chr19.12157_chr19_9869144_9871370_+_0.R 

fitVsDatCorrelation=0.893907890722097
cont.fitVsDatCorrelation=0.256530652735576

fstatistic=6526.65702749868,42,462
cont.fstatistic=1395.13790503447,42,462

residuals=-0.52232233454403,-0.0930309461900459,-0.00452963828551343,0.0951478845843235,0.821854730170363
cont.residuals=-0.776811899614282,-0.301942789728456,-0.0820392884896112,0.29901219995564,1.10910523085503

predictedValues:
Include	Exclude	Both
chr19.12157_chr19_9869144_9871370_+_0.R.tl.Lung	150.539850521143	66.5356916510956	78.1806523563143
chr19.12157_chr19_9869144_9871370_+_0.R.tl.cerebhem	87.9514649194949	55.3334778356183	72.514698879462
chr19.12157_chr19_9869144_9871370_+_0.R.tl.cortex	121.859357788786	65.032117247815	77.3906848363018
chr19.12157_chr19_9869144_9871370_+_0.R.tl.heart	123.951257370244	64.7318562908002	65.5570189590767
chr19.12157_chr19_9869144_9871370_+_0.R.tl.kidney	163.242388317108	59.4388739662256	63.4468282722068
chr19.12157_chr19_9869144_9871370_+_0.R.tl.liver	143.179932728054	56.6145567482377	58.3024441368788
chr19.12157_chr19_9869144_9871370_+_0.R.tl.stomach	116.251003205531	53.0910068982362	64.8604007596912
chr19.12157_chr19_9869144_9871370_+_0.R.tl.testicle	108.939786299612	54.971203771546	65.067949590709


diffExp=84.0041588700472,32.6179870838766,56.827240540971,59.2194010794437,103.803514350882,86.5653759798161,63.1599963072945,53.9685825280662
diffExpScore=0.998152139037598
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.5459205459222	84.7748963651837	73.702572108797
cerebhem	69.8721105766058	90.4956972989297	77.1645410392387
cortex	72.7749269643149	76.8369111878034	78.0109311198065
heart	80.3960596336054	73.643741586111	73.3166109180848
kidney	79.5834597031356	83.906995977565	73.3440461352484
liver	73.6570973725835	74.9566658359587	74.4553565240155
stomach	74.9372396115317	73.4290258572404	75.710900438821
testicle	86.634571607951	83.4680539459998	88.2643870266785
cont.diffExp=-18.2289758192615,-20.6235867223239,-4.06198422348851,6.75231804749438,-4.32353627442933,-1.29956846337524,1.50821375429130,3.16651766195125
cont.diffExpScore=1.57343882694450

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=-1,-1,0,0,0,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.429991226843425
cont.tran.correlation=-0.200661565858576

tran.covariance=0.0079187756092825
cont.tran.covariance=-0.00142761644403030

tran.mean=93.2289890974716
cont.tran.mean=77.8695858794026

weightedLogRatios:
wLogRatio
Lung	3.76073605679355
cerebhem	1.96720060648398
cortex	2.81895170529902
heart	2.92015518987231
kidney	4.63731372213052
liver	4.17543270945026
stomach	3.42016499539287
testicle	2.97451921581617

cont.weightedLogRatios:
wLogRatio
Lung	-1.04564798140183
cerebhem	-1.1317859159739
cortex	-0.234337793934363
heart	0.381002985825951
kidney	-0.232944288786928
liver	-0.075348317594476
stomach	0.0875579610942986
testicle	0.165438026948334

varWeightedLogRatios=0.71858175075336
cont.varWeightedLogRatios=0.303630645504026

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.52264227143701	0.0947145175535798	47.7502540080887	8.80644555407945e-181	***
df.mm.trans1	0.51398466657647	0.0760938193831036	6.75461779607555	4.31950260338465e-11	***
df.mm.trans2	-0.370037417562153	0.0760938193831036	-4.86291029366202	1.58853452697541e-06	***
df.mm.exp2	-0.646570198259997	0.102170423532633	-6.3283499852918	5.86691443490221e-10	***
df.mm.exp3	-0.224061740577851	0.102170423532633	-2.19301959246832	0.0288037529248776	*  
df.mm.exp4	-0.0457225825054678	0.102170423532633	-0.447512899766575	0.654714522647524	   
df.mm.exp5	0.177038239971599	0.102170423532633	1.73277386791935	0.0838031763568056	.  
df.mm.exp6	0.081780080432693	0.102170423532633	0.800428123962629	0.423874192161549	   
df.mm.exp7	-0.297422205534569	0.102170423532633	-2.91104015478190	0.00377642903917474	** 
df.mm.exp8	-0.330771463811298	0.102170423532633	-3.23744829838793	0.00129281119235844	** 
df.mm.trans1:exp2	0.109127488926283	0.080772811966797	1.35104234047394	0.177343131266126	   
df.mm.trans2:exp2	0.462209789193277	0.080772811966797	5.72234366909593	1.89154421225071e-08	***
df.mm.trans1:exp3	0.0127014786555403	0.080772811966797	0.157249430176598	0.875116971517013	   
df.mm.trans2:exp3	0.201204479706621	0.080772811966797	2.49099263486493	0.0130886767398125	*  
df.mm.trans1:exp4	-0.148616851546583	0.080772811966797	-1.83993658172597	0.0664188568958598	.  
df.mm.trans2:exp4	0.0182375118545586	0.080772811966797	0.225787754697156	0.821466349761341	   
df.mm.trans1:exp5	-0.0960299354857802	0.080772811966797	-1.18888934466284	0.235093927523553	   
df.mm.trans2:exp5	-0.289828304020977	0.080772811966797	-3.58819133522448	0.000368546549535167	***
df.mm.trans1:exp6	-0.131905806961347	0.080772811966797	-1.63304710767739	0.103140379876040	   
df.mm.trans2:exp6	-0.243252462164497	0.080772811966797	-3.01156362198322	0.00274156972485288	** 
df.mm.trans1:exp7	0.0389460430349922	0.080772811966797	0.482167725583227	0.629915146749538	   
df.mm.trans2:exp7	0.0716912375852053	0.080772811966797	0.887566445187957	0.375235830529078	   
df.mm.trans1:exp8	0.00733893747628524	0.080772811966797	0.0908590068561936	0.927644002274284	   
df.mm.trans2:exp8	0.139842423905399	0.080772811966797	1.73130562747876	0.0840648556417016	.  
df.mm.trans1:probe2	0.00730158229060768	0.05418404948747	0.134755197510592	0.89286411614724	   
df.mm.trans1:probe3	-0.11569571443279	0.05418404948747	-2.13523565564336	0.0332671348558230	*  
df.mm.trans1:probe4	0.0445534880636116	0.05418404948747	0.822262058392564	0.411351929172719	   
df.mm.trans1:probe5	-0.0512697766502241	0.05418404948747	-0.946215300170213	0.344533399963033	   
df.mm.trans1:probe6	-0.220876099651317	0.05418404948747	-4.07640443526455	5.384739209565e-05	***
df.mm.trans2:probe2	0.0915473433637945	0.05418404948747	1.6895625969219	0.091786309458253	.  
df.mm.trans2:probe3	0.115397203107634	0.05418404948747	2.12972644531339	0.0337221499991019	*  
df.mm.trans2:probe4	0.320504038862274	0.05418404948747	5.91509940460228	6.4613119290388e-09	***
df.mm.trans2:probe5	0.0687151015242051	0.05418404948747	1.26817951360567	0.205372539704362	   
df.mm.trans2:probe6	0.0808413086016357	0.05418404948747	1.49197613257625	0.136387904268060	   
df.mm.trans3:probe2	-0.354711849787284	0.05418404948747	-6.5464256205013	1.57057910848574e-10	***
df.mm.trans3:probe3	-0.55472249408632	0.05418404948747	-10.2377452282262	2.62934104382567e-22	***
df.mm.trans3:probe4	-0.489361908064166	0.05418404948747	-9.03147536393216	4.62206491737545e-18	***
df.mm.trans3:probe5	-0.692960875013304	0.05418404948747	-12.7890196758651	2.83397849729041e-32	***
df.mm.trans3:probe6	0.245833211593762	0.05418404948747	4.53700330483071	7.28345958154332e-06	***
df.mm.trans3:probe7	-0.168253060417280	0.05418404948747	-3.10521384076671	0.00201831461114419	** 
df.mm.trans3:probe8	-0.441265872395437	0.05418404948747	-8.14383340797515	3.60085085781106e-15	***
df.mm.trans3:probe9	-0.312662994343906	0.05418404948747	-5.77038809947583	1.45116646476849e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27828111763013	0.204227236290009	20.9486315113957	5.40362476658732e-69	***
df.mm.trans1	-0.100989945520754	0.164076541091720	-0.615505085911703	0.538524291405131	   
df.mm.trans2	0.143696813110205	0.164076541091720	0.875791335885597	0.381598585053095	   
df.mm.exp2	0.068174835547045	0.220303959388755	0.309458058476115	0.757112631455538	   
df.mm.exp3	-0.0656463453839641	0.220303959388755	-0.297980778766316	0.765851790584507	   
df.mm.exp4	0.0535631198133904	0.220303959388755	0.243132805974092	0.80801042883404	   
df.mm.exp5	0.173499931497344	0.220303959388755	0.787547949563544	0.431365016160688	   
df.mm.exp6	-0.0317230534108973	0.220303959388755	-0.143996746581017	0.885565827446328	   
df.mm.exp7	-0.0518058932890265	0.220303959388755	-0.235156433106172	0.814191512484139	   
df.mm.exp8	0.067972197891998	0.220303959388755	0.308538249065476	0.757811872592508	   
df.mm.trans1:exp2	-0.0194005001231619	0.174165572305425	-0.111391131245733	0.911354568660162	   
df.mm.trans2:exp2	-0.00287199517151015	0.174165572305425	-0.0164900280433935	0.986850576175354	   
df.mm.trans1:exp3	0.155125587555388	0.174165572305425	0.890678826486743	0.373565086599358	   
df.mm.trans2:exp3	-0.0326679810378801	0.174165572305425	-0.187568533812135	0.851297243006598	   
df.mm.trans1:exp4	0.135509801760562	0.174165572305425	0.778051597493248	0.436936861728178	   
df.mm.trans2:exp4	-0.194323421126988	0.174165572305425	-1.11573957214812	0.265113619241911	   
df.mm.trans1:exp5	0.00541410320923943	0.174165572305425	0.0310859553789712	0.975214414615215	   
df.mm.trans2:exp5	-0.183790402327651	0.174165572305425	-1.05526252918313	0.291856796174649	   
df.mm.trans1:exp6	0.133251314025444	0.174165572305425	0.76508412231878	0.444612091889641	   
df.mm.trans2:exp6	-0.0913662544328541	0.174165572305425	-0.524594230785346	0.600116910430687	   
df.mm.trans1:exp7	0.170564607451170	0.174165572305425	0.97932447379474	0.327932216501403	   
df.mm.trans2:exp7	-0.091874267154865	0.174165572305425	-0.527511068569567	0.598091963142747	   
df.mm.trans1:exp8	0.195834504388965	0.174165572305425	1.12441570280916	0.261420574407852	   
df.mm.trans2:exp8	-0.0835076922553794	0.174165572305425	-0.479473016107547	0.631829045407351	   
df.mm.trans1:probe2	0.152594302003323	0.116833817704526	1.30607990906568	0.192175282006060	   
df.mm.trans1:probe3	-0.0338837418250827	0.116833817704526	-0.290016559338796	0.771933689327206	   
df.mm.trans1:probe4	0.0789881019824275	0.116833817704526	0.676072249750405	0.499333062352586	   
df.mm.trans1:probe5	0.107033658607541	0.116833817704526	0.91611881483006	0.360082516390881	   
df.mm.trans1:probe6	0.00428375575653018	0.116833817704526	0.0366653751516009	0.970767653509205	   
df.mm.trans2:probe2	-0.060169851328851	0.116833817704526	-0.515003725043217	0.606796723621362	   
df.mm.trans2:probe3	0.212928872805747	0.116833817704526	1.82249349537004	0.0690263933085955	.  
df.mm.trans2:probe4	0.0830215400449187	0.116833817704526	0.710595114292004	0.477693724358652	   
df.mm.trans2:probe5	-0.0310373190079035	0.116833817704526	-0.265653554918468	0.79062453683573	   
df.mm.trans2:probe6	0.0655797795092052	0.116833817704526	0.561308196527972	0.57485961029942	   
df.mm.trans3:probe2	0.0575415976759785	0.116833817704526	0.492508066641304	0.622594187154478	   
df.mm.trans3:probe3	-0.0356709519760049	0.116833817704526	-0.305313587083298	0.760264834810251	   
df.mm.trans3:probe4	0.0640233200714582	0.116833817704526	0.547986202362864	0.58396603684994	   
df.mm.trans3:probe5	-0.0713599433254587	0.116833817704526	-0.610781576152282	0.541644551694825	   
df.mm.trans3:probe6	-0.105120466595264	0.116833817704526	-0.899743487464519	0.368725499734427	   
df.mm.trans3:probe7	-0.0385378699028208	0.116833817704526	-0.329852012542152	0.741661289796108	   
df.mm.trans3:probe8	-0.0802468138273853	0.116833817704526	-0.686845772945044	0.492524523043208	   
df.mm.trans3:probe9	0.0208185455908458	0.116833817704526	0.178189380436889	0.858652354610335