fitVsDatCorrelation=0.897967217708916
cont.fitVsDatCorrelation=0.2692453288643

fstatistic=5503.80989269451,54,738
cont.fstatistic=1138.33142366782,54,738

residuals=-0.793245122001247,-0.103020302635932,-0.00635406792445297,0.0943039113884193,0.935723714539177
cont.residuals=-0.790190901389963,-0.316661395379057,-0.107468593519601,0.200761701819314,2.04999548097230

predictedValues:
Include	Exclude	Both
Lung	63.2058013866942	50.2399890333932	99.901300513238
cerebhem	75.6958208896362	47.2877062660654	97.494183808364
cortex	147.539411224969	54.8850550037306	321.146009155957
heart	79.2266536618574	54.1320249825617	114.182259218306
kidney	59.0194139354476	50.8122924344902	92.2059226750125
liver	60.9546892131454	53.9788785421196	86.1082974909128
stomach	64.3353413071202	51.9807993783892	98.9351654047975
testicle	66.7115255394501	50.422749306655	102.036268981607


diffExp=12.9658123533010,28.4081146235708,92.6543562212382,25.0946286792957,8.2071215009574,6.97581067102573,12.3545419287310,16.2887762327951
diffExpScore=0.99509681731879
diffExp1.5=0,1,1,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,1,1,1,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=0,1,1,1,0,0,0,1
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,0,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	72.333383007372	66.0551012934371	68.6690765449002
cerebhem	71.2187146445103	68.5483143964771	76.4960717267647
cortex	76.2292043902768	58.7518227364667	79.5722837101559
heart	74.7322264203509	69.5743805572978	70.9312436583709
kidney	69.3808543679846	72.8977531294359	69.8359463138418
liver	80.0916086086277	63.2797822030004	68.0710666912866
stomach	74.0205468046022	59.3236848917296	71.61815908374
testicle	68.3783775891476	55.453549823814	72.8422955087724
cont.diffExp=6.27828171393485,2.67040024803323,17.4773816538102,5.15784586305314,-3.51689876145137,16.8118264056273,14.6968619128726,12.9248277653336
cont.diffExpScore=1.08209189492235

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=0,0,1,0,0,1,1,1
cont.diffExp1.2Score=0.8

tran.correlation=0.46882405910978
cont.tran.correlation=-0.122725089918050

tran.covariance=0.00632044053483861
cont.tran.covariance=-0.000438844972086425

tran.mean=64.4017595066078
cont.tran.mean=68.7668315540332

weightedLogRatios:
wLogRatio
Lung	0.925594876784291
cerebhem	1.92493236644433
cortex	4.449516014995
heart	1.59281887991308
kidney	0.599362088067565
liver	0.492151159274408
stomach	0.865198054798184
testicle	1.13665206153695

cont.weightedLogRatios:
wLogRatio
Lung	0.3846037168375
cerebhem	0.162293325806462
cortex	1.0946945590311
heart	0.30595220329488
kidney	-0.210857951704254
liver	1.00494301826192
stomach	0.928203347037774
testicle	0.863248123983434

varWeightedLogRatios=1.65437832569828
cont.varWeightedLogRatios=0.222678602806122

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.39035934663554	0.110165197389866	30.7752305352599	1.90079520370737e-134	***
df.mm.trans1	0.839580684593747	0.0970457297923185	8.65139235276484	3.16013380571079e-17	***
df.mm.trans2	0.532136508086721	0.0875673254845503	6.07688433033857	1.96307509670307e-09	***
df.mm.exp2	0.144155890447671	0.116613612599972	1.23618407177023	0.216783444723957	   
df.mm.exp3	-0.231584143948705	0.116613612599972	-1.98591003902026	0.0474128972871186	*  
df.mm.exp4	0.166918120073886	0.116613612599972	1.43137766125535	0.152745335144662	   
df.mm.exp5	0.0229556849596496	0.116613612599972	0.196852532460306	0.843997138673898	   
df.mm.exp6	0.184093151944543	0.116613612599972	1.57865919629855	0.114842750027243	   
df.mm.exp7	0.0614940875640106	0.116613612599972	0.527331982887435	0.598121552832863	   
df.mm.exp8	0.0364671635367568	0.116613612599972	0.312717895652994	0.75458334321646	   
df.mm.trans1:exp2	0.0361709712250345	0.109994882630885	0.328842309386473	0.74236817334319	   
df.mm.trans2:exp2	-0.204716842416081	0.0899958130616336	-2.27473740668225	0.0232077929006643	*  
df.mm.trans1:exp3	1.07928338775072	0.109994882630885	9.81212363644693	1.91173937948172e-21	***
df.mm.trans2:exp3	0.320013929375196	0.0899958130616335	3.55587575119784	0.00040075347640799	***
df.mm.trans1:exp4	0.0589985672322204	0.109994882630886	0.536375564217877	0.591860652588678	   
df.mm.trans2:exp4	-0.0923034542233885	0.0899958130616335	-1.02564165024182	0.305396581101713	   
df.mm.trans1:exp5	-0.0914853364588333	0.109994882630885	-0.831723569957655	0.405834022971583	   
df.mm.trans2:exp5	-0.0116286864655936	0.0899958130616335	-0.129213638612606	0.897223811155365	   
df.mm.trans1:exp6	-0.220358454601437	0.109994882630885	-2.00335187720417	0.0455047027027373	*  
df.mm.trans2:exp6	-0.112311623879174	0.0899958130616336	-1.24796498924075	0.212439683814837	   
df.mm.trans1:exp7	-0.0437810668197998	0.109994882630885	-0.39802821524632	0.690724499533587	   
df.mm.trans2:exp7	-0.0274309837682959	0.0899958130616335	-0.304802888435597	0.760602216863085	   
df.mm.trans1:exp8	0.0175144801173435	0.109994882630885	0.159229954143572	0.873531270334861	   
df.mm.trans2:exp8	-0.0328360190420478	0.0899958130616336	-0.364861629946718	0.715319227272197	   
df.mm.trans1:probe2	-0.161645025718464	0.0642231749934862	-2.51692672831667	0.0120491913683534	*  
df.mm.trans1:probe3	-0.329276943141017	0.0642231749934862	-5.12707357078521	3.76319647458903e-07	***
df.mm.trans1:probe4	-0.372559511523017	0.0642231749934862	-5.80101359923118	9.77812360084063e-09	***
df.mm.trans1:probe5	-0.20178484702637	0.0642231749934862	-3.14193197466235	0.00174544005043052	** 
df.mm.trans1:probe6	-0.221368207769830	0.0642231749934862	-3.44685867355953	0.000599269877433521	***
df.mm.trans1:probe7	-0.119085399447843	0.0642231749934862	-1.85424341695845	0.0641030957811028	.  
df.mm.trans1:probe8	-0.379326441015764	0.0642231749934862	-5.90637945031894	5.33589490000125e-09	***
df.mm.trans1:probe9	0.270881994101323	0.0642231749934862	4.21782314762228	2.77343407040345e-05	***
df.mm.trans1:probe10	0.183773274337513	0.0642231749934862	2.86147912114516	0.00433599090350004	** 
df.mm.trans1:probe11	-0.422999027495046	0.0642231749934862	-6.58639233482226	8.56203451640087e-11	***
df.mm.trans1:probe12	-0.286265653509934	0.0642231749934862	-4.45735754326952	9.58670677363803e-06	***
df.mm.trans1:probe13	-0.412675165378184	0.0642231749934862	-6.4256425413418	2.35414322879001e-10	***
df.mm.trans1:probe14	-0.408912778909655	0.0642231749934862	-6.36705953810488	3.38545748324216e-10	***
df.mm.trans1:probe15	-0.425582728474821	0.0642231749934862	-6.6266223760813	6.62533020356507e-11	***
df.mm.trans1:probe16	-0.427864042889486	0.0642231749934862	-6.66214404586634	5.27709839641983e-11	***
df.mm.trans1:probe17	-0.0559180169538934	0.0642231749934862	-0.870682848668955	0.384210468738853	   
df.mm.trans1:probe18	0.068586066113883	0.0642231749934862	1.06793328297518	0.285899807480973	   
df.mm.trans1:probe19	0.519364238542891	0.0642231749934862	8.08686644027747	2.50917827325867e-15	***
df.mm.trans1:probe20	0.393250839974734	0.0642231749934862	6.12319213453118	1.4899257170552e-09	***
df.mm.trans1:probe21	0.295122481083623	0.0642231749934862	4.59526457721151	5.0825425001948e-06	***
df.mm.trans1:probe22	0.238598509806562	0.0642231749934862	3.71514659358965	0.000218420742468107	***
df.mm.trans2:probe2	0.0153844317307657	0.0642231749934862	0.239546421246319	0.810748418307991	   
df.mm.trans2:probe3	0.0101061306116765	0.0642231749934862	0.157359560823666	0.875004523677217	   
df.mm.trans2:probe4	-0.0220888483179858	0.0642231749934862	-0.343938902432434	0.730990204631737	   
df.mm.trans2:probe5	-0.0362657686896809	0.0642231749934862	-0.564683522628072	0.572460633649793	   
df.mm.trans2:probe6	-0.0296660046485937	0.0642231749934862	-0.461920555182807	0.644274425909646	   
df.mm.trans3:probe2	-0.289697719473092	0.0642231749934862	-4.51079722393784	7.51174565023846e-06	***
df.mm.trans3:probe3	-0.0796428563544703	0.0642231749934862	-1.24009528277834	0.215334290912727	   
df.mm.trans3:probe4	-0.415775421112556	0.0642231749934862	-6.4739157034003	1.74137786843435e-10	***
df.mm.trans3:probe5	-0.00435318103839557	0.0642231749934862	-0.0677820901697415	0.945977460315697	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17696946085898	0.241095323083012	17.324970917917	1.11659796582656e-56	***
df.mm.trans1	0.0453247528075374	0.212383512510803	0.213409940685635	0.8310661681729	   
df.mm.trans2	-0.0641393598806642	0.191640128910212	-0.334686478481315	0.737956699731466	   
df.mm.exp2	-0.0864209818131806	0.255207608861905	-0.338630114511764	0.73498471308986	   
df.mm.exp3	-0.212075049384209	0.255207608861905	-0.83099030757725	0.406247867337918	   
df.mm.exp4	0.0521207672403019	0.255207608861905	0.20422889220558	0.83823094039575	   
df.mm.exp5	0.0400438670957435	0.255207608861905	0.156907026692184	0.875361036824349	   
df.mm.exp6	0.067708672368432	0.255207608861905	0.265308204055427	0.790846133938215	   
df.mm.exp7	-0.126473350487331	0.255207608861905	-0.49557045360574	0.620345034434428	   
df.mm.exp8	-0.290170419605443	0.255207608861905	-1.13699752487574	0.255908353542749	   
df.mm.trans1:exp2	0.070890860213906	0.240722591105808	0.294491929021927	0.768464844270011	   
df.mm.trans2:exp2	0.123470536316653	0.196954847268388	0.626897677458028	0.53092018818422	   
df.mm.trans1:exp3	0.264533946756509	0.240722591105808	1.09891616545551	0.27216300032313	   
df.mm.trans2:exp3	0.0949079654284671	0.196954847268388	0.481876768938503	0.630036356396031	   
df.mm.trans1:exp4	-0.0194951084929065	0.240722591105808	-0.080985787014637	0.935475212713557	   
df.mm.trans2:exp4	-0.000213624905104795	0.196954847268388	-0.00108463898232314	0.999134876570977	   
df.mm.trans1:exp5	-0.081718662913263	0.240722591105808	-0.339472346728539	0.734350506745146	   
df.mm.trans2:exp5	0.0585246883738474	0.196954847268388	0.297147743178397	0.766437332108686	   
df.mm.trans1:exp6	0.0341766631676904	0.240722591105808	0.141975304480951	0.887138260455384	   
df.mm.trans2:exp6	-0.110632052513603	0.196954847268388	-0.561712768423748	0.574482272810156	   
df.mm.trans1:exp7	0.149530313062905	0.240722591105808	0.621172746504626	0.534677775820799	   
df.mm.trans2:exp7	0.0189927229035571	0.196954847268388	0.09643186327715	0.923203766866705	   
df.mm.trans1:exp8	0.233941325530698	0.240722591105808	0.971829542279526	0.331453762545811	   
df.mm.trans2:exp8	0.115226888016161	0.196954847268388	0.585042153642164	0.558698198488893	   
df.mm.trans1:probe2	0.109245689632255	0.140551712258773	0.777263313812359	0.437252507099071	   
df.mm.trans1:probe3	0.263781290939594	0.140551712258773	1.87675615401924	0.0609458624557437	.  
df.mm.trans1:probe4	-0.0891611046991333	0.140551712258773	-0.634365126302957	0.526039149098823	   
df.mm.trans1:probe5	0.0190577120236007	0.140551712258773	0.135592172570001	0.892180668287434	   
df.mm.trans1:probe6	-0.0260958198750244	0.140551712258773	-0.185667036392831	0.852756897110747	   
df.mm.trans1:probe7	0.158552197938430	0.140551712258773	1.12807019843712	0.259656868474799	   
df.mm.trans1:probe8	0.0321084775400153	0.140551712258773	0.228446007693593	0.819362809567502	   
df.mm.trans1:probe9	0.268176620695714	0.140551712258773	1.90802812990259	0.0567753749995962	.  
df.mm.trans1:probe10	0.251790631041219	0.140551712258773	1.79144477854273	0.0736315820361257	.  
df.mm.trans1:probe11	0.00744368214459376	0.140551712258773	0.0529604515303879	0.957777750643169	   
df.mm.trans1:probe12	0.292922415406893	0.140551712258773	2.08408998154065	0.0374953853035157	*  
df.mm.trans1:probe13	-0.130110565396028	0.140551712258773	-0.92571312938884	0.354897688295364	   
df.mm.trans1:probe14	0.132964749462566	0.140551712258773	0.946020132559905	0.344447948761681	   
df.mm.trans1:probe15	0.00104886620859078	0.140551712258773	0.00746249328261249	0.994047863811479	   
df.mm.trans1:probe16	0.0940886150672551	0.140551712258773	0.66942347094304	0.503434753269759	   
df.mm.trans1:probe17	0.139895687637598	0.140551712258773	0.995332503527475	0.319900803793508	   
df.mm.trans1:probe18	-0.0483235451945366	0.140551712258773	-0.343813279951845	0.731084643276398	   
df.mm.trans1:probe19	0.079620085025689	0.140551712258773	0.566482497766365	0.571238054305756	   
df.mm.trans1:probe20	-0.0427269144208798	0.140551712258773	-0.303994264703189	0.761217950501418	   
df.mm.trans1:probe21	0.126910034281810	0.140551712258773	0.902941929644748	0.366851330938163	   
df.mm.trans1:probe22	-0.048417280986851	0.140551712258773	-0.344480193152744	0.730583327693234	   
df.mm.trans2:probe2	0.0923939710326809	0.140551712258773	0.657366385281541	0.511150321301384	   
df.mm.trans2:probe3	0.22632787574649	0.140551712258773	1.61028188208617	0.107763940697585	   
df.mm.trans2:probe4	0.0166066921607422	0.140551712258773	0.118153609755868	0.905978096811314	   
df.mm.trans2:probe5	0.361760789288361	0.140551712258773	2.57386255545799	0.0102510112737020	*  
df.mm.trans2:probe6	0.157161440813498	0.140551712258773	1.11817521314962	0.26385601488441	   
df.mm.trans3:probe2	0.082585149989049	0.140551712258773	0.587578398454511	0.556995044894321	   
df.mm.trans3:probe3	0.116609103825253	0.140551712258773	0.829652673391562	0.407003461809824	   
df.mm.trans3:probe4	0.0113594072032521	0.140551712258773	0.0808201267753896	0.935606913392984	   
df.mm.trans3:probe5	0.145166944151800	0.140551712258773	1.03283653979633	0.302018739078317