Package 'methylclock'

Check wheter input data contains the required CpGs for the implemented clocks.

Description

Check wheter input data contains the required CpGs for the implemented clocks.

Usage

checkClocks(x, ...)

Arguments

x	data.frame or tibble (Individual in columns, CpGs in rows, CpG names in first colum - i.e. Horvath's format), ExpressionSet or GenomicRatioSet. A matrix is also possible having the CpG names in the rownames.
...	other parameters

Details

To be supplied

Value

a list with the different clocks when there are more than 80 the required CpGs

Examples

TestDataset <- get_TestDataset()
checkClocks(TestDataset)

---

Check wheter input data contains the required CpGs for the implemented clocks for Gestational Age.

Description

Check wheter input data contains the required CpGs for the implemented clocks for Gestational Age.

Usage

checkClocksGA(x, ...)

Arguments

x	data.frame or tibble (Individual in columns, CpGs in rows, CpG names in first colum - i.e. Horvath's format), ExpressionSet or GenomicRatioSet. A matrix is also possible having the CpG names in the rownames.
...	other parameters

Details

To be supplied

Value

a list with the different GA clocks when there are more than 80

Examples

TestDataset <- get_TestDataset()
checkClocksGA(TestDataset)

---

Get common CpGs

Description

Show the required CpGs contained on input data for the implemented clocks

Usage

commonClockCpgs(object, clock)

Arguments

object	resulting object from checkClocks functions
clock	string with the implemented clock, possible values are : "Knight", "Bohlin", "Mayne" and "Lee", "Horvath", "Hannum", "Levine", "skinHorvath", "PedBE", "Wu" and "TL"

Value

The common CpGs between input data and defined GA clock

Examples

TestDataset <- get_TestDataset()
cpgs.missing.GA <- checkClocksGA(TestDataset)
cpgs.missing <- checkClocks(TestDataset)
commonClockCpgs(cpgs.missing.GA, "Bohlin")
commonClockCpgs(cpgs.missing, "Hannum")

---

DNAm age estimation using different DNA methylation clocks.

Description

DNAm age estimation using different DNA methylation clocks.

Usage

DNAmAge(
  x,
  clocks = "all",
  toBetas = FALSE,
  fastImp = FALSE,
  normalize = FALSE,
  age,
  cell.count = TRUE,
  cell.count.reference = "blood gse35069 complete",
  min.perc = 0.8,
  ...
)

Arguments

x	data.frame (Individual in columns, CpGs in rows, CpG names in first colum - i.e. Horvath's format), matrix (individuals in columns and Cpgs in rows having CpG names in the rownames), ExpressionSet or GenomicRatioSet.
clocks	the methods used for estimating DNAmAge. Currrently "Horvath", "Hannum", "Levine", "BNN", "skinHorvath", "PedBE", "Wu", "TL", "BLUP", "EN" and "all" are available. Default is "all" and all clocks are estimated.
toBetas	Should data be transformed to beta values? Default is FALSE. If TRUE, it implies data are M values.
fastImp	Is fast imputation performed if necessary? (see details). Default is FALSE
normalize	Is Horvath's normalization performed? By default is FALSE
age	individual's chronological age.
cell.count	Are cell counts estimated? Default is TRUE.
cell.count.reference	Used when 'cell.count' is TRUE. Default is "blood gse35069 complete".See 'meffil::meffil.list.cell.count.references()' for possible values.
min.perc	Indicates the minimum conicidence percentage required between CpGs in or dataframee x and CpGs in clock coefficients to perform the calculation. If min.prec is too low, the estimated gestational DNAm age can be poor
...	Other arguments to be passed through impute package

Details

Imputation is performed when having missing data. Fast imputation is performed by ... what about imputing only when CpGs for the clock are missing?

Value

The estimated chronological and biological mDNA age

Examples

MethylationData <- get_MethylationDataExample()
age.example55 <- DNAmAge(MethylationData)

---

Gestational DNAm age estimation using different DNA methylation clocks.

Description

Gestational DNAm age estimation using different DNA methylation clocks.

Usage

DNAmGA(
  x,
  toBetas = FALSE,
  fastImp = FALSE,
  normalize = FALSE,
  age,
  cell.count = TRUE,
  cell.count.reference = "andrews and bakulski cord blood",
  min.perc = 0.8,
  ...
)

Arguments

x	data.frame (Individual in columns, CpGs in rows, CpG names in first colum - i.e. Horvath's format), matrix (individuals in columns and Cpgs in rows having CpG names in the rownames), ExpressionSet or GenomicRatioSet.
toBetas	Should data be transformed to beta values? Default is FALSE. If TRUE, it implies data are M values.
fastImp	Is fast imputation performed if necessary? (see details). Default is FALSE
normalize	Is Horvath's normalization performed? By default is FALSE
age	individual's chronological age. Required to compute gestational age difference output
cell.count	Are cell counts estimated? Default is TRUE.
cell.count.reference	Used when 'cell.count' is TRUE. Default is "blood gse35069 complete". See 'meffil::meffil.list.cell.count.references()' for possible values.
min.perc	Indicates the minimum conicidence percentage required between CpGs in or dataframee x and CpGs in clock coefficients to perform the calculation. If min.prec is too low, the estimated gestational DNAm age can be poor
...	Other arguments to be passed through impute package

Details

Imputation is performed when having missing data. Fast imputation is performed by ... what about imputing only when CpGs for the clock are missing?

Value

the estimated gestational DNAm age

Examples

TestDataset <- get_TestDataset()
TestDataset[1:5, ]
ga.test <- DNAmGA(TestDataset)

---

Get cell type reference

Description

Get cell type reference

Usage

getCellTypeReference(name)

Arguments

name	string with predefined datasets andrews and bakulski cord blood, blood gse35069, blood gse35069 chen, blood gse35069 complete, "combined cord blood", "cord blood gse68456", "gervin and lyle cord blood", "guintivano dlpfc" or "saliva gse48472"

Details

ORIGINAL AUTHOR: Matthew Suderman at githug : https://github.com/perishky/meffil The original meffilListCellTypeReferences and getCellTypeReference function from meffil v1.0.0

Value

name and reference.globals

Examples

name <- "andrews and bakulski cord blood"
getCellTypeReference(name)

---

Loads DNAm clock data from methylclockData

Description

Loads DNAm clock data from methylclockData

Usage

load_DNAm_Clocks_data()

Value

void

Examples

load_DNAm_Clocks_data()

---

Loads DNAmGA clock data from methylclockData

Description

Loads DNAmGA clock data from methylclockData

Usage

load_DNAmGA_Clocks_data()

Value

void

Examples

load_DNAm_Clocks_data()

---

Estimate cell counts for a beta matrix from a reference

Description

Estimate cell type ratios from methylation profiles of purified cell populations (Infinium HumanMethylation450 BeadChip).

Usage

meffilEstimateCellCountsFromBetas(beta, cellTypeReference, verbose = FALSE)

Arguments

beta	Matrix of Illumina 450K methylation levels (rows = CpG sites, columns = subjects).
cellTypeReference	Character string name of the cell type reference to use for estimating cell counts. See meffilListCellTypeReferences() for a list of available references. New references can be created using
verbose	If TRUE, then status messages are printed during execution (Default: FALSE).

Details

ORIGINAL AUTHOR: Matthew Suderman The original meffil.list.cellTypeReferences and get.cellTypeReference function from meffil v1.0.0 downloaded from githug : https://github.com/perishky/meffil

Value

A matrix of cell count estimates.

Results should be nearly identical to minfi::estimateCellCounts()

betas

Examples

cell.count.reference <- "andrews and bakulski cord blood"
TestDataset <- get_TestDataset()
cpgs <- t(as.matrix(TestDataset[, -1]))
colnames(cpgs) <- TestDataset$CpGName
meffilEstimateCellCountsFromBetas(t(cpgs), cell.count.reference)

---

List of available cell type references

Description

List of available cell type references

Usage

meffilListCellTypeReferences()

Details

ORIGINAL AUTHOR: Matthew Suderman The original meffilListCellTypeReferences and getCellTypeReference function from meffil v1.0.0 at githug : https://github.com/perishky/meffil

Value

a list with reference globals

Examples

meffilListCellTypeReferences()

---

methylclock

Description

Package to estimate DNA methylation age (DNAmAge) using different methylation clocks.

Author(s)

Juan R Gonzalez <[email protected]>

---

Plot correlation among DNAm clockx

Description

Plot correlation among DNAm clockx

Usage

plotCorClocks(x, ...)

Arguments

x	a tible or data.frame with the different DNAm clocks
...	other arguments to be passs through function 'chart.Correlation' from 'PerformanceAnalytics' package

Details

To be supplied

Value

Plot with Correlation Clocks

Examples

library(Biobase)
library(GEOquery)

dd <- GEOquery::getGEO("GSE109446")
gse109446 <- dd[[1]]
controls <- Biobase::pData(gse109446)$`diagnosis:ch1` == "control"
gse <- gse109446[, controls]
age <- as.numeric(Biobase::pData(gse)$`age:ch1`)
age.gse <- DNAmAge(gse, age = age)
plotCorClocks(age.gse)

---

Plot DNAm age estimation vs chronological age.

Description

Plot DNAm age estimation vs chronological age.

Usage

plotDNAmAge(x, y, tit = "Horvath's method", clock = "chronological", ...)

Arguments

x	DNAm age estimation
y	Chronological age
tit	Plot title. Default is "Horvath's method".
clock	Type of clock 'chronological' or 'GA', default 'chronological'
...	Other plot parameters for ggplot

Value

Plot with estimated DNAmAge

Examples

library(tidyverse)

path <- system.file("extdata", package = "methylclock")
covariates <- read_csv(file.path(
path,
"SampleAnnotationExample55.csv"
))
age <- covariates$Age
MethylationData <- get_MethylationDataExample()

age.example55 <- DNAmAge(MethylationData)
plotDNAmAge(age.example55$Horvath, age)

---

PROGRESS cohor data

Description

The PROGRESS cohort data is available in the additional file 8 of : Knight, A.K., Craig, J.M., Theda, C. et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol 17, 206 (2016). https://doi.org/10.1186/s13059-016-1068-z

Usage

data(progress_data)

Format

A data frame with 148 obs. and 151 variables

Details

A dataset containing data from the PROGRESS (Programming Research in Obesity, Growth, Environment and Social Stressors) cohort

Examples

data(progress_data)

---

PROGRESS cohort variables

Description

The PROGRESS cohort data is available in the additional file 8 of : Knight, A.K., Craig, J.M., Theda, C. et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol 17, 206 (2016). https://doi.org/10.1186/s13059-016-1068-z

Usage

data(progress_vars)

Format

A data frame with 150 obs. and 3 variables

Details

A dataset containing data from the PROGRESS (Programming Research in Obesity, Growth, Environment and Social Stressors) cohort

Examples

data(progress_vars)

---