fitVsDatCorrelation=0.884828438836549
cont.fitVsDatCorrelation=0.220075655651418

fstatistic=9432.80287997597,65,991
cont.fstatistic=2140.11464651099,65,991

residuals=-0.759510129787856,-0.0936150790037037,-0.0103212929910335,0.083112903235938,0.905882770918522
cont.residuals=-0.644772278724114,-0.220098887203721,-0.0674737743320256,0.129634743511246,2.02310139064961

predictedValues:
Include	Exclude	Both
Lung	52.063327631039	48.3990283128842	62.802452359099
cerebhem	55.3618160726249	58.0221930307328	67.3800082123966
cortex	53.9649474427718	48.5063768102304	80.123942269862
heart	52.8320135030411	51.3325080004729	65.157984415105
kidney	54.1189506023061	52.7465021702261	169.196489776470
liver	55.4568957300255	50.2873780160761	77.879007732262
stomach	54.1105347271899	49.451693940662	60.5857923839956
testicle	53.1815186985944	51.0953942362681	67.2498741214587


diffExp=3.6642993181548,-2.66037695810785,5.4585706325414,1.49950550256822,1.37244843207997,5.16951771394939,4.65884078652788,2.08612446232633
diffExpScore=1.19420052728693
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	63.7923725127786	55.8983896674355	58.2895907151067
cerebhem	60.6214753745393	66.9017514944148	63.3676173826082
cortex	56.2593156009688	61.9971637133415	56.9640039620833
heart	60.4230095450465	60.1937919811688	57.3127328124373
kidney	62.6138545245781	78.1372849732856	64.3734546849835
liver	63.3805992795103	70.3768603222004	59.9683735216962
stomach	58.5850046455721	65.026260065401	59.4687299079906
testicle	59.8035500656465	69.9120892417855	57.3992269548431
cont.diffExp=7.89398284534305,-6.28027611987548,-5.73784811237264,0.229217563877711,-15.5234304487075,-6.99626104269005,-6.44125541982894,-10.1085391761390
cont.diffExpScore=1.34678961572591

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

tran.correlation=0.523466626957089
cont.tran.correlation=0.174865317059504

tran.covariance=0.000668271468292016
cont.tran.covariance=0.000650800372597781

tran.mean=52.5581924328216
cont.tran.mean=63.3701733129796

weightedLogRatios:
wLogRatio
Lung	0.285791737457597
cerebhem	-0.189495248755810
cortex	0.419627812623968
heart	0.113811035737153
kidney	0.102191453974426
liver	0.388147397337158
stomach	0.355269698258151
testicle	0.158213667657501

cont.weightedLogRatios:
wLogRatio
Lung	0.540226182814691
cerebhem	-0.409478254425057
cortex	-0.396094608872014
heart	0.0155811057037028
kidney	-0.940789843483201
liver	-0.439926415195118
stomach	-0.430042026987382
testicle	-0.651111279419358

varWeightedLogRatios=0.040773093220713
cont.varWeightedLogRatios=0.197861808794296

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.77467403783635	0.0777535000280436	48.546676824515	3.29068584229159e-264	***
df.mm.trans1	0.101534356225789	0.0668632993666908	1.51853643459853	0.129198165629324	   
df.mm.trans2	0.123469009100532	0.0583423432839014	2.11628471108395	0.0345694117908910	*  
df.mm.exp2	0.172420900510560	0.0738765094035066	2.33390697398565	0.0197997183311975	*  
df.mm.exp3	-0.205491160866571	0.0738765094035066	-2.78154940624221	0.00551264999243001	** 
df.mm.exp4	0.0366802776256069	0.0738765094035066	0.496507995867301	0.619646159031706	   
df.mm.exp5	-0.866325258453793	0.0738765094035066	-11.7266674542243	7.85124185896539e-30	***
df.mm.exp6	-0.11374269341043	0.0738765094035067	-1.53963275104374	0.123969121811520	   
df.mm.exp7	0.0960183594585062	0.0738765094035066	1.29971435079672	0.194001131335323	   
df.mm.exp8	0.00704372621310695	0.0738765094035066	0.0953445996566347	0.9240603939853	   
df.mm.trans1:exp2	-0.110991601634544	0.0681533854649743	-1.62855595327081	0.103724887740318	   
df.mm.trans2:exp2	0.00892493796836173	0.0469714793934601	0.190007597878732	0.849342088817005	   
df.mm.trans1:exp3	0.241365058554756	0.0681533854649743	3.54149771002645	0.000416378873347291	***
df.mm.trans2:exp3	0.207706693449517	0.0469714793934601	4.42197469893691	1.08608789857207e-05	***
df.mm.trans1:exp4	-0.0220237709788142	0.0681533854649743	-0.323150065525839	0.746649768585123	   
df.mm.trans2:exp4	0.0221642207368163	0.0469714793934601	0.471865502705504	0.637126668490298	   
df.mm.trans1:exp5	0.90504885469169	0.0681533854649743	13.2795876318839	3.70350152685328e-37	***
df.mm.trans2:exp5	0.95234298170124	0.0469714793934601	20.2749198875315	9.97511617788307e-77	***
df.mm.trans1:exp6	0.176887942222770	0.0681533854649743	2.59543881812998	0.00958669254813382	** 
df.mm.trans2:exp6	0.152017067611888	0.0469714793934601	3.23636959224779	0.00125072178881411	** 
df.mm.trans1:exp7	-0.0574502823031877	0.0681533854649743	-0.842955664069144	0.399456643308443	   
df.mm.trans2:exp7	-0.074501783692358	0.0469714793934601	-1.58610681746445	0.113034092627925	   
df.mm.trans1:exp8	0.0142064002206358	0.0681533854649743	0.208447461908358	0.834922434315355	   
df.mm.trans2:exp8	0.0471708972307482	0.0469714793934601	1.00424550897402	0.315505432051815	   
df.mm.trans1:probe2	0.223924615732458	0.0493818192650607	4.53455581558318	6.47791259100636e-06	***
df.mm.trans1:probe3	0.385668389153187	0.0493818192650607	7.80992670770356	1.45226344741273e-14	***
df.mm.trans1:probe4	0.283037824094911	0.0493818192650607	5.73162002346824	1.31947372086556e-08	***
df.mm.trans1:probe5	-0.067515986649607	0.0493818192650607	-1.36722355827374	0.171865269922819	   
df.mm.trans1:probe6	0.375692505751314	0.0493818192650607	7.60791140024137	6.46391360438584e-14	***
df.mm.trans1:probe7	0.302732348011887	0.0493818192650607	6.13044137533589	1.26361301262064e-09	***
df.mm.trans1:probe8	0.0884136487662676	0.0493818192650607	1.79040890113223	0.0736933672127789	.  
df.mm.trans1:probe9	-0.0562430939038914	0.0493818192650607	-1.13894333463096	0.255002026770603	   
df.mm.trans1:probe10	0.422252308776691	0.0493818192650607	8.5507645336074	4.58237214636444e-17	***
df.mm.trans1:probe11	-0.102316268617769	0.0493818192650607	-2.07194206573432	0.0385290517835932	*  
df.mm.trans1:probe12	-0.11756659248212	0.0493818192650607	-2.3807667322071	0.0174648018179304	*  
df.mm.trans1:probe13	-0.0212120749601353	0.0493818192650607	-0.429552318562381	0.667614703530822	   
df.mm.trans1:probe14	0.0575976580813733	0.0493818192650607	1.16637375735822	0.243743974571624	   
df.mm.trans1:probe15	0.00114620713552984	0.0493818192650607	0.0232111160056191	0.981486544370346	   
df.mm.trans1:probe16	-0.0159181837974367	0.0493818192650607	-0.322349075719440	0.747256248198672	   
df.mm.trans1:probe17	0.0104766392335431	0.0493818192650607	0.212155797203602	0.832029117041913	   
df.mm.trans1:probe18	0.377455851136531	0.0493818192650607	7.64361979275223	4.97661445489878e-14	***
df.mm.trans1:probe19	0.0630019667113671	0.0493818192650607	1.27581299451928	0.202320585502057	   
df.mm.trans1:probe20	0.102364965024356	0.0493818192650607	2.07292818587555	0.0384369661518686	*  
df.mm.trans1:probe21	0.154977279767644	0.0493818192650607	3.13834690730594	0.00174924250723142	** 
df.mm.trans1:probe22	0.148825512364699	0.0493818192650607	3.01377135511891	0.00264585394321295	** 
df.mm.trans1:probe23	-0.0229556448048544	0.0493818192650607	-0.464860249105815	0.642133657521488	   
df.mm.trans1:probe24	0.303752184778070	0.0493818192650607	6.15109344489026	1.11485315704651e-09	***
df.mm.trans2:probe2	-0.0695965489729916	0.0493818192650607	-1.40935571043721	0.159043730076435	   
df.mm.trans2:probe3	-0.0958754893616765	0.0493818192650607	-1.9415139172386	0.0524791358769481	.  
df.mm.trans2:probe4	0.0115464287765067	0.0493818192650606	0.233819428857619	0.815173457593136	   
df.mm.trans2:probe5	-0.124616443305433	0.0493818192650607	-2.52352880392164	0.0117737594774058	*  
df.mm.trans2:probe6	-0.0947241390545456	0.0493818192650607	-1.91819865011669	0.0553729299667841	.  
df.mm.trans3:probe2	0.00408393169377427	0.0493818192650607	0.082701118641528	0.9341058961927	   
df.mm.trans3:probe3	0.506558151108146	0.0493818192650607	10.2579888438123	1.55807045873419e-23	***
df.mm.trans3:probe4	0.661999829526568	0.0493818192650607	13.4057399945764	8.82048402261385e-38	***
df.mm.trans3:probe5	-0.157990438436564	0.0493818192650606	-3.19936447842350	0.0014210320849309	** 
df.mm.trans3:probe6	-0.268327635571241	0.0493818192650607	-5.43373329627595	6.94671594441624e-08	***
df.mm.trans3:probe7	-0.211384815815851	0.0493818192650607	-4.28062025583196	2.04467560738379e-05	***
df.mm.trans3:probe8	0.342922405570991	0.0493818192650607	6.94430482057231	6.87365756387329e-12	***
df.mm.trans3:probe9	0.236620876635615	0.0493818192650607	4.79165976784966	1.9068915321545e-06	***
df.mm.trans3:probe10	-0.0414094523721943	0.0493818192650607	-0.83855663862698	0.401920365242511	   
df.mm.trans3:probe11	0.243421413646101	0.0493818192650607	4.92937314317073	9.66603734285003e-07	***
df.mm.trans3:probe12	-0.0999368737876296	0.0493818192650607	-2.02375844541512	0.0432629564642908	*  
df.mm.trans3:probe13	0.437746075889381	0.0493818192650607	8.8645190153839	3.50569204630773e-18	***
df.mm.trans3:probe14	0.310130609857142	0.0493818192650607	6.28025889837093	5.05076826883937e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15477216646565	0.162791238362071	25.5220871114994	7.79213615397085e-111	***
df.mm.trans1	-0.00369170761036604	0.139990602364545	-0.0263711102603344	0.978966646027398	   
df.mm.trans2	-0.125280545947639	0.122150415205822	-1.02562521573539	0.305318555019501	   
df.mm.exp2	0.0451757208952358	0.154674046150031	0.292070467022090	0.77029387786144	   
df.mm.exp3	0.000895044611008263	0.154674046150031	0.00578665027059605	0.995384111483334	   
df.mm.exp4	0.0366707247497022	0.154674046150031	0.237083891334505	0.812640708938025	   
df.mm.exp5	0.217006889479144	0.154674046150031	1.40299484548721	0.160931594606205	   
df.mm.exp6	0.195459342834048	0.154674046150031	1.26368545789806	0.206640127857456	   
df.mm.exp7	0.0460736611198589	0.154674046150031	0.297875838039229	0.765860365836263	   
df.mm.exp8	0.174527102129601	0.154674046150031	1.12835415167398	0.259443469623402	   
df.mm.trans1:exp2	-0.0961601415920642	0.142691634645504	-0.67390174505296	0.500530932264677	   
df.mm.trans2:exp2	0.134513854314004	0.0983434224234534	1.36779716425574	0.171685658581072	   
df.mm.trans1:exp3	-0.126557036666982	0.142691634645504	-0.88692681236286	0.375333491238583	   
df.mm.trans2:exp3	0.102658020462958	0.0983434224234534	1.04387276681227	0.296798825389207	   
df.mm.trans1:exp4	-0.0909343697329029	0.142691634645504	-0.637278912382046	0.524090379322575	   
df.mm.trans2:exp4	0.0373629266453968	0.0983434224234534	0.379922985438895	0.704083989785822	   
df.mm.trans1:exp5	-0.235653947688833	0.142691634645504	-1.65149098105352	0.0989551719349515	.  
df.mm.trans2:exp5	0.117924881507648	0.0983434224234534	1.19911305303043	0.230770624464909	   
df.mm.trans1:exp6	-0.20193516338747	0.142691634645504	-1.41518571771322	0.157328205937849	   
df.mm.trans2:exp6	0.0348696053291897	0.0983434224234534	0.35456977670602	0.72298735745074	   
df.mm.trans1:exp7	-0.131228520815931	0.142691634645504	-0.919665130629056	0.357971576720791	   
df.mm.trans2:exp7	0.105181955829803	0.0983434224234534	1.06953727293427	0.285088077069617	   
df.mm.trans1:exp8	-0.239095707401282	0.142691634645504	-1.67561124375147	0.094129816604375	.  
df.mm.trans2:exp8	0.0491759103164932	0.0983434224234534	0.500042698379444	0.61715602568295	   
df.mm.trans1:probe2	0.0268019667104920	0.103389911808881	0.259231933189345	0.795510132145918	   
df.mm.trans1:probe3	-0.0377869686282317	0.103389911808881	-0.365480228845557	0.714830955428893	   
df.mm.trans1:probe4	0.0528141947823069	0.103389911808881	0.510825416699605	0.609587083950837	   
df.mm.trans1:probe5	0.031909119321288	0.103389911808881	0.30862894418822	0.75766866632493	   
df.mm.trans1:probe6	0.122468419631333	0.103389911808881	1.18452968465356	0.236487458208267	   
df.mm.trans1:probe7	-0.00794719253459088	0.103389911808881	-0.0768662280057025	0.938745479557436	   
df.mm.trans1:probe8	-0.0540844042295177	0.103389911808881	-0.523111039397097	0.601013922347479	   
df.mm.trans1:probe9	-0.0161755433923744	0.103389911808881	-0.156451854048153	0.875708732066074	   
df.mm.trans1:probe10	0.00478424869889518	0.103389911808881	0.0462738444708127	0.96310130896069	   
df.mm.trans1:probe11	0.0254936169310164	0.103389911808881	0.246577412486260	0.805286353270975	   
df.mm.trans1:probe12	-0.00999985511169531	0.103389911808881	-0.0967198340412588	0.922968444353152	   
df.mm.trans1:probe13	-0.0450710834280651	0.103389911808881	-0.435933087082813	0.66298021986643	   
df.mm.trans1:probe14	0.0631532762176219	0.103389911808881	0.610826289651573	0.541454653399425	   
df.mm.trans1:probe15	-0.0503836956713162	0.103389911808881	-0.487317329029664	0.626141244873407	   
df.mm.trans1:probe16	0.00331730432998787	0.103389911808881	0.0320853773056699	0.974410425362845	   
df.mm.trans1:probe17	0.0995737399278277	0.103389911808881	0.963089514109393	0.335737543532597	   
df.mm.trans1:probe18	-0.0078299004508929	0.103389911808881	-0.0757317644816905	0.939647789230164	   
df.mm.trans1:probe19	0.0800923700606402	0.103389911808881	0.774663297988816	0.438723428696132	   
df.mm.trans1:probe20	0.0128416087841722	0.103389911808881	0.124205626637058	0.901177655173105	   
df.mm.trans1:probe21	-0.040392242458269	0.103389911808881	-0.390678759190117	0.696118668691574	   
df.mm.trans1:probe22	-0.117910083986723	0.103389911808881	-1.14044089915352	0.254378199771208	   
df.mm.trans1:probe23	-0.0298502336229211	0.103389911808881	-0.288715147354997	0.77285974952655	   
df.mm.trans1:probe24	0.0672018458915906	0.103389911808881	0.649984555706125	0.51585283647851	   
df.mm.trans2:probe2	-0.0863931163009223	0.103389911808881	-0.835604894030887	0.403578628695668	   
df.mm.trans2:probe3	0.0119939033606055	0.103389911808881	0.116006515053196	0.907670881038019	   
df.mm.trans2:probe4	-0.0317948104262356	0.103389911808881	-0.307523334433337	0.75850967968121	   
df.mm.trans2:probe5	0.0125982848199289	0.103389911808881	0.121852167194196	0.903040801933254	   
df.mm.trans2:probe6	-0.0255252244993898	0.103389911808881	-0.246883124792426	0.805049809524348	   
df.mm.trans3:probe2	0.124814611414221	0.103389911808881	1.20722234143061	0.227634587880408	   
df.mm.trans3:probe3	0.00315400866808063	0.103389911808881	0.0305059614898493	0.975669681414801	   
df.mm.trans3:probe4	-0.0251164693387865	0.103389911808881	-0.242929594380688	0.808110210065468	   
df.mm.trans3:probe5	0.0293468556741231	0.103389911808881	0.283846413645963	0.776587374733546	   
df.mm.trans3:probe6	0.0133224534881861	0.103389911808881	0.128856416018742	0.897497409933116	   
df.mm.trans3:probe7	0.0379243320277174	0.103389911808881	0.366808824615516	0.7138399300376	   
df.mm.trans3:probe8	0.0301731044996944	0.103389911808881	0.291837994363225	0.77047157296654	   
df.mm.trans3:probe9	0.0935188664718281	0.103389911808881	0.90452603001249	0.365936373205773	   
df.mm.trans3:probe10	0.180713315846843	0.103389911808881	1.74788151653419	0.080794233056215	.  
df.mm.trans3:probe11	0.0284126029800534	0.103389911808881	0.274810206169582	0.783519336220038	   
df.mm.trans3:probe12	0.00844879739470908	0.103389911808881	0.0817178121819752	0.934887614591423	   
df.mm.trans3:probe13	0.0332412317560217	0.103389911808881	0.321513300228645	0.747889233342896	   
df.mm.trans3:probe14	-0.00322306452875065	0.103389911808881	-0.0311738783055409	0.975137148893992