fitVsDatCorrelation=0.883944426455187
cont.fitVsDatCorrelation=0.275965240350203

fstatistic=9107.45460747456,58,830
cont.fstatistic=2144.50107649774,58,830

residuals=-0.66573106584877,-0.097042260826082,-0.0114057542685329,0.0886099030157079,1.44610917635824
cont.residuals=-0.677044343763791,-0.265656255794456,-0.0563723159840295,0.182045022439295,1.65970334413106

predictedValues:
Include	Exclude	Both
Lung	78.1512053313884	115.857631769604	72.6749841924685
cerebhem	79.179947129488	164.784198376046	69.9304712693782
cortex	69.1499553986855	117.593850828567	77.4223495394716
heart	69.6882663704929	111.199091289415	65.4897890105304
kidney	81.9640443138303	113.315920670261	68.2623382203918
liver	79.01456436597	119.732814464496	66.8081246692961
stomach	73.1021660578525	136.462005839617	69.5281620309527
testicle	73.5022294354267	125.262281643077	71.3007177498458


diffExp=-37.7064264382160,-85.6042512465581,-48.443895429881,-41.5108249189222,-31.3518763564306,-40.7182500985256,-63.3598397817645,-51.7600522076501
diffExpScore=0.997509063375522
diffExp1.5=0,-1,-1,-1,0,-1,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.4Score=0.875
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	77.6437814623854	74.7737406793713	75.8006912289863
cerebhem	73.8870771159767	64.2796638476543	89.6759892749874
cortex	88.5815530706556	72.6802945863781	75.8871346187715
heart	81.4937230623086	77.50868808859	80.2805951902021
kidney	71.7098357811296	74.6058946019913	74.9376919296576
liver	78.5466318609768	65.3521562359024	84.5086541332575
stomach	80.0909907226	75.2361748149799	78.1475709061066
testicle	73.7085963871416	92.3233429281441	72.5047193774295
cont.diffExp=2.87004078301408,9.60741326832233,15.9012584842775,3.98503497371863,-2.89605882086171,13.1944756250744,4.85481590762005,-18.6147465410025
cont.diffExpScore=2.40530005795539

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=0,0,1,0,0,1,0,-1
cont.diffExp1.2Score=1.5

tran.correlation=0.215611844181773
cont.tran.correlation=-0.146428936843205

tran.covariance=0.00175645380522579
cont.tran.covariance=-0.000903649586911974

tran.mean=100.497510830264
cont.tran.mean=76.4013840778866

weightedLogRatios:
wLogRatio
Lung	-1.7935776732172
cerebhem	-3.47267675107858
cortex	-2.39024993206819
heart	-2.09237484904560
kidney	-1.47964522485322
liver	-1.90252686424988
stomach	-2.87373273319848
testicle	-2.43296810328511

cont.weightedLogRatios:
wLogRatio
Lung	0.163212407992027
cerebhem	0.589618939780266
cortex	0.867586271524926
heart	0.21936781085657
kidney	-0.169943933002018
liver	0.785582012143445
stomach	0.272129428931837
testicle	-0.993642957366026

varWeightedLogRatios=0.40864657894156
cont.varWeightedLogRatios=0.358101753216273

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71690546689731	0.0824409027459025	57.2155969887381	2.78005983009358e-290	***
df.mm.trans1	-0.379532125180224	0.069783973107385	-5.43867177920915	7.0649566032978e-08	***
df.mm.trans2	0.0867591622429106	0.0623898352322558	1.39059771387336	0.164720176048390	   
df.mm.exp2	0.403847946207377	0.0796501531743844	5.07027205990678	4.90108154246251e-07	***
df.mm.exp3	-0.170771710443376	0.0796501531743844	-2.14402237330908	0.0323209886909093	*  
df.mm.exp4	-0.0515504448862396	0.0796501531743844	-0.64721086943017	0.517674431425652	   
df.mm.exp5	0.0880917694747304	0.0796501531743844	1.10598367942701	0.269054045393786	   
df.mm.exp6	0.128059832688212	0.0796501531743844	1.60777886274544	0.108263922556330	   
df.mm.exp7	0.141161972428702	0.0796501531743845	1.77227496499153	0.0767157088753318	.  
df.mm.exp8	0.0358087602838432	0.0796501531743844	0.449575535723632	0.65313390892741	   
df.mm.trans1:exp2	-0.390770352310062	0.0707377846359517	-5.52420964723649	4.43041919509635e-08	***
df.mm.trans2:exp2	-0.0515733419804208	0.052630595975167	-0.9799117989231	0.327415190817902	   
df.mm.trans1:exp3	0.0484036435053723	0.0707377846359517	0.6842685808508	0.493996590271316	   
df.mm.trans2:exp3	0.185646330657268	0.052630595975167	3.52734616086929	0.000442783793408024	***
df.mm.trans1:exp4	-0.0630630763299123	0.0707377846359517	-0.89150482524245	0.372916768351518	   
df.mm.trans2:exp4	0.0105105298007634	0.052630595975167	0.199703795976823	0.841761160105582	   
df.mm.trans1:exp5	-0.0404565824052286	0.0707377846359517	-0.571923231882879	0.56752883996299	   
df.mm.trans2:exp5	-0.11027421849219	0.052630595975167	-2.09524928321582	0.0364513339120411	*  
df.mm.trans1:exp6	-0.117073118205183	0.0707377846359517	-1.65502946986104	0.0982965622586505	.  
df.mm.trans2:exp6	-0.0951592434855197	0.052630595975167	-1.80805939439522	0.0709591093052026	.  
df.mm.trans1:exp7	-0.207949454732463	0.0707377846359517	-2.93972246660909	0.00337621681782990	** 
df.mm.trans2:exp7	0.0225221329429131	0.052630595975167	0.427928518110260	0.668814173711473	   
df.mm.trans1:exp8	-0.0971385021251474	0.0707377846359517	-1.37321945584055	0.170054972914832	   
df.mm.trans2:exp8	0.0422389068936182	0.052630595975167	0.802554219860023	0.422462174008647	   
df.mm.trans1:probe2	-0.337812141657220	0.0517747568898322	-6.5246495000648	1.18453526891998e-10	***
df.mm.trans1:probe3	-0.348422249068431	0.0517747568898322	-6.7295776938135	3.17089841020801e-11	***
df.mm.trans1:probe4	0.201145537273467	0.0517747568898322	3.8850117191563	0.000110472448896095	***
df.mm.trans1:probe5	0.32645000258006	0.0517747568898322	6.30519624215116	4.67260926686239e-10	***
df.mm.trans1:probe6	0.388299548377714	0.0517747568898322	7.49978506328766	1.64373262236098e-13	***
df.mm.trans1:probe7	0.213230075224124	0.0517747568898322	4.11841770069227	4.19669676972970e-05	***
df.mm.trans1:probe8	0.291824501811739	0.0517747568898322	5.63642437631705	2.37901002486855e-08	***
df.mm.trans1:probe9	0.498831882498536	0.0517747568898322	9.63465426906716	6.74555960701141e-21	***
df.mm.trans1:probe10	0.433946916468908	0.0517747568898322	8.3814380315155	2.22422604066226e-16	***
df.mm.trans1:probe11	-0.136468453990253	0.0517747568898322	-2.63581061868884	0.00855034544584482	** 
df.mm.trans1:probe12	-0.220988693160795	0.0517747568898322	-4.26827099605741	2.19719598310564e-05	***
df.mm.trans1:probe13	-0.111687426555425	0.0517747568898322	-2.15717915958690	0.0312781253220594	*  
df.mm.trans1:probe14	-0.216134327485011	0.0517747568898322	-4.17451168230317	3.3016317891835e-05	***
df.mm.trans1:probe15	-0.270499800285047	0.0517747568898322	-5.22454988751804	2.20951043469852e-07	***
df.mm.trans1:probe16	-0.0522790835613053	0.0517747568898322	-1.00974078299481	0.312913765213564	   
df.mm.trans2:probe2	-0.55933199472957	0.0517747568898322	-10.8031795478969	1.51638080834306e-25	***
df.mm.trans2:probe3	-0.432792183803486	0.0517747568898322	-8.3591350264453	2.64837466667792e-16	***
df.mm.trans2:probe4	-0.153442556106400	0.0517747568898322	-2.96365575280052	0.00312699001898644	** 
df.mm.trans2:probe5	-0.525246953986892	0.0517747568898322	-10.1448463602548	7.0667384628914e-23	***
df.mm.trans2:probe6	0.593461101908306	0.0517747568898322	11.4623638537036	2.43260636156292e-28	***
df.mm.trans3:probe2	-0.411666607206616	0.0517747568898322	-7.95110652248106	6.03054647573294e-15	***
df.mm.trans3:probe3	-0.524303011311942	0.0517747568898322	-10.1266146440353	8.34284898744168e-23	***
df.mm.trans3:probe4	-0.0975622054690266	0.0517747568898322	-1.88435854323029	0.0598657354551111	.  
df.mm.trans3:probe5	-0.0104903272830993	0.0517747568898322	-0.202614708658525	0.839485847694242	   
df.mm.trans3:probe6	0.153092213904352	0.0517747568898322	2.95688909230623	0.00319569325824328	** 
df.mm.trans3:probe7	-0.425161366749235	0.0517747568898322	-8.21175013248069	8.3124242099678e-16	***
df.mm.trans3:probe8	-0.253626582366763	0.0517747568898322	-4.89865327434443	1.16012777023371e-06	***
df.mm.trans3:probe9	0.265236837209612	0.0517747568898322	5.12289874724067	3.74356937501137e-07	***
df.mm.trans3:probe10	-0.236261374059406	0.0517747568898322	-4.56325414645846	5.7972339402346e-06	***
df.mm.trans3:probe11	0.0996214986854363	0.0517747568898322	1.92413262118089	0.0546795993045745	.  
df.mm.trans3:probe12	-0.0364873324921645	0.0517747568898322	-0.704732087295036	0.48117467587567	   
df.mm.trans3:probe13	-0.176373023736250	0.0517747568898322	-3.40654470114735	0.000689537223464165	***
df.mm.trans3:probe14	-0.185864787218654	0.0517747568898322	-3.58987271758981	0.000350256842279159	***
df.mm.trans3:probe15	-0.232183057280850	0.0517747568898322	-4.48448377603965	8.33744868133487e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30675773947968	0.169462974011564	25.4141517614685	7.6275324544243e-106	***
df.mm.trans1	0.0437546681272706	0.143445780276930	0.30502582956989	0.760422951758286	   
df.mm.trans2	-0.013136503637576	0.128246618782629	-0.102431578799295	0.918438860644746	   
df.mm.exp2	-0.368912614380217	0.163726395367240	-2.25322626539687	0.024505196817989	*  
df.mm.exp3	0.102255948980414	0.163726395367240	0.624553840271463	0.532435561569424	   
df.mm.exp4	0.0268972828761306	0.163726395367240	0.164281897343429	0.869549229144225	   
df.mm.exp5	-0.0703003693096971	0.163726395367240	-0.429377127322768	0.667760229863498	   
df.mm.exp6	-0.231861838693393	0.163726395367240	-1.41615429920951	0.157105494055632	   
df.mm.exp7	0.00670574842719932	0.163726395367240	0.0409570394080824	0.967339994515536	   
df.mm.exp8	0.203273977001160	0.163726395367240	1.24154676797968	0.214754572499476	   
df.mm.trans1:exp2	0.319319094969901	0.145406405802533	2.19604558139994	0.0283646690854928	*  
df.mm.trans2:exp2	0.217689162826444	0.108185827918971	2.01217818464622	0.0445238027357697	*  
df.mm.trans1:exp3	0.0295362201384676	0.145406405802533	0.203128740961926	0.839084193786515	   
df.mm.trans2:exp3	-0.130652415171459	0.108185827918971	-1.20766663882551	0.227519525132726	   
df.mm.trans1:exp4	0.0214972545698678	0.145406405802533	0.147842555155801	0.882502931171177	   
df.mm.trans2:exp4	0.00902598866077035	0.108185827918971	0.0834304162975082	0.933529421028379	   
df.mm.trans1:exp5	-0.00920317503428756	0.145406405802533	-0.0632927757445966	0.949548607365027	   
df.mm.trans2:exp5	0.0680531266022082	0.108185827918971	0.629039199599956	0.529496476103266	   
df.mm.trans1:exp6	0.243422861363091	0.145406405802533	1.67408622762925	0.094490403650514	.  
df.mm.trans2:exp6	0.0971855105048253	0.108185827918971	0.898320162393314	0.369275400994532	   
df.mm.trans1:exp7	0.0243261617535776	0.145406405802533	0.167297730930873	0.867176540207785	   
df.mm.trans2:exp7	-0.000540347974473376	0.108185827918971	-0.00499462808454065	0.99601608010668	   
df.mm.trans1:exp8	-0.255286006542516	0.145406405802533	-1.75567235249046	0.0795132146365269	.  
df.mm.trans2:exp8	0.00755627242184094	0.108185827918971	0.0698453075341848	0.94433360894779	   
df.mm.trans1:probe2	0.0526792599904537	0.106426591522407	0.494982120886231	0.620743743797873	   
df.mm.trans1:probe3	-0.0430999453630168	0.106426591522407	-0.40497346336552	0.685601367667555	   
df.mm.trans1:probe4	-0.0764034302020836	0.106426591522407	-0.717897934239466	0.473022231091958	   
df.mm.trans1:probe5	-0.0182412752325456	0.106426591522407	-0.171397720923018	0.863952823535719	   
df.mm.trans1:probe6	0.00867203998911284	0.106426591522407	0.0814837707856782	0.93507687542894	   
df.mm.trans1:probe7	-0.00527015223812403	0.106426591522407	-0.0495191301603833	0.96051750576983	   
df.mm.trans1:probe8	0.143512300531252	0.106426591522407	1.34846280876182	0.177877409265578	   
df.mm.trans1:probe9	0.091985142012919	0.106426591522407	0.864306003763658	0.387669523528920	   
df.mm.trans1:probe10	-0.0172260168445500	0.106426591522407	-0.161858202899632	0.871456910363569	   
df.mm.trans1:probe11	0.0858890795472214	0.106426591522407	0.807026498909703	0.419882410346939	   
df.mm.trans1:probe12	-0.0488443289599787	0.106426591522407	-0.458948541537149	0.646391279963199	   
df.mm.trans1:probe13	-0.0424083519399835	0.106426591522407	-0.398475149239884	0.690382455832426	   
df.mm.trans1:probe14	0.0134921528159226	0.106426591522407	0.126774264053001	0.89914977011676	   
df.mm.trans1:probe15	-0.147363138044168	0.106426591522407	-1.38464584777332	0.16653288596378	   
df.mm.trans1:probe16	0.0528173561491015	0.106426591522407	0.496279692824525	0.619828488846954	   
df.mm.trans2:probe2	0.0902983860607085	0.106426591522407	0.84845699527732	0.396428134111310	   
df.mm.trans2:probe3	0.0379328215156064	0.106426591522407	0.356422403207566	0.721614837273827	   
df.mm.trans2:probe4	0.118567573249963	0.106426591522407	1.11407846059788	0.265568033780223	   
df.mm.trans2:probe5	0.0136876589169827	0.106426591522407	0.128611268304133	0.897696405983865	   
df.mm.trans2:probe6	0.177269628642165	0.106426591522407	1.66565165816517	0.0961601639381924	.  
df.mm.trans3:probe2	0.0942727203783212	0.106426591522407	0.885800428537381	0.375981637471012	   
df.mm.trans3:probe3	0.0740529632390609	0.106426591522407	0.695812598897989	0.486740976933763	   
df.mm.trans3:probe4	0.0503629145781244	0.106426591522407	0.47321739668343	0.636182514601783	   
df.mm.trans3:probe5	0.169951334455304	0.106426591522407	1.59688788322721	0.110671331615543	   
df.mm.trans3:probe6	-0.105515074078851	0.106426591522407	-0.991435247239272	0.321762033229373	   
df.mm.trans3:probe7	-0.0743840925233531	0.106426591522407	-0.698923938644527	0.484795362687084	   
df.mm.trans3:probe8	-0.112547265491145	0.106426591522407	-1.05751075817785	0.290586229821457	   
df.mm.trans3:probe9	-0.0559099758599199	0.106426591522407	-0.525338405187471	0.599488269089214	   
df.mm.trans3:probe10	-0.0805519532563923	0.106426591522407	-0.75687807064114	0.449337706393568	   
df.mm.trans3:probe11	-0.0760305837535548	0.106426591522407	-0.714394613845612	0.475184063043244	   
df.mm.trans3:probe12	0.0136340568791334	0.106426591522407	0.128107615625958	0.898094841797601	   
df.mm.trans3:probe13	-0.0239149225937890	0.106426591522407	-0.224708150958249	0.822261630425456	   
df.mm.trans3:probe14	-0.0461362006248891	0.106426591522407	-0.433502567026921	0.664762343130749	   
df.mm.trans3:probe15	0.0336886949512102	0.106426591522407	0.316543962080354	0.751669256021358