Description Usage Arguments Value Author(s) References See Also Examples
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.
1 2 3 4 5 6 | 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)
|
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 called. |
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. |
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. |
Chin Lin <xup6fup@gmail.com>
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | #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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.