chr2.13940_chr2_104649369_104652414_-_2.R 

fitVsDatCorrelation=0.867046868968472
cont.fitVsDatCorrelation=0.220045331931392

fstatistic=7257.7286755323,54,738
cont.fstatistic=1883.15376078481,54,738

residuals=-0.851893208740171,-0.108938644329808,-0.000269298530139285,0.107179497940729,0.976426146340784
cont.residuals=-0.906659342646207,-0.279816296875607,-0.0360909455996876,0.190280674752844,1.84839507324152

predictedValues:
Include	Exclude	Both
chr2.13940_chr2_104649369_104652414_-_2.R.tl.Lung	95.7323845222826	102.978102086500	121.144600106375
chr2.13940_chr2_104649369_104652414_-_2.R.tl.cerebhem	109.311632969916	88.176184601441	117.633262661803
chr2.13940_chr2_104649369_104652414_-_2.R.tl.cortex	106.433235485967	82.9308783629163	178.964714543612
chr2.13940_chr2_104649369_104652414_-_2.R.tl.heart	95.8114311607934	83.6506479775624	163.103558092406
chr2.13940_chr2_104649369_104652414_-_2.R.tl.kidney	95.292092557845	104.724812133184	130.543022305551
chr2.13940_chr2_104649369_104652414_-_2.R.tl.liver	89.9452260470285	93.806127999724	125.939664582243
chr2.13940_chr2_104649369_104652414_-_2.R.tl.stomach	90.165722879453	81.04271831756	132.229076393478
chr2.13940_chr2_104649369_104652414_-_2.R.tl.testicle	100.354930475033	85.0656541970438	139.943170250390


diffExp=-7.24571756421757,21.1354483684753,23.5023571230507,12.1607831832310,-9.43271957533895,-3.86090195269539,9.12300456189304,15.2892762779894
diffExpScore=1.64987325448234
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=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	99.6080635208326	90.685604885106	99.9932403066696
cerebhem	101.804705677431	102.701216855530	88.9468189731694
cortex	98.2343978674377	97.310374741164	89.8507187452168
heart	106.397592601661	81.831417933827	105.339731714639
kidney	95.908780523652	97.006575032566	120.181012830379
liver	96.037504408117	95.902529682302	91.9856954335626
stomach	100.129151049152	87.5172016734516	83.382316302279
testicle	104.262113776546	95.1229408604868	106.121119778108
cont.diffExp=8.92245863572658,-0.896511178098308,0.924023126273681,24.5661746678336,-1.09779450891415,0.134974725815084,12.6119493757003,9.13917291605901
cont.diffExpScore=1.05403925895749

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,1,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.235736245158655
cont.tran.correlation=-0.476551659206517

tran.covariance=-0.00156059906703764
cont.tran.covariance=-0.00129571567660941

tran.mean=94.0888613608907
cont.tran.mean=96.9037606930789

weightedLogRatios:
wLogRatio
Lung	-0.335471488065088
cerebhem	0.985540482972507
cortex	1.13346663217266
heart	0.610053308207475
kidney	-0.434581412216705
liver	-0.189981489290772
stomach	0.474514612726221
testicle	0.748113130336433

cont.weightedLogRatios:
wLogRatio
Lung	0.427397906920507
cerebhem	-0.0405717188183897
cortex	0.0433096574914732
heart	1.19077797451416
kidney	-0.0520018119775907
liver	0.00641897928733009
stomach	0.61108507805898
testicle	0.422089592020979

varWeightedLogRatios=0.375969301644819
cont.varWeightedLogRatios=0.18684788766304

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46574604927328	0.102616032076727	43.5189897611146	5.9188435595938e-206	***
df.mm.trans1	0.185153677383748	0.0903955873290523	2.04826012922249	0.0408878320073282	*  
df.mm.trans2	0.220763254727817	0.0815666988640315	2.70653658665064	0.0069560182612534	** 
df.mm.exp2	0.00687976284404336	0.108622564064343	0.0633364062366275	0.949515776227472	   
df.mm.exp3	-0.500751426905875	0.108622564064343	-4.61001294914426	4.74436522061044e-06	***
df.mm.exp4	-0.504442277754649	0.108622564064343	-4.64399162457478	4.04547046640456e-06	***
df.mm.exp5	-0.0625080670376005	0.108622564064343	-0.575461162936398	0.565154861982896	   
df.mm.exp6	-0.194459997495586	0.108622564064343	-1.79023575046890	0.073825841312833	.  
df.mm.exp7	-0.386998228682771	0.108622564064343	-3.56277935451357	0.000390531605016256	***
df.mm.exp8	-0.288187973634659	0.108622564064343	-2.65311333899233	0.0081468180598963	** 
df.mm.trans1:exp2	0.125766420760845	0.102457388283725	1.22749977202788	0.220026198721693	   
df.mm.trans2:exp2	-0.162059216673698	0.0838287722321389	-1.93321710861902	0.0535916472154749	.  
df.mm.trans1:exp3	0.606712681150082	0.102457388283725	5.92160986448309	4.88480949872586e-09	***
df.mm.trans2:exp3	0.284242532406448	0.0838287722321389	3.39075146680334	0.000734108439685246	***
df.mm.trans1:exp4	0.505267641316676	0.102457388283725	4.93149054236564	1.00876748620755e-06	***
df.mm.trans2:exp4	0.29657508692743	0.0838287722321389	3.53786747712532	0.00042860865055419	***
df.mm.trans1:exp5	0.0578982625679125	0.102457388283725	0.565096022236879	0.572180189875455	   
df.mm.trans2:exp5	0.079327775438116	0.0838287722321389	0.946307256158318	0.344301623067617	   
df.mm.trans1:exp6	0.132104245598651	0.102457388283725	1.28935792539263	0.197677631143224	   
df.mm.trans2:exp6	0.101173817336406	0.0838287722321389	1.20691040370047	0.227853352976000	   
df.mm.trans1:exp7	0.327090933627511	0.102457388283725	3.19245824148601	0.00147050762991110	** 
df.mm.trans2:exp7	0.147458266447419	0.0838287722321389	1.75904122798169	0.0789848764711082	.  
df.mm.trans1:exp8	0.33534454297771	0.102457388283725	3.27301474881511	0.00111362560783128	** 
df.mm.trans2:exp8	0.0970949697691075	0.0838287722321389	1.15825351110036	0.247135275613151	   
df.mm.trans1:probe2	-0.266445336715307	0.0598222264503205	-4.45395219344731	9.73604320725325e-06	***
df.mm.trans1:probe3	1.12430862154547	0.0598222264503205	18.7941621076099	1.08281708371213e-64	***
df.mm.trans1:probe4	0.155343577566738	0.0598222264503205	2.59675352764984	0.00959831236259796	** 
df.mm.trans1:probe5	0.135886351534296	0.0598222264503205	2.27150274400341	0.0234036779339381	*  
df.mm.trans1:probe6	-0.0257036723853382	0.0598222264503205	-0.429667598658902	0.667562873971553	   
df.mm.trans1:probe7	-0.198517156153270	0.0598222264503205	-3.31845148421765	0.000949584370542594	***
df.mm.trans1:probe8	-0.130093181773991	0.0598222264503205	-2.17466298888135	0.0299723035246698	*  
df.mm.trans1:probe9	0.0121206181591372	0.0598222264503205	0.202610616126146	0.839495234531703	   
df.mm.trans1:probe10	-0.593641457433091	0.0598222264503205	-9.92342633596365	7.17475384360603e-22	***
df.mm.trans1:probe11	-0.542202152411225	0.0598222264503205	-9.06355688485613	1.11903256742418e-18	***
df.mm.trans1:probe12	-0.413114448443934	0.0598222264503205	-6.90570165901474	1.07866867980528e-11	***
df.mm.trans1:probe13	-0.540857031620489	0.0598222264503205	-9.04107158347984	1.34695318809101e-18	***
df.mm.trans1:probe14	-0.365028397663857	0.0598222264503205	-6.10188585954747	1.69187668186207e-09	***
df.mm.trans1:probe15	-0.399053118556306	0.0598222264503205	-6.67064972728323	4.99652671331918e-11	***
df.mm.trans1:probe16	-0.442875610138035	0.0598222264503205	-7.40319503998772	3.63325223523281e-13	***
df.mm.trans1:probe17	0.0112054170383885	0.0598222264503205	0.187311935768457	0.851467549088915	   
df.mm.trans1:probe18	0.0343679534814955	0.0598222264503205	0.574501410609257	0.565803617841638	   
df.mm.trans1:probe19	0.116754813034861	0.0598222264503205	1.95169621665319	0.0513521810932027	.  
df.mm.trans1:probe20	-0.0543179396769516	0.0598222264503205	-0.907989269206823	0.364180286976844	   
df.mm.trans1:probe21	-0.00610937067090898	0.0598222264503205	-0.102125431188733	0.918684856540813	   
df.mm.trans1:probe22	-0.024291886553939	0.0598222264503205	-0.406067911466188	0.684810413555668	   
df.mm.trans2:probe2	-0.216588045268140	0.0598222264503205	-3.62052798967632	0.000314099780316927	***
df.mm.trans2:probe3	-0.0595676026084691	0.0598222264503205	-0.99574365821936	0.319701076519075	   
df.mm.trans2:probe4	-0.11276756147491	0.0598222264503205	-1.88504454224147	0.0598164757383405	.  
df.mm.trans2:probe5	-0.12431088634546	0.0598222264503205	-2.07800501120924	0.0380540089452759	*  
df.mm.trans2:probe6	-0.0586882385413728	0.0598222264503205	-0.981044037037146	0.326892519285533	   
df.mm.trans3:probe2	-0.193472729524738	0.0598222264503205	-3.23412786525771	0.00127447819237192	** 
df.mm.trans3:probe3	-0.322551396972095	0.0598222264503205	-5.39183203487016	9.39601808466831e-08	***
df.mm.trans3:probe4	0.334988049732894	0.0598222264503205	5.59972554701028	3.02982627021825e-08	***
df.mm.trans3:probe5	-0.192354458642058	0.0598222264503205	-3.21543463117006	0.00135922012409903	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46961285238927	0.200914364165296	22.2463578995877	2.66355564896783e-84	***
df.mm.trans1	0.106348596219865	0.176987665416505	0.600881400235404	0.548103477908272	   
df.mm.trans2	0.0219792379626268	0.159701375191312	0.137627105191155	0.890572672082737	   
df.mm.exp2	0.263302474963353	0.212674695672055	1.23805267068240	0.216090230019284	   
df.mm.exp3	0.163573268236772	0.212674695672055	0.769124261444825	0.442065621525296	   
df.mm.exp4	-0.0888856310976821	0.212674695672055	-0.417941734049753	0.676111292525223	   
df.mm.exp5	-0.15436191638802	0.212674695672055	-0.725812329954131	0.468183930287085	   
df.mm.exp6	0.102898898614322	0.212674695672055	0.483832353864007	0.628648382122688	   
df.mm.exp7	0.151320808842596	0.212674695672055	0.711512991070328	0.476991236311455	   
df.mm.exp8	0.0339579744749848	0.212674695672055	0.15967096775513	0.873183960806286	   
df.mm.trans1:exp2	-0.241489267544122	0.200603751718541	-1.20381231893881	0.229048102945382	   
df.mm.trans2:exp2	-0.138877142682367	0.164130342315170	-0.846139359264173	0.397749273401166	   
df.mm.trans1:exp3	-0.177459950786981	0.200603751718541	-0.884629271719544	0.376644727770902	   
df.mm.trans2:exp3	-0.0930662916319472	0.164130342315170	-0.567026732042254	0.570868439401859	   
df.mm.trans1:exp4	0.154825461466964	0.200603751718541	0.771797437189478	0.440481469464281	   
df.mm.trans2:exp4	-0.0138517499390228	0.164130342315170	-0.084394815386567	0.932765416948265	   
df.mm.trans1:exp5	0.116516332890246	0.200603751718541	0.580828284077787	0.561533513271274	   
df.mm.trans2:exp5	0.221742043209774	0.164130342315170	1.35101188532207	0.177105666542255	   
df.mm.trans1:exp6	-0.139403232878662	0.200603751718541	-0.694918373581834	0.487325182636117	   
df.mm.trans2:exp6	-0.0469651719633833	0.164130342315170	-0.286145579792911	0.774846925624984	   
df.mm.trans1:exp7	-0.146103066003326	0.200603751718541	-0.728316717666962	0.466650757602415	   
df.mm.trans2:exp7	-0.186884077471982	0.164130342315170	-1.13863210687223	0.255226106519039	   
df.mm.trans1:exp8	0.0117069583906643	0.200603751718541	0.0583586213636217	0.953478771565182	   
df.mm.trans2:exp8	0.0138135615769664	0.164130342315170	0.0841621444403074	0.93295033982852	   
df.mm.trans1:probe2	-0.0111489244972946	0.117127356680793	-0.0951863408620984	0.924192658030358	   
df.mm.trans1:probe3	-0.00949302753295709	0.117127356680793	-0.0810487643704655	0.935425145879618	   
df.mm.trans1:probe4	-0.0512630760526354	0.117127356680793	-0.437669537718182	0.661753911452432	   
df.mm.trans1:probe5	-0.00669613670936849	0.117127356680793	-0.05716970739481	0.954425483412416	   
df.mm.trans1:probe6	0.0545837390509137	0.117127356680793	0.46602041229079	0.641338399718954	   
df.mm.trans1:probe7	-0.0453041456855916	0.117127356680793	-0.386793888032997	0.699020304510071	   
df.mm.trans1:probe8	0.0229424856682798	0.117127356680793	0.195876405977511	0.844760824822768	   
df.mm.trans1:probe9	0.00595864757921782	0.117127356680793	0.0508732353232977	0.95944029875018	   
df.mm.trans1:probe10	0.0761425327018671	0.117127356680793	0.650083250058977	0.515840751874828	   
df.mm.trans1:probe11	0.0276366553627368	0.117127356680793	0.235953889389435	0.813533916696982	   
df.mm.trans1:probe12	0.131667017666732	0.117127356680793	1.12413548293046	0.261321061866974	   
df.mm.trans1:probe13	0.0288703297886442	0.117127356680793	0.246486650145485	0.805374080255282	   
df.mm.trans1:probe14	-0.0743461036020898	0.117127356680793	-0.634745850234672	0.525790909040887	   
df.mm.trans1:probe15	0.140823111304867	0.117127356680793	1.20230760170446	0.229629993658134	   
df.mm.trans1:probe16	0.235231087152393	0.117127356680793	2.00833600124237	0.0449714868355797	*  
df.mm.trans1:probe17	-0.0362774894768177	0.117127356680793	-0.309726869152223	0.756856101213098	   
df.mm.trans1:probe18	0.01873028925574	0.117127356680793	0.159913873125179	0.872992677052302	   
df.mm.trans1:probe19	0.0500533404387908	0.117127356680793	0.427341159718997	0.66925551066781	   
df.mm.trans1:probe20	0.0247674946001122	0.117127356680793	0.211457812265080	0.832588428149782	   
df.mm.trans1:probe21	0.0413546339997823	0.117127356680793	0.353074082534671	0.724133725454476	   
df.mm.trans1:probe22	0.0583726762074123	0.117127356680793	0.498369278207952	0.618372275523185	   
df.mm.trans2:probe2	0.037961573399984	0.117127356680793	0.324105097867448	0.745950300619008	   
df.mm.trans2:probe3	0.0808226708410284	0.117127356680793	0.690040936049587	0.490385376943236	   
df.mm.trans2:probe4	0.025070910818553	0.117127356680793	0.214048293490296	0.830568521324705	   
df.mm.trans2:probe5	0.0530984356336006	0.117127356680793	0.453339314898991	0.650437688850514	   
df.mm.trans2:probe6	-0.0230816190857834	0.117127356680793	-0.197064287454960	0.843831488573664	   
df.mm.trans3:probe2	-0.0605340650221311	0.117127356680793	-0.516822600095931	0.605434842430862	   
df.mm.trans3:probe3	-0.0120999208998116	0.117127356680793	-0.103305677193651	0.917748435075607	   
df.mm.trans3:probe4	0.0835786472980277	0.117127356680793	0.713570677820419	0.475718285461582	   
df.mm.trans3:probe5	0.0248648396151742	0.117127356680793	0.212288916268624	0.831940260152922