fitVsDatCorrelation=0.883515334602875
cont.fitVsDatCorrelation=0.283120230789985

fstatistic=13513.3294847326,63,945
cont.fstatistic=3211.77298192427,63,945

residuals=-0.687921968661447,-0.0857606688520542,-0.00514188433550733,0.0733075377337947,0.762263021440608
cont.residuals=-0.508778479080323,-0.197743397598595,-0.0568189338719776,0.134974202665613,1.3315571965504

predictedValues:
Include	Exclude	Both
Lung	53.1072246573046	42.6669065334442	65.7343692577723
cerebhem	59.688997136636	47.2584466060489	58.1196133790858
cortex	53.8937817953033	44.1615548468452	65.311401703773
heart	54.2734352942486	44.7514983926796	59.87221441111
kidney	54.0980645464776	43.4896641607418	62.4687147791209
liver	59.8199667708633	46.2903223647245	60.1127582260029
stomach	55.7325050596586	44.4332420303156	61.1860264423523
testicle	55.7038291382638	45.3506506692308	62.3685944866422


diffExp=10.4403181238604,12.4305505305871,9.73222694845815,9.521936901569,10.6084003857357,13.5296444061388,11.299263029343,10.3531784690330
diffExpScore=0.98875336933805
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=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	60.2159096267489	60.9257616436631	57.5100439893542
cerebhem	59.0793624414218	57.8393920137926	57.2224674688273
cortex	53.9972100419845	54.4241833489353	58.6576361253775
heart	56.4028667107209	62.6915771600198	56.6402120997714
kidney	55.4975453015941	53.0092619361258	53.9468462124796
liver	54.3520834555662	55.4663338960419	57.6529683138444
stomach	53.8423425089079	57.6670795774642	63.5551270255477
testicle	56.2100148184509	60.2930032130984	56.3232958193104
cont.diffExp=-0.709852016914212,1.23997042762920,-0.426973306950764,-6.28871044929888,2.48828336546828,-1.11425044047574,-3.82473706855633,-4.08298839464753
cont.diffExpScore=1.47061638762869

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=0.918017164980519
cont.tran.correlation=0.523553968711518

tran.covariance=0.00138884473745594
cont.tran.covariance=0.00127575407495548

tran.mean=50.2950056251742
cont.tran.mean=56.9946204809085

weightedLogRatios:
wLogRatio
Lung	0.845540842226774
cerebhem	0.927617705180122
cortex	0.774223450400367
heart	0.75188197746442
kidney	0.847269932581217
liver	1.01617419600251
stomach	0.885293601047745
testicle	0.805479314945215

cont.weightedLogRatios:
wLogRatio
Lung	-0.0480944571185059
cerebhem	0.086294639886541
cortex	-0.0314487367783704
heart	-0.431853061813323
kidney	0.183185925428576
liver	-0.0812874163897106
stomach	-0.275903237709821
testicle	-0.284983149537311

varWeightedLogRatios=0.00741502042619247
cont.varWeightedLogRatios=0.0424352674251959

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.95804150531946	0.0633640297166618	46.6832920593374	1.29133309232666e-247	***
df.mm.trans1	0.97587885966977	0.0540455468334402	18.0566007163786	7.66300033215276e-63	***
df.mm.trans2	0.791636301699897	0.0475296833040386	16.6556191135536	8.24545429881563e-55	***
df.mm.exp2	0.342161255584509	0.0601657388541838	5.68697837175677	1.72321582259919e-08	***
df.mm.exp3	0.0555884296184981	0.0601657388541838	0.923921665006405	0.355762951577396	   
df.mm.exp4	0.162832621877102	0.0601657388541838	2.70640110099436	0.00692426855272713	** 
df.mm.exp5	0.0885411854019878	0.0601657388541838	1.47162134278071	0.141456081065120	   
df.mm.exp6	0.289935653646788	0.0601657388541838	4.81894944146649	1.68059607554045e-06	***
df.mm.exp7	0.160517936035847	0.0601657388541838	2.66792927491299	0.00776266766303655	** 
df.mm.exp8	0.161296898497073	0.0601657388541838	2.68087621907192	0.00747086368442238	** 
df.mm.trans1:exp2	-0.225326531309927	0.0545121357504204	-4.13351134032919	3.88993848718837e-05	***
df.mm.trans2:exp2	-0.239953450585285	0.0381938761131227	-6.28251109875809	5.07670914697664e-10	***
df.mm.trans1:exp3	-0.0408863005067119	0.0545121357504204	-0.750040334025926	0.453417102227087	   
df.mm.trans2:exp3	-0.0211574159886593	0.0381938761131227	-0.553947861327172	0.579745578739907	   
df.mm.trans1:exp4	-0.141110712314549	0.0545121357504204	-2.58861096473295	0.00978441633348682	** 
df.mm.trans2:exp4	-0.115131291194807	0.0381938761131227	-3.01439138708548	0.00264371487588587	** 
df.mm.trans1:exp5	-0.0700557522397723	0.0545121357504204	-1.28514047881956	0.199058078969383	   
df.mm.trans2:exp5	-0.0694414781534997	0.0381938761131227	-1.81813120898826	0.06936057067816	.  
df.mm.trans1:exp6	-0.170909132499799	0.0545121357504204	-3.13524924582470	0.00177011674348068	** 
df.mm.trans2:exp6	-0.208426331716004	0.0381938761131227	-5.45706152207979	6.18530085161532e-08	***
df.mm.trans1:exp7	-0.112267362102805	0.0545121357504204	-2.05949300201357	0.0397205107777678	*  
df.mm.trans2:exp7	-0.119953649466315	0.0381938761131227	-3.14065137329962	0.00173812661809195	** 
df.mm.trans1:exp8	-0.113560984746173	0.0545121357504204	-2.08322391304026	0.0374991349117956	*  
df.mm.trans2:exp8	-0.100295971429318	0.0381938761131227	-2.62596996262598	0.00877995356126113	** 
df.mm.trans1:probe2	0.460644930891085	0.0402598803959222	11.4417858761881	1.75711087923848e-28	***
df.mm.trans1:probe3	-0.0937041186113799	0.0402598803959222	-2.32748129626513	0.0201500762752391	*  
df.mm.trans1:probe4	0.507038529672841	0.0402598803959222	12.5941389961058	9.84848761440289e-34	***
df.mm.trans1:probe5	0.224493534290603	0.0402598803959222	5.57611031336648	3.20991436164798e-08	***
df.mm.trans1:probe6	-0.0294443215592313	0.0402598803959222	-0.731356409151519	0.464742790369394	   
df.mm.trans1:probe7	0.359674011183341	0.0402598803959222	8.9338072454823	2.11390867144518e-18	***
df.mm.trans1:probe8	0.881654100049452	0.0402598803959222	21.8990740006955	2.66466958550046e-86	***
df.mm.trans1:probe9	-0.0666020331068228	0.0402598803959222	-1.65430280596583	0.0983980838225161	.  
df.mm.trans1:probe10	0.0822811513785018	0.0402598803959222	2.04375051712364	0.0412550271770609	*  
df.mm.trans1:probe11	-0.082756104879754	0.0402598803959222	-2.05554770818783	0.040100454085512	*  
df.mm.trans1:probe12	-0.0317343125056442	0.0402598803959222	-0.7882366314446	0.430755984726611	   
df.mm.trans1:probe13	-0.0775829130303557	0.0402598803959222	-1.92705274500055	0.0542725927847616	.  
df.mm.trans1:probe14	-0.094540006120744	0.0402598803959222	-2.34824359116376	0.0190674921856681	*  
df.mm.trans1:probe15	-0.161324932664919	0.0402598803959222	-4.00708921830923	6.63052501777558e-05	***
df.mm.trans1:probe16	-0.0212819250564634	0.0402598803959222	-0.528613717854438	0.597197565416823	   
df.mm.trans1:probe17	-0.215333113252278	0.0402598803959222	-5.34857806666729	1.11230813013511e-07	***
df.mm.trans1:probe18	-0.141191172513471	0.0402598803959222	-3.50699433592386	0.000474524313724211	***
df.mm.trans1:probe19	0.0613965290766936	0.0402598803959222	1.52500525269599	0.127592370141374	   
df.mm.trans1:probe20	-0.179553816360396	0.0402598803959222	-4.45986959212581	9.18614045031888e-06	***
df.mm.trans2:probe2	-0.0189089004828388	0.0402598803959222	-0.469671054580531	0.638698404313392	   
df.mm.trans2:probe3	0.0603457140488732	0.0402598803959222	1.49890445414700	0.134232390109127	   
df.mm.trans2:probe4	0.0203482861271027	0.0402598803959222	0.505423412265371	0.613379337049175	   
df.mm.trans2:probe5	0.0071840777896814	0.0402598803959222	0.17844260139454	0.85841364112506	   
df.mm.trans2:probe6	0.0134382045840618	0.0402598803959222	0.333786500404579	0.738614722898478	   
df.mm.trans3:probe2	-0.511738841347795	0.0402598803959222	-12.7108882668123	2.76311246458957e-34	***
df.mm.trans3:probe3	-0.582640770689692	0.0402598803959222	-14.4719945752423	5.05852811681112e-43	***
df.mm.trans3:probe4	-0.391135359252901	0.0402598803959222	-9.71526381614681	2.48290092835450e-21	***
df.mm.trans3:probe5	-0.494610493980548	0.0402598803959222	-12.2854437001916	2.72606207629154e-32	***
df.mm.trans3:probe6	-0.20875449227058	0.0402598803959222	-5.18517417880168	2.63991561740688e-07	***
df.mm.trans3:probe7	-0.726787312832323	0.0402598803959222	-18.0523962238581	8.10934154953385e-63	***
df.mm.trans3:probe8	-0.691023020663299	0.0402598803959222	-17.1640604459742	1.09606928424081e-57	***
df.mm.trans3:probe9	-0.408635672641125	0.0402598803959222	-10.1499475066129	4.79013225357314e-23	***
df.mm.trans3:probe10	-0.616072319301436	0.0402598803959222	-15.3023882148402	2.16859890714656e-47	***
df.mm.trans3:probe11	-0.80370996618867	0.0402598803959222	-19.9630490275891	2.9688722441882e-74	***
df.mm.trans3:probe12	-0.691364315822133	0.0402598803959222	-17.1725377478309	9.80609239648654e-58	***
df.mm.trans3:probe13	-0.393801552502214	0.0402598803959222	-9.78148838569578	1.37272525919331e-21	***
df.mm.trans3:probe14	-0.804523895966574	0.0402598803959222	-19.9832659226693	2.23343664889732e-74	***
df.mm.trans3:probe15	-0.679620857542494	0.0402598803959222	-16.8808464123339	4.44318749842804e-56	***
df.mm.trans3:probe16	-0.63453719001105	0.0402598803959222	-15.7610301811855	7.27091067746042e-50	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12928914264628	0.129742024095327	31.826920933593	1.17966948833002e-151	***
df.mm.trans1	-0.0339423841018434	0.110661816662608	-0.306721732260448	0.759122756125197	   
df.mm.trans2	-0.0252895081797313	0.0973201569415698	-0.259858892283896	0.795029224572951	   
df.mm.exp2	-0.0660279637982705	0.123193312910148	-0.535970356170455	0.592105164070703	   
df.mm.exp3	-0.241609870618485	0.123193312910148	-1.96122553173567	0.0501460358848195	.  
df.mm.exp4	-0.0216051926659718	0.123193312910148	-0.175376342721863	0.860821497984185	   
df.mm.exp5	-0.156826876678866	0.123193312910148	-1.27301452468649	0.203325966186014	   
df.mm.exp6	-0.198815633862908	0.123193312910148	-1.61385085899845	0.106893620543132	   
df.mm.exp7	-0.26679409926358	0.123193312910148	-2.16565406807566	0.0305871792208446	*  
df.mm.exp8	-0.0584303120194427	0.123193312910148	-0.474297757233456	0.635397163236435	   
df.mm.trans1:exp2	0.0469730330448963	0.111617188200374	0.420840497796545	0.673967245040878	   
df.mm.trans2:exp2	0.0140419293101160	0.0782044768478426	0.179554034194699	0.857541184935342	   
df.mm.trans1:exp3	0.132605653045658	0.111617188200374	1.18803972025890	0.235116259462723	   
df.mm.trans2:exp3	0.128762371788822	0.0782044768478426	1.64648338533543	0.0999967667593968	.  
df.mm.trans1:exp4	-0.0438114188306491	0.111617188200374	-0.392514983910893	0.694766256951896	   
df.mm.trans2:exp4	0.0501761949498354	0.0782044768478426	0.641602590698996	0.521286836668031	   
df.mm.trans1:exp5	0.0752290707398297	0.111617188200374	0.673991810336406	0.500481343926809	   
df.mm.trans2:exp5	0.0176374277098707	0.0782044768478426	0.225529642557250	0.821616031229656	   
df.mm.trans1:exp6	0.0963619838312529	0.111617188200374	0.863325670400015	0.388177488119083	   
df.mm.trans2:exp6	0.104935773328202	0.0782044768478426	1.34181286747009	0.179978998821606	   
df.mm.trans1:exp7	0.154917695449663	0.111617188200374	1.38793762813265	0.165483175138092	   
df.mm.trans2:exp7	0.211824464605153	0.0782044768478426	2.70859768063261	0.00687895505226295	** 
df.mm.trans1:exp8	-0.0104113442532112	0.111617188200374	-0.0932772489710172	0.925703077437601	   
df.mm.trans2:exp8	0.0479902753046539	0.0782044768478426	0.613651254237344	0.539593484966177	   
df.mm.trans1:probe2	0.0487108794688247	0.0824347566870292	0.590902204682423	0.554727245091247	   
df.mm.trans1:probe3	-0.0407499986217357	0.0824347566870292	-0.49433030749938	0.621187813435688	   
df.mm.trans1:probe4	0.0521470921084584	0.0824347566870292	0.632586231878374	0.527156908541435	   
df.mm.trans1:probe5	2.13524216424439e-05	0.0824347566870292	0.000259022073947647	0.999793384906931	   
df.mm.trans1:probe6	0.0804245959241558	0.0824347566870292	0.975615130757222	0.329504894797075	   
df.mm.trans1:probe7	0.115122560811395	0.0824347566870293	1.39652939413005	0.162883042972588	   
df.mm.trans1:probe8	-0.0434981296423455	0.0824347566870292	-0.527667350405241	0.597854104216913	   
df.mm.trans1:probe9	0.0431342571336214	0.0824347566870292	0.523253283774275	0.600920674412076	   
df.mm.trans1:probe10	-0.0297016902615693	0.0824347566870292	-0.360305427652736	0.71869925541402	   
df.mm.trans1:probe11	0.00504468340370478	0.0824347566870293	0.0611960731910371	0.951215996716122	   
df.mm.trans1:probe12	0.087650001769507	0.0824347566870292	1.06326512374238	0.287933359682631	   
df.mm.trans1:probe13	-0.0639966416838099	0.0824347566870292	-0.776330813066857	0.437747941578398	   
df.mm.trans1:probe14	-0.0204848877163579	0.0824347566870292	-0.248498188623648	0.803802962850212	   
df.mm.trans1:probe15	-0.0195985939881746	0.0824347566870293	-0.237746731789145	0.812129071617335	   
df.mm.trans1:probe16	0.051394613287329	0.0824347566870293	0.623458057654651	0.533134001313586	   
df.mm.trans1:probe17	-0.135028933320502	0.0824347566870293	-1.63800972729441	0.101752559130735	   
df.mm.trans1:probe18	0.0287800901696432	0.0824347566870292	0.349125676186676	0.727072737664562	   
df.mm.trans1:probe19	-0.046268806183647	0.0824347566870293	-0.561277888637563	0.574741182263171	   
df.mm.trans1:probe20	-0.0198682638047445	0.0824347566870293	-0.241018043883796	0.80959341298377	   
df.mm.trans2:probe2	-0.00215386558989026	0.0824347566870292	-0.0261281245490613	0.979160660859652	   
df.mm.trans2:probe3	0.0734668695213512	0.0824347566870292	0.891212305026563	0.373042133811742	   
df.mm.trans2:probe4	0.169519442631705	0.0824347566870292	2.05640738742397	0.0400174024058638	*  
df.mm.trans2:probe5	-0.0871419351395198	0.0824347566870292	-1.05710186627179	0.290735251473464	   
df.mm.trans2:probe6	-0.0292482528509159	0.0824347566870292	-0.354804866616631	0.722814947203217	   
df.mm.trans3:probe2	0.0441500487368792	0.0824347566870292	0.535575654144267	0.592377876751554	   
df.mm.trans3:probe3	-0.0827055221520912	0.0824347566870292	-1.00328460319341	0.315980387962111	   
df.mm.trans3:probe4	-0.0974921761017117	0.0824347566870292	-1.18265862628611	0.237241933275026	   
df.mm.trans3:probe5	-0.0663489371696837	0.0824347566870292	-0.804866052089936	0.421099427459423	   
df.mm.trans3:probe6	-0.0229995352527701	0.0824347566870292	-0.279002888794709	0.780303652529438	   
df.mm.trans3:probe7	0.0475967760507255	0.0824347566870292	0.577387232808011	0.563815427610636	   
df.mm.trans3:probe8	-0.181406531974683	0.0824347566870292	-2.20060735623214	0.0280051099961979	*  
df.mm.trans3:probe9	0.0545824323961617	0.0824347566870292	0.662128871240424	0.508050066610216	   
df.mm.trans3:probe10	-0.128456435879527	0.0824347566870293	-1.55828003917357	0.119501693164213	   
df.mm.trans3:probe11	-0.0061870601655834	0.0824347566870292	-0.075054023499737	0.940187613292813	   
df.mm.trans3:probe12	0.0447384138383308	0.0824347566870292	0.54271299675432	0.58745539222304	   
df.mm.trans3:probe13	0.000518760497395853	0.0824347566870292	0.00629298269618691	0.994980287583532	   
df.mm.trans3:probe14	0.0394045243111608	0.0824347566870292	0.478008620329451	0.632754622469498	   
df.mm.trans3:probe15	0.088171698963692	0.0824347566870292	1.06959373093613	0.285075339418354	   
df.mm.trans3:probe16	-0.0231286919401201	0.0824347566870293	-0.280569663448273	0.779101897307895