.trans1:probe7	-0.00644459975647719	0.085496200291218	-0.0753787856597782	0.93992482062415	   
df.mm.trans1:probe8	-0.00410266826418033	0.085496200291218	-0.0479865567148689	0.961734254258538	   
df.mm.trans1:probe9	-0.0224562867765296	0.085496200291218	-0.262658301772930	0.792855164420972	   
df.mm.trans1:probe10	-0.0373742507492391	0.085496200291218	-0.437145166942327	0.66207795877765	   
df.mm.trans1:probe11	-0.0641717663945016	0.085496200291218	-0.750580331943632	0.453039746513949	   
df.mm.trans1:probe12	0.107344215572924	0.085496200291218	1.25554369910343	0.209504843853361	   
df.mm.trans1:probe13	-0.0469915737040906	0.085496200291218	-0.549633475453032	0.582664264975748	   
df.mm.trans1:probe14	0.00166513130686187	0.085496200291218	0.0194760855007600	0.984464273518982	   
df.mm.trans1:probe15	-0.0381285476444049	0.085496200291218	-0.445967744935227	0.655694074752709	   
df.mm.trans1:probe16	0.0284539536745204	0.085496200291218	0.332809570221838	0.739331163142074	   
df.mm.trans1:probe17	-0.100847364644201	0.085496200291218	-1.17955376146184	0.238391420307904	   
df.mm.trans1:probe18	0.00337436826935698	0.085496200291218	0.0394680495491399	0.968523226510195	   
df.mm.trans1:probe19	-0.0590685890866596	0.085496200291218	-0.690891395003048	0.489755914981784	   
df.mm.trans1:probe20	0.0789045891829995	0.085496200291218	0.922901706909008	0.356227989655721	   
df.mm.trans1:probe21	-0.121867959527939	0.085496200291218	-1.42541959891588	0.154273619361936	   
df.mm.trans1:probe22	0.000342612946517271	0.085496200291218	0.00400734705577862	0.996803216953835	   
df.mm.trans2:probe2	-0.0258793682241429	0.085496200291218	-0.302696121418173	0.762169400176758	   
df.mm.trans2:probe3	0.055834918617084	0.085496200291218	0.653069006890348	0.513826125326933	   
df.mm.trans2:probe4	0.0554095791675926	0.085496200291218	0.648094055394929	0.517037432443517	   
df.mm.trans2:probe5	-0.00204559561036248	0.085496200291218	-0.023926158161354	0.980915144616174	   
df.mm.trans2:probe6	-0.066951437378482	0.085496200291218	-0.783092548562759	0.433713986273804	   
df.mm.trans3:probe2	-0.0328568380057085	0.085496200291218	-0.384307582018747	0.700812719388741	   
df.mm.trans3:probe3	0.0497771520280534	0.085496200291218	0.582214786838502	0.560521970306612	   
df.mm.trans3:probe4	-0.0425886373173886	0.085496200291218	-0.498134854792644	0.618472419232011	   
df.mm.trans3:probe5	-0.0495137404339388	0.085496200291218	-0.579133812558741	0.562598225307733	   
df.mm.trans3:probe6	-0.0872484555278892	0.085496200291218	-1.02049512411900	0.307681689960611	   
df.mm.trans3:probe7	0.00743056077742361	0.085496200291218	0.086911006010952	0.930755502941701	   
df.mm.trans3:probe8	-0.029871666141616	0.085496200291218	-0.349391739514351	0.726851260783127	   
df.mm.trans3:probe9	0.0439276899171352	0.085496200291218	0.513796984749126	0.607480387992584	   
df.mm.trans3:probe10	-0.0291449408857155	0.085496200291218	-0.34089165116627	0.733239687923622	   
df.mm.trans3:probe11	-0.0993489677843335	0.085496200291218	-1.16202787312103	0.245435322840135	   
df.mm.trans3:probe12	0.0246690367378799	0.085496200291218	0.288539568470318	0.772979290259828	   
df.mm.trans3:probe13	0.0274337737054945	0.085496200291218	0.320877110468645	0.748354584310865	   
df.mm.trans3:probe14	0.0345466443351635	0.085496200291218	0.404072277101092	0.686225374850321	   
df.mm.trans3:probe15	-0.0347224297028694	0.085496200291218	-0.406128337687494	0.684714484035389	   
df.mm.trans3:probe16	-0.101098246705666	0.085496200291218	-1.18248818498722	0.237226137941939	   
df.mm.trans3:probe17	0.0516771946023499	0.085496200291218	0.604438494650365	0.545656577857697	   
df.mm.trans3:probe18	0.0140524755446594	0.085496200291218	0.164363743614263	0.869470101981723	   
df.mm.trans3:probe19	-0.100946614054149	0.085496200291218	-1.18071462486407	0.237929949770858	   
df.mm.trans3:probe20	-0.0114279792883424	0.085496200291218	-0.133666516750643	0.893686766920628	   
df.mm.trans3:probe21	-0.0404634391263117	0.085496200291218	-0.473277630917921	0.636093791046137	   
df.mm.trans3:probe22	0.0623032776678981	0.085496200291218	0.728725691383712	0.466299427442171	   
df.mm.trans3:probe23	-0.00477909841730996	0.085496200291218	-0.0558983721034543	0.955431272189564	   
df.mm.trans3:probe24	-0.0653471387419014	0.085496200291218	-0.764327987902566	0.444809145427278	   
df.mm.trans3:probe25	-0.0964881509610259	0.085496200291218	-1.12856654017801	0.259286894061777	   
df.mm.trans3:probe26	-0.0302460122627715	0.085496200291218	-0.353770251306459	0.72356786666864	   
df.mm.trans3:probe27	-0.0136575193953096	0.085496200291218	-0.159744168147698	0.873107176214836	   
df.mm.trans3:probe28	-0.00283554403900748	0.085496200291218	-0.0331657316857243	0.973547466063703	   
df.mm.trans3:probe29	-0.0370072729947004	0.085496200291218	-0.432852838706818	0.665192773331287	   
df.mm.trans3:probe30	-0.0792869027001504	0.085496200291218	-0.927373408760654	0.353903126836749