chr5.17953_chr5_120425108_120430586_+_2.R 

fitVsDatCorrelation=0.805457534558967
cont.fitVsDatCorrelation=0.250388826474335

fstatistic=5381.20358722587,43,485
cont.fstatistic=2009.45570302126,43,485

residuals=-0.721789675349172,-0.100115008805721,-0.0099803766219424,0.102752788287007,1.4962809121377
cont.residuals=-0.856936696452093,-0.205806491939826,-0.0585384550002047,0.127040395883094,1.53580903040545

predictedValues:
Include	Exclude	Both
chr5.17953_chr5_120425108_120430586_+_2.R.tl.Lung	80.8125821039378	77.1373438355682	95.3386208306354
chr5.17953_chr5_120425108_120430586_+_2.R.tl.cerebhem	100.725771395857	150.984175229532	101.154106178509
chr5.17953_chr5_120425108_120430586_+_2.R.tl.cortex	84.0740985449634	98.6205193397997	167.010222526554
chr5.17953_chr5_120425108_120430586_+_2.R.tl.heart	85.9698988338449	85.9059701610216	103.084907282452
chr5.17953_chr5_120425108_120430586_+_2.R.tl.kidney	76.908965391145	89.531333098011	153.091792106613
chr5.17953_chr5_120425108_120430586_+_2.R.tl.liver	74.9701854272415	74.0628714688817	107.449610450084
chr5.17953_chr5_120425108_120430586_+_2.R.tl.stomach	101.711326948341	86.4167911683943	98.1873963766232
chr5.17953_chr5_120425108_120430586_+_2.R.tl.testicle	81.2202202749357	89.9757473160805	126.923029111760


diffExp=3.6752382683696,-50.2584038336758,-14.5464207948363,0.0639286728232662,-12.6223677068660,0.907313958359751,15.2945357799464,-8.75552704114473
diffExpScore=1.57824284334666
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	90.0711392162262	94.4396030473174	81.4649165923627
cerebhem	96.437471516327	90.1133734985818	81.2882753353931
cortex	103.99055979828	88.942572614746	108.963876086843
heart	95.8227472840784	90.4815321660157	99.3737743417353
kidney	92.7428418887651	86.2478567277119	95.1095516280643
liver	89.8787683915028	90.3364882359556	104.435760335318
stomach	105.942443792573	96.7832653670379	102.876817297921
testicle	94.0768399197413	90.9268587418912	98.2030681739334
cont.diffExp=-4.36846383109120,6.32409801774514,15.0479871835340,5.34121511806272,6.49498516105322,-0.457719844452782,9.15917842553552,3.14998117785008
cont.diffExpScore=1.20753431435823

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=0.626608633683748
cont.tran.correlation=0.29521019362371

tran.covariance=0.0158874525320604
cont.tran.covariance=0.000599100796633201

tran.mean=89.9392375335972
cont.tran.mean=93.577147637922

weightedLogRatios:
wLogRatio
Lung	0.203349263175103
cerebhem	-1.94889797587412
cortex	-0.719947065484888
heart	0.0033130231469351
kidney	-0.671478694188618
liver	0.0524914550257981
stomach	0.739930905162435
testicle	-0.455404172756319

cont.weightedLogRatios:
wLogRatio
Lung	-0.214273143491459
cerebhem	0.307590810653376
cortex	0.713730616313093
heart	0.260034408117909
kidney	0.326254010701737
liver	-0.0228637932333665
stomach	0.417539681776281
testicle	0.154176448466552

varWeightedLogRatios=0.655538142754877
cont.varWeightedLogRatios=0.0787308979283516

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53585438299989	0.113342379187264	40.019050381021	8.87568031706662e-156	***
df.mm.trans1	-0.189591228396691	0.0985759631770407	-1.92330079551126	0.0550273772804068	.  
df.mm.trans2	-0.294185549532978	0.0925436650806147	-3.1788837115616	0.00157291919417808	** 
df.mm.exp2	0.832646355477672	0.125742079951619	6.6218592518753	9.43202014346003e-11	***
df.mm.exp3	-0.275362348681699	0.125742079951619	-2.18989815332821	0.0290071949116887	*  
df.mm.exp4	0.0914123503071732	0.125742079951619	0.726982966580044	0.467587135052516	   
df.mm.exp5	-0.374111788424976	0.125742079951619	-2.97523143063102	0.00307375149773384	** 
df.mm.exp6	-0.235302351671525	0.125742079951619	-1.87130952312909	0.061903985860708	.  
df.mm.exp7	0.31415760365335	0.125742079951619	2.49842855927170	0.0128044194545706	*  
df.mm.exp8	-0.127161646446928	0.125742079951619	-1.01128951020895	0.312382131993317	   
df.mm.trans1:exp2	-0.612377338506902	0.114786289361866	-5.33493452842933	1.46932316973036e-07	***
df.mm.trans2:exp2	-0.161058843109852	0.102667978359238	-1.56873492284325	0.117361880526573	   
df.mm.trans1:exp3	0.314928211703988	0.114786289361866	2.74360477592554	0.00630197095140092	** 
df.mm.trans2:exp3	0.521054176382772	0.102667978359238	5.07513817560127	5.5268364715714e-07	***
df.mm.trans1:exp4	-0.0295478013948809	0.114786289361866	-0.257415772904121	0.796967005008799	   
df.mm.trans2:exp4	0.0162534584095848	0.102667978359238	0.158310883971179	0.874277725683376	   
df.mm.trans1:exp5	0.324601570689239	0.114786289361866	2.82787754960808	0.00487963100404479	** 
df.mm.trans2:exp5	0.523112923768181	0.102667978359238	5.09519065367973	4.99941579951646e-07	***
df.mm.trans1:exp6	0.160260186023768	0.114786289361866	1.39616139623212	0.163304593341579	   
df.mm.trans2:exp6	0.194629179451189	0.102667978359238	1.89571454081015	0.0585920003171695	.  
df.mm.trans1:exp7	-0.0841516032230427	0.114786289361866	-0.733115459092443	0.463842197408896	   
df.mm.trans2:exp7	-0.200563123649359	0.102667978359238	-1.95351196015163	0.0513334858730393	.  
df.mm.trans1:exp8	0.132193208266687	0.114786289361867	1.15164632467511	0.25003382664849	   
df.mm.trans2:exp8	0.281114287045906	0.102667978359238	2.73809119005227	0.00640693893179571	** 
df.mm.trans1:probe2	0.176344958377047	0.0628710399758095	2.80486784447813	0.00523583601785324	** 
df.mm.trans1:probe3	0.13309852193823	0.0628710399758095	2.11700843487624	0.0347676274635048	*  
df.mm.trans1:probe4	0.179380988441880	0.0628710399758095	2.85315764636467	0.00451374594743994	** 
df.mm.trans1:probe5	0.200488190419847	0.0628710399758095	3.18887981647810	0.00152061748376134	** 
df.mm.trans1:probe6	0.111030789033868	0.0628710399758095	1.76600846870973	0.0780235030950457	.  
df.mm.trans1:probe7	-0.196617880397365	0.0628710399758095	-3.12732031270703	0.00187009108177382	** 
df.mm.trans1:probe8	-0.0245305960000783	0.0628710399758095	-0.390173218218067	0.696579762191071	   
df.mm.trans1:probe9	0.00498730913852213	0.0628710399758095	0.0793260162459705	0.936806032461673	   
df.mm.trans1:probe10	0.0743394367941349	0.0628710399758095	1.18241143812378	0.237621835426637	   
df.mm.trans1:probe11	-0.146492907354296	0.0628710399758095	-2.33005382781421	0.0202126960507614	*  
df.mm.trans1:probe12	0.221883476152658	0.0628710399758095	3.52918412416957	0.000456551342160327	***
df.mm.trans2:probe2	0.151619319336401	0.0628710399758095	2.41159235467934	0.0162535595195784	*  
df.mm.trans2:probe3	0.204850123414449	0.0628710399758095	3.25825886597817	0.00119962160598888	** 
df.mm.trans2:probe4	0.235772307009868	0.0628710399758095	3.75009395582742	0.00019811042351808	***
df.mm.trans2:probe5	0.198057065981446	0.0628710399758095	3.15021138599984	0.00173229635389259	** 
df.mm.trans2:probe6	0.248888041096688	0.0628710399758095	3.95870723933389	8.66368125531076e-05	***
df.mm.trans3:probe2	0.261037890435085	0.0628710399758095	4.15195757117304	3.89464553044556e-05	***
df.mm.trans3:probe3	1.10279605193721	0.0628710399758095	17.5406045829929	1.05934940438902e-53	***
df.mm.trans3:probe4	0.657595572835313	0.0628710399758095	10.4594352676261	3.16817406381852e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.64185183095697	0.185153551069829	25.0702824986939	1.41429260155205e-89	***
df.mm.trans1	-0.140979666334037	0.161031467340230	-0.875478989682018	0.381746698912151	   
df.mm.trans2	-0.0998265162376275	0.151177241395142	-0.660327674432847	0.509357058748522	   
df.mm.exp2	0.0235737800237153	0.205409422220464	0.114764842668287	0.908679012325197	   
df.mm.exp3	-0.207113227964077	0.205409422220464	-1.00829468154477	0.313815784324821	   
df.mm.exp4	-0.179630217977718	0.205409422220464	-0.874498433596304	0.382279700049381	   
df.mm.exp5	-0.216361520570888	0.205409422220464	-1.05331838350954	0.292719567415012	   
df.mm.exp6	-0.294956794118845	0.205409422220464	-1.43594578539961	0.151662418488408	   
df.mm.exp7	-0.0465501060069752	0.205409422220464	-0.226621084387324	0.820813877577325	   
df.mm.exp8	-0.181257977971367	0.205409422220464	-0.882422899650746	0.377985296487309	   
df.mm.trans1:exp2	0.0447212609044236	0.187512290123752	0.238497758599764	0.8115957885367	   
df.mm.trans2:exp2	-0.0704657058992563	0.167716090933349	-0.420148749634641	0.674562863640034	   
df.mm.trans1:exp3	0.350813557978808	0.187512290123752	1.87088301117373	0.0619632215648167	.  
df.mm.trans2:exp3	0.147143628929879	0.167716090933349	0.87733757751577	0.380737681553004	   
df.mm.trans1:exp4	0.241530526486546	0.187512290123752	1.28807837783403	0.198332877319066	   
df.mm.trans2:exp4	0.136815474413422	0.167716090933349	0.815756399114939	0.415039915953473	   
df.mm.trans1:exp5	0.24559224884242	0.187512290123752	1.30973947723820	0.190904034934072	   
df.mm.trans2:exp5	0.125626217657551	0.167716090933349	0.749040935538353	0.454195654434687	   
df.mm.trans1:exp6	0.292818744696513	0.187512290123752	1.56159761316585	0.119035056066608	   
df.mm.trans2:exp6	0.250537741859573	0.167716090933349	1.4938205419964	0.135872803565579	   
df.mm.trans1:exp7	0.208846275725104	0.187512290123752	1.11377379897218	0.265927900549833	   
df.mm.trans2:exp7	0.07106369792234	0.167716090933349	0.423714251428512	0.671962068330514	   
df.mm.trans1:exp8	0.22477007843278	0.187512290123752	1.19869518037692	0.231231856820083	   
df.mm.trans2:exp8	0.143352902236548	0.167716090933349	0.854735532164993	0.393119581536099	   
df.mm.trans1:probe2	-0.0304762092202907	0.102704711110232	-0.29673623430556	0.766794924837994	   
df.mm.trans1:probe3	-0.0396468910606939	0.102704711110232	-0.386027969234451	0.699645231972626	   
df.mm.trans1:probe4	0.0753687790378771	0.102704711110232	0.733839550524459	0.463401124150042	   
df.mm.trans1:probe5	0.11811511722801	0.102704711110232	1.15004575691993	0.250691772490684	   
df.mm.trans1:probe6	-0.0743450249398155	0.102704711110232	-0.723871613445478	0.469493558145167	   
df.mm.trans1:probe7	0.0768129790007045	0.102704711110232	0.747901222547248	0.454882222160091	   
df.mm.trans1:probe8	0.0955429774237867	0.102704711110232	0.930268693528977	0.352694775855091	   
df.mm.trans1:probe9	-0.0480956559830765	0.102704711110232	-0.468290650576446	0.639787135450217	   
df.mm.trans1:probe10	-0.0858095286239457	0.102704711110232	-0.835497492727934	0.403849053274779	   
df.mm.trans1:probe11	-0.0290398342005461	0.102704711110232	-0.282750751028138	0.77748854201177	   
df.mm.trans1:probe12	-0.0627846412480077	0.102704711110232	-0.6113121839233	0.541279338206932	   
df.mm.trans2:probe2	0.0662788477836167	0.102704711110232	0.645334055927388	0.51901589169523	   
df.mm.trans2:probe3	-0.0124576138123492	0.102704711110232	-0.121295446700381	0.903507262705749	   
df.mm.trans2:probe4	0.0771125932433335	0.102704711110232	0.750818462072	0.453126035855735	   
df.mm.trans2:probe5	-0.0506378926326284	0.102704711110232	-0.493043523371379	0.622204987557573	   
df.mm.trans2:probe6	-0.0209439919526232	0.102704711110232	-0.203924354844289	0.83849815003906	   
df.mm.trans3:probe2	0.00318612335662646	0.102704711110232	0.0310221733957931	0.975264618024686	   
df.mm.trans3:probe3	-0.0385679639202848	0.102704711110232	-0.375522831458921	0.70743584949666	   
df.mm.trans3:probe4	0.0318890722548964	0.102704711110232	0.310492789572915	0.75631962652791