fitVsDatCorrelation=0.876664533309878
cont.fitVsDatCorrelation=0.282720787138123

fstatistic=7886.52445217035,64,968
cont.fstatistic=1972.67190018525,64,968

residuals=-0.526920991286588,-0.0997098602205901,-0.00478020457803694,0.0752529445484734,1.19679662068849
cont.residuals=-0.598014706871455,-0.249179659714900,-0.0860059547748376,0.166777686482888,1.79869624079207

predictedValues:
Include	Exclude	Both
Lung	80.231895754531	57.869380950426	55.7626013065339
cerebhem	91.9183386292783	70.5353925523876	57.8943331998058
cortex	81.548825662371	54.0030657999627	61.5389364966127
heart	80.5394251012528	49.2201368626067	60.7371283887895
kidney	77.4230759219859	56.3011738701286	55.1845484398273
liver	97.4309331219839	48.0741332783947	54.0353918085343
stomach	89.8575965081697	60.607827767289	58.9128994421035
testicle	78.9951398601552	54.9222901391103	55.861957542581


diffExp=22.3625148041049,21.3829460768907,27.5457598624083,31.3192882386461,21.1219020518573,49.3567998435892,29.2497687408807,24.0728497210449
diffExpScore=0.995602691368761
diffExp1.5=0,0,1,1,0,1,0,0
diffExp1.5Score=0.75
diffExp1.4=0,0,1,1,0,1,1,1
diffExp1.4Score=0.833333333333333
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	57.2104803194665	56.3140090150836	61.8697929235662
cerebhem	61.141539472779	50.7452792460478	63.1011200874346
cortex	57.755602554157	46.3562088266781	63.2116237158845
heart	66.5967304315183	52.0577005052683	62.9533779540831
kidney	66.660296935342	61.4449049702813	53.2198953496339
liver	60.8720629545598	68.1226029266095	60.606887730723
stomach	63.2935471536322	64.4948669396867	57.768391935414
testicle	64.428775501004	59.0062444584596	62.1945161203705
cont.diffExp=0.896471304382935,10.3962602267312,11.3993937274789,14.5390299262500,5.21539196506065,-7.25053997204967,-1.20131978605450,5.42253104254445
cont.diffExpScore=1.39348871922107

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=0,1,1,1,0,0,0,0
cont.diffExp1.2Score=0.75

tran.correlation=0.160041011259777
cont.tran.correlation=0.28322268328576

tran.covariance=0.00131085844024204
cont.tran.covariance=0.00240699165401264

tran.mean=70.5924144862521
cont.tran.mean=59.7813032631609

weightedLogRatios:
wLogRatio
Lung	1.37932007621997
cerebhem	1.16201516432610
cortex	1.72906588087579
heart	2.03996145903302
kidney	1.33480620236377
liver	2.98520467340035
stomach	1.69387086126014
testicle	1.52207381235186

cont.weightedLogRatios:
wLogRatio
Lung	0.0637885207455652
cerebhem	0.74922000544382
cortex	0.867651041596696
heart	1.00380789023606
kidney	0.338817776081011
liver	-0.468711855090866
stomach	-0.0781644434679534
testicle	0.362359201966521

varWeightedLogRatios=0.331130099329847
cont.varWeightedLogRatios=0.255774970723638

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.89322609429588	0.0849989955297974	57.5680461139156	0	***
df.mm.trans1	-0.144811150801004	0.072811060887374	-1.98886198108006	0.0469976047518432	*  
df.mm.trans2	-0.856250777063136	0.0637449115858594	-13.4324568935959	7.60911243516903e-38	***
df.mm.exp2	0.296389517992643	0.0806755012450455	3.67384786482309	0.000252025417861917	***
df.mm.exp3	-0.151433480038479	0.0806755012450455	-1.87706897015132	0.060809216182195	.  
df.mm.exp4	-0.24351167802823	0.0806755012450455	-3.01840923539579	0.00260761355216146	** 
df.mm.exp5	-0.0526888742098933	0.0806755012450456	-0.653096335278475	0.513849257113807	   
df.mm.exp6	0.040242636223261	0.0806755012450455	0.498821024997751	0.618018805673018	   
df.mm.exp7	0.104583999971116	0.0806755012450455	1.29635389129409	0.195162553980383	   
df.mm.exp8	-0.0695841338008186	0.0806755012450455	-0.862518766254235	0.388615739616467	   
df.mm.trans1:exp2	-0.160410097194165	0.073806246833217	-2.17339458483305	0.0299924696256085	*  
df.mm.trans2:exp2	-0.0984633304090632	0.0511621304565156	-1.92453538448229	0.0545803583191238	.  
df.mm.trans1:exp3	0.167714270300973	0.073806246833217	2.27235874329125	0.0232834511794895	*  
df.mm.trans2:exp3	0.0822858807811206	0.0511621304565155	1.60833569765157	0.108087753889782	   
df.mm.trans1:exp4	0.247337356888905	0.073806246833217	3.35117103905613	0.000835825593794521	***
df.mm.trans2:exp4	0.0816260853338054	0.0511621304565156	1.59543952930542	0.110940389618062	   
df.mm.trans1:exp5	0.0170526104133946	0.073806246833217	0.231045624795541	0.817328181859696	   
df.mm.trans2:exp5	0.0252158411234581	0.0511621304565156	0.492861436739619	0.622222274224479	   
df.mm.trans1:exp6	0.153979974041393	0.073806246833217	2.08627291927400	0.0372151891244938	*  
df.mm.trans2:exp6	-0.225686791724895	0.0511621304565156	-4.41120785454222	1.14321073677784e-05	***
df.mm.trans1:exp7	0.00872101775718177	0.073806246833217	0.118160970532603	0.905964628131183	   
df.mm.trans2:exp7	-0.0583483624437663	0.0511621304565156	-1.14045998325575	0.254376802102867	   
df.mm.trans1:exp8	0.0540493251331605	0.073806246833217	0.732313692298946	0.464154392912691	   
df.mm.trans2:exp8	0.0173149950945579	0.0511621304565156	0.338433816966917	0.735109684316697	   
df.mm.trans1:probe2	-0.97833905275048	0.0540206412514454	-18.1104672230136	2.34737395803767e-63	***
df.mm.trans1:probe3	-0.725961943693692	0.0540206412514454	-13.4386028539465	7.09485580703233e-38	***
df.mm.trans1:probe4	-0.959378672792948	0.0540206412514454	-17.7594832376648	2.68441113124898e-61	***
df.mm.trans1:probe5	-0.840864760232185	0.0540206412514454	-15.5656197474273	6.33631044337994e-49	***
df.mm.trans1:probe6	-0.879406646517437	0.0540206412514454	-16.2790856632770	7.5369490888429e-53	***
df.mm.trans1:probe7	-0.848358928918337	0.0540206412514454	-15.7043476209316	1.11359379124757e-49	***
df.mm.trans1:probe8	-0.662412374429572	0.0540206412514454	-12.2622086499547	3.10178559736774e-32	***
df.mm.trans1:probe9	-0.844425064608112	0.0540206412514454	-15.6315261175379	2.77711217519457e-49	***
df.mm.trans1:probe10	-0.433983979069959	0.0540206412514454	-8.03366951995126	2.73670257273197e-15	***
df.mm.trans1:probe11	-0.0355465827671632	0.0540206412514454	-0.658018526690704	0.510682678736958	   
df.mm.trans1:probe12	-0.068036726180871	0.0540206412514454	-1.25945795171490	0.208168560585928	   
df.mm.trans1:probe13	0.00440455709947795	0.0540206412514454	0.0815347059464996	0.935033580242009	   
df.mm.trans1:probe14	0.0167734724636821	0.0540206412514454	0.310501172794452	0.756246709153584	   
df.mm.trans1:probe15	-0.226961399941205	0.0540206412514454	-4.20138292851406	2.89798419856582e-05	***
df.mm.trans1:probe16	-0.154740196226935	0.0540206412514454	-2.86446426110862	0.00426744770360442	** 
df.mm.trans1:probe17	-0.942921659386807	0.0540206412514454	-17.4548401785508	1.58090247756334e-59	***
df.mm.trans1:probe18	-0.977376213705795	0.0540206412514454	-18.0926436833003	2.98938767531267e-63	***
df.mm.trans1:probe19	-0.909411357324194	0.0540206412514454	-16.8345161452496	5.67088592554667e-56	***
df.mm.trans1:probe20	-0.97770612005217	0.0540206412514454	-18.0987507256961	2.75176174690226e-63	***
df.mm.trans1:probe21	-1.00262188960906	0.0540206412514454	-18.5599775638026	5.08248427613208e-66	***
df.mm.trans1:probe22	-1.00199524673398	0.0540206412514454	-18.5483775001869	5.95972356848117e-66	***
df.mm.trans2:probe2	0.0944483353391424	0.0540206412514454	1.74837493874835	0.080716121046312	.  
df.mm.trans2:probe3	0.128793649725023	0.0540206412514454	2.38415625474599	0.0173101540250631	*  
df.mm.trans2:probe4	-0.0143746705848309	0.0540206412514454	-0.266095889493838	0.790222026202179	   
df.mm.trans2:probe5	0.0798388884391067	0.0540206412514454	1.47793300097063	0.139750998279149	   
df.mm.trans2:probe6	0.156768919145961	0.0540206412514454	2.90201884898519	0.00379171558113152	** 
df.mm.trans3:probe2	0.809949972838202	0.0540206412514454	14.9933424349444	7.4860456334933e-46	***
df.mm.trans3:probe3	0.034479277412097	0.0540206412514454	0.638261164868613	0.523454651342168	   
df.mm.trans3:probe4	0.0924959463356663	0.0540206412514454	1.71223340176828	0.0871739384874613	.  
df.mm.trans3:probe5	0.0815445500801927	0.0540206412514454	1.50950725854278	0.131495525462116	   
df.mm.trans3:probe6	-0.141474379529103	0.0540206412514454	-2.61889485670105	0.0089594136977257	** 
df.mm.trans3:probe7	-0.0276837242853775	0.0540206412514454	-0.512465673195555	0.608442046759198	   
df.mm.trans3:probe8	0.355921602004925	0.0540206412514454	6.58862230731852	7.27764278669862e-11	***
df.mm.trans3:probe9	0.254995993982358	0.0540206412514454	4.7203437070554	2.70145263427824e-06	***
df.mm.trans3:probe10	-0.0291091153758274	0.0540206412514454	-0.538851718555795	0.590113071423713	   
df.mm.trans3:probe11	0.0151090875630097	0.0540206412514454	0.279691007233378	0.779774329539097	   
df.mm.trans3:probe12	0.134030653160993	0.0540206412514454	2.48110074327203	0.0132666335649203	*  
df.mm.trans3:probe13	-0.116734889144182	0.0540206412514454	-2.16093120036886	0.0309453462380801	*  
df.mm.trans3:probe14	0.136047938882533	0.0540206412514454	2.51844361212378	0.0119477465488839	*  
df.mm.trans3:probe15	0.334515847642604	0.0540206412514454	6.19237091402822	8.74660081432453e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02620416284565	0.169467595847824	23.7579588162744	1.15609634994493e-98	***
df.mm.trans1	0.00849813695619765	0.145167779487314	0.0585401043276292	0.953330500747428	   
df.mm.trans2	-0.0328713729501438	0.127092053813750	-0.258642235794819	0.795966276885034	   
df.mm.exp2	-0.0573768045142807	0.160847585957921	-0.356715359901583	0.721382646840284	   
df.mm.exp3	-0.206560980430471	0.160847585957921	-1.28420317408126	0.199378142214699	   
df.mm.exp4	0.0559654187041978	0.160847585957921	0.347940681676372	0.727960378924937	   
df.mm.exp5	0.390669773760512	0.160847585957921	2.42881962718865	0.0153292532237532	*  
df.mm.exp6	0.273026479948755	0.160847585957921	1.69742354740829	0.0899380487499965	.  
df.mm.exp7	0.305278874222171	0.160847585957921	1.89793879966613	0.0580014224926842	.  
df.mm.exp8	0.160288430293324	0.160847585957921	0.99652369253	0.319244796365016	   
df.mm.trans1:exp2	0.123831196158816	0.147151941401374	0.841519282583248	0.400264924668251	   
df.mm.trans2:exp2	-0.0467479338424567	0.102005008328351	-0.458290574242948	0.646846560003318	   
df.mm.trans1:exp3	0.216044235080745	0.147151941401374	1.46817115033134	0.142382594160043	   
df.mm.trans2:exp3	0.0119728865695498	0.102005008328351	0.117375477594291	0.906586864499397	   
df.mm.trans1:exp4	0.0959529610463368	0.147151941401374	0.652067245138234	0.514512589430065	   
df.mm.trans2:exp4	-0.134556022370069	0.102005008328351	-1.31911191984748	0.187443645740708	   
df.mm.trans1:exp5	-0.237797350386142	0.147151941401374	-1.61599872975867	0.106420427651341	   
df.mm.trans2:exp5	-0.303472186776663	0.102005008328351	-2.97507143766701	0.00300194941085549	** 
df.mm.trans1:exp6	-0.210989250833834	0.147151941401374	-1.43381900928059	0.151946847863985	   
df.mm.trans2:exp6	-0.0826607461525121	0.102005008328351	-0.810359682403336	0.417932655400263	   
df.mm.trans1:exp7	-0.20423259494014	0.147151941401374	-1.38790282340259	0.165486000535292	   
df.mm.trans2:exp7	-0.169636568087441	0.102005008328351	-1.66302195222990	0.0966317510225641	.  
df.mm.trans1:exp8	-0.0414651764005068	0.147151941401374	-0.281784772974253	0.778168776188525	   
df.mm.trans2:exp8	-0.113588485878820	0.102005008328351	-1.11355792955952	0.265745312410636	   
df.mm.trans1:probe2	0.0997950641238813	0.107704192761089	0.926566195479945	0.354382762696128	   
df.mm.trans1:probe3	0.0355885135877333	0.107704192761089	0.330428302514426	0.74114786054197	   
df.mm.trans1:probe4	-0.0891672901110755	0.107704192761089	-0.827890612474738	0.407936508576987	   
df.mm.trans1:probe5	-0.0159872846159998	0.107704192761089	-0.148436975443129	0.882028836173476	   
df.mm.trans1:probe6	0.069185229160936	0.107704192761089	0.642363378688548	0.520789372932269	   
df.mm.trans1:probe7	-0.0167015335407474	0.107704192761089	-0.155068555017120	0.876799599037006	   
df.mm.trans1:probe8	0.0530095600236152	0.107704192761089	0.49217731143672	0.622705601351684	   
df.mm.trans1:probe9	-0.0443631383910055	0.107704192761089	-0.411897970299191	0.680505398764445	   
df.mm.trans1:probe10	-0.0311437604711755	0.107704192761089	-0.289160149412743	0.772520741721397	   
df.mm.trans1:probe11	0.161530941351589	0.107704192761089	1.49976465363701	0.134001437738285	   
df.mm.trans1:probe12	-0.0108888324772468	0.107704192761089	-0.101099429818861	0.919492466672234	   
df.mm.trans1:probe13	-0.0262261593468653	0.107704192761089	-0.243501749324099	0.80766832290274	   
df.mm.trans1:probe14	0.104016467711845	0.107704192761089	0.965760617533022	0.33440518955707	   
df.mm.trans1:probe15	-0.0166208381756249	0.107704192761089	-0.154319323598604	0.877390129336334	   
df.mm.trans1:probe16	-0.0324075794271671	0.107704192761089	-0.300894316148434	0.763559700508082	   
df.mm.trans1:probe17	0.0897045551122485	0.107704192761089	0.832878951251528	0.405118444192738	   
df.mm.trans1:probe18	-0.160157243895025	0.107704192761089	-1.48701029912817	0.137337730833592	   
df.mm.trans1:probe19	0.124620865315134	0.107704192761089	1.15706605397962	0.247530817028132	   
df.mm.trans1:probe20	0.053396489191604	0.107704192761089	0.495769828664411	0.620169349050846	   
df.mm.trans1:probe21	0.143480910599238	0.107704192761089	1.33217572056372	0.183115995026849	   
df.mm.trans1:probe22	-0.0453771814716126	0.107704192761089	-0.42131304555867	0.6736200378666	   
df.mm.trans2:probe2	0.263653720125077	0.107704192761089	2.44794295715039	0.0145441079101433	*  
df.mm.trans2:probe3	0.165484942787278	0.107704192761089	1.53647632970388	0.124748378974756	   
df.mm.trans2:probe4	0.209390250022367	0.107704192761089	1.9441234798244	0.0521699930645301	.  
df.mm.trans2:probe5	-0.0358458577624391	0.107704192761089	-0.332817663300748	0.739343988537313	   
df.mm.trans2:probe6	0.187138332586141	0.107704192761089	1.73752133309474	0.0826132380563564	.  
df.mm.trans3:probe2	0.124572928405143	0.107704192761089	1.15662097464926	0.247712603143642	   
df.mm.trans3:probe3	0.0616496729820125	0.107704192761089	0.572398078492307	0.567185228835698	   
df.mm.trans3:probe4	0.0761992176258502	0.107704192761089	0.707486084547109	0.479434748925997	   
df.mm.trans3:probe5	0.17995574915744	0.107704192761089	1.6708332753268	0.095077859491605	.  
df.mm.trans3:probe6	0.0889137928934243	0.107704192761089	0.82553696949063	0.409270205169154	   
df.mm.trans3:probe7	0.255775676292780	0.107704192761089	2.37479776539567	0.0177526281904352	*  
df.mm.trans3:probe8	0.110835799547422	0.107704192761089	1.02907599700672	0.303701024914974	   
df.mm.trans3:probe9	0.132667530202890	0.107704192761089	1.2317768398968	0.218331597300712	   
df.mm.trans3:probe10	0.133410171734680	0.107704192761089	1.23867203601453	0.215767260732259	   
df.mm.trans3:probe11	0.201976046662920	0.107704192761089	1.87528490289088	0.0610543879880527	.  
df.mm.trans3:probe12	0.0457902858228699	0.107704192761089	0.42514859123862	0.67082284825737	   
df.mm.trans3:probe13	0.124791000500873	0.107704192761089	1.15864570637177	0.246886386420256	   
df.mm.trans3:probe14	0.218154909436539	0.107704192761089	2.02550062206448	0.0430899487289998	*  
df.mm.trans3:probe15	0.093324254614569	0.107704192761089	0.866486737629442	0.386438014116268