fitVsDatCorrelation=0.893603667826932
cont.fitVsDatCorrelation=0.237721396412449

fstatistic=7691.4873919511,58,830
cont.fstatistic=1631.18523433048,58,830

residuals=-0.69380268148316,-0.106467757272363,0.00459965383919962,0.114283386820196,0.683142804714186
cont.residuals=-0.751465444820309,-0.310297649266462,-0.0666703619429848,0.228852745420376,1.42838549524739

predictedValues:
Include	Exclude	Both
Lung	62.9535199417174	90.7399424853285	89.6725829706136
cerebhem	56.2622907857518	66.4068611938343	76.4043736846886
cortex	57.8683947498818	136.792273408031	103.425718149304
heart	114.188731527204	88.446165124781	126.771297137791
kidney	59.968404860012	77.5756307523245	79.950787306602
liver	60.9403138529481	67.4389154008723	68.2027849834494
stomach	57.0351871384318	101.262565450967	87.3280367161803
testicle	57.3926302872729	55.540007768046	68.1917525496407


diffExp=-27.7864225436111,-10.1445704080825,-78.9238786581488,25.7425664024229,-17.6072258923124,-6.49860154792426,-44.227378312535,1.85262251922686
diffExpScore=1.34169487910848
diffExp1.5=0,0,-1,0,0,0,-1,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,-1,0,0,0,-1,0
diffExp1.4Score=0.75
diffExp1.3=-1,0,-1,0,0,0,-1,0
diffExp1.3Score=0.75
diffExp1.2=-1,0,-1,1,-1,0,-1,0
diffExp1.2Score=1.25

cont.predictedValues:
Include	Exclude	Both
Lung	72.1366292973315	71.4634000588193	85.8187333273175
cerebhem	72.3583039153935	93.614401386628	84.8185551283889
cortex	70.78931729205	68.2237842358387	71.7311312834809
heart	71.0661171905051	82.9228435021412	83.0582678870117
kidney	76.7307928875381	80.4473148464524	72.6249299801434
liver	74.0036874989264	87.7499295337191	81.5880664263164
stomach	74.4425064651768	62.1721039090578	81.5353639364234
testicle	74.174253987629	85.4178104793932	76.4568048013213
cont.diffExp=0.673229238512221,-21.2560974712346,2.56553305621134,-11.8567263116361,-3.71652195891434,-13.7462420347927,12.2704025561191,-11.2435564917642
cont.diffExpScore=1.63450312325822

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

tran.correlation=0.0428286274071247
cont.tran.correlation=0.0722308732293437

tran.covariance=0.00702100931190352
cont.tran.covariance=0.000282080502806767

tran.mean=75.6757396704627
cont.tran.mean=76.1070747804125

weightedLogRatios:
wLogRatio
Lung	-1.58129625473886
cerebhem	-0.681821608176404
cortex	-3.86126425214196
heart	1.17769545156745
kidney	-1.08703057473904
liver	-0.421578197903345
stomach	-2.48602718270770
testicle	0.132348656269992

cont.weightedLogRatios:
wLogRatio
Lung	0.0400740560276092
cerebhem	-1.13591807532787
cortex	0.156565162577780
heart	-0.669778962294188
kidney	-0.206412334108531
liver	-0.747832484606142
stomach	0.760103337362819
testicle	-0.61775694841508

varWeightedLogRatios=2.44559499076117
cont.varWeightedLogRatios=0.370520553565993

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89466413127634	0.0904899216889595	43.0397557936169	1.26236991708326e-213	***
df.mm.trans1	0.0477310097194699	0.078030952420596	0.61169328630252	0.540908212663893	   
df.mm.trans2	0.58197192548471	0.0691128192950472	8.42060751421865	1.63551876030580e-16	***
df.mm.exp2	-0.264444285214404	0.0889858293200748	-2.97175727006172	0.00304650939935706	** 
df.mm.exp3	0.183551997014412	0.0889858293200748	2.06271041599433	0.0394507576862187	*  
df.mm.exp4	0.223632783397161	0.0889858293200748	2.51312804640807	0.0121550104334495	*  
df.mm.exp5	-0.0905693536347924	0.0889858293200748	-1.01779524140885	0.309071855137557	   
df.mm.exp6	-0.0555974906254908	0.0889858293200748	-0.624790385731094	0.532280355911932	   
df.mm.exp7	0.0374843861809579	0.0889858293200748	0.421239948735316	0.673688935483752	   
df.mm.exp8	-0.309533348511493	0.0889858293200748	-3.47845663603501	0.000530554731431615	***
df.mm.trans1:exp2	0.152072129892481	0.0819677281199615	1.85526832791949	0.0639122891333536	.  
df.mm.trans2:exp2	-0.0477529728043545	0.0609859413025988	-0.783016081811622	0.433841022212762	   
df.mm.trans1:exp3	-0.267777296108957	0.0819677281199615	-3.26686248662472	0.00113211327644366	** 
df.mm.trans2:exp3	0.226913885248194	0.0609859413025988	3.72075728276944	0.000212003384024060	***
df.mm.trans1:exp4	0.371823160418421	0.0819677281199615	4.53621405578364	6.57149764791023e-06	***
df.mm.trans2:exp4	-0.249236360545101	0.0609859413025988	-4.08678385906098	4.7986534167301e-05	***
df.mm.trans1:exp5	0.0419905160995107	0.0819677281199615	0.512281077719474	0.608590591978202	   
df.mm.trans2:exp5	-0.0661749455880214	0.0609859413025988	-1.08508525365995	0.278198961001157	   
df.mm.trans1:exp6	0.0230957389698637	0.0819677281199615	0.281766245077119	0.778192991340065	   
df.mm.trans2:exp6	-0.241177918636239	0.0609859413025988	-3.9546478005409	8.31886759990836e-05	***
df.mm.trans1:exp7	-0.136212665986809	0.0819677281199615	-1.66178408394409	0.0969336832664968	.  
df.mm.trans2:exp7	0.0722347749296604	0.0609859413025988	1.18444961882686	0.236574188738682	   
df.mm.trans1:exp8	0.217052575990247	0.0819677281199615	2.64802478937304	0.00825002906868435	** 
df.mm.trans2:exp8	-0.181360670141161	0.0609859413025988	-2.97381111560268	0.00302640943568961	** 
df.mm.trans1:probe2	-0.0432315798546223	0.0561194670984418	-0.770349080093505	0.441312056863809	   
df.mm.trans1:probe3	0.174226224586069	0.0561194670984418	3.10455949769534	0.00197030966246317	** 
df.mm.trans1:probe4	-0.113257080854351	0.0561194670984418	-2.01814248620147	0.0438976876550321	*  
df.mm.trans1:probe5	0.170360475387837	0.0561194670984418	3.03567521567872	0.00247494056520415	** 
df.mm.trans1:probe6	0.0925601185308567	0.0561194670984418	1.64934065336887	0.0994562514434268	.  
df.mm.trans1:probe7	0.064971693370336	0.0561194670984418	1.15773895814738	0.247303622890668	   
df.mm.trans1:probe8	-0.101231810565360	0.0561194670984418	-1.80386264872730	0.0716153062081363	.  
df.mm.trans1:probe9	0.264601253536051	0.0561194670984418	4.71496375886645	2.83487642488314e-06	***
df.mm.trans1:probe10	0.366400791946332	0.0561194670984418	6.52894282306015	1.15269063172777e-10	***
df.mm.trans1:probe11	0.178221415708394	0.0561194670984418	3.17575032200088	0.00154969167406155	** 
df.mm.trans1:probe12	0.225033093275925	0.0561194670984418	4.00989362356531	6.62198698522442e-05	***
df.mm.trans1:probe13	0.455706797970594	0.0561194670984418	8.12029802726419	1.67611931612483e-15	***
df.mm.trans1:probe14	0.129352724876675	0.0561194670984418	2.30495283659361	0.0214151334911976	*  
df.mm.trans1:probe15	0.283998755002878	0.0561194670984418	5.060610331611	5.14823399771959e-07	***
df.mm.trans1:probe16	0.613502746975572	0.0561194670984418	10.9320843317952	4.40325271653058e-26	***
df.mm.trans1:probe17	0.57188894286973	0.0561194670984418	10.1905626057809	4.65591992809813e-23	***
df.mm.trans1:probe18	0.580897073426581	0.0561194670984418	10.3510796424279	1.06378171771017e-23	***
df.mm.trans1:probe19	0.717325771478445	0.0561194670984418	12.7821201548502	2.79032287142726e-34	***
df.mm.trans1:probe20	0.784001228662179	0.0561194670984418	13.9702186994564	5.46905252977945e-40	***
df.mm.trans1:probe21	0.784718930075514	0.0561194670984418	13.9830075132218	4.72885609757749e-40	***
df.mm.trans2:probe2	0.110820377764872	0.0561194670984418	1.97472256054173	0.0486307016034707	*  
df.mm.trans2:probe3	0.0734964238552064	0.0561194670984418	1.30964222675677	0.190679281854525	   
df.mm.trans2:probe4	0.109821097873388	0.0561194670984418	1.95691626366918	0.05069220589992	.  
df.mm.trans2:probe5	0.172387782237182	0.0561194670984418	3.07180005709585	0.00219715669254160	** 
df.mm.trans2:probe6	0.0352596564171490	0.0561194670984418	0.628296351340941	0.529982664717283	   
df.mm.trans3:probe2	-0.279093206713547	0.0561194670984418	-4.97319773589398	8.00471235175546e-07	***
df.mm.trans3:probe3	-0.294096815826877	0.0561194670984418	-5.24054897583022	2.03187474727149e-07	***
df.mm.trans3:probe4	-0.307793319819188	0.0561194670984418	-5.48460874154905	5.50318650223659e-08	***
df.mm.trans3:probe5	-0.0125566887646767	0.0561194670984418	-0.223749251621553	0.823007480589444	   
df.mm.trans3:probe6	0.127415860367321	0.0561194670984418	2.27043959886175	0.0234364608574115	*  
df.mm.trans3:probe7	-0.330723436043534	0.0561194670984418	-5.89320343087714	5.50419623570855e-09	***
df.mm.trans3:probe8	-0.0535941916029448	0.0561194670984418	-0.955001791248885	0.339854845253782	   
df.mm.trans3:probe9	0.953829752471065	0.0561194670984418	16.9964150015521	8.07924686027203e-56	***
df.mm.trans3:probe10	-0.0854079204529999	0.0561194670984418	-1.52189471619860	0.128416332828414	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07582206148660	0.195821627235755	20.8139525701091	9.48851808884621e-78	***
df.mm.trans1	0.119569889661379	0.168860219929013	0.708099810077499	0.47908207716678	   
df.mm.trans2	0.222391319369389	0.149561238252879	1.48695826517141	0.137405600928255	   
df.mm.exp2	0.284790062533498	0.192566747469141	1.47891609676346	0.139542144027569	   
df.mm.exp3	0.114066447232610	0.192566747469141	0.592347581977459	0.553779188940419	   
df.mm.exp4	0.166468805154287	0.192566747469141	0.864473266242208	0.387577724182405	   
df.mm.exp5	0.347087283310564	0.192566747469141	1.80242584907441	0.0718411034603464	.  
df.mm.exp6	0.281412914376919	0.192566747469141	1.46137855094642	0.144290052008953	   
df.mm.exp7	-0.0566133898398352	0.192566747469141	-0.293993592268091	0.768836302572115	   
df.mm.exp8	0.321735741630463	0.192566747469141	1.67077517722535	0.095143082905958	.  
df.mm.trans1:exp2	-0.281721791736146	0.177379465046296	-1.58824355267177	0.112612113996875	   
df.mm.trans2:exp2	-0.0147912612687844	0.131974545247465	-0.112076622359632	0.91078975173651	   
df.mm.trans1:exp3	-0.132920293544889	0.177379465046296	-0.749355589217708	0.453855236003204	   
df.mm.trans2:exp3	-0.16045863207043	0.131974545247465	-1.21583015701668	0.224395314155663	   
df.mm.trans1:exp4	-0.181420083323573	0.177379465046296	-1.02277951552183	0.30671010832455	   
df.mm.trans2:exp4	-0.0177436574117833	0.131974545247465	-0.134447573799267	0.89308125909249	   
df.mm.trans1:exp5	-0.285346133621414	0.177379465046296	-1.60867625543317	0.108067423393780	   
df.mm.trans2:exp5	-0.228670218456426	0.131974545247465	-1.73268426898268	0.083523252873405	.  
df.mm.trans1:exp6	-0.255859941269753	0.177379465046296	-1.44244397852352	0.149554405149814	   
df.mm.trans2:exp6	-0.0761072864175016	0.131974545247465	-0.576681558362585	0.564310951701393	   
df.mm.trans1:exp7	0.0880785420703978	0.177379465046296	0.496554333656432	0.61963484385369	   
df.mm.trans2:exp7	-0.0826656325766145	0.131974545247465	-0.626375581909438	0.531240846447426	   
df.mm.trans1:exp8	-0.29388058288992	0.177379465046296	-1.65679033259694	0.0979398039190859	.  
df.mm.trans2:exp8	-0.143366540309048	0.131974545247465	-1.08631963868655	0.277652988150067	   
df.mm.trans1:probe2	0.153863702516277	0.121443417805069	1.26695794055505	0.205525716657838	   
df.mm.trans1:probe3	-0.00926289567816393	0.121443417805069	-0.0762733447854041	0.939220012657582	   
df.mm.trans1:probe4	0.077958594220688	0.121443417805069	0.641933466874431	0.52109362906567	   
df.mm.trans1:probe5	0.249413416268459	0.121443417805069	2.05374174060876	0.0403134912116984	*  
df.mm.trans1:probe6	0.0700186252546304	0.121443417805069	0.576553480790688	0.564397450688674	   
df.mm.trans1:probe7	-0.0289843380278954	0.121443417805069	-0.238665368216321	0.811423986200486	   
df.mm.trans1:probe8	0.052968637509634	0.121443417805069	0.436158982240229	0.662834806694228	   
df.mm.trans1:probe9	0.150202593362864	0.121443417805069	1.23681131573518	0.216506975516970	   
df.mm.trans1:probe10	0.186065512488151	0.121443417805069	1.53211689732586	0.125874636363227	   
df.mm.trans1:probe11	0.0784877963736723	0.121443417805069	0.646291069472817	0.518269526384985	   
df.mm.trans1:probe12	0.153133391562667	0.121443417805069	1.260944350302	0.207683217082389	   
df.mm.trans1:probe13	0.231088480754694	0.121443417805069	1.90284895576323	0.0574060460191539	.  
df.mm.trans1:probe14	0.135229544384514	0.121443417805069	1.1135189278152	0.265807988951475	   
df.mm.trans1:probe15	0.115405182798218	0.121443417805069	0.950279437815695	0.342246903201974	   
df.mm.trans1:probe16	0.088099643644924	0.121443417805069	0.725437781949897	0.46838798249583	   
df.mm.trans1:probe17	0.118135772740578	0.121443417805069	0.972763900059202	0.330954061189725	   
df.mm.trans1:probe18	0.32060699996944	0.121443417805069	2.63997016688093	0.00844699045301836	** 
df.mm.trans1:probe19	0.118272796699898	0.121443417805069	0.97389219471523	0.330393809536609	   
df.mm.trans1:probe20	0.0784470751174012	0.121443417805069	0.645955758947083	0.518486554694077	   
df.mm.trans1:probe21	0.239119430035408	0.121443417805069	1.96897809990182	0.0492879148069445	*  
df.mm.trans2:probe2	-0.0825411168818615	0.121443417805069	-0.679667275293171	0.496904667044852	   
df.mm.trans2:probe3	-0.0372735842047409	0.121443417805069	-0.306921403221452	0.758980181383704	   
df.mm.trans2:probe4	-0.105740770824711	0.121443417805069	-0.870699892475332	0.384169791765959	   
df.mm.trans2:probe5	-0.145400130506025	0.121443417805069	-1.19726645654365	0.231544540039721	   
df.mm.trans2:probe6	-0.09349158965143	0.121443417805069	-0.769836614788747	0.441615856292018	   
df.mm.trans3:probe2	0.0638234902223099	0.121443417805069	0.525540958710121	0.599347555740006	   
df.mm.trans3:probe3	0.0265609546385845	0.121443417805069	0.218710533009026	0.826929309673008	   
df.mm.trans3:probe4	0.0532443599099227	0.121443417805069	0.438429359715370	0.6611891555989	   
df.mm.trans3:probe5	-0.021401953547852	0.121443417805069	-0.176229835545345	0.86015637079717	   
df.mm.trans3:probe6	0.0730724697050246	0.121443417805069	0.60169971354326	0.547538351646328	   
df.mm.trans3:probe7	0.0215887039034792	0.121443417805069	0.177767591637874	0.858948874826945	   
df.mm.trans3:probe8	0.112281961071062	0.121443417805069	0.92456193263012	0.355462538571726	   
df.mm.trans3:probe9	-0.0371022690351201	0.121443417805069	-0.305510744885932	0.760053790404882	   
df.mm.trans3:probe10	0.0524727760593831	0.121443417805069	0.432075916568883	0.665798461312818