fitVsDatCorrelation=0.912843782614545
cont.fitVsDatCorrelation=0.245686097945028

fstatistic=11262.5284117729,53,715
cont.fstatistic=1987.16785153421,53,715

residuals=-0.627015830444707,-0.0840955234123865,0.00487395611589783,0.0875685989202492,0.567729849900871
cont.residuals=-0.640917409010675,-0.273795237740611,-0.0544625163247477,0.236703197750032,1.01798635951600

predictedValues:
Include	Exclude	Both
Lung	64.2475015724163	44.1743969214757	85.762918818423
cerebhem	54.6548400409736	45.3051484691821	74.8288347827116
cortex	64.5004525147664	44.4880262788295	86.1587795395303
heart	58.5889119357936	44.6472436709869	69.6178472225237
kidney	60.6943859817504	45.4366384792131	74.7998851212037
liver	60.9763901409965	48.5816679562205	80.3967995752218
stomach	67.6110627691942	43.9186294740161	90.2949662449676
testicle	55.6172050709225	42.778441718632	73.3564907389357


diffExp=20.0731046509406,9.34969157179156,20.0124262359369,13.9416682648067,15.2577475025374,12.3947221847760,23.6924332951782,12.8387633522906
diffExpScore=0.99222156450717
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=1,0,1,0,0,0,1,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,0,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	71.0352854374616	63.7684384128107	64.4634943621813
cerebhem	68.3223329915754	72.6805708548893	59.4394095588626
cortex	68.6361819849196	71.0828463238583	62.5680968827428
heart	65.7785796153878	65.4261338641797	68.3179025634846
kidney	70.4596990247638	60.078612101022	60.6537312883127
liver	63.6708954171024	62.2668878232396	67.6913128999911
stomach	63.3922004995165	54.8577380308771	63.4908769451332
testicle	67.5467525056969	49.3454641149486	62.466106244259
cont.diffExp=7.26684702465091,-4.35823786331389,-2.44666433893870,0.352445751208094,10.3810869237418,1.40400759386284,8.53446246863948,18.2012883907483
cont.diffExpScore=1.31262503137329

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

tran.correlation=-0.0198255148499348
cont.tran.correlation=0.268612349203417

tran.covariance=-2.89012816795463e-06
cont.tran.covariance=0.00145380599705914

tran.mean=52.8888089372106
cont.tran.mean=64.8967886876406

weightedLogRatios:
wLogRatio
Lung	1.48919130616459
cerebhem	0.733063001992957
cortex	1.47873104573300
heart	1.06925767307937
kidney	1.14686249145848
liver	0.908249882618967
stomach	1.72489302216365
testicle	1.02024462953859

cont.weightedLogRatios:
wLogRatio
Lung	0.454251852376334
cerebhem	-0.26312775044099
cortex	-0.148732977091893
heart	0.0224763025120035
kidney	0.665496075726194
liver	0.0923702600443088
stomach	0.589530414504016
testicle	1.27342664746541

varWeightedLogRatios=0.113157699258956
cont.varWeightedLogRatios=0.259595652562343

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.16739191307441	0.0721866339263985	43.8778169973112	5.23045010899726e-205	***
df.mm.trans1	0.680288901977079	0.0626309380351451	10.8618667278357	1.52395976694619e-25	***
df.mm.trans2	0.613163572035635	0.0563128780125784	10.8885142027136	1.18599847734368e-25	***
df.mm.exp2	-4.63202296414781e-05	0.0738587586484535	-0.000627146062147471	0.999499784800402	   
df.mm.exp3	0.00639898079514906	0.0738587586484535	0.0866380766783039	0.930983455143292	   
df.mm.exp4	0.127015604416833	0.0738587586484535	1.71970943922022	0.0859180222942198	.  
df.mm.exp5	0.108052179629292	0.0738587586484535	1.46295688699007	0.143918605665866	   
df.mm.exp6	0.107457158874827	0.0738587586484535	1.45490068938597	0.146135620933619	   
df.mm.exp7	-0.0062729739161072	0.0738587586484535	-0.0849320247306722	0.932339206910447	   
df.mm.exp8	-0.0201055875963588	0.0738587586484535	-0.272216700690241	0.785533990456493	   
df.mm.trans1:exp2	-0.161658741720012	0.0682812651579891	-2.36754168725442	0.0181719515506502	*  
df.mm.trans2:exp2	0.0253216329216439	0.0542644470498568	0.466633943553853	0.640903943000643	   
df.mm.trans1:exp3	-0.00246957827203733	0.0682812651579892	-0.0361677286781846	0.971158712848437	   
df.mm.trans2:exp3	0.000675733704392837	0.0542644470498568	0.0124526046265982	0.990067989502575	   
df.mm.trans1:exp4	-0.219212978837047	0.0682812651579891	-3.21044108262834	0.00138459299532303	** 
df.mm.trans2:exp4	-0.116368396972936	0.0542644470498568	-2.14446849271346	0.0323318086198647	*  
df.mm.trans1:exp5	-0.164943810772733	0.0682812651579891	-2.4156525276895	0.0159567517419527	*  
df.mm.trans2:exp5	-0.0798787511631611	0.0542644470498568	-1.47202736793338	0.141453496007127	   
df.mm.trans1:exp6	-0.159713253479214	0.0682812651579891	-2.33904941728408	0.0196073671572667	*  
df.mm.trans2:exp6	-0.0123562677543864	0.0542644470498568	-0.227704665322283	0.81994094239877	   
df.mm.trans1:exp7	0.0573017570208055	0.0682812651579891	0.839201747188204	0.401636595627319	   
df.mm.trans2:exp7	0.000466199495887756	0.0542644470498568	0.00859125120098293	0.99314765409588	   
df.mm.trans1:exp8	-0.124144652280127	0.0682812651579891	-1.81813638035091	0.0694616041266895	.  
df.mm.trans2:exp8	-0.0120055009575572	0.0542644470498568	-0.221240639318168	0.824968233813481	   
df.mm.trans1:probe2	0.249945719128603	0.0433919867210277	5.76018149930617	1.24825210714479e-08	***
df.mm.trans1:probe3	0.0770754502579887	0.0433919867210277	1.77625999826917	0.0761152556097934	.  
df.mm.trans1:probe4	0.373056812013196	0.0433919867210277	8.59736647717061	5.12007345209826e-17	***
df.mm.trans1:probe5	0.513982037844502	0.0433919867210277	11.8450911489477	1.10932119422074e-29	***
df.mm.trans1:probe6	0.239599427277966	0.0433919867210277	5.52174365323209	4.70014986447195e-08	***
df.mm.trans1:probe7	0.84670507104879	0.0433919867210278	19.5129362592305	2.62985547616585e-68	***
df.mm.trans1:probe8	0.178489200898596	0.0433919867210277	4.11341389012963	4.35185828125567e-05	***
df.mm.trans1:probe9	0.167609510308897	0.0433919867210277	3.86268348085739	0.000122326691350790	***
df.mm.trans1:probe10	0.191012998276477	0.0433919867210277	4.40203394014943	1.23614904074436e-05	***
df.mm.trans1:probe11	0.26974330189154	0.0433919867210277	6.21643124168874	8.64145042004959e-10	***
df.mm.trans1:probe12	0.192127175181548	0.0433919867210277	4.42771096001565	1.10127589228879e-05	***
df.mm.trans1:probe13	0.233685502722109	0.0433919867210277	5.38545294605893	9.81318423442158e-08	***
df.mm.trans1:probe14	0.496342921826838	0.0433919867210277	11.4385848478865	6.099650951401e-28	***
df.mm.trans1:probe15	0.66449530530151	0.0433919867210277	15.3137792370197	5.4262372968913e-46	***
df.mm.trans1:probe16	0.95751432743797	0.0433919867210277	22.0666164375912	1.06375490212244e-82	***
df.mm.trans1:probe17	0.607322064188693	0.0433919867210277	13.9961801724645	1.62552207692612e-39	***
df.mm.trans1:probe18	0.844829277843914	0.0433919867210277	19.4697072359333	4.56580505624249e-68	***
df.mm.trans1:probe19	1.08807646686938	0.0433919867210277	25.0755162206505	4.73998277189011e-100	***
df.mm.trans2:probe2	-0.00789963360520951	0.0433919867210277	-0.182052821319226	0.85559287393159	   
df.mm.trans2:probe3	-0.0353546791038018	0.0433919867210277	-0.814774380603987	0.415472763925054	   
df.mm.trans2:probe4	0.0942034307180632	0.0433919867210277	2.17098680739622	0.0302604437933267	*  
df.mm.trans2:probe5	0.0164978518127994	0.0433919867210277	0.380205034603878	0.703906137788698	   
df.mm.trans2:probe6	0.0312214827918036	0.0433919867210277	0.719521855326197	0.472054550513141	   
df.mm.trans3:probe2	0.0893988457576027	0.0433919867210277	2.06026164075774	0.0397348406478637	*  
df.mm.trans3:probe3	-0.219156758779821	0.0433919867210277	-5.05062743931974	5.59534208970308e-07	***
df.mm.trans3:probe4	-0.224363698749890	0.0433919867210277	-5.17062517077982	3.03179896822475e-07	***
df.mm.trans3:probe5	-0.123375521232052	0.0433919867210277	-2.84327892210255	0.00459261170827924	** 
df.mm.trans3:probe6	0.0410175664475216	0.0433919867210277	0.945279752024917	0.344835398104996	   
df.mm.trans3:probe7	0.371675977258238	0.0433919867210277	8.5655441325558	6.57989489372708e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26802394703775	0.171375295752311	24.9045460624986	4.65377173819051e-99	***
df.mm.trans1	0.0486738371095279	0.148689514182936	0.327352183353315	0.743497346013551	   
df.mm.trans2	-0.151271253982523	0.133690069742125	-1.13150703170633	0.258221102038982	   
df.mm.exp2	0.173017308683553	0.175345017751942	0.986724977428888	0.324111264892244	   
df.mm.exp3	0.104074343454490	0.175345017751942	0.593540351410055	0.553007298739423	   
df.mm.exp4	-0.109291841350903	0.175345017751942	-0.623295961026489	0.533288819306287	   
df.mm.exp5	-0.00682236659423466	0.175345017751942	-0.0389082431979114	0.968974405373658	   
df.mm.exp6	-0.182136526996255	0.175345017751942	-1.03873226243543	0.299280407912664	   
df.mm.exp7	-0.249148158175020	0.175345017751942	-1.42090240925742	0.155781087501835	   
df.mm.exp8	-0.275294291737696	0.175345017751942	-1.57001490699411	0.116854051637429	   
df.mm.trans1:exp2	-0.211957343364187	0.162103721621422	-1.30754150024509	0.191449148428323	   
df.mm.trans2:exp2	-0.0422015830508422	0.128826974692942	-0.327583436244072	0.743322539045556	   
df.mm.trans1:exp3	-0.138431245165739	0.162103721621422	-0.853967100700082	0.393409230163306	   
df.mm.trans2:exp3	0.0045133307542333	0.128826974692942	0.0350340506325695	0.97206236715153	   
df.mm.trans1:exp4	0.0324093573421216	0.162103721621422	0.199929754961522	0.841592367101694	   
df.mm.trans2:exp4	0.134955248140337	0.128826974692942	1.04756980020684	0.295190842174646	   
df.mm.trans1:exp5	-0.00131346365874364	0.162103721621422	-0.00810261260880314	0.993537381385556	   
df.mm.trans2:exp5	-0.0527820992896918	0.128826974692942	-0.409713100967382	0.68213913639078	   
df.mm.trans1:exp6	0.0726873527518957	0.162103721621422	0.448400271288344	0.654000194711528	   
df.mm.trans2:exp6	0.158307943657194	0.128826974692942	1.2288415841055	0.219535490231300	   
df.mm.trans1:exp7	0.135312259957128	0.162103721621422	0.83472642456129	0.404150569185559	   
df.mm.trans2:exp7	0.0986330389452996	0.128826974692942	0.765624118554288	0.444152646300384	   
df.mm.trans1:exp8	0.224937547991879	0.162103721621422	1.38761495258696	0.165686619829102	   
df.mm.trans2:exp8	0.0188817686041294	0.128826974692942	0.146566886703145	0.883515206075168	   
df.mm.trans1:probe2	-0.0210670924202824	0.103015117247032	-0.204504862813127	0.838017190757566	   
df.mm.trans1:probe3	-0.0745983123316772	0.103015117247032	-0.724149176598902	0.469211026587902	   
df.mm.trans1:probe4	-0.139310514794076	0.103015117247032	-1.35233078908222	0.176697068649227	   
df.mm.trans1:probe5	-0.0336505618907513	0.103015117247032	-0.326656541195375	0.744023270513205	   
df.mm.trans1:probe6	-0.0232623246890118	0.103015117247032	-0.225814669833637	0.821410098701355	   
df.mm.trans1:probe7	-0.177531625310158	0.103015117247032	-1.72335507694890	0.085256658245995	.  
df.mm.trans1:probe8	-0.165950367230206	0.103015117247032	-1.61093217835451	0.107635839842313	   
df.mm.trans1:probe9	-0.105249926757941	0.103015117247032	-1.02169399570308	0.307271311577307	   
df.mm.trans1:probe10	-0.0550359793473725	0.103015117247032	-0.534251484812618	0.593333622647293	   
df.mm.trans1:probe11	-0.108386559053739	0.103015117247032	-1.05214226756474	0.293089728838780	   
df.mm.trans1:probe12	-0.0726008410682397	0.103015117247032	-0.704759097581202	0.481189663073687	   
df.mm.trans1:probe13	-0.0393174351005479	0.103015117247032	-0.381666653897643	0.702822033957347	   
df.mm.trans1:probe14	-0.166280382039770	0.103015117247032	-1.61413573544772	0.106939187011036	   
df.mm.trans1:probe15	-0.098129686800804	0.103015117247032	-0.952575596895042	0.341127052387559	   
df.mm.trans1:probe16	-0.0184360072081719	0.103015117247032	-0.178964094793603	0.858016593628533	   
df.mm.trans1:probe17	-0.0462275323038653	0.103015117247032	-0.448745131192843	0.653751491072728	   
df.mm.trans1:probe18	0.0296524885863057	0.103015117247032	0.287845991721764	0.773548042855132	   
df.mm.trans1:probe19	-0.0761647069610198	0.103015117247032	-0.739354659747421	0.459934324628713	   
df.mm.trans2:probe2	0.117459906972421	0.103015117247032	1.14022009692762	0.254576415145183	   
df.mm.trans2:probe3	0.129281882181087	0.103015117247032	1.25497971206564	0.20989615897385	   
df.mm.trans2:probe4	0.128848808954898	0.103015117247032	1.25077573465181	0.211425448064951	   
df.mm.trans2:probe5	0.120903658638631	0.103015117247032	1.17364967268544	0.240926173190138	   
df.mm.trans2:probe6	0.00407957245323558	0.103015117247032	0.0396016872305519	0.968421737098394	   
df.mm.trans3:probe2	-0.00552057802745179	0.103015117247032	-0.0535899795581786	0.957276809555393	   
df.mm.trans3:probe3	0.0704583061542324	0.103015117247032	0.68396084028398	0.494221497948829	   
df.mm.trans3:probe4	-0.0569276805588802	0.103015117247032	-0.552614820816705	0.580699903203523	   
df.mm.trans3:probe5	-0.0787603315203801	0.103015117247032	-0.764551200106988	0.444791086309809	   
df.mm.trans3:probe6	-0.00655080041347943	0.103015117247032	-0.0635906708504784	0.949313933494596	   
df.mm.trans3:probe7	0.0820089660728648	0.103015117247032	0.796086712945307	0.426245862637403