fitVsDatCorrelation=0.83562892950606
cont.fitVsDatCorrelation=0.238477040483681

fstatistic=11799.6949790640,62,922
cont.fstatistic=3764.82690724952,62,922

residuals=-0.470893771850328,-0.0813053368125042,-0.0117866594349109,0.0803149630464687,0.958157238296732
cont.residuals=-0.646618983776801,-0.191929981756775,-0.0221431861832685,0.159174571257729,0.985956908674909

predictedValues:
Include	Exclude	Both
Lung	58.8235005634142	68.6985962340811	67.1900094952261
cerebhem	54.5970325359196	60.4646389734224	61.0170540693862
cortex	54.0660695551925	55.5344596935682	62.211668832281
heart	57.5049542308084	63.290642839604	61.9985939072504
kidney	63.1891587972695	70.0987502399976	71.7914190491297
liver	55.9279025208479	62.4523555553244	68.2029619252668
stomach	62.8003703161248	61.4083356399499	66.9289886773629
testicle	57.2523361767263	63.902301831952	63.7872786669233


diffExp=-9.87509567066696,-5.86760643750281,-1.46839013837569,-5.7856886087956,-6.90959144272806,-6.5244530344765,1.39203467617492,-6.64996565522576
diffExpScore=1.04179248838564
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	62.636972272649	61.9161896968706	61.5967694464941
cerebhem	63.8247331798625	64.801179144446	60.7380391888158
cortex	62.0699887546213	60.2677084397925	60.647226621956
heart	64.8895852798675	59.3934865602035	60.3699632072766
kidney	61.2143270837863	62.5118687868477	63.7481457875389
liver	59.8246659805875	63.8860292575521	66.7210179670097
stomach	62.9533335925803	60.7694168079853	61.7106377221331
testicle	61.2131565223785	55.9055607161066	57.9339650473769
cont.diffExp=0.720782575778486,-0.976445964583455,1.80228031482872,5.49609871966391,-1.29754170306142,-4.06136327696462,2.18391678459499,5.30759580627189
cont.diffExpScore=2.14696129007312

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=0.65116452638457
cont.tran.correlation=-0.042158045595279

tran.covariance=0.00282625700151700
cont.tran.covariance=-4.45936555664963e-05

tran.mean=60.6257128565127
cont.tran.mean=61.7548876297586

weightedLogRatios:
wLogRatio
Lung	-0.644358724398228
cerebhem	-0.413524719456784
cortex	-0.107284267967720
heart	-0.393033442709472
kidney	-0.435637724706843
liver	-0.450104882398876
stomach	0.092547592904119
testicle	-0.450801471363012

cont.weightedLogRatios:
wLogRatio
Lung	0.0478188739381331
cerebhem	-0.0632180191736298
cortex	0.121209894607381
heart	0.365377187107906
kidney	-0.0865198439874037
liver	-0.270892295497693
stomach	0.145632473408604
testicle	0.369052524536497

varWeightedLogRatios=0.0535441092376139
cont.varWeightedLogRatios=0.049054574436715

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93868168339453	0.0694445063847346	56.7169656527409	6.84223868218557e-303	***
df.mm.trans1	0.0469420597278716	0.0596409460976939	0.787077717563013	0.43143864019207	   
df.mm.trans2	0.306992713587149	0.0523685942123997	5.86215303664692	6.35887318701783e-09	***
df.mm.exp2	-0.105860795507198	0.0666323441125512	-1.58872987161286	0.112464224967602	   
df.mm.exp3	-0.220077693530564	0.0666323441125512	-3.30286584483389	0.000993834553384477	***
df.mm.exp4	-0.0242487474592756	0.0666323441125512	-0.363918571111894	0.716002250502152	   
df.mm.exp5	0.0255271149969402	0.0666323441125513	0.383103961550885	0.701730991383898	   
df.mm.exp6	-0.160766274705201	0.0666323441125513	-2.41273628965604	0.0160275256753884	*  
df.mm.exp7	-0.0428712730850672	0.0666323441125512	-0.643400343422584	0.52012435090881	   
df.mm.exp8	-0.0474755872060124	0.0666323441125512	-0.71250063071201	0.476335085485882	   
df.mm.trans1:exp2	0.0312988831182636	0.0611691428087069	0.511677647930166	0.608999154389094	   
df.mm.trans2:exp2	-0.0218092558546526	0.0433564685523939	-0.503021961493412	0.61506896956673	   
df.mm.trans1:exp3	0.135743057993595	0.0611691428087068	2.21914272067049	0.0267197680027683	*  
df.mm.trans2:exp3	0.0073526512411599	0.0433564685523939	0.169586026875658	0.865372912724775	   
df.mm.trans1:exp4	0.00157840758293129	0.0611691428087069	0.0258039840098368	0.97941926782014	   
df.mm.trans2:exp4	-0.0577425225021356	0.0433564685523939	-1.33180871113515	0.183252189783424	   
df.mm.trans1:exp5	0.0460641889198133	0.0611691428087069	0.753062521472125	0.451604582220552	   
df.mm.trans2:exp5	-0.00535091510633325	0.0433564685523939	-0.123416765363788	0.901803979809989	   
df.mm.trans1:exp6	0.110288236469458	0.0611691428087069	1.80300444644713	0.0717139211096897	.  
df.mm.trans2:exp6	0.0654414638802684	0.0433564685523939	1.50938178466233	0.131543843139110	   
df.mm.trans1:exp7	0.108290798791572	0.0611691428087068	1.77035011149704	0.0769992400651044	.  
df.mm.trans2:exp7	-0.0693119071136463	0.0433564685523939	-1.59865204496272	0.110240550860697	   
df.mm.trans1:exp8	0.0204025908843247	0.0611691428087068	0.333543841674051	0.738799640223904	   
df.mm.trans2:exp8	-0.0248977953956383	0.0433564685523939	-0.574257918759013	0.56593335119982	   
df.mm.trans1:probe2	0.506474506122324	0.0438185398826688	11.5584523692138	5.93299439060346e-29	***
df.mm.trans1:probe3	0.113090405869005	0.0438185398826688	2.58088028884172	0.0100083971355649	*  
df.mm.trans1:probe4	0.470053324598374	0.0438185398826688	10.7272703713318	2.21708696426506e-25	***
df.mm.trans1:probe5	-0.188851199465721	0.0438185398826688	-4.30984692715459	1.80928629156834e-05	***
df.mm.trans1:probe6	-0.167168808865661	0.0438185398826688	-3.81502462914744	0.000145241595201720	***
df.mm.trans1:probe7	0.253945445047342	0.0438185398826688	5.79538811031407	9.3524107378893e-09	***
df.mm.trans1:probe8	0.0212188771990739	0.0438185398826688	0.484244277784948	0.628327519970056	   
df.mm.trans1:probe9	0.135049192441176	0.0438185398826688	3.08201032719922	0.00211717181863871	** 
df.mm.trans1:probe10	0.137484336426274	0.0438185398826688	3.13758369846211	0.00175755447099362	** 
df.mm.trans1:probe11	0.339177266838008	0.0438185398826688	7.74049677935891	2.5977694435465e-14	***
df.mm.trans1:probe12	0.466243937147518	0.0438185398826688	10.6403348536022	5.10275432428722e-25	***
df.mm.trans1:probe13	0.353332499031846	0.0438185398826688	8.06353885770613	2.29204822752756e-15	***
df.mm.trans1:probe14	0.418362129866957	0.0438185398826688	9.54760544251791	1.15289219674929e-20	***
df.mm.trans1:probe15	0.103002846856938	0.0438185398826688	2.35066816769214	0.0189495689241972	*  
df.mm.trans1:probe16	0.241093637889628	0.0438185398826688	5.50209200341215	4.86327359568469e-08	***
df.mm.trans1:probe17	-0.00438055704553002	0.0438185398826688	-0.099970401963636	0.920389556021478	   
df.mm.trans1:probe18	-0.0812938397861796	0.0438185398826688	-1.85523844481941	0.0638811920366215	.  
df.mm.trans1:probe19	0.148899662638665	0.0438185398826688	3.39809731308637	0.000707613733418822	***
df.mm.trans1:probe20	-0.174838598025873	0.0438185398826688	-3.99005988090957	7.12959593718447e-05	***
df.mm.trans1:probe21	0.117818717432907	0.0438185398826688	2.68878693238948	0.00730061678635312	** 
df.mm.trans1:probe22	-0.0965942338667829	0.0438185398826688	-2.20441470951404	0.0277415634340142	*  
df.mm.trans2:probe2	-0.0206662798573875	0.0438185398826688	-0.471633238184678	0.637300183164317	   
df.mm.trans2:probe3	0.0263463518930319	0.0438185398826688	0.601260378907614	0.547814366947384	   
df.mm.trans2:probe4	0.0136762138982081	0.0438185398826688	0.312110214873165	0.755027332152186	   
df.mm.trans2:probe5	-0.140191191513635	0.0438185398826688	-3.19935789483218	0.00142436809104236	** 
df.mm.trans2:probe6	-0.182132087905547	0.0438185398826688	-4.15650745992987	3.53240726586861e-05	***
df.mm.trans3:probe2	0.43337727981512	0.0438185398826688	9.8902720395421	5.44867889413079e-22	***
df.mm.trans3:probe3	0.136191460176163	0.0438185398826688	3.10807846497938	0.00194080996432697	** 
df.mm.trans3:probe4	-0.375953137627361	0.0438185398826688	-8.57977327939351	3.98686744011317e-17	***
df.mm.trans3:probe5	0.303665453664108	0.0438185398826688	6.93006783149829	7.8952359628338e-12	***
df.mm.trans3:probe6	0.0983310164283344	0.0438185398826688	2.24405050217628	0.025065736939436	*  
df.mm.trans3:probe7	-0.394894137271654	0.0438185398826688	-9.01203322449919	1.14318091354533e-18	***
df.mm.trans3:probe8	-0.173446443661439	0.0438185398826688	-3.95828898283397	8.12921028000877e-05	***
df.mm.trans3:probe9	-0.351561404171160	0.0438185398826688	-8.02312001067408	3.11984424328501e-15	***
df.mm.trans3:probe10	-0.115060902648266	0.0438185398826688	-2.62584976487943	0.00878652947305771	** 
df.mm.trans3:probe11	-0.177281458763315	0.0438185398826688	-4.04580935918939	5.65091494320852e-05	***
df.mm.trans3:probe12	-0.175570418507855	0.0438185398826688	-4.00676104174107	6.65199069727527e-05	***
df.mm.trans3:probe13	-0.313991857392782	0.0438185398826688	-7.165730721141	1.58368692479193e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95819546360419	0.122777344910609	32.2388097452837	2.69727762269852e-153	***
df.mm.trans1	0.165309804112962	0.105444726891187	1.56773893760997	0.117285188705219	   
df.mm.trans2	0.159454526849842	0.0925872655567309	1.72220797202548	0.085367220144652	.  
df.mm.exp2	0.0783664239150873	0.117805463977024	0.665218923380083	0.506076841858168	   
df.mm.exp3	-0.0205428438261607	0.117805463977024	-0.174379380485842	0.861605599654978	   
df.mm.exp4	0.0138520391013761	0.117805463977024	0.117584012097076	0.906422882933177	   
df.mm.exp5	-0.0477303753489696	0.117805463977024	-0.405162661710482	0.68545193771489	   
df.mm.exp6	-0.0945292432358818	0.117805463977024	-0.802418156549325	0.422517891435151	   
df.mm.exp7	-0.0155039432997061	0.117805463977024	-0.131606317536594	0.895324400284264	   
df.mm.exp8	-0.0638058161560027	0.117805463977024	-0.541620176195281	0.588211042917179	   
df.mm.trans1:exp2	-0.0595813564399303	0.108146566740689	-0.550931557381687	0.581814062465033	   
df.mm.trans2:exp2	-0.0328243155336736	0.0766538977766194	-0.428214565544057	0.6685949228777	   
df.mm.trans1:exp3	0.0114497279312176	0.108146566740689	0.105872320095666	0.915706685186352	   
df.mm.trans2:exp3	-0.00644240245957763	0.0766538977766194	-0.0840453342418637	0.933038632008653	   
df.mm.trans1:exp4	0.021479383237834	0.108146566740689	0.198613639666775	0.842608769154175	   
df.mm.trans2:exp4	-0.055449164098636	0.0766538977766194	-0.723370444386571	0.469635795632059	   
df.mm.trans1:exp5	0.0247559249977800	0.108146566740689	0.228910872937271	0.818988953648885	   
df.mm.trans2:exp5	0.057305123167247	0.0766538977766194	0.747582638709938	0.454902559602226	   
df.mm.trans1:exp6	0.0485915785920108	0.108146566740689	0.449312262575310	0.653312009971568	   
df.mm.trans2:exp6	0.125848254763462	0.0766538977766194	1.64177241358556	0.100978196203555	   
df.mm.trans1:exp7	0.0205419434366399	0.108146566740689	0.189945405163853	0.849393721508071	   
df.mm.trans2:exp7	-0.00319109879302989	0.0766538977766194	-0.041629961236012	0.966802701376763	   
df.mm.trans1:exp8	0.0408122432781805	0.108146566740689	0.377379000630127	0.705978818250516	   
df.mm.trans2:exp8	-0.0383120239773099	0.0766538977766194	-0.499805294819537	0.617331436548982	   
df.mm.trans1:probe2	-0.0136218453850647	0.0774708362796619	-0.175831913520221	0.86046460658352	   
df.mm.trans1:probe3	0.0727084419776134	0.0774708362796619	0.93852661813464	0.348219633585606	   
df.mm.trans1:probe4	-0.0129572065769167	0.0774708362796619	-0.167252700489027	0.867207894326783	   
df.mm.trans1:probe5	-0.0323716387126531	0.0774708362796619	-0.417855805709838	0.676149839861632	   
df.mm.trans1:probe6	-0.0507616278419456	0.0774708362796619	-0.655235315373404	0.512479703070976	   
df.mm.trans1:probe7	0.0185973996834850	0.0774708362796619	0.240056782352914	0.810339597930353	   
df.mm.trans1:probe8	-0.0110912492528901	0.0774708362796619	-0.143166768109380	0.886189756468013	   
df.mm.trans1:probe9	-0.0176963267801741	0.0774708362796619	-0.228425658350868	0.819365995625662	   
df.mm.trans1:probe10	0.0726631353347836	0.0774708362796619	0.937941796219639	0.348519949808242	   
df.mm.trans1:probe11	9.48841545408871e-05	0.0774708362796619	0.00122477256084296	0.999023038081989	   
df.mm.trans1:probe12	0.00861887752417121	0.0774708362796619	0.111253188142412	0.91143978879325	   
df.mm.trans1:probe13	0.0657834544625183	0.0774708362796619	0.849138303155096	0.396024754764187	   
df.mm.trans1:probe14	-0.0107712680759070	0.0774708362796619	-0.139036424455569	0.889451728379878	   
df.mm.trans1:probe15	-0.0102109099550622	0.0774708362796619	-0.131803275211873	0.895168651561834	   
df.mm.trans1:probe16	0.136454055122763	0.0774708362796619	1.76136029602388	0.0785088586812475	.  
df.mm.trans1:probe17	-0.00238518098586121	0.0774708362796619	-0.0307881146041969	0.975445182238996	   
df.mm.trans1:probe18	0.133001396670075	0.0774708362796619	1.71679309346751	0.0863528383458684	.  
df.mm.trans1:probe19	0.0373295583485339	0.0774708362796619	0.48185304485133	0.630024697600024	   
df.mm.trans1:probe20	0.0596206356159401	0.0774708362796619	0.76958812475853	0.441741365771953	   
df.mm.trans1:probe21	-0.00367319742002985	0.0774708362796619	-0.0474139378948999	0.962193588617502	   
df.mm.trans1:probe22	0.0454342721703729	0.0774708362796619	0.586469365147418	0.55770369473921	   
df.mm.trans2:probe2	0.0778427077936774	0.0774708362796619	1.00480014844132	0.315256718504564	   
df.mm.trans2:probe3	0.0312839327205407	0.0774708362796619	0.403815606270324	0.686441959133131	   
df.mm.trans2:probe4	-0.00196759291967904	0.0774708362796619	-0.0253978531040536	0.979743119284296	   
df.mm.trans2:probe5	0.0745211307375333	0.0774708362796619	0.96192495545704	0.336339503394965	   
df.mm.trans2:probe6	-0.0271778587474047	0.0774708362796619	-0.350814061814118	0.72580798603276	   
df.mm.trans3:probe2	-0.189179824307486	0.0774708362796619	-2.44194891125954	0.0147952004360959	*  
df.mm.trans3:probe3	-0.241314566516624	0.0774708362796619	-3.11490850112297	0.00189689044733841	** 
df.mm.trans3:probe4	-0.212215920436513	0.0774708362796619	-2.7393007566155	0.00627582295310088	** 
df.mm.trans3:probe5	-0.0826348173281192	0.0774708362796619	-1.06665709699862	0.286405928924487	   
df.mm.trans3:probe6	-0.193777963645121	0.0774708362796619	-2.50130207637881	0.0125461865995736	*  
df.mm.trans3:probe7	-0.150206473439505	0.0774708362796619	-1.9388776558094	0.0528210874684728	.  
df.mm.trans3:probe8	-0.136226542355565	0.0774708362796619	-1.75842354229663	0.0790072108544935	.  
df.mm.trans3:probe9	-0.118868222283646	0.0774708362796619	-1.53436090265689	0.125283948449733	   
df.mm.trans3:probe10	-0.209623752952405	0.0774708362796619	-2.70584084307138	0.00693894251066523	** 
df.mm.trans3:probe11	-0.251794727651114	0.0774708362796619	-3.25018729295964	0.00119505751607652	** 
df.mm.trans3:probe12	-0.193194071002355	0.0774708362796619	-2.49376514156817	0.0128138726857927	*  
df.mm.trans3:probe13	-0.131517920972884	0.0774708362796619	-1.69764426574817	0.0899123765825066	.