fitVsDatCorrelation=0.956682946820227
cont.fitVsDatCorrelation=0.246616166897766

fstatistic=11172.7521977749,52,692
cont.fstatistic=996.194934337903,52,692

residuals=-0.66464149349335,-0.102905725878566,0.00369994732373722,0.0902275029129664,1.13346994705760
cont.residuals=-1.14102012781232,-0.443115023052713,-0.0375574625340724,0.399127738825004,1.86708096230468

predictedValues:
Include	Exclude	Both
Lung	172.031445963251	392.574040644959	74.1050218483565
cerebhem	113.501675217787	140.416432396248	60.9391225100054
cortex	150.329951813985	214.184144072909	73.1244300996803
heart	147.190158060619	411.814040817183	98.1986644817683
kidney	155.061959679588	207.255767513766	56.5387506989765
liver	158.303767087060	200.732324283472	60.2599393303032
stomach	142.666887734684	323.309253625940	78.8608428660108
testicle	150.087416221607	321.361064284185	75.3383591508255


diffExp=-220.542594681707,-26.914757178461,-63.8541922589241,-264.623882756563,-52.1938078341774,-42.4285571964113,-180.642365891255,-171.273648062579
diffExpScore=0.999022935424166
diffExp1.5=-1,0,0,-1,0,0,-1,-1
diffExp1.5Score=0.8
diffExp1.4=-1,0,-1,-1,0,0,-1,-1
diffExp1.4Score=0.833333333333333
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	117.608081066369	108.632843358220	107.655030695231
cerebhem	126.622092778075	128.050920463231	130.329045918502
cortex	132.081686555894	120.940783722020	133.423843828110
heart	116.619244099614	137.282308798550	120.492524378826
kidney	110.568854242844	113.892179796072	114.491020016796
liver	109.257240591804	118.482939049758	107.057078486511
stomach	121.326540181280	102.314713539351	106.344383616585
testicle	151.871314539267	108.066160817579	113.612954552235
cont.diffExp=8.97523770814841,-1.42882768515639,11.1409028338739,-20.6630646989365,-3.32332555322844,-9.2256984579542,19.0118266419291,43.8051537216879
cont.diffExpScore=2.38524607427924

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

tran.correlation=0.499445671839535
cont.tran.correlation=-0.200733937664640

tran.covariance=0.0267069132636887
cont.tran.covariance=-0.00197222091841665

tran.mean=212.551270588578
cont.tran.mean=120.226118974995

weightedLogRatios:
wLogRatio
Lung	-4.58743218893715
cerebhem	-1.02954762623493
cortex	-1.83721990753137
heart	-5.66498199237258
kidney	-1.50544643287780
liver	-1.23079517729736
stomach	-4.39282084461664
testicle	-4.10510266568073

cont.weightedLogRatios:
wLogRatio
Lung	0.375300843694293
cerebhem	-0.0543861291562744
cortex	0.426442217033338
heart	-0.789603515661338
kidney	-0.139790169751362
liver	-0.383775570060519
stomach	0.80329242510792
testicle	1.65138901142576

varWeightedLogRatios=3.3380258861647
cont.varWeightedLogRatios=0.577478043168769

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	6.69938561800221	0.0805108898726762	83.2109249891157	0	***
df.mm.trans1	-1.51771834994707	0.0673893972266039	-22.5216193111741	1.10921313546706e-84	***
df.mm.trans2	-0.673134521777186	0.0618574671517041	-10.8820252876882	1.44235267817701e-25	***
df.mm.exp2	-1.24836439541625	0.0800705353039211	-15.5908086623609	3.44056553268869e-47	***
df.mm.exp3	-0.727412965820935	0.0800705353039211	-9.08465221395007	1.07682210106442e-18	***
df.mm.exp4	-0.389614524260799	0.0800705353039211	-4.86589133920427	1.41212519469577e-06	***
df.mm.exp5	-0.472066999075569	0.0800705353039211	-5.8956393545236	5.83358247031305e-09	***
df.mm.exp6	-0.547098604861481	0.0800705353039211	-6.83270822138102	1.82874975123554e-11	***
df.mm.exp7	-0.443482195584932	0.0800705353039212	-5.53864407052633	4.33279088762686e-08	***
df.mm.exp8	-0.35312536400271	0.0800705353039211	-4.41017863390526	1.19732296058894e-05	***
df.mm.trans1:exp2	0.832504706386192	0.0691161346769144	12.0450125036326	1.8125608402356e-30	***
df.mm.trans2:exp2	0.220251762000323	0.0560605879517712	3.92881648315578	9.39297969751223e-05	***
df.mm.trans1:exp3	0.592568237455031	0.0691161346769145	8.57351529024315	6.53880465796113e-17	***
df.mm.trans2:exp3	0.121523939140354	0.0560605879517712	2.16772502002477	0.0305198598633326	*  
df.mm.trans1:exp4	0.233662581848377	0.0691161346769145	3.3807240948968	0.00076356934772732	***
df.mm.trans2:exp4	0.437461256459813	0.0560605879517712	7.80336547372924	2.22879678546616e-14	***
df.mm.trans1:exp5	0.368214490517757	0.0691161346769145	5.32747515813764	1.34848218462090e-07	***
df.mm.trans2:exp5	-0.166704536538856	0.0560605879517712	-2.97364945016758	0.00304512564071562	** 
df.mm.trans1:exp6	0.463937083133353	0.0691161346769145	6.71242807923275	3.9890506545512e-11	***
df.mm.trans2:exp6	-0.123654252607598	0.0560605879517712	-2.20572521847216	0.0277312017938767	*  
df.mm.trans1:exp7	0.256317366568926	0.0691161346769145	3.70850262051097	0.000225188842623892	***
df.mm.trans2:exp7	0.249366344300815	0.0560605879517712	4.44815784870727	1.008918144277e-05	***
df.mm.trans1:exp8	0.216665977691711	0.0691161346769145	3.13481039853462	0.00179237505811877	** 
df.mm.trans2:exp8	0.152965507842802	0.0560605879517712	2.72857480507336	0.0065223478154098	** 
df.mm.trans1:probe2	0.0593392328316182	0.0495113706815783	1.19849707278851	0.231133842053600	   
df.mm.trans1:probe3	0.496305969567699	0.0495113706815783	10.0240805846314	3.56705676615726e-22	***
df.mm.trans1:probe4	0.382851521749354	0.0495113706815783	7.7325979159733	3.72546799287542e-14	***
df.mm.trans1:probe5	0.0266990910711837	0.0495113706815783	0.539251705287926	0.589886638425266	   
df.mm.trans1:probe6	0.0282238184331633	0.0495113706815783	0.570047204200398	0.568830702374605	   
df.mm.trans1:probe7	-0.456401168940247	0.0495113706815783	-9.21810813672465	3.57679486285433e-19	***
df.mm.trans1:probe8	-0.124915798664568	0.0495113706815783	-2.52297193442567	0.0118597479551784	*  
df.mm.trans1:probe9	-0.259396967254102	0.0495113706815783	-5.23913928625321	2.14392016025415e-07	***
df.mm.trans1:probe10	-0.311620235363243	0.0495113706815783	-6.29391251087272	5.49417566769275e-10	***
df.mm.trans1:probe11	-0.258715629197339	0.0495113706815783	-5.22537804217163	2.30311930969708e-07	***
df.mm.trans1:probe12	-0.432119398845279	0.0495113706815783	-8.72767998333881	1.93218763125046e-17	***
df.mm.trans2:probe2	-0.147383125377445	0.0495113706815783	-2.97675308416138	0.00301486096790628	** 
df.mm.trans2:probe3	-0.331573410497878	0.0495113706815783	-6.69691438417896	4.40747830054492e-11	***
df.mm.trans2:probe4	-0.202774167140250	0.0495113706815783	-4.09550703906681	4.71055107440451e-05	***
df.mm.trans2:probe5	-0.215259476893145	0.0495113706815783	-4.34767759263908	1.58262373584646e-05	***
df.mm.trans2:probe6	-0.120002646018562	0.0495113706815783	-2.42373912025851	0.0156169847118989	*  
df.mm.trans3:probe2	-0.165818598749517	0.0495113706815783	-3.34910135726082	0.000854698730375452	***
df.mm.trans3:probe3	-0.269537519487511	0.0495113706815783	-5.44395187968	7.24287032679535e-08	***
df.mm.trans3:probe4	-0.292421590637793	0.0495113706815783	-5.90615017545039	5.49024822800328e-09	***
df.mm.trans3:probe5	-0.347077241954589	0.0495113706815783	-7.01005116959377	5.67033796469771e-12	***
df.mm.trans3:probe6	-0.140028821223693	0.0495113706815783	-2.8282154037758	0.00481621565981127	** 
df.mm.trans3:probe7	0.0855237739542599	0.0495113706815783	1.72735621690394	0.0845499088392845	.  
df.mm.trans3:probe8	-0.263247348315629	0.0495113706815783	-5.31690689818803	1.42589781519362e-07	***
df.mm.trans3:probe9	0.122047815515984	0.0495113706815783	2.46504618708515	0.0139412897774251	*  
df.mm.trans3:probe10	-0.298234279991117	0.0495113706815783	-6.02355127490099	2.77096367335958e-09	***
df.mm.trans3:probe11	-0.436064821660245	0.0495113706815783	-8.8073671897452	1.02212740964893e-17	***
df.mm.trans3:probe12	-0.325324090145936	0.0495113706815783	-6.57069448224706	9.85188955171541e-11	***
df.mm.trans3:probe13	-0.309460062816437	0.0495113706815783	-6.25028268368214	7.16269481802719e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.65502240303519	0.268002885716722	17.3692995528247	2.32622922848277e-56	***
df.mm.trans1	0.146959095947225	0.224324348569518	0.655118790645604	0.512608981701978	   
df.mm.trans2	0.0348594978540829	0.205909780974987	0.169295007206664	0.865614078052559	   
df.mm.exp2	0.0471730471347468	0.266537043079142	0.17698495709934	0.859571992825268	   
df.mm.exp3	0.0087911651716035	0.266537043079142	0.0329829020013296	0.973697733317112	   
df.mm.exp4	0.112966463360732	0.266537043079142	0.423830256596601	0.67182131924449	   
df.mm.exp5	-0.0760053220932827	0.266537043079142	-0.285158570138091	0.77560798349616	   
df.mm.exp6	0.0187123653485436	0.266537043079142	0.0702054961380639	0.944050382792266	   
df.mm.exp7	-0.0165432317991438	0.266537043079142	-0.0620672894395084	0.95052716819931	   
df.mm.exp8	0.196580068992985	0.266537043079142	0.7375337653709	0.461047822307125	   
df.mm.trans1:exp2	0.0266762062116825	0.230072274350617	0.115947070488943	0.907728128742935	   
df.mm.trans2:exp2	0.117281166987757	0.186613256539719	0.628471787923569	0.529902293554392	   
df.mm.trans1:exp3	0.107271653904021	0.230072274350617	0.466251981933926	0.641181877053609	   
df.mm.trans2:exp3	0.0985360830990503	0.186613256539719	0.528022954671915	0.597652677803921	   
df.mm.trans1:exp4	-0.121409908980998	0.230072274350617	-0.527703345931966	0.597874405920902	   
df.mm.trans2:exp4	0.121099203600499	0.186613256539719	0.648931409514971	0.5165980121108	   
df.mm.trans1:exp5	0.0142860146607941	0.230072274350617	0.062093595158811	0.950506227303616	   
df.mm.trans2:exp5	0.12328374487423	0.186613256539719	0.660637658654172	0.509064568670908	   
df.mm.trans1:exp6	-0.0923650077835172	0.230072274350617	-0.401460836792348	0.688204810410479	   
df.mm.trans2:exp6	0.068082823789453	0.186613256539719	0.364833801477347	0.715346939200826	   
df.mm.trans1:exp7	0.0476710720290892	0.230072274350617	0.207200420666252	0.835914294870148	   
df.mm.trans2:exp7	-0.0433770650295575	0.186613256539719	-0.232443642182114	0.816262182913504	   
df.mm.trans1:exp8	0.059095728744866	0.230072274350617	0.256857237194985	0.797365332133816	   
df.mm.trans2:exp8	-0.201810216077138	0.186613256539719	-1.08143558404804	0.279880118013386	   
df.mm.trans1:probe2	-0.00264017287808739	0.164812365624549	-0.0160192645016810	0.98722364017633	   
df.mm.trans1:probe3	-0.215257712469112	0.164812365624549	-1.30607743935598	0.191960196770594	   
df.mm.trans1:probe4	0.092378354503424	0.164812365624549	0.560506210522254	0.575315623192909	   
df.mm.trans1:probe5	0.0297019202497417	0.164812365624549	0.180216576208876	0.857035306986303	   
df.mm.trans1:probe6	-0.184673495289489	0.164812365624549	-1.12050752132388	0.262886268127802	   
df.mm.trans1:probe7	-0.213736144048358	0.164812365624549	-1.29684531399337	0.195116655416161	   
df.mm.trans1:probe8	-0.0568436174104004	0.164812365624549	-0.344898983732162	0.730275114694803	   
df.mm.trans1:probe9	-0.234798056819213	0.164812365624549	-1.42463859389104	0.154712510698244	   
df.mm.trans1:probe10	0.0378850872263398	0.164812365624549	0.229867990079361	0.818262280113256	   
df.mm.trans1:probe11	0.0118736361888063	0.164812365624549	0.0720433575709666	0.94258821974495	   
df.mm.trans1:probe12	-0.129483532453836	0.164812365624549	-0.785642096472335	0.432346107168027	   
df.mm.trans2:probe2	-0.0162153285785956	0.164812365624549	-0.0983866017403993	0.921653804643201	   
df.mm.trans2:probe3	-0.079398538691306	0.164812365624549	-0.481751101565887	0.630135085490714	   
df.mm.trans2:probe4	0.209371109670208	0.164812365624549	1.27036044217195	0.204383213540203	   
df.mm.trans2:probe5	-0.040029753988301	0.164812365624549	-0.242880768300425	0.808169736883032	   
df.mm.trans2:probe6	-0.109981655951298	0.164812365624549	-0.667314345829136	0.504793825094876	   
df.mm.trans3:probe2	-0.107585717227524	0.164812365624549	-0.652776973498515	0.514116866179289	   
df.mm.trans3:probe3	-0.183350005217303	0.164812365624549	-1.11247723750889	0.266319325163779	   
df.mm.trans3:probe4	-0.231354708747224	0.164812365624549	-1.40374605916563	0.160843074911526	   
df.mm.trans3:probe5	-0.156614830740237	0.164812365624549	-0.950261408764766	0.342311199848398	   
df.mm.trans3:probe6	-0.191030973166805	0.164812365624549	-1.15908155582199	0.246822748272861	   
df.mm.trans3:probe7	-0.384292721840959	0.164812365624549	-2.3316983551853	0.0200026657272491	*  
df.mm.trans3:probe8	-0.0556428078217916	0.164812365624549	-0.337613064474475	0.735757180280268	   
df.mm.trans3:probe9	-0.184383244893281	0.164812365624549	-1.11874642533386	0.263636527394784	   
df.mm.trans3:probe10	-0.206152940815824	0.164812365624549	-1.25083418367679	0.211417724425346	   
df.mm.trans3:probe11	0.0223007071987619	0.164812365624549	0.135309672391719	0.892406386339966	   
df.mm.trans3:probe12	-0.171306948720960	0.164812365624549	-1.03940592122320	0.298979018935515	   
df.mm.trans3:probe13	-0.203403356092953	0.164812365624549	-1.23415106216190	0.217565576707173