fitVsDatCorrelation=0.91289958059598
cont.fitVsDatCorrelation=0.196367335484846

fstatistic=6991.3422608543,57,807
cont.fstatistic=1199.87215755844,57,807

residuals=-1.16818008644949,-0.128341050196382,0.00192717690289238,0.120956510876487,1.16896988927544
cont.residuals=-1.16298872472859,-0.369501815255722,-0.00364170844535675,0.331757605813631,1.78324354749470

predictedValues:
Include	Exclude	Both
Lung	120.047042781096	147.848987179534	147.013755500286
cerebhem	101.224641154664	180.151550346855	118.144993700467
cortex	116.367951326051	161.522233546825	164.050508536874
heart	135.607373806575	183.460672885968	175.475996820074
kidney	114.541072203691	139.657573195649	130.478590904057
liver	115.453041329362	151.837446892261	107.038635545371
stomach	150.621398480828	205.418486688449	171.276256136258
testicle	161.591566874029	180.817146460162	223.65997200115


diffExp=-27.8019443984387,-78.9269091921911,-45.1542822207735,-47.8532990793925,-25.1165009919576,-36.3844055628986,-54.7970882076209,-19.2255795861326
diffExpScore=0.99702611082935
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,-1,-1,0,-1,-1,0
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,0
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	113.956655847777	98.7900545068358	112.27317628792
cerebhem	118.771242776641	106.610694907917	141.637925222289
cortex	115.351606235254	109.426345301779	113.83228636937
heart	121.652902043301	118.171543636205	113.778867181783
kidney	113.069824618450	88.1528294300655	112.993644018525
liver	127.376225600920	118.205837317557	123.250932068362
stomach	120.939571046904	114.930154825511	105.583111003038
testicle	118.028541023440	106.384610284181	133.382747483148
cont.diffExp=15.1666013409416,12.1605478687241,5.9252609334751,3.48135840709534,24.9169951883847,9.17038828336247,6.00941622139213,11.6439307392589
cont.diffExpScore=0.98882363118687

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=0.594432610821421
cont.tran.correlation=0.843801774272975

tran.covariance=0.0115799185358967
cont.tran.covariance=0.00326774321570010

tran.mean=147.885511572
cont.tran.mean=113.113664962671

weightedLogRatios:
wLogRatio
Lung	-1.01904912213414
cerebhem	-2.82784655946220
cortex	-1.61342684898523
heart	-1.52958365285243
kidney	-0.959591244295714
liver	-1.33845869193930
stomach	-1.60411897187134
testicle	-0.577953398946761

cont.weightedLogRatios:
wLogRatio
Lung	0.666176411614234
cerebhem	0.510177801628081
cortex	0.248986193457681
heart	0.138978584299498
kidney	1.14597519760232
liver	0.359375807469395
stomach	0.243099996736341
testicle	0.490140838247557

varWeightedLogRatios=0.450324323162806
cont.varWeightedLogRatios=0.102765856816767

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.59392525364803	0.105371614075044	43.597370069479	2.46688887474284e-214	***
df.mm.trans1	0.777442752805019	0.0915013754374073	8.49651438668087	9.36046255087467e-17	***
df.mm.trans2	0.526122507603805	0.081331578259966	6.46885894580973	1.71054899017690e-10	***
df.mm.exp2	0.245679101722913	0.105706186984948	2.32416955648866	0.0203638244696331	*  
df.mm.exp3	-0.0523233198026654	0.105706186984948	-0.494988243309884	0.620743156899647	   
df.mm.exp4	0.160712889809438	0.105706186984948	1.52037354097658	0.128808809700429	   
df.mm.exp5	0.0153689018420855	0.105706186984948	0.145392642384064	0.884437179197791	   
df.mm.exp6	0.304935621966654	0.105706186984948	2.88474715306945	0.00402161796725121	** 
df.mm.exp7	0.402991976506167	0.105706186984948	3.8123783290334	0.000148117546556654	***
df.mm.exp8	0.0788823953473194	0.105706186984948	0.746241990154768	0.455738571598237	   
df.mm.trans1:exp2	-0.416220573974585	0.0983220141460808	-4.23323888947383	2.56827236565145e-05	***
df.mm.trans2:exp2	-0.0480720546499121	0.0751643075490608	-0.63955960238887	0.522640459823756	   
df.mm.trans1:exp3	0.0211967958018675	0.0983220141460808	0.215585451396212	0.829365371793472	   
df.mm.trans2:exp3	0.140774726059852	0.0751643075490608	1.87289327408447	0.0614444477991128	.  
df.mm.trans1:exp4	-0.0388328258139307	0.0983220141460808	-0.394955556506759	0.692980053924049	   
df.mm.trans2:exp4	0.055096042067756	0.0751643075490609	0.733008044167692	0.463766383630741	   
df.mm.trans1:exp5	-0.06231912310285	0.0983220141460808	-0.633826754304077	0.526373493380051	   
df.mm.trans2:exp5	-0.0723667770426806	0.0751643075490608	-0.962781131129902	0.335945767509601	   
df.mm.trans1:exp6	-0.343955432390003	0.0983220141460808	-3.49825454021899	0.000493939072845181	***
df.mm.trans2:exp6	-0.278316497657877	0.0751643075490608	-3.70277471759074	0.000227761394610366	***
df.mm.trans1:exp7	-0.176106272184667	0.0983220141460808	-1.79111741876055	0.0736491067126674	.  
df.mm.trans2:exp7	-0.0741340756606197	0.0751643075490609	-0.986293602348844	0.32428466995057	   
df.mm.trans1:exp8	0.218305875055686	0.0983220141460808	2.22031532766752	0.0266744325377215	*  
df.mm.trans2:exp8	0.122412488779389	0.0751643075490609	1.6285986363872	0.103788301284156	   
df.mm.trans1:probe2	-0.54011241588353	0.0643668674721335	-8.39115583987931	2.14353655019765e-16	***
df.mm.trans1:probe3	-0.488135336269175	0.0643668674721335	-7.58364288087353	9.24868224147427e-14	***
df.mm.trans1:probe4	-0.96280620965715	0.0643668674721335	-14.9581026305806	7.85467198134097e-45	***
df.mm.trans1:probe5	-0.872429099853017	0.0643668674721335	-13.5540089818837	7.42646315583883e-38	***
df.mm.trans1:probe6	-0.692620319139598	0.0643668674721334	-10.7605099695034	2.51276959813854e-25	***
df.mm.trans1:probe7	-0.365042117736129	0.0643668674721335	-5.67127362371904	1.97450706550585e-08	***
df.mm.trans1:probe8	-1.45225007691232	0.0643668674721335	-22.5620747745888	8.93739680024776e-88	***
df.mm.trans1:probe9	-0.620760069232243	0.0643668674721335	-9.64409320526574	6.63205844807765e-21	***
df.mm.trans1:probe10	-0.54187726481671	0.0643668674721335	-8.41857443274379	1.72917271883375e-16	***
df.mm.trans1:probe11	-1.47546880395501	0.0643668674721335	-22.9227996001793	5.96771008396302e-90	***
df.mm.trans1:probe12	-1.31692320036408	0.0643668674721334	-20.4596441008134	2.85620303574673e-75	***
df.mm.trans1:probe13	-1.33941185578821	0.0643668674721335	-20.8090265751721	2.52031202361380e-77	***
df.mm.trans1:probe14	-1.49334328522379	0.0643668674721335	-23.2004965267311	1.24806116011716e-91	***
df.mm.trans1:probe15	-1.17201362717928	0.0643668674721335	-18.2083371959446	2.51979872409754e-62	***
df.mm.trans1:probe16	-1.22679751186733	0.0643668674721334	-19.0594565192792	3.73527330795311e-67	***
df.mm.trans1:probe17	-0.475430817330778	0.0643668674721335	-7.38626619567292	3.77019473704265e-13	***
df.mm.trans1:probe18	-0.464298344920024	0.0643668674721335	-7.21331273610657	1.2589353578765e-12	***
df.mm.trans1:probe19	-0.255798761769265	0.0643668674721334	-3.97407504536412	7.69802124488679e-05	***
df.mm.trans1:probe20	-0.580207188742242	0.0643668674721335	-9.01406595549229	1.41704145438926e-18	***
df.mm.trans1:probe21	-0.647568716910049	0.0643668674721334	-10.0605908341027	1.64024429621726e-22	***
df.mm.trans1:probe22	-0.521234495936549	0.0643668674721335	-8.09786954697164	2.05817917116891e-15	***
df.mm.trans2:probe2	-0.365149204606336	0.0643668674721335	-5.67293731925701	1.95614895988096e-08	***
df.mm.trans2:probe3	-0.582352213675569	0.0643668674721335	-9.0473909411187	1.07463178860279e-18	***
df.mm.trans2:probe4	-0.418248602401516	0.0643668674721335	-6.49788654982456	1.42457040056624e-10	***
df.mm.trans2:probe5	-0.309811223172577	0.0643668674721335	-4.81320958032803	1.77297641637137e-06	***
df.mm.trans2:probe6	-0.0584278703612695	0.0643668674721335	-0.90773207794468	0.364290769567365	   
df.mm.trans3:probe2	-0.82491782047422	0.0643668674721335	-12.8158764418877	2.33175921733966e-34	***
df.mm.trans3:probe3	-1.16593536874598	0.0643668674721334	-18.1139057178875	8.54420489344963e-62	***
df.mm.trans3:probe4	-0.700634783319653	0.0643668674721334	-10.8850222301556	7.67026499808878e-26	***
df.mm.trans3:probe5	-0.468040307342334	0.0643668674721334	-7.27144765193	8.4170125064576e-13	***
df.mm.trans3:probe6	-1.08602558591511	0.0643668674721334	-16.8724318669895	6.17864404483668e-55	***
df.mm.trans3:probe7	-1.34829266481003	0.0643668674721334	-20.9469983201178	3.86659607912743e-78	***
df.mm.trans3:probe8	-1.66186832398542	0.0643668674721334	-25.8186919645406	1.26385633915296e-107	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.67788872102372	0.253121180566329	18.4808268931011	7.32528058919122e-64	***
df.mm.trans1	0.0488526062793314	0.219802423807085	0.222256904328808	0.824170133283092	   
df.mm.trans2	-0.0976691163562934	0.195372779350459	-0.499911587893699	0.61727361123615	   
df.mm.exp2	-0.114770956720525	0.253924883638391	-0.451987828353080	0.651399208326362	   
df.mm.exp3	0.100630268275889	0.253924883638391	0.396299357644658	0.691988962860947	   
df.mm.exp4	0.231172324521894	0.253924883638391	0.910396496828177	0.36288528024877	   
df.mm.exp5	-0.128134143871645	0.253924883638391	-0.504614364829712	0.613967473280835	   
df.mm.exp6	0.197470101559651	0.253924883638391	0.77767132834789	0.43699071815195	   
df.mm.exp7	0.272237052270178	0.253924883638391	1.07211647936744	0.283988223757892	   
df.mm.exp8	-0.0631155268004503	0.253924883638391	-0.248559833507029	0.803764520921439	   
df.mm.trans1:exp2	0.156152105943086	0.236186799592828	0.661138159339484	0.508712415990723	   
df.mm.trans2:exp2	0.190957854085499	0.180557908600689	1.05759894742584	0.2905547961314	   
df.mm.trans1:exp3	-0.0884635231041162	0.236186799592828	-0.374548972493900	0.708094327820581	   
df.mm.trans2:exp3	0.00162447220653325	0.180557908600689	0.00899695958555786	0.992823785273159	   
df.mm.trans1:exp4	-0.165818564132348	0.236186799592828	-0.702065333110104	0.482840853028305	   
df.mm.trans2:exp4	-0.0520319329135765	0.180557908600689	-0.288173103669733	0.773288230900243	   
df.mm.trans1:exp5	0.120321524495297	0.236186799592828	0.509433739322959	0.610587503368225	   
df.mm.trans2:exp5	0.0142092133017495	0.180557908600689	0.0786961557755621	0.937293811971337	   
df.mm.trans1:exp6	-0.086143152307534	0.236186799592828	-0.364724669016388	0.715412496328339	   
df.mm.trans2:exp6	-0.0180395495184363	0.180557908600689	-0.0999100491262969	0.920440552061932	   
df.mm.trans1:exp7	-0.212764208491108	0.236186799592828	-0.900830227844659	0.367947342768095	   
df.mm.trans2:exp7	-0.120909394562328	0.180557908600689	-0.66964330446319	0.503276747441508	   
df.mm.trans1:exp8	0.0982238308655028	0.236186799592828	0.415873499428566	0.677613136257182	   
df.mm.trans2:exp8	0.137179516259632	0.180557908600689	0.75975357337025	0.447623780585411	   
df.mm.trans1:probe2	0.0743816460993414	0.154620555326214	0.48105923525118	0.630604799221031	   
df.mm.trans1:probe3	-0.099382719297805	0.154620555326214	-0.642752311218454	0.52056740029798	   
df.mm.trans1:probe4	-0.0551874522363337	0.154620555326214	-0.356921834357021	0.721243631061357	   
df.mm.trans1:probe5	-0.0121285806686712	0.154620555326214	-0.0784409333098221	0.93749675881963	   
df.mm.trans1:probe6	-0.0236653201471904	0.154620555326214	-0.153054166034147	0.878393820802918	   
df.mm.trans1:probe7	0.0369102547493984	0.154620555326214	0.23871505746132	0.811387145417753	   
df.mm.trans1:probe8	0.098268393197745	0.154620555326214	0.635545468003403	0.525252895405357	   
df.mm.trans1:probe9	-0.00731716895400688	0.154620555326214	-0.0473233907262157	0.962267194330015	   
df.mm.trans1:probe10	-0.0943534012422537	0.154620555326214	-0.610225471271847	0.541884256461591	   
df.mm.trans1:probe11	-0.0638531253511891	0.154620555326214	-0.41296660212139	0.679740780994364	   
df.mm.trans1:probe12	0.0159649779643894	0.154620555326214	0.103252623370204	0.917788128180003	   
df.mm.trans1:probe13	0.119611325931920	0.154620555326214	0.773579720235565	0.439405909807044	   
df.mm.trans1:probe14	-0.055096290834626	0.154620555326214	-0.356332252968471	0.721684896418925	   
df.mm.trans1:probe15	0.137534042834490	0.154620555326214	0.889493913304895	0.374002764609449	   
df.mm.trans1:probe16	0.0145543031808473	0.154620555326214	0.0941291612240111	0.925029921135663	   
df.mm.trans1:probe17	-0.0751218986589335	0.154620555326214	-0.485846778265952	0.627207692212592	   
df.mm.trans1:probe18	-0.029616916396039	0.154620555326214	-0.191545789843750	0.84814619225751	   
df.mm.trans1:probe19	0.0584007074090822	0.154620555326214	0.377703386757796	0.705750267909561	   
df.mm.trans1:probe20	0.111244713906418	0.154620555326214	0.719469113739226	0.472060221038558	   
df.mm.trans1:probe21	0.0523854328641938	0.154620555326214	0.338799927045098	0.734848589945157	   
df.mm.trans1:probe22	0.0687721835786653	0.154620555326214	0.444780342649602	0.65659776550524	   
df.mm.trans2:probe2	-0.00337305257841423	0.154620555326214	-0.0218150333977126	0.982600894725525	   
df.mm.trans2:probe3	-0.0161230608686088	0.154620555326214	-0.104275015922642	0.916977013447873	   
df.mm.trans2:probe4	0.161837231911265	0.154620555326214	1.04667346181642	0.295563595181718	   
df.mm.trans2:probe5	-0.0616018703276942	0.154620555326214	-0.398406733165124	0.690435778528704	   
df.mm.trans2:probe6	0.098143401729176	0.154620555326214	0.634737092504394	0.525779802274745	   
df.mm.trans3:probe2	0.0849145648676949	0.154620555326214	0.54918031233651	0.583033635727676	   
df.mm.trans3:probe3	0.115309146631563	0.154620555326214	0.745755610489737	0.456032219808162	   
df.mm.trans3:probe4	0.106968465067921	0.154620555326214	0.691812707839794	0.489253939175741	   
df.mm.trans3:probe5	-0.0482402964332622	0.154620555326214	-0.311991483483462	0.755127585599451	   
df.mm.trans3:probe6	0.238853193342596	0.154620555326214	1.54476998765572	0.122793942523427	   
df.mm.trans3:probe7	0.0695921206733768	0.154620555326214	0.450083241044848	0.652771297265302	   
df.mm.trans3:probe8	0.167504922926522	0.154620555326214	1.08332894402769	0.278986007440752