fitVsDatCorrelation=0.881878455135069
cont.fitVsDatCorrelation=0.263152950643521

fstatistic=11146.2985108488,56,784
cont.fstatistic=2651.40501146535,56,784

residuals=-0.552354348808415,-0.0948741812875964,-0.00065365003764505,0.0919147216970603,0.562543594821978
cont.residuals=-0.803263041650633,-0.215234986368672,-0.041398981383506,0.182628431948110,1.20841944234298

predictedValues:
Include	Exclude	Both
Lung	65.7184626370832	81.438112219495	98.6456270693248
cerebhem	59.2964520746576	73.1303880011877	96.2037866470704
cortex	77.6526127803957	94.9815155503605	135.120712519601
heart	69.9103086132892	93.1768554667528	124.193981970006
kidney	59.5532296195386	71.298871520383	80.4815247829307
liver	58.2109882229546	70.7622746603312	82.4779951520014
stomach	67.7171820302581	86.4192151951102	95.0291879655166
testicle	61.5796699757357	77.6356137283518	91.161872150373


diffExp=-15.7196495824118,-13.8339359265301,-17.3289027699649,-23.2665468534636,-11.7456419008444,-12.5512864373767,-18.7020331648521,-16.0559437526161
diffExpScore=0.992319740884803
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,-1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	76.7550597251803	76.1402966582516	74.957561922026
cerebhem	72.2794346816259	81.784339120043	75.3566599177564
cortex	73.0716072962478	67.8587703710908	68.4423100408673
heart	74.6080871800538	77.2551079118629	81.1104926150473
kidney	78.9569403941456	75.0819981061934	75.8803882852009
liver	75.6084772052962	70.422407016943	74.70345342282
stomach	79.5256904137016	61.7145573447606	89.1317761680342
testicle	78.2349553517813	68.3192241401575	78.0515992194331
cont.diffExp=0.614763066928688,-9.5049044384172,5.21283692515706,-2.64702073180908,3.87494228795221,5.18607018835316,17.8111330689410,9.91573121162375
cont.diffExpScore=1.74066178708848

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

tran.correlation=0.959269323495401
cont.tran.correlation=-0.523731516226572

tran.covariance=0.0112552471137977
cont.tran.covariance=-0.00166042488997182

tran.mean=73.0301095184928
cont.tran.mean=74.2260595573335

weightedLogRatios:
wLogRatio
Lung	-0.92060839652388
cerebhem	-0.878074108712251
cortex	-0.896992114187511
heart	-1.26143259625381
kidney	-0.751879244401532
liver	-0.812579751059507
stomach	-1.0577317082248
testicle	-0.981499289447588

cont.weightedLogRatios:
wLogRatio
Lung	0.0348734586913373
cerebhem	-0.536475919554489
cortex	0.314875968393828
heart	-0.150950546649141
kidney	0.218584994813360
liver	0.304837005178183
stomach	1.07745368727299
testicle	0.581668567197437

varWeightedLogRatios=0.0251970965165738
cont.varWeightedLogRatios=0.232209057582092

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.6247965247123	0.0762811334031909	47.5189127769393	4.89801871810587e-233	***
df.mm.trans1	0.360232099787895	0.0664858459400281	5.41817727810568	8.0166309416768e-08	***
df.mm.trans2	0.734839112486302	0.0593314427929037	12.3853234961984	2.64799934585721e-32	***
df.mm.exp2	-0.185364629159705	0.0776239515586395	-2.38798238736500	0.0171769705472534	*  
df.mm.exp3	0.006069581952842	0.0776239515586395	0.0781921279575268	0.937695175390765	   
df.mm.exp4	-0.033821609115288	0.0776239515586394	-0.435710994302295	0.663166363277654	   
df.mm.exp5	-0.0279659931534633	0.0776239515586395	-0.360275309256022	0.718738298621132	   
df.mm.exp6	-0.0828207484660888	0.0776239515586394	-1.06694836842368	0.286323588712233	   
df.mm.exp7	0.126676538305779	0.0776239515586395	1.63192591670734	0.103096663789203	   
df.mm.exp8	-0.0339680652656312	0.0776239515586394	-0.437597733477542	0.661798436661647	   
df.mm.trans1:exp2	0.0825342029900222	0.0724839483217834	1.13865490085642	0.255194930279276	   
df.mm.trans2:exp2	0.0777652415473311	0.0565370897813715	1.37547301865110	0.169377706436215	   
df.mm.trans1:exp3	0.160795714969191	0.0724839483217835	2.21836308164889	0.0268158540702454	*  
df.mm.trans2:exp3	0.147769345044260	0.0565370897813716	2.61367087721853	0.00912931616469388	** 
df.mm.trans1:exp4	0.0956548237899028	0.0724839483217834	1.31966905783409	0.187330756049980	   
df.mm.trans2:exp4	0.168477595548039	0.0565370897813715	2.97994814023044	0.00297204310024147	** 
df.mm.trans1:exp5	-0.0705433790469389	0.0724839483217834	-0.973227599768301	0.330740369028379	   
df.mm.trans2:exp5	-0.10499687927267	0.0565370897813715	-1.85713271904677	0.0636671058503608	.  
df.mm.trans1:exp6	-0.0384850138120216	0.0724839483217834	-0.530945329318599	0.595607036467726	   
df.mm.trans2:exp6	-0.0576966091440929	0.0565370897813715	-1.02050900333224	0.307801919008909	   
df.mm.trans1:exp7	-0.0967164942585034	0.0724839483217834	-1.33431603131141	0.182487722761873	   
df.mm.trans2:exp7	-0.067309861648532	0.0565370897813716	-1.19054344517588	0.234193214809546	   
df.mm.trans1:exp8	-0.0310800518056722	0.0724839483217834	-0.428785303853706	0.668197262051868	   
df.mm.trans2:exp8	-0.0138490455450344	0.0565370897813715	-0.244955048068242	0.806555337181091	   
df.mm.trans1:probe2	0.286816164152780	0.0460627452609303	6.22664069473197	7.76287341558947e-10	***
df.mm.trans1:probe3	-0.0472331142709006	0.0460627452609303	-1.02540814715538	0.305486918053218	   
df.mm.trans1:probe4	0.07677331471016	0.0460627452609303	1.66671166200070	0.0959711621479633	.  
df.mm.trans1:probe5	0.483483789161543	0.0460627452609303	10.4962000510991	3.31629304545557e-24	***
df.mm.trans1:probe6	0.45561830045682	0.0460627452609303	9.8912537208951	8.08093331343037e-22	***
df.mm.trans1:probe7	0.224825385511650	0.0460627452609303	4.88085076645102	1.28002375195901e-06	***
df.mm.trans1:probe8	0.0392641823720885	0.0460627452609303	0.85240647620262	0.394248842688611	   
df.mm.trans1:probe9	0.249758871341709	0.0460627452609303	5.42214472730419	7.84697495900642e-08	***
df.mm.trans1:probe10	0.174911613193912	0.0460627452609303	3.79724682502302	0.000157613849044114	***
df.mm.trans1:probe11	0.393522423496599	0.0460627452609303	8.5431821587581	6.76033857810853e-17	***
df.mm.trans1:probe12	0.318300040337977	0.0460627452609303	6.91014047328078	1.00280803592044e-11	***
df.mm.trans1:probe13	0.278180714054168	0.0460627452609303	6.03916923488529	2.39072567438116e-09	***
df.mm.trans1:probe14	0.547662864620108	0.0460627452609303	11.8894968486524	4.34201746779275e-30	***
df.mm.trans1:probe15	0.249596099354499	0.0460627452609303	5.41861102590866	7.99791115045897e-08	***
df.mm.trans1:probe16	0.243173824024384	0.0460627452609303	5.27918652366213	1.68150210507263e-07	***
df.mm.trans1:probe17	0.358531511351387	0.0460627452609303	7.78354631970856	2.22787404112912e-14	***
df.mm.trans1:probe18	0.252456130843374	0.0460627452609303	5.48070093116018	5.71358091004653e-08	***
df.mm.trans1:probe19	0.202688676679916	0.0460627452609303	4.40027348634457	1.23073413265617e-05	***
df.mm.trans1:probe20	0.771333142063439	0.0460627452609303	16.7452707756364	4.885484849432e-54	***
df.mm.trans1:probe21	0.126203934214928	0.0460627452609303	2.7398265887112	0.00628687814205377	** 
df.mm.trans1:probe22	0.124319129842779	0.0460627452609303	2.69890839416001	0.00710647673179159	** 
df.mm.trans2:probe2	0.0290235300553691	0.0460627452609303	0.630086849816709	0.528821356146035	   
df.mm.trans2:probe3	-0.0507295233089789	0.0460627452609303	-1.10131350230241	0.271098233659719	   
df.mm.trans2:probe4	-0.0464107837932751	0.0460627452609303	-1.00755574880249	0.313978641191166	   
df.mm.trans2:probe5	0.485774627359906	0.0460627452609303	10.5459330443323	2.08815265145056e-24	***
df.mm.trans2:probe6	0.105042708833048	0.0460627452609303	2.28042658417372	0.0228500151790128	*  
df.mm.trans3:probe2	-0.238700026889176	0.0460627452609303	-5.18206254397168	2.79367954897528e-07	***
df.mm.trans3:probe3	-0.322101986699485	0.0460627452609303	-6.99267889646793	5.77884764576965e-12	***
df.mm.trans3:probe4	-0.0366260716262042	0.0460627452609303	-0.795134363328317	0.426775996005528	   
df.mm.trans3:probe5	0.220037483435198	0.0460627452609303	4.77690771986685	2.12479202433037e-06	***
df.mm.trans3:probe6	-0.753875624155614	0.0460627452609303	-16.3662764753850	5.19112473812233e-52	***
df.mm.trans3:probe7	0.232929393314816	0.0460627452609303	5.05678487018843	5.31247141976948e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24178025600041	0.156089409570748	27.1753238587132	4.17633499372857e-115	***
df.mm.trans1	0.078295834337964	0.136045912987924	0.57551037453741	0.56511131557373	   
df.mm.trans2	0.03557372185247	0.121406296175164	0.293013813724657	0.769589129708586	   
df.mm.exp2	0.00611826497239248	0.158837136088352	0.0385191090891315	0.969283601953373	   
df.mm.exp3	-0.0733975237818672	0.158837136088353	-0.462092968869951	0.644142853727439	   
df.mm.exp4	-0.0927252313532629	0.158837136088352	-0.583775517720774	0.559539261724836	   
df.mm.exp5	0.00205036393874151	0.158837136088352	0.0129085929728738	0.989704003047796	   
df.mm.exp6	-0.0897212622436154	0.158837136088352	-0.564863258386303	0.572328360285516	   
df.mm.exp7	-0.347790789736632	0.158837136088352	-2.18960627408426	0.0288468510367325	*  
df.mm.exp8	-0.129737240088444	0.158837136088352	-0.816794128145687	0.414294209126284	   
df.mm.trans1:exp2	-0.0661979295595636	0.148319462390559	-0.446319913062044	0.655489417733818	   
df.mm.trans2:exp2	0.0653898598390194	0.115688382816471	0.565224080820234	0.572083063836453	   
df.mm.trans1:exp3	0.0242180969547837	0.148319462390559	0.163283338305339	0.870337431685487	   
df.mm.trans2:exp3	-0.0417514842779116	0.115688382816471	-0.360896083612357	0.718274352295008	   
df.mm.trans1:exp4	0.0643548308742503	0.148319462390559	0.433893366635791	0.664485247642763	   
df.mm.trans2:exp4	0.107260619614018	0.115688382816471	0.927151171126463	0.354133378204822	   
df.mm.trans1:exp5	0.0262329725756357	0.148319462390559	0.176868039789399	0.859657723465955	   
df.mm.trans2:exp5	-0.0160471867134323	0.115688382816471	-0.138710441988714	0.889714586985189	   
df.mm.trans1:exp6	0.0746703624355134	0.148319462390559	0.503442779740458	0.614794417136619	   
df.mm.trans2:exp6	0.0116551090899120	0.115688382816471	0.100745717125303	0.919778076378912	   
df.mm.trans1:exp7	0.383251600000715	0.148319462390559	2.58396028291638	0.00994710976594395	** 
df.mm.trans2:exp7	0.137732983167915	0.115688382816471	1.19055154730977	0.234190034265409	   
df.mm.trans1:exp8	0.148834478096223	0.148319462390559	1.00347234069867	0.315942622687435	   
df.mm.trans2:exp8	0.0213507860743840	0.115688382816471	0.184554278956902	0.853626377200728	   
df.mm.trans1:probe2	-0.0171066912621173	0.0942553733828715	-0.181493008283240	0.856027531650842	   
df.mm.trans1:probe3	0.104401209860511	0.0942553733828715	1.10764199550116	0.268356162836162	   
df.mm.trans1:probe4	0.00318255824089542	0.0942553733828715	0.0337652711635618	0.973072924073786	   
df.mm.trans1:probe5	0.128779647210642	0.0942553733828715	1.36628440998829	0.172241452222762	   
df.mm.trans1:probe6	-0.0237327270379265	0.0942553733828715	-0.251791767261084	0.801268008703307	   
df.mm.trans1:probe7	-0.0864927259358178	0.0942553733828715	-0.917642388243254	0.359088320585421	   
df.mm.trans1:probe8	0.0473142556156962	0.0942553733828715	0.501979398283242	0.615822956315729	   
df.mm.trans1:probe9	0.102252033393330	0.0942553733828715	1.08484036212955	0.278325840248556	   
df.mm.trans1:probe10	0.0330903302459221	0.0942553733828715	0.351071021823944	0.725629385472452	   
df.mm.trans1:probe11	-0.0118478570247481	0.0942553733828715	-0.125699539448232	0.900001992334586	   
df.mm.trans1:probe12	-0.0913961299517087	0.0942553733828715	-0.969664929133023	0.332512581107145	   
df.mm.trans1:probe13	0.110457680453989	0.0942553733828715	1.17189796708250	0.241594021545611	   
df.mm.trans1:probe14	0.00676766279727652	0.0942553733828715	0.0718013472800732	0.942778312824548	   
df.mm.trans1:probe15	-0.0835823259063778	0.0942553733828715	-0.886764572740706	0.375477595779005	   
df.mm.trans1:probe16	-0.00954455581923466	0.0942553733828715	-0.101262723563399	0.919367797268018	   
df.mm.trans1:probe17	0.0252107420886836	0.0942553733828715	0.267472730560155	0.789175554213443	   
df.mm.trans1:probe18	0.0671987682921877	0.0942553733828715	0.712943632605665	0.476092778421157	   
df.mm.trans1:probe19	-0.0107389685378286	0.0942553733828715	-0.113934815092251	0.909318636573775	   
df.mm.trans1:probe20	0.0196122518055868	0.0942553733828715	0.208075689498577	0.83522390922998	   
df.mm.trans1:probe21	0.193360628763739	0.0942553733828715	2.05145470039460	0.040554437874711	*  
df.mm.trans1:probe22	0.088567555414445	0.0942553733828715	0.93965523912019	0.347683838634072	   
df.mm.trans2:probe2	0.172094692765145	0.0942553733828715	1.82583429027526	0.0682554649499477	.  
df.mm.trans2:probe3	0.175449991097843	0.0942553733828715	1.86143224307386	0.0630572117727616	.  
df.mm.trans2:probe4	0.159016309626145	0.0942553733828715	1.68707951514032	0.0919858536213524	.  
df.mm.trans2:probe5	0.0976911676317316	0.0942553733828715	1.03645197218522	0.300310901921043	   
df.mm.trans2:probe6	0.113655539969936	0.0942553733828715	1.20582557673672	0.228248533484178	   
df.mm.trans3:probe2	0.0371748142953636	0.0942553733828715	0.394405252041781	0.693389127467032	   
df.mm.trans3:probe3	0.00598237683031108	0.0942553733828715	0.0634698756749949	0.949408524909399	   
df.mm.trans3:probe4	-0.097275738013894	0.0942553733828715	-1.03204448216181	0.30236953725924	   
df.mm.trans3:probe5	-0.0174524932740409	0.0942553733828715	-0.185161785982723	0.853150030558793	   
df.mm.trans3:probe6	-0.0712707885172886	0.0942553733828715	-0.756145628194394	0.449789024675553	   
df.mm.trans3:probe7	-0.128252070218974	0.0942553733828715	-1.36068709523865	0.174003624506757