etma (version 1.1-1)

ETMA: Epistasis Test in Meta-Analysis (ETMA)

Description

This function is a Markov chain Monte Carlo (MCMC) based method, called "Epistasis Test in Meta-Analysis (ETMA)", using the genotype summary data for estimating a consistent estimate of epistasis in meta-analysis.

Usage

ETMA(case.x1.0, case.x1.1, ctrl.x1.0, ctrl.x1.1,
case.x2.0, case.x2.1, ctrl.x2.0, ctrl.x2.1,
data = NULL, sig.level = 0.05, max.step.EM = 20,
iterations.step1 = 20000, iterations.step2 = 200000, start.seed = NULL,
show.detailed.plot = TRUE, show.final.plot = TRUE,
show.p.matrix = FALSE, progress.bar = TRUE)

Arguments

case.x1.0

the number of wild type of SNP1 in case group.

case.x1.1

the number of mutation type of SNP1 in case group.

ctrl.x1.0

the number of wild type of SNP1 in control group.

ctrl.x1.1

the number of mutation type of SNP1 in control group.

case.x2.0

the number of wild type of SNP2 in case group.

case.x2.1

the number of mutation type of SNP2 in case group.

ctrl.x2.0

the number of wild type of SNP2 in control group.

ctrl.x2.1

the number of mutation type of SNP1 in control group.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is c

sig.level

the significance level used to calculate confidence intervals.

max.step.EM

the maximum number of iterations if convergence is too slow.

iterations.step1

the length of chain to obtain the study-level parameters [p(disease|base),p(SNP1=1),p(SNP2=1)] in step 1.

iterations.step2

the length of chain to obtain the global-level parameters [OR(SNP1),OR(SNP2),OR(interaction)] in step 2.

start.seed

the start seed of this algorithm (if you want your results can be reproduced). A NULL value means a random seed in this algorithm.

show.detailed.plot

a logical indicating whether showing the MCMC plot in each step.

show.final.plot

a logical indicating whether showing the MCMC plot in the last step.

show.p.matrix

a logical indicating whether a p.matrix should be printed.

progress.bar

a logical indicating whether a progress bar should be presented.

Value

• b
  the beta values of each SNP and interaction term (Sequence is SNP1, SNP2, and interaction).
• vcov
  the variance covariance matrix of beta value.
• LKK
  the log of likelihood value in the last step.
• se
  the stardard errors of each SNP and interaction term (Sequence is SNP1, SNP2, and interaction).
• df
  the degree of freedom in this analysis.
• OR
  the odds ratios of each SNP and interaction term (Sequence is SNP1, SNP2, and interaction).
• ci.l
  the lower bounds of confidence interval based on a specific significance level (please see the sig.level).
• ci.u
  the upper bounds of confidence interval based on a specific significance level (please see the sig.level).
• t
  the t value of each each SNP and interaction term (Sequence is SNP1, SNP2, and interaction).
• pval
  the p value of each each SNP and interaction term (Sequence is SNP1, SNP2, and interaction).
• sig.level
  the significance level for calculating the confidence interval.
• p.matrix
  the p matrix in iterations process.

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

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)

#Following examples are complete examples.
#They need 20,000/200,000 learning time in step 1/step 2, respectively (default).
#Please note they need more than 15 mins, and one of example need about 3 hrs.
#The complete learning time is necessary in real data analysis.
#Please use default setting as following to analysis your data.
#
#Example 1 (Note: the computing time in this example is about 3 hrs)
#
#data(data.GST)
#ggint1=ETMA(case.GSTM1.0,case.GSTM1.1,ctrl.GSTM1.0,ctrl.GSTM1.1,
#                  case.GSTT1.0,case.GSTT1.1,ctrl.GSTT1.0,ctrl.GSTT1.1,
#                  data=data.GST,start.seed=1,show.detailed.plot=TRUE,show.p.matrix=TRUE)
#print(ggint1)
#summary(ggint1)
#
#Example 2 (Note: the computing time in this example is about 15 mins)
#
#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)
#summary(ggint2)
#
#Example 3 (Note: the computing time in this example is about 15 mins)
#
#data(data.RAS)
#ggint3=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,start.seed=1,show.detailed.plot=TRUE,show.p.matrix=TRUE)
#print(ggint3)
#summary(ggint3)