fitVsDatCorrelation=0.967496984574513
cont.fitVsDatCorrelation=0.29528832028518

fstatistic=9825.42371715243,58,830
cont.fstatistic=675.045038430612,58,830

residuals=-1.13826608206099,-0.103863867696232,-0.00651982040511978,0.105512293096030,1.16109445223040
cont.residuals=-1.10577428341504,-0.431676122543572,-0.197179654382868,0.149839317820656,2.31982298034917

predictedValues:
Include	Exclude	Both
Lung	132.208513260090	92.6193372296257	90.4804429886444
cerebhem	123.097296745053	99.0921418612545	88.9377599408693
cortex	122.800421616037	91.06221315879	78.4305626171277
heart	125.868221992491	96.3124009952747	84.644400271786
kidney	113.884668547193	93.961517020678	83.7063290236044
liver	107.421055495173	99.0222473462437	80.9026158449104
stomach	121.514350492413	103.567859298018	83.940074740356
testicle	123.941571967914	96.7593781268637	78.6528250211342


diffExp=39.5891760304638,24.0051548837989,31.7382084572469,29.5558209972159,19.9231515265152,8.39880814892965,17.9464911943948,27.1821938410504
diffExpScore=0.994983420331607
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,1,0,0,0,0
diffExp1.3Score=0.75
diffExp1.2=1,1,1,1,1,0,0,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	105.011442022247	88.5182317988484	86.800610601244
cerebhem	101.186841597386	99.7550576667365	90.2889799102571
cortex	104.047032632274	92.2072512668279	133.460952276304
heart	130.949637650457	93.7188627704552	98.6249028687699
kidney	109.347536212827	123.959181088571	104.510159858274
liver	111.938304671959	106.124841794921	110.078119388279
stomach	112.872253295124	94.8422196774486	106.249460330065
testicle	92.917746755293	132.445312878098	95.9220911867471
cont.diffExp=16.4932102233981,1.43178393064939,11.8397813654459,37.2307748800018,-14.6116448757442,5.81346287703833,18.0300336176753,-39.5275661228048
cont.diffExpScore=3.84559387192049

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,-1
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,1,0,0,0,-1
cont.diffExp1.3Score=2
cont.diffExp1.2=0,0,0,1,0,0,0,-1
cont.diffExp1.2Score=2

tran.correlation=-0.283972451976020
cont.tran.correlation=-0.443090763090394

tran.covariance=-0.000743334826259207
cont.tran.covariance=-0.00654596662930318

tran.mean=108.945824697070
cont.tran.mean=106.240109611217

weightedLogRatios:
wLogRatio
Lung	1.67493874583648
cerebhem	1.02052606806839
cortex	1.39373609432235
heart	1.25827961608942
kidney	0.892090996310864
liver	0.377428579100846
stomach	0.754301854947193
testicle	1.16265442196494

cont.weightedLogRatios:
wLogRatio
Lung	0.780601192646322
cerebhem	0.065694722743986
cortex	0.553819987030212
heart	1.57474025580282
kidney	-0.596658548126393
liver	0.250194112809976
stomach	0.807421910542762
testicle	-1.66910891810668

varWeightedLogRatios=0.161160713186647
cont.varWeightedLogRatios=0.980925002171472

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.97728428127741	0.0856346865840464	58.1222922605367	8.07652904361283e-295	***
df.mm.trans1	-0.152399981542146	0.0735670357616747	-2.07157974987406	0.0386130726984251	*  
df.mm.trans2	-0.328090465489232	0.0651913100915528	-5.03273312084802	5.93071929878506e-07	***
df.mm.exp2	0.0133438780742521	0.0836863077008442	0.159451150861535	0.873352234126896	   
df.mm.exp3	0.0521451627694722	0.0836863077008442	0.623102681933071	0.533388218769081	   
df.mm.exp4	0.0566290931257373	0.0836863077008442	0.67668289689839	0.498795702816587	   
df.mm.exp5	-0.0569875678848887	0.0836863077008442	-0.680966450194024	0.496082649038746	   
df.mm.exp6	-0.0288899415962622	0.0836863077008442	-0.345217065849480	0.7300186563128	   
df.mm.exp7	0.102411722189941	0.0836863077008442	1.22375720716506	0.221391112398289	   
df.mm.exp8	0.119249432139653	0.0836863077008442	1.42495750399142	0.154545391156844	   
df.mm.trans1:exp2	-0.0847491271218102	0.0765827308352063	-1.10663495800609	0.268772414094693	   
df.mm.trans2:exp2	0.0542083198497528	0.0566432106716357	0.957013545083132	0.338839082169217	   
df.mm.trans1:exp3	-0.125965035874847	0.0765827308352063	-1.64482298425611	0.100384966022668	   
df.mm.trans2:exp3	-0.0691001739818786	0.0566432106716357	-1.21991979555073	0.222841793453132	   
df.mm.trans1:exp4	-0.105773912834856	0.0765827308352063	-1.38117186056560	0.167597850644261	   
df.mm.trans2:exp4	-0.0175299532240299	0.0566432106716357	-0.309480218655898	0.757033933697655	   
df.mm.trans1:exp5	-0.0922064973921003	0.0765827308352063	-1.20401161445279	0.228928337946337	   
df.mm.trans2:exp5	0.0713749277084166	0.0566432106716357	1.26007913149877	0.207994981602479	   
df.mm.trans1:exp6	-0.178734170270542	0.0765827308352063	-2.3338704211939	0.0198403421950598	*  
df.mm.trans2:exp6	0.0957365419289934	0.0566432106716357	1.69016799707884	0.0913713113031878	.  
df.mm.trans1:exp7	-0.186759677503106	0.0765827308352063	-2.43866568175771	0.0149502688634106	*  
df.mm.trans2:exp7	0.00931737584034487	0.0566432106716357	0.164492367750096	0.869383604363174	   
df.mm.trans1:exp8	-0.183819493556320	0.0765827308352063	-2.40027342393770	0.0166021275020217	*  
df.mm.trans2:exp8	-0.0755201186085876	0.0566432106716357	-1.33325985079452	0.182812351319678	   
df.mm.trans1:probe2	-0.136854948564087	0.0533498695973739	-2.56523492178926	0.0104852508918564	*  
df.mm.trans1:probe3	-0.561471201788621	0.0533498695973739	-10.5243219154234	2.12039977890866e-24	***
df.mm.trans1:probe4	-0.378352099482802	0.0533498695973739	-7.09190298567152	2.83315004021791e-12	***
df.mm.trans1:probe5	-0.570695756595663	0.0533498695973739	-10.6972287074485	4.15618546585216e-25	***
df.mm.trans1:probe6	-0.568105407276154	0.0533498695973739	-10.6486747121144	6.58106969417655e-25	***
df.mm.trans1:probe7	-0.152260507287871	0.0533498695973739	-2.85399961493750	0.0044247474103125	** 
df.mm.trans1:probe8	-0.467178022771801	0.0533498695973739	-8.75687281520172	1.11325332349595e-17	***
df.mm.trans1:probe9	-0.461692546042174	0.0533498695973739	-8.65405200662197	2.55493448800251e-17	***
df.mm.trans1:probe10	-0.602818473957367	0.0533498695973739	-11.2993429694726	1.22558616661164e-27	***
df.mm.trans1:probe11	-0.67403556670313	0.0533498695973739	-12.6342495640572	1.35832113286476e-33	***
df.mm.trans1:probe12	-0.583688909845729	0.0533498695973739	-10.9407748182099	4.04948509858323e-26	***
df.mm.trans1:probe13	-0.504788600710404	0.0533498695973739	-9.46185256908766	3.03095113673075e-20	***
df.mm.trans1:probe14	-0.653890807277221	0.0533498695973739	-12.2566524006913	7.30866954724387e-32	***
df.mm.trans1:probe15	1.37022393267627	0.0533498695973739	25.6837353683751	1.60484488235004e-107	***
df.mm.trans1:probe16	1.58696100632679	0.0533498695973739	29.7462958823223	6.30740333699994e-133	***
df.mm.trans1:probe17	1.5077941865314	0.0533498695973739	28.2623781072113	1.23862640351179e-123	***
df.mm.trans1:probe18	1.3906674247453	0.0533498695973739	26.0669320326465	6.5829642149381e-110	***
df.mm.trans1:probe19	1.15726020534907	0.0533498695973739	21.6919031683263	5.22842045263638e-83	***
df.mm.trans1:probe20	1.14730278808515	0.0533498695973739	21.5052594644323	6.9431297276966e-82	***
df.mm.trans2:probe2	-0.380806726454343	0.0533498695973739	-7.1379129757627	2.06884049451926e-12	***
df.mm.trans2:probe3	-0.515142685306769	0.0533498695973739	-9.6559314801423	5.59797731448335e-21	***
df.mm.trans2:probe4	-0.444713127949372	0.0533498695973739	-8.3357865971478	3.17801484550088e-16	***
df.mm.trans2:probe5	-0.289849829329473	0.0533498695973739	-5.432999771451	7.28535227783108e-08	***
df.mm.trans2:probe6	-0.421317430532712	0.0533498695973739	-7.89725324002386	9.02216128448616e-15	***
df.mm.trans3:probe2	-0.49988709241789	0.0533498695973739	-9.36997777483782	6.67973784821083e-20	***
df.mm.trans3:probe3	0.396048954213107	0.0533498695973739	7.42361616255201	2.82587856319433e-13	***
df.mm.trans3:probe4	0.245564498004285	0.0533498695973739	4.60290718342022	4.81805347729424e-06	***
df.mm.trans3:probe5	0.0144542118780129	0.0533498695973739	0.270932468759480	0.786510334585656	   
df.mm.trans3:probe6	0.370967897114874	0.0533498695973739	6.95349210625126	7.21932843145427e-12	***
df.mm.trans3:probe7	-0.120981628562410	0.0533498695973739	-2.26770242318202	0.0236036471493716	*  
df.mm.trans3:probe8	-0.275493827680422	0.0533498695973739	-5.16390817371337	3.02954250830941e-07	***
df.mm.trans3:probe9	0.215856620870931	0.0533498695973739	4.04605714128223	5.69496041531716e-05	***
df.mm.trans3:probe10	-0.106920304452668	0.0533498695973739	-2.00413431672063	0.0453801656564803	*  
df.mm.trans3:probe11	-0.147870500094011	0.0533498695973739	-2.77171249358199	0.00570103488535444	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.66121026365389	0.323533910748959	14.4071768330667	3.63401566384175e-42	***
df.mm.trans1	0.0917309001804876	0.277941471284806	0.330036751106103	0.741455473390165	   
df.mm.trans2	-0.185213965067990	0.246297386516009	-0.751993221235224	0.452268325430763	   
df.mm.exp2	0.043006868772951	0.316172797339798	0.136023304771316	0.891835840881025	   
df.mm.exp3	-0.398591333832557	0.316172797339798	-1.26067560899043	0.207780016333183	   
df.mm.exp4	0.150124289323353	0.316172797339798	0.474817222058516	0.63504216429327	   
df.mm.exp5	0.191535052637571	0.316172797339798	0.60579232068382	0.544818288609279	   
df.mm.exp6	0.00770953280522135	0.316172797339798	0.0243839219252494	0.980552233922724	   
df.mm.exp7	-0.060982585244343	0.316172797339798	-0.192877394125731	0.847102174152037	   
df.mm.exp8	0.180683974232898	0.316172797339798	0.571472232124741	0.567834291278906	   
df.mm.trans1:exp2	-0.0801074599756404	0.289334980850681	-0.276867524763560	0.78195076400714	   
df.mm.trans2:exp2	0.0765023493141016	0.214002061512602	0.357484169887759	0.720820254475182	   
df.mm.trans1:exp3	0.389365051733463	0.289334980850681	1.34572408282151	0.178758996425914	   
df.mm.trans2:exp3	0.439421568624745	0.214002061512602	2.05335203557780	0.0403513390788496	*  
df.mm.trans1:exp4	0.0706191986823673	0.289334980850681	0.244074181679443	0.807233686399609	   
df.mm.trans2:exp4	-0.0930333499123173	0.214002061512602	-0.434731092096694	0.663870629125149	   
df.mm.trans1:exp5	-0.151073152833759	0.289334980850681	-0.52213926013918	0.60171269084248	   
df.mm.trans2:exp5	0.145208734181203	0.214002061512602	0.678538950301899	0.497619173973646	   
df.mm.trans1:exp6	0.0561690197114307	0.289334980850681	0.194131451186050	0.846120457732934	   
df.mm.trans2:exp6	0.173698081258907	0.214002061512602	0.8116654579455	0.417216324560973	   
df.mm.trans1:exp7	0.133169946783442	0.289334980850681	0.460262172212656	0.645448601608417	   
df.mm.trans2:exp7	0.129988710787978	0.214002061512602	0.607418030785297	0.543739664440199	   
df.mm.trans1:exp8	-0.303038631783017	0.289334980850681	-1.04736257915322	0.295237280412715	   
df.mm.trans2:exp8	0.22227731317671	0.214002061512602	1.03866902779168	0.299261172082673	   
df.mm.trans1:probe2	-0.343067061844121	0.201559585692475	-1.70206274569123	0.0891180720939437	.  
df.mm.trans1:probe3	-0.280356970939249	0.201559585692475	-1.39093841642936	0.164616863312162	   
df.mm.trans1:probe4	-0.201037975121012	0.201559585692475	-0.997412127189729	0.31885519540386	   
df.mm.trans1:probe5	-0.269046967252651	0.201559585692475	-1.33482595892583	0.182299337445669	   
df.mm.trans1:probe6	-0.0865689965830992	0.201559585692475	-0.429495805350482	0.667673914039617	   
df.mm.trans1:probe7	-0.458522113811897	0.201559585692475	-2.27487128551393	0.0231679570218368	*  
df.mm.trans1:probe8	-0.115460099166164	0.201559585692475	-0.572833580548852	0.566912522880756	   
df.mm.trans1:probe9	-0.194816402290738	0.201559585692475	-0.966544962976727	0.334053098405580	   
df.mm.trans1:probe10	-0.307063988446328	0.201559585692475	-1.52344026403598	0.128029495401856	   
df.mm.trans1:probe11	0.38992057886549	0.201559585692475	1.93451766397457	0.0533890576521913	.  
df.mm.trans1:probe12	-0.0579508311837115	0.201559585692475	-0.287512156688636	0.77379196316225	   
df.mm.trans1:probe13	-0.0226286113087200	0.201559585692475	-0.112267601815997	0.91063837457512	   
df.mm.trans1:probe14	-0.186117445284647	0.201559585692475	-0.923386722815609	0.356074060298522	   
df.mm.trans1:probe15	-0.243775344949395	0.201559585692475	-1.20944555483126	0.226836094081245	   
df.mm.trans1:probe16	-0.146597757538646	0.201559585692475	-0.72731722004189	0.467236748793078	   
df.mm.trans1:probe17	-0.0785394431503812	0.201559585692475	-0.389658685199973	0.69688891205593	   
df.mm.trans1:probe18	-0.389358156432494	0.201559585692475	-1.93172731078415	0.0537332835386526	.  
df.mm.trans1:probe19	-0.054350594409572	0.201559585692475	-0.26965025862127	0.787496354750735	   
df.mm.trans1:probe20	-0.0196891063276415	0.201559585692475	-0.097683800351136	0.92220696312481	   
df.mm.trans2:probe2	0.0847609957081074	0.201559585692475	0.420525748834538	0.674210275204579	   
df.mm.trans2:probe3	0.367856093743608	0.201559585692475	1.82504886820345	0.0683529391225104	.  
df.mm.trans2:probe4	-0.254896609826832	0.201559585692475	-1.26462162020780	0.206361973786908	   
df.mm.trans2:probe5	-0.0327616962908013	0.201559585692475	-0.162540998376463	0.870919408194829	   
df.mm.trans2:probe6	-0.0423506822952315	0.201559585692475	-0.210114950126197	0.833629523811121	   
df.mm.trans3:probe2	0.216178931200526	0.201559585692475	1.07253113493871	0.283793275204668	   
df.mm.trans3:probe3	-0.0311616414955796	0.201559585692475	-0.154602627250503	0.87717218416029	   
df.mm.trans3:probe4	-0.204664940973323	0.201559585692475	-1.01540663655454	0.310207927959812	   
df.mm.trans3:probe5	-0.0779867626438847	0.201559585692475	-0.386916664746826	0.69891708343356	   
df.mm.trans3:probe6	0.0243956194353750	0.201559585692475	0.121034280515916	0.903693176884165	   
df.mm.trans3:probe7	-0.0206583296398548	0.201559585692475	-0.102492419642962	0.918390585672637	   
df.mm.trans3:probe8	-0.375344394561459	0.201559585692475	-1.86220066523718	0.0629279937215099	.  
df.mm.trans3:probe9	-0.104965291311015	0.201559585692475	-0.520765563941787	0.60266898915402	   
df.mm.trans3:probe10	-0.366962437155595	0.201559585692475	-1.82061515901049	0.069025344777961	.  
df.mm.trans3:probe11	-0.204079720886240	0.201559585692475	-1.01250317708834	0.311592591634375