fitVsDatCorrelation=0.905147013878044
cont.fitVsDatCorrelation=0.234062454910537

fstatistic=10251.1461686373,53,715
cont.fstatistic=1948.93226720223,53,715

residuals=-0.525206422120638,-0.0892410342013044,-0.00331588784392803,0.0816097246296618,0.881150081755339
cont.residuals=-0.623568060144214,-0.246086609022478,-0.0934576737144512,0.196252090178103,1.56276558345995

predictedValues:
Include	Exclude	Both
Lung	57.2219006747372	44.1609006808973	65.3188499734675
cerebhem	55.1449711588717	47.3439023023684	52.5432198451637
cortex	59.5258581897285	43.1880009861409	66.8240387481188
heart	63.507226480711	46.8286152330374	74.2889683752979
kidney	56.6548796379342	43.5194753269263	65.5423554989633
liver	55.8455059624615	47.4774640311444	63.7115282256128
stomach	68.1081226599886	46.2698923591169	63.1859270330867
testicle	57.026305124325	45.0054101341826	61.1010259172531


diffExp=13.0609999938399,7.80106885650329,16.3378572035876,16.6786112476735,13.1354043110080,8.3680419313171,21.8382303008717,12.0208949901424
diffExpScore=0.990928973678075
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,1,1,0,1,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	55.0493869700262	63.1276945907924	55.5597818004358
cerebhem	57.345702666303	55.2148844713997	66.5349424504409
cortex	61.1558531436427	54.5514674658008	65.4275130780913
heart	55.1947412163975	60.9268369225298	63.2243577106513
kidney	62.3786341722899	52.9414935002425	59.342590141742
liver	59.6792454178116	57.8346797978221	69.1720407065287
stomach	57.5153787815738	62.0112743510615	60.8000303512698
testicle	59.1500668800514	56.8785468220701	55.4298222086866
cont.diffExp=-8.0783076207662,2.1308181949033,6.60438567784197,-5.73209570613231,9.43714067204737,1.84456561998954,-4.49589556948772,2.27152005798138
cont.diffExpScore=8.14806484610826

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=0.119028384576085
cont.tran.correlation=-0.836986551044754

tran.covariance=0.000304562444959837
cont.tran.covariance=-0.00243828855534819

tran.mean=52.3017769339107
cont.tran.mean=58.1847429481134

weightedLogRatios:
wLogRatio
Lung	1.01498317703788
cerebhem	0.599997797478461
cortex	1.25964542199043
heart	1.2182787426999
kidney	1.03004480417140
liver	0.639826418414808
stomach	1.55716531127534
testicle	0.92920000271284

cont.weightedLogRatios:
wLogRatio
Lung	-0.558217162583191
cerebhem	0.15260360069756
cortex	0.463556163289859
heart	-0.40117938717375
kidney	0.664541083658582
liver	0.127883759943762
stomach	-0.307805359552475
testicle	0.159007005951652

varWeightedLogRatios=0.101943169757015
cont.varWeightedLogRatios=0.182491529314678

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.0168487439898	0.0782335115641412	38.5621031661909	8.20902611480928e-177	***
df.mm.trans1	0.780879150433273	0.0694728723444799	11.2400585160968	4.17133103674579e-27	***
df.mm.trans2	0.763472702764008	0.0631946116804512	12.0812943138977	1.03635987833650e-30	***
df.mm.exp2	0.250271683025071	0.0851957274476269	2.93760838158137	0.00341392285881441	** 
df.mm.exp3	-0.0055852057626107	0.085195727447627	-0.0655573457723468	0.947748574346925	   
df.mm.exp4	0.0341898485898577	0.085195727447627	0.401309427293469	0.688312231008888	   
df.mm.exp5	-0.0280057553111633	0.0851957274476269	-0.32872253280987	0.742461675287235	   
df.mm.exp6	0.0729828502803748	0.0851957274476269	0.85664918261588	0.391925807665062	   
df.mm.exp7	0.254010515498754	0.0851957274476269	2.98149359256195	0.00296589708247966	** 
df.mm.exp8	0.082270865967498	0.0851957274476269	0.965668918292565	0.33453637767976	   
df.mm.trans1:exp2	-0.287242830307708	0.0808973401904414	-3.55070796680714	0.000409368591070322	***
df.mm.trans2:exp2	-0.180673448553286	0.0681216567280562	-2.65221747724868	0.00817380935057538	** 
df.mm.trans1:exp3	0.0450593112954109	0.0808973401904414	0.556993730440782	0.577706017183117	   
df.mm.trans2:exp3	-0.0166918901333162	0.0681216567280562	-0.245030595775888	0.806503048488525	   
df.mm.trans1:exp4	0.0700271496442865	0.0808973401904414	0.865629815262587	0.386983552751759	   
df.mm.trans2:exp4	0.0244648064714084	0.0681216567280562	0.359134049969933	0.719600839178815	   
df.mm.trans1:exp5	0.0180471716322988	0.0808973401904414	0.223087330063186	0.823531256194268	   
df.mm.trans2:exp5	0.0133745042646519	0.0681216567280562	0.196332633512473	0.844405608973678	   
df.mm.trans1:exp6	-0.0973304982428433	0.0808973401904414	-1.20313595000424	0.229321938209239	   
df.mm.trans2:exp6	-0.000567490886826788	0.0681216567280562	-0.00833055028435713	0.993355583158178	   
df.mm.trans1:exp7	-0.0798507380142605	0.0808973401904414	-0.987062588538548	0.323945855304687	   
df.mm.trans2:exp7	-0.207358836561502	0.0681216567280562	-3.04394881923212	0.00242058337011766	** 
df.mm.trans1:exp8	-0.0856949152143617	0.0808973401904414	-1.05930448408596	0.289818852567327	   
df.mm.trans2:exp8	-0.0633279558340921	0.0681216567280562	-0.929630294913398	0.352876279421486	   
df.mm.trans1:probe2	0.087922363874971	0.044309298064474	1.98428699427908	0.047605752517638	*  
df.mm.trans1:probe3	0.104529231250912	0.044309298064474	2.35908118198606	0.0185882392008893	*  
df.mm.trans1:probe4	0.143892718633555	0.044309298064474	3.24746102780004	0.00121876710624857	** 
df.mm.trans1:probe5	-0.0551160275672434	0.044309298064474	-1.24389304220177	0.213946577061121	   
df.mm.trans1:probe6	0.51343775040349	0.044309298064474	11.5875848373042	1.41955549032829e-28	***
df.mm.trans1:probe7	0.53594464420742	0.044309298064474	12.0955345180050	8.97467876069202e-31	***
df.mm.trans1:probe8	0.396022320876008	0.044309298064474	8.93767985897136	3.34567509337666e-18	***
df.mm.trans1:probe9	0.04566499907664	0.044309298064474	1.03059630983532	0.303078653183299	   
df.mm.trans1:probe10	0.218627414926684	0.044309298064474	4.93412047757023	1.00237957971204e-06	***
df.mm.trans1:probe11	0.594499102953385	0.044309298064474	13.4170282293422	9.02210849994969e-37	***
df.mm.trans1:probe12	1.04289886783519	0.044309298064474	23.5367950608849	3.82891579748917e-91	***
df.mm.trans1:probe13	0.578404405557544	0.044309298064474	13.053793014638	4.38258478421097e-35	***
df.mm.trans1:probe14	0.746235517848452	0.044309298064474	16.8415107087143	7.45930821284938e-54	***
df.mm.trans1:probe15	0.589590131215676	0.044309298064474	13.3062394795280	2.96885682323214e-36	***
df.mm.trans1:probe16	0.88111225760969	0.044309298064474	19.8854934765067	2.23186928406924e-70	***
df.mm.trans1:probe17	0.0642564864674365	0.044309298064474	1.45018064546944	0.147446661451791	   
df.mm.trans1:probe18	0.0086497006231246	0.044309298064474	0.195211862994049	0.845282551067987	   
df.mm.trans1:probe19	-0.0103112486258730	0.044309298064474	-0.232710719336361	0.816052642093302	   
df.mm.trans1:probe20	-0.0469011161843182	0.044309298064474	-1.05849377519077	0.290187850118633	   
df.mm.trans1:probe21	-0.0564638132850014	0.044309298064474	-1.27431071471368	0.202967331295599	   
df.mm.trans1:probe22	0.0965333422199249	0.044309298064474	2.17862494863855	0.0296854390788075	*  
df.mm.trans2:probe2	0.0552842609827146	0.044309298064474	1.24768983932607	0.212553136559146	   
df.mm.trans2:probe3	-0.0344518274180362	0.044309298064474	-0.777530426410855	0.437103074221347	   
df.mm.trans2:probe4	0.0383793241636829	0.044309298064474	0.866168633676785	0.386688244639598	   
df.mm.trans2:probe5	0.00618792391843742	0.044309298064474	0.139652943935908	0.88897353512586	   
df.mm.trans2:probe6	0.00978382696011664	0.044309298064474	0.220807536735976	0.825305332069927	   
df.mm.trans3:probe2	-0.444373089282353	0.044309298064474	-10.0288902937652	3.09230123095725e-22	***
df.mm.trans3:probe3	-0.492831017572475	0.044309298064474	-11.1225191799554	1.28797871381089e-26	***
df.mm.trans3:probe4	-0.590475634264375	0.044309298064474	-13.3262240671287	2.39588974416168e-36	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21567178685234	0.178923886138802	23.5612576823980	2.76644668100083e-91	***
df.mm.trans1	-0.173101845688771	0.158887873656393	-1.08945913684462	0.276318512550195	   
df.mm.trans2	-0.089541436454985	0.144529182940083	-0.619538799247942	0.535758835815993	   
df.mm.exp2	-0.273327497733512	0.194846816058536	-1.40278144268674	0.161116083464673	   
df.mm.exp3	-0.204303380596699	0.194846816058536	-1.04853332853703	0.294747248611475	   
df.mm.exp4	-0.162078913081205	0.194846816058536	-0.831827362436926	0.405784116833128	   
df.mm.exp5	-0.116847749735768	0.194846816058536	-0.599690321347947	0.54890262738767	   
df.mm.exp6	-0.225954550581109	0.194846816058536	-1.15965225992314	0.246577516433423	   
df.mm.exp7	-0.0641524160727743	0.194846816058536	-0.329245390663718	0.742066637345302	   
df.mm.exp8	-0.0300525076927534	0.194846816058536	-0.154236585953373	0.877466719902833	   
df.mm.trans1:exp2	0.314194679558446	0.185016193134821	1.69820097492489	0.0899049247045424	.  
df.mm.trans2:exp2	0.139400487618275	0.155797577129134	0.894753886337604	0.371219763003936	   
df.mm.trans1:exp3	0.309498228721363	0.185016193134822	1.67281697605696	0.094800779496085	.  
df.mm.trans2:exp3	0.0582884205405638	0.155797577129134	0.374129184899012	0.708419126488366	   
df.mm.trans1:exp4	0.164715866776417	0.185016193134821	0.890278110178108	0.373616027748953	   
df.mm.trans2:exp4	0.126593089421117	0.155797577129134	0.81254851168956	0.416747417225198	   
df.mm.trans1:exp5	0.241839838162731	0.185016193134821	1.30712795493799	0.191589463444528	   
df.mm.trans2:exp5	-0.0591244159474406	0.155797577129134	-0.379495092522748	0.704432929756985	   
df.mm.trans1:exp6	0.306708135362573	0.185016193134821	1.65773671031635	0.0978092618608387	.  
df.mm.trans2:exp6	0.138383569564331	0.155797577129134	0.888226711315484	0.37471751550244	   
df.mm.trans1:exp7	0.107974057955976	0.185016193134821	0.583592474401932	0.559678567189996	   
df.mm.trans2:exp7	0.0463090556868543	0.155797577129134	0.297238612693384	0.766370678538146	   
df.mm.trans1:exp8	0.101899501571395	0.185016193134821	0.550759908334837	0.581970309192847	   
df.mm.trans2:exp8	-0.074188828545104	0.155797577129134	-0.476187306068387	0.634086413771117	   
df.mm.trans1:probe2	-0.110418232017595	0.101337542483674	-1.08960834564726	0.276252797595078	   
df.mm.trans1:probe3	-0.0136089437771528	0.101337542483674	-0.134293209047824	0.893208514785521	   
df.mm.trans1:probe4	-0.0195544744679831	0.101337542483674	-0.192963772247915	0.847042138216113	   
df.mm.trans1:probe5	-0.00526894039254016	0.101337542483674	-0.0519939625868568	0.958548026392894	   
df.mm.trans1:probe6	-0.147817192429801	0.101337542483674	-1.45866170430978	0.145097375139495	   
df.mm.trans1:probe7	-0.00845060923585412	0.101337542483674	-0.0833907062352094	0.93356421887575	   
df.mm.trans1:probe8	-0.0897505242996796	0.101337542483674	-0.885659175267041	0.376098971696815	   
df.mm.trans1:probe9	-0.085497847408567	0.101337542483674	-0.843693712252209	0.399122753887615	   
df.mm.trans1:probe10	-0.0199013456297362	0.101337542483674	-0.196386700742643	0.844363309177323	   
df.mm.trans1:probe11	-0.131803636939496	0.101337542483674	-1.3006397600448	0.193800827563593	   
df.mm.trans1:probe12	0.0615038757285666	0.101337542483674	0.606920931978147	0.54409605559514	   
df.mm.trans1:probe13	0.0466853649735304	0.101337542483674	0.460691702495663	0.645159894778788	   
df.mm.trans1:probe14	-0.0519014501155934	0.101337542483674	-0.512164088881027	0.60869433299567	   
df.mm.trans1:probe15	0.0140703343928614	0.101337542483674	0.138846216792046	0.889610768414641	   
df.mm.trans1:probe16	-0.169405225251735	0.101337542483674	-1.67169265308587	0.095022485629169	.  
df.mm.trans1:probe17	-0.0212103300121993	0.101337542483674	-0.209303773235041	0.834270724812723	   
df.mm.trans1:probe18	-0.0197742937833297	0.101337542483674	-0.195132951704601	0.845344302121505	   
df.mm.trans1:probe19	0.0274569150476743	0.101337542483674	0.27094514406739	0.786511422021383	   
df.mm.trans1:probe20	-0.00968207068977938	0.101337542483674	-0.0955427816037595	0.923910481546349	   
df.mm.trans1:probe21	-0.053688069381735	0.101337542483674	-0.529794467735236	0.596418907581996	   
df.mm.trans1:probe22	-0.0848028665647872	0.101337542483674	-0.836835633530873	0.402964565730955	   
df.mm.trans2:probe2	0.138783094125907	0.101337542483674	1.36951312144032	0.171268905923544	   
df.mm.trans2:probe3	0.050382583000697	0.101337542483674	0.497175891243009	0.619217863937412	   
df.mm.trans2:probe4	-0.0144651963583331	0.101337542483674	-0.142742719073373	0.8865335920547	   
df.mm.trans2:probe5	-0.0207126123562632	0.101337542483674	-0.204392289852500	0.838105119990392	   
df.mm.trans2:probe6	0.0363043603273196	0.101337542483674	0.35825183281081	0.720260603522666	   
df.mm.trans3:probe2	0.142455551908464	0.101337542483674	1.40575297581756	0.160231877934109	   
df.mm.trans3:probe3	0.112423455179450	0.101337542483674	1.10939591018365	0.267632415391447	   
df.mm.trans3:probe4	0.00284533647958568	0.101337542483674	0.0280778121301301	0.977607925329021