chr9.23930_chr9_109415714_109416307_-_2.R 

fitVsDatCorrelation=0.793373413058116
cont.fitVsDatCorrelation=0.237512850217154

fstatistic=8646.6316876202,52,692
cont.fstatistic=3387.55842131058,52,692

residuals=-0.473139583557604,-0.0846766177982776,-0.00327711707761482,0.072770458127476,1.30293196030144
cont.residuals=-0.562614091736082,-0.17748693729,-0.0308015676191536,0.127240585072607,1.67779159541547

predictedValues:
Include	Exclude	Both
chr9.23930_chr9_109415714_109416307_-_2.R.tl.Lung	59.3441754865521	58.9727963684026	76.3884062803421
chr9.23930_chr9_109415714_109416307_-_2.R.tl.cerebhem	69.8790857392567	75.6090475786832	74.6744652034269
chr9.23930_chr9_109415714_109416307_-_2.R.tl.cortex	63.1453523507612	54.8414006390086	96.397042664369
chr9.23930_chr9_109415714_109416307_-_2.R.tl.heart	61.2614883887499	56.4351963992635	90.1654555829713
chr9.23930_chr9_109415714_109416307_-_2.R.tl.kidney	60.1373908320365	53.2923655146115	85.3160183152676
chr9.23930_chr9_109415714_109416307_-_2.R.tl.liver	56.9147649834476	56.5047953763982	72.0346725925908
chr9.23930_chr9_109415714_109416307_-_2.R.tl.stomach	59.4278288115436	55.9163546023851	73.3462186290653
chr9.23930_chr9_109415714_109416307_-_2.R.tl.testicle	59.7553409171979	59.3930161772053	76.9685656800658


diffExp=0.371379118149562,-5.72996183942658,8.30395171175256,4.82629198948642,6.84502531742499,0.409969607049362,3.51147420915848,0.362324739992573
diffExpScore=1.52561229156868
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	63.264835833342	57.7039711527292	63.892409172058
cerebhem	63.8066920119469	66.1109266882647	61.1179780713262
cortex	61.7773080799822	65.3013332988193	56.8666916968466
heart	64.5598129095471	64.2847081626123	60.7399645037332
kidney	61.9348085839271	62.100304525844	66.1199285393474
liver	61.3905405994642	61.1934664982276	61.052669895613
stomach	59.7571327645625	61.7466414848774	59.4691161552994
testicle	63.593008637419	60.6173087295827	58.8059913697898
cont.diffExp=5.5608646806128,-2.30423467631781,-3.52402521883710,0.275104746934815,-0.165495941916930,0.197074101236595,-1.98950872031489,2.97569990783626
cont.diffExpScore=8.3891311670628

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=0.815839777066278
cont.tran.correlation=0.145144693049313

tran.covariance=0.00526329499248748
cont.tran.covariance=0.000148897907203323

tran.mean=60.051900010344
cont.tran.mean=62.4464249975718

weightedLogRatios:
wLogRatio
Lung	0.0256144430555221
cerebhem	-0.337791226463692
cortex	0.574541823933426
heart	0.334315883862164
kidney	0.487730691929404
liver	0.0291914365092454
stomach	0.246930610002305
testicle	0.0248581626977744

cont.weightedLogRatios:
wLogRatio
Lung	0.377337185093209
cerebhem	-0.148062340532393
cortex	-0.230297504702757
heart	0.0177879309604967
kidney	-0.0110141570442933
liver	0.0132331837924836
stomach	-0.134497456156927
testicle	0.19785237587079

varWeightedLogRatios=0.0881480912092959
cont.varWeightedLogRatios=0.0391918925599219

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.72018213466085	0.089720343164586	41.4641986805203	9.41818445674972e-190	***
df.mm.trans1	0.34916085887254	0.0805819887769014	4.33298884989329	1.68902972882401e-05	***
df.mm.trans2	0.353132837944413	0.0741060827180171	4.76523417501539	2.29980212681820e-06	***
df.mm.exp2	0.434604849929243	0.101506980899301	4.2815267095806	2.11833544347532e-05	***
df.mm.exp3	-0.243190181974782	0.101506980899301	-2.39579760741813	0.0168488876344516	*  
df.mm.exp4	-0.178001269575017	0.101506980899301	-1.75358648240757	0.0799440896638493	.  
df.mm.exp5	-0.198536650872066	0.101506980899301	-1.95589159595854	0.0508798981773453	.  
df.mm.exp6	-0.0258665634289878	0.101506980899301	-0.254825463232411	0.798933618218604	   
df.mm.exp7	-0.0111707318969507	0.101506980899301	-0.110048903021090	0.912402497869832	   
df.mm.exp8	0.00643881812916606	0.101506980899301	0.0634322691121476	0.94944060984428	   
df.mm.trans1:exp2	-0.271192425895012	0.0971855351394952	-2.79046079754313	0.0054082622922896	** 
df.mm.trans2:exp2	-0.186105156012453	0.0845891507494172	-2.20010668464756	0.0281291147682441	*  
df.mm.trans1:exp3	0.305275453233887	0.0971855351394952	3.14116141662142	0.00175449628666740	** 
df.mm.trans2:exp3	0.170559317731376	0.0845891507494172	2.01632616263795	0.0441517649518684	*  
df.mm.trans1:exp4	0.209798689092482	0.0971855351394952	2.15874398171855	0.0312131472872927	*  
df.mm.trans2:exp4	0.134018023998671	0.0845891507494172	1.58434057809233	0.113573099584576	   
df.mm.trans1:exp5	0.211814464740240	0.0971855351394952	2.17948550096691	0.0296320883921175	*  
df.mm.trans2:exp5	0.0972534765862892	0.0845891507494172	1.14971572269815	0.250658163078993	   
df.mm.trans1:exp6	-0.0159326169071967	0.0971855351394952	-0.163940208636273	0.86982608316068	   
df.mm.trans2:exp6	-0.0168841871763418	0.0845891507494172	-0.199602277913378	0.84185028344724	   
df.mm.trans1:exp7	0.0125793692154550	0.0971855351394952	0.129436640930146	0.897049768522124	   
df.mm.trans2:exp7	-0.0420486208909709	0.0845891507494172	-0.497092363718531	0.619281810741792	   
df.mm.trans1:exp8	0.000465778573370613	0.0971855351394952	0.00479267385524047	0.996177395431059	   
df.mm.trans2:exp8	0.000661569478200783	0.0845891507494172	0.00782097316664858	0.993762083958565	   
df.mm.trans1:probe2	-0.184024870557952	0.0485927675697476	-3.78708354682230	0.000165671437023945	***
df.mm.trans1:probe3	0.136885829585661	0.0485927675697476	2.81700006876913	0.0049856383669929	** 
df.mm.trans1:probe4	-0.23239211197118	0.0485927675697476	-4.78244240025259	2.11713927208577e-06	***
df.mm.trans1:probe5	-0.201947737222242	0.0485927675697476	-4.15592170033073	3.64589005132383e-05	***
df.mm.trans1:probe6	-0.085430015758905	0.0485927675697476	-1.75808088387399	0.079175803785718	.  
df.mm.trans1:probe7	-0.175268724101731	0.0485927675697476	-3.60688910855219	0.000332187780545918	***
df.mm.trans1:probe8	-0.286834213906054	0.0485927675697476	-5.90281698802907	5.59693565836813e-09	***
df.mm.trans1:probe9	0.260065308420087	0.0485927675697476	5.35193448380568	1.18461587719344e-07	***
df.mm.trans1:probe10	-0.0731859450662765	0.0485927675697476	-1.50610777542623	0.132495828160299	   
df.mm.trans1:probe11	0.118886793618065	0.0485927675697476	2.44659441237671	0.0146690947403142	*  
df.mm.trans1:probe12	0.0439306950820448	0.0485927675697476	0.904058304952252	0.366279099240786	   
df.mm.trans1:probe13	0.251240677873807	0.0485927675697476	5.17033069814739	3.06229917146402e-07	***
df.mm.trans1:probe14	-0.055767300656715	0.0485927675697476	-1.14764610961229	0.251511288159335	   
df.mm.trans1:probe15	0.073398652493733	0.0485927675697476	1.51048512288131	0.131376154988472	   
df.mm.trans1:probe16	-0.131224651940241	0.0485927675697476	-2.70049759466546	0.00709289468683647	** 
df.mm.trans1:probe17	0.112322523833429	0.0485927675697476	2.31150702976954	0.0210977335489793	*  
df.mm.trans1:probe18	0.145738448012979	0.0485927675697476	2.99917982246625	0.00280421287531493	** 
df.mm.trans1:probe19	0.125363978309835	0.0485927675697476	2.57988965394684	0.0100882586528563	*  
df.mm.trans1:probe20	0.140283536612042	0.0485927675697476	2.88692214146243	0.00401160960675267	** 
df.mm.trans1:probe21	0.250908582820387	0.0485927675697476	5.16349644955385	3.17197879655171e-07	***
df.mm.trans1:probe22	0.117325152428557	0.0485927675697476	2.41445709508425	0.0160171085660308	*  
df.mm.trans2:probe2	-0.0878604556174486	0.0485927675697476	-1.8080973776877	0.0710253637058732	.  
df.mm.trans2:probe3	0.0394452149215752	0.0485927675697476	0.811750737698928	0.417213804317464	   
df.mm.trans2:probe4	0.00807793875863977	0.0485927675697476	0.166237470361101	0.868018630589598	   
df.mm.trans2:probe5	0.00177531041626131	0.0485927675697476	0.0365344578020406	0.9708667399707	   
df.mm.trans2:probe6	0.0724135697457838	0.0485927675697476	1.49021291371900	0.136623902788372	   
df.mm.trans3:probe2	0.311660026533366	0.0485927675697476	6.41371220698688	2.63131621421495e-10	***
df.mm.trans3:probe3	-0.571594717766971	0.0485927675697476	-11.7629586943475	2.99914948223247e-29	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04549437702608	0.143170514144727	28.2564772585548	2.12705266680557e-117	***
df.mm.trans1	0.0730511187831008	0.128588058817718	0.568101886401874	0.570150071445232	   
df.mm.trans2	0.0328056757375688	0.118254183942735	0.277416617694095	0.781543076648068	   
df.mm.exp2	0.188930958929829	0.161978946268322	1.16639207305905	0.243857777070856	   
df.mm.exp3	0.216383689456344	0.161978946268322	1.33587539887992	0.182029163142425	   
df.mm.exp4	0.178856941599684	0.161978946268322	1.10419869816539	0.269890754091992	   
df.mm.exp5	0.0179079659617262	0.161978946268322	0.110557367943740	0.911999411253187	   
df.mm.exp6	0.0741041641179125	0.161978946268322	0.45749256817091	0.647460641180308	   
df.mm.exp7	0.0824159320492004	0.161978946268322	0.508806446441974	0.611050080777437	   
df.mm.exp8	0.137385176365932	0.161978946268322	0.848166873109237	0.396638319889878	   
df.mm.trans1:exp2	-0.180402542343287	0.155083034043099	-1.16326420524602	0.245123282519739	   
df.mm.trans2:exp2	-0.0529229155967859	0.134982455223602	-0.392072551274295	0.695125326076829	   
df.mm.trans1:exp3	-0.240177234325723	0.155083034043099	-1.54870089953860	0.121910827298739	   
df.mm.trans2:exp3	-0.0926972306132441	0.134982455223602	-0.68673540172083	0.492479537672119	   
df.mm.trans1:exp4	-0.158594474306583	0.155083034043099	-1.02264232374064	0.306834353246242	   
df.mm.trans2:exp4	-0.0708611541001171	0.134982455223602	-0.524965663002158	0.599775201748172	   
df.mm.trans1:exp5	-0.0391552672894615	0.155083034043099	-0.25247937358886	0.800745529702527	   
df.mm.trans2:exp5	0.0555169314802919	0.134982455223602	0.411289981267023	0.680987257458746	   
df.mm.trans1:exp6	-0.104178061464514	0.155083034043099	-0.671756663179301	0.501962816717548	   
df.mm.trans2:exp6	-0.0153897321466316	0.134982455223602	-0.114012833157747	0.909260690433231	   
df.mm.trans1:exp7	-0.139457030282631	0.155083034043099	-0.899241049435974	0.368837077033124	   
df.mm.trans2:exp7	-0.0147023422123144	0.134982455223602	-0.108920394046468	0.913297206237363	   
df.mm.trans1:exp8	-0.132211297879855	0.155083034043099	-0.85251941771472	0.394220823052039	   
df.mm.trans2:exp8	-0.0881306967558596	0.134982455223602	-0.652904828333941	0.514034481456255	   
df.mm.trans1:probe2	0.0840216015153951	0.0775415170215494	1.08356922514225	0.278933234136383	   
df.mm.trans1:probe3	0.0464202290527884	0.0775415170215495	0.59864999855352	0.549602209692446	   
df.mm.trans1:probe4	0.0410577409139767	0.0775415170215495	0.529493650511976	0.596632867585542	   
df.mm.trans1:probe5	0.0136038172994321	0.0775415170215494	0.175439143080622	0.860785907607026	   
df.mm.trans1:probe6	-0.0627369724980302	0.0775415170215494	-0.809075897761906	0.418749576581971	   
df.mm.trans1:probe7	-0.0137599687600894	0.0775415170215494	-0.177452921849148	0.859204569474788	   
df.mm.trans1:probe8	0.0330877846003901	0.0775415170215495	0.426710565788836	0.669722870383426	   
df.mm.trans1:probe9	0.0739684828309558	0.0775415170215494	0.953921017696872	0.34045671520662	   
df.mm.trans1:probe10	-0.0444152267051189	0.0775415170215495	-0.572792852282933	0.566971018491033	   
df.mm.trans1:probe11	-0.0576428363214838	0.0775415170215495	-0.743380301748087	0.457503771439104	   
df.mm.trans1:probe12	-0.00421713658403703	0.0775415170215494	-0.0543855310809182	0.956643703402535	   
df.mm.trans1:probe13	0.0753344622468014	0.0775415170215494	0.97153712153794	0.331620336515783	   
df.mm.trans1:probe14	0.0305005877910739	0.0775415170215494	0.39334525506636	0.694185651860646	   
df.mm.trans1:probe15	0.0206648618648143	0.0775415170215494	0.266500613588349	0.78993307686822	   
df.mm.trans1:probe16	0.155197213990963	0.0775415170215494	2.001472500826	0.0457314680620194	*  
df.mm.trans1:probe17	0.150390986442377	0.0775415170215495	1.93948986580417	0.0528477889063933	.  
df.mm.trans1:probe18	0.0173680103389717	0.0775415170215494	0.223983370536134	0.82283635384552	   
df.mm.trans1:probe19	0.00539493846585259	0.0775415170215494	0.0695748377524429	0.94455216483693	   
df.mm.trans1:probe20	0.162907714590110	0.0775415170215495	2.10090956235531	0.0360100662048793	*  
df.mm.trans1:probe21	-0.0310489594613968	0.0775415170215495	-0.400417230072607	0.68897282019481	   
df.mm.trans1:probe22	0.0235067414807715	0.0775415170215495	0.303150394571707	0.761866362452106	   
df.mm.trans2:probe2	-0.0364163455365496	0.0775415170215495	-0.469636743454853	0.638762515658476	   
df.mm.trans2:probe3	-0.0611156714071747	0.0775415170215495	-0.788167084610818	0.430868881985857	   
df.mm.trans2:probe4	-0.00112079072389968	0.0775415170215495	-0.0144540726948662	0.988471886223548	   
df.mm.trans2:probe5	-0.0734811817682005	0.0775415170215495	-0.947636628617666	0.343645270409946	   
df.mm.trans2:probe6	-0.0346325278882406	0.0775415170215495	-0.44663206522792	0.655280475506673	   
df.mm.trans3:probe2	0.0434010804638777	0.0775415170215495	0.559714100664502	0.575855582983668	   
df.mm.trans3:probe3	-0.0258529090214504	0.0775415170215495	-0.333407315390356	0.738927837355486