fitVsDatCorrelation=0.855010141443355
cont.fitVsDatCorrelation=0.256725144091992

fstatistic=8104.23733725514,71,1129
cont.fstatistic=2322.16917922965,71,1129

residuals=-0.917201142651166,-0.106063266505077,9.0945195148729e-05,0.0933451430220958,1.05229279017420
cont.residuals=-0.772842932195177,-0.261816067864466,-0.0681786536459858,0.186654143569339,1.16822748623513

predictedValues:
Include	Exclude	Both
Lung	63.6766632740254	59.4035546383068	64.764091066731
cerebhem	61.4349862044989	52.8837087049493	66.4141916425557
cortex	60.1753856015657	56.439889068585	59.9078363986914
heart	60.3943296153565	58.5722710244102	60.7558912497547
kidney	73.84863170459	60.1690832645305	78.163701394941
liver	62.6795089597175	56.0308657977999	63.9032352440249
stomach	91.9179957756741	62.7780145302766	92.4962163589587
testicle	62.0619715207251	58.8873095453799	63.3514109624369


diffExp=4.27310863571862,8.55127749954963,3.73549653298060,1.82205859094627,13.6795484400595,6.64864316191765,29.1399812453975,3.17466197534527
diffExpScore=0.986115888803838
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,1,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	62.4635699478796	66.7013684269675	66.6718464859824
cerebhem	66.9320749798917	76.0895835598554	69.857282728743
cortex	70.8007151279048	72.015362199562	69.5194414811753
heart	63.875311954131	73.4080686374224	66.3930548081604
kidney	67.5540795807251	79.5481664273123	63.8793074650741
liver	64.6219259023269	76.8671551620833	65.8370169367537
stomach	68.3623482883672	69.5078791361722	66.721129962259
testicle	69.4073161266202	60.5644999960751	68.846622254944
cont.diffExp=-4.23779847908794,-9.15750857996366,-1.21464707165711,-9.53275668329144,-11.9940868465871,-12.2452292597564,-1.14553084780498,8.8428161305451
cont.diffExpScore=1.40028153241660

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

tran.correlation=0.732305668304633
cont.tran.correlation=-0.155374605731673

tran.covariance=0.00550721170747539
cont.tran.covariance=-0.000595635346505125

tran.mean=62.5846355768995
cont.tran.mean=69.294964090831

weightedLogRatios:
wLogRatio
Lung	0.286128505487863
cerebhem	0.605987238159727
cortex	0.260528675406698
heart	0.125156895413820
kidney	0.860322687027001
liver	0.457718775891352
stomach	1.65108960268727
testicle	0.215380737799863

cont.weightedLogRatios:
wLogRatio
Lung	-0.273556610711217
cerebhem	-0.547272469664943
cortex	-0.0726065780708683
heart	-0.587907746218789
kidney	-0.701892175741407
liver	-0.738403367271347
stomach	-0.0703457738911847
testicle	0.56855385381758

varWeightedLogRatios=0.25224957719067
cont.varWeightedLogRatios=0.193887804853325

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15429726348118	0.086260989815307	48.1596289629406	5.31583778530681e-276	***
df.mm.trans1	-0.0416937395998341	0.0734765445460333	-0.567442846658539	0.570526158171749	   
df.mm.trans2	-0.0769842394652676	0.0639086185970449	-1.20459870914543	0.228610711084148	   
df.mm.exp2	-0.17725686423959	0.0799041356814978	-2.21836908349957	0.0267280323226008	*  
df.mm.exp3	-0.0297886773382894	0.0799041356814978	-0.372805200684839	0.709363408307387	   
df.mm.exp4	-0.00312834819363463	0.0799041356814978	-0.0391512675401984	0.968776708441935	   
df.mm.exp5	-0.0270501383692485	0.0799041356814978	-0.338532394331525	0.735024965498831	   
df.mm.exp6	-0.0608536019159085	0.0799041356814978	-0.761582631448192	0.446468195627775	   
df.mm.exp7	0.0659131056766896	0.0799041356814978	0.824902304674476	0.4096013082767	   
df.mm.exp8	-0.0123590519554230	0.0799041356814978	-0.154673495308013	0.877106371757829	   
df.mm.trans1:exp2	0.141418203176416	0.07251182158635	1.9502779006594	0.0513902056130328	.  
df.mm.trans2:exp2	0.0609981247097277	0.0474701301712259	1.28497909084525	0.199063145537662	   
df.mm.trans1:exp3	-0.0267660728386118	0.07251182158635	-0.369127023057029	0.712102261989835	   
df.mm.trans2:exp3	-0.0213892281279861	0.0474701301712259	-0.450582883401303	0.652376720486248	   
df.mm.trans1:exp4	-0.0497945736413645	0.07251182158635	-0.686709733006323	0.492406678294506	   
df.mm.trans2:exp4	-0.0109643248970274	0.0474701301712259	-0.23097313737036	0.817377520089999	   
df.mm.trans1:exp5	0.175249477311390	0.07251182158635	2.41684008865639	0.0158136335558594	*  
df.mm.trans2:exp5	0.0398547247550764	0.0474701301712259	0.839574793903438	0.401324579935911	   
df.mm.trans1:exp6	0.04507004346242	0.07251182158635	0.621554423491468	0.534360377697276	   
df.mm.trans2:exp6	0.00240224879679009	0.0474701301712259	0.0506054815549298	0.959648846157797	   
df.mm.trans1:exp7	0.301165581790815	0.07251182158635	4.15333079768483	3.52365464336636e-05	***
df.mm.trans2:exp7	-0.0106622475686882	0.0474701301712259	-0.224609613039384	0.822323628744457	   
df.mm.trans1:exp8	-0.0133256633790249	0.07251182158635	-0.183772288262765	0.854225096180279	   
df.mm.trans2:exp8	0.00363059479116345	0.0474701301712259	0.0764816691689659	0.939049447721612	   
df.mm.trans1:probe2	0.816443221101909	0.0556636616689969	14.6674364679218	1.02642912175274e-44	***
df.mm.trans1:probe3	0.664065134498545	0.055663661668997	11.9299577962980	5.50924114291477e-31	***
df.mm.trans1:probe4	0.318709641360531	0.0556636616689969	5.72563197972373	1.32001509597291e-08	***
df.mm.trans1:probe5	0.535246272203698	0.055663661668997	9.61572157050199	4.29891217656469e-21	***
df.mm.trans1:probe6	-0.104979672685271	0.055663661668997	-1.88596419167555	0.0595562951297805	.  
df.mm.trans1:probe7	0.382035837355796	0.0556636616689969	6.86328972800184	1.10761359803544e-11	***
df.mm.trans1:probe8	0.286785646253815	0.0556636616689969	5.15211607815493	3.03789839616421e-07	***
df.mm.trans1:probe9	-0.0558198326955315	0.055663661668997	-1.00280561899544	0.316169533036297	   
df.mm.trans1:probe10	0.371883102675362	0.055663661668997	6.6808954266566	3.72101200671732e-11	***
df.mm.trans1:probe11	-0.226104567506456	0.055663661668997	-4.06197797139152	5.20205585803596e-05	***
df.mm.trans1:probe12	-0.169447936465267	0.0556636616689969	-3.04413923526781	0.00238748824435575	** 
df.mm.trans1:probe13	-0.270916353291915	0.055663661668997	-4.86702356921675	1.29375827535757e-06	***
df.mm.trans1:probe14	-0.0934629689573113	0.055663661668997	-1.67906612958895	0.0934158601075207	.  
df.mm.trans1:probe15	-0.322610151524561	0.055663661668997	-5.79570480725751	8.81904011921735e-09	***
df.mm.trans1:probe16	-0.174619356978614	0.055663661668997	-3.13704402015421	0.00175075794027931	** 
df.mm.trans1:probe17	0.100927817240058	0.0556636616689969	1.81317243986254	0.070070683356314	.  
df.mm.trans1:probe18	-0.0286007006911821	0.055663661668997	-0.513812778994952	0.607483435502077	   
df.mm.trans1:probe19	0.109158211782345	0.055663661668997	1.96103182056998	0.050120837863295	.  
df.mm.trans1:probe20	-0.112054277301919	0.055663661668997	-2.01305975823595	0.0443453527031399	*  
df.mm.trans1:probe21	-0.0470818111237859	0.0556636616689969	-0.845826697563611	0.397828656023038	   
df.mm.trans1:probe22	-0.166114065586016	0.055663661668997	-2.98424610608282	0.00290394461014939	** 
df.mm.trans2:probe2	0.0689474938193268	0.055663661668997	1.23864459778665	0.215734611206791	   
df.mm.trans2:probe3	0.0358326537717329	0.055663661668997	0.643735117262159	0.519877954380314	   
df.mm.trans2:probe4	0.0790106888159027	0.055663661668997	1.41943031498248	0.156049632904816	   
df.mm.trans2:probe5	0.0882566599597042	0.055663661668997	1.58553457162989	0.113124960370285	   
df.mm.trans2:probe6	-0.0748982935443076	0.055663661668997	-1.34555096266733	0.178717538523216	   
df.mm.trans3:probe2	-0.148418790615091	0.055663661668997	-2.66634975430939	0.00777754748718577	** 
df.mm.trans3:probe3	0.499708425877689	0.055663661668997	8.97728268127952	1.1339582807602e-18	***
df.mm.trans3:probe4	-0.191143830242998	0.055663661668997	-3.43390687051153	0.000616536662160939	***
df.mm.trans3:probe5	-0.250446592799415	0.055663661668997	-4.4992834695046	7.52334349635468e-06	***
df.mm.trans3:probe6	0.238881786385958	0.055663661668997	4.2915212406698	1.92663742813264e-05	***
df.mm.trans3:probe7	0.161034370326660	0.055663661668997	2.89298916920428	0.00388921712133766	** 
df.mm.trans3:probe8	0.0629681261837362	0.055663661668997	1.13122500920214	0.258200628543921	   
df.mm.trans3:probe9	0.750835071365943	0.055663661668997	13.4887833256599	1.48195946658561e-38	***
df.mm.trans3:probe10	-0.230480004008607	0.055663661668997	-4.14058287036798	3.72220340175164e-05	***
df.mm.trans3:probe11	-0.15186734851434	0.055663661668997	-2.72830324058479	0.00646490953047285	** 
df.mm.trans3:probe12	0.234735391569594	0.055663661668997	4.21703108511696	2.67343951679647e-05	***
df.mm.trans3:probe13	0.380946780806725	0.055663661668997	6.84372478174395	1.26310260196164e-11	***
df.mm.trans3:probe14	0.00241666732107079	0.055663661668997	0.0434155290652897	0.965377975968746	   
df.mm.trans3:probe15	-0.189059472822144	0.055663661668997	-3.39646130264269	0.000706399108594352	***
df.mm.trans3:probe16	0.164854627468790	0.055663661668997	2.96162024785748	0.00312427849241277	** 
df.mm.trans3:probe17	0.096168007245726	0.055663661668997	1.72766225509180	0.0843222622371857	.  
df.mm.trans3:probe18	0.492763924617408	0.055663661668997	8.8525244269344	3.24630551429818e-18	***
df.mm.trans3:probe19	0.0657223771962733	0.055663661668997	1.18070524334332	0.237968518919397	   
df.mm.trans3:probe20	0.0363785674513406	0.055663661668997	0.653542479250918	0.513539690036851	   
df.mm.trans3:probe21	0.568903524721934	0.055663661668997	10.2203755136504	1.63660632175256e-23	***
df.mm.trans3:probe22	0.378002723530838	0.055663661668997	6.79083467017719	1.79864018542503e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04140119995626	0.160756059787485	25.1399617861926	4.06858646675986e-111	***
df.mm.trans1	0.0957445349752064	0.136930955850497	0.699217604817875	0.484560071966355	   
df.mm.trans2	0.158085912617207	0.119100160270814	1.32733585125113	0.184665951344807	   
df.mm.exp2	0.154109130707534	0.148909420589593	1.03491861090690	0.300928542374143	   
df.mm.exp3	0.160115911797070	0.148909420589593	1.07525710034399	0.282489369351026	   
df.mm.exp4	0.122348131430554	0.148909420589593	0.821627879190769	0.411462186239526	   
df.mm.exp5	0.297269476510681	0.148909420589593	1.99631074604729	0.0461399333029512	*  
df.mm.exp6	0.188423987451202	0.148909420589593	1.26535975162051	0.206003479120561	   
df.mm.exp7	0.1307144279924	0.148909420589593	0.877811675546437	0.380232688084873	   
df.mm.exp8	-0.0232061727416594	0.148909420589593	-0.155840863860572	0.876186318452492	   
df.mm.trans1:exp2	-0.0850143378033675	0.135133097257438	-0.629115586993536	0.529400656092816	   
df.mm.trans2:exp2	-0.0224232223860522	0.0884653781537196	-0.253468903361144	0.799952036100121	   
df.mm.trans1:exp3	-0.0348303164391406	0.135133097257438	-0.257748228568953	0.796648194292033	   
df.mm.trans2:exp3	-0.0834619205146104	0.0884653781537196	-0.943441629442706	0.345656899937011	   
df.mm.trans1:exp4	-0.0999987050639552	0.135133097257438	-0.74000157691532	0.459452885441029	   
df.mm.trans2:exp4	-0.0265397437688113	0.0884653781537196	-0.300001473149136	0.764231257653846	   
df.mm.trans1:exp5	-0.218924526398401	0.135133097257438	-1.62006592642019	0.105497375478148	   
df.mm.trans2:exp5	-0.121132240161059	0.0884653781537196	-1.36926154264074	0.171189849594928	   
df.mm.trans1:exp6	-0.154453729963893	0.135133097257438	-1.1429748381305	0.253291349525953	   
df.mm.trans2:exp6	-0.0465707820612881	0.0884653781537196	-0.526429469168894	0.598693245320999	   
df.mm.trans1:exp7	-0.0404757245123324	0.135133097257438	-0.299524878314773	0.764594724084723	   
df.mm.trans2:exp7	-0.0894997818391942	0.0884653781537196	-1.01169275152679	0.311901748150579	   
df.mm.trans1:exp8	0.128614948422731	0.135133097257438	0.951764971224709	0.341419899052037	   
df.mm.trans2:exp8	-0.0733103833391865	0.0884653781537196	-0.828690103057046	0.40745494099846	   
df.mm.trans1:probe2	0.0638472738705294	0.103734850972736	0.615485280711587	0.538358333724746	   
df.mm.trans1:probe3	0.154785150422843	0.103734850972736	1.49212293623022	0.135946284828719	   
df.mm.trans1:probe4	-0.165385123892267	0.103734850972736	-1.59430627548435	0.111147339962829	   
df.mm.trans1:probe5	-0.0891163032672749	0.103734850972736	-0.859077758647345	0.390479976689864	   
df.mm.trans1:probe6	-0.0431630082233555	0.103734850972736	-0.416089750152526	0.67742341042164	   
df.mm.trans1:probe7	0.057467254921739	0.103734850972736	0.553982141805385	0.57970077053221	   
df.mm.trans1:probe8	0.150808371669070	0.103734850972736	1.45378694098385	0.146283299053146	   
df.mm.trans1:probe9	-0.0163544710359985	0.103734850972737	-0.157656475934947	0.874755689892454	   
df.mm.trans1:probe10	-0.0386300640267408	0.103734850972736	-0.372392341286474	0.709670646146311	   
df.mm.trans1:probe11	-0.0674266252163487	0.103734850972736	-0.649990090929708	0.515830852380668	   
df.mm.trans1:probe12	0.0230707514301991	0.103734850972736	0.222401162327428	0.824041832392115	   
df.mm.trans1:probe13	-0.0894616278506335	0.103734850972736	-0.862406674437174	0.388646913096416	   
df.mm.trans1:probe14	-0.070188841873681	0.103734850972737	-0.67661775397092	0.49878710656227	   
df.mm.trans1:probe15	0.139841455648301	0.103734850972737	1.34806628955445	0.177907465939989	   
df.mm.trans1:probe16	0.00705662488413383	0.103734850972737	0.0680255942719622	0.94577730528818	   
df.mm.trans1:probe17	-0.126866982788738	0.103734850972736	-1.22299286690141	0.221587608524975	   
df.mm.trans1:probe18	0.0938765604755932	0.103734850972736	0.904966456261318	0.365676229303308	   
df.mm.trans1:probe19	0.0218827996378657	0.103734850972736	0.210949352437176	0.832964860834875	   
df.mm.trans1:probe20	-0.040148606238995	0.103734850972736	-0.387031030193959	0.698806176614529	   
df.mm.trans1:probe21	0.0153406465901161	0.103734850972736	0.147883247011633	0.882461315781555	   
df.mm.trans1:probe22	-0.0939733095573568	0.103734850972736	-0.905899113713045	0.36518253982022	   
df.mm.trans2:probe2	0.0878300402923044	0.103734850972736	0.846678232712629	0.397353922888363	   
df.mm.trans2:probe3	-0.108909551379343	0.103734850972737	-1.04988391421092	0.293996166871784	   
df.mm.trans2:probe4	-0.0103538757937204	0.103734850972737	-0.0998109670629551	0.920512120833359	   
df.mm.trans2:probe5	0.0255889827054633	0.103734850972737	0.246676815607404	0.805203194669273	   
df.mm.trans2:probe6	0.0265183801299054	0.103734850972737	0.255636171269721	0.798278350548183	   
df.mm.trans3:probe2	-0.150819574471610	0.103734850972736	-1.45389493557231	0.146253356087374	   
df.mm.trans3:probe3	-0.113679608501928	0.103734850972736	-1.09586708262400	0.273370652609355	   
df.mm.trans3:probe4	-0.147043308491674	0.103734850972736	-1.41749187580479	0.156615078962445	   
df.mm.trans3:probe5	-0.0991209583234477	0.103734850972736	-0.95552225114295	0.339518205549451	   
df.mm.trans3:probe6	-0.0385776938425893	0.103734850972736	-0.371887494712150	0.71004640238502	   
df.mm.trans3:probe7	-0.135439314168260	0.103734850972737	-1.30562981387861	0.191944460391576	   
df.mm.trans3:probe8	-0.0513350035102347	0.103734850972736	-0.494867472492215	0.620789960204029	   
df.mm.trans3:probe9	-0.107432052631490	0.103734850972737	-1.03564088273212	0.300591480517170	   
df.mm.trans3:probe10	-0.131743915743226	0.103734850972737	-1.27000631425066	0.204344063552487	   
df.mm.trans3:probe11	0.0366851203403237	0.103734850972736	0.353643158459496	0.723672352473673	   
df.mm.trans3:probe12	0.0701414219139079	0.103734850972736	0.676160627370472	0.499077152750313	   
df.mm.trans3:probe13	-0.158902891754021	0.103734850972736	-1.53181780533703	0.125847545076319	   
df.mm.trans3:probe14	-0.204638278274775	0.103734850972736	-1.97270518399412	0.0487728361685295	*  
df.mm.trans3:probe15	-0.125566428351985	0.103734850972736	-1.21045557182114	0.226357478017025	   
df.mm.trans3:probe16	-0.124005002976742	0.103734850972736	-1.19540349086088	0.232180450134418	   
df.mm.trans3:probe17	0.00609719761546782	0.103734850972736	0.058776752058672	0.95314032699217	   
df.mm.trans3:probe18	-0.184940880232731	0.103734850972736	-1.78282301944345	0.074883739934461	.  
df.mm.trans3:probe19	-0.0189942415008778	0.103734850972736	-0.183103762359189	0.85474947949458	   
df.mm.trans3:probe20	-0.178952228611772	0.103734850972737	-1.72509264662466	0.0847844412636327	.  
df.mm.trans3:probe21	-0.138110980780356	0.103734850972736	-1.33138457794338	0.183331260519560	   
df.mm.trans3:probe22	-0.103496636442418	0.103734850972737	-0.997703621029141	0.318636879863182