Type: Package
Title: Epigenome-Wide Mediation Analysis Study
Version: 0.2.3
Date: 2025-09-05
Author: Xiuquan Nie [aut, cph, cre], Mengyi Wang [ctb, rev]
Maintainer: Xiuquan Nie <niexiuquan1995@foxmail.com>
Depends: R (≥ 4.1.0)
Imports: mediation, parallel, multilevel, ggplot2, qqman, lavaan, minfi, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19
Description: DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-09-04 15:35:33 UTC; niexi
Repository: CRAN
Date/Publication: 2025-09-04 16:20:08 UTC

An Emas results data.

Description

An Emas results data for 2000 CpGs. The data were fabricated.

Usage

E.result

Format

A data.frame produced by Emas contains 2000 obs of 13 variables.

Plotting the manhattan plot from the EMAS results

Description

Function to plot a manhattan plot from the Emas results.

Usage

EMAS.manhattan(E.result, type = "EPIC", ...)

Arguments

E.result

A data.frame produced by Emas.

type

A character string indicating the type of annotation, only "EPIC" and "450k" are available.

...

Other arguments passed to manhattan.

Details

This function can plot a manhattan plot from the Emas results according to the annotation from 450k or EPIC.

Value

No return value, called for side effects.

Author(s)

Xiuquan Nie, niexiuquan1995@foxmail.com

References

Turner, (2018). qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software, 3(25), 731. doi:10.21105/joss.00731.

Examples

data(E.result)
EMAS.manhattan(E.result, type = "EPIC",
               genomewideline = -log10(0.05/2000),
               suggestiveline = -log10(1/100), ylim=c(0,5))

Plotting the volcano plot from the EMAS results

Description

Function to plot a volcano plot from the Emas results.

Usage

EMAS.volcano(
  E.result,
  epiwideline = -log10(1e-07),
  suggestiveline = -log10(1e-05)
)

Arguments

E.result

A data.frame produced by Emas.

epiwideline

Where to draw a "epigenome-wide sigificant" line. Default -log10(1.0e-7).

suggestiveline

Where to draw a "suggestive" line. Default -log10(1.0e-5). Set to FALSE to disable.

Details

This function can plot a volcano plot from the Emas results.

Value

No return value, called for side effects.

Author(s)

Xiuquan Nie, niexiuquan1995@foxmail.com

Examples

data(E.result)
EMAS.volcano(E.result, 
             epiwideline = -log10(0.05/2000),
             suggestiveline = -log10(1/100))

Epigenome-Wide Mediation Analysis Study

Description

This function can perform the Epigenome-Wide Mediation Analysis Study (EMAS).

Usage

Emas(
  data,
  M.matrix,
  id = "",
  x = "",
  y = "",
  x.cov = c(),
  y.cov = c(),
  m.cov = c(),
  mem.sav = FALSE,
  p.th = 0.1,
  ini.sims = 100,
  boot = FALSE,
  cl.n = 1,
  ...
)

Arguments

data

A data.frame included id, x, y, x.cov, y.cov, m.cov.

M.matrix

A matrix with the epigenome-wide CpG information, maybe a M-value matrix or a beta value matrix.

id

Variable name of the id.

x, y

Variable name of exposure(x) and outcome(y).

x.cov

Variable names of covariates related to exposure(x).

y.cov

Variable names of covariates related to outcome(y).

m.cov

Variable names of covariates related to mediator(m).

mem.sav

A logical value. If 'TRUE', the memory required for the function will decrease, but the speed will also decrease.

p.th

Sobel indirect effects P-value threshold for subsequent nonparametric bootstrap or quasi-Bayesian approximation mediation analyses.

ini.sims

Initial number of Monte Carlo draws for nonparametric bootstrap or quasi-Bayesian approximation.

boot

A logical value. If 'FALSE' a quasi-Bayesian approximation is used for confidence intervals; if 'TRUE' nonparametric bootstrap will be used. Default is 'FALSE'.

cl.n

Number of cores used for parallel computing.

...

Other arguments passed to makeCluster.

Details

This function can perform the Epigenome-Wide Mediation Analysis Study (EMAS) to explore the potential mediating CpG sites of exposure variables affecting outcome variables within the epigenome-wide.

Value

Emas returns a data.frame with the average mediation effects(AME), average direct effects(ADE), total effects, mediation proportion.

AMEEst:

Point estimates for average mediation effects under the exposure conditions.

AMElow95, AMEupp95:

95 percentage confidence intervals for average mediation effects.

AME.P:

Two-sided p-values for average mediation effects.

ADEEst:

Point estimates for average direct effect under the exposure conditions.

ADElow95, ADEupp95:

95 percentage confidence intervals for average direct effects.

ADE.P:

Two-sided p-values for average direct effects.

TotEst:

Point estimate for total effect.

Totlow95, Totupp95:

95 percentage confidence interval for total effect.

Tot.P:

Two-sided p-values for total effect.

PropEst:

The "proportions mediated", or the size of the average mediation effects relative to the total effect.

Author(s)

Xiuquan Nie, niexiuquan1995@foxmail.com

References

Tingley, D., Yamamoto, T., Hirose, K., Imai, K. and Keele, L. (2014). mediation: R package for Causal Mediation Analysis. Journal of Statistical Software, 59(5), 1–38. doi:10.18637/jss.v059.i05.

Examples

data(data.m)
data(Mvalue)
E.result <- Emas(data.m, Mvalue, id = "ID", x = "x", y = "y",
                 x.cov = c("age", "gender"),
                 y.cov = c("age", "gender"),
                 m.cov = c("age", "gender", "CD8T", "CD4T"), 
                 p.th = 0.1, ini.sims = 100, boot = FALSE, cl.n = 1)

Epigenome-Wide Mediation Analysis Study: Parallel multiple mediation model

Description

This function can perform the parallel multiple mediation model after the Epigenome-Wide Mediation Analysis Study (EMAS).

Usage

Emas.parallel(
  data,
  M.matrix,
  id = "",
  x = "",
  y = "",
  x.cov = c(),
  y.cov = c(),
  m.cov = c(),
  m.cor = TRUE,
  boot = FALSE,
  lavaan = FALSE,
  ...
)

Arguments

data

A data.frame included id, x, y, x.cov, y.cov, m.cov.

M.matrix

A matrix with the CpG information screened from EMAS., maybe a M-value matrix or a beta value matrix.

id

Variable name of the id.

x, y

Variable name of exposure(x) and outcome(y).

x.cov

Variable names of covariates related to exposure(x).

y.cov

Variable names of covariates related to outcome(y).

m.cov

Variable names of covariates related to mediator(m).

m.cor

A logical value. If 'TRUE', the mediators in the parallel multiple mediation model are set to correlate with each other.

boot

A logical value or a numeric value. If a numeric value, the number for bootstrap.

lavaan

A logical value. If 'TRUE', a lavaan object will be given.

...

Other arguments passed to sem.

Details

This function can perform the parallel multiple mediation model after the Epigenome-Wide Mediation Analysis Study (EMAS) to further explore the potential parallel mediating CpG sites of exposure variables affecting outcome variables.

Value

Emas.parallel returns a data.frame with the average mediation effects(AME), average direct effects(ADE), and total effects(Tot). If lavaan is 'TRUE', a lavaan object will be given.

Author(s)

Xiuquan Nie, niexiuquan1995@foxmail.com

References

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. doi:10.18637/jss.v048.i02.

Examples

data(data.m)
data(Mvalue)
EP.result <- Emas.parallel(data.m, Mvalue, 
                           id = "ID", x = "x", y = "y", 
                           x.cov = c("age", "gender"), 
                           y.cov = c("age", "gender"), 
                           m.cov = c("age", "gender", "CD8T", "CD4T"), 
                           m.cor = TRUE, boot = FALSE, lavaan = FALSE)

A M-value matrix for 221 participants

Description

A M-value matrix for 221 participants. The data were fabricated.

Usage

Mvalue

Format

A matrix contains 221 obs of 10 CpGs.

A data for 221 participants

Description

A data for 221 participants. The data were fabricated.

Usage

data.m

Format

A data.frame contains 221 obs of 7 variables. The variables are:

ID

The ID of these 221 people.

age

A numeric.

gender

A two level factor: "1" or "2"

CD8T

A numeric.

CD4T

A numeric.

x

A numeric.

y

A numeric.