chr4.17278_chr4_150808193_150809369_-_0.R 

fitVsDatCorrelation=0.83172408561994
cont.fitVsDatCorrelation=0.243071146661008

fstatistic=7477.72998853622,43,485
cont.fstatistic=2442.04728712977,43,485

residuals=-0.456907704707823,-0.101825023628886,-0.0100814965160880,0.0825377675525814,0.989004823376046
cont.residuals=-0.640498945911058,-0.208846086841935,-0.0270293207275061,0.155399641931171,1.34319221828103

predictedValues:
Include	Exclude	Both
chr4.17278_chr4_150808193_150809369_-_0.R.tl.Lung	76.8718659975345	92.614017319219	74.8230893229775
chr4.17278_chr4_150808193_150809369_-_0.R.tl.cerebhem	70.1382998810008	114.109507888701	79.2144158851352
chr4.17278_chr4_150808193_150809369_-_0.R.tl.cortex	70.7207780107668	83.8912477333829	59.0251674342072
chr4.17278_chr4_150808193_150809369_-_0.R.tl.heart	74.6437237929452	101.136868468903	59.3871721281598
chr4.17278_chr4_150808193_150809369_-_0.R.tl.kidney	85.799316216314	84.9716767945248	80.2081257520723
chr4.17278_chr4_150808193_150809369_-_0.R.tl.liver	75.9498509515478	78.4850312907322	64.8705383609738
chr4.17278_chr4_150808193_150809369_-_0.R.tl.stomach	80.6138057068209	106.871749673441	70.8054239177411
chr4.17278_chr4_150808193_150809369_-_0.R.tl.testicle	76.1632153408067	87.0653234192777	66.4708168028198


diffExp=-15.7421513216846,-43.9712080077004,-13.1704697226161,-26.4931446759577,0.827639421789243,-2.53518033918439,-26.2579439666205,-10.9021080784711
diffExpScore=1.00470595628363
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,-1,0,-1,0,0,-1,0
diffExp1.3Score=0.75
diffExp1.2=-1,-1,0,-1,0,0,-1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	71.086794812709	78.1935680955851	87.0436106497547
cerebhem	69.7661509806992	73.551078807611	74.6488122007989
cortex	76.7865735904518	85.1070728185354	77.9319895847427
heart	70.710865078213	82.4172042061355	77.0939913899073
kidney	70.1979115900188	81.6645739737683	71.1159274301899
liver	77.5448603720927	71.6668829106248	71.2469349522557
stomach	72.3343765704899	75.8481419926666	77.3947357663136
testicle	73.14476550068	72.5045992141917	79.230233444115
cont.diffExp=-7.10677328287612,-3.7849278269118,-8.32049922808359,-11.7063391279225,-11.4666623837495,5.87797746146794,-3.51376542217668,0.640166286488267
cont.diffExpScore=1.2980693914979

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=-0.233143001116965
cont.tran.correlation=-0.0813888897995449

tran.covariance=-0.00190342319521917
cont.tran.covariance=-0.000258945324294094

tran.mean=85.00289240537
cont.tran.mean=75.1578387821545

weightedLogRatios:
wLogRatio
Lung	-0.826296997775803
cerebhem	-2.18709233617222
cortex	-0.741898708314547
heart	-1.35611478327400
kidney	0.043106588751153
liver	-0.142715522723044
stomach	-1.27746014983171
testicle	-0.588601370353063

cont.weightedLogRatios:
wLogRatio
Lung	-0.410828975618805
cerebhem	-0.225671732476105
cortex	-0.45189881368535
heart	-0.664130307124787
kidney	-0.654677984392758
liver	0.339861781336966
stomach	-0.204202949890873
testicle	0.0376943587305966

varWeightedLogRatios=0.513651677790083
cont.varWeightedLogRatios=0.118350782018579

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.56931575945598	0.0882442716018464	51.780310228776	1.01529965510322e-199	***
df.mm.trans1	-0.230427585195764	0.0706441541994545	-3.26180683747989	0.00118502966860337	** 
df.mm.trans2	-0.0244379273496099	0.0706441541994545	-0.345929930459817	0.729545379804753	   
df.mm.exp2	0.0600153425251687	0.0945973352349274	0.634429525695663	0.526099678800537	   
df.mm.exp3	0.0548428453316116	0.0945973352349274	0.579750425267397	0.562352140245669	   
df.mm.exp4	0.289668987782788	0.0945973352349273	3.06212629630011	0.00231976376508512	** 
df.mm.exp5	-0.0457497493898782	0.0945973352349274	-0.483626195983863	0.628869453037684	   
df.mm.exp6	-0.0348663220497528	0.0945973352349274	-0.368576154530825	0.71260450003045	   
df.mm.exp7	0.245909884358226	0.0945973352349274	2.59954346227113	0.00961928643438118	** 
df.mm.exp8	0.0473203375878394	0.0945973352349274	0.500229075907074	0.617140680538558	   
df.mm.trans1:exp2	-0.151686294827224	0.0742082551225805	-2.04406227550644	0.0414869656178341	*  
df.mm.trans2:exp2	0.148702735173951	0.0742082551225805	2.00385704970852	0.0456410266553177	*  
df.mm.trans1:exp3	-0.138243383563189	0.0742082551225805	-1.86291111864620	0.0630790763691764	.  
df.mm.trans2:exp3	-0.153762060271717	0.0742082551225805	-2.07203443899505	0.038790148372925	*  
df.mm.trans1:exp4	-0.319082500227267	0.0742082551225805	-4.29982486045781	2.06740805291071e-05	***
df.mm.trans2:exp4	-0.201634760084261	0.0742082551225805	-2.71714730054213	0.00682020383262776	** 
df.mm.trans1:exp5	0.155620828561291	0.0742082551225805	2.09708243785316	0.0365034767478906	*  
df.mm.trans2:exp5	-0.040372768963723	0.0742082551225805	-0.54404687048676	0.586659229981786	   
df.mm.trans1:exp6	0.0227996308507574	0.0742082551225805	0.307238471152515	0.758793742145998	   
df.mm.trans2:exp6	-0.130666260672596	0.0742082551225805	-1.76080491930117	0.078901600756336	.  
df.mm.trans1:exp7	-0.198379920612329	0.0742082551225805	-2.67328641920816	0.00776442775478851	** 
df.mm.trans2:exp7	-0.102720875109688	0.0742082551225805	-1.38422436883888	0.166926343857148	   
df.mm.trans1:exp8	-0.056581687544431	0.0742082551225805	-0.762471607113884	0.446149238502472	   
df.mm.trans2:exp8	-0.109102161909447	0.0742082551225805	-1.47021597164934	0.142151687380141	   
df.mm.trans1:probe2	-0.165466243377040	0.0508069191046695	-3.25676593450108	0.00120581129523779	** 
df.mm.trans1:probe3	-0.373488047152917	0.0508069191046695	-7.35112566820827	8.41592860323733e-13	***
df.mm.trans1:probe4	0.504133639531826	0.0508069191046695	9.92253906388692	2.9936649643922e-21	***
df.mm.trans1:probe5	-0.218471894980108	0.0508069191046695	-4.300042175949	2.06545644378891e-05	***
df.mm.trans1:probe6	0.305321082357493	0.0508069191046695	6.0094390240134	3.66050571373978e-09	***
df.mm.trans2:probe2	-0.234749393569495	0.0508069191046695	-4.62042173991849	4.91443071681859e-06	***
df.mm.trans2:probe3	0.189640119341008	0.0508069191046695	3.73256482941471	0.000212005922798882	***
df.mm.trans2:probe4	-0.068978930808141	0.0508069191046695	-1.35766805040933	0.175200379362096	   
df.mm.trans2:probe5	-0.197748464770808	0.0508069191046695	-3.89215619162851	0.000113256283984103	***
df.mm.trans2:probe6	0.0488394382443404	0.0508069191046695	0.961275336214033	0.336892739133718	   
df.mm.trans3:probe2	-0.0825025682960138	0.0508069191046695	-1.62384513270814	0.105058575026046	   
df.mm.trans3:probe3	-0.0272376470790096	0.0508069191046695	-0.536101136597087	0.592134435159934	   
df.mm.trans3:probe4	0.0519564338762261	0.0508069191046695	1.02262516192309	0.306994906253668	   
df.mm.trans3:probe5	-0.0804943543349526	0.0508069191046695	-1.58431874542762	0.113772998455222	   
df.mm.trans3:probe6	0.103695320363856	0.0508069191046695	2.04096847813640	0.0417947970111485	*  
df.mm.trans3:probe7	0.256565291461899	0.0508069191046695	5.04981006491139	6.27030721321853e-07	***
df.mm.trans3:probe8	-0.132722780700093	0.0508069191046695	-2.61229736104771	0.00927256828827028	** 
df.mm.trans3:probe9	-0.250367214421622	0.0508069191046695	-4.9278172901181	1.14309323734357e-06	***
df.mm.trans3:probe10	0.167870231391542	0.0508069191046695	3.30408208861681	0.00102336836893107	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17680070110753	0.154177513844280	27.0908551899866	4.09531768225504e-99	***
df.mm.trans1	0.0658696598180863	0.123427162629284	0.533672316651458	0.593812756237457	   
df.mm.trans2	0.187449822733640	0.123427162629284	1.51870802779976	0.129487579454636	   
df.mm.exp2	0.0736547290609378	0.165277379461193	0.445643132176063	0.656053906259932	   
df.mm.exp3	0.272423705418907	0.165277379461193	1.64828185385691	0.0999424484592553	.  
df.mm.exp4	0.168688376448043	0.165277379461193	1.02063801469971	0.307934801893217	   
df.mm.exp5	0.232947809236475	0.165277379461193	1.40943551982666	0.159347296413729	   
df.mm.exp6	0.200053846758824	0.165277379461193	1.21041274620279	0.226710106475299	   
df.mm.exp7	0.104434209707775	0.165277379461193	0.631872371453567	0.527767999680107	   
df.mm.exp8	0.0470528799265555	0.165277379461193	0.284690379772166	0.776002878762246	   
df.mm.trans1:exp2	-0.0924073724790951	0.129654243542785	-0.712721542728381	0.476360830826218	   
df.mm.trans2:exp2	-0.134862009196027	0.129654243542785	-1.04016656540458	0.298780772296826	   
df.mm.trans1:exp3	-0.195295496183290	0.129654243542785	-1.50627924583774	0.132646511945322	   
df.mm.trans2:exp3	-0.187700956193524	0.129654243542785	-1.44770391669890	0.148345959242103	   
df.mm.trans1:exp4	-0.17399072932968	0.129654243542785	-1.34195938810336	0.180237061188259	   
df.mm.trans2:exp4	-0.116081567168104	0.129654243542785	-0.895316373735176	0.371062110964208	   
df.mm.trans1:exp5	-0.245530840670852	0.129654243542785	-1.89373547646227	0.058854961108133	.  
df.mm.trans2:exp5	-0.189514907250705	0.129654243542785	-1.46169459689275	0.144472517348013	   
df.mm.trans1:exp6	-0.113098826947622	0.129654243542785	-0.872311031688676	0.383470355021782	   
df.mm.trans2:exp6	-0.287212484697795	0.129654243542785	-2.21521854472134	0.0272088841720578	*  
df.mm.trans1:exp7	-0.0870363148996173	0.129654243542785	-0.671295535891162	0.502351866694435	   
df.mm.trans2:exp7	-0.134888394776698	0.129654243542785	-1.0403700726709	0.298686347457316	   
df.mm.trans1:exp8	-0.0185139060467988	0.129654243542785	-0.142794447300056	0.88651182525037	   
df.mm.trans2:exp8	-0.122590277392394	0.129654243542785	-0.945516892024747	0.344865857833651	   
df.mm.trans1:probe2	-0.00540055558069906	0.088768192330815	-0.0608388594934169	0.951512603759114	   
df.mm.trans1:probe3	0.0799367494336408	0.088768192330815	0.90051117787482	0.368295199998186	   
df.mm.trans1:probe4	0.0665917349754328	0.088768192330815	0.750175634164808	0.453512689914426	   
df.mm.trans1:probe5	0.120096784442881	0.088768192330815	1.35292587681985	0.176709639174493	   
df.mm.trans1:probe6	0.0784749934179268	0.088768192330815	0.884044063052132	0.377110447259905	   
df.mm.trans2:probe2	0.00887254652762043	0.088768192330815	0.0999518667064307	0.92042384014074	   
df.mm.trans2:probe3	-0.053659657677145	0.088768192330815	-0.604491949967506	0.545799296682769	   
df.mm.trans2:probe4	-0.0279567170096993	0.088768192330815	-0.314940704272902	0.752942125620052	   
df.mm.trans2:probe5	0.000284672211734891	0.088768192330815	0.00320691685005813	0.997442573552128	   
df.mm.trans2:probe6	-0.0085509094867653	0.088768192330815	-0.0963285300989163	0.923299442953524	   
df.mm.trans3:probe2	0.0286077028202155	0.088768192330815	0.322274252398903	0.747383805471161	   
df.mm.trans3:probe3	0.168847812020677	0.088768192330815	1.90212065366192	0.057747515192486	.  
df.mm.trans3:probe4	0.00173591460291231	0.088768192330815	0.0195555925758072	0.984405931338233	   
df.mm.trans3:probe5	-0.0607626286197804	0.088768192330815	-0.684509023157017	0.493980753814544	   
df.mm.trans3:probe6	0.0296929726438634	0.088768192330815	0.334500138666852	0.738146789305711	   
df.mm.trans3:probe7	0.0927418332078793	0.088768192330815	1.04476424237925	0.296652381638844	   
df.mm.trans3:probe8	-0.00358414118951866	0.088768192330815	-0.0403764129403642	0.967809649917792	   
df.mm.trans3:probe9	0.0274404721006524	0.088768192330815	0.309125052342952	0.757359153582652	   
df.mm.trans3:probe10	0.0781708747814238	0.088768192330815	0.880618076462593	0.378960730329559