fitVsDatCorrelation=0.789876237364013
cont.fitVsDatCorrelation=0.229637797870470

fstatistic=9710.87364409046,62,922
cont.fstatistic=3846.56521872187,62,922

residuals=-0.523652552707488,-0.0931355568951776,-0.00847668648437334,0.0753645972881756,2.06901577839846
cont.residuals=-0.52421712958729,-0.183727696238348,-0.0416808816061264,0.163237847252312,2.04361682534341

predictedValues:
Include	Exclude	Both
Lung	63.9658162084002	77.0430782973656	69.2596911239357
cerebhem	61.1101087037489	85.708033158611	58.109539751729
cortex	56.3517651461918	70.7249969635848	51.8454726284913
heart	61.3146260039317	79.7818830958882	63.1911891853043
kidney	60.5675657704174	79.2359112809403	57.3249047413079
liver	60.8635088597276	76.6852704529888	55.9021002734266
stomach	57.4653325602041	72.7341622431249	48.9021930393649
testicle	56.9338768752593	76.7215130225742	58.6878700209873


diffExp=-13.0772620889654,-24.5979244548621,-14.3732318173930,-18.4672570919565,-18.6683455105229,-15.8217615932613,-15.2688296829207,-19.7876361473149
diffExpScore=0.992910931085863
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,-1,-1,0,0,-1
diffExp1.3Score=0.8
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	62.6680858180055	57.8637665040776	64.6478325225312
cerebhem	63.0972494706311	61.3961003270773	68.9967824738217
cortex	61.4926446942104	60.2952451602394	64.5824684899495
heart	66.2624865737222	53.4267716773819	57.8175449232276
kidney	65.417512554336	60.4365757173799	70.9172468304793
liver	63.8483566102386	65.624882460448	66.6800338645337
stomach	63.0046201913282	59.697757654609	58.7420418129391
testicle	63.7885940182944	64.9623735443168	66.7133928695596
cont.diffExp=4.80431931392791,1.7011491435538,1.19739953397099,12.8357148963403,4.98093683695614,-1.77652585020944,3.3068625367192,-1.17377952602244
cont.diffExpScore=1.18234100063748

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

tran.correlation=0.588776479670849
cont.tran.correlation=-0.335992915913560

tran.covariance=0.00157820099126531
cont.tran.covariance=-0.000542160526649181

tran.mean=68.5754655401849
cont.tran.mean=62.0801889360185

weightedLogRatios:
wLogRatio
Lung	-0.79081997688137
cerebhem	-1.44840534909555
cortex	-0.941730809966978
heart	-1.11831491529018
kidney	-1.13864925056569
liver	-0.976104077732465
stomach	-0.982337844723587
testicle	-1.25015098795929

cont.weightedLogRatios:
wLogRatio
Lung	0.326858028620378
cerebhem	0.112904095703432
cortex	0.0808023770829691
heart	0.87975777075856
kidney	0.327963867752330
liver	-0.114448229874503
stomach	0.221921926404968
testicle	-0.0759383005698379

varWeightedLogRatios=0.0418074340642876
cont.varWeightedLogRatios=0.0983770640100157

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87515865017654	0.0889281384574236	43.576293368964	4.14833217032646e-226	***
df.mm.trans1	-0.0067977693805594	0.08045931025586	-0.0844870451777744	0.932687543295906	   
df.mm.trans2	0.391724539939641	0.0731981428445806	5.35156391564985	1.10074209906768e-07	***
df.mm.exp2	0.236443545541098	0.100024047252479	2.36386701034272	0.0182918129931999	*  
df.mm.exp3	0.077294561346676	0.100024047252479	0.772759785969968	0.43986260642328	   
df.mm.exp4	0.084299518167742	0.100024047252479	0.842792513233885	0.399563070127140	   
df.mm.exp5	0.162603522350778	0.100024047252479	1.62564430071837	0.104367079595765	   
df.mm.exp6	0.159891031449237	0.100024047252479	1.59852591293014	0.110268598569115	   
df.mm.exp7	0.18332032886172	0.100024047252479	1.83276255957715	0.0671602255422952	.  
df.mm.exp8	0.0449891660426662	0.100024047252479	0.449783499852842	0.652972278990529	   
df.mm.trans1:exp2	-0.282115066736989	0.0970375792761751	-2.90727642673435	0.00373328730858326	** 
df.mm.trans2:exp2	-0.129861712346379	0.0828616081075723	-1.56721206976566	0.117408252250281	   
df.mm.trans1:exp3	-0.204029815585185	0.0970375792761751	-2.10258558701782	0.0357726207374053	*  
df.mm.trans2:exp3	-0.162860210914265	0.0828616081075723	-1.96544834976938	0.0496621893638631	*  
df.mm.trans1:exp4	-0.126629925479678	0.0970375792761751	-1.30495758884587	0.192232879820527	   
df.mm.trans2:exp4	-0.049367792183036	0.0828616081075723	-0.595786074039812	0.551464368272877	   
df.mm.trans1:exp5	-0.217192809787993	0.0970375792761751	-2.23823400591897	0.025443828536053	*  
df.mm.trans2:exp5	-0.134538624854692	0.0828616081075723	-1.62365452381797	0.104791374540226	   
df.mm.trans1:exp6	-0.209606052951791	0.0970375792761751	-2.16005030747149	0.0310258983944916	*  
df.mm.trans2:exp6	-0.164546106370918	0.0828616081075723	-1.98579426744049	0.0473519878262286	*  
df.mm.trans1:exp7	-0.290487293801484	0.0970375792761751	-2.99355462047068	0.0028310612673433	** 
df.mm.trans2:exp7	-0.240873871458398	0.0828616081075723	-2.90694179077108	0.00373724644518033	** 
df.mm.trans1:exp8	-0.161447445334641	0.0970375792761751	-1.66376208618263	0.0964997973846198	.  
df.mm.trans2:exp8	-0.0491717381266163	0.0828616081075723	-0.593420031901635	0.553045627829675	   
df.mm.trans1:probe2	0.0490353091469744	0.0485187896380876	1.01064576245079	0.312451152235800	   
df.mm.trans1:probe3	0.237375172941888	0.0485187896380876	4.89243805776117	1.17499176446457e-06	***
df.mm.trans1:probe4	0.344764795890982	0.0485187896380876	7.10579959769523	2.39333077257352e-12	***
df.mm.trans1:probe5	0.426717144375652	0.0485187896380876	8.79488436456535	6.93087084427265e-18	***
df.mm.trans1:probe6	0.81537291449826	0.0485187896380876	16.8053020403087	1.71987470805899e-55	***
df.mm.trans1:probe7	0.460213053029532	0.0485187896380876	9.48525419661875	1.99051605042386e-20	***
df.mm.trans1:probe8	0.530467144747812	0.0485187896380876	10.9332312018639	3.01431793754985e-26	***
df.mm.trans1:probe9	0.674833601861622	0.0485187896380876	13.9087064392034	4.61061520631332e-40	***
df.mm.trans1:probe10	0.306200092855894	0.0485187896380876	6.31095901484577	4.30171354989055e-10	***
df.mm.trans1:probe11	0.101463755913652	0.0485187896380876	2.09122603161563	0.0367810105823164	*  
df.mm.trans1:probe12	0.207388413490784	0.0485187896380876	4.27439379749041	2.11594306775168e-05	***
df.mm.trans1:probe13	0.220968043947354	0.0485187896380876	4.55427774673697	5.96161231559924e-06	***
df.mm.trans1:probe14	0.159775800758791	0.0485187896380876	3.29307062172396	0.00102869288762905	** 
df.mm.trans1:probe15	0.336370549477868	0.0485187896380876	6.93278937885571	7.75215819934173e-12	***
df.mm.trans1:probe16	0.246488488092734	0.0485187896380876	5.08026869448612	4.56183753856014e-07	***
df.mm.trans1:probe17	0.164134650496421	0.0485187896380876	3.38290900743275	0.000747415148712153	***
df.mm.trans1:probe18	0.362212936706583	0.0485187896380876	7.46541575765615	1.92124133388368e-13	***
df.mm.trans1:probe19	0.240999373086416	0.0485187896380876	4.96713489524541	8.0958124247154e-07	***
df.mm.trans1:probe20	0.217699640321140	0.0485187896380876	4.48691407895806	8.13885970526443e-06	***
df.mm.trans1:probe21	0.237899678421442	0.0485187896380876	4.9032484156342	1.11366814610124e-06	***
df.mm.trans1:probe22	0.262200271584763	0.0485187896380876	5.4040975370691	8.29584178105642e-08	***
df.mm.trans1:probe23	0.317279889613526	0.0485187896380876	6.5393199620227	1.02242845035560e-10	***
df.mm.trans1:probe24	0.00751627416868315	0.0485187896380876	0.154914708811755	0.876922463167381	   
df.mm.trans1:probe25	0.376945604131997	0.0485187896380876	7.76906445819688	2.10294083338567e-14	***
df.mm.trans1:probe26	0.314663023990783	0.0485187896380876	6.48538486507854	1.44130044028092e-10	***
df.mm.trans1:probe27	0.758815784350238	0.0485187896380876	15.6396272456591	4.43829285882286e-49	***
df.mm.trans1:probe28	0.433478274691537	0.0485187896380876	8.93423512674054	2.18936243572807e-18	***
df.mm.trans1:probe29	0.527300525349444	0.0485187896380876	10.8679653652265	5.69049656465946e-26	***
df.mm.trans1:probe30	0.54848384722962	0.0485187896380876	11.3045657428984	7.68333097370739e-28	***
df.mm.trans1:probe31	0.157715262594017	0.0485187896380876	3.25060175182543	0.00119333683735602	** 
df.mm.trans1:probe32	0.104798516647462	0.0485187896380876	2.15995735732853	0.0310331162019899	*  
df.mm.trans2:probe2	0.208014089640536	0.0485187896380876	4.28728934073087	1.99906999774463e-05	***
df.mm.trans2:probe3	0.206375158635587	0.0485187896380876	4.25351003549316	2.31914480560769e-05	***
df.mm.trans2:probe4	0.0927539444202153	0.0485187896380876	1.91171183601420	0.0562229039159089	.  
df.mm.trans2:probe5	0.0195708503627541	0.0485187896380876	0.403366417603107	0.686772211298516	   
df.mm.trans2:probe6	0.170619760297908	0.0485187896380876	3.51657082896335	0.000458471463510577	***
df.mm.trans3:probe2	-0.00423361187557078	0.0485187896380876	-0.0872571617542447	0.930486050681485	   
df.mm.trans3:probe3	-0.0624334124188633	0.0485187896380876	-1.28678833261440	0.198491052998523	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93858586842468	0.141132229040514	27.9070620169547	9.95036478629409e-125	***
df.mm.trans1	0.120664536205333	0.127691886960036	0.944966348904355	0.344923621649103	   
df.mm.trans2	0.130734072218346	0.116168147005884	1.12538656755645	0.260718123578455	   
df.mm.exp2	0.000974490542889017	0.158741844721677	0.00613883846819091	0.995103274286705	   
df.mm.exp3	0.02323869249931	0.158741844721677	0.146392985038410	0.883643165881066	   
df.mm.exp4	0.0876544753978927	0.158741844721677	0.552182542363533	0.580957115092016	   
df.mm.exp5	-0.00611831699867791	0.158741844721677	-0.0385425595211219	0.96926344267291	   
df.mm.exp6	0.113571072040276	0.158741844721677	0.715445081537266	0.474515197246165	   
df.mm.exp7	0.132357698295616	0.158741844721677	0.833792114030678	0.404614095762912	   
df.mm.exp8	0.101987769650264	0.158741844721677	0.642475648617287	0.520724116432676	   
df.mm.trans1:exp2	0.00585036811093604	0.154002210115969	0.0379888581243769	0.969704789095067	   
df.mm.trans2:exp2	0.0582804357197825	0.131504422075609	0.443182326494497	0.657737827499025	   
df.mm.trans1:exp3	-0.0421734432448537	0.154002210115969	-0.273849597438215	0.784261543370933	   
df.mm.trans2:exp3	0.0179231607756423	0.131504422075609	0.136293217313539	0.891619238143089	   
df.mm.trans1:exp4	-0.0318828716439800	0.154002210115969	-0.207028662900169	0.836033186465377	   
df.mm.trans2:exp4	-0.167433907073822	0.131504422075609	-1.27321883501040	0.203261316588004	   
df.mm.trans1:exp5	0.0490559957389797	0.154002210115969	0.318540855368496	0.750146851278451	   
df.mm.trans2:exp5	0.0496214026127127	0.131504422075609	0.37733638024874	0.706010476505676	   
df.mm.trans1:exp6	-0.0949125481195244	0.154002210115969	-0.616306402668193	0.537844442538484	   
df.mm.trans2:exp6	0.0122924635059229	0.131504422075609	0.0934756665358012	0.925545988431916	   
df.mm.trans1:exp7	-0.127001958160200	0.154002210115969	-0.824676204741237	0.409768741124123	   
df.mm.trans2:exp7	-0.101154633111866	0.131504422075609	-0.769210886716091	0.441965131612598	   
df.mm.trans1:exp8	-0.0842656923622035	0.154002210115969	-0.547171967848696	0.584393004199624	   
df.mm.trans2:exp8	0.0137290698022194	0.131504422075609	0.104400061880245	0.916874579109145	   
df.mm.trans1:probe2	0.0977842198939326	0.0770011050579848	1.26990670874525	0.204438264026372	   
df.mm.trans1:probe3	0.0600474695006887	0.0770011050579847	0.779826074644912	0.435693388588446	   
df.mm.trans1:probe4	0.0686701678315811	0.0770011050579847	0.89180756275991	0.37272876382526	   
df.mm.trans1:probe5	0.119153809380503	0.0770011050579848	1.54742986208802	0.122102781975927	   
df.mm.trans1:probe6	0.112597985807765	0.0770011050579847	1.4622905180773	0.144002376378009	   
df.mm.trans1:probe7	0.0506255706929556	0.0770011050579848	0.657465508512282	0.511045736572931	   
df.mm.trans1:probe8	0.0690354855793056	0.0770011050579847	0.896551881006373	0.370192109039432	   
df.mm.trans1:probe9	0.144817064668011	0.0770011050579848	1.88071410869958	0.0603258015184841	.  
df.mm.trans1:probe10	0.0559782014564824	0.0770011050579847	0.726979196133987	0.467423236613666	   
df.mm.trans1:probe11	0.177266045114326	0.0770011050579848	2.30212339135702	0.0215502673441220	*  
df.mm.trans1:probe12	0.142357572606802	0.0770011050579847	1.84877311175731	0.0648105688310429	.  
df.mm.trans1:probe13	0.15590824502271	0.0770011050579847	2.02475334484233	0.0431804963881602	*  
df.mm.trans1:probe14	0.0949940298740222	0.0770011050579847	1.23367099475375	0.217639918371721	   
df.mm.trans1:probe15	0.0396917331496922	0.0770011050579848	0.515469656179646	0.606348438598647	   
df.mm.trans1:probe16	0.092915667519063	0.0770011050579847	1.20667966322164	0.227865043014052	   
df.mm.trans1:probe17	0.0935277241616312	0.0770011050579848	1.21462833671285	0.224818858280677	   
df.mm.trans1:probe18	0.152027538176756	0.0770011050579847	1.97435527791807	0.048639443034959	*  
df.mm.trans1:probe19	0.0401257234852572	0.0770011050579848	0.521105813417106	0.602418193288818	   
df.mm.trans1:probe20	0.0843522200439337	0.0770011050579848	1.09546765569680	0.273597876402984	   
df.mm.trans1:probe21	0.073152079946168	0.0770011050579848	0.95001337826362	0.342354406465828	   
df.mm.trans1:probe22	0.058247474598645	0.0770011050579848	0.756449852957077	0.449572765344738	   
df.mm.trans1:probe23	0.0846009437364621	0.0770011050579848	1.09869778716493	0.272186725872954	   
df.mm.trans1:probe24	0.0417220898611264	0.0770011050579848	0.541837546743102	0.588061337402037	   
df.mm.trans1:probe25	0.0735566802418435	0.0770011050579847	0.955267852149038	0.339692649362277	   
df.mm.trans1:probe26	0.139590921374649	0.0770011050579847	1.81284309192098	0.0701811287914176	.  
df.mm.trans1:probe27	0.0507775098264254	0.0770011050579847	0.659438715693599	0.509778759338355	   
df.mm.trans1:probe28	-0.0117751769350711	0.0770011050579847	-0.152922181132387	0.878493103231428	   
df.mm.trans1:probe29	0.0332462665277577	0.0770011050579847	0.431763498753973	0.666014255982719	   
df.mm.trans1:probe30	0.111621703505147	0.0770011050579847	1.4496117090929	0.147506878652913	   
df.mm.trans1:probe31	0.08653027339949	0.0770011050579847	1.12375365696803	0.261410047364428	   
df.mm.trans1:probe32	0.157919795711244	0.0770011050579847	2.05087700484719	0.0405611161041413	*  
df.mm.trans2:probe2	-0.0497454236555008	0.0770011050579847	-0.646035191547453	0.518417322914384	   
df.mm.trans2:probe3	-0.0538945535959208	0.0770011050579847	-0.699919222657079	0.484154308354664	   
df.mm.trans2:probe4	-0.00454652388540125	0.0770011050579847	-0.059044917368102	0.95292912621698	   
df.mm.trans2:probe5	-0.0337392656452440	0.0770011050579847	-0.438165992810585	0.661368594947298	   
df.mm.trans2:probe6	0.0408688495606792	0.0770011050579847	0.530756662906376	0.595715231001773	   
df.mm.trans3:probe2	-0.0702671799699801	0.0770011050579847	-0.91254768249191	0.361719024562778	   
df.mm.trans3:probe3	0.0071774843915604	0.0770011050579848	0.0932127452736618	0.925754799700461