etma (version 1.1-1)

etma-package: \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}etmaEpistasis Test in Meta-Analysis

Description

\Sexpr[results=rd,stage=build]{tools:::Rd_package_description("#1")}etmaTraditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).

References

Lin C, Chu CM, Su SL (2016) Epistasis Test in Meta-Analysis: A Multi-Parameter Markov Chain Monte Carlo Model for Consistency of Evidence. PLoS ONE 11(4): e0152891. doi:10.1371/journal.pone.0152891

See Also

ETMA, data.GST, data.PAH, data.RAS

Examples

#Detailed tutorial is shown in website <http://rpubs.com/chinlin/ETMA>
#The simple toy example (just test this algorithm)
#Note: the computing time in this example is about 3-5 secs

data(data.RAS)
ggint.toy=ETMA(case.ACE.0,case.ACE.1,ctrl.ACE.0,ctrl.ACE.1,
                  case.AGT.0,case.AGT.1,ctrl.AGT.0,ctrl.AGT.1,
                  data=data.RAS,iterations.step1=100,iterations.step2=300,
                  start.seed=1,show.detailed.plot=FALSE,show.final.plot=FALSE)
print(ggint.toy)
summary(ggint.toy)

#The fastest complete example (Note: the computing time in this example is about 15 mins)
#Other examples can refer the help(ETMA)
#Note: the complete example need about 20,000/200,000 learning time in step 1/2, respectively.
#
#data(data.PAH)
#ggint2=ETMA(case.CYP1A1.0,case.CYP1A1.1,ctrl.CYP1A1.0,ctrl.CYP1A1.1,
#                  case.GSTM1.0,case.GSTM1.1,ctrl.GSTM1.0,ctrl.GSTM1.1,
#                  data=data.PAH,start.seed=1,show.detailed.plot=TRUE,show.p.matrix=TRUE)
#
#print(ggint2)
#
#Epistasis Test in Meta-Analysis (ETMA)
#A MCMC algorithm for detecting gene-gene interaction in meta-analysis.
#
#This analysis include 13 studies. (df = 10)
#
#                      b      se    OR 95%ci.l 95%ci.u t value p value
#SNP1(mutation) -0.19967 0.14580 0.819   0.592   1.133 -1.3695  0.2008
#SNP2(mutation) -0.01963 0.14025 0.981   0.717   1.340 -0.1400  0.8915
#Interaction     0.79747 0.28886 2.220   1.166   4.225  2.7608  0.0201
#
#summary(ggint2)
#
#Epistasis Test in Meta-Analysis (ETMA)
#A MCMC algorithm for detecting gene-gene interaction in meta-analysis.
#
#This analysis include 13 studies. (df = 10)
#
#                      b      se    OR 95%ci.l 95%ci.u t value p value
#SNP1(mutation) -0.19967 0.14580 0.819   0.592   1.133 -1.3695  0.2008
#SNP2(mutation) -0.01963 0.14025 0.981   0.717   1.340 -0.1400  0.8915
#Interaction     0.79747 0.28886 2.220   1.166   4.225  2.7608  0.0201
#
#                                    OR 95%ci.l 95%ci.u t value p value
#SNP1(wild type) & SNP2(mutation) 0.981   0.717   1.340 -0.1400  0.8915
#SNP1(mutation) & SNP2(wild type) 0.819   0.592   1.133 -1.3695  0.2008
#SNP1(mutation) & SNP2(mutation)  1.783   1.506   2.110  7.6478 <0.0001