Help for package rplec

Title:

Placental Epigenetic Clock to Estimate Aging by DNA Methylation

Version:

0.1.3

Description:

Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (https://www.synapse.org/Synapse:syn59520082/wiki/628063). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at https://herdiantrisufriyana.github.io/rplec/doc/placental_aging_analysis.html.

Depends:

R (≥ 4.4)

Imports:

doParallel, dplyr, foreach, ggplot2, parallel, pbapply, purrr, RPMM, stats, stringr, tibble, tidyr, utils

Suggests:

tidyverse, knitr, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.2

LazyData:

true

LazyDataCompression:

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-01-23 05:06:06 UTC; rstudio

Author:

Herdiantri Sufriyana

[aut, cre], Emily Chia-Yu Su

[aut]

Maintainer:

Herdiantri Sufriyana <herdi@nycu.edu.tw>

Repository:

CRAN

Date/Publication:

2025-01-27 18:30:05 UTC

Residual DNA-methylation-based (DNAm) gestational age

Description

A data frame of DNAm gestational age for both case and control groups.

Usage

aging

Format

A data frame vector with 10 rows and 1 column:

output: A numeric value of residual DNAm gestational age (weeks).

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Indices for random beta values

Description

A list of indices for rand.idx in bmiq_norm_450k function. The input probes must be already filtered and ordered the same way to the that when we developed our placental epigenetic clock. Run data(probe_info_450k) and find the required probes in prob_info_450k$probeID.

Usage

beta_v_indices

Format

A list of 2 elements where each has a length of nfit of 10000:

beta1.v: An integer indicating the selected indices.
beta2.v: An integer indicating the selected indices.

Source

Derived from ChAMP R package.

Normalize DNA methylation values

Description

This function normalize DNA methylation values from 450k probes. The probes are filtered and ordered the same way to the input when we developed our placental epigenetic clock.

Usage

bmiq_norm_450k(beta, cores = 1, verbose = FALSE)

Arguments

beta

A data frame of beta values where each column represents a sample and each row represent a probe. The rows must be named according to the probe IDs. They include all the required probes. Run data(probe_info_450k) and find the required probes in prob_info_450k$probeID. Meanwhile, the columns must be named according to the sample IDs.

cores

An integer indicating the number of threads.

verbose

A logical scalar indicating whether to show a progress bar.

Value

A data frame of normalized beta values.

Examples



  beta_values_case <- download_beta_values_case()
  norm_beta_values_case <- bmiq_norm_450k(beta_values_case)

Access Beta values at individual CpG sites for case group

Description

Downloads and loads beta values for the case group. Data contains beta values for 5 samples and 452,453 probes.

Usage

download_beta_values_case()

Value

A data frame with 452,453 rows and 5 columns:

GSM1931565: Beta values for sample GSM1931565.
GSM5114811: Beta values for sample GSM5114811.
GSM2589558: Beta values for sample GSM2589558.
GSM1842848: Beta values for sample GSM1842848.
GSM1843045: Beta values for sample GSM1843045.

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Examples


beta_values_case <- download_beta_values_case()
head(beta_values_case)

Access Beta values at individual CpG sites for control group

Description

Downloads and loads beta values for the control group. Data contains beta values for 5 samples and 452,453 probes.

Usage

download_beta_values_control()

Value

A data frame with 452,453 rows and 5 columns:

GSM7115144: Beta values for sample GSM7115144.
GSM1702248: Beta values for sample GSM1702248.
GSM3179749: Beta values for sample GSM3179749.
GSM4281756: Beta values for sample GSM4281756.
GSM5210472: Beta values for sample GSM5210472.

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Examples


beta_values_control <- download_beta_values_control()
head(beta_values_control)

Gestational age for both case and control groups

Description

A data frame of gestational age for 10 samples.

Usage

ga

Format

A data frame vector with 10 rows and 1 column:

GA: A numeric value of gestational age (weeks' gestation).

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Identify placental aging

Description

This function identifies placental aging based on the case-control aging difference. Placental aging is defined as the residual DNA-methylation-based (DNAm) gestational ages (GA). Only GA from 5 to 44 weeks' gestation are shown in the placental aging plot.

Usage

ipla(
  aging,
  ga,
  phenotype,
  case = "Case",
  control = "Control",
  method = NULL,
  from = NULL,
  to = NULL
)

Arguments

aging

A data frame of residual DNA-methylation-based GA. This data frame must be the output of plec function with argument type="residual". The rows must be named according to the sample IDs.

ga

A data frame of GA. There is only one column, i.e., GA, as shown in ga. Use data(ga) to load this data frame. The rows must be named according to the sample IDs.

phenotype

A data frame of phenotype (optional. There is only one column, i.e., phenotype, as shown in phenotype. Use data(phenotype) to load this data frame. The rows must be named according to the sample IDs.

case

A character of of case name in phenotype (default="Case").

control

A character of of case name in phenotype (default="Control").

method

A character of of the method of statistical test (optional), i.e., "Mann-Whitney U" or "Permutation".

from

An integer from 5 to 44 indicating minimum GA (weeks) to be included in the statistical test. If it is undefined, the minimum GA in either case or control is applied.

to

An integer from 5 to 44 indicating maximum GA (weeks) to be included in the statistical test. If it is undefined, the maximum GA in either case or control is applied.

Value

An ggplot object consisting the aging plot without or with statistical test results.

Examples


# Prepare data
data(aging)
data(ga)
data(phenotype)

# Identify placental aging
set.seed(1)
ipla(aging, ga, phenotype)

## Conduct statistical test
set.seed(1)
ipla(aging, ga, phenotype, method = "Mann-Whitney U")

## Conduct statistical test for a specific range of GA
set.seed(1)
ipla(aging, ga, phenotype, method = "Mann-Whitney U", from = 5, to = 20)

Phenotype for both case and control groups

Description

A data frame of phenotype for 10 samples.

Usage

phenotype

Format

A data frame vector with 10 rows and 1 column:

phenotype: A character value of phenotype (case/control).

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Estimate DNA-methylation-based gestational age

Description

This function estimate gestational age (GA) using BMIQ-normalized beta values. The estimated GA is a sum of normal and residual GAs. The latter is a sum of condition- and trimester-specific, residual GAs.

Usage

plec(norm_beta, type = "stack", verbose = FALSE)

Arguments

norm_beta

A data frame of normalized beta values where each column represents a sample and each row represent a probe. This data frame must be the output of bmiq_norm_450k function. The rows must be named according to the probe IDs. Meanwhile, the columns must be named according to the sample IDs.

type

An character indicating the type of outputs which are primarily: (1) "stack" (default) for the estimated GA; (2) "normal" for the estimated normal GA; (3) "residual" for the estimated residual GA; (4) "condition" for the condition-specific, estimated residual GA; and (5) "trimester" for the trimester-specific, estimated residual GA. In addition, a user can obtain the output of a single submodel using the column name (except predictor) in plec_int_coef. Use data(plec_int_coef) to load this data frame.

verbose

A logical scalar indicating whether to show a progress bar.

Value

A data frame of the estimated GA.

Examples



  beta_values_case <- download_beta_values_case()
  norm_beta_values_case <- bmiq_norm_450k(beta_values_case)
  dnam_ga_case <- plec(norm_beta_values_case)

Intercept and coefficients of placental epigenetic clock

Description

A data frame of intercept and coefficients for all submodels in our placental epigenetic_clock.

Usage

plec_int_coef

Format

A data frame with 10,447 rows and 32 columns:

predictor: A character value of predictor name.
ga_est: Estimate normal GA.
fgr_pred: Predict FGR.
pe_pred: Predict PE.
pe_onset_pred: Predict PE onset.
preterm_pred: Predict preterm delivery.
anencephaly_pred: Predict anencephaly.
spina_bifida_pred: Predict spina bifida.
gdm_pred: Predict GDM.
diandric_triploid_pred: Predict diandric triploid.
miscarriage_pred: Predict miscarriage.
lga_pred: Predict LGA.
subfertility_pred: Predict subfertility.
hellp_pred: Predict HELLP.
chorioamnionitis_pred: Predict chorioamnionitis.
ga_res_conds_fgr_est: Estimate GA in FGR.
ga_res_conds_pe_est: Estimate GA in PE.
ga_res_conds_pe_onset_est: Estimate GA in EOPE.
ga_res_conds_preterm_est: Estimate GA in preterm delivery.
ga_res_conds_anencephaly_est: Estimate GA in anencephaly.
ga_res_conds_spina_bifida_est: Estimate GA in spina bifida.
ga_res_conds_gdm_est: Estimate GA in GDM.
ga_res_conds_diandric_triploid_est: Estimate GA in diandric triploid.
ga_res_conds_miscarriage_est: Estimate GA in miscarriage.
ga_res_conds_lga_est: Estimate GA in LGA.
ga_res_conds_subfertility_est: Estimate GA in subfertility.
ga_res_conds_hellp_est: Estimate GA in HELLP.
ga_res_conds_chorioamnionitis_est: Estimate GA in chorioamnionitis.
ga_res_conds_pred_est: Estimate residual GA.
ga_res_comb_pr_est: Estimate residual GA for preterm.
ga_res_comb_tb_est: Estimate residual GA for term before the date.
ga_res_comb_ta_est: Estimate residual GA for term after the date.

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Mean values of the scalers of placental epigenetic clock

Description

A data frame of mean values of the scalers for all submodels in our placental epigenetic_clock.

Usage

plec_scaler_mean

Format

A data frame with 10,446 rows and 32 columns:

predictor: A character value of predictor name.
ga_est: Estimate normal GA.
fgr_pred: Predict FGR.
pe_pred: Predict PE.
pe_onset_pred: Predict PE onset.
preterm_pred: Predict preterm delivery.
anencephaly_pred: Predict anencephaly.
spina_bifida_pred: Predict spina bifida.
gdm_pred: Predict GDM.
diandric_triploid_pred: Predict diandric triploid.
miscarriage_pred: Predict miscarriage.
lga_pred: Predict LGA.
subfertility_pred: Predict subfertility.
hellp_pred: Predict HELLP.
chorioamnionitis_pred: Predict chorioamnionitis.
ga_res_conds_fgr_est: Estimate GA in FGR.
ga_res_conds_pe_est: Estimate GA in PE.
ga_res_conds_pe_onset_est: Estimate GA in EOPE.
ga_res_conds_preterm_est: Estimate GA in preterm delivery.
ga_res_conds_anencephaly_est: Estimate GA in anencephaly.
ga_res_conds_spina_bifida_est: Estimate GA in spina bifida.
ga_res_conds_gdm_est: Estimate GA in GDM.
ga_res_conds_diandric_triploid_est: Estimate GA in diandric triploid.
ga_res_conds_miscarriage_est: Estimate GA in miscarriage.
ga_res_conds_lga_est: Estimate GA in LGA.
ga_res_conds_subfertility_est: Estimate GA in subfertility.
ga_res_conds_hellp_est: Estimate GA in HELLP.
ga_res_conds_chorioamnionitis_est: Estimate GA in chorioamnionitis.
ga_res_conds_pred_est: Estimate residual GA.
ga_res_comb_pr_est: Estimate residual GA for preterm.
ga_res_comb_tb_est: Estimate residual GA for term before the date.
ga_res_comb_ta_est: Estimate residual GA for term after the date.

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Scale values of the scalers of placental epigenetic clock

Description

A data frame of scale values of the scalers for all submodels in our placental epigenetic_clock.

Usage

plec_scaler_scale

Format

A data frame with 10,446 rows and 32 columns:

predictor: A character value of predictor name.
ga_est: Estimate normal GA.
fgr_pred: Predict FGR.
pe_pred: Predict PE.
pe_onset_pred: Predict PE onset.
preterm_pred: Predict preterm delivery.
anencephaly_pred: Predict anencephaly.
spina_bifida_pred: Predict spina bifida.
gdm_pred: Predict GDM.
diandric_triploid_pred: Predict diandric triploid.
miscarriage_pred: Predict miscarriage.
lga_pred: Predict LGA.
subfertility_pred: Predict subfertility.
hellp_pred: Predict HELLP.
chorioamnionitis_pred: Predict chorioamnionitis.
ga_res_conds_fgr_est: Estimate GA in FGR.
ga_res_conds_pe_est: Estimate GA in PE.
ga_res_conds_pe_onset_est: Estimate GA in EOPE.
ga_res_conds_preterm_est: Estimate GA in preterm delivery.
ga_res_conds_anencephaly_est: Estimate GA in anencephaly.
ga_res_conds_spina_bifida_est: Estimate GA in spina bifida.
ga_res_conds_gdm_est: Estimate GA in GDM.
ga_res_conds_diandric_triploid_est: Estimate GA in diandric triploid.
ga_res_conds_miscarriage_est: Estimate GA in miscarriage.
ga_res_conds_lga_est: Estimate GA in LGA.
ga_res_conds_subfertility_est: Estimate GA in subfertility.
ga_res_conds_hellp_est: Estimate GA in HELLP.
ga_res_conds_chorioamnionitis_est: Estimate GA in chorioamnionitis.
ga_res_conds_pred_est: Estimate residual GA.
ga_res_comb_pr_est: Estimate residual GA for preterm.
ga_res_comb_tb_est: Estimate residual GA for term before the date.
ga_res_comb_ta_est: Estimate residual GA for term after the date.

Source

Derived from the 2024 Placental Clock DREAM Challenge.

Probe info for 450K

Description

A list of 450K probe information for bmiq_norm_450k function. The probes are already filtered and ordered the same way to the input when we developed our placental epigenetic clock.

Usage

probe_info_450k

Format

A list of 2 elements where each has a length of 346407:

Design: An integer indicating design type 1 or 2.
probeID: A character for each probe identifier.

Source

Derived from ChAMP R package.

Perform quality control

Description

This function evaluates the precision of DNA-methylation-based (DNAm) gestational age (GA) based on calibration, root mean square error (RMSE), mean absolute error (MAE), and Pearson's correlation coefficient (r). The sample identifiers (IDs) are automatically matched among the DNAm-GA, GA, and phenotype (optional). Only GA from 5 to 44 weeks' gestation are shown in the calibration plot.

Usage

qc(dnam_ga, ga, phenotype = NULL)

Arguments

dnam_ga

A data frame of DNA-methylation-based GA. This data frame must be the output of plec function. The rows must be named according to the sample IDs.

ga

A data frame of GA. There is only one column, i.e., GA, as shown in ga. Use data(ga) to load this data frame. The rows must be named according to the sample IDs.

phenotype

A data frame of phenotype (optional). There is only one column, i.e., phenotype, as shown in phenotype. Use data(phenotype) to load this data frame. The rows must be named according to the sample IDs.

Value

A ggplot object of calibration plot with RMSE, MAE, and r.

Examples



  beta_values_case <- download_beta_values_case()
  norm_beta_values_case <- bmiq_norm_450k(beta_values_case)
  dnam_ga_case <- plec(norm_beta_values_case)
  
  data(ga)
  ga_case <- ga[phenotype$phenotype == "Case", , drop = FALSE]
  set.seed(1)
  qc(dnam_ga_case, ga_case)