fitVsDatCorrelation=0.890074280040119
cont.fitVsDatCorrelation=0.250771158422883

fstatistic=8713.31239645246,52,692
cont.fstatistic=1921.47375838816,52,692

residuals=-0.759078284241315,-0.0919035241520207,-4.6061845070593e-05,0.0902153108214074,0.88361794573636
cont.residuals=-0.937070310523839,-0.264767538062233,-0.0831834345209987,0.192656782922863,1.22209141630331

predictedValues:
Include	Exclude	Both
Lung	83.9142679910488	51.0272467508133	76.1276849162062
cerebhem	65.9953262393905	48.3002565767807	64.201523466603
cortex	79.2631412431831	50.507244228946	72.5675382561424
heart	99.1464941162716	52.6622512025097	84.0005682191172
kidney	67.261800210865	52.179329021727	67.12751473747
liver	70.6912346058415	52.9670897837072	71.2196558645281
stomach	74.5326051113021	53.7448811640094	68.0133093405108
testicle	72.8931979364728	49.6259109778384	70.2008066782953


diffExp=32.8870212402355,17.6950696626099,28.7558970142371,46.4842429137619,15.0824711891381,17.7241448221343,20.7877239472927,23.2672869586344
diffExpScore=0.995090430773179
diffExp1.5=1,0,1,1,0,0,0,0
diffExp1.5Score=0.75
diffExp1.4=1,0,1,1,0,0,0,1
diffExp1.4Score=0.8
diffExp1.3=1,1,1,1,0,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	68.1523174389988	69.0122523407847	61.8831744097649
cerebhem	72.1331907449518	64.907655491633	81.0003087329125
cortex	66.2944966155284	68.0471036945773	99.7437858038317
heart	73.3591018172798	64.6209371724597	65.9613754437022
kidney	73.8989035057794	69.2140629245453	62.9001897565252
liver	73.5175268725472	64.6327270517297	81.0959619030235
stomach	71.2603961987299	71.5408468007124	66.5857581230351
testicle	72.1929936040194	70.4174414212297	69.8772479000508
cont.diffExp=-0.85993490178592,7.22553525331882,-1.75260707904897,8.73816464482007,4.68484058123407,8.8847998208175,-0.280450601982494,1.77555218278971
cont.diffExpScore=1.16270062047662

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=0.296111192794447
cont.tran.correlation=-0.288288461341728

tran.covariance=0.00148085342138941
cont.tran.covariance=-0.000460428331321683

tran.mean=64.0445173225442
cont.tran.mean=69.5751221059691

weightedLogRatios:
wLogRatio
Lung	2.07981822534569
cerebhem	1.25904838091179
cortex	1.86907281032972
heart	2.70811160560733
kidney	1.03635295002431
liver	1.18750833724077
stomach	1.35626309546899
testicle	1.57512920262669

cont.weightedLogRatios:
wLogRatio
Lung	-0.0530146373983485
cerebhem	0.446021383633755
cortex	-0.109778436322304
heart	0.53673058786733
kidney	0.279655860309064
liver	0.545238629096812
stomach	-0.0167652622775239
testicle	0.106254228618882

varWeightedLogRatios=0.312208267699654
cont.varWeightedLogRatios=0.0732781897927205

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01700967225346	0.0921430946693626	43.595341427024	1.19076035493299e-200	***
df.mm.trans1	0.354854104979672	0.0827579739289978	4.28785394485514	2.0604227466588e-05	***
df.mm.trans2	-0.0679189998052685	0.0761071965912535	-0.892412319035202	0.372482185122135	   
df.mm.exp2	-0.124749546005379	0.104248011328352	-1.19666115847959	0.231848464495626	   
df.mm.exp3	-0.0193710985330671	0.104248011328353	-0.185817439452667	0.852642381341374	   
df.mm.exp4	0.0999304311930493	0.104248011328352	0.9585835731513	0.338103357080943	   
df.mm.exp5	-0.0730585273487107	0.104248011328353	-0.700814590300399	0.483654305138005	   
df.mm.exp6	-0.0675198908627401	0.104248011328353	-0.647685169264966	0.517403414662263	   
df.mm.exp7	0.0460382954999628	0.104248011328352	0.441622769713609	0.658900166990826	   
df.mm.exp8	-0.0875947814822808	0.104248011328353	-0.840253740729705	0.401056348168880	   
df.mm.trans1:exp2	-0.115462187533105	0.0998098719754545	-1.15682131684829	0.247744548302253	   
df.mm.trans2:exp2	0.0698266787189231	0.0868733427736271	0.803775663391658	0.421802561479686	   
df.mm.trans1:exp3	-0.0376513408556712	0.0998098719754545	-0.377230629700942	0.706117877032046	   
df.mm.trans2:exp3	0.00912813452499942	0.0868733427736271	0.105074056477662	0.916347493445222	   
df.mm.trans1:exp4	0.0668724052527293	0.0998098719754545	0.669997906311058	0.503082632767618	   
df.mm.trans2:exp4	-0.0683912683553605	0.0868733427736271	-0.787252638977795	0.431403532263096	   
df.mm.trans1:exp5	-0.148144660245214	0.0998098719754546	-1.48426861304507	0.138193011273175	   
df.mm.trans2:exp5	0.0953852079783815	0.0868733427736271	1.09798017358368	0.272595036508087	   
df.mm.trans1:exp6	-0.103954182539050	0.0998098719754545	-1.04152205069069	0.297997059424735	   
df.mm.trans2:exp6	0.104830923990368	0.0868733427736271	1.20670991403582	0.227956227722932	   
df.mm.trans1:exp7	-0.164597271858422	0.0998098719754545	-1.64910813530450	0.099579334113393	.  
df.mm.trans2:exp7	0.00585039273524216	0.0868733427736271	0.0673439348418651	0.946327353742839	   
df.mm.trans1:exp8	-0.053205549129125	0.0998098719754545	-0.533069004859654	0.594156961980499	   
df.mm.trans2:exp8	0.0597481374776398	0.0868733427736271	0.687761464795137	0.491833462376004	   
df.mm.trans1:probe2	-0.191878147523114	0.0499049359877273	-3.84487313179404	0.000131734021208679	***
df.mm.trans1:probe3	0.110418692293976	0.0499049359877273	2.21258058163085	0.0272522912639870	*  
df.mm.trans1:probe4	-0.149248150093721	0.0499049359877273	-2.99064906386062	0.00288269928407011	** 
df.mm.trans1:probe5	-0.302852216295911	0.0499049359877273	-6.0685824017566	2.12523068590658e-09	***
df.mm.trans1:probe6	0.288857578242816	0.0499049359877273	5.78815647241492	1.07899042110758e-08	***
df.mm.trans1:probe7	-0.0198688633567202	0.0499049359877273	-0.398134231884525	0.690654041030392	   
df.mm.trans1:probe8	-0.321681358534605	0.0499049359877273	-6.44588259994389	2.15500183763786e-10	***
df.mm.trans1:probe9	0.0961056305727215	0.0499049359877273	1.92577404760836	0.0545419976029262	.  
df.mm.trans1:probe10	0.465511909000113	0.0499049359877273	9.32797327131314	1.43048231474829e-19	***
df.mm.trans1:probe11	-0.255061847895844	0.0499049359877273	-5.11095431439026	4.15185312288002e-07	***
df.mm.trans1:probe12	-0.184217691977952	0.0499049359877273	-3.69137217254932	0.000240596592001685	***
df.mm.trans1:probe13	-0.203896863601347	0.0499049359877273	-4.08570534288412	4.90898681322253e-05	***
df.mm.trans1:probe14	-0.28357229271352	0.0499049359877273	-5.6822494028097	1.95914788285885e-08	***
df.mm.trans1:probe15	-0.127226083193605	0.0499049359877273	-2.54936872827355	0.0110064152165212	*  
df.mm.trans1:probe16	-0.159137397204422	0.0499049359877273	-3.18881076700624	0.00149299093348774	** 
df.mm.trans1:probe17	0.426648897402345	0.0499049359877273	8.54923243478895	7.91080779403455e-17	***
df.mm.trans1:probe18	0.509814440845125	0.0499049359877273	10.2157117478419	6.48126674351726e-23	***
df.mm.trans1:probe19	0.19549068894721	0.0499049359877273	3.91726159102349	9.84429464558452e-05	***
df.mm.trans1:probe20	0.397572310802675	0.0499049359877273	7.96659294183739	6.71762334670571e-15	***
df.mm.trans1:probe21	0.590928987945655	0.0499049359877273	11.8410929951093	1.3844160711465e-29	***
df.mm.trans1:probe22	0.565588808078655	0.0499049359877273	11.3333239865841	1.98070795943335e-27	***
df.mm.trans2:probe2	0.0372652858722526	0.0499049359877273	0.746725451795329	0.455482923568275	   
df.mm.trans2:probe3	-0.127916458603063	0.0499049359877273	-2.56320253841265	0.0105814129528846	*  
df.mm.trans2:probe4	-0.103408660412902	0.0499049359877273	-2.07211287553465	0.0386247446190183	*  
df.mm.trans2:probe5	0.0473892027111288	0.0499049359877273	0.94958948996113	0.342652392760355	   
df.mm.trans2:probe6	-0.00390776088523574	0.0499049359877273	-0.0783040957350741	0.937608795708017	   
df.mm.trans3:probe2	0.197185928435544	0.0499049359877273	3.95123096609193	8.57276366895746e-05	***
df.mm.trans3:probe3	-0.111784274989174	0.0499049359877273	-2.23994426155891	0.0254112561299433	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31492030286215	0.195690557892140	22.0497112857148	5.12014162743458e-82	***
df.mm.trans1	-0.0420985480332133	0.175758738582649	-0.239524636855634	0.810769721957048	   
df.mm.trans2	-0.0778220496646084	0.161634030352371	-0.481470699548554	0.630334213734899	   
df.mm.exp2	-0.273754007284482	0.221398592799538	-1.23647582318804	0.216701242908896	   
df.mm.exp3	-0.5190786006168	0.221398592799538	-2.3445433597981	0.0193321887252002	*  
df.mm.exp4	-0.0559452003667448	0.221398592799538	-0.252689954616828	0.800582851545365	   
df.mm.exp5	0.067571975542352	0.221398592799538	0.305205081423142	0.760301739507873	   
df.mm.exp6	-0.260169334141336	0.221398592799538	-1.17511737925499	0.240351933185242	   
df.mm.exp7	0.00733768019618968	0.221398592799538	0.0331423976250539	0.973570589801172	   
df.mm.exp8	-0.043737019320657	0.221398592799538	-0.197548768344061	0.843456133867501	   
df.mm.trans1:exp2	0.330523126314486	0.211973014365385	1.55926983113404	0.119389721770731	   
df.mm.trans2:exp2	0.212435523316765	0.184498827332948	1.15141936882559	0.249957415800787	   
df.mm.trans1:exp3	0.491440324517366	0.211973014365385	2.31840985037018	0.0207176124018049	*  
df.mm.trans2:exp3	0.504994708387033	0.184498827332948	2.73711608733271	0.00635718186609092	** 
df.mm.trans1:exp4	0.129566622207308	0.211973014365385	0.611241117626275	0.541240696839982	   
df.mm.trans2:exp4	-0.00980039550833271	0.184498827332948	-0.0531190124620512	0.95765241357953	   
df.mm.trans1:exp5	0.0133808522228404	0.211973014365385	0.0631252627269593	0.949684986554772	   
df.mm.trans2:exp5	-0.0646519711022876	0.184498827332948	-0.350419414783684	0.726130595859215	   
df.mm.trans1:exp6	0.335948010245151	0.211973014365385	1.58486216394539	0.113454506066640	   
df.mm.trans2:exp6	0.194606168364058	0.184498827332948	1.05478268440628	0.291892849935211	   
df.mm.trans1:exp7	0.037257877330946	0.211973014365385	0.175767077910792	0.86052835548658	   
df.mm.trans2:exp7	0.0286468313194349	0.184498827332948	0.155268365298271	0.876655032028182	   
df.mm.trans1:exp8	0.101334856476342	0.211973014365385	0.47805545804839	0.632761713337555	   
df.mm.trans2:exp8	0.063893940271123	0.184498827332948	0.346310820479197	0.729214407022237	   
df.mm.trans1:probe2	-0.00803321135699543	0.105986507182692	-0.0757946607594901	0.939604370555823	   
df.mm.trans1:probe3	-0.156109419157352	0.105986507182692	-1.47291785819737	0.141227870298927	   
df.mm.trans1:probe4	-0.205682109562228	0.105986507182692	-1.94064428604753	0.0527072255554635	.  
df.mm.trans1:probe5	-0.0744382519312115	0.105986507182692	-0.702337060725097	0.482705157319155	   
df.mm.trans1:probe6	-0.075550591267986	0.105986507182692	-0.712832163982505	0.476189912547875	   
df.mm.trans1:probe7	-0.111650256567887	0.105986507182692	-1.05343840018647	0.292507795159109	   
df.mm.trans1:probe8	0.0735336715145176	0.105986507182692	0.693802196800063	0.488039087890444	   
df.mm.trans1:probe9	-0.0289432657369055	0.105986507182692	-0.273084437880523	0.784869785215144	   
df.mm.trans1:probe10	-0.137478200101128	0.105986507182692	-1.29712926442752	0.195019007752480	   
df.mm.trans1:probe11	-0.0974735333061243	0.105986507182692	-0.919678701536094	0.358061153362743	   
df.mm.trans1:probe12	0.00485129622909766	0.105986507182692	0.0457727720070568	0.96350456639709	   
df.mm.trans1:probe13	-0.00381778575713635	0.105986507182692	-0.0360214319597825	0.971275657947024	   
df.mm.trans1:probe14	0.0228191533116656	0.105986507182692	0.215302437246390	0.829594968324708	   
df.mm.trans1:probe15	-0.157990608218597	0.105986507182692	-1.49066718413753	0.136504558710996	   
df.mm.trans1:probe16	-0.0186503517115809	0.105986507182692	-0.175969113496991	0.860369689000158	   
df.mm.trans1:probe17	-0.0535309153383292	0.105986507182692	-0.505072926368413	0.613668426547066	   
df.mm.trans1:probe18	-0.0487080339537187	0.105986507182692	-0.459568253058468	0.645970490256676	   
df.mm.trans1:probe19	-0.00893701305889464	0.105986507182692	-0.084322177383293	0.932824667835654	   
df.mm.trans1:probe20	0.0492761984706307	0.105986507182692	0.464928978041439	0.642128575437469	   
df.mm.trans1:probe21	-0.144654056815562	0.105986507182692	-1.36483464415161	0.172748623339107	   
df.mm.trans1:probe22	-0.0957475229405668	0.105986507182692	-0.903393511926227	0.366631445326020	   
df.mm.trans2:probe2	-6.28857871573467e-07	0.105986507182692	-5.93337669378503e-06	0.999995268769963	   
df.mm.trans2:probe3	-0.0111908567569443	0.105986507182692	-0.105587560666135	0.915940193333096	   
df.mm.trans2:probe4	0.027877521238984	0.105986507182692	0.263028964535369	0.792606547662658	   
df.mm.trans2:probe5	0.0412370169358433	0.105986507182692	0.389077987679712	0.697338149446295	   
df.mm.trans2:probe6	-0.083250800429315	0.105986507182692	-0.785484894655627	0.432438173988105	   
df.mm.trans3:probe2	-0.0453483323505557	0.105986507182692	-0.427868919884182	0.668879678459662	   
df.mm.trans3:probe3	-0.163905811634907	0.105986507182692	-1.54647809416322	0.122446324905823