fitVsDatCorrelation=0.77749360739616
cont.fitVsDatCorrelation=0.258707964625362

fstatistic=6501.7484818878,55,761
cont.fstatistic=2747.94713579220,55,761

residuals=-0.645511781052378,-0.0928501442657322,-0.009760615794045,0.078210538536249,1.66261712167908
cont.residuals=-0.512877694404507,-0.173397952656175,-0.0556649722808903,0.101220766782332,2.17818554956130

predictedValues:
Include	Exclude	Both
Lung	52.3366317238626	45.3103986598314	94.4990644936107
cerebhem	60.2804502816478	50.4194348824979	57.6843123545
cortex	50.6400212792178	42.1169808226438	66.7798139409336
heart	52.6311247696786	44.2882022172616	74.768347021655
kidney	50.4711212358538	43.2297265834407	68.7208671573123
liver	52.3807889716959	48.5786254777399	68.1381770609539
stomach	61.6429716876002	44.0625515995382	72.8544516517719
testicle	52.8802734611801	48.3913012592625	66.0476167553324


diffExp=7.02623306403119,9.86101539914994,8.52304045657395,8.34292255241699,7.24139465241311,3.80216349395603,17.580420088062,4.48897220191757
diffExpScore=0.985265116342546
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=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,0,0,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	55.3539229276247	55.6085956203698	52.4978620323925
cerebhem	55.4755006121616	53.8526602247526	68.4159050248822
cortex	52.8207212882164	59.8435673736527	49.6639924459474
heart	55.2524847508535	52.5117241887098	56.8239127874838
kidney	52.7096599388566	56.1437340790961	55.0141091490361
liver	56.8301552865632	56.8001904788893	61.9788048159062
stomach	55.1035882464299	54.4690064081156	53.0796505631831
testicle	60.0196486909146	54.6419867970571	55.7993279787455
cont.diffExp=-0.254672692745039,1.62284038740898,-7.02284608543626,2.74076056214367,-3.43407414023950,0.0299648076738421,0.634581838314361,5.37766189385749
cont.diffExpScore=16.1722089118778

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=0.365426074840347
cont.tran.correlation=-0.401903785326712

tran.covariance=0.00185681179668578
cont.tran.covariance=-0.000659724096056548

tran.mean=49.9787878070595
cont.tran.mean=55.4648216820165

weightedLogRatios:
wLogRatio
Lung	0.560150420063641
cerebhem	0.716255862944812
cortex	0.706313802675295
heart	0.669131131806128
kidney	0.595326144595877
liver	0.295461526632240
stomach	1.32737842015914
testicle	0.348070387600274

cont.weightedLogRatios:
wLogRatio
Lung	-0.0184346607744620
cerebhem	0.118791318554512
cortex	-0.50298141033583
heart	0.202819283705755
kidney	-0.252235434031853
liver	0.00213062687466665
stomach	0.0463715978737597
testicle	0.37995871290677

varWeightedLogRatios=0.099235558553369
cont.varWeightedLogRatios=0.0740934107969092

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.57959155005346	0.0934074744373018	27.6165431684481	7.71451833035115e-117	***
df.mm.trans1	1.27781123187460	0.0817918874870997	15.6227135860649	6.28044385353248e-48	***
df.mm.trans2	1.24328846468432	0.0733484822706237	16.9504320498021	6.45757526932512e-55	***
df.mm.exp2	0.741756139963132	0.0967291139249327	7.66838555493	5.32531108849478e-14	***
df.mm.exp3	0.241149190513612	0.0967291139249328	2.49303628172132	0.0128770455726755	*  
df.mm.exp4	0.216988205599487	0.0967291139249327	2.24325641779249	0.0251672404931660	*  
df.mm.exp5	0.235233630169412	0.0967291139249328	2.43188033699933	0.0152503507587617	*  
df.mm.exp6	0.397542708090414	0.0967291139249327	4.10985578136206	4.38910458219625e-05	***
df.mm.exp7	0.395862669935077	0.0967291139249327	4.09248729645439	4.72356181165921e-05	***
df.mm.exp8	0.434331327032096	0.0967291139249328	4.49018200837818	8.21898334818181e-06	***
df.mm.trans1:exp2	-0.600444837662568	0.090739952077452	-6.61720470328276	6.90341237931919e-11	***
df.mm.trans2:exp2	-0.63491598360317	0.0723854407266645	-8.77132165293687	1.14631492423748e-17	***
df.mm.trans1:exp3	-0.274103533736788	0.0907399520774521	-3.02075907537205	0.00260582861375214	** 
df.mm.trans2:exp3	-0.314234743326923	0.0723854407266645	-4.34113186536376	1.60911195315951e-05	***
df.mm.trans1:exp4	-0.211377076461861	0.0907399520774521	-2.32948190540631	0.0200943324414345	*  
df.mm.trans2:exp4	-0.239806436789986	0.0723854407266645	-3.31290981145672	0.00096693679477823	***
df.mm.trans1:exp5	-0.271528855441009	0.0907399520774521	-2.99238482305173	0.00285756270662599	** 
df.mm.trans2:exp5	-0.282241813340591	0.0723854407266646	-3.89915168723455	0.000105053779926172	***
df.mm.trans1:exp6	-0.396699347928231	0.0907399520774521	-4.3718267295273	1.40345648714966e-05	***
df.mm.trans2:exp6	-0.327895636047167	0.0723854407266645	-4.52985618040702	6.85042222345313e-06	***
df.mm.trans1:exp7	-0.232199991533594	0.0907399520774521	-2.55896092313777	0.0106908874371735	*  
df.mm.trans2:exp7	-0.423788975445773	0.0723854407266645	-5.85461622104433	7.10780175287172e-09	***
df.mm.trans1:exp8	-0.423997501413135	0.0907399520774521	-4.67266613774742	3.51556927074266e-06	***
df.mm.trans2:exp8	-0.368547812646313	0.0723854407266646	-5.09146326867016	4.48256195815003e-07	***
df.mm.trans1:probe2	-0.0185823297378429	0.055566645468589	-0.334415179846465	0.738158428523325	   
df.mm.trans1:probe3	0.607219668561993	0.055566645468589	10.9277726492459	6.37423733541219e-26	***
df.mm.trans1:probe4	0.0409662403119414	0.055566645468589	0.73724515789061	0.461200515833604	   
df.mm.trans1:probe5	0.22332942484223	0.055566645468589	4.01912735524902	6.42234259654529e-05	***
df.mm.trans1:probe6	0.0705819264902748	0.055566645468589	1.27022111727392	0.204394099610089	   
df.mm.trans1:probe7	0.146673027071855	0.055566645468589	2.63958757695328	0.00847063337148906	** 
df.mm.trans1:probe8	0.0131644585831177	0.055566645468589	0.236912962301448	0.812788072287042	   
df.mm.trans1:probe9	0.0768922225282229	0.055566645468589	1.38378377675667	0.166830399806454	   
df.mm.trans1:probe10	0.527112926503816	0.055566645468589	9.48613906883735	2.9888940543082e-20	***
df.mm.trans1:probe11	0.00960873363987492	0.055566645468589	0.17292268696167	0.862758179542201	   
df.mm.trans1:probe12	0.102967141264796	0.055566645468589	1.85303864209333	0.0642637403273209	.  
df.mm.trans1:probe13	0.267848422063483	0.055566645468589	4.82030937453105	1.73090521603446e-06	***
df.mm.trans1:probe14	0.0638328857012352	0.055566645468589	1.14876262842462	0.251014814876634	   
df.mm.trans1:probe15	0.0257774065246824	0.055566645468589	0.463900714309882	0.642851545021715	   
df.mm.trans1:probe16	0.140297790898627	0.055566645468589	2.52485622832739	0.0117767095722081	*  
df.mm.trans1:probe17	0.0416618661981208	0.055566645468589	0.749763924865172	0.453628617063179	   
df.mm.trans1:probe18	0.204547215699193	0.055566645468589	3.68111506415878	0.00024861495985358	***
df.mm.trans1:probe19	0.0884392662205547	0.055566645468589	1.59158908144902	0.111892345528186	   
df.mm.trans1:probe20	0.0751821712724813	0.055566645468589	1.35300899736661	0.176454563590243	   
df.mm.trans1:probe21	0.00872367485445635	0.055566645468589	0.156994808322336	0.875290581603512	   
df.mm.trans1:probe22	0.0919811219336905	0.055566645468589	1.65532976047089	0.0982699202725123	.  
df.mm.trans2:probe2	-0.0182655092411887	0.055566645468589	-0.328713549057301	0.742462646401761	   
df.mm.trans2:probe3	-0.0490728242424546	0.055566645468589	-0.883134546428482	0.377442472318876	   
df.mm.trans2:probe4	-0.0317263737575269	0.055566645468589	-0.570960753343682	0.568194809141943	   
df.mm.trans2:probe5	-0.00531750785225276	0.055566645468589	-0.0956960386471174	0.923787180631015	   
df.mm.trans2:probe6	-0.00773927612972215	0.055566645468589	-0.139279167645581	0.889266401515299	   
df.mm.trans3:probe2	-0.790062927618132	0.055566645468589	-14.2182944634429	7.37248089782126e-41	***
df.mm.trans3:probe3	-0.637248954193899	0.055566645468589	-11.4681919129728	3.39251042304273e-28	***
df.mm.trans3:probe4	-0.495878788085237	0.055566645468589	-8.92403678328125	3.31600400836349e-18	***
df.mm.trans3:probe5	-0.645490238847338	0.055566645468589	-11.6165054306225	7.81911194304316e-29	***
df.mm.trans3:probe6	-0.743926961456556	0.055566645468589	-13.3880128120580	7.38494703225057e-37	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0725599647878	0.143470798927750	28.3859851288533	1.87477492137585e-121	***
df.mm.trans1	-0.0909622233382014	0.125629640607182	-0.724050653162507	0.469257158805555	   
df.mm.trans2	-0.0507419756267208	0.112660848769312	-0.450395822337728	0.652553339771176	   
df.mm.exp2	-0.294724904089883	0.148572727589361	-1.98370797165730	0.0476473439405072	*  
df.mm.exp3	0.0820444641999808	0.148572727589361	0.552217526939016	0.580961466266966	   
df.mm.exp4	-0.138320334910467	0.148572727589361	-0.930994114160504	0.352151852443339	   
df.mm.exp5	-0.0861887333959978	0.148572727589361	-0.58011140264056	0.562011211796687	   
df.mm.exp6	-0.118498574584956	0.148572727589361	-0.797579586157112	0.425363291702309	   
df.mm.exp7	-0.0362598124070707	0.148572727589361	-0.244054295801104	0.80725454993312	   
df.mm.exp8	0.00239986426104025	0.148572727589361	0.0161527912960797	0.98711673141025	   
df.mm.trans1:exp2	0.296918865394043	0.139373572593018	2.13038139060315	0.033460243073773	*  
df.mm.trans2:exp2	0.262638920321226	0.111181648731577	2.36225063504238	0.0184151477410423	*  
df.mm.trans1:exp3	-0.128888433412767	0.139373572593018	-0.92476953137401	0.355378946187793	   
df.mm.trans2:exp3	-0.00864830390550047	0.111181648731577	-0.0777853540054966	0.938019243855906	   
df.mm.trans1:exp4	0.136486115585568	0.139373572593018	0.97928260750062	0.327751571272293	   
df.mm.trans2:exp4	0.081019010660265	0.111181648731577	0.728708483680318	0.466404188340816	   
df.mm.trans1:exp5	0.0372399411157471	0.139373572593018	0.267195139099222	0.7893913112566	   
df.mm.trans2:exp5	0.0957660290211652	0.111181648731577	0.86134744459826	0.38931802400124	   
df.mm.trans1:exp6	0.144818130787770	0.139373572593018	1.03906449474931	0.299104761203476	   
df.mm.trans2:exp6	0.139700467011769	0.111181648731577	1.25650652428302	0.209317942703865	   
df.mm.trans1:exp7	0.0317271173076715	0.139373572593018	0.227640841211104	0.819986611621219	   
df.mm.trans2:exp7	0.0155538757553273	0.111181648731577	0.139896070374695	0.88877912271339	   
df.mm.trans1:exp8	0.0785245909560545	0.139373572593018	0.56341090706882	0.573321080215491	   
df.mm.trans2:exp8	-0.0199350748698495	0.111181648731577	-0.179301846098525	0.857748424838942	   
df.mm.trans1:probe2	0.0647557831849994	0.0853485341204112	0.758721679901858	0.448253986358182	   
df.mm.trans1:probe3	0.0370042107117744	0.0853485341204112	0.433565861360527	0.66472660281887	   
df.mm.trans1:probe4	0.0440565948348381	0.0853485341204112	0.516196268499261	0.605867287718697	   
df.mm.trans1:probe5	-0.0490003994586177	0.0853485341204111	-0.574121160528512	0.566055465410122	   
df.mm.trans1:probe6	0.0929833488925812	0.0853485341204112	1.08945455069444	0.276298353485831	   
df.mm.trans1:probe7	0.00978217358515738	0.0853485341204112	0.114614429948574	0.908780974417239	   
df.mm.trans1:probe8	0.155600049944228	0.0853485341204112	1.82311332640705	0.0686784211881181	.  
df.mm.trans1:probe9	0.0106071162888019	0.0853485341204112	0.124280005487115	0.901126397001579	   
df.mm.trans1:probe10	0.0505370112327397	0.0853485341204111	0.592125122634693	0.553942681828084	   
df.mm.trans1:probe11	0.0824148000951229	0.0853485341204112	0.965626427500801	0.334537903338969	   
df.mm.trans1:probe12	-0.0228941258328471	0.0853485341204112	-0.268242753888985	0.788585161763113	   
df.mm.trans1:probe13	0.0798295325325862	0.0853485341204112	0.935335719064153	0.349912016034673	   
df.mm.trans1:probe14	0.041250601557611	0.0853485341204112	0.483319391278753	0.629007997951981	   
df.mm.trans1:probe15	0.0950217971754252	0.0853485341204112	1.11333836198483	0.265914670981138	   
df.mm.trans1:probe16	-0.0779496145729385	0.0853485341204112	-0.913309354123949	0.361369119070919	   
df.mm.trans1:probe17	0.115552274094331	0.0853485341204112	1.35388703842656	0.176174347453536	   
df.mm.trans1:probe18	-0.042869591095801	0.0853485341204112	-0.502288545873791	0.615609870462174	   
df.mm.trans1:probe19	0.00626780200878399	0.0853485341204112	0.0734377230186668	0.94147709778981	   
df.mm.trans1:probe20	0.0410178968520896	0.0853485341204112	0.480592868697907	0.630944011883467	   
df.mm.trans1:probe21	0.0799549454693189	0.0853485341204112	0.936805140162303	0.349155996932277	   
df.mm.trans1:probe22	0.086271916084928	0.0853485341204112	1.01081895517050	0.312424368062521	   
df.mm.trans2:probe2	0.0992868735313775	0.0853485341204112	1.16331082372548	0.245068043960560	   
df.mm.trans2:probe3	0.0175025515537539	0.0853485341204112	0.205071495768878	0.837571104708208	   
df.mm.trans2:probe4	-0.084939129506772	0.0853485341204112	-0.995203144168105	0.319953804561114	   
df.mm.trans2:probe5	-0.0251054202538143	0.0853485341204112	-0.294151745106660	0.7687221644602	   
df.mm.trans2:probe6	-0.0485073037026067	0.0853485341204112	-0.568343723796963	0.569969257228527	   
df.mm.trans3:probe2	0.0157322319254411	0.0853485341204111	0.184329257527093	0.853804270649694	   
df.mm.trans3:probe3	0.0968117983373624	0.0853485341204111	1.13431120212069	0.257021207914843	   
df.mm.trans3:probe4	0.0126352427850431	0.0853485341204111	0.148042879883761	0.882348144120932	   
df.mm.trans3:probe5	-0.0314964448915787	0.0853485341204111	-0.369033226125981	0.712205608903141	   
df.mm.trans3:probe6	0.0858172503615091	0.0853485341204112	1.00549179017459	0.314979700799119