chr6.19368_chr6_114628586_114631681_-_2.R 

fitVsDatCorrelation=0.929767402388386
cont.fitVsDatCorrelation=0.252642560256289

fstatistic=4104.7214487575,56,784
cont.fstatistic=582.280481306595,56,784

residuals=-1.45998105764134,-0.115060614150078,-0.00179790987141001,0.108442284312009,1.45250439611586
cont.residuals=-1.16023288459049,-0.478278681510209,-0.169377284624939,0.296801579292907,3.16492245018957

predictedValues:
Include	Exclude	Both
chr6.19368_chr6_114628586_114631681_-_2.R.tl.Lung	69.5449763643348	114.837787774202	67.4384249875485
chr6.19368_chr6_114628586_114631681_-_2.R.tl.cerebhem	68.5127514766843	67.4738315317301	59.6769251132257
chr6.19368_chr6_114628586_114631681_-_2.R.tl.cortex	66.7785849044234	85.7647083398874	63.6258116665433
chr6.19368_chr6_114628586_114631681_-_2.R.tl.heart	69.4552958920547	104.362638936185	71.4037093617767
chr6.19368_chr6_114628586_114631681_-_2.R.tl.kidney	73.4381094169715	123.957529733990	71.2128799253602
chr6.19368_chr6_114628586_114631681_-_2.R.tl.liver	76.6904025434074	124.451219923938	71.0502032966155
chr6.19368_chr6_114628586_114631681_-_2.R.tl.stomach	401.412647528513	108.187825305184	685.65148976098
chr6.19368_chr6_114628586_114631681_-_2.R.tl.testicle	77.5006193031527	116.239930321187	64.9658767698636


diffExp=-45.2928114098676,1.03891994495424,-18.986123435464,-34.9073430441308,-50.5194203170181,-47.7608173805305,293.224822223329,-38.7393110180341
diffExpScore=8.9821925429337
diffExp1.5=-1,0,0,-1,-1,-1,1,0
diffExp1.5Score=1.25
diffExp1.4=-1,0,0,-1,-1,-1,1,-1
diffExp1.4Score=1.2
diffExp1.3=-1,0,0,-1,-1,-1,1,-1
diffExp1.3Score=1.2
diffExp1.2=-1,0,-1,-1,-1,-1,1,-1
diffExp1.2Score=1.16666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	101.628041384169	110.083523690266	98.6495972935622
cerebhem	87.8510927453194	87.1536366917352	108.890314867273
cortex	81.7767771586112	95.8640595687085	69.0917735129547
heart	84.5617025353342	104.921910239011	80.6292302785852
kidney	106.151374159383	83.8664931037937	116.242497913583
liver	82.3707344677943	105.821723914156	87.615004473819
stomach	95.5723155971761	100.275604574456	89.0123107064666
testicle	100.481750089037	92.6935657660835	95.9224895559183
cont.diffExp=-8.45548230609734,0.697456053584176,-14.0872824100973,-20.3602077036771,22.2848810555895,-23.4509894463612,-4.70328897728001,7.78818432295347
cont.diffExpScore=2.46635598719910

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,-1,1,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.075650264731762
cont.tran.correlation=-0.285099751209726

tran.covariance=0.0181182490460358
cont.tran.covariance=-0.00298521536465688

tran.mean=109.288053705990
cont.tran.mean=95.0671441053147

weightedLogRatios:
wLogRatio
Lung	-2.25332342813180
cerebhem	0.0644722896330035
cortex	-1.08259787970054
heart	-1.80965837270222
kidney	-2.38619476702948
liver	-2.21824198391087
stomach	7.00063856256277
testicle	-1.84564016691223

cont.weightedLogRatios:
wLogRatio
Lung	-0.372528935401495
cerebhem	0.0356424809528404
cortex	-0.712591784875766
heart	-0.980590330962394
kidney	1.07146580415123
liver	-1.13650772050897
stomach	-0.220207246443137
testicle	0.368664921052435

varWeightedLogRatios=9.99894643183918
cont.varWeightedLogRatios=0.537114902932316

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.06634406966289	0.131673188200556	38.4766567810766	9.39519052521698e-183	***
df.mm.trans1	-0.746612212709823	0.114764987285418	-6.50557483052749	1.37879475932243e-10	***
df.mm.trans2	-0.359072192026993	0.102415366481088	-3.50603824762274	0.000480678068875076	***
df.mm.exp2	-0.424465026652551	0.133991102733458	-3.16785979063788	0.00159518113057703	** 
df.mm.exp3	-0.274308508353166	0.133991102733458	-2.04721435048441	0.0409698787601726	*  
df.mm.exp4	-0.154074069573232	0.133991102733458	-1.14988283871150	0.250542765586840	   
df.mm.exp5	0.0764289900808217	0.133991102733458	0.570403470989101	0.5685675079749	   
df.mm.exp6	0.126024368589563	0.133991102733458	0.940542812310883	0.347228895139758	   
df.mm.exp7	-0.625789999585005	0.133991102733458	-4.67038472569226	3.5366500016169e-06	***
df.mm.exp8	0.157800870306937	0.133991102733458	1.17769663125202	0.239274940976016	   
df.mm.trans1:exp2	0.409511222343142	0.125118651796203	3.27298301623459	0.00111080403742256	** 
df.mm.trans2:exp2	-0.107315723167878	0.0975918753559429	-1.09963788252321	0.2718274717329	   
df.mm.trans1:exp3	0.233717267036627	0.125118651796203	1.86796503703790	0.0621397921689184	.  
df.mm.trans2:exp3	-0.0176044859998623	0.097591875355943	-0.180388848309905	0.856893925410317	   
df.mm.trans1:exp4	0.152783705413971	0.125118651796203	1.22111054763306	0.222411256557744	   
df.mm.trans2:exp4	0.0584252248572344	0.0975918753559429	0.598668942923193	0.549566596917361	   
df.mm.trans1:exp5	-0.0219596725304836	0.125118651796203	-0.175510782886729	0.860723618155586	   
df.mm.trans2:exp5	-1.05768080807956e-05	0.0975918753559429	-0.000108377957101647	0.999913554355016	   
df.mm.trans1:exp6	-0.0282214830554750	0.125118651796203	-0.225557761775143	0.821604178708055	   
df.mm.trans2:exp6	-0.0456311288211987	0.0975918753559429	-0.467570980215004	0.640221356002299	   
df.mm.trans1:exp7	2.37880625879467	0.125118651796203	19.0124032240161	1.42550098614375e-66	***
df.mm.trans2:exp7	0.566138247861903	0.097591875355943	5.80107970870577	9.55688182498329e-09	***
df.mm.trans1:exp8	-0.0494886281687174	0.125118651796203	-0.395533579192701	0.692556747521683	   
df.mm.trans2:exp8	-0.145665042004090	0.0975918753559429	-1.49259394260856	0.135945797078255	   
df.mm.trans1:probe2	-0.472503491828295	0.0795115155633361	-5.94257936703414	4.21878686913618e-09	***
df.mm.trans1:probe3	-0.52779847407508	0.0795115155633361	-6.63801300145831	5.93355033337703e-11	***
df.mm.trans1:probe4	-0.357438076893468	0.0795115155633361	-4.4954252772197	7.99071435239126e-06	***
df.mm.trans1:probe5	-0.558151504010466	0.0795115155633361	-7.01975682460563	4.8171131270556e-12	***
df.mm.trans1:probe6	0.254635788063969	0.0795115155633361	3.20250200565143	0.00141739357926520	** 
df.mm.trans1:probe7	-0.322312854968947	0.0795115155633361	-4.05366257560773	5.54511846008447e-05	***
df.mm.trans1:probe8	0.0542151532686089	0.0795115155633361	0.68185284715675	0.495533380716629	   
df.mm.trans1:probe9	-0.588012483626569	0.0795115155633361	-7.39531223195185	3.62986502225511e-13	***
df.mm.trans1:probe10	0.518803831273704	0.0795115155633361	6.52488922639693	1.22036305069136e-10	***
df.mm.trans1:probe11	0.116129110802231	0.0795115155633361	1.46053197426134	0.144544541776564	   
df.mm.trans1:probe12	0.43452666492366	0.0795115155633361	5.46495261529169	6.22434388669452e-08	***
df.mm.trans1:probe13	0.547443685460819	0.0795115155633361	6.88508679003539	1.18413629932971e-11	***
df.mm.trans1:probe14	0.429949632068626	0.0795115155633361	5.407388213172	8.49618895666576e-08	***
df.mm.trans1:probe15	0.219357038534119	0.0795115155633361	2.75880841888099	0.00593644986676276	** 
df.mm.trans1:probe16	0.238715648819227	0.0795115155633361	3.00227768428177	0.00276477816930808	** 
df.mm.trans1:probe17	-0.384400370929775	0.0795115155633361	-4.83452451140333	1.60628645301884e-06	***
df.mm.trans1:probe18	-0.365983617826149	0.0795115155633361	-4.60290079032161	4.85874320315402e-06	***
df.mm.trans1:probe19	-0.288003517920605	0.0795115155633361	-3.62216109050508	0.000310959760278068	***
df.mm.trans1:probe20	-0.461346162040681	0.0795115155633361	-5.80225592195118	9.49280909201648e-09	***
df.mm.trans1:probe21	-0.265623990835523	0.0795115155633361	-3.34069837499118	0.000875220083682184	***
df.mm.trans1:probe22	-0.477188989168731	0.0795115155633361	-6.0015079047087	2.98615490678440e-09	***
df.mm.trans2:probe2	0.116544376465888	0.0795115155633361	1.46575468521982	0.143116064853111	   
df.mm.trans2:probe3	-0.0781189235585163	0.0795115155633361	-0.982485656386332	0.32616373473828	   
df.mm.trans2:probe4	-0.0304908559372799	0.0795115155633361	-0.383477232464421	0.701469910867133	   
df.mm.trans2:probe5	0.0701468295229724	0.0795115155633361	0.882222267126777	0.377927028260342	   
df.mm.trans2:probe6	0.39315185399759	0.0795115155633361	4.9445901164394	9.3371000759226e-07	***
df.mm.trans3:probe2	-0.00482056817609848	0.0795115155633361	-0.060627295831867	0.951671485621263	   
df.mm.trans3:probe3	0.081965899321865	0.0795115155633361	1.03086828041372	0.302920499824409	   
df.mm.trans3:probe4	0.320138436462678	0.0795115155633361	4.02631536066831	6.21601682830917e-05	***
df.mm.trans3:probe5	0.939100230945598	0.0795115155633361	11.8108707184376	9.62218440011058e-30	***
df.mm.trans3:probe6	0.297100471937721	0.0795115155633361	3.73657161271264	0.000200084271428082	***
df.mm.trans3:probe7	0.120402589031792	0.0795115155633361	1.51427863220497	0.130358212082631	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.0291449861031	0.346061483572158	14.5325187136998	1.51799673290948e-42	***
df.mm.trans1	-0.323897852717628	0.301623605419497	-1.07384782522957	0.283221285743666	   
df.mm.trans2	-0.282740368373701	0.269166518631329	-1.05042919086443	0.293844505896829	   
df.mm.exp2	-0.478010065034265	0.352153391522528	-1.3573916268919	0.175047410018085	   
df.mm.exp3	0.000504046884369537	0.352153391522528	0.00143132764444011	0.998858330269577	   
df.mm.exp4	-0.0301481340373718	0.352153391522528	-0.0856108013244653	0.931797669387015	   
df.mm.exp5	-0.392570936741195	0.352153391522528	-1.11477255704942	0.265289505150541	   
df.mm.exp6	-0.130950957863881	0.352153391522528	-0.371857721709613	0.7100992658376	   
df.mm.exp7	-0.0519537781865121	0.352153391522528	-0.147531670678766	0.88275031546727	   
df.mm.exp8	-0.15525001267196	0.352153391522528	-0.440859058607216	0.659436572847373	   
df.mm.trans1:exp2	0.332333823413242	0.328834950037	1.01064021137610	0.312500464723465	   
df.mm.trans2:exp2	0.244443181296955	0.25648949214191	0.95303390113818	0.340866625420245	   
df.mm.trans1:exp3	-0.217830236338423	0.328834950037	-0.662430305275985	0.507890166697525	   
df.mm.trans2:exp3	-0.138812289057063	0.25648949214191	-0.541200685836522	0.5885230078694	   
df.mm.trans1:exp4	-0.153689885492201	0.328834950037000	-0.467376978861002	0.640360063144664	   
df.mm.trans2:exp4	-0.0178748884458864	0.25648949214191	-0.0696905292166768	0.944457751283702	   
df.mm.trans1:exp5	0.436117575315752	0.328834950037	1.32625067763229	0.185142902394498	   
df.mm.trans2:exp5	0.120557719447220	0.25648949214191	0.470029857521486	0.638464399977872	   
df.mm.trans1:exp6	-0.0791383274718927	0.328834950037000	-0.240662762467883	0.809879420158201	   
df.mm.trans2:exp6	0.0914674022718348	0.25648949214191	0.35661266864386	0.721477745228272	   
df.mm.trans1:exp7	-0.009482524437447	0.328834950037	-0.0288367292965059	0.977002146040995	   
df.mm.trans2:exp7	-0.0413631649515828	0.25648949214191	-0.161266508838879	0.871925047120154	   
df.mm.trans1:exp8	0.143906637587908	0.328834950037000	0.437625737689125	0.661778141505558	   
df.mm.trans2:exp8	-0.0166903103171691	0.25648949214191	-0.0650721016981652	0.94813318137504	   
df.mm.trans1:probe2	-0.218501377110673	0.208970963739470	-1.04560640005032	0.296065059028301	   
df.mm.trans1:probe3	-0.0112257364872634	0.208970963739470	-0.0537191210031399	0.957172633548086	   
df.mm.trans1:probe4	0.0317566244426683	0.208970963739470	0.151966684147852	0.87925230953856	   
df.mm.trans1:probe5	-0.299308069894042	0.208970963739470	-1.43229501619755	0.152457981293814	   
df.mm.trans1:probe6	-0.0202957263230460	0.208970963739470	-0.0971222315285351	0.922654148409047	   
df.mm.trans1:probe7	-0.295663408602116	0.208970963739470	-1.41485402235465	0.157508346579888	   
df.mm.trans1:probe8	0.0441370388679084	0.208970963739470	0.211211347634570	0.832777244294736	   
df.mm.trans1:probe9	0.142332755618816	0.208970963739470	0.681112596084239	0.496001368632924	   
df.mm.trans1:probe10	0.107309022102470	0.208970963739470	0.513511639044051	0.607738151437453	   
df.mm.trans1:probe11	-0.248326713945078	0.208970963739470	-1.18833118966075	0.235062797284288	   
df.mm.trans1:probe12	-0.0090601333517046	0.208970963739470	-0.0433559437616419	0.965428835969787	   
df.mm.trans1:probe13	-0.242039350319559	0.208970963739470	-1.15824393010560	0.247117205843796	   
df.mm.trans1:probe14	-0.0217135393099253	0.208970963739470	-0.103906968324059	0.917269751196001	   
df.mm.trans1:probe15	-0.0186560300411710	0.208970963739470	-0.0892757046592846	0.928885602339377	   
df.mm.trans1:probe16	-0.201197065432671	0.208970963739470	-0.962799146026378	0.335945188811885	   
df.mm.trans1:probe17	-0.0599817962292446	0.208970963739470	-0.287034117830961	0.774162039789254	   
df.mm.trans1:probe18	-0.0732787735372617	0.208970963739470	-0.350664859011802	0.725933990524969	   
df.mm.trans1:probe19	-0.276100322914428	0.208970963739470	-1.32123773549061	0.186807570182684	   
df.mm.trans1:probe20	-0.358756350085715	0.208970963739470	-1.71677607101906	0.0864150673499911	.  
df.mm.trans1:probe21	-0.210549435704741	0.208970963739470	-1.00755354685180	0.313979698082161	   
df.mm.trans1:probe22	-0.194783126671882	0.208970963739470	-0.932106179663902	0.351568602787981	   
df.mm.trans2:probe2	0.0209144647207766	0.208970963739470	0.100083113684881	0.920303927697514	   
df.mm.trans2:probe3	-0.154964837130896	0.208970963739470	-0.741561575626816	0.458575084828736	   
df.mm.trans2:probe4	-0.209323807059285	0.208970963739470	-1.00168848012901	0.316803130290939	   
df.mm.trans2:probe5	-0.243136903481465	0.208970963739470	-1.16349611032368	0.244982255109780	   
df.mm.trans2:probe6	-0.000636956306126281	0.208970963739470	-0.00304806129391446	0.9975687781068	   
df.mm.trans3:probe2	0.183766974635899	0.208970963739470	0.879389994415714	0.379459308087626	   
df.mm.trans3:probe3	0.292923728894290	0.208970963739470	1.40174368559397	0.161387565805914	   
df.mm.trans3:probe4	0.406356116254665	0.208970963739470	1.94455779397792	0.0521855161115476	.  
df.mm.trans3:probe5	-0.136574986675334	0.208970963739470	-0.653559634464843	0.513587187386064	   
df.mm.trans3:probe6	0.118591016204912	0.208970963739470	0.567499972640996	0.570537008244924	   
df.mm.trans3:probe7	0.318408847609181	0.208970963739470	1.52369899583824	0.127987169563592