fitVsDatCorrelation=0.947825659167615
cont.fitVsDatCorrelation=0.229742631605333

fstatistic=7355.81090997633,62,922
cont.fstatistic=775.925335067848,62,922

residuals=-0.64831462371286,-0.114345866101241,-0.00787003154853496,0.101468865128684,0.890445973835634
cont.residuals=-1.05180451536828,-0.484797482804281,-0.207587085471544,0.486516369796991,1.86619534000780

predictedValues:
Include	Exclude	Both
Lung	74.8333718440713	250.209501875624	59.7307285610009
cerebhem	79.9374436066863	235.616566234573	59.0539145735022
cortex	67.8427912351121	183.847157097067	58.8792382353793
heart	70.6072547288382	198.710948850444	59.3906870008876
kidney	81.9865838052714	252.375557950516	65.7763373663219
liver	73.295327406572	209.035688817827	67.8930920634776
stomach	72.0023118185744	204.534503085656	63.3185890821069
testicle	77.9837399232508	204.872367320363	64.6505682562752


diffExp=-175.376130031553,-155.679122627886,-116.004365861955,-128.103694121605,-170.388974145244,-135.740361411255,-132.532191267082,-126.888627397112
diffExpScore=0.999124123495935
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	90.5989774729443	84.950504795219	72.4254730525979
cerebhem	90.2170616787282	100.309769980006	86.9581659079299
cortex	85.2985188771565	92.915229312813	84.0470925482421
heart	83.5654345104532	75.0954286256256	81.210067487751
kidney	86.06228820939	112.596744581175	88.3437110512924
liver	88.6657611933755	95.1502040716148	99.979727146094
stomach	86.664695365053	70.2160263346706	87.726184414754
testicle	93.6373718330807	95.3760819341612	85.9729937766128
cont.diffExp=5.6484726777253,-10.0927083012775,-7.61671043565646,8.47000588482764,-26.5344563717851,-6.48444287823924,16.4486690303824,-1.73871010108051
cont.diffExpScore=3.62596545858521

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,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,-1,0,1,0
cont.diffExp1.2Score=2

tran.correlation=0.775355011172325
cont.tran.correlation=0.264354060163862

tran.covariance=0.00588763530251178
cont.tran.covariance=0.00174556792360107

tran.mean=146.105694725028
cont.tran.mean=89.4575061734667

weightedLogRatios:
wLogRatio
Lung	-5.93713936628529
cerebhem	-5.32019477449354
cortex	-4.70108517407155
heart	-4.94025457534237
kidney	-5.58666232733674
liver	-5.04982817969565
stomach	-5.01004554878565
testicle	-4.67435506631796

cont.weightedLogRatios:
wLogRatio
Lung	0.288026357160323
cerebhem	-0.483058394554770
cortex	-0.383939654667228
heart	0.467257709114989
kidney	-1.23337348977969
liver	-0.319046511413977
stomach	0.91697820452375
testicle	-0.0836868445668422

varWeightedLogRatios=0.191508313094426
cont.varWeightedLogRatios=0.438087134873451

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.61154257487059	0.09748528516502	57.5629703023544	1.62457492802568e-307	***
df.mm.trans1	-1.44139171258973	0.0847930001184174	-16.9989469717637	1.40131952169857e-56	***
df.mm.trans2	-0.097684912552414	0.0745253314140807	-1.31076119621197	0.190264844603012	   
df.mm.exp2	0.0172834019701426	0.0960362077179322	0.179967559953071	0.857217580890148	   
df.mm.exp3	-0.391906560597671	0.0960362077179323	-4.08082086861175	4.87641966836901e-05	***
df.mm.exp4	-0.282869185027805	0.0960362077179322	-2.94544309640609	0.00330599996370045	** 
df.mm.exp5	0.00349784669221148	0.0960362077179322	0.0364221659239710	0.970953623996704	   
df.mm.exp6	-0.328648337815608	0.0960362077179322	-3.42212948246436	0.000648645721140474	***
df.mm.exp7	-0.298459948158489	0.0960362077179322	-3.10778564929485	0.0019427136128036	** 
df.mm.exp8	-0.237825268995131	0.0960362077179322	-2.47641253904618	0.0134495072456594	*  
df.mm.trans1:exp2	0.048697038766929	0.0900069034540578	0.541036708276331	0.588612970972958	   
df.mm.trans2:exp2	-0.0773762120098786	0.0660470104638278	-1.17153238983096	0.241687502160251	   
df.mm.trans1:exp3	0.293835762486234	0.0900069034540579	3.26459139477248	0.00113658432367079	** 
df.mm.trans2:exp3	0.08371273062764	0.0660470104638278	1.26747191189656	0.205306618070451	   
df.mm.trans1:exp4	0.224738149312408	0.0900069034540578	2.49689902316349	0.0127019591482162	*  
df.mm.trans2:exp4	0.0524218610529317	0.0660470104638277	0.793705281810474	0.427571242266305	   
df.mm.trans1:exp5	0.0877938419211067	0.0900069034540579	0.975412313411263	0.329611621541587	   
df.mm.trans2:exp5	0.00512186630531683	0.0660470104638278	0.0775487984898567	0.938203797850372	   
df.mm.trans1:exp6	0.307881265383531	0.0900069034540579	3.42064056831689	0.000652161771885744	***
df.mm.trans2:exp6	0.148854760664017	0.0660470104638278	2.25376984694168	0.0244448160565282	*  
df.mm.trans1:exp7	0.259894242116213	0.0900069034540578	2.88749231606296	0.00397403135953084	** 
df.mm.trans2:exp7	0.096898054222621	0.0660470104638277	1.46710734584557	0.142687862841236	   
df.mm.trans1:exp8	0.279061678296792	0.0900069034540578	3.10044749444395	0.00199098464623813	** 
df.mm.trans2:exp8	0.0379138813688961	0.0660470104638278	0.574043868187805	0.566078125487832	   
df.mm.trans1:probe2	-0.118663708847335	0.0603784663702602	-1.96533161540823	0.0496757125226329	*  
df.mm.trans1:probe3	-0.260616434937814	0.0603784663702602	-4.31638050128054	1.75761110751416e-05	***
df.mm.trans1:probe4	0.201639827850199	0.0603784663702602	3.33959836961872	0.00087263951711215	***
df.mm.trans1:probe5	0.0643697340892195	0.0603784663702602	1.06610415863304	0.286655647419229	   
df.mm.trans1:probe6	-0.0280514422200172	0.0603784663702602	-0.464593486823543	0.642332285593499	   
df.mm.trans1:probe7	-0.219867911299081	0.0603784663702602	-3.64149546215334	0.000286155899534484	***
df.mm.trans1:probe8	0.723733497613554	0.0603784663702602	11.9866161087197	7.19109304533992e-31	***
df.mm.trans1:probe9	-0.180448111914077	0.0603784663702602	-2.98861701467393	0.00287675767136533	** 
df.mm.trans1:probe10	0.0541716470440394	0.0603784663702602	0.897201441186688	0.369845645210381	   
df.mm.trans1:probe11	-0.25408480757469	0.0603784663702602	-4.20820240806647	2.82579419256286e-05	***
df.mm.trans1:probe12	-0.288409066542746	0.0603784663702602	-4.77668751594531	2.07232296587267e-06	***
df.mm.trans1:probe13	-0.212738640465470	0.0603784663702602	-3.52341908058558	0.000446939655749776	***
df.mm.trans1:probe14	-0.22850371800568	0.0603784663702602	-3.78452338627519	0.000163950471239878	***
df.mm.trans1:probe15	-0.230628523361131	0.0603784663702602	-3.81971482923801	0.000142549921507363	***
df.mm.trans1:probe16	-0.295509166759455	0.0603784663702602	-4.89428076803571	1.16431347922636e-06	***
df.mm.trans1:probe17	0.90954555404872	0.0603784663702602	15.0640718244	5.20046463331266e-46	***
df.mm.trans1:probe18	1.40934315388359	0.0603784663702602	23.3418176811754	4.70077498138924e-95	***
df.mm.trans1:probe19	1.01284317995248	0.0603784663702602	16.7749073608693	2.54582797626349e-55	***
df.mm.trans1:probe20	1.08815501246332	0.0603784663702602	18.0222366992630	1.94398202146165e-62	***
df.mm.trans1:probe21	1.14572859319649	0.0603784663702602	18.9757816333147	4.85351327171979e-68	***
df.mm.trans1:probe22	0.953918737769546	0.0603784663702602	15.7989891945882	6.10647451030534e-50	***
df.mm.trans1:probe23	-0.0237485801991245	0.0603784663702602	-0.393328642259486	0.694167697780233	   
df.mm.trans1:probe24	0.094389498888284	0.0603784663702602	1.56329738999095	0.118325808507376	   
df.mm.trans1:probe25	0.000952204101204654	0.0603784663702602	0.0157705910475670	0.987420822330851	   
df.mm.trans1:probe26	-0.238563048907902	0.0603784663702602	-3.95112799727235	8.37215984152893e-05	***
df.mm.trans2:probe2	0.140024922782696	0.0603784663702602	2.31912022945429	0.0206062942169579	*  
df.mm.trans2:probe3	-0.233629118171743	0.0603784663702602	-3.86941126889599	0.000116776819441363	***
df.mm.trans2:probe4	-0.0458307972548588	0.0603784663702602	-0.759058651370997	0.448011479978635	   
df.mm.trans2:probe5	0.445805325794283	0.0603784663702602	7.38351522644615	3.4444928904152e-13	***
df.mm.trans2:probe6	-0.179756651435543	0.0603784663702602	-2.97716491063581	0.00298535172946581	** 
df.mm.trans3:probe2	0.441192711414859	0.0603784663702602	7.3071202025789	5.90838246172416e-13	***
df.mm.trans3:probe3	-0.238782797189519	0.0603784663702602	-3.95476751140424	8.24783635695498e-05	***
df.mm.trans3:probe4	-0.219922535651017	0.0603784663702602	-3.64240016138173	0.000285166090238006	***
df.mm.trans3:probe5	-0.0187690482524520	0.0603784663702602	-0.310856657692400	0.755979857717135	   
df.mm.trans3:probe6	-0.244164614836662	0.0603784663702602	-4.04390223062914	5.696290494053e-05	***
df.mm.trans3:probe7	0.414301169216752	0.0603784663702602	6.86173720736999	1.24708562201456e-11	***
df.mm.trans3:probe8	0.0473788762651005	0.0603784663702602	0.784698239510722	0.432832082506160	   
df.mm.trans3:probe9	-0.0248078703427969	0.0603784663702602	-0.410872813341548	0.681261270556515	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.70893506957833	0.297618953279485	15.8220268490640	4.57985114899556e-50	***
df.mm.trans1	-0.204104850197535	0.258869878648372	-0.788445729040475	0.430638701856938	   
df.mm.trans2	-0.210402742139365	0.227523067617021	-0.924753451784174	0.35533613190725	   
df.mm.exp2	-0.0208991324498778	0.293194973677917	-0.0712806641522995	0.943189842069527	   
df.mm.exp3	-0.119486253885840	0.293194973677917	-0.407531726710634	0.683712100593866	   
df.mm.exp4	-0.318603207027417	0.293194973677916	-1.08665985310312	0.277471168504746	   
df.mm.exp5	0.0316954635828773	0.293194973677917	0.108103707185975	0.913936947547836	   
df.mm.exp6	-0.230590532387788	0.293194973677917	-0.786475052744589	0.431791319232957	   
df.mm.exp7	-0.426550888109153	0.293194973677917	-1.4548369733573	0.146054753450062	   
df.mm.exp8	-0.0227294539483238	0.293194973677917	-0.0775233410832368	0.938224043400804	   
df.mm.trans1:exp2	0.0166747686826336	0.274787731795408	0.0606823622498866	0.951625329386402	   
df.mm.trans2:exp2	0.187093439888267	0.201639068790835	0.92786304266433	0.353721447488177	   
df.mm.trans1:exp3	0.0592004177270578	0.274787731795408	0.215440541469061	0.829471512059748	   
df.mm.trans2:exp3	0.209105028230249	0.201639068790835	1.03702635349481	0.299995469616706	   
df.mm.trans1:exp4	0.237790252010491	0.274787731795408	0.865359783192709	0.387066450689874	   
df.mm.trans2:exp4	0.195294103084144	0.201639068790835	0.968533053912915	0.333032210460678	   
df.mm.trans1:exp5	-0.0830670748047546	0.274787731795408	-0.302295427317701	0.762495042917526	   
df.mm.trans2:exp5	0.250048549984518	0.201639068790835	1.24007986886658	0.215261355276059	   
df.mm.trans1:exp6	0.209021413529794	0.274787731795408	0.760665012823134	0.447051659062886	   
df.mm.trans2:exp6	0.343978480500465	0.201639068790835	1.70591186798865	0.0883612764075998	.  
df.mm.trans1:exp7	0.38215455759538	0.274787731795408	1.39072641670885	0.164643959930640	   
df.mm.trans2:exp7	0.236058678088158	0.201639068790835	1.17069910857914	0.242022230408748	   
df.mm.trans1:exp8	0.0557161026504223	0.274787731795408	0.202760517314162	0.839366953909317	   
df.mm.trans2:exp8	0.138488497127275	0.201639068790835	0.68681381022907	0.492372765822272	   
df.mm.trans1:probe2	0.123436254836932	0.184333214303254	0.66963653459578	0.503257175583045	   
df.mm.trans1:probe3	-0.0179230106626956	0.184333214303254	-0.0972315853680594	0.92256361689923	   
df.mm.trans1:probe4	-0.0197253749179978	0.184333214303254	-0.107009336285683	0.914804852290634	   
df.mm.trans1:probe5	-0.0648647708032391	0.184333214303254	-0.351888676429889	0.725002159795173	   
df.mm.trans1:probe6	0.0713126099244806	0.184333214303254	0.386867934756247	0.69894325316333	   
df.mm.trans1:probe7	0.178202710150582	0.184333214303254	0.966742270643715	0.333926397166944	   
df.mm.trans1:probe8	-0.0687428147225218	0.184333214303254	-0.372926902958629	0.709288563902638	   
df.mm.trans1:probe9	-0.0553822820614108	0.184333214303254	-0.300446570471555	0.763904275996309	   
df.mm.trans1:probe10	0.153062931910419	0.184333214303254	0.830360022142342	0.406550216984333	   
df.mm.trans1:probe11	0.0587940043704136	0.184333214303254	0.318955021712415	0.749832863908666	   
df.mm.trans1:probe12	-0.147382814818420	0.184333214303254	-0.799545623806867	0.424180036979567	   
df.mm.trans1:probe13	-0.114552134552193	0.184333214303254	-0.621440552562269	0.534463410454383	   
df.mm.trans1:probe14	0.085411594381529	0.184333214303254	0.463354337439236	0.643219758355629	   
df.mm.trans1:probe15	0.121816071180632	0.184333214303254	0.660847105829924	0.508875453750781	   
df.mm.trans1:probe16	0.08522018235796	0.184333214303254	0.46231593519419	0.643963849902045	   
df.mm.trans1:probe17	-0.114907547184363	0.184333214303254	-0.623368651269346	0.533196465196698	   
df.mm.trans1:probe18	0.103119325140538	0.184333214303254	0.55941803830802	0.57601231623084	   
df.mm.trans1:probe19	-0.0117701241232903	0.184333214303254	-0.0638524324971993	0.94910156872546	   
df.mm.trans1:probe20	-0.184885134291699	0.184333214303254	-1.00299414291956	0.316126841447825	   
df.mm.trans1:probe21	-0.0109029238900150	0.184333214303254	-0.0591479073981649	0.952847117823635	   
df.mm.trans1:probe22	0.157296412800526	0.184333214303254	0.853326479414346	0.393699908577514	   
df.mm.trans1:probe23	-0.101888293628175	0.184333214303254	-0.552739743693477	0.580575612660604	   
df.mm.trans1:probe24	-0.175051240808529	0.184333214303254	-0.94964568089474	0.342541169609498	   
df.mm.trans1:probe25	-0.176636171674430	0.184333214303254	-0.958243864742895	0.33819100339577	   
df.mm.trans1:probe26	0.183387301269785	0.184333214303254	0.994868461242621	0.320061264287591	   
df.mm.trans2:probe2	-0.0227645611391281	0.184333214303254	-0.123496794786409	0.90174062803942	   
df.mm.trans2:probe3	0.00215977469858264	0.184333214303254	0.0117166876666595	0.990654184389228	   
df.mm.trans2:probe4	-0.0992970568191093	0.184333214303254	-0.538682392071522	0.590236055703895	   
df.mm.trans2:probe5	-0.335239465605499	0.184333214303254	-1.81866011978711	0.0692876128903272	.  
df.mm.trans2:probe6	-0.391811747274109	0.184333214303254	-2.12556238849892	0.0338050003585521	*  
df.mm.trans3:probe2	-0.091283725249746	0.184333214303254	-0.495210402502783	0.620569603865139	   
df.mm.trans3:probe3	0.0257181358992663	0.184333214303254	0.139519814681668	0.889069869115606	   
df.mm.trans3:probe4	0.187267697088448	0.184333214303254	1.01591944672742	0.309934259556509	   
df.mm.trans3:probe5	-0.214151508743549	0.184333214303254	-1.16176300377011	0.245632401324729	   
df.mm.trans3:probe6	-0.101230190664819	0.184333214303254	-0.549169562563376	0.583022063494585	   
df.mm.trans3:probe7	0.158160537992793	0.184333214303254	0.858014322544158	0.391107539116667	   
df.mm.trans3:probe8	-0.063293789792969	0.184333214303254	-0.343366169966753	0.731401285266933	   
df.mm.trans3:probe9	-0.00801177441317167	0.184333214303254	-0.043463542061341	0.96534143702729