fitVsDatCorrelation=0.9250865181419
cont.fitVsDatCorrelation=0.219390111292662

fstatistic=8375.7697178819,64,968
cont.fstatistic=1256.15668417267,64,968

residuals=-0.935944237899158,-0.116457382002744,4.8775884311265e-05,0.116832159797124,0.773249163080751
cont.residuals=-1.00321539658992,-0.389290240080777,-0.106113594935486,0.305889907520436,1.74318689127604

predictedValues:
Include	Exclude	Both
Lung	91.505921318181	307.37222242711	142.059546925365
cerebhem	81.7248240576423	428.209173431639	119.207881200545
cortex	85.3053119815717	303.85463831111	139.7414609158
heart	79.2190861579547	188.652723954288	100.340658662340
kidney	84.3474069053048	139.082626736635	104.014005573494
liver	84.4101568498788	132.146464400777	88.2068555549028
stomach	78.3660708561945	185.553642368967	91.8420932194924
testicle	84.0774492067173	200.919530905435	103.792408262555


diffExp=-215.866301108929,-346.484349373997,-218.549326329538,-109.433637796333,-54.7352198313303,-47.7363075508983,-107.187571512772,-116.842081698718
diffExpScore=0.99917887056279
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	96.133190520214	92.6739410436844	111.589678061873
cerebhem	96.1304704970991	90.4928069894944	85.371524620531
cortex	101.081474379916	79.876148955731	88.1479735290707
heart	93.100681241828	99.8707371600485	94.5903818642802
kidney	91.9211124609448	83.407018034257	91.2612615850638
liver	88.4935858144993	78.4554486769402	95.8018827340256
stomach	95.2437337877661	99.6190557084462	90.3413246533904
testicle	85.0262487321714	96.9312878719713	101.491850588466
cont.diffExp=3.45924947652971,5.63766350760474,21.2053254241854,-6.77005591822045,8.51409442668776,10.0381371375591,-4.37532192068007,-11.9050391397999
cont.diffExpScore=2.68261247534921

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

tran.correlation=0.211646112362070
cont.tran.correlation=-0.152265017055372

tran.covariance=0.00447911446836389
cont.tran.covariance=-0.000735763627027022

tran.mean=159.671703116838
cont.tran.mean=91.7785588671883

weightedLogRatios:
wLogRatio
Lung	-6.20638149329222
cerebhem	-8.66466762199304
cortex	-6.45495814143878
heart	-4.17017545468327
kidney	-2.34308460092520
liver	-2.08862992595883
stomach	-4.13079535569752
testicle	-4.24024541231334

cont.weightedLogRatios:
wLogRatio
Lung	0.166650374103583
cerebhem	0.274106636507881
cortex	1.05909975366733
heart	-0.320706454199165
kidney	0.434702943325351
liver	0.532492956884228
stomach	-0.205657666976765
testicle	-0.590801913681571

varWeightedLogRatios=4.88528206587743
cont.varWeightedLogRatios=0.280504978500840

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.48901773852322	0.0911701714643735	60.20628951727	0	***
df.mm.trans1	-0.703761777364122	0.0780973570832109	-9.01133922642581	1.06356573470780e-18	***
df.mm.trans2	0.268176261841255	0.0683729787986353	3.92225505679749	9.39155228413066e-05	***
df.mm.exp2	0.393883895149811	0.0865327788362703	4.55184613792518	5.99401898195957e-06	***
df.mm.exp3	-0.0652246911967974	0.0865327788362703	-0.753757039516897	0.451178424160386	   
df.mm.exp4	-0.284662675073483	0.0865327788362703	-3.28965137722084	0.00103930016023822	** 
df.mm.exp5	-0.562730133003096	0.0865327788362703	-6.50308635144891	1.25905179220871e-10	***
df.mm.exp6	-0.448302898801397	0.0865327788362703	-5.18072925462889	2.689190580661e-07	***
df.mm.exp7	-0.223552476774062	0.0865327788362703	-2.58344271131115	0.00992760961959127	** 
df.mm.exp8	-0.195966791237095	0.0865327788362703	-2.26465385571271	0.0237543647564403	*  
df.mm.trans1:exp2	-0.506929779447841	0.0791647964424164	-6.40347480482157	2.36505069636486e-10	***
df.mm.trans2:exp2	-0.0623315600478858	0.0548766509195746	-1.13584847113278	0.256301123277560	   
df.mm.trans1:exp3	-0.00494226618218017	0.0791647964424164	-0.0624301003006447	0.950233213129328	   
df.mm.trans2:exp3	0.0537146507313086	0.0548766509195746	0.97882523498074	0.327910898746982	   
df.mm.trans1:exp4	0.140476247656534	0.0791647964424164	1.77447873258558	0.0762982445702618	.  
df.mm.trans2:exp4	-0.203488903489456	0.0548766509195746	-3.70811447272325	0.000220672468957969	***
df.mm.trans1:exp5	0.481270515392069	0.0791647964424164	6.07935012808553	1.73404812363919e-09	***
df.mm.trans2:exp5	-0.230261137584481	0.0548766509195746	-4.19597649867416	2.96672249547109e-05	***
df.mm.trans1:exp6	0.367586950938494	0.0791647964424164	4.64331328390231	3.90124857131052e-06	***
df.mm.trans2:exp6	-0.395845679010853	0.0548766509195746	-7.21337166859894	1.10037239415950e-12	***
df.mm.trans1:exp7	0.0685398567196721	0.0791647964424164	0.865787064450134	0.386821470191903	   
df.mm.trans2:exp7	-0.281162969855734	0.0548766509195746	-5.12354462497715	3.61861782087623e-07	***
df.mm.trans1:exp8	0.111301495604409	0.0791647964424164	1.40594684261418	0.160060808504863	   
df.mm.trans2:exp8	-0.229188188681657	0.0548766509195746	-4.17642448730239	3.22849328403432e-05	***
df.mm.trans1:probe2	-0.640901682959421	0.0579426979673324	-11.0609568667437	7.29758260446174e-27	***
df.mm.trans1:probe3	-0.747223039387513	0.0579426979673324	-12.8958965598873	3.14644682896162e-35	***
df.mm.trans1:probe4	-0.520572752737521	0.0579426979673324	-8.98426844105559	1.33524051944635e-18	***
df.mm.trans1:probe5	-0.62750454952216	0.0579426979673324	-10.8297433763947	7.06672668187085e-26	***
df.mm.trans1:probe6	-0.363923076132953	0.0579426979673325	-6.28074095441895	5.08276922685693e-10	***
df.mm.trans1:probe7	-0.599059495022576	0.0579426979673324	-10.338826392936	7.75108907230199e-24	***
df.mm.trans1:probe8	-0.627319034096484	0.0579426979673324	-10.8265416713968	7.29055208052421e-26	***
df.mm.trans1:probe9	-0.636359216176077	0.0579426979673324	-10.9825610214915	1.58230108752340e-26	***
df.mm.trans1:probe10	-0.603516989561031	0.0579426979673324	-10.4157557506433	3.75428032173243e-24	***
df.mm.trans1:probe11	-0.634329496057339	0.0579426979673324	-10.9475312387933	2.23297947159633e-26	***
df.mm.trans1:probe12	-0.536707017030816	0.0579426979673324	-9.2627205128316	1.25234887642182e-19	***
df.mm.trans1:probe13	-0.388814045044821	0.0579426979673324	-6.71032000035675	3.30083679999895e-11	***
df.mm.trans1:probe14	-0.661015799693446	0.0579426979673324	-11.4080949434926	2.25523918383089e-28	***
df.mm.trans1:probe15	-0.470328960868171	0.0579426979673324	-8.1171394734387	1.44174646618203e-15	***
df.mm.trans1:probe16	-0.485840635559962	0.0579426979673324	-8.38484662612492	1.77772573317523e-16	***
df.mm.trans1:probe17	-0.148565392898711	0.0579426979673324	-2.5640054417637	0.0104971494271512	*  
df.mm.trans1:probe18	0.120729127800323	0.0579426979673324	2.08359520760302	0.0374588602589497	*  
df.mm.trans1:probe19	-0.254145069742986	0.0579426979673324	-4.38614490968769	1.28022310416465e-05	***
df.mm.trans1:probe20	-0.15953925483463	0.0579426979673324	-2.75339707040526	0.0060084546930068	** 
df.mm.trans1:probe21	-0.327138999528880	0.0579426979673324	-5.64590554125247	2.15847487106939e-08	***
df.mm.trans1:probe22	-0.635458874073053	0.0579426979673325	-10.9670225302819	1.84370597472491e-26	***
df.mm.trans2:probe2	-0.119246610624043	0.0579426979673324	-2.05800928861257	0.0398565265957575	*  
df.mm.trans2:probe3	-0.00348583712356788	0.0579426979673324	-0.0601600761761761	0.952040560351684	   
df.mm.trans2:probe4	-0.0899511416805057	0.0579426979673324	-1.55241548695608	0.120889704449657	   
df.mm.trans2:probe5	-0.143468576052572	0.0579426979673324	-2.47604238472738	0.0134548584416254	*  
df.mm.trans2:probe6	-0.255673096812163	0.0579426979673325	-4.41251625798145	1.13645749271682e-05	***
df.mm.trans3:probe2	-0.0104542097317305	0.0579426979673325	-0.180423247423247	0.856858069441236	   
df.mm.trans3:probe3	-0.3862504574496	0.0579426979673325	-6.66607650315773	4.40645782298694e-11	***
df.mm.trans3:probe4	-0.583180434795623	0.0579426979673325	-10.0647787426884	9.9079835777413e-23	***
df.mm.trans3:probe5	0.194974175142978	0.0579426979673325	3.36494816401028	0.000795647975221601	***
df.mm.trans3:probe6	0.288101157746169	0.0579426979673325	4.97217367939267	7.82889311856923e-07	***
df.mm.trans3:probe7	-0.450177630384941	0.0579426979673325	-7.76935914580206	2.00668629641784e-14	***
df.mm.trans3:probe8	-0.334018173816448	0.0579426979673325	-5.76462928952263	1.09973731384182e-08	***
df.mm.trans3:probe9	0.107843637843337	0.0579426979673324	1.86121188047091	0.0630172304419156	.  
df.mm.trans3:probe10	0.255444185547598	0.0579426979673324	4.40856560893342	1.15696568288444e-05	***
df.mm.trans3:probe11	0.162838163759587	0.0579426979673324	2.81033105934062	0.00504885465666343	** 
df.mm.trans3:probe12	-0.447151928248895	0.0579426979673324	-7.71714027712335	2.95466906532852e-14	***
df.mm.trans3:probe13	0.0478009433698988	0.0579426979673324	0.824969237656978	0.409592300093074	   
df.mm.trans3:probe14	-0.279973568283603	0.0579426979673325	-4.83190424514664	1.57162745785986e-06	***
df.mm.trans3:probe15	-0.0235844920180345	0.0579426979673325	-0.407031305848603	0.684074980277753	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.51911612218659	0.234226491199456	19.2937873894816	1.95708990144878e-70	***
df.mm.trans1	0.0213263895089582	0.200640951176664	0.106291309844222	0.915373249027489	   
df.mm.trans2	-0.00820987184715258	0.175657922537935	-0.0467378398226223	0.962731811765139	   
df.mm.exp2	0.243970694985153	0.222312504572599	1.09742227705181	0.27272974982229	   
df.mm.exp3	0.137393847614663	0.222312504572599	0.618021230424291	0.536706716726364	   
df.mm.exp4	0.208009025332517	0.222312504572599	0.935660482672442	0.349681379644405	   
df.mm.exp5	0.0509433929991941	0.222312504572599	0.229152170711827	0.818799051158026	   
df.mm.exp6	-0.096814732318333	0.222312504572599	-0.435489368915445	0.663304339369121	   
df.mm.exp7	0.274204302009432	0.222312504572599	1.23341825749567	0.217719170764967	   
df.mm.exp8	0.0169904685815927	0.222312504572599	0.0764260589581199	0.93909593067615	   
df.mm.trans1:exp2	-0.243998989703986	0.203383323727456	-1.19970007979099	0.230549392406135	   
df.mm.trans2:exp2	-0.267787650630045	0.140984328396181	-1.89941430850054	0.0578070625317791	.  
df.mm.trans1:exp3	-0.0872016101017918	0.203383323727456	-0.428754966255966	0.668196948559757	   
df.mm.trans2:exp3	-0.286003873011062	0.140984328396181	-2.02862173593764	0.0427701670216031	*  
df.mm.trans1:exp4	-0.240062154931617	0.203383323727456	-1.18034335623953	0.238153643078566	   
df.mm.trans2:exp4	-0.133219626321615	0.140984328396181	-0.944925069595353	0.344932924727767	   
df.mm.trans1:exp5	-0.09574728820945	0.203383323727456	-0.470772561165123	0.637909223296343	   
df.mm.trans2:exp5	-0.156298260495958	0.140984328396181	-1.10862152037738	0.267868852665576	   
df.mm.trans1:exp6	0.0140101738651133	0.203383323727456	0.0688855586010958	0.945094940410452	   
df.mm.trans2:exp6	-0.0697416591917618	0.140984328396181	-0.494676677792019	0.620940616881494	   
df.mm.trans1:exp7	-0.28349970833998	0.203383323727456	-1.39391815977933	0.163662213519303	   
df.mm.trans2:exp7	-0.201938155757653	0.140984328396181	-1.43234470139252	0.152368028028888	   
df.mm.trans1:exp8	-0.139765082317279	0.203383323727456	-0.687200306081	0.492121114402442	   
df.mm.trans2:exp8	0.0279245640406307	0.140984328396181	0.198068568033744	0.843033003512397	   
df.mm.trans1:probe2	0.153400557589929	0.148861350346605	1.03049285279729	0.303036108967304	   
df.mm.trans1:probe3	0.064742139029179	0.148861350346605	0.434915704300915	0.663720556103237	   
df.mm.trans1:probe4	-0.05982875104067	0.148861350346605	-0.401909232325022	0.687839548330042	   
df.mm.trans1:probe5	0.0210471618095492	0.148861350346605	0.141387685658793	0.887593100064843	   
df.mm.trans1:probe6	-0.120359478574596	0.148861350346605	-0.80853410434847	0.418981844291933	   
df.mm.trans1:probe7	0.111418545095764	0.148861350346605	0.748471949477417	0.454357351692224	   
df.mm.trans1:probe8	0.0248545013838794	0.148861350346605	0.166964099989747	0.867433166926865	   
df.mm.trans1:probe9	-0.0592429695413689	0.148861350346605	-0.397974151137479	0.69073699146207	   
df.mm.trans1:probe10	0.0625509471279623	0.148861350346605	0.420196021212493	0.674435515018768	   
df.mm.trans1:probe11	-0.0690323528181981	0.148861350346605	-0.463735903627537	0.642941227409032	   
df.mm.trans1:probe12	0.163205441695442	0.148861350346605	1.09635873459053	0.273194490252001	   
df.mm.trans1:probe13	0.0680493541129965	0.148861350346605	0.457132452141218	0.64767841542289	   
df.mm.trans1:probe14	-0.0741594639146624	0.148861350346605	-0.498178094864728	0.618471683833906	   
df.mm.trans1:probe15	-0.0192754506514017	0.148861350346605	-0.129485931751400	0.897000055989495	   
df.mm.trans1:probe16	0.0105401064781153	0.148861350346605	0.0708048560192018	0.943567691940318	   
df.mm.trans1:probe17	0.159158862509831	0.148861350346605	1.06917518979406	0.285257273154666	   
df.mm.trans1:probe18	0.136262378209030	0.148861350346605	0.915364383648004	0.360228334245440	   
df.mm.trans1:probe19	0.194489929576728	0.148861350346605	1.30651730031928	0.191687027674464	   
df.mm.trans1:probe20	0.0527051238512719	0.148861350346605	0.354055123969752	0.72337467738318	   
df.mm.trans1:probe21	0.0891262681083139	0.148861350346605	0.598720002880497	0.549499691050969	   
df.mm.trans1:probe22	0.0261555704516648	0.148861350346605	0.175704240158811	0.860563051698467	   
df.mm.trans2:probe2	0.0114541134129038	0.148861350346605	0.0769448442207085	0.938683323060029	   
df.mm.trans2:probe3	0.0711487097990083	0.148861350346605	0.47795287113376	0.632791661380637	   
df.mm.trans2:probe4	0.00762645546317859	0.148861350346605	0.0512319379437399	0.959151272033428	   
df.mm.trans2:probe5	0.147791667355585	0.148861350346605	0.992814232918556	0.321048595854328	   
df.mm.trans2:probe6	0.143781567512899	0.148861350346605	0.965875743959879	0.334347601115439	   
df.mm.trans3:probe2	0.0858837654851324	0.148861350346605	0.576937971375128	0.564115500200199	   
df.mm.trans3:probe3	0.315934732983822	0.148861350346605	2.12234224832845	0.0340623935397402	*  
df.mm.trans3:probe4	0.184911872065264	0.148861350346605	1.24217516255711	0.214472795285398	   
df.mm.trans3:probe5	0.211585418423256	0.148861350346605	1.42135898895587	0.155534495319193	   
df.mm.trans3:probe6	0.165130779231332	0.148861350346605	1.10929249833383	0.267579528050725	   
df.mm.trans3:probe7	0.114914902463330	0.148861350346605	0.771959290949363	0.440326954137967	   
df.mm.trans3:probe8	0.230087655744960	0.148861350346605	1.54565073613285	0.122515786292329	   
df.mm.trans3:probe9	0.309031274773016	0.148861350346605	2.07596716040447	0.0381604782989100	*  
df.mm.trans3:probe10	0.254704486657088	0.148861350346605	1.71101824660357	0.0873981180406382	.  
df.mm.trans3:probe11	0.294380485308154	0.148861350346605	1.97754813202169	0.0482630187012644	*  
df.mm.trans3:probe12	0.202109157783679	0.148861350346605	1.35770068800997	0.174875086384769	   
df.mm.trans3:probe13	-0.0275401901573305	0.148861350346605	-0.185005645140305	0.853263361756615	   
df.mm.trans3:probe14	0.138149658794995	0.148861350346605	0.92804249372541	0.353616868807098	   
df.mm.trans3:probe15	0.259654730487762	0.148861350346605	1.74427230361131	0.0814290185421255	.